Salivary Protein Panel to Diagnose Systolic Heart Failure

Advancing intrauterine adhesion severity prediction: Integrative machine learning approach with hysteroscopic cold knife system, clinical characteristics and hematological parameters

Intrauterine Adhesion (IUA) constitute a significant determinant impacting female fertility, potentially leading to infertility, miscarriage, menstrual irregularities, and placental complications. The precise assessment of the severity of IUA is pivotal for the customization of personalized treatment plans, aimed at enhancing the success rate of treatments and mitigating reproductive health risks. This study proposes bTLSMA-SVM-FS, a novel feature selection machine learning model that integrates an enhanced slime mould algorithm (SMA), termed TLSMA, with support vector machines (SVM), aiming to develop a predictive model for assessing the severity of IUA. Initially, a series of optimization comparative experiments were conducted on the TLSMA using the CEC 2017 benchmark functions. By comparing it with eleven meta-heuristic algorithms as well as eleven SOTA algorithms, the experimental outcomes corroborated the superior performance of the TLSMA. Subsequently, the developed bTLSMA-SVM-FS model was employed to conduct a thorough analysis of the clinical features of 107 IUA patients from Wenzhou People's Hospital, comprising 61 cases of moderate IUA and 46 cases of severe IUA. The evaluation results of the model demonstrated exceptional performance in predicting the severity of IUA, achieving an accuracy of 86.700 % and a specificity of 87.609 %. Moreover, the model successfully identified critical factors influencing the prediction of IUA severity, including the preoperative Chinese IUA score, production times, thrombin time, preoperative endometrial thickness, and menstruation. The identification of these key factors not only further validated the efficacy of the proposed model but also provided vital scientific evidence for a deeper understanding of the pathogenesis of IUA and the enhancement of targeted treatment strategies.

Paper electrochemical immunosensor for the rapid screening of Galectin-3 patients with heart failure

A paper electrochemical immunosensor for the combined binding and quantification of the heart failure (HF) biomarker Galectin-3 has been developed. The simple design of the new sensor is comprised of paper material that is decorated with gold nanostructures, to maximize its electroactive surface area, and functionalized with target-specific recognition molecules to selectively bind the protein from aqueous solutions. The binding of the protein caused the blockage of the electron flow to the sensor electroactive surface, thus causing its oxidation potential to shift and the corresponding current to reduce quantitatively with the increase in the protein concentration within the working range of 0.5ng/mL-8ng/mL (LOQ-0.5 ng/mL). This novel sensor was able to quantify Galectin-3 concentration in saliva samples from HF patients and healthy controls within 20 min with good reproducibility (RSD = 3.64%), without the need for complex sample processing steps. The electrochemical measurements of the patient samples were cross validated by ELISA where the percent agreement between the two methods was found to be 92.7% (RSD = 7.20%). Therefore, the new paper immunosensor sensor has a strong potential for rapid and cost-effective screening of the Galectin 3 biomarker at points of care, thus supporting the timely diagnosis of heart failure.

Proteome profiling of early gestational plasma reveals novel biomarkers of congenital heart disease

Prenatal diagnosis of congenital heart disease (CHD) relies primarily on fetal echocardiography conducted at mid-gestational age-the sensitivity of which varies among centers and practitioners. An objective method for early diagnosis is needed. Here, we conducted a case-control study recruiting 103 pregnant women with healthy offspring and 104 cases with CHD offspring, including VSD (42/104), ASD (20/104), and other CHD phenotypes. Plasma was collected during the first trimester and proteomic analysis was performed. Principal component analysis revealed considerable differences between the controls and the CHDs. Among the significantly altered proteins, 25 upregulated proteins in CHDs were enriched in amino acid metabolism, extracellular matrix receptor, and actin skeleton regulation, whereas 49 downregulated proteins were enriched in carbohydrate metabolism, cardiac muscle contraction, and cardiomyopathy. The machine learning model reached an area under the curve of 0.964 and was highly accurate in recognizing CHDs. This study provides a highly valuable proteomics resource to better recognize the cause of CHD and has developed a reliable objective method for the early recognition of CHD, facilitating early intervention and better prognosis.

Salivary inflammatory biomarkers are predictive of mild cognitive impairment and Alzheimer's disease in a feasibility study

