A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers

Identification and validation of diagnostic markers and drugs for pediatric bronchopulmonary dysplasia based on integrating bioinformatics and molecular docking analysis

BACKGROUND

BPD is a prevalent chronic lung disease in infancy with lifelong impacts. Its early diagnosis and treatment are hindered by complex pathophysiology and limited mechanistic understanding. This study seeks to establish a foundation for early diagnosis and targeted therapy by identifying diagnostic markers and exploring drug-gene associations.

METHODS

Gene expression data were retrieved from the GEO database. Functional enrichment analyses were conducted on the differentially expressed genes (DEGs). DEGs were used to construct a PPI network. Three algorithms were applied to identify diagnostic markers. Immune cell infiltration was analyzed using the CIBERSORT tool, assessing relationships between immune cells and diagnostic markers. Molecular docking was performed to evaluate interactions between predict candidate drugs and diagnostic markers.

RESULTS

Six hub genes were identified as diagnostic markers. Diagnostic markers showed significant correlations with specific immune cells. Resveratrol and progesterone were found to stably bind to all six diagnostic markers in molecular docking analyses, suggesting therapeutic potential.

CONCLUSION

In conclusion, our results show that IL7R, CXCL10, DEFA4, PRTN3, NCAPG and CCNB1 are BPD diagnostic indicators, and revealing immunological features associated with BPD. The molecular interactions of resveratrol and progesterone with the aforementioned key targets suggest their potential as therapeutic drugs for treating BPD.

Machine learning identifies potential diagnostic biomarkers associated with ferroptosis in obstructive sleep apnea

Obstructive sleep apnea (OSA) is the most common sleep apnea-related disorder, with a high prevalence and a range of associated complications. Ferroptosis is a new mode of cell death that is involved in the development of OSA, but the mechanism has remained elusive. In the present study, ferroptosis-related genes in OSA were assessed and their potential clinical value was discussed. Data were downloaded and merged, and screened for differentially expressed genes (DEGs) through the Gene Expression Omnibus database. The OSA ferroptosis-related genes were obtained after intersecting with the downloaded ferroptosis-related genes. Subsequently, key ferroptosis-associated differential genes were obtained using two machine learning methods (the least absolute shrinkage and selection operators and random forest). The immune infiltration in the samples and the correlation between key differential genes and immune infiltrating cells were then analyzed. A competing endogenous (ce)RNA visualization network was constructed to find possible therapeutic targets. Finally, the expression levels of key DEGs were verified by reverse transcription-quantitative (RT-q)PCR. In this study, 3 key ferroptosis-related differential genes were identified: TXN, EGR1 and CDKN1A. Functional enrichment analysis showed that the three key differential genes in OSA can influence the development of OSA by affecting metabolism, immune response and other processes. RT-qPCR experiments verified the expression of these key genes, further confirming the findings. A persistent state of immune activation may promote the progression of OSA, with marked infiltration of T cells and natural killer cells in OSA tissues. Genipin is a possible targeted therapeutic agent for OSA. Meanwhile, ceRNA network analysis identified several long non-coding RNAs that can regulate OSA disease progression. A total of 3 key ferroptosis-related markers were identified (TXN, EGR1 and CDKN1A) that are closely associated with metabolic disorders and immune responses, and which may be targets for early diagnosis and treatment of OSA.

Physical activity and sedentary behavior in peritoneal dialysis patients: a comparative analysis of ActiGraph GT3X data collected via wrist and waist with placement-specific cut-points

BACKGROUND

The increasing adoption of accelerometers for the assessment of sedentary behaviour and physical activity among dialysis patients demands robust validation of these monitoring devices. This study aims to determine the comparability of wrist- versus waist-worn ActiGraph GT3X accelerometers, using placement-specific cut-points for peritoneal dialysis patients, to refine research and clinical practices.

METHODS

This was a cross-sectional study. Thirty-one participants wore two ActiGraph GT3X accelerometers, positioned on the right waist and nondominant wrist, and monitored over a seven-day period in a naturalistic setting. Data were processed with ActiLife v6.13.3 and analysed using intraclass correlation coefficients (ICC), limits of agreement, and pairwise 90% equivalence test within a ± 10% threshold.

RESULTS

The sedentary time measurements from both wrist- and waist-worn GT3X accelerometers were deemed equivalent, with high ICC values (0.98, 95% confidence intervals (CI) 0.97-0.99) and a ratio of 1.0 within the 90% CI of 0.9 to 1.0. Although agreement between accelerometers was good for classification of light-intensity activity (ICC = 0.76), the waist-worn device's estimates exceeded the equivalence criteria compared to the wrist-worn device (ratio 1.4; 90% CI 1.2-1.6). Conversely, the waist-worn device reported a significantly lower duration of moderate-to-vigorous physical activity (MVPA) than the wrist-worn device (Ln transformed ratio 0.3; 90% CI 0.1-0.4).

CONCLUSIONS

The use of placement-specific cut-points did not ensure equivalence in physical activity parameter estimates between wrist- and waist-worn ActiGraph GT3X devices. The findings underscore the necessity for consistent accelerometer placement for reliable monitoring of physical activity in peritoneal dialysis patients.

CLINICAL TRIAL NUMBER

Not applicable.

Integrated bioinformatics analysis and biological experiments to identify key immune genes in vascular dementia

Objectives

This study aimed to identify key immune genes to provide new perspectives on the mechanisms and diagnosis of vascular dementia (VaD) based on bioinformatic methods combined with biological experiments in mice.

Methods

We obtained gene expression profiles from a Gene Expression Omnibus database (GSE186798). The gene expression data were analysed using integrated bioinformatics and machine learning techniques to pinpoint potential key immune-related genes for diagnosing VaD. Moreover, the diagnostic accuracy was evaluated through receiver operating characteristic curve analysis. The microRNA, transcription factor (TF), and drug-regulating hub genes were predicted using the database. Immune cell infiltration has been studied to investigate the dysregulation of immune cells in patients with VaD. To evaluate cognitive impairment, mice with bilateral common carotid artery stenosis (BCAS) were subjected to behavioural tests 30 d after chronic cerebral hypoperfusion. The expression of hub genes in the BCAS mice was determined using a quantitative polymerase chain reaction(qPCR).

Results

The results of gene set enrichment and gene set variation analyses indicated that immune-related pathways were upregulated in patients with VaD. A total of 1620 immune genes were included in the combined immune dataset, and 323 differentially expressed genes were examined using the GSE186798 dataset. Thirteen potential genes were identified using differential gene analysis. Protein-protein interaction network design and functional enrichment analysis were performed using the immune system as the main subject. To evaluate the diagnostic value, two potential core genes were selected using machine learning. Two putative hub genes, Rac family small GTPase 1(RAC1) and CKLF-like MARVEL transmembrane domain containing 5 (CMTM5) exhibit good diagnostic value. Their high confidence levels were confirmed by validating each biomarker using a different dataset. According to GeneMANIA, VaD pathophysiology is strongly associated with immune and inflammatory responses. The data were used to construct miRNA hub gene, TFs-hub gene, and drug-hub gene networks. Varying levels of immune cell dysregulation were also observed. In the animal experiments, a BCAS mouse model was employed to mimic VaD in humans, further confirmed using the Morris water maze test. The mRNA expression of RAC1 and CMTM5 was significantly reduced in the BCAS group, which was consistent with the results of the integrated bioinformatics analysis.

Conclusions

RAC1 and CMTM5 are differentially expressed in the frontal lobes of BCAS mice, suggesting their potential as biomarkers for diagnosing and prognosis of VaD. These findings pave the way for exploring novel molecular mechanisms aimed at preventing or treating VaD.

Bioinformatics and machine learning approaches to explore key biomarkers in muscle aging linked to adipogenesis

Adipogenesis is intricately linked to the onset and progression of muscle aging; however, the relevant biomarkers remain unclear. This study sought to identify key genes associated with adipogenesis in the context of muscle aging. Firstly, gene expression profiles from biopsies of the vastus lateralis muscle in both young and elderly population were retrieved from the GEO database. After intersecting with the results of differential gene analysis, weighted gene co-expression network analysis, and sets of adipogenesis-related genes, 29 adipogenesis-related differential expressed genes (ARDEGs) were selected. Connectivity Map (cMAP) analysis identified tamsulosin, fraxidin, and alaproclate as key target compounds. In further, using three machine learning algorithms and the friends analysis, four hub ARDEGs, ESRRA, RXRG, GADD45A, and CEBPB were identified and verified in vivo aged mice muscles. Immune infiltration analysis showed a strong link between several immune cells and hub ARDEGs. In all, these findings suggested that ESRRA, RXRG, GADD45A, and CEBPB could serve as adipogenesis related biomarkers in muscle aging.

Methodological Choices on 24-h Movement Behavior Assessment by Accelerometry: A Scoping Review

BACKGROUND

There are no reviews describing current measurement protocols and accelerometer processing decisions that are being used in 24-h MovBeh studies, across the lifespan. We aim to synthesise information on methods for assessing 24-h movement behaviors using accelerometry across all age groups.

MAIN BODY

PubMed, PsycINFO, SPORTDiscus, and EMBASE were searched until December 2022. Observational or intervention reports describing accelerometry methods in studies on combinations of movement behaviors, with a 24-h protocol across all ages, were included. This review included 102 studies: three studies in toddlers, 15 in preschoolers, 17 in children, 23 in adolescents and 44 in adults and older adults. The Actigraph GT3X was the most commonly used device; the majority of the included reports collected data for seven days, including three weekdays and one weekend day, with a ≥ 16 h/day per 24-h period for valid data. The criteria for non-wear time varied between ≥ 20 and  ≥ 90 min of consecutive zero counts, depending on the age group. The most common epoch used was 15 or 60 s for youth and adults, respectively. The choice of sleep algorithms and SB/PA cut-points, of the included reports, depended on age and the original validation/calibration study. To deal with non-compliant participants, exclusion of non-compliant participants from the analysis was most frequently used. Most studies used diaries/logs to complement the accelerometer data.

CONCLUSIONS

Accelerometer protocols and methodological decisions varied considerably between reports. Therefore, consensus on methodological decisions is needed to improve precision and comparability between studies, which is challenging given the complexity of the procedures, the number of available brands and types of accelerometers, and the plethora of programming options.

