Identification and validation of protein biomarkers for predicting gastrointestinal stromal tumor recurrence

We conducted a proteomic analysis using mass spectrometry to identify and validate protein biomarkers for accurately predicting recurrence risk in gastrointestinal stromal tumors (GIST) patients, focusing on differentially expressed proteins in metastatic versus primary GIST tissues. We selected five biomarkers-GPX4, RBM4, TPM3, PFKFB2, and PGAM5-and validated their expressions in primary tumors of recurrent and non-recurrent GIST patients via immunohistochemistry. Our analysis of the association between these biomarkers with recurrence-free survival (RFS) and overall survival (OS), along with their interrelationships, revealed that immunohistochemistry confirmed significantly higher expressions of these biomarkers in primary GIST tissues of recurrent patients. Kaplan-Meier survival analysis showed that high expressions of GPX4, RBM4, TPM3, PFKFB2, and PGAM5 correlated with lower RFS, and GPX4 and RBM4 with lower OS. All biomarker pairs showed positive associations, with high expressions correlating with increased recurrence rates, and GPX4 and RBM4 with higher mortality rates. In conclusion, the biomarkers GPX4, RBM4, TPM3, PFKFB2, and PGAM5 are clinically relevant for predicting GIST recurrence, with their high expressions in primary tumors linked to poorer RFS and OS. They serve as potential prognostic indicators, enabling early treatment and improved outcomes. The observed interrelationships among these biomarkers further validate their accuracy in predicting GIST recurrence.

Decoding phenotypic screening: A comparative analysis of image representations

Biomedical imaging techniques such as high content screening (HCS) are valuable for drug discovery, but high costs limit their use to pharmaceutical companies. To address this issue, The JUMP-CP consortium released a massive open image dataset of chemical and genetic perturbations, providing a valuable resource for deep learning research. In this work, we aim to utilize the JUMP-CP dataset to develop a universal representation model for HCS data, mainly data generated using U2OS cells and CellPainting protocol, using supervised and self-supervised learning approaches. We propose an evaluation protocol that assesses their performance on mode of action and property prediction tasks using a popular phenotypic screening dataset. Results show that the self-supervised approach that uses data from multiple consortium partners provides representation that is more robust to batch effects whilst simultaneously achieving performance on par with standard approaches. Together with other conclusions, it provides recommendations on the training strategy of a representation model for HCS images.

Advanced methods to mechanically isolate stromal vascular fraction: A concise review

Adipose tissue is a highly attractive reservoir of stem cells due to its accessibility and abundance, and the SVF within it holds great promise for stem cell-based therapies. The use of mechanical methods for SVF isolation from adipose tissue is preferred over enzymatic methods, as it can be readily applied in clinical settings without additional processing steps. However, there is a lack of consensus on the optimal approach for mechanically isolating SVF. This comprehensive review aims to present and compare the latest mechanical isolation methods for SVF from adipose tissue, including centrifugation, filtration/washing, emulsification, vibration, and mincing/adiponizing. Each of these methods possesses unique advantages and limitations, and yet, no conclusive evidence has emerged demonstrating the superiority of one approach over the others, primarily due to the dearth of well-controlled prospective studies in this field.

Comparison of bioactive substances in novel-developed freeze-dried platelet-rich plasma (PRP) and activated normal PRP, and investigation of bioactive substance levels after long-term storage

Introduction

In recent years, biotherapy in orthopedics has become widespread, and platelet-rich plasma (PRP) has been readily used to treat sports injuries and osteoarthritis. Production of freeze-dried PRP (PRP-FD) results in PRP that is in powder form, allowing it to be stored for long periods at room temperature. Using this technology, we have developed Valuable Platelet-Derived Factor Concentrate Freeze Dry (VFD). However, whether VFD contains sufficient levels of bioactive substances (BS) remains unknown and retains the same levels of BS during long-term storage. In this study, we examined whether VFD contains sufficient amounts of BS and whether they retain these BS levels during long-term storage.

Methods

Peripheral blood was collected from 10 healthy men (mean ± SD: 46.5 ± 15 years old) and various BS, including transforming growth factor β (TGF-β), basic fibroblast growth factor (bFGF), epidermal growth factor (EGF), tissue inhibitors of metalloproteinases-1 (TIMP-1), interleukin-1 receptor antagonist (IL-1ra), matrix metallopeptidase-9 (MMP-9), and interleukin-6 (IL-6), were compared between VFD and normal PRP samples, including both leukocyte-rich PRP (LR-PRP) and leukocyte-poor PRP (LP-PRP). VFD was prepared using two rounds of centrifugation. LP-PRP and LR-PRP were activated by freezing and thawing before measurement. To evaluate the effects of long-term storage, the BS of VFD purified from five professional football players was compared between baseline and 1 year after storage.

Results

In terms of the growth factors, the TGF-β and EGF levels were higher in LR-PRP than in VFD and LP-PRP (p < 0.05), while the bFGF levels were higher in VFD than in the LR-PRP and LP-PRP groups (p < 0.01). In terms of anti-inflammatory cytokines, the TIMP-1 level was lower in VFD than that in the other groups (p < 0.01), whereas the IL-1ra levels were higher in VFD than those in LP-PRP (p < 0.05) and lower than those in LR-PRP (p < 0.01). In terms of inflammatory enzymes and cytokines, the IL-1ra level was higher in VFD than that in LP-PRP (p < 0.05) and lower than that in LR-PRP (p < 0.01), whereas the IL-6 levels did not differ among the groups. Furthermore, the TGF-β, bFGF, TIMP-1, and IL-1ra levels were 5.61 → 3.38 (x103 pg/μL), 61.0 → 63.0 (pg/μL), 3.4 → 2.7 (x105 pg/μL), and 14.9 → 14.5 (x103 pg/μL) at baseline and 1 year later, respectively. No significant differences in the BS levels were observed between baseline and 1 year after storage.

Conclusions

The VFD samples prepared in this study exhibited higher levels of anti-inflammatory cytokines than LP-PRP and contained growth factor levels similar to LP-PRP and LR-PRP. In addition, the BS levels in VFD samples were maintained after one year of storage. These results suggest that VFD can be prepared and stored and may serve as a novel treatment strategy for sports injuries in high-risk groups, such as athletes.

LiViT-Net: A U-Net-like, lightweight Transformer network for retinal vessel segmentation

The intricate task of precisely segmenting retinal vessels from images, which is critical for diagnosing various eye diseases, presents significant challenges for models due to factors such as scale variation, complex anatomical patterns, low contrast, and limitations in training data. Building on these challenges, we offer novel contributions spanning model architecture, loss function design, robustness, and real-time efficacy. To comprehensively address these challenges, a new U-Net-like, lightweight Transformer network for retinal vessel segmentation is presented. By integrating MobileViT+ and a novel local representation in the encoder, our design emphasizes lightweight processing while capturing intricate image structures, enhancing vessel edge precision. A novel joint loss is designed, leveraging the characteristics of weighted cross-entropy and Dice loss to effectively guide the model through the task's challenges, such as foreground-background imbalance and intricate vascular structures. Exhaustive experiments were performed on three prominent retinal image databases. The results underscore the robustness and generalizability of the proposed LiViT-Net, which outperforms other methods in complex scenarios, especially in intricate environments with fine vessels or vessel edges. Importantly, optimized for efficiency, LiViT-Net excels on devices with constrained computational power, as evidenced by its fast performance. To demonstrate the model proposed in this study, a freely accessible and interactive website was established (https://hz-t3.matpool.com:28765?token=aQjYR4hqMI), revealing real-time performance with no login requirements.

Applications of blood plasma derivatives for cutaneous wound healing: A mini-review of clinical studies

Skin injuries are a global healthcare problem. Chronic ulcers do not heal in a timely fashion, so it is essential to help the body with skin repair. There are some treatments that have been applied to chronic ulcers. One of these treatments is growth factor (GF) therapy. Platelet-rich plasma (PRP) and Platelet-poor plasma (PPP) are two types of plasma derivatives containing many GFs important for wound healing. Several works have reported their application in wound healing and tissue regeneration. The use of autologous PRP is now an adequate alternative in regenerative medicine. It was also demonstrated that PPP is a hemostatic agent for wounds. This review has studied the latest clinical studies, which have applied PRP and PPP to patients with chronic wounds.

