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.

BBDash: An Electron-based tool for analyzing blood product utilization

Blood transfusions can be associated with side effects ranging from occasional febrile reactions to extremely rare fatal reactions. Monitoring blood product orders and ensuring appropriate utilization is therefore an important strategy to ensure patient safety. However, data extracted from laboratory information systems can be difficult to interpret. We created BBDash, an Electron-based tool that reads Sunquest reports to create easy-to-interpret graphs related to blood product utilization.

Intrauterine injection of bioengineered hydrogel loaded exosomes derived from HUCM stem cells and spermidine prominently augments the pregnancy rate in thin endometrium rats

The endometrium is essential to the development of embryos and pregnancy. Human umbilical cord mesenchymal stem cells (HUCMSCs) are promising stem cell sources. HUCMSCs self-renew quickly and are painless to collect. Spermidine is an inherent polyamine needed for cellular and molecular processes that regulate physiology and function. HUCMSCs and spermidine (SN) may heal intrauterine adhesions. HUCMSCs were investigated for endometrial repair in rats. Composite hydrogels are used for medical exosome implantation, including their materials, properties, and embedding procedures. This study examined whether bioengineered hydrogel-loaded exosomes from HUCMSCs and spermidine prenatally improved conception rates in mice with poor endometrial lining. The data show that HUCMSC and SN provide a good experimental base for HUCMSC safety and intrauterine treatment in rats. Western blots, exosome structural analysis, pregnancy outcomes, flow cytometry, H&E staining, immunohistochemistry, and immunofluorescence labelling found and recovered the aberrant area. HUCM-derived stem cells and spermidine-derived exosomes biophysically match. These traits strengthen and prolong endometrial function. Pregnant rats with HUCMSC and SN had thinner endometrium. Hydrogel-incorporated HEHUCMSC and SN exosomes may improve IUI in rats with thin endometrium.

Mobile robots for isolation-room hospital settings: A scenario-based preliminary study

Isolated patients pose physical challenges to medical staff owing to the need for protective gear. Additionally, communication issues arise within isolation rooms, hampering patient care. Mobile robots offer potential solutions, allowing for contactless communication and efficient task delegation, thereby reducing the risk of cross-contamination and minimizing staff workload. This preliminary study assessed the usability, acceptability, and potential for improvement of mobile robots in clinical nursing scenarios, focusing on nurses' perspectives. A preliminary test was conducted using mobile robots in a simulated hospital environment with 30 experienced nurses responsible for isolated patient care. Data were collected through interviews, surveys, and scenario-based tasks. Two scenarios were designed to evaluate the usability and effectiveness of mobile robots in real-world nursing situations. Nurses regarded mobile robots as highly usable and useful in healthcare settings. Robots efficiently handled tasks like remote supply delivery and medication distribution. Nurses recognized the potential for improved communication and efficiency with mobile robots; however, concerns were raised about the robots' limitations in providing emotional support and potential safety issues during emergencies. This research emphasizes the promising role of mobile robots in enhancing healthcare delivery within isolation rooms. While these findings indicate the potential for mobile robots, careful planning, training, and scenario development are crucial for their safe and effective integration into clinical settings. Further research, tailored scenarios, and a reevaluation of the evolving role of nurses in a technology-augmented healthcare environment are necessary, emphasizing the importance of understanding the capabilities and limitations of robotic assistance in patient care.

Alternative polyadenylation regulates the translation of metabolic and inflammation-related proteins in adipose tissue of gestational diabetes mellitus