Identification and validation of key biomarkers associated with immune and oxidative stress for preeclampsia by WGCNA and machine learning

Background

Preeclampsia (PE), a major obstetric disorder marked by dysfunction in both placental and maternal vascular systems, continues to pose critical challenges in global maternal healthcare. This multisystem pregnancy complication contributes significantly to adverse perinatal outcomes and remains a leading cause of pregnancy-related morbidity worldwide. However, the available treatment options at present remain restricted. Our investigation employs an integrative bioinformatics approach to elucidate critical molecular signatures linked to the interplay between immunological dysregulation and oxidative stress mechanisms in PE pathogenesis.

Methods

In this study, we sourced the dataset from the GEO database with the aim of pinpointing differentially expressed genes (DEGs) between PE samples and control samples. Genes associated with oxidative stress were procured from the Genecards database. Next, we employed a comprehensive approach. This involved integrating WGCNA, GO and KEGG pathway analyses, constructing PPI networks, applying machine learning algorithms, performing gene GSEA, and conducting immune infiltration analysis to identify the key hub genes related to oxidative stress. Diagnostic potential of candidate biomarkers was quantitatively assessed through ROC curve modeling. Additionally, we constructed a miRNA - gene regulatory network for the identified diagnostic genes and predicted potential candidate drugs. In the final step, we validated the significant hub gene using independent external datasets, the hypoxia model of the HTR-8/SVneo cell line, and human placental tissue samples.

Results

At last, leptin (LEP) was identified as a core gene through screening and was found to be upregulated. The results of quantitative real-time polymerase chain reaction (qRT -PCR) and immunohistochemistry validation were consistent with those obtained from the datasets. KEGG analysis revealed that LEP was significantly enriched in "allograft rejection," "antigen processing," "ECM receptor interaction" and "graft versus host disease." GO analysis revealed that LEP was involved in biological processes such as "antigen processing and presentation," "peptide antigen assembly with MHC protein complex," "complex of collagen trimers," "MHC class II protein complex" and "mitochondrial protein containing complex." Moreover, immune cell analysis indicated that T follicular helper cells, plasmacytoid dendritic cells, neutrophils, and activated dendritic cells were positively correlated with LEP expression, whereas γδT cells, eosinophils, and central memory CD4+ T cells showed a negative correlation. These findings suggest that LEP influences the immune microenvironment of PE through its interaction with arious immune cells. In addition, 28 miRNAs and 15 drugs were predicted to target LEP. Finally, the overexpression of LEP was verified using independent external datasets, the hypoxia model of the HTR-8/SVneo cell line, and human placental tissue.

Conclusion

Through an integrated analytical framework employing WGCNA coupled with three distinct machine learning-driven phenotypic classification models, we discovered a pivotal regulatory gene. This gene has the potential to act as a novel diagnostic biomarker for PE. Moreover, it can be considered as a promising target for drug development related to PE. Notably, it shows a strong correlation with the immune microenvironment, suggesting its crucial role in the complex pathophysiological processes underlying PE.

Spatiotemporal Variation Assessment and Improved Prediction Of Cyanobacteria Blooms in Lakes Using Improved Machine Learning Model Based on Multivariate Data

Cyanobacterial blooms in shallow lakes pose a significant threat to aquatic ecosystems and public health worldwide, highlighting the urgent need for advanced predictive methodologies. As impounded lakes along the Eastern Route of the South-to-North Water Diversion Project, Lakes Hongze and Luoma play a key role in water resource management, making the prediction of cyanobacterial blooms in these lakes particularly important. To address this, satellite remote sensing data were utilized to analyze the spatiotemporal dynamics of cyanobacterial blooms in these lakes. Subsequently, a precise machine learning model, integrating the Projection Pursuit Model and Random Forest (PP-RF) algorithms, was developed to predict the extent of cyanobacterial blooms, considering a range of influencing factors, including physical, chemical, climatic, and hydrologic variables. The findings indicated pronounced seasonal fluctuations in cyanobacterial blooms, with higher levels in summer than in other seasons. Key determinants for cyanobacterial blooms prediction included solar radiation, temperature and total nitrogen for Lake Hongze, while for Lake Luoma, significant predictors were identified as temperature, water temperature, and solar radiation. Compared with traditional data preprocessing methods, PP-RF model has advantages in addressing multicollinearity. This study provides a feasible method for predicting cyanobacterial blooms in impounded lakes within inter-basin water transfer projects. By inputting region-specific data, this model could be applied broadly, contributing to against the adverse effects of cyanobacterial blooms and provide scientific guidance for the protection and management of aquatic ecosystems.

Effective analysis of thyroid toxicity and mechanisms of acetyltributyl citrate using network toxicology, molecular docking, and machine learning strategies

The growing prevalence of environmental pollutants has raised concerns about their potential role in thyroid dysfunction and related disorders. Previous research suggests that various chemicals, including plasticizers like acetyl tributyl citrate (ATBC), may adversely affect thyroid health, yet the precise mechanisms remain poorly understood. The objective of this study was to elucidate the complex effects of acetyl tributyl citrate (ATBC) on the thyroid gland and to clarify the potential molecular mechanisms by which environmental pollutants influence the disease process. Through an exhaustive exploration of databases such as ChEMBL, STITCH, and GEO, we identified a comprehensive list of 19 potential targets closely associated with ATBC and the thyroid gland. After rigorous screening using the STRING platform and Cytoscape software, we narrowed this list to 15 candidate targets, ultimately identifying five core targets: CBX5, HADHB, TRIM33, TP53, and CUL4A, utilizing three well-established machine learning methods. In-depth Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses conducted in the DAVID database revealed that the primary pathways through which ATBC affects the thyroid gland involve key signaling cascades, including the FoxO signaling pathway and metabolic pathways such as fatty acid metabolism. Furthermore, molecular docking simulations using Molecular Operating Environment software confirmed strong binding interactions between ATBC and these core targets, enhancing our understanding of their interactions. Overall, our findings provide a theoretical framework for comprehending the intricate molecular mechanisms underlying ATBC's effects on thyroid damage and pave the way for the development of preventive and therapeutic strategies against thyroid disorders caused by exposure to ATBC-containing plastics or overexposure to ATBC.

Relationships between multivitamins, blood biochemistry markers, and BMC and BMD based on RF: A cross-sectional and population-based study of NHANES, 2017-2018

BACKGROUND

Previous studies have separately suggested a possible association between the vitamin exposure, blood biochemical indicators, and bone density. Our study aimed to investigate the relationship between vitamin exposure serum concentrations, blood biochemical indicator serum concentrations, and BMC and BMD using the NHANES 2017-2018 nutrient survey data. This population-based cross-sectional study aimed to explore these associations.

METHODS

In this study, we measured vitamin serum concentrations, serum ion serum concentrations, and serum biochemical indicators in adults participating in the NHANES. Skeletal status was assessed by evaluating BMC and BMD in the whole body. Given the inclusion of multiple variables and diverse data types, we used the RF to fit a multivariable model to estimate the associations between vitamin serum concentrations, blood biochemical indicator serum concentrations, and skeletal status.

RESULTS

Under the dimension reduction and comparison selection of RF model, we identified ALP, CPK, and creatinine serum concentrations as the most important factors associated with BMC and BMD in multiple skeletal sites, and the gender, age, height, weight, and body mass index which were found to be related to BMC and BMD in different skeletal sites. Vitamin D and blood calcium serum concentrations were not the important factors associated with BMC and BMD and the three blood biochemical indexes were more important than the vitamin level for BMC and BMD.

CONCLUSION

The effect of vitamin serum concentrations and blood calcium serum concentrations on human bone density was not significant. ALP, CPK and creatinine serum concentrations body development indicators were identified as the most important factors related to bone status. The RF model can be used to comprehensively evaluate the effects of vitamin content and blood biochemistry serum concentrations in adults on BMC and BMD.

Identification of hub biomarkers and immune cell infiltrations participating in the pathogenesis of endometriosis

Endometriosis (EM) is a chronic disease that can cause pain and infertility in patients. As is well known, immune cell infiltrations (ICIs) play important roles in the pathogenesis of EM. However, the pathogenesis and biomarkers of EM that can be used in clinical practice and their relationship with ICIs still need to be elucidated. The gene expression datasets of EM and the healthy control were obtained from the Gene Expression Omnibus (GEO). To identify the central modules and explore the correlation between the gene network and EM, weighted gene co-expression network analysis (WGCNA) was executed. The hub genes were screened using machine learning. The qRT-PCR results showed that only CHMP4C and KAT2B differentially expressed in ectopic tissues compared to the normal. Subsequently, the samples were clustered based on the expression of CHMP4C and KAT2B. Depending on the differential expression genes of the two 2rG Clusters, the samples were divided into two gene Clusters. Significant differences in immune cell infiltrations were observed among the two 2rG Clusters and the two gene Clusters. Furthermore, varied immune checkpoint genes were shown to be correlated with EM. The qRT-PCR results showed that the two genes were significantly related to the ICI genes in EM. Hub genes CHMP4C and KAT2B are involved in the pathogenesis of EM by regulating ICI.

Deciphering hub genes and immune landscapes related to neutrophil extracellular traps in rheumatoid arthritis: insights from integrated bioinformatics analyses and experiments

Background

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation and progressive joint destruction. Neutrophil extracellular traps (NETs), a microreticular structure formed after neutrophil death, have recently been implicated in RA pathogenesis and pathological mechanisms. However, the underlying molecular mechanisms and key genes involved in NET formation in RA remain largely unknown.

Methods

We obtained single-cell RNA sequencing data of synovial tissues from the Gene Expression Omnibus (GEO) database and performed cellular annotation and intercellular communication analyses. Subsequently, three microarray datasets were collected for a training cohort and correlated with a bulk RNA-seq dataset associated with NETs. Differentially expressed genes were identified, and weighted gene correlation network analysis was used to characterize gene association. Using three machine learning techniques, we identified the most important hub genes to develop and evaluate a nomogram diagnostic model. CIBERSORT was used to elucidate the relationship between hub genes and immune cells. An external validation dataset was used to verify pivotal gene expression and to construct co-regulatory networks using the NetworkAnalyst platform. We further investigated hub gene expression using immunohistochemistry (IHC) in an adjuvant-induced arthritis rat model and real-time quantitative polymerase chain reaction (RT-qPCR) in a clinical cohort.

Results

Seven cellular subpopulations were identified through downscaling and clustering, with neutrophils likely the most crucial cell clusters in RA. Intercellular communication analysis highlighted the network between neutrophils and fibroblasts. In this context, 4 key hub genes (CRYBG1, RMM2, MMP1, and SLC19A2) associated with NETs were identified. A nomogram model with a diagnostic value was developed and evaluated. Immune cell infiltration analysis indicated associations between the hub genes and the immune landscape in NETs and RA. IHC and RT-qPCR findings showed high expression of CRYBG1, RMM2, and MMP1 in synovial and neutrophilic cells, with lower expression of SLC19A2. Correlation analysis further emphasized close associations between hub genes and laboratory markers in patients with RA.