Mesenchymal stem cell therapy in ischemic stroke trials. A systematic review

Cerebrovascular accidents, also known as strokes, are the leading cause of permanent disability in society, presenting significant socioeconomic and healthcare costs. They can be caused by ischemic factors or hemorrhages, with ischemic strokes being the most common among the population. Therapies for patients suffering from this condition are limited and primarily focus on acute-phase treatment. In recent years, there has been an increase in cellular therapies, employing Stem Cells to mitigate or eliminate the consequences arising from this disease. Mesenchymal Stem Cells (MSCs) hold substantial therapeutic potential in Nervous System pathologies due to their low antigenicity and capacity to differentiate into various human tissues, such as adipogenic, chondrogenic, and osteogenic tissues. This study conducts a literature review using the "clinical trials" and "Pubmed" database, summarizing all ongoing clinical trials for ischemic strokes that utilize MSCs as treatment.

Microcomputed tomography as a diagnostic tool for detection of lymph node metastasis in non-small cell lung cancer: A decision-support approach for pathological examination "A pilot study for method validation"

Background

Non-small cell lung cancer (NSCLC) patients without lymph node (LN) metastases (pN0) may exhibit different survival rates, even when their T stage is similar. This divergence could be attributed to the current pathology practice, wherein LNs are examined solely in two-dimensional (2D). Unfortunately, adhering to the protocols of 2D pathological examination does not ensure the exhaustive sampling of all excised LNs, thereby leaving room for undetected metastatic foci in the unexplored depths of tissues. The employment of micro-computed tomography (micro-CT) facilitates a three-dimensional (3D) evaluation of all LNs without compromising sample integrity. In our study, we utilized quantitative micro-CT parameters to appraise the metastatic status of formalin-fixed paraffin-embedded (FFPE) LNs.

Methods

Micro-CT scans were conducted on 12 FFPEs obtained from 8 NSCLC patients with histologically confirmed mediastinal LN metastases. Simultaneously, whole-slide images from these FFPEs underwent scanning, and 47 regions of interest (ROIs) (17 metastatic foci, 11 normal lymphoid tissues, 10 adipose tissues, and 9 anthracofibrosis) were marked on scanned images. Quantitative structural variables obtained via micro-CT analysis from tumoral and non-tumoral ROIs, were analyzed.

Result

Significant distinctions were observed in linear density, connectivity, connectivity density, and closed porosity between tumoral and non-tumoral ROIs, as indicated by kappa coefficients of 1, 0.90, 1, and 1, respectively. Receiver operating characteristic analysis substantiated the differentiation between tumoral and non-tumoral ROIs based on thickness, linear density, connectivity, connectivity density, and the percentage of closed porosity.

Conclusions

Quantitative micro-CT parameters demonstrate the ability to distinguish between tumoral and non-tumoral regions of LNs in FFPEs. The discriminatory characteristics of these quantitative micro-CT parameters imply their potential usefulness in developing an artificial intelligence algorithm specifically designed for the 3D identification of LN metastases while preserving the FFPE tissue.

Pilot testing "Teach Ted": A digital application for children undergoing blood tests and their parents

Objective

In this pilot study, we evaluated the acceptability and preliminary evidence of the impact of a new educational mobile application, "Teach Ted", for children undergoing blood tests.

Methods

Parents of children (4-10 years) completed questionnaires on anxiety and pain before their child had a blood test, and after using Teach Ted and receiving their blood test. Health professionals at each patient's blood test (e.g. technician/nurse) completed questionnaires on patient outcomes and procedure-related outcomes (e.g. time taken).

Results

Nine parents and eight health professionals participated. All but one parent (n = 8/9) reported Teach Ted was useful. Seventy-eight percent (n = 7/9) reported they would use Teach Ted again. All health professionals who completed the acceptability measure (n = 3/3) strongly agreed that Teach Ted was relevant/helpful. Many parents perceived Teach Ted helped reduce their/child's anxiety (n = 3/5, 60% and n = 4/6, 67% respectively), although child's pain and child's/parent's anxiety remained similar before and after using Teach Ted (all p > 0.05). The average blood test duration was 7.6 min (range ≤1 to 22), which health professionals (n = 3/3) reported was not elongated by offering Teach Ted.

Conclusions

Participants reported that Teach Ted was acceptable. Further evaluation of Teach Ted's impact on patient's outcomes are needed.

Innovation

Teach Ted is an innovative mobile application with potential to educate young children about their upcoming procedure and mitigate negative outcomes.

Computational methods for biofabrication in tissue engineering and regenerative medicine - a literature review

This literature review rigorously examines the growing scientific interest in computational methods for Tissue Engineering and Regenerative Medicine biofabrication, a leading-edge area in biomedical innovation, emphasizing the need for accurate, multi-stage, and multi-component biofabrication process models. The paper presents a comprehensive bibliometric and contextual analysis, followed by a literature review, to shed light on the vast potential of computational methods in this domain. It reveals that most existing methods focus on single biofabrication process stages and components, and there is a significant gap in approaches that utilize accurate models encompassing both biological and technological aspects. This analysis underscores the indispensable role of these methods in understanding and effectively manipulating complex biological systems and the necessity for developing computational methods that span multiple stages and components. The review concludes that such comprehensive computational methods are essential for developing innovative and efficient Tissue Engineering and Regenerative Medicine biofabrication solutions, driving forward advancements in this dynamic and evolving field.

HiOmics: A cloud-based one-stop platform for the comprehensive analysis of large-scale omics data

Analyzing the vast amount of omics data generated comprehensively by high-throughput sequencing technology is of utmost importance for scientists. In this context, we propose HiOmics, a cloud-based platform equipped with nearly 300 plugins designed for the comprehensive analysis and visualization of omics data. HiOmics utilizes the Element Plus framework to craft a user-friendly interface and harnesses Docker container technology to ensure the reliability and reproducibility of data analysis results. Furthermore, HiOmics employs the Workflow Description Language and Cromwell engine to construct workflows, ensuring the portability of data analysis and simplifying the examination of intricate data. Additionally, HiOmics has developed DataCheck, a tool based on Golang, which verifies and converts data formats. Finally, by leveraging the object storage technology and batch computing capabilities of public cloud platforms, HiOmics enables the storage and processing of large-scale data while maintaining resource independence among users.

Organic antibacterial modifications of high-viscosity glass ionomer cement for atraumatic restorative treatment: A review

High viscosity glass ionomer cement (HVGIC) has been employed as a restorative material for Atraumatic Restorative Treatment (ART). As residual caries persist after caries removal in ART, the antibacterial activity of HVGIC gains importance. Organic and inorganic substances with antibacterial properties have been incorporated into HVGIC over the years, and their effects on the antibacterial and physical properties have been studied. The objective of this paper is to review the various alterations made to HVGIC using organic compounds, their effect on the antibacterial activity, and the physical properties of the cement. Various in vitro investigations have been conducted by adding antiseptics, antibiotics, and naturally occurring antibacterial substances. Most of these compounds render superior antibacterial properties to HVGIC, but higher concentrations affect physical properties in a dose-dependent manner. However, some naturally occurring antibacterial substances, such as chitosan, improve the physical properties of HVGIC, as they enhance cross-linking and polysalt bridging. There is potential for clinical benefits to be gained from the addition of organic antibacterial compounds to HVGIC. In-depth research is required to determine the optimum concentration at which the antibacterial effect is maximum without affecting the physical properties of the cement.