In gestational diabetes mellitus (GDM), adipose tissue undergoes metabolic disturbances and chronic low-grade inflammation. Alternative polyadenylation (APA) is a post-transcriptional modification mechanism that generates mRNA with variable lengths of 3' untranslated regions (3'UTR), and it is associated with inflammation and metabolism. However, the role of APA in GDM adipose tissue has not been well characterized. In this study, we conducted transcriptomic and proteomic sequencing on subcutaneous and omental adipose tissues from both control and GDM patients. Using Dapars, a novel APA quantitative algorithm, we delineated the APA landscape of adipose tissue, revealing significant 3'UTR elongation of mRNAs in the GDM group. Omental adipose tissue exhibited a significant correlation between elongated 3'UTRs and reduced translation levels of genes related to metabolism and inflammation. Validation experiments in THP-1 derived macrophages (TDMs) demonstrated the impact of APA on translation levels by overexpressing long and short 3'UTR isoforms of a representative gene LRRC25. Additionally, LRRC25 was validated to suppress proinflammatory polarization in TDMs. Further exploration revealed two underexpressed APA trans-acting factors, CSTF3 and PPP1CB, in GDM omental adipose tissue. In conclusion, this study provides preliminary insights into the APA landscape of GDM adipose tissue. Reduced APA regulation in GDM omental adipose tissue may contribute to metabolic disorders and inflammation by downregulating gene translation levels. These findings advance our understanding of the molecular mechanisms underlying GDM-associated adipose tissue changes.

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

Objective

Classification tasks are an open challenge in the field of biomedicine. While several machine-learning techniques exist to accomplish this objective, several peculiarities associated with biomedical data, especially when it comes to omics measurements, prevent their use or good performance achievements. Omics approaches aim to understand a complex biological system through systematic analysis of its content at the molecular level. On the other hand, omics data are heterogeneous, sparse and affected by the classical "curse of dimensionality" problem, i.e. having much fewer observation, samples (n) than omics features (p). Furthermore, a major problem with multi-omics data is the imbalance either at the class or feature level. The objective of this work is to study whether feature extraction and/or feature selection techniques can improve the performances of classification machine-learning algorithms on omics measurements.

Methods

Among all omics, metabolomics has emerged as a powerful tool in cancer research, facilitating a deeper understanding of the complex metabolic landscape associated with tumorigenesis and tumor progression. Thus, we selected three publicly available metabolomics datasets, and we applied several feature extraction techniques both linear and non-linear, coupled or not with feature selection methods, and evaluated the performances regarding patient classification in the different configurations for the three datasets.

Results

We provide general workflow and guidelines on when to use those techniques depending on the characteristics of the data available. To further test the extension of our approach to other omics data, we have included a transcriptomics and a proteomics data. Overall, for all datasets, we showed that applying supervised feature selection improves the performances of feature extraction methods for classification purposes. Scripts used to perform all analyses are available at: https://github.com/Plant-Net/Metabolomic_project/.

Dopamine promotes osteogenic differentiation of PDLSCs by activating DRD1 and DRD2 during orthodontic tooth movement via ERK1/2 signaling pathway

Introduction

Orthodontic tooth movement (OTM) involves complex interactions between mechanical forces and periodontal tissue adaptation, mainly mediated by periodontal ligament cells, including periodontal ligament stem cells (PDLSCs), osteoblasts, and osteoclasts. Dopamine (DA), a neurotransmitter known for its critical role in bone metabolism, is investigated in this study for its potential to enhance osteogenic differentiation in PDLSCs, which are pivotal in OTM. This study examined the potential of DA to facilitate OTM by binding to DA receptors (D1R and D2R) and activating the ERK1/2 signaling pathway. We propose that DA's interaction with these receptors on PDLSCs could enhance osteogenic differentiation, thereby accelerating bone remodeling and reducing the duration of orthodontic treatments, which offering a novel approach to improve clinical outcomes in orthodontic care.

Methods

This study utilized a rat OTM model, micro-CT, histological analyses, and in vitro assays to investigate dopamine's effect on osteogenesis. PDLSCs were cultured and treated with DA, and cytotoxicity, osteogenic differentiation, gene and protein expression assessed.

Results

Dopamine administration significantly increased trabecular bone density and osteogenic marker expression in an OTM rat model. In vitro, DA at 10 nM optimally promoted human PDLSCs osteogenesis without affecting proliferation. Blocking DA receptors or inhibiting the ERK1/2 pathway attenuated these effects, underscoring the importance of dopaminergic signaling in tension-induced osteogenesis during OTM.

Conclusion

Taken together, our study reveals that local dopamine administration at a concentration of 10 nM not only enhances tension-induced osteogenesis in vivo but also significantly promotes osteogenic differentiation of PDLSCs in vitro through D1 and D2 receptor-mediated ERK1/2 signaling pathway activation.