Conclusion

This study first elucidated neutrophil heterogeneity in the RA synovial microenvironment and mechanisms of communication with fibroblasts. CRYBG1, RMM2, MMP1, and SLC19A2 were identified and validated as potential NET-associated biomarkers, offering insights for diagnostic tools and immunotherapeutic strategies in RA.

Common biomarkers of idiopathic pulmonary fibrosis and systemic sclerosis based on WGCNA and machine learning

Interstitial lung disease (ILD) is known to be a major complication of systemic sclerosis (SSc) and a leading cause of death in SSc patients. As the most common type of ILD, the pathogenesis of idiopathic pulmonary fibrosis (IPF) has not been fully elucidated. In this study, weighted correlation network analysis (WGCNA), protein‒protein interaction, Kaplan-Meier curve, univariate Cox analysis and machine learning methods were used on datasets from the Gene Expression Omnibus database. CCL2 was identified as a common characteristic gene of IPF and SSc. The genes associated with CCL2 expression in both diseases were enriched mainly in chemokine-related pathways and lipid metabolism-related pathways according to Gene Set Enrichment Analysis. Single-cell RNA sequencing (sc-RNAseq) revealed a significant difference in CCL2 expression in alveolar epithelial type 1/2 cells, mast cells, ciliated cells, club cells, fibroblasts, M1/M2 macrophages, monocytes and plasma cells between IPF patients and healthy donors. Statistical analyses revealed that CCL2 was negatively correlated with lung function in IPF patients and decreased after mycophenolate mofetil (MMF) treatment in SSc patients. Finally, we identified CCL2 as a common biomarker from IPF and SSc, revealing the common mechanism of these two diseases and providing clues for the study of the treatment and mechanism of these two diseases.

Identification of O-glycosylation related genes and subtypes in ulcerative colitis based on machine learning

Ulcerative colitis (UC) is an immune-related inflammatory bowel disease, with its underlying mechanisms being a central area of clinical research. O-GlcNAcylation plays a critical role in regulating immunity progression and the occurrence of inflammatory diseases and tumors. Yet, the mechanism of O-GlcNAc-associated colitis remains to be elucidated. To this end, the transcriptional and clinical data of GSE75214 and GSE92415 from the GEO database was hereby examined, and genes MUC1, ADAMTS1, GXYLT2, and SEMA5A were found to be significantly related to O-GlcNAcylation using machine learning methods. Based on the four hub genes, two UC subtypes were built. Notably, subtype B might be prone to developing colitis-associated colorectal cancer (CAC). This study delved into the role of intestinal glycosylation changes, especially the O-GlcNAcylation, and forged a foundation for further research on the occurrence and development of UC. Overall, understanding the role of O-GlcNAcylation in UC could have significant implications for diagnosis and treatment, offering valuable insights into the disease's progression.

Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning

PURPOSE

This study aimed to identify novel biomarkers for preeclampsia (PE) diagnosis by integrating Weighted Gene Co-expression Network Analysis (WGCNA) with machine learning techniques.

PATIENTS AND METHODS

We obtained the PE dataset GSE25906 from the gene expression omnibus (GEO) database. Analysis of differentially expressed genes (DEGs) and module genes with Limma and Weighted Gene Co-expression Network analysis (WGCNA). Candidate hub genes for PE were identified using machine learning. Subsequently, we used western-blotting (WB) and real-time fluorescence quantitative (qPCR) to verify the expression of F13A1 and SCCPDH in preeclampsia patients. Finally, we estimated the extent of immune cell infiltration in PE samples by employing the CIBERSORT algorithms.

RESULTS

Our findings revealed that F13A1 and SCCPDH were the hub genes of PE. The nomogram and two candidate hub genes had high diagnostic values (AUC: 0.90 and 0.88, respectively). The expression levels of F13A1 and SCCPDH were verified by WB and qPCR. CIBERSORT analysis confirmed that the PE group had a significantly larger proportion of plasma cells and activated dendritic cells and a lower portion of resting memory CD4 + T cells.

CONCLUSION

The study proposes F13A1 and SCCPDH as potential biomarkers for diagnosing PE and points to an improvement in early detection. Integration of WGCNA with machine learning could enhance biomarker discovery in complex conditions like PE and offer a path toward more precise and reliable diagnostic tools.

Exploring Pyroptosis-related Signature Genes and Potential Drugs in Ulcerative Colitis by Transcriptome Data and Animal Experimental Validation

Ulcerative colitis (UC) is an idiopathic, relapsing inflammatory disorder of the colonic mucosa. Pyroptosis contributes significantly to UC. However, the molecular mechanisms of UC remain unexplained. Herein, using transcriptome data and animal experimental validation, we sought to explore pyroptosis-related molecular mechanisms, signature genes, and potential drugs in UC. Gene profiles (GSE48959, GSE59071, GSE53306, and GSE94648) were selected from the Gene Expression Omnibus (GEO) database, which contained samples derived from patients with active and inactive UC, as well as health controls. Gene Set Enrichment Analysis (GSEA), Weighted Gene Co-expression Network Analysis (WGCNA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on microarrays to unravel the association between UC and pyroptosis. Then, differential expressed genes (DEGs) and pyroptosis-related DEGs were obtained by differential expression analyses and the public database. Subsequently, pyroptosis-related DEGs and their association with the immune infiltration landscape were analyzed using the CIBERSORT method. Besides, potential signature genes were selected by machine learning (ML) algorithms, and then validated by testing datasets which included samples of colonic mucosal tissue and peripheral blood. More importantly, the potential drug was screened based on this. And these signature genes and the drug effect were finally observed in the animal experiment. GSEA and KEGG enrichment analyses on key module genes derived from WGCNA revealed a close association between UC and pyroptosis. Then, a total of 20 pyroptosis-related DEGs of UC and 27 pyroptosis-related DEGs of active UC were screened. Next, 6 candidate genes (ZBP1, AIM2, IL1β, CASP1, TLR4, CASP11) in UC and 2 candidate genes (TLR4, CASP11) in active UC were respectively identified using the binary logistic regression (BLR), least absolute shrinkage and selection operator (LASSO), random forest (RF) analysis and artificial neural network (ANN), and these genes also showed high diagnostic specificity for UC in testing sets. Specially, TLR4 was elevated in UC and further elevated in active UC. The results of the drug screen revealed that six compounds (quercetin, cyclosporine, resveratrol, cisplatin, paclitaxel, rosiglitazone) could target TLR4, among which the effect of quercetin on intestinal pathology, pyroptosis and the expression of TLR4 in UC and active UC was further determined by the murine model. These findings demonstrated that pyroptosis may promote UC, and especially contributes to the activation of UC. Pyroptosis-related DEGs offer new ideas for the diagnosis of UC. Besides, quercetin was verified as an effective treatment for pyroptosis and intestinal inflammation. This study might enhance our comprehension on the pathogenic mechanism and diagnosis of UC and offer a treatment option for UC.

Identification of Ferroptosis-Related Gene in Age-Related Macular Degeneration Using Machine Learning

BACKGROUND

Age-related macular degeneration (AMD) is a major cause of irreversible visual impairment, with dry AMD being the most prevalent form. Programmed cell death of retinal pigment epithelium (RPE) cells is a central mechanism in the pathogenesis of dry AMD. Ferroptosis, a recently identified form of programmed cell death, is characterized by iron accumulation-induced lipid peroxidation. This study aimed to investigate the involvement of ferroptosis in the progression of AMD.

METHODS

A total of 41 samples of AMD and 50 normal samples were obtained from the data set GSE29801 for differential gene expression analysis and functional enrichment. Differentially expressed genes (DEGs) were selected and intersected with genes from the ferroptosis database to obtain differentially expressed ferroptosis-associated genes (DEFGs). Machine learning algorithms were employed to screen diagnostic genes. The diagnostic genes were subjected to Gene Set Enrichment Analysis (GSEA). Expression differences of diagnostic genes were validated in in vivo and in vitro models.

RESULTS

We identified 462 DEGs when comparing normal and AMD samples. The GO enrichment analysis indicated significant involvement in key biological processes like collagen-containing extracellular matrix composition, positive cell adhesion regulation, and extracellular matrix organization. Through the intersection with ferroptosis gene sets, we pinpointed 10 DEFGs. Leveraging machine learning algorithms, we pinpointed five ferroptosis feature diagnostic genes: VEGFA, SLC2A1, HAMP, HSPB1, and FADS2. The subsequent experiments validated the increased expression of SLC2A1 and FADS2 in the AMD ferroptosis model.

CONCLUSION

The occurrence of ferroptosis could potentially contribute to the advancement of AMD. SLC2A1 and FADS2 have demonstrated promise as emerging diagnostic biomarkers and plausible therapeutic targets for AMD.

A Data-Driven Approach to Predicting Recreational Activity Participation Using Machine Learning

Purpose: With the popularity of recreational activities, the study aimed to develop prediction models for recreational activity participation and explore the key factors affecting participation in recreational activities. Methods: A total of 12,712 participants, excluding individuals under 20, were selected from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018. The mean age of the sample was 46.86 years (±16.97), with a gender distribution of 6,721 males and 5,991 females. The variables included demographic, physical-related variables, and lifestyle variables. This study developed 42 prediction models using six machine learning methods, including logistic regression, Support Vector Machine (SVM), decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The relative importance of each variable was evaluated by permutation feature importance. Results: The results illustrated that the LightGBM was the most effective algorithm for predicting recreational activity participation (accuracy: .838, precision: .783, recall: .967, F1-score: .865, AUC: .826). In particular, prediction performance increased when the demographic and lifestyle datasets were used together. Next, as the result of the permutation feature importance based on the top models, education level and moderate-vigorous physical activity (MVPA) were found to be essential variables. Conclusion: These findings demonstrated the potential of a data-driven approach utilizing machine learning in a recreational discipline. Furthermore, this study interpreted the prediction model through feature importance analysis to overcome the limitation of machine learning interpretability.

Device-measured stationary behaviour and cardiovascular and orthostatic circulatory disease incidence

BACKGROUND

Previous studies have indicated that standing may be beneficially associated with surrogate metabolic markers, whereas more time spent sitting has an adverse association. Studies assessing the dose-response associations of standing, sitting and composite stationary behaviour time with cardiovascular disease (CVD) and orthostatic circulatory disease are scarce and show an unclear picture.