Alzheimer's disease (AD) is an insidious disease. Its distinctive pathology forms over a considerable length of time without symptoms. There is a need to detect this disease, before even subtle changes occur in cognition. Hallmark AD biomarkers, tau and amyloid-β, have shown promising results in CSF and blood. However, detecting early changes in these biomarkers and others will involve screening a wide group of healthy, asymptomatic individuals. Saliva is a feasible alternative. Sample collection is economical, non-invasive and saliva is an abundant source of proteins including tau and amyloid-β. This work sought to extend an earlier promising untargeted mass spectrometry study in saliva from individuals with mild cognitive impairment (MCI) or AD with age- and gender-matched cognitively normal from the South Australian Neurodegenerative Disease cohort. Five proteins, with key roles in inflammation, were chosen from this study and measured by ELISA from individuals with AD (n = 16), MCI (n = 15) and cognitively normal (n = 29). The concentrations of Cystatin-C, Interleukin-1 receptor antagonist, Stratifin, Matrix metalloproteinase 9 and Haptoglobin proteins had altered abundance in saliva from AD and MCI, consistent with the earlier study. Receiver operating characteristic analysis showed that combinations of these proteins demonstrated excellent diagnostic accuracy for distinguishing both MCI (area under curve = 0.97) and AD (area under curve = 0.97) from cognitively normal. These results provide evidence for saliva being a valuable source of biomarkers for early detection of cognitive impairment in individuals on the AD continuum and potentially other neurodegenerative diseases.

High S100A7 expression is associated with early muscle invasion and poor survival in bladder carcinoma

Muscle-invasive bladder carcinoma (MIBC) accounts for 25% of newly diagnosed bladder carcinomas (BCs) and presents a high risk of progression and metastasis. This study aimed to identify reliable biomarkers associated with muscle invasion and prognosis to identify potential therapeutic targets for MIBC. Four gene datasets were downloaded from the Gene Expression Omnibus, and the integrated differentially expressed genes (DEGs) were then subjected to gene ontology (GO) terms and pathway enrichment analyses. Correlation analysis between the expression of the top-ranking DEGs and pathological T stages was performed to identify the genes associated with early muscle invasion. The corresponding prognostic values were evaluated, and co-expressed genes mined in the cBioPortal database were loaded into ClueGo in Cytoscape for pathway enrichment analysis. Using data mining from the STRING and TCGA databases, protein-protein interaction and competitive endogenous RNA networks were constructed. In total, 645 integrated DEGs were identified and these were mainly enriched in 26 pathways, including cell cycle, bladder cancer, DNA replication, and PPAR signaling pathway. S100A7 expression was significantly increased from the T2 stage and showed significantly worse overall survival and disease-specific survival in patients with BC. In total, 144 genes co-expressed with S100A7 in BC were significantly enriched in the IL-17 pathway. S100A7 was predicted to directly interact with LYZ, which potentially shows competitive binding with hsa-mir-140 to affect the expression of six lncRNAs in MIBC. In conclusion, high S100A7 expression was predicted to be associated with early muscle invasion and poor survival in patients with BC.

Identification of Salivary Proteomic Biomarkers for Oral Cancer Screening

BACKGROUND/AIM

The current study aimed to identify biomarkers for differentiating between patients with oral cancer (OC) and healthy controls (HCs) on the basis of the comprehensive proteomic analyses of saliva samples by using liquid chromatography-mass spectrometry (LC-MS/MS).

PATIENTS AND METHODS

Unstimulated saliva samples were collected from 39 patients with OC and from 31 HCs. Proteins in the saliva were comprehensively analyzed using LC-MS/MS. To differentiate between patients with OC and HCs, a multiple logistic regression model was developed for evaluating the discriminatory ability of a combination of multiple markers.

RESULTS

A total of 23 proteins were significantly differentially expressed between the patients with OC and the HCs. Six out of the 23 proteins, namely α-2-macroglobulin-like protein 1, cornulin, hemoglobin subunit β, Ig k chain V-II region Vk167, kininogen-1 and transmembrane protease serine 11D, were selected using the forward-selection method and applied to the multiple logistic regression model. The area under the curve for discriminating between patients with OC and HCs was 0.957 when the combination of the six metabolites was used (95% confidence interval=0.915-0.998; p<0.001). Furthermore, these candidate proteins did not show a stage-specific difference.

CONCLUSION

The results of the current study showed that six salivary proteins are potential non-invasive biomarkers for OC screening.

Saliva proteomic patterns in patients with molar incisor hypomineralization

Molar incisor hypomineralization (MIH) is an endemic pediatric disease with an unclear pathogenesis. Considering that saliva controls enamel remineralization and that MIH is associated with higher saliva flow rate, we hypothesized that the protein composition of saliva is linked to disease. To test this, we enrolled 5 children aged 6-14 years with MIH showing at least one hypersensitive molar and 5 caries-free children without hypomineralization. Saliva samples were subjected to proteomic analysis followed by protein classification in to biological pathways. Among 618 salivary proteins identified with high confidence, 88 proteins were identified exclusively in MIH patients and 16 proteins in healthy controls only. Biological pathway analysis classified these 88 patient-only proteins to neutrophil-mediated adaptive immunity, the activation of the classical pathway of complement activation, extracellular matrix degradation, heme scavenging as well as glutathione -and drug metabolism. The 16 controls-only proteins were associated with adaptive immunity related to platelet degranulation and the lysosome. This report suggests that the proteaneous composition of saliva is affected in MIH patients, reflecting a catabolic environment which is linked to inflammation.