GETdb: A comprehensive database for genetic and evolutionary features of drug targets

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.

SAEDC: Development of a technological solution for exploratory data analysis and statistics in cytotoxicity

INTRODUCTION

The intergovernmental organizations Organisation for Economic Co-operation and Development (OECD) and Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) have developed guidelines for the use of in vitro models for toxicological evaluation of chemicals. However, the presence of manual steps and the requirement of multiple tools for data analysis, apart from being costly and time-consuming, can inadvertently introduce errors by researchers.

OBJECTIVES

We have developed the SAEDC platform (Technological Solution for Exploratory Data Analysis and Statistics for Cytotoxicity, in Portuguese), which enables analysis of cytotoxicity data from assays following OECD Guideline No. 129.

METHODOLOGY

In vitro experimental data were used to compare with the analysis methodology suggested in the Guideline. We analyzed 117 data sets covering chemicals from Category I to Unclassified according to GHS classification.

RESULTS

The four-parameters of non-linear regression (4PL) calculated by the SAEDC platform showed no significant differences compared to standard methodology in any of the data sets (p > 0.05). The coefficient of determination (R-squared) also demonstrated not only a good fit of the 4PL model to the data but also significant similarity to values obtained by the conventional methodology. Finally, the SAEDC platform predicted LD50 values for the chemicals from IC50, using the Registry of Cytotoxicity (RC) regression models.

CONCLUSION

The comparison with the standard data analysis methodology revealed that SAEDC platform fulfills the requirements for cytotoxicity data analysis, generating reliable and accurate results with fewer steps performed by researchers. The use of SAEDC platform for obtaining toxicity values can reduce analysis time compared to the standard methodology proposed by regulatory agencies. Thus, automation of the analysis using the SAEDC platform has the potential to save time and resources for cytotoxicity researchers and laboratories while generating reliable results.

iSanXoT: A standalone application for the integrative analysis of mass spectrometry-based quantitative proteomics data

Many bioinformatics tools are available for the quantitative analysis of proteomics experiments. Most of these tools use a dedicated statistical model to derive absolute quantitative protein values from mass spectrometry (MS) data. Here, we present iSanXoT, a standalone application that processes relative abundances between MS signals and then integrates them sequentially to upper levels using the previously published Generic Integration Algorithm (GIA). iSanXoT offers unique capabilities that complement conventional quantitative software applications, including statistical weighting and independent modeling of error distributions in each integration, aggregation of technical or biological replicates, quantification of posttranslational modifications, and analysis of coordinated protein behavior. iSanXoT is a standalone, user-friendly application that accepts output from popular proteomics pipelines and enables unrestricted creation of quantification workflows and fully customizable reports that can be reused across projects or shared among users. Numerous publications attest the successful application of diverse integrative workflows constructed using the GIA for the analysis of high-throughput quantitative proteomics experiments. iSanXoT has been tested with the main operating systems. Download links for the corresponding distributions are available at https://github.com/CNIC-Proteomics/iSanXoT/releases.

SCInter: A comprehensive single-cell transcriptome integration database for human and mouse

Single-cell RNA sequencing (scRNA-seq), which profiles gene expression at the cellular level, has effectively explored cell heterogeneity and reconstructed developmental trajectories. With the increasing research on diseases and biological processes, scRNA-seq datasets are accumulating rapidly, highlighting the urgent need for collecting and processing these data to support comprehensive and effective annotation and analysis. Here, we have developed a comprehensive Single-Cell transcriptome integration database for human and mouse (SCInter, https://bio.liclab.net/SCInter/index.php), which aims to provide a manually curated database that supports the provision of gene expression profiles across various cell types at the sample level. The current version of SCInter includes 115 integrated datasets and 1016 samples, covering nearly 150 tissues/cell lines. It contains 8016,646 cell markers in 457 identified cell types. SCInter enabled comprehensive analysis of cataloged single-cell data encompassing quality control (QC), clustering, cell markers, multi-method cell type automatic annotation, predicting cell differentiation trajectories and so on. At the same time, SCInter provided a user-friendly interface to query, browse, analyze and visualize each integrated dataset and single cell sample, along with comprehensive QC reports and processing results. It will facilitate the identification of cell type in different cell subpopulations and explore developmental trajectories, enhancing the study of cell heterogeneity in the fields of immunology and oncology.