OBJECTIVE

To examine associations of daily sitting, standing and stationary time with CVD and orthostatic circulatory disease incidence.

METHODS

We used accelerometer data from 83 013 adults (mean age ± standard deviation = 61.3 ± 7.8; female = 55.6%) from the UK Biobank to assess daily time spent sitting and standing. Major CVD was defined as coronary heart disease, heart failure and stroke. Orthostatic circulatory disease was defined as orthostatic hypotension, varicose vein, chronic venous insufficiency and venous ulcers. To estimate the dose-response hazard ratios (HR) we used Cox proportional hazards regression models and restricted cubic splines. The Fine-Gray subdistribution method was used to account for competing risks.

RESULTS

During 6.9 (±0.9) years of follow-up, 6829 CVD and 2042 orthostatic circulatory disease events occurred. When stationary time exceeded 12 h/day, orthostatic circulatory disease risk was higher by an average HR (95% confidence interval) of 0.22 (0.16, 0.29) per hour. Every additional hour above 10 h/day of sitting was associated with a 0.26 (0.18, 0.36) higher risk. Standing more than 2 h/day was associated with an 0.11 (0.05, 0.18) higher risk for every additional 30 min/day. For major CVD, when stationary time exceeded 12 h/day, risk was higher by an average of 0.13 (0.10, 0.16) per hour. Sitting time was associated with a 0.15 (0.11, 0.19) higher risk per extra hour. Time spent standing was not associated with major CVD risk.

CONCLUSIONS

Time spent standing was not associated with CVD risk but was associated with higher orthostatic circulatory disease risk. Time spent sitting above 10 h/day was associated with both higher orthostatic circulatory disease and major CVD risk. The deleterious associations of overall stationary time were primarily driven by sitting. Collectively, our findings indicate increasing standing time as a prescription may not lower major CVD risk and may lead to higher orthostatic circulatory disease risk.

Identification of Potential Key Genes for the Comorbidity of Myasthenia Gravis With Thymoma by Integrated Bioinformatics Analysis and Machine Learning

Background

Thymoma is a key risk factor for myasthenia gravis (MG). The purpose of our study was to investigate the potential key genes responsible for MG patients with thymoma.

Methods

We obtained MG and thymoma dataset from GEO database. Differentially expressed genes (DEGs) were determined and functional enrichment analyses were conducted by R packages. Weighted gene co-expression network analysis (WGCNA) was used to screen out the crucial module genes related to thymoma. Candidate genes were obtained by integrating DEGs of MG and module genes. Subsequently, we identified several candidate key genes by machine learning for diagnosing MG patients with thymoma. The nomogram and receiver operating characteristics (ROC) curves were applied to assess the diagnostic value of candidate key genes. Finally, we investigated the infiltration of immunocytes and analyzed the relationship among key genes and immune cells.

Results

We obtained 337 DEGs in MG dataset and 2150 DEGs in thymoma dataset. Biological function analyses indicated that DEGs of MG and thymoma were enriched in many common pathways. Black module (containing 207 genes) analyzed by WGCNA was considered as the most correlated with thymoma. Then, 12 candidate genes were identified by intersecting with MG DEGs and thymoma module genes as potential causes of thymoma-associated MG pathogenesis. Furthermore, five candidate key genes (JAM3, MS4A4A, MS4A6A, EGR1, and FOS) were screened out through integrating least absolute shrinkage and selection operator (LASSO) regression and Random forest (RF). The nomogram and ROC curves (area under the curve from 0.833 to 0.929) suggested all five candidate key genes had high diagnostic values. Finally, we found that five key genes and immune cell infiltrations presented varying degrees of correlation.

Conclusions

Our study identified five key potential pathogenic genes that predisposed thymoma to the development of MG, which provided potential diagnostic biomarkers and promising therapeutic targets for MG patients with thymoma.

Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics

Introduction

Heart failure (HF) and kidney failure (KF) are closely related conditions that often coexist, posing a complex clinical challenge. Understanding the shared mechanisms between these two conditions is crucial for developing effective therapies.

Methods

This study employed transcriptomic analysis to unveil molecular signatures and novel biomarkers for both HF and KF. A total of 2869 shared differentially expressed genes (DEGs) were identified in patients with HF and KF compared to healthy controls. Functional enrichment analysis was performed to explore the common mechanisms underlying these conditions. A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. These genes were further analyzed using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA), with their diagnostic values validated in both training and validation sets. Molecular docking studies were conducted. Additionally, immune cell infiltration and correlation analyses were performed to assess the relationship between immune responses and the identified biomarkers.

Results

The functional enrichment analysis indicated that the common mechanisms are associated with cellular homeostasis, cell communication, cellular replication, inflammation, and extracellular matrix (ECM) production, with the PI3K-Akt signaling pathway being notably enriched. The PPI network revealed two key protein clusters related to the cell cycle and inflammation. CDK2 and CCND1 were identified as signature genes for both HF and KF. Their diagnostic value was validated in both training and validation sets. Additionally, docking studies with CDK2 and CCND1 were performed to evaluate potential drug candidates. Immune cell infiltration and correlation analyses highlighted the immune microenvironment, and that CDK2 and CCND1 are associated with immune responses in HF and KF.

Discussion

This study identifies CDK2 and CCND1 as novel biomarkers linking cell cycle regulation and inflammation in heart and kidney failure. These findings offer new insights into the molecular mechanisms of HF and KF and present potential targets for diagnosis and therapy.

Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review

BACKGROUND

Monitoring free-living physical activity (PA) through wearable devices enables the real-time assessment of activity features associated with health outcomes and provision of treatment recommendations and adjustments. The conclusions of studies on PA and health depend crucially on reliable statistical analyses of digital data. Data analytics, however, are challenging due to the various metrics adopted for measuring PA, different aims of studies, and complex temporal variations within variables. The application, interpretation, and appropriateness of these analytical tools have yet to be summarized.

OBJECTIVE

This research aimed to review studies that used analytical methods for analyzing PA monitored by accelerometers. Specifically, this review addressed three questions: (1) What metrics are used to describe an individual's free-living daily PA? (2) What are the current analytical tools for analyzing PA data, particularly under the aims of classification, association with health outcomes, and prediction of health events? and (3) What challenges exist in the analyses, and what recommendations for future research are suggested regarding the use of statistical methods in various research tasks?

METHODS

This scoping review was conducted following an existing framework to map research studies by exploring the information about PA. Three databases, PubMed, IEEE Xplore, and the ACM Digital Library, were searched in February 2024 to identify related publications. Eligible articles were classification, association, or prediction studies involving human PA monitored through wearable accelerometers.

RESULTS

After screening 1312 articles, 428 (32.62%) eligible studies were identified and categorized into at least 1 of the following 3 thematic categories: classification (75/428, 17.5%), association (342/428, 79.9%), and prediction (32/428, 7.5%). Most articles (414/428, 96.7%) derived PA variables from 3D acceleration, rather than 1D acceleration. All eligible articles (428/428, 100%) considered PA metrics represented in the time domain, while a small fraction (16/428, 3.7%) also considered PA metrics in the frequency domain. The number of studies evaluating the influence of PA on health conditions has increased greatly. Among the studies in our review, regression-type models were the most prevalent (373/428, 87.1%). The machine learning approach for classification research is also gaining popularity (32/75, 43%). In addition to summary statistics of PA, several recent studies used tools to incorporate PA trajectories and account for temporal patterns, including longitudinal data analysis with repeated PA measurements and functional data analysis with PA as a continuum for time-varying association (68/428, 15.9%).

CONCLUSIONS

Summary metrics can quickly provide descriptions of the strength, frequency, and duration of individuals' overall PA. When the distribution and profile of PA need to be evaluated or detected, considering PA metrics as longitudinal or functional data can provide detailed information and improve the understanding of the role PA plays in health. Depending on the research goal, appropriate analytical tools can ensure the reliability of the scientific findings.

Identification of neutrophil extracellular trap-related biomarkers in non-alcoholic fatty liver disease through machine learning and single-cell analysis

Non-alcoholic Fatty Liver Disease (NAFLD), noted for its widespread prevalence among adults, has become the leading chronic liver condition globally. Simultaneously, the annual disease burden, particularly liver cirrhosis caused by NAFLD, has increased significantly. Neutrophil Extracellular Traps (NETs) play a crucial role in the progression of this disease and are key to the pathogenesis of NAFLD. However, research into the specific roles of NETs-related genes in NAFLD is still a field requiring thorough investigation. Utilizing techniques like AddModuleScore, ssGSEA, and WGCNA, our team conducted gene screening to identify the genes linked to NETs in both single-cell and bulk transcriptomics. Using algorithms including Random Forest, Support Vector Machine, Least Absolute Shrinkage, and Selection Operator, we identified ZFP36L2 and PHLDA1 as key hub genes. The pivotal role of these genes in NAFLD diagnosis was confirmed using the training dataset GSE164760. This study identified 116 genes linked to NETs across single-cell and bulk transcriptomic analyses. These genes demonstrated enrichment in immune and metabolic pathways. Additionally, two NETs-related hub genes, PHLDA1 and ZFP36L2, were selected through machine learning for integration into a prognostic model. These hub genes play roles in inflammatory and metabolic processes. scRNA-seq results showed variations in cellular communication among cells with different expression patterns of these key genes. In conclusion, this study explored the molecular characteristics of NETs-associated genes in NAFLD. It identified two potential biomarkers and analyzed their roles in the hepatic microenvironment. These discoveries could aid in NAFLD diagnosis and management, with the ultimate goal of enhancing patient outcomes.

Design of a Real-Time Physical Activity Detection and Classification Framework for Individuals With Type 1 Diabetes

BACKGROUND

Managing glycemia during and after exercise events in type 1 diabetes (T1D) is challenging since these events can have wide-ranging effects on glycemia depending on the event timing, type, intensity. To this end, advanced physical activity-informed technologies can be beneficial for improving glucose control.

METHODS

We propose a real-time physical activity detection and classification framework, which builds upon random forest models. This module automatically detects exercise sessions and predicts the activity type and intensity from tri-axial accelerometer, heart rate, and continuous glucose monitoring records.