Digital analysis of the prostate tumor microenvironment with high-order chromogenic multiplexing

As our understanding of the tumor microenvironment grows, the pathology field is increasingly utilizing multianalyte diagnostic assays to understand important characteristics of tumor growth. In clinical settings, brightfield chromogenic assays represent the gold-standard and have developed significant trust as the first-line diagnostic method. However, conventional brightfield tests have been limited to low-order assays that are visually interrogated. We have developed a hybrid method of brightfield chromogenic multiplexing that overcomes these limitations and enables high-order multiplex assays. However, how compatible high-order brightfield multiplexed images are with advanced analytical algorithms has not been extensively evaluated. In the present study, we address this gap by developing a novel 6-marker prostate cancer assay that targets diverse aspects of the tumor microenvironment such as prostate-specific biomarkers (PSMA and p504s), immune biomarkers (CD8 and PD-L1), a prognostic biomarker (Ki-67), as well as an adjunctive diagnostic biomarker (basal cell cocktail) and apply the assay to 143 differentially graded adenocarcinoma prostate tissues. The tissues were then imaged on our spectroscopic multiplexing imaging platform and mined for proteomic and spatial features that were correlated with cancer presence and disease grade. Extracted features were used to train a UMAP model that differentiated healthy from cancerous tissue with an accuracy of 89% and identified clusters of cells based on cancer grade. For spatial analysis, cell-to-cell distances were calculated for all biomarkers and differences between healthy and adenocarcinoma tissues were studied. We report that p504s positive cells were at least 2× closer to cells expressing PD-L1, CD8, Ki-67, and basal cell in adenocarcinoma tissues relative to the healthy control tissues. These findings offer a powerful insight to understand the fingerprint of the prostate tumor microenvironment and indicate that high-order chromogenic multiplexing is compatible with digital analysis. Thus, the presented chromogenic multiplexing system combines the clinical applicability of brightfield assays with the emerging diagnostic power of high-order multiplexing in a digital pathology friendly format that is well-suited for translational studies to better understand mechanisms of tumor development and growth.

Circle-seq based method for eccDNA synthesis and its application as a canonical promoter independent vector for robust microRNA overexpression

Extrachromosomal circular DNA (eccDNA) has recently gained increasing attention due to its significant role in cancer and other pathophysiologic states. The majority of circular DNAs detected by Circle-seq are small-size eccDNAs with enigmatic functions. One major technical hurdle is to synthesize eccDNA for functional identification. Here, we describe CAES (Circle-seq based Artificial EccDNA Synthesis), a promising and reliable method for artificial eccDNA synthesis. Eight eccDNAs carrying different microRNA genes (eccMIR) found in gastric cancer tissues, ranging from 329 bp to 2189 bp in size, were created utilizing the CAES method. Exonuclease V and single restriction-endonuclease digestion identified the circular structure of synthetic eccDNAs. The DNA circularization efficiency afforded by CAES ranged from 15.6% to 31.1%, which was negatively correlated with the eccDNA length. In addition, we demonstrated that CAES-synthesized eccMIRs can express both miRNA-3p and - 5p molecules efficiently independent of a canonical promoter in human cell lines. Further assays proved that these eccMIRs were functional as they were able to repress the luciferase gene containing a miRNA-target sequence in the 3'UTR as well as the endogenous mRNA targets. Finally, kinetics study revealed that eccDNA exhibited a decay rate similar to the standard plasmids and linear DNA in cultured cells. Together, this study offers a rapid and convenient method for Circle-seq users to synthesize artificial eccDNAs. It also demonstrates the promising potential of eccMIR as a bacterial DNA-free vector for safe and robust miRNA overexpression in both basic research and therapeutic applications.

Salivary biomarkers for early detection of oral squamous cell carcinoma (OSCC) and head/neck squamous cell carcinoma (HNSCC): A systematic review and network meta-analysis

Oral cancer became a very common condition. WHO estimates that there are 4 cases of lip and oral cavity cancer for every 100,000 people worldwide. The early diagnosis of cancers is currently a top focus in the health sector. Recent systematic reviews and meta-analyses have identified promising biomarkers for early detection in several original research investigations. However, it is still unclear the quality of these evidence and which biomarker performs the best in terms of early detection. Therefore, the objective was, to map the methodological and reporting quality of available oral squamous cell carcinoma (OSCC) or head/neck squamous cell carcinoma (HNSCC) systematic reviews and meta-analysis. Secondly, to evaluate diagnostic accuracy of salivary biomarkers for common craniofacial cancers and to compare the diagnostic value of different salivary biomarkers. PubMed, Scopus, Web of Science, Embase and Cochrane Library electronic databases were used to map the methodological and reporting quality of the systematic reviews and meta-analysis conducted on the HNSCC, OSCC using the AMSTAR-2 checklist. The inclusion criteria were systematic reviews and meta-analysis published in the topic of HNSCC and OSCC biomarkers. Exclusion criteria were no animal studies; original primary studies, due to limitation of competency in other languages articles with language other than English were excluded. The sensitivity and specificity were calculated for salivary biomarkers and ranked according to network meta-analysis principles. A total of N = 5893 patients were included from four meta-analysis studies. All together, these included n = 37 primary studies. n = 94 biomarkers were pooled from these four meta-analyses and categorised into the stages at which they were detected (I-IV). In OSCC, Chemerin and MMP-9 displayed the highest sensitivity, registering 0.94 (95% CI 0.78, 1.00) and a balanced accuracy of 0.93. Phytosphingosine closely followed, with a sensitivity of 0.91 (95% CI 0.68, 0.99) and a balanced accuracy of 0.87. For HNSCC, the top three biomarkers are Actin, IL-1β Singleplex, and IL-8 ELISA. Actin leads with a sensitivity of 0.91 (95% CI 0.68-0.99), a specificity of 0.67, and an overall accuracy of 0.79. Subsequently, IL-1β Singleplex exhibits a sensitivity of 0.62 (95% CI 0.30-0.88), a specificity of 0.89, and an accuracy of 0.75, followed by IL-8 ELISA with a sensitivity of 0.81 (95% CI 0.54-0.97), a specificity of 0.59, and an accuracy of 0.70. In conclusion, there was highest sensitivity for MMP-9 and chemerin salivary biomarkers. There is need of further more studies to identify biomarkers for HNSCC and OSCC.

Patient's informational needs and outreach preferences: A cross-sectional survey study in patients with hepatobiliary malignancies

Objective

Hepatobiliary tumors have evolving management guidelines. Patient educational needs and interest in community engagement are unknown. This study serves as a needs assessment.

Methods

A prospective, needs assessment, survey study of hepatobiliary patients was performed (2016-2019). Surveys (n = 169) were distributed covering three domains of interest: informational needs, interest in outreach, and engagement preferences.

Results

Seventy patients completed the survey (response rate = 41.4%). Most patients had completed surgical treatment (84.3%). Cancer treatment was ranked as their primary topic of interest (n = 39, 55.7bold%), followed by symptom management, nutrition, and survivorship. Most patients did not participate in screening (n = 57, 81.4%), though were interested in learning more about these programs. Thirty-nine patients (55.7%) stated they would want to receive more education. Only 17 (24.3%) were interested in attending in-person events. Patients preferred online methods for education (n = 49, 70%). While patients were aware of their case presentation at tumor board, only 38 (54.3%) felt well-informed about recommendations.

Conclusion

Multidisciplinary care is complex and difficult for patients to navigate. Most patients have interest in educational resources and prefer online modalities. Patients understand multidisciplinary tumor boards, but communication could be improved.

Innovation

These data inform a new, innovative, approach to outreach efforts in this population.

Are nocturnal awakenings at age 1 predictive of sleep duration and efficiency at age 6: Results from two birth cohorts

Objective

To investigate the association of nighttime awakenings at 12 months with the duration and efficiency of nighttime sleep at 6 years of age.

Methods

Data from two population-based prospective studies (The Pelotas 2004 and The Pelotas 2015 Birth Cohorts) were used. Information on nighttime awakenings was provided by mothers during the 12-month follow-up interview. Infants who awakened >3 times after sleep onset at 12 months were considered frequent wakeners. Sleep duration and sleep efficiency were obtained by actigraphy at the 6-year follow-up. Children wore the device at the wrist of the non-dominant arm continuously for 3-7 days, including at least one weekend day. Unadjusted and adjusted beta coefficients were obtained by linear regression for each cohort separately.