Secure and privacy-preserving automated machine learning operations into end-to-end integrated IoT-edge-artificial intelligence-blockchain monitoring system for diabetes mellitus prediction

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure to date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to be able to monitor and predict the incidence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. This paper proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by blockchain to obtain a cohesive view of the risk factors data from patients across different hospitals and ensure security and privacy of the user's data. We provide a comparative analysis of different medical sensors, devices, and methods to measure and collect the risk factors values in the system. Numerical experiments and comparative analysis were carried out within our proposed system, using the most accurate random forest (RF) model, and the two most used state-of-the-art machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM), using three real-life diabetes datasets. The results show that the proposed system predicts diabetes using RF with 4.57% more accuracy on average in comparison with the other models LR and SVM, with 2.87 times more execution time. Data balancing without feature selection does not show significant improvement. When using feature selection, the performance is improved by 1.14% for PIMA Indian and 0.02% for Sylhet datasets, while it is reduced by 0.89% for MIMIC III.

Peritendinous adhesion: Therapeutic targets and progress of drug therapy

Peritendinous adhesion (PA) is one of the most common complications following hand surgery and characterized with abnormal hyperplasia of connective tissue and excessive deposition of extracellular matrix. Subsequently, various clinical symptoms such as chronic pain, limb dyskinesia and even joint stiffness occur and patients are always involved in the vicious cycle of "adhesion - release - re-adhesion", which seriously compromise the quality of life. Until present, the underlying mechanism remains controversial and lack of specific treatment, with symptomatic treatment being the only option to relieve symptoms, but not contributing no more to the fundamentally rehabilitation of basic structure and function. Recently, novel strategies have been proposed to inhibit the formation of adhesion tissues including implantation of anti-adhesion barriers, anti-inflammation, restraint of myofibroblast transformation and regulation of collagen overproduction. Furthermore, gene therapy has also been considered as a promising anti-adhesion treatment. In this review, we provide an overview of anti-adhesion targets and relevant drugs to summarize the potential pharmacological roles and present subsequent challenges and prospects of anti-adhesion drugs.

Predicting zeta potential of liposomes from their structure: A nano-QSPR model for DOPE, DC-Chol, DOTAP, and EPC formulations

Liposomes, nanoscale spherical structures composed of amphiphilic lipids, hold great promise for various pharmaceutical applications, especially as nanocarriers in targeted drug delivery, due to their biocompatibility, biodegradability, and low immunogenicity. Understanding the factors influencing their physicochemical properties is crucial for designing and optimizing liposomes. In this study, we have presented the kernel-weighted local polynomial regression (KwLPR) nano-quantitative structure-property relationships (nano-QSPR) model to predict the zeta potential (ZP) based on the structure of 12 liposome formulations, including 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE), 3ß-[N-(N',N'-dimethylaminoethane)-carbamoyl]cholesterol (DC-Chol), 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), and L-α-phosphatidylcholine (EPC). The developed model is well-fitted (R 2 = 0.96, RMSE C = 5.76), flexible (Q CVloo 2 = 0.83, RMSE CVloo = 10.77), and reliable (Q Ext 2 = 0.89 RMSE Ext = 5.17). Furthermore, we have established the formula for computing molecular nanodescriptors for liposomes, based on constituent lipids' molar fractions. Through the correlation matrix and principal component analysis (PCA), we have identified two key structural features affecting liposomes' zeta potential: hydrophilic-lipophilic balance (HLB) and enthalpy of formation. Lower HLB values, indicating a more lipophilic nature, are associated with a higher zeta potential, and thus stability. Higher enthalpy of formation reflects reduced zeta potential and decreased stability of liposomes. We have demonstrated that the nano-QSPR approach allows for a better understanding of how the composition and molecular structure of liposomes affect their zeta potential, filling a gap in ZP nano-QSPR modeling methodologies for nanomaterials (NMs). The proposed proof-of-concept study is the first step in developing a comprehensive and computationally based system for predicting the physicochemical properties of liposomes as one of the most important drug nano-vehicles.