RESULTS

Data from 19 adults with T1D who performed structured sessions of either aerobic, resistance, or high-intensity interval exercise at varying times of day were used to train and test this framework. The exercise onset and completion were both predicted within 1 minute with an average accuracy of 81% and 78%, respectively. Activity type and intensity were identified within 2.38 minutes and from the exercise onset. On participants assigned to the test set, the average accuracy for activity type and intensity classification was 74% and 73%, respectively, if exercise was announced. For unannounced exercise events, the classification accuracy was 65% for the activity type and 70% for its intensity.

CONCLUSIONS

The proposed module showed high performance in detection and classification of exercise in real-time within a minute of exercise onset. Integration of this module into insulin therapy decisions can help facilitate glucose management around physical activity.

Identification of potential diagnostic genes for atherosclerosis in women with polycystic ovary syndrome

Polycystic ovary syndrome (PCOS), which is the most prevalent endocrine disorder among women in their reproductive years, is linked to a higher occurrence and severity of atherosclerosis (AS). Nevertheless, the precise manner in which PCOS impacts the cardiovascular well-being of women remains ambiguous. The Gene Expression Omnibus database provided four PCOS datasets and two AS datasets for this study. Through the examination of genes originating from differentially expressed (DEGs) and critical modules utilizing functional enrichment analyses, weighted gene co-expression network (WGCNA), and machine learning algorithm, the research attempted to discover potential diagnostic genes. Additionally, the study investigated immune infiltration and conducted gene set enrichment analysis (GSEA) to examine the potential mechanism of the simultaneous occurrence of PCOS and AS. Two verification datasets and cell experiments were performed to assess biomarkers' reliability. The PCOS group identified 53 genes and AS group identified 175 genes by intersecting DEGs and key modules of WGCNA. Then, 18 genes from two groups were analyzed by machine learning algorithm. Death Associated Protein Kinase 1 (DAPK1) was recognized as an essential gene. Immune infiltration and single-gene GSEA results suggest that DAPK1 is associated with T cell-mediated immune responses. The mRNA expression of DAPK1 was upregulated in ox-LDL stimulated RAW264.7 cells and in granulosa cells. Our research discovered the close association between AS and PCOS, and identified DAPK1 as a crucial diagnostic biomarker for AS in PCOS.

Identification of macrophage differentiation related genes and subtypes linking atherosclerosis plaque processing and metabolic syndrome via integrated bulk and single-cell sequence analysis

Metabolic syndrome(MS) is a separate risk factor for the advancement of atherosclerosis(AS) plaque but mechanism behind this remains unclear. There may be a significant role for the immune system in this process. This study aims to identify potential diagnostic genes in MS patients at a higher risk of developing and progressing to AS. Datasets were retrevied from gene expression omnibus(GEO) database and differentially expressed genes were identified. Hub genes, immune cell dysregulation and AS subtypes were identified using a conbination of muliple bioinformatic analysis, machine learning and consensus clustering. Diagnostic value of hub genes was estimated using a nomogram and ROC analysis. Finally, enrichment analysis, competing endogenous RNA(ceRNA) network, single-cell RNA(scRNA) sequencing analysis and drug-protein interaction prediction was constructed to identify the functional roles, potential regulators and distribution for hub genes. Four hub genes and two macrophage-related subtypes were identified. Their strong diagnostic value was validated and functional process were identified. ScRNA analysis identified the macrophage differentiation regulation function of F13A1. CeRNA network and drug-protein binding modes revealed the potential therapeutic method. Four immune-correlated hub genes(F13A1, MMRN1, SLCO2A1 and ZNF521) were identified with their diagnostic value being assesed, which F13A1 was found strong correlated with macrophage differentiation and could be potential diagnostic and therapeutic marker for AS progression in MS patients.

Random forest model used to predict the medical out-of-pocket costs of hypertensive patients

Background

Precise prediction of out-of-pocket (OOP) costs to improve health policy design is important for governments of countries with national health insurance. Controlling the medical expenses for hypertension, one of the leading causes of stroke and ischemic heart disease, is an important issue for the Japanese government. This study aims to explore the importance of OOP costs for outpatients with hypertension.

Methods

To obtain a precise prediction of the highest quartile group of OOP costs of hypertensive outpatients, we used nationwide longitudinal data, and estimated a random forest (RF) model focusing on complications with other lifestyle-related diseases and the nonlinearities of the data.

Results

The results of the RF models showed that the prediction accuracy of OOP costs for hypertensive patients without activities of daily living (ADL) difficulties was slightly better than that for all hypertensive patients who continued physician visits during the past two consecutive years. Important variables of the highest quartile of OOP costs were age, diabetes or lipidemia, lack of habitual exercise, and moderate or vigorous regular exercise.

Conclusion

As preventing complications of diabetes or lipidemia is important for reducing OOP costs in outpatients with hypertension, regular exercise of moderate or vigorous intensity is recommended for hypertensive patients that do not have ADL difficulty. For hypertensive patients with ADL difficulty, habitual exercise is not recommended.

A review of the evidence for the utility of physical activity monitor use in patients with idiopathic inflammatory myopathies

Few proven therapies exist for patients with idiopathic inflammatory myopathies (IIMs), partly due to the lack of reliable and valid outcome measures for assessing treatment responses. The current core set measures developed by the International Myositis Assessment and Clinical Studies group were developed to standardize assessments of disease activity and treatment effect. None of the current measures address functional improvement in muscle weakness. Therefore, supplemental measures to more objectively assess physical activity levels and fatiguability in free-living settings are needed to assess disease activity more comprehensively. Validated physical activity monitors (PAMs) have the potential to serve as an objective functional outcome measure in clinical trials and observational studies. This review examines the current evidence for the use of body-worn PAMs in clinical settings with IIM patients. A practical overview of methods for PAM use in clinical patient populations (including measurement details and data processing) that focuses on IIM patients is also presented.

A quantitative analysis of fidgeting in ADHD and its relation to performance and sustained attention on a cognitive task

Introduction

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder where hyperactivity often manifests as fidgeting, a non-goal-directed motoric action. Many studies demonstrate fidgeting varies under different conditions as a self-regulating mechanism for attention and alertness during cognitively demanding tasks. Fidgeting has also been associated with reaction time variability. However, a lack of standard variables to define and quantify fidgeting can lead to discrepancies in data and interpretability issues across studies. Furthermore, little is known about fidgeting in adults with ADHD compared to youth. This study aims to design a framework to quantify meaningful fidgeting variables and to apply them to test the relation between fidgeting and performance on a cognitive task, the Flanker, in adults with ADHD.

Method

Our study included 70 adult participants diagnosed with ADHD, aged 18-50 years (30.5 ± 7.2 years). Screening included a structured clinical interview, childhood, current self and current observer ratings of ADHD symptoms. Actigraphy devices were attached to the left wrist and right ankle during completion of a cognitive control, attention task (the Flanker). Laboratory testing was subsequently completed on a single day. The relation between task performance, reaction time variability and fidgeting was examined.

Results and Discussion

Our analysis revealed increased fidgeting during correct trials as defined by our new variables, consistent with previous observations. Furthermore, differences in fidgeting were observed between early and later trials while the percentage of correct trials were not significantly different. This suggests a relation between the role of fidgeting and sustaining attention. Participants with low reaction time variability, that is, those with more consistent reaction times, fidgeted more during later trials. This observation supports the theory that fidgeting aids arousal and improves sustained attention. Finally, a correlation analysis using ADHD-symptom rating scales validated the relevance of the fidget variables in relation to ADHD symptom severity. These findings suggest fidgeting may be a compensatory mechanism that aids in sustained attention for those with ADHD, although alternative explanations exist.

Conclusion

Our study suggests that fidgeting may aid in sustained attention during the attention-demanding, cognitive control processes for adults with ADHD, with more fidgeting observed during correct trials and among participants with lower reaction time variability. Furthermore, the newly defined fidget variables were validated through a significant correlation with ADHD rating scales. By sharing our implementation of fidget variables, we hope to standardize and encourage further quantitative research into the role of fidgeting in ADHD.

Analysis and experimental validation of necroptosis-related molecular classification, immune signature and feature genes in Alzheimer's disease

Necroptosis, a programmed cell death pathway, has been demonstrated to be activated in Alzheimer's disease (AD). However, the precise role of necroptosis and its correlation with immune cell infiltration in AD remains unclear. In this study, we conducted non-negative matrix factorization clustering analysis to identify three subtypes of AD based on necroptosis-relevant genes. Notably, these subtypes exhibited varying necroptosis scores, clinical characteristics and immune infiltration signatures. Cluster B, characterized by high necroptosis scores, showed higher immune cell infiltration and was associated with a more severe pathology, potentially representing a high-risk subgroup. To identify potential biomarkers for AD within cluster B, we employed two machine learning algorithms: the least absolute shrinkage and selection operator regression and Random Forest. Subsequently, we identified eight feature genes (CARTPT, KLHL35, NRN1, NT5DC3, PCYOX1L, RHOQ, SLC6A12, and SLC38A2) that were utilized to develop a diagnosis model with remarkable predictive capacity for AD. Moreover, we conducted validation using bulk RNA-seq, single-nucleus RNA-seq, and in vivo experiments to confirm the expression of these feature genes. In summary, our study identified a novel necroptosis-related subtype of AD and eight diagnostic biomarkers, explored the roles of necroptosis in AD progression and shed new light for the clinical diagnosis and treatment of this disease.

Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning

BACKGROUND

There is mounting evidence that asthma might exacerbate depression. We sought to examine candidates for diagnostic genes in patients suffering from asthma and depression.

METHODS

Microarray data were downloaded from the Gene Expression Omnibus(GEO) database and used to screen for differential expressed genes(DEGs) in the SA and MDD datasets. A weighted gene co-expression network analysis(WGCNA) was used to identify the co-expression modules of SA and MDD. The least absolute shrinkage and selection operatoes(LASSO) and support vector machine(SVM) were used to determine critical biomarkers. Immune cell infiltration analysis was used to investigate the correlation between immune cell infiltration and common biomarkers of SA and MDD. Finally, validation of these analytical results was accomplished via the use of both in vivo and in vitro studies.

RESULTS

The number of DEGs that were included in the MDD dataset was 5177, whereas the asthma dataset had 1634 DEGs. The intersection of DEGs for SA and MDD included 351 genes, the strongest positive modules of SA and MDD was 119 genes, which played a function in immunity. The intersection of DEGs and modular hub genes was 54, following the analysis using machine learning algorithms,three hub genes were identified and employed to formulate a nomogram and for the evaluation of diagnostic effectiveness, which demonstrated a significant diagnostic value (area under the curve from 0.646 to 0.979). Additionally, immunocyte disorder was identified by immune infiltration. In vitro studies have revealed that STK11IP deficiency aggravated the LPS/IFN-γinduced up-regulation in M1 macrophage activation.