Results

2500 children from the 2004 and 2793 from the 2015 cohort had full information on nighttime awakenings at 12 months and actigraphy at 6 years and were analyzed. Prevalence of frequent wakeners was 6.3 % and 5.9 % in the 2004 and 2015 cohort, respectively. Mean bedtime and wake-up time at 6 years were, respectively, 23:23 and 08:41 h in the 2004 cohort, and 00:10 and 09:00 h int the 2015 cohort. Nighttime sleep lasted on average 7.54 and 7.24 h respectively in the 2004 and the 2015 cohort, and the sleep efficiency was 81.1 and 82.5 % respectively. In adjusted analyses, no associations were found between awakening at 12 months and sleep duration or sleep efficiency at 6 years of age.

Conclusion

In both cohorts sleep duration and efficiency were below the recommendation for school-age children (respectively 9-11 h and 85 %). There was no relationship between the number of nighttime awakenings at 12 months and sleep duration or efficiency at 6 years.

Prognostic iron-metabolism signature robustly stratifies single-cell characteristics of hepatocellular carcinoma

Cancer immunotherapy has shown to be a promising method in treating hepatocellular carcinoma (HCC), but suboptimal responses in patients are attributed to cellular and molecular heterogeneity. Iron metabolism-related genes (IRGs) are important in maintaining immune system homeostasis and have the potential to help develop new strategies for HCC treatment. Herein, we constructed and validated the iron-metabolism gene prognostic index (IPX) using univariate Cox proportional hazards regression and LASSO Cox regression analysis, successfully categorizing HCC patients into two groups with distinct survival risks. Then, we performed single-sample gene set enrichment analysis, weighted correlation network analysis, gene ontology enrichment analysis, cellular lineage analysis, and SCENIC analysis to reveal the key determinants underlying the ability of this model based on bulk and single-cell transcriptomic data. We identified several driver transcription factors specifically activated in specific malignant cell sub-populations to contribute to the adverse survival outcomes in the IPX-high subgroup. Within the tumor microenvironment (TME), T cells displayed significant diversity in their cellular characteristics and experienced changes in their developmental paths within distinct clusters identified by IPX. Interestingly, the proportion of Treg cells was increased in the high-risk group compared with the low-risk group. These results suggest that iron-metabolism could be involved in reshaping the TME, thereby disrupting the cell cycle of immune cells. This study utilized IRGs to construct a novel and reliable model, which can be used to assess the prognosis of patients with HCC and further clarify the molecular mechanisms of IRGs in HCC at single-cell resolution.

Genome-wide analysis for root and leaf architecture traits associated with drought tolerance at the seedling stage in a highly ecologically diverse wheat population

Drought stress occurred at early growth stages in wheat affecting the following growth stages. Therefore, selecting promising drought-tolerant genotypes with highly adapted traits at the seedling stage is an important task for wheat breeders and geneticists. Few research efforts were conducted on the genetic control for drought-adaptive traits at the seedling stage in wheat. In this study, a set of 146 highly diverse spring wheat core collections representing 28 different countries was evaluated under drought stress at the seedling stage. All genotypes were exposed to drought stress for 13 days by water withholding. Leaf traits including seedling length, leaf wilting, days to wilting, leaf area, and leaf rolling were scored. Moreover, root traits such as root length, maximum width, emergence angle, tip angle, and number of roots were scored. Considerable significant genetic variation was found among all genotypes tested in these experiments. The heritability estimates ranged from 0.74 (leaf witling) to 0.99 (root tip angle). A set of nine genotypes were selected and considered drought-tolerant genotypes. Among all leaf traits, shoot length had significant correlations with all root traits under drought stress. The 146 genotypes were genotyped using the Infinium Wheat 15 K single nucleotide polymorphism (SNP) array and diversity arrays technology (DArT) marker platform. The result of genotyping revealed 12,999 SNPs and 2150 DArT markers which were used to run a genome-wide association study (GWAS). The results of GWAS revealed 169 markers associated with leaf and root traits under drought stress. Out of the 169 markers, 82 were considered major quantitative trait loci (QTL). The GWAS revealed 95 candidate genes were identified with 53 genes showing evidence for drought tolerance in wheat, while the remaining candidate genes were considered novel. No shared markers were found between leaf and root traits. The results of the study provided mapping novel markers associated with new root traits at the seedling stage. Also, the selected genotypes from different countries could be employed in future wheat breeding programs not only for improving adaptive drought-tolerant traits but also for expanding genetic diversity.

Embedding an Education Intervention about Shared Decision Making into an RCT: Ensuring competency and fidelity

Objective

To describe the outcomes of training nephrology clinicians and clinical research participants, to use the Best Case/Worst Case Communication intervention, for discussions about dialysis initiation for patients with life-limiting illness, during a randomized clinical trial to ensure competency, fidelity to the intervention, and adherence to study protocols and the intervention throughout the trial.

Methods

We enrolled 68 nephrologists at ten study sites and randomized them to receive training or wait-list control. We collected copies of completed graphic aids (component of the intervention), used with study-enrolled patients, to measure fidelity and adherence.

Results

We trained 34 of 36 nephrologists to competence and 27 completed the entire program. We received 60 graphic aids for study-enrolled patients for a 73% return rate in the intervention arm. The intervention fidelity score for the graphic aid reflected completion of all elements throughout the study.

Conclusion

We successfully taught the Best Case/Worst Case Communication intervention to clinicians as research participants within a randomized clinical trial.

Innovation

Decisions about dialysis are an opportunity to discuss prognosis and uncertainty in relation to consideration of prolonged life supporting therapy. Our study reveals a strategy to evaluate adherence to a communication intervention in real time during a clinical study.

Reliability and validity of modified upper limb neurodynamic tests in patients with cervical radiculopathy

Background

Neurodynamic Tests (NDTs) are used to assess neural mechanosensitivity in various conditions such as neural sliding, tension or inflammatory dysfunction. But in some upper quadrant dysfunctions, standard testing procedure of NDT cannot be assessed or tolerated by patient.

Objective

The purpose of the study was to determine the validity, intra-rater and inter-rater reliability of modified NDTs via median and ulnar nerve in patients with cervical radiculopathy.

Methods

Thirty-three patients (18 men and 15 women, mean age ± SD - 40 . 18 ± 9 . 01 ) with cervical radiculopathy having positive response to standard NDTs were included in the study. Modified neurodynamic tests for median & ulnar nerve were performed with modification in the sequencing of standard neurodynamic test at lower degrees of glenohumeral abduction and external rotation. Outcome measures used were angle of elbow extension for median nerve and angle elbow flexion for ulnar nerve at the point of pain onset indicated by "OP" (Onset of Pain).

Results

Reliability of OP was evaluated using measurement of Intra-class Correlation Coefficient (ICC), Standard Error of Measurement (SEM) and Minimal Detectable Change (MDC) values. Results indicated high ICC values and low SEM values for OP during modified median and modified ulnar NDTs (M-MNT1 and M-UNT) on symptomatic side of patients with cervical radiculopathy. Spearman correlation analysis for validity of test score showed strong correlation (r = 0 . 767 ) with standard NDT.

Conclusion

There was strong correlation between Modified NDTs and standard tests depicting good validity and substantial reliability of OP during M-MNT1 and M-UNT for positive NDT response in patients with cervical radiculopathy.

Cat-E: A comprehensive web tool for exploring cancer targeting strategies

Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.

The molecular subtypes of autoimmune diseases

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

DeepNeuropePred: A robust and universal tool to predict cleavage sites from neuropeptide precursors by protein language model

Neuropeptides play critical roles in many biological processes such as growth, learning, memory, metabolism, and neuronal differentiation. A few approaches have been reported for predicting neuropeptides that are cleaved from precursor protein sequences. However, these models for cleavage site prediction of precursors were developed using a limited number of neuropeptide precursor datasets and simple precursors representation models. In addition, a universal method for predicting neuropeptide cleavage sites that can be applied to all species is still lacking. In this paper, we proposed a novel deep learning method called DeepNeuropePred, using a combination of pre-trained language model and Convolutional Neural Networks for feature extraction and predicting the neuropeptide cleavage sites from precursors. To demonstrate the model's effectiveness and robustness, we evaluated the performance of DeepNeuropePred and four models from the NeuroPred server in the independent dataset and our model achieved the highest AUC score (0.916), which are 6.9%, 7.8%, 8.8%, and 10.9% higher than Mammalian (0.857), insects (0.850), Mollusc (0.842) and Motif (0.826), respectively. For the convenience of researchers, we provide a web server (http://isyslab.info/NeuroPepV2/deepNeuropePred.jsp).