The prevalence of sleep disturbance and its possible associated factors among iranian medical students: A cross-sectional study with a national meta-analysis

Background

The aim of this cross-sectional study was to investigate the prevalence of sleep disturbance and its possible associated factors among Iranian medical students. Additionally, a national meta-analysis was conducted to provide a comprehensive overview of sleep disturbance in this population.

Methods

A sample of medical students from Guilan University of Medical Sciences, Iran was included in the study. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep disturbance. Demographic and lifestyle factors, as well as academic performance, were collected through a self-administered questionnaire. The data collected from this study were combined with existing studies through a meta-analysis to estimate the overall prevalence of sleep disturbance among Iranian medical students using the random effects model.

Results

A total of 249 medical students participated in the study. The prevalence of sleep disturbance among Guilan University of Medical Sciences medical students was found to be 71.1%. A significant difference was observed in total PSQI means regarding medical students' residency (p < 0.001) and their duration of sleep in the last 24 h (p = 0.006). The national prevalence of sleep disturbances was 59% (95% CI: [51%-66%], I2 = 97%).

Conclusion

Sleep disturbance is highly prevalent among Iranian medical students, with various factors contributing to its occurrence. The findings of this study highlight the need for interventions and strategies to improve sleep quality and overall well-being among this population. The national meta-analysis provides valuable insights into the overall burden of sleep disturbance among Iranian medical students and can serve as a reference for future studies and public health initiatives targeting this issue.

Belief in misinformation and acceptance of COVID-19 vaccine boosters: A survey analysis

Objective

This study examines the impact of misinformation belief on the willingness of American adults to receive ongoing COVID-19 vaccine boosters.

Methods

A representative survey of 600 adults in the state of Florida was fielded in August of 2023. For this study, responses were analyzed for the 443 previously vaccinated respondents in that sample using both descriptive and inferential statistical methods.

Results

Among previously vaccinated individuals, belief in misinformation remained relatively high. 49% of respondents believe that COVID-19 vaccines contain a live strain of the virus, and roughly 40% believe that vaccines can cause you to "get sick" with COVID-19. Belief in misinformation was associated with a statistically significant decrease in the likelihood of receiving ongoing vaccine boosters, ceteris paribus.

Conclusion

While confidence in public health guidelines is the most compelling determinant of vaccine acceptance, misinformation continues to undermine vaccination efforts. Addressing common myths about COVID-19 vaccines may help to improve booster shot acceptance among previously vaccinated Americans.

Innovation

Throughout the COVID-19 pandemic, health professionals and public health agencies have been forced to innovate in real-time, as digital platforms have fueled the spread of viral misinformation. This study aims to inform these efforts by exploring and deepening our understanding of the impact that belief in misinformation has on vaccination behaviors.

Feasibility and acceptability of chaplain decision coaching on Periviable resuscitation decision quality: A pilot study

Objective

To pilot test and assess the feasibility and acceptability of chaplain-led decision coaching alongside the GOALS (Getting Optimal Alignment around Life Support) decision support tool to enhance decision-making in threatened periviable delivery.

Methods

Pregnant people admitted for threatened periviable delivery and their 'important other' (IO) were enrolled. Decisional conflict, acceptability, and knowledge were measured before and after the intervention. Chaplains journaled their impressions of training and coaching encounters. Descriptive analysis and conventional content analysis were completed.

Results

Eight pregnant people and two IOs participated. Decisional conflict decreased by a mean of 6.7 (SD = 9.4) and knowledge increased by a mean of 1.4 (SD = 1.8). All rated their experience as "good" or "excellent," and the amount of information was "just right." Participants found it "helpful to have someone to talk to" and noted chaplains helped them reach a decision. Chaplains found the intervention a valuable use of their time and skillset.

Conclusion

This is the first small-scale pilot study to utilize chaplains as decision coaches. Our results suggest that chaplain coaching with a decision support tool is feasible and well-accepted by parents and chaplains.

Innovations

Our findings recognize chaplains as an underutilized, yet practical resource in value-laden clinical decision-making.

Clustering human dental pulp fibroblasts spontaneously activate NLRP3 and AIM2 inflammasomes and induce IL-1β secretion

Objectives

The objective of the present study was to investigate whether