CONCLUSION

Asthma and MDD pathophysiology may be associated with alterations in inflammatory processes and immune pathways. Additionally, STK11IP may serve as a diagnostic marker for individuals with the two conditions.

SLCO4A1, as a novel prognostic biomarker of non‑small cell lung cancer, promotes cell proliferation and migration

Solute carrier organic anion transporter family member 4A1 (SLCO4A1) is a membrane transporter protein. The role of this molecule in non‑small cell lung cancer (NSCLC) remains unclear. Bulk sequencing was carried out using early‑stage NSCLC tissues with lymph node metastasis to identify SLCO4A1 that influences NSCLC cell proliferation, metastasis and prognosis. The in vitro functional assays carried out included the following: Cell Counting Kit‑8, plate colony formation, Transwell and wound healing assays. The molecular techniques used included reverse transcription‑quantitative PCR, western blotting and immunohistochemistry. The present study revealed the role of SLCO4A in NSCLC. SLCO4A1 was found to be expressed at high levels in NSCLC tissues and cells, and promotes cell proliferation, migration and invasion. Kaplan‑Meier survival analysis indicated that patients with NSCLC and high expression of SLCO4A1 had a poor prognosis. SLCO4A was revealed to regulate the expression of the proliferation‑related proteins Ki‑67 and PCNA, and that of the extracellular matrix proteins vimentin and E‑cadherin. Mechanistically, SLCO4A1 may affect the MAPK signaling pathway to promote NSCLC cell proliferation, migration and invasion. In addition, bioinformatics analysis demonstrated a strong association between SLCO4A1 and tumor infiltrating immune cells, highlighting its critical role in immune therapies such as immune checkpoint inhibitor treatment of patients with NSCLC.

The Temporal Relationship Between Moderate to Vigorous Physical Activity and Secondary Conditions During the First Year After Moderate to Severe Traumatic Brain Injury

OBJECTIVE

To determine the cross-sectional and temporal relationships between minutes per week of moderate to vigorous physical activity (MVPA) as measured by a wrist-worn accelerometer and secondary conditions in the first year after moderate to severe traumatic brain injury (TBI).

DESIGN

Prospective longitudinal cohort study.

SETTING

Four inpatient rehabilitation centers.

PARTICIPANTS

Individuals (N = 180) with moderate-severe TBI enrolled in the TBI Model Systems Study.

INTERVENTIONS

Participants wore a wrist accelerometer for 7 days immediately post discharge, and for 7 consecutive days at 6- and 12-months post injury.

MAIN OUTCOME MEASURES

Minutes per week of MVPA from daily averages based on wrist worn accelerometer. Secondary conditions included depression (Patient Health Questionnaire-9), fatigue (PROMIS Fatigue), Pain (Numeric Rating Scale), Sleep (Pittsburgh Sleep Quality Index), and cognition (Brief Test of Adult Cognition by Telephone).

RESULTS

At baseline, 6 and 12 months, 61%, 70% and 79% of the sample achieved at least 150 minutes per week of MVPA. The correlations between minutes of MVPA between baseline, 6 and 12 months were significant (r = 0.53-0.73), as were secondary conditions over these time points. However, no significant correlations were observed between minutes of MVPA and any secondary outcomes cross-sectionally or longitudinally at any time point.

CONCLUSIONS

Given the robust relationships physical activity has with outcomes in the general population, further research is needed to understand the effect of physical activity in individuals with moderate-severe TBI.

Identification of immune-associated genes in vascular dementia by integrated bioinformatics and inflammatory infiltrates

OBJECTIVE

Dysregulation of the immune system plays a vital role in the pathological process of vascular dementia, and this study aims to spot critical biomarkers and immune infiltrations in vascular dementia employing a bioinformatics approach.

METHODS

We acquired gene expression profiles from the Gene Expression Database. The gene expression data were analyzed using the bioinformatics method to identify candidate immune-related central genes for the diagnosis of vascular dementia. and the diagnostic value of nomograms and Receiver Operating Characteristic (ROC) curves were evaluated. We also examined the role of the VaD hub genes. Using the database and potential therapeutic drugs, we predicted the miRNA and lncRNA controlling the Hub genes. Immune cell infiltration was initiated to examine immune cell dysregulation in vascular dementia.

RESULTS

1321 immune genes were included in the combined immune dataset, and 2816 DEGs were examined in GSE122063. Twenty potential genes were found using differential gene analysis and co-expression network analysis. PPI network design and functional enrichment analysis were also done using the immune system as the main subject. To create the nomogram for evaluating the diagnostic value, four potential core genes were chosen by machine learning. All four putative center genes and nomograms have a solid diagnostic value (AUC ranged from 0.81 to 0.92). Their high confidence level became unquestionable by validating each of the four biomarkers using a different dataset. According to GeneMANIA and GSEA enrichment investigations, the pathophysiology of VaD is strongly related to inflammatory responses, drug reactions, and central nervous system degeneration. The data and Hub genes were used to construct a ceRNA network that includes three miRNAs, 90 lncRNA, and potential VaD therapeutics. Immune cells with varying dysregulation were also found.

CONCLUSION

Using bioinformatic techniques, our research identified four immune-related candidate core genes (HMOX1, EBI3, CYBB, and CCR5). Our study confirms the role of these Hub genes in the onset and progression of VaD at the level of immune infiltration. It predicts potential RNA regulatory pathways control VaD progression, which may provide ideas for treating clinical disease.

Cathepsin B serves as a potential prognostic biomarker and correlates with ferroptosis in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a long-term, systemic, and progressive autoimmune disorder. It has been established that ferroptosis, a type of iron-dependent lipid peroxidation cell death, is closely associated with RA. Fibroblast-like synoviocytes (FLS) are the main drivers of RA joint destruction, and they possess a high concentration of endoplasmic reticulum structure. Therefore, targeting ferroptosis and RA-FLS may be a potential treatment for RA.

METHODS

Four machine learning algorithms were utilized to detect the essential genes linked to RA, and an XGBoost model was created based on the identified genes. SHAP values were then used to visualize the factors that affect the development and progression of RA, and to analyze the importance of individual features in predicting the outcomes. Moreover, WGCNA and PPI were employed to identify the key genes related to RA, and CIBERSORT was used to analyze the correlation between the chosen genes and immune cells. Finally, the findings were validated through in vitro cell experiments, such as CCK-8 assay, lipid peroxidation assay, iron assay, GSH assay, and Western blot.

RESULTS

Bioinformatics and machine learning were employed to identify cathepsin B (CTSB) as a potential biomarker for RA. CTSB is highly expressed in RA patients and has been found to have a positive correlation with macrophages M2, neutrophils, and T cell follicular helper cells, and a negative correlation with CD8 T cells, monocytes, Tregs, and CD4 memory T cells. To investigate the effect of CTSB on RA-FLS from RA patients, the CTSB inhibitor CA-074Me was used and it was observed to reduce the proliferation and migration of RA-FLS, as indicated by the accumulation of lipid ROS and ferrous ions, and induce ferroptosis in RA-FLS.

CONCLUSIONS

This study identified CTSB, a gene associated with ferroptosis, as a potential biomarker for diagnosing and managing RA. Moreover, CA-074Me, a CTSB inhibitor, was observed to cause ferroptosis and reduce the migratory capacity of RA-FLS.

Identification of Neutrophil Extracellular Trap-Related Gene Expression Signatures in Ischemia Reperfusion Injury During Lung Transplantation: A Transcriptome Analysis and Clinical Validation

Purpose

Ischemia reperfusion injury (IRI) unavoidably occurs during lung transplantation, further contributing to primary graft dysfunction (PGD). Neutrophils are the end effectors of IRI and activated neutrophils release neutrophil extracellular traps (NETs) to further amplify damage. Nevertheless, potential contributions of NETs in IRI remain incompletely understood. This study aimed to explore NET-related gene biomarkers in IRI during lung transplantation.

Methods

Differential expression analysis was applied to identify differentially expressed genes (DEGs) for IRI during lung transplantation based on matrix data (GSE145989, 127003) downloaded from GEO database. The CIBERSORT and weighted gene co-expression network analysis (WGCNA) algorithms were utilized to identify key modules associated with neutrophil infiltration. Moreover, the least absolute shrinkage and selection operator regression and random forest were applied to identify potential NET-associated hub genes. Subsequently, the screened hub genes underwent further validation of an external dataset (GSE18995) and nomogram model. Based on clinical peripheral blood samples, immunofluorescence staining and dsDNA quantification were used to assess NET formation, and ELISA was applied to validate the expression of hub genes.

Results

Thirty-eight genes resulted from the intersection between 586 DEGs and 75 brown module genes, primarily enriched in leukocyte migration and NETs formation. Subsequently, four candidate hub genes (FCAR, MMP9, PADI4, and S100A12) were screened out via machine learning algorithms. Validation using an external dataset and nomogram model achieved better predictive value. Substantial NETs formation was demonstrated in IRI, with more pronounced NETs observed in patients with PGD ≥ 2. PADI4, S100A12, and MMP9 were all confirmed to be up-regulated after reperfusion through ELISA, with higher levels of S100A12 in PGD ≥ 2 patients compared with non-PGD patients.

Conclusion

We identified three potential NET-related biomarkers for IRI that provide new insights into early detection and potential therapeutic targets of IRI and PGD after lung transplantation.

Identification of 4 autophagy-related genes in heart failure by bioinformatics analysis and machine learning

Introduction

Autophagy refers to the process of breaking down and recycling damaged or unnecessary components within a cell to maintain cellular homeostasis. Heart failure (HF) is a severe medical condition that poses a serious threat to the patient's life. Autophagy is known to play a pivotal role in the pathogenesis of HF. However, our understanding of the specific mechanisms involved remains incomplete. Here, we identify autophagy-related genes (ARGs) associated with HF, which we believe will contribute to further comprehending the pathogenesis of HF.

Methods

By searching the GEO (Gene Expression Omnibus) database, we found the GSE57338 dataset, which was related to HF. ARGs were obtained from the HADb and HAMdb databases. Annotation of GO and enrichment analysis of KEGG pathway were carried out on the differentially expressed ARGs (AR-DEGs). We employed machine learning algorithms to conduct a thorough screening of significant genes and validated these genes by analyzing external dataset GSE76701 and conducting mouse models experimentation. At last, immune infiltration analysis was conducted, target drugs were screened and a TF regulatory network was constructed.