Application of mesenchymal stem/stromal cells in periodontal regeneration: Opportunities and challenges

Guided tissue regeneration (GTR) has been widely used in the periodontal treatment of intrabony and furcation defects for nearly four decades. The treatment outcomes have shown effectiveness in reducing pocket depth, improving attachment gain and bone filling in periodontal tissue. Although applying GTR could reconstruct the periodontal tissue, the surgical indications are relatively narrow, and some complications and race ethic problems bring new challenges. Therefore, it is challenging to achieve a consensus concerning the clinical benefits of GTR. With the appearance of stem cell-based regenerative medicine, mesenchymal stem/stromal cells (MSCs) have been considered a promising cell resource for periodontal regeneration. In this review, we highlight preclinical and clinical periodontal regeneration using MSCs derived from distinct origins, including non-odontogenic and odontogenic tissues and induced pluripotent stem cells, and discuss the transplantation procedures, therapeutic mechanisms, and concerns to evaluate the effectiveness of MSCs.

Effects of age on differential resistance to duck hepatitis A virus genotype 3 in Pekin ducks by 16 S and transcriptomics

Duck hepatitis A virus genotype 3 (DHAV-3) is the major cause of viral hepatitis in ducks in Asia. Previous studies have shown that ducklings younger than 21 days are more susceptible to DHAV-3. To elucidate the mechanism by which age affects the differential susceptibility of Pekin ducks to DHAV-3, intestinal (n = 520), liver (n = 40) and blood (n = 260) samples were collected from control and DHAV-3-infected ducks at 7, 10, 14, and 21 days of age. Comparisons of plasma markers, mortality rates, and intestinal histopathological data showed that the resistance of Pekin ducks to DHAV-3 varied with age. 16 S sequencing revealed that the ileal microbial composition was influenced by age, and this correlation was greater than that recorded for caecal microbes. Candidatus Arthromitus, Bacteroides, Corynebacterium, Enterococcus, Romboutsia, and Streptococcus were the differntially abundant microbes in the ileum at the genus level after DHAV-3 infection and were significantly correlated with 7 differentially expressed genes (DEGs) in 7- and 21-day-old ducklings. 3 immunity-related pathways were significantly different between 7- and 21-day-old ducklings, especially for IFIH1-mediated induction of the interferon-alpha/beta pathway, which induces differential production of CD8(+) T cells and was influenced by a combination of differentially abundant microbiota and DEGs. We found that microbes in the ileum changed regularly with age. The intestinal microbiota was associated with the expression of genes in the liver through IFIH1-mediated induction of the interferon-alpha/beta pathway, which may partially explain why younger ducklings were more susceptible to DHAV-3 infection.

Healthcare professionals' perspectives of barriers to cancer care delivery for American Indian, rural, and frontier populations

Objective

This descriptive qualitative study sought to understand the barriers affecting cancer care delivery from the perspective of healthcare professionals (HCPs) serving American Indian (AI), rural, and frontier populations.

Methods

One-on-one, semi-structured interviews with multidisciplinary HCPs (N = 18) who provide cancer care to AI, rural, and frontier populations were conducted between January and April 2022. Interviews were conducted via Zoom. Data were analyzed following thematic content analysis methodologies.

Results

Thematic content analysis revealed three major themes: (a) Access, (b) Time, and (c) Isolation. The themes represent the HCP perspectives of the needs and barriers of persons with cancer to whom they provide cancer care. Furthermore, these themes also reflect the barriers HCPs experience while providing cancer care to AI, rural and frontier populations.

Conclusions

This study provides preliminary evidence for the need and strong multidisciplinary support for an early palliative care intervention in rural and frontier South Dakota (SD). This intervention could support the needs of persons with advanced cancer as well as the HCPs delivering cancer care in rural settings.

Innovation

This study is the initial step to develop the first culturally responsive, nurse-led, early palliative care intervention for AI, rural, and frontier persons with advanced cancer in SD.

Breast MRI radiomics and machine learning-based predictions of response to neoadjuvant chemotherapy - How are they affected by variations in tumor delineation?

Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variability. A quantitative assessment of the impact of variations in these delineations on the performance of the radiomics predictors is required to develop robust radiomics based prediction models. In this study, we developed radiomics models for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with two different breast cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired prior to treatment (baseline MRI scans). Different mathematical operations such as erosion, smoothing, dilation, randomization, and ellipse fitting were applied to the original VOIs delineated by experts to simulate variations of segmentation masks. The effects of such VOI modifications on various steps of the radiomics workflow, including feature extraction, feature selection, and prediction performance, were evaluated. Using manual tumor VOIs and radiomics features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was achieved for human epidermal growth receptor 2 positive and triple-negative breast cancer, respectively. For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes. At most 28% of the selected features were similar to manual VOIs when different VOI delineation data were used. Differences in VOI delineation affect different steps of radiomics analysis, and their quantification is therefore important for development of standardized radiomics research.

Curvature-enhanced graph convolutional network for biomolecular interaction prediction

Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local geometric properties and enhance the learning capability of GCNs. More specifically, ORCs are evaluated based on the local topology from node neighborhoods, and further incorporated into the weight function for the feature aggregation in message-passing procedure. Our CGCN model is extensively validated on fourteen real-world bimolecular interaction networks and analyzed in details using a series of well-designed simulated data. It has been found that our CGCN can achieve the state-of-the-art results. It outperforms all existing models, as far as we know, in thirteen out of the fourteen real-world datasets and ranks as the second in the rest one. The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negative-curvature ratios, network densities, and network sizes (when larger than 500).

Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis

The accuracy of artificial intelligence-aided (AI) caries diagnosis can vary considerably depending on numerous factors. This review aimed to assess the diagnostic accuracy of AI models for caries detection and classification on bitewing radiographs. Publications after 2010 were screened in five databases. A customized risk of bias (RoB) assessment tool was developed and applied to the 14 articles that met the inclusion criteria out of 935 references. Dataset sizes ranged from 112 to 3686 radiographs. While 86 % of the studies reported a model with an accuracy of ≥80 %, most exhibited unclear or high risk of bias. Three studies compared the model's diagnostic performance to dentists, in which the models consistently showed higher average sensitivity. Five studies were included in a bivariate diagnostic random-effects meta-analysis for overall caries detection. The diagnostic odds ratio was 55.8 (95 % CI= 28.8 - 108.3), and the summary sensitivity and specificity were 0.87 (0.76 - 0.94) and 0.89 (0.75 - 0.960), respectively. Independent meta-analyses for dentin and enamel caries detection were conducted and showed sensitivities of 0.84 (0.80 - 0.87) and 0.71 (0.66 - 0.75), respectively. Despite the promising diagnostic performance of AI models, the lack of high-quality, adequately reported, and externally validated studies highlight current challenges and future research needs.

Comparing the immediate effects of different neural mobilization exercises on hamstring flexibility in recreational soccer players

Background

Hamstring strain injuries remain a challenge for both athletes and clinicians given the high incidence rate, slow healing, and persistent symptoms. Increased tension in the neural structures is a known causative factor for hamstring tightness for which neural mobilization has emerged as a significant adjunct to routine stretching techniques.

Objective

To compare the short-term effects of neural sliding and neural tensioning on hamstring length in male recreational soccer players with hamstring tightness.

Methods

Sixty-two participants between ages 18 and 30 years were randomly assigned to one of the two groups viz. neural sliding or neural tensioning. Participants in either group performed the given stretching protocol in three sets. The Active Knee Extension Test (AKET) and Sit and Reach Test (SRT) were recorded before intervention, immediately after intervention, and after 60 min. between- and within group-analysis was done using analysis of variance.

Results

Between-group analysis showed that neural tensioning was more effective than neural sliding in improving hamstring length on both measures, however this difference was negligible. Within-group analysis demonstrated that the mean post-test scores on the AKET test and SRT were significantly greater than the pre-test scores in both groups (p < 0 . 05 ). A reduction in the post-test scores was observed after 60 min, irrespective of the type of stretching (p < 0 . 05 ).