Results

Through processing the dataset with R language, we obtained a total of 442 DEGs. Additionally, we retrieved 803 ARGs from the database. The intersection of these two sets resulted in 15 AR-DEGs. Upon performing functional enrichment analysis, it was discovered that these genes exhibited significant enrichment in domains related to "regulation of cell growth", "icosatetraenoic acid binding", and "IL-17 signaling pathway". After screening and verification, we ultimately identified 4 key genes. Finally, an analysis of immune infiltration illustrated significant discrepancies in 16 distinct types of immune cells between the HF and control group and up to 194 potential drugs and 16 TFs were identified based on the key genes.

Discussion

In this study, TPCN1, MAP2K1, S100A9, and CD38 were considered as key autophagy-related genes in HF. With these relevant data, further exploration of the molecular mechanisms of autophagy in HF can be carried out.

IMU-Based Energy Expenditure Estimation for Various Walking Conditions Using a Hybrid CNN-LSTM Model

In ubiquitous healthcare systems, energy expenditure estimation based on wearable sensors such as inertial measurement units (IMUs) is important for monitoring the intensity of physical activity. Although several studies have reported data-driven methods to estimate energy expenditure during activities of daily living using wearable sensor signals, few have evaluated the performance while walking at various speeds and inclines. In this study, we present a hybrid model comprising a convolutional neural network (CNN) and long short-term memory (LSTM) to estimate the steady-state energy expenditure under various walking conditions based solely on IMU data. To implement and evaluate the model, we performed level/inclined walking and level running experiments on a treadmill. With regard to the model inputs, the performance of the proposed model based on fixed-size sequential data was compared with that of a method based on stride-segmented data under different conditions in terms of the sensor location, input sequence format, and neural network model. Based on the experimental results, the following conclusions were drawn: (i) the CNN-LSTM model using a two-second sequence from the IMU attached to the lower body yielded optimal performance, and (ii) although the stride-segmented data-based method showed superior performance, the performance difference between the two methods was not significant; therefore, the proposed model based on fixed-size sequential data may be considered more practical as it does not require heel-strike detection.

Estimating Physical Activity and Sleep using the Combination of Movement and Heart Rate: A Systematic Review and Meta-Analysis

The purpose of this meta-analysis was to quantify the difference in physical activity and sleep estimates assessed via 1) movement, 2) heart rate (HR), or 3) the combination of movement and HR (MOVE+HR) compared to criterion indicators of the outcomes. Searches in four electronic databases were executed September 21-24 of 2021. Weighted mean was calculated from standardized group-level estimates of mean percent error (MPE) and mean absolute percent error (MAPE) of the proxy signal compared to the criterion measurement method for physical activity, HR, or sleep. Standardized mean difference (SMD) effect sizes between the proxy and criterion estimates were calculated for each study across all outcomes, and meta-regression analyses were conducted. Two-One-Sided-Tests method were conducted to metaanalytically evaluate the equivalence of the proxy and criterion. Thirty-nine studies (physical activity k = 29 and sleep k = 10) were identified for data extraction. Sample size weighted means for MPE were -38.0%, 7.8%, -1.4%, and -0.6% for physical activity movement only, HR only, MOVE+HR, and sleep MOVE+HR, respectively. Sample size weighted means for MAPE were 41.4%, 32.6%, 13.3%, and 10.8% for physical activity movement only, HR only, MOVE+HR, and sleep MOVE+HR, respectively. Few estimates were statistically equivalent at a SMD of 0.8. Estimates of physical activity from MOVE+HR were not statistically significantly different from estimates based on movement or HR only. For sleep, included studies based their estimates solely on the combination of MOVE+HR, so it was impossible to determine if the combination produced significantly different estimates than either method alone.

Potential diagnostic markers and biological mechanism for osteoarthritis with obesity based on bioinformatics analysis

Numerous observational studies have shown that obesity (OB) is a significant risk factor in the occurrence and progression of osteoarthritis (OA), but the underlying molecular mechanism between them remains unclear. The study aimed to identify the key genes and pathogeneses for OA with OB. We obtained two OA and two OB datasets from the gene expression omnibus (GEO) database. First, the identification of differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were used to identify key genes for diagnosing OA with OB, and then the nomogram and receiver operating characteristic (ROC) curve were conducted to assess the diagnostic value of key genes. Second, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore the pathogenesis of OA with OB. Third, CIBERSORT was created to investigate immunocyte dysregulation in OA and OB. In this study, two genes (SOD2, ZNF24) were finally identified as key genes for OA with OB. These two key genes had high diagnostic values via nomogram and ROC curve calculation. Additionally, functional analysis emphasized that oxidative stress and inflammation response were shared pathogenesis of OB and AD. Finally, in OA and OB, immune infiltration analysis showed that SOD2 closely correlated to M2 macrophages, regulatory T cells, and CD8 T cells, and ZNF24 correlated to regulatory T cells. Overall, our findings might be new biomarkers or potential therapeutic targets for OA and OB comorbidity.

A convenient machine learning model to predict full stomach and evaluate the safety and comfort improvements of preoperative oral carbohydrate in patients undergoing elective painless gastrointestinal endoscopy

BACKGROUND AND AIMS

Assessment of the patient's gastric contents is the key to avoiding aspiration incidents, however, there is no effective method to determine whether elective painless gastrointestinal endoscopy (GIE) patients have a full stomach or an empty stomach. And previous studies have shown that preoperative oral carbohydrates (POCs) can improve the discomfort induced by fasting, but there are different perspectives on their safety. This study aimed to develop a convenient, accurate machine learning (ML) model to predict full stomach. And based on the model outcomes, evaluate the safety and comfort improvements of POCs in empty- and full stomach groups.

METHODS

We enrolled 1386 painless GIE patients between October 2022 and January 2023 in Nanjing First Hospital, and 1090 patients without POCs were used to construct five different ML models to identify full stomach. The metrics of discrimination and calibration validated the robustness of the models. For the best-performance model, we further interpreted it through SHapley Additive exPlanations (SHAP) and constructed a web calculator to facilitate clinical use. We evaluated the safety and comfort improvements of POCs by propensity score matching (PSM) in the two groups, respectively.

RESULTS

Random Forest (RF) model showed the greatest discrimination with the area under the receiver operating characteristic curve (AUROC) 0.837 [95% confidence interval (CI): 79.1-88.2], F1 71.5%, and best calibration with a Brier score of 15.2%. The web calculator can be visited at https://medication.shinyapps.io/RF_model/. PSM results demonstrated that POCs significantly reduced the full stomach incident in empty stomach group (p < 0.05), but no differences in full stomach group (p > 0.05). Comfort improved in both groups and was more significant in empty stomach group.

CONCLUSIONS

The developed convenient RF model predicted full stomach with high accuracy and interpretability. POCs were safe and comfortably improved in both groups, with more benefit in empty stomach group. These findings may guide the patients' gastrointestinal preparation.

Novel insights into molecular signatures and pathogenic cell populations shared by systemic lupus erythematosus and vascular dementia

Although vascular dementia (VD) and systemic lupus erythematosus (SLE) may share immune-mediated pathophysiologic processes, the underlying mechanisms are unclear. This study investigated shared gene signatures in SLE versus VD, as well as their potential molecular mechanisms. Bulk RNA sequencing (RNAseq) and single-cell or single-nucleus RNAseq (sc/snRNAseq) datasets from SLE blood samples and VD brain samples were obtained from Gene Expression Omnibus. The identification of genes associated with both SLE and VD was performed using the weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. For the sc/snRNAseq data, an unbiased clustering pipeline based on Seurat and CellChat was used to determine the cellular landscape profile and examine intracellular communication, respectively. The results were subsequently validated using a mice model of SLE with cognitive dysfunction (female MRL/lpr mice). WGCNA and machine learning identified C1QA, LY96, CD163, and MS4A4A as key genes for SLE and VD. sc/snRNAseq analyses revealed that CD163 and MS4A4A were upregulated in mononuclear phagocytes (MPs) from SLE and VD samples and were associated with monocyte-macrophage differentiation. Intriguingly, LGALS9-associated molecular pathway, as the only signaling pathway common between SLE and VD via CellChat analysis, exhibited significant upregulation in cortical microglia of MRL/lpr mice. Our analyses identified C1QA, LY96, CD163, and MS4A4A as potential biomarkers for SLE and VD. Moreover, the upregulation of CD163/MS4A4A and activation of LGALS9 signaling in MPs may contribute to the pathogenesis of VD with SLE. These findings offer novel insight into the mechanisms underlying VD in SLE patients.

Identification of immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning

Background: There is increasing evidence indicating that immune system dysregulation plays a pivotal role in the pathogenesis of retinopathy of prematurity (ROP) and sepsis. This study aims to identify key diagnostic candidate genes in ROP with sepsis. Methods: We obtained publicly available data on ROP and sepsis from the gene expression omnibus database. Differential analysis and weighted gene correlation network analysis (WGCNA) were performed to identify differentially expressed genes (DEGs) and key module genes. Subsequently, we conducted functional enrichment analysis to gain insights into the biological functions and pathways. To identify immune-related pathogenic genes and potential mechanisms, we employed several machine learning algorithms, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). We evaluated the diagnostic performance using nomogram and Receiver Operating Characteristic (ROC) curves. Furthermore, we used CIBERSORT to investigate immune cell dysregulation in sepsis and performed cMAP analysis to identify potential therapeutic drugs. Results: The sepsis dataset comprised 352 DEGs, while the ROP dataset had 307 DEGs and 420 module genes. The intersection between DEGs for sepsis and module genes for ROP consisted of 34 genes, primarily enriched in immune-related pathways. After conducting PPI network analysis and employing machine learning algorithms, we pinpointed five candidate hub genes. Subsequent evaluation using nomograms and ROC curves underscored their robust diagnostic potential. Immune cell infiltration analysis revealed immune cell dysregulation. Finally, through cMAP analysis, we identified some small molecule compounds that have the potential for sepsis treatment. Conclusion: Five immune-associated candidate hub genes (CLEC5A, KLRB1, LCN2, MCEMP1, and MMP9) were recognized, and the nomogram for the diagnosis of ROP with sepsis was developed.

Association between physical activity measured by accelerometry and cognitive function in older adults: a systematic review

OBJECTIVE

To analyze studies that investigated the association between physical activity assessed by accelerometry and cognitive function in older people.

METHODS

A systematic review was carried out in four electronic databases (PubMed, Web of Science, Scopus, and SportsDiscus).