Conclusion

There was no difference in short-term effects of neural sliding or neural tensioning on hamstring flexibility in male recreational soccer players. Both groups showed improved flexibility immediately after the intervention with reduction in the effect after 60 min.

Role of artificial intelligence in digital pathology for gynecological cancers

The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality and poor prognosis, underscoring the critical importance of early detection, treatment, and identification of prognostic factors. This review highlights the various clinical applications of AI in gynecological cancers using digitized histopathology slides. Particularly, deep learning models have shown promise in accurately diagnosing, classifying histopathological subtypes, and predicting treatment response and prognosis. Furthermore, the integration with transcriptomics, proteomics, and other multi-omics techniques can provide valuable insights into the molecular features of diseases. Despite the considerable potential of AI, substantial challenges remain. Further improvements in data acquisition and model optimization are required, and the exploration of broader clinical applications, such as the biomarker discovery, need to be explored.

Platelet-rich plasma (PRP) in nerve repair

Platelet-rich plasma (PRP) has the capability of assisting in the recovery of damaged tissues by releasing a variety of biologically active factors to initiate a hemostatic cascade reaction and promote the synthesis of new connective tissue and revascularization. It is now widely used for tissue engineering repair. In addition, PRP has demonstrated nerve repair and pain relief, and has been studied and applied to the facial nerve, median nerve, sciatic nerve, and central nerve. These suggest that PRP injection therapy has a positive effect on nerve repair. This indicates that PRP has high clinical value and potential application in nerve repair. It is worthwhile for scientists and medical workers to further explore and study PRP to expand its application in nerve repair, and to provide a more reliable scientific basis for the opening of a new approach to nerve repair.

Cysteine hyperoxidation rewires communication pathways in the nucleosome and destabilizes the dyad

Gene activity is tightly controlled by reversible chemical modifications called epigenetic marks, which are of various types and modulate gene accessibility without affecting the DNA sequence. Despite an increasing body of evidence demonstrating the role of oxidative-type modifications of histones in gene expression regulation, there remains a complete absence of structural data at the atomistic level to understand the molecular mechanisms behind their regulatory action. Owing to μs time-scale MD simulations and protein communication networks analysis, we describe the impact of histone H3 hyperoxidation (i.e., S-sulfonylation) on the nucleosome core particle dynamics. Our results reveal the atomic-scale details of the intrinsic structural networks within the canonical histone core and their perturbation by hyperoxidation of the histone H3 C110. We show that this modification involves local rearrangements of the communication networks and destabilizes the dyad, and that one modification is enough to induce a maximal structural signature. Our results suggest that cysteine hyperoxidation in the nucleosome core particle might favor its disassembly.

Depression assessment using integrated multi-featured EEG bands deep neural network models: Leveraging ensemble learning techniques

Mental Status Assessment (MSA) holds significant importance in psychiatry. In recent years, several studies have leveraged Electroencephalogram (EEG) technology to gauge an individual's mental state or level of depression. This study introduces a novel multi-tier ensemble learning approach to integrate multiple EEG bands for conducting mental state or depression assessments. Initially, the EEG signal is divided into eight sub-bands, and then a Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN) model is trained for each band. Subsequently, the integration of multi-band EEG frequency models and the evaluation of mental state or depression level are facilitated through a two-tier ensemble learning approach based on Multiple Linear Regression (MLR). The authors conducted numerous experiments to validate the performance of the proposed method under different evaluation metrics. For clarity and conciseness, the research employs the simplest commercialized one-channel EEG sensor, positioned at FP1, to collect data from 57 subjects (49 depressed and 18 healthy subjects). The obtained results, including an accuracy of 0.897, F1-score of 0.921, precision of 0.935, negative predictive value of 0.829, recall of 0.908, specificity of 0.875, and AUC of 0.8917, provide evidence of the superior performance of the proposed method compared to other ensemble learning techniques. This method not only proves effective but also holds the potential to significantly enhance the accuracy of depression assessment.

Sleep health inequities in vulnerable populations: Beyond sleep deserts

Despite the importance of sleep to overall health and well-being, there is a high prevalence of undiagnosed sleep disorders and adverse sleep health, particularly among vulnerable populations. Such vulnerable populations include people experiencing homelessness (PEH), refugees, and incarcerated individuals. In this narrative review, we provide an overview of the literature on sleep health and disorders among key and vulnerable populations (e.g., PEH, refugees, and incarcerated individuals). The limited research among these populations indicated a high prevalence of sleep disorders, mainly insomnia, short sleep duration, and fatigue. Substance abuse and PTSD were commonly found among PEH and refugee populations, respectively, which were was related to poor sleep. Similar across the included vulnerable populations, the individuals reside in environments/facilities with inopportune light exposure, noise disruption, inadequate bedding, and forced sleep schedules. Studies also found a high prevalence of psychosocial stress and reports of threats to safety, which were associated with poor sleep health outcomes. Additionally, several studies reported environmental barriers to adherence to sleep disorder treatment. This paper highlighted the conditions in which these vulnerable populations reside, which may inform interventions within these various facilities (homeless shelters, refugee camps, prisons/jails). The improvement of these facilities with a sleep equity focus may in turn improve quality of life and daily functioning.

Understanding patients' experiences during transitions from one electronic health record to another: A scoping review

Objectives

Identify existing research on impacts of transitions between electronic health record (EHR) systems on patients' healthcare experiences.

Methods

Scoping review. We searched MedLine, OVID, Embase, CINAHL, and PsycInfo databases for articles on patient experiences with EHR-to-EHR transitions.

Results

Three studies met inclusion criteria. All three used validated surveys to compare patient satisfaction with care pre- and post-transition. The surveys did not include specific questions about the EHR transition; one study focused on patient perceptions of provider computer use. Satisfaction levels initially decreased following EHR implementation, then returned to baseline between six and 15 months later in two of three studies. Factors associated with changes in observed satisfaction are unknown.

Conclusions

Patient experience has been given limited attention in studies of EHR-to-EHR transitions. Future research should look beyond satisfaction, and examine how an EHR-to-EHR transition can impact the quality of patients' care, including safety, effectiveness, timeliness, efficiency, and equity.

Innovation

To our knowledge, this is the first literature review on EHR transitions that specifically focused on patient experiences. In preparation for a transition from one EHR to another, healthcare system leaders should consider the multiple ways patients' experiences with care may be impacted and develop strategies to minimize disruptions in care.

scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data

Single-cell RNA sequencing (scRNA-seq) is currently an important technology for identifying cell types and studying diseases at the genetic level. Identifying rare cell types is biologically important as one of the downstream data analyses of single-cell RNA sequencing. Although rare cell identification methods have been developed, most of these suffer from insufficient mining of intercellular similarities, low scalability, and being time-consuming. In this paper, we propose a single-cell similarity division algorithm (scSID) for identifying rare cells. It takes cell-to-cell similarity into consideration by analyzing both inter-cluster and intra-cluster similarities, and discovers rare cell types based on the similarity differences. We show that scSID outperforms other existing methods by benchmarking it on different experimental datasets. Application of scSID to multiple datasets, including 68K PBMC and intestine, highlights its exceptional scalability and remarkable ability to identify rare cell populations.

Holding back in communications with clinicians: Patient and care partner perspectives in Parkinson's disease

Objectives

To describe how patients with Parkinson's disease (PD) and care partners choose to share or withhold information from clinicians.

Methods

This is a qualitative, descriptive study nested within a multisite, randomized clinical trial of outpatient palliative care compared to standard neurologic care for PD. Interviews with patients (n = 30) and care partners (n = 30) explored experiences communicating with neurology clinicians. Thematic analyses identified themes relevant to patient-care partner-clinician communication.

Results

There were four themes relevant to sharing and/or withholding information from clinicians: (1) Suppressing Concerns During Visits, (2) Care Partner Awareness of Patients' Communication Barriers due to Cognitive Impairment, (3) Limited Sharing of Sensitive or Intimate Issues by Patients and Care Partners, and (4) Patient and Care Partner Suggestions to Overcome 'Holding Back'.

Conclusion

Limitations to communication between patients, care partners, and clinicians should be acknowledged and recognized in routine Parkinson's disease care to foster accurate disclosure of unmet palliative care and other needs. Triadic communication strategies may help patients and care partners talk about unmet palliative care needs.

Innovation

By recognizing that cognitive impairment and sensitive topics can be barriers, clinicians can adjust or adopt targeted communication strategies for identifying and discussing care needs.

Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions

Host-pathogen interactions (HPIs) are vital in numerous biological activities and are intrinsically linked to the onset and progression of infectious diseases. HPIs are pivotal in the entire lifecycle of diseases: from the onset of pathogen introduction, navigating through the mechanisms that bypass host cellular defenses, to its subsequent proliferation inside the host. At the heart of these stages lies the synergy of proteins from both the host and the pathogen. By understanding these interlinking protein dynamics, we can gain crucial insights into how diseases progress and pave the way for stronger plant defenses and the swift formulation of countermeasures. In the framework of current study, we developed a web-based R/Shiny app, Deep-HPI-pred, that uses network-driven feature learning method to predict the yet unmapped interactions between pathogen and host proteins. Leveraging citrus and CLas bacteria training datasets as case study, we spotlight the effectiveness of Deep-HPI-pred in discerning Protein-protein interaction (PPIs) between them. Deep-HPI-pred use Multilayer Perceptron (MLP) models for HPI prediction, which is based on a comprehensive evaluation of topological features and neural network architectures. When subjected to independent validation datasets, the predicted models consistently surpassed a Matthews correlation coefficient (MCC) of 0.80 in host-pathogen interactions. Remarkably, the use of Eigenvector Centrality as the leading topological feature further enhanced this performance. Further, Deep-HPI-pred also offers relevant gene ontology (GO) term information for each pathogen and host protein within the system. This protein annotation data contributes an additional layer to our understanding of the intricate dynamics within host-pathogen interactions. In the additional benchmarking studies, the Deep-HPI-pred model has proven its robustness by consistently delivering reliable results across different host-pathogen systems, including plant-pathogens (accuracy of 98.4% and 97.9%), human-virus (accuracy of 94.3%), and animal-bacteria (accuracy of 96.6%) interactomes. These results not only demonstrate the model's versatility but also pave the way for gaining comprehensive insights into the molecular underpinnings of complex host-pathogen interactions. Taken together, the Deep-HPI-pred applet offers a unified web service for both identifying and illustrating interaction networks. Deep-HPI-pred applet is freely accessible at its homepage: https://cbi.gxu.edu.cn/shiny-apps/Deep-HPI-pred/ and at github: https://github.com/tahirulqamar/Deep-HPI-pred.

Spontaneous regeneration of cholecystokinergic reticulospinal axons after a complete spinal cord injury in sea lampreys

In contrast to humans, lampreys spontaneously recover their swimming capacity after a complete spinal cord injury (SCI). This recovery process involves the regeneration of descending axons. Spontaneous axon regeneration in lampreys has been mainly studied in giant descending neurons. However, the regeneration of neurochemically distinct descending neuronal populations with small-caliber axons, as those found in mammals, has been less studied. Cholecystokinin (CCK) is a regulatory neuropeptide found in the brain and spinal cord that modulates several processes such as satiety, or locomotion. CCK shows high evolutionary conservation and is present in all vertebrate species. Work in lampreys has shown that all CCKergic spinal cord axons originate in a single neuronal population located in the caudal rhombencephalon. Here, we investigate the spontaneous regeneration of CCKergic descending axons in larval lampreys following a complete SCI. Using anti-CCK-8 immunofluorescence, confocal microscopy and lightning adaptive deconvolution, we demonstrate the partial regeneration of CCKergic axons (81% of the number of axonal profiles seen in controls) 10 weeks after the injury. Our data also revealed a preference for regeneration of CCKergic axons in lateral spinal cord regions. Regenerated CCKergic axons exhibit colocalization with synaptic vesicle marker SV2, indicative of functional synaptic connections. We also extracted swimming dynamics in injured animals by using DeepLabCut. Interestingly, the degree of CCKergic reinnervation correlated with improved swimming performance in injured animals, suggesting a potential role in locomotor recovery. These findings open avenues for further exploration into the role of specific neuropeptidergic systems in post-SCI spinal locomotor networks.

Current status of dental journals published by Japanese organization

The publication status of dental journals in Japan was examined, with a focus on metrics such as Journal Impact Factor (JIF), Eigenfactor, Article Influence Score, and percentage of open access. A total of 18 journals published by Japanese dental organizations were identified in the Journal Citation Reports (JCR), with JIF values ranging from 0.4 to 6.6. The highest JIF was observed in The Japanese Dental Science Review. Additionally, 16 journals were not listed on the JCR. The authors explored the implications of these findings on the visibility and impact of Japanese dental research, and discussed the potential benefits of embracing open-access publications for greater global dissemination. This study highlighted the opportunities for journals to enhance their international recognition by meeting the criteria for JIF inclusion and embracing open-access publications. By adopting effective publication strategies, the dental community in Japan will be able to contribute to the advancement of dentistry globally, ensuring broader accessibility and recognition of its research contributions.

Metagenomic shotgun sequencing reveals the enrichment of Salmonella and Mycobacterium in larynx due to prolonged ethanol exposure

The exposure of ethanol increases the risk of head and neck inflammation and tumor progression. However, limited studies have investigated the composition and functionality of laryngeal microbiota under ethanol exposure. We established an ethanol-exposed mouse model to investigate the changes in composition and function of laryngeal microbiota using Metagenomic shotgun sequencing. In the middle and late stages of the experiment, the laryngeal microbiota of mice exposed to ethanol exhibited obvious distinguished from that of the control group on principal-coordinate analysis (PCoA) plots. Among the highly abundant species, Salmonella enterica and Mycobacterium marinum were likely to be most impacted. Our findings indicated that the exposure to ethanol significantly increased their abundance in larynxes in mice of the same age, which has been confirmed through FISH experiments. Among the species-related functions and genes, metabolism is most severely affected by ethanol. The difference was most obvious in the second month of the experiment, which may be alleviated later because the animal established tolerance. Notable enrichments concerning energy, amino acid, and carbohydrate metabolic pathways occurred during the second month under ethanol exposure. Finally, based on the correlation between species and functional variations, a network was established to investigate relationships among microbiota, functional pathways, and related genes affected by ethanol. Our data first demonstrated the continuous changes of abundance, function and their interrelationship of laryngeal microbiota under ethanol exposure by Metagenomic shotgun sequencing.

Importance

Ethanol may participate in the inflammation and tumor progression by affecting the composition of the laryngeal microbiota. Here, we applied the metagenomic shotgun sequencing instead of 16 S rRNA sequencing method to identify the laryngeal microbiota under ethanol exposure. Salmonella enterica and Mycobacterium marinum are two dominant species that may play a role in the reconstruction of the laryngeal microenvironment, as their local abundance increases following exposure to ethanol. The metabolic function is most evidently impacted, and several potential metabolic pathways could be associated with alterations in microbiota composition. These findings could help us better understand the impact of prolonged ethanol exposure on the microbial composition and functionality in the larynx.

Current status of Er:YAG laser in periodontal surgery

Lasers have numerous advantageous tissue interactions such as ablation or vaporization, hemostasis, bacterial killing, as well as biological effects, which induce various beneficial therapeutic effects and biological responses in the tissues. Thus, lasers are considered an effective and suitable device for treating a variety of inflammatory and infectious conditions of periodontal disease. Among various laser systems, the Er:YAG laser, which can be effectively and safely used in both soft and hard tissues with minimal thermal side effects, has been attracting much attention in periodontal therapy. This laser can effectively and precisely debride the diseased root surface including calculus removal, ablate diseased connective tissues within the bone defects, and stimulate the irradiated surrounding periodontal tissues during surgery, resulting in favorable wound healing as well as regeneration of periodontal tissues. The safe and effective performance of Er:YAG laser-assisted periodontal surgery has been reported with comparable and occasionally superior clinical outcomes compared to conventional surgery. This article explains the characteristics of the Er:YAG laser and introduces its applications in periodontal surgery including conventional flap surgery, regenerative surgery, and flapless surgery, based on scientific evidence from currently available basic and clinical studies as well as cases reports.

Have a vital end-user been overlooked? Developing a shared decision intervention for patients with potential pancreatic cancer regarding the choice of surgery

Objective

To develop a patient decision aid facilitating shared decision making for patients with potential pancreatic cancer deciding about no treatment, surgical or medical treatment.

Methods

Based on a user-centred design by Wittemann et al., we developed a shared decision making intervention in three phases: 1) Understanding decision needs 2) Development of a patient decision aid (PtDA) based on a generic template 3) Assessment of the intervention from interviews with patients (n = 11), relatives (n = 11), nurses (n = 4) and surgeons (n = 2) analysed with thematic analysis, and measuring patients' perceptions of choice of options with the Decisional Conflict Scale.

Results

Results showed varying experiences with the use of the PtDA, with surgeons not finding PtDA useful as it was impractical and constraining with patients' conversations. There was no difference in patients' perceptions in choosing options for those being presented vs those patients not being presented for the PtDA.

Conclusion

The format and structure of the PtDA was not feasible for the surgeons as fundamental users in the present clinic.

Innovation

This study highlights the urgent need to consider clinical context before introducing a predefined tool and shows the importance of a multistakeholder approach. Research should focus on finding means to successful implement shared decision making.

Developing and testing a web-based platform for antiretroviral therapy (ART) adherence support among adolescents and young adults (AYA) living with HIV

Objective

Describe the development and testing of a web-based platform for antiretroviral treatment (ART) adherence support among HIV+ adolescents and young adults (AYA) in a randomized controlled trial (RCT).

Methods

A seven-member multi-disciplinary team operationalized the flat, password protected, web-based platform. Manualized protocols guided the objectives and content for each of the eight web-based sessions. Team members evaluated usability and content validity. Client satisfaction and perceived ease of use was evaluated with the first ten HIV+ AYA participants.

Results

The web-based platform was developed, evaluated, refined, implemented and pilot tested between September 2020 to April 2022. Usability was rated as high; the evaluation of content validity showed an excellent fit between session content and objectives. HIV+ AYA participants (mean age = 24.2 years) were satisfied with the quality, type, and amount of support/education received, and found the platform easy to use, operate, and navigate. Average time spent per session was 6.5 min.

Conclusion

Findings support the usability, validity, acceptability, and feasibility of this web-based platform for ART adherence support among HIV+ AYA.

Innovation

Our research and findings are responsive to research gaps and the need for transparency in the methodological development and testing of web-based control arms for ART adherence support among HIV+ AYA.

Utility of artificial intelligence in a binary classification of soft tissue tumors

Soft tissue tumors (STTs) pose diagnostic and therapeutic challenges due to their rarity, complexity, and morphological overlap. Accurate differentiation between benign and malignant STTs is important to set treatment directions, however, this task can be difficult. The integration of machine learning and artificial intelligence (AI) models can potentially be helpful in classifying these tumors. The aim of this study was to investigate AI and machine learning tools in the classification of STT into benign and malignant categories. This study consisted of three components: (1) Evaluation of whole-slide images (WSIs) to classify STT into benign and malignant entities. Five specialized soft tissue pathologists from different medical centers independently reviewed 100 WSIs, representing 100 different cases, with limited clinical information and no additional workup. The results showed an overall concordance rate of 70.4% compared to the reference diagnosis. (2) Identification of cell-specific parameters that can distinguish benign and malignant STT. Using an image analysis software (QuPath) and a cohort of 95 cases, several cell-specific parameters were found to be statistically significant, most notably cell count, nucleus/cell area ratio, nucleus hematoxylin density mean, and cell max caliper. (3) Evaluation of machine learning library (Scikit-learn) in differentiating benign and malignant STTs. A total of 195 STT cases (156 cases in the training group and 39 cases in the validation group) achieved approximately 70% sensitivity and specificity, and an AUC of 0.68. Our limited study suggests that the use of WSI and AI in soft tissue pathology has the potential to enhance diagnostic accuracy and identify parameters that can differentiate between benign and malignant STTs. We envision the integration of AI as a supportive tool to augment the pathologists' diagnostic capabilities.

Improving cardiac differentiation of human pluripotent stem cells by targeting ferroptosis

Generation of cardiomyocytes from human pluripotent stem cells (hPSCs) is of high interest for disease modelling and regenerative medicine. hPSCs can provide an unlimited source of patient-specific cardiomyocytes that are otherwise difficult to obtain from individuals. Moreover, the low proliferation rate of adult cardiomyocytes and low viability ex vivo limits the quantity of study material. Most protocols for the differentiation of cardiomyocytes from hPSCs are based on the temporal modulation of the Wnt pathway. However, during the initial stage of GSK-3 inhibition, a substantial number of cells are lost due to detachment. In this study, we aimed to increase the efficiency of generating cardiomyocytes from hPSCs. We identified cell death as a detrimental factor during this initial stage of in vitro cardiomyocyte differentiation. Through pharmacological targeting of different types of cell death, we discovered that ferroptosis was the main cell death type during the first 48 h of the in vitro differentiation procedure. Inhibiting ferroptosis using ferrostatin-1 during cardiomyocyte differentiation resulted in increased robustness and cell yield.

Students' perceptions and experiences of remote learning amid covid-19 pandemic in Ghana

Background

The COVID-19 pandemic had a significant impact on students, instructors, and educational organisations all around the world. Remote learning was an emergency response by most universities in Ghana during this pandemic to ensure the continuation of their academic calendar. Conducting this study among Ghanaian undergraduate students is crucial because factors like socioeconomic status, technological resources, and individual learning preferences can significantly impact their experience and the perception of remote learning, which may differ from studies conducted elsewhere.

Objective

To determine the perceptions and experiences of remote learning among allied health sciences students during the COVID-19 pandemic.

Methods

This cross-sectional study involved 218 second, third, and final year Allied Health Sciences students in the University of Ghana. A questionnaire was used to obtain data concerning health professions students' perception and experience of remote learning. The readiness of students in respect to the emergency remote learning, attitudes towards remote learning, perception of remote learning, satisfaction, and the level of anxiety was calculated using mean and mean percentages. Kruskal-Wallis test was used to analyse differences between programmes of study and the perceptions and experiences of remote learning.

Results

One hundred and fifteen (53.1%) of the participants had moderate perceptions of independence and responsibility in their learning experiences while 80 (36.7%) students reported that their satisfaction levels regarding remote learning was high. Seventy-seven (38.4%) students reported that they had a burden of anxiety. There was no statistically significant difference between anxiety level and programme of study.

Conclusion

Ghana Allied Health Sciences students had positive perceptions and experiences towards remote learning. They could adapt to the new teaching method with appropriate technology integration. Despite a number of students who were anxious about using remote learning. Adequate support towards transitioning into the use of technology may be a good consideration.

Physiotherapy students' rating on lecturers' and supervisors' clinical education attributes

Background

Clinical education is considered a vital aspect of education of health science students. Attributes of clinical educators play a crucial role in determining the outcome of clinical teaching and learning. A good clinical educator ensures that students get maximum benefits of the clinical learning experience.

Objective

To determine the ratings of physiotherapy students on clinical education attributes of lecturers and clinical supervisors.

Methods

The study was conducted with 81 clinical physiotherapy students from two universities in Ghana. Two copies of McGill clinical teachers' evaluation (CTE) tool were used to obtain students' ratings on their clinical supervisors' and lecturers' clinical education attributes. Independent t-test was used to compare the means of students' level of study and ratings regarding the clinical education attributes of clinical supervisors and lecturers.

Results

Students had a high rating on their clinical education attributes of supervisors and lecturers with a mean score of (121 . 22 ± 18 . 12 ) and (122 . 11 ± 16 . 71 ), respectively. Rating on clinical education attributes of supervisors (p = 0 . 111 ) and lecturers (p = 0 . 124 ) did not differ significantly between the different levels of study.

Conclusion

Clinical physiotherapy students rated the clinical education attributes of their lecturers and supervisors high.