RESULTS

In total, 195 records were identified. Fifty-two studies were selected for a full evaluation; 23 were selected according to the inclusion criteria adopted and divided into four chapters (characteristics of the studies, the association between physical activity level and cognitive function decline, effects of physical activity in reducing the chances of cognitive function decline and effects of physical activity on brain plasticity. The cross-sectional studies had an average score of 7 points, and the cohort studies obtained 10 points, indicating the high quality of the selected studies. Seven studies indicated an association between Moderate to vigorous physical activities (MVPA) and cognitive function, two specifically indicated a reduction in the chances of cognitive function decline according to the interquartile of MVPA, and three studies indicated improvements in MVPA in brain plasticity.

CONCLUSION

Measured by accelerometry, seems to be favorably associated with important outcomes in cognitive function assessed through questionnaires, imaging analyses, and biochemical markers with older adults.

Identification of co-diagnostic effect genes for aortic dissection and metabolic syndrome by multiple machine learning algorithms

Aortic dissection (AD) is a life-threatening condition in which the inner layer of the aorta tears. It has been reported that metabolic syndrome (MS) has a close linkage with aortic dissection. However, the inter-relational mechanisms between them were still unclear. This article explored the hub gene signatures and potential molecular mechanisms in AD and MS. We obtained five bulk RNA-seq datasets of AD, one single cell RNA-seq (scRNA-seq) dataset of ascending thoracic aortic aneurysm (ATAA), and one bulk RNA-seq dataset of MS from the gene expression omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and key modules via weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and machine learning algorithms (Random Forest and LASSO regression) were used to identify hub genes for diagnosing AD with MS. XGBoost further improved the diagnostic performance of the model. The receiver operating characteristic (ROC) and precision-recall (PR) curves were developed to assess the diagnostic value. Then, immune cell infiltration and metabolism-associated pathways analyses were created to investigate immune cell and metabolism-associated pathway dysregulation in AD and MS. Finally, the scRNA-seq dataset was performed to confirm the expression levels of identified hub genes. 406 common DEGs were identified between the merged AD and MS datasets. Functional enrichment analysis revealed these DEGs were enriched for applicable terms of metabolism, cellular processes, organismal systems, and human diseases. Besides, the positively related key modules of AD and MS were mainly enriched in transcription factor binding and inflammatory response. In contrast, the negatively related modules were significantly associated with adaptive immune response and regulation of nuclease activity. Through machine learning, nine genes with common diagnostic effects were found in AD and MS, including MAD2L2, IMP4, PRPF4, CHSY1, SLC20A1, SLC9A1, TIPRL, DPYD, and MAPKAPK2. In the training set, the AUC of the hub gene on RP and RR curves was 1. In the AD verification set, the AUC of the Hub gene on RP and RR curves were 0.946 and 0.955, respectively. In the MS set, the AUC of the Hub gene on RP and RR curves were 0.978 and 0.98, respectively. scRNA-seq analysis revealed that the SLC20A1 was found to be relevant in fatty acid metabolic pathways and expressed in endothelial cells. Our study revealed the common pathogenesis of AD and MS. These common pathways and hub genes might provide new ideas for further mechanism research.

Assessment of 24-hour physical behaviour in adults via wearables: a systematic review of validation studies under laboratory conditions

BACKGROUND

Wearable technology is used by consumers and researchers worldwide for continuous activity monitoring in daily life. Results of high-quality laboratory-based validation studies enable us to make a guided decision on which study to rely on and which device to use. However, reviews in adults that focus on the quality of existing laboratory studies are missing.

METHODS

We conducted a systematic review of wearable validation studies with adults. Eligibility criteria were: (i) study under laboratory conditions with humans (age ≥ 18 years); (ii) validated device outcome must belong to one dimension of the 24-hour physical behavior construct (i.e., intensity, posture/activity type, and biological state); (iii) study protocol must include a criterion measure; (iv) study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in five electronic databases as well as back- and forward citation searches. The risk of bias was assessed based on the QUADAS-2 tool with eight signaling questions.

RESULTS

Out of 13,285 unique search results, 545 published articles between 1994 and 2022 were included. Most studies (73.8% (N = 420)) validated an intensity measure outcome such as energy expenditure; only 14% (N = 80) and 12.2% (N = 70) of studies validated biological state or posture/activity type outcomes, respectively. Most protocols validated wearables in healthy adults between 18 and 65 years. Most wearables were only validated once. Further, we identified six wearables (i.e., ActiGraph GT3X+, ActiGraph GT9X, Apple Watch 2, Axivity AX3, Fitbit Charge 2, Fitbit, and GENEActiv) that had been used to validate outcomes from all three dimensions, but none of them were consistently ranked with moderate to high validity. Risk of bias assessment resulted in 4.4% (N = 24) of all studies being classified as "low risk", while 16.5% (N = 90) were classified as "some concerns" and 79.1% (N = 431) as "high risk".

CONCLUSION

Laboratory validation studies of wearables assessing physical behaviour in adults are characterized by low methodological quality, large variability in design, and a focus on intensity. Future research should more strongly aim at all components of the 24-hour physical behaviour construct, and strive for standardized protocols embedded in a validation framework.

Wrist-Worn Accelerometry, Aging, and Gait Speed in the Baltimore Longitudinal Study of Aging

Wrist-worn accelerometry metrics are not well defined in older adults. Accelerometry data from 720 participants (mean age 70 years, 55% women) were summarized into (a) total activity counts per day, (b) active minutes per day, (c) active bouts per day, and (d) activity fragmentation (the reciprocal of the mean active bout length). Linear regression and mixed-effects models were utilized to estimate associations between age and gait speed with wrist accelerometry. Activity counts per day, daily active minutes per day, and active bouts per day were negatively associated with age among all participants, while positive associations with activity fragmentation were only observed among those ≥65 years. More activity counts, more daily active minutes, and lower activity fragmentation were associated with faster gait speed. There were baseline age interactions with annual changes in total activity counts per day, active minutes per day, and activity fragmentation (Baseline age × Time, p < .01 for all). These results help define and characterize changes in wrist-based physical activity patterns among older adults.

Exploring the shared gene signatures of smoking-related osteoporosis and chronic obstructive pulmonary disease using machine learning algorithms

Objectives: Cigarette smoking has been recognized as a predisposing factor for both osteoporosis (OP) and chronic obstructive pulmonary disease (COPD). This study aimed to investigate the shared gene signatures affected by cigarette smoking in OP and COPD through gene expression profiling. Materials and methods: Microarray datasets (GSE11784, GSE13850, GSE10006, and GSE103174) were obtained from Gene Expression Omnibus (GEO) and analyzed for differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA). Least absolute shrinkage and selection operator (LASSO) regression method and a random forest (RF) machine learning algorithm were used to identify candidate biomarkers. The diagnostic value of the method was assessed using logistic regression and receiver operating characteristic (ROC) curve analysis. Finally, immune cell infiltration was analyzed to identify dysregulated immune cells in cigarette smoking-induced COPD. Results: In the smoking-related OP and COPD datasets, 2858 and 280 DEGs were identified, respectively. WGCNA revealed 982 genes strongly correlated with smoking-related OP, of which 32 overlapped with the hub genes of COPD. Gene Ontology (GO) enrichment analysis showed that the overlapping genes were enriched in the immune system category. Using LASSO regression and RF machine learning, six candidate genes were identified, and a logistic regression model was constructed, which had high diagnostic values for both the training set and external validation datasets. The area under the curves (AUCs) were 0.83 and 0.99, respectively. Immune cell infiltration analysis revealed dysregulation in several immune cells, and six immune-associated genes were identified for smoking-related OP and COPD, namely, mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1), tissue-type plasminogen activator (PLAT), sodium channel 1 subunit alpha (SCNN1A), sine oculis homeobox 3 (SIX3), sperm-associated antigen 9 (SPAG9), and vacuolar protein sorting 35 (VPS35). Conclusion: The findings suggest that immune cell infiltration profiles play a significant role in the shared pathogenesis of smoking-related OP and COPD. The results could provide valuable insights for developing novel therapeutic strategies for managing these disorders, as well as shedding light on their pathogenesis.

WGCNA combined with machine learning algorithms for analyzing key genes and immune cell infiltration in heart failure due to ischemic cardiomyopathy

Background

Ischemic cardiomyopathy (ICM) induced heart failure (HF) is one of the most common causes of death worldwide. This study aimed to find candidate genes for ICM-HF and to identify relevant biomarkers by machine learning (ML).

Methods

The expression data of ICM-HF and normal samples were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between ICM-HF and normal group were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and gene ontology (GO) annotation analysis, protein–protein interaction (PPI) network, gene pathway enrichment analysis (GSEA), and single-sample gene set enrichment analysis (ssGSEA) were performed. Weighted gene co-expression network analysis (WGCNA) was applied to screen for disease-associated modules, and relevant genes were derived using four ML algorithms. The diagnostic values of candidate genes were assessed using receiver operating characteristic (ROC) curves. The immune cell infiltration analysis was performed between the ICM-HF and normal group. Validation was performed using another gene set.

Results

A total of 313 DEGs were identified between ICM-HF and normal group of GSE57345, which were mainly enriched in biological processes and pathways related to cell cycle regulation, lipid metabolism pathways, immune response pathways, and intrinsic organelle damage regulation. GSEA results showed positive correlations with pathways such as cholesterol metabolism in the ICM-HF group compared to normal group and lipid metabolism in adipocytes. GSEA results also showed a positive correlation with pathways such as cholesterol metabolism and a negative correlation with pathways such as lipolytic presentation in adipocytes compared to normal group. Combining multiple ML and cytohubba algorithms yielded 11 relevant genes. After validation using the GSE42955 validation sets, the 7 genes obtained by the machine learning algorithm were well verified. The immune cell infiltration analysis showed significant differences in mast cells, plasma cells, naive B cells, and NK cells.

Conclusion

Combined analysis using WGCNA and ML identified coiled-coil-helix-coiled-coil-helix domain containing 4 (CHCHD4), transmembrane protein 53 (TMEM53), acid phosphatase 3 (ACPP), aminoadipate-semialdehyde dehydrogenase (AASDH), purinergic receptor P2Y1 (P2RY1), caspase 3 (CASP3) and aquaporin 7 (AQP7) as potential biomarkers of ICM-HF. ICM-HF may be closely related to pathways such as mitochondrial damage and disorders of lipid metabolism, while the infiltration of multiple immune cells was identified to play a critical role in the progression of the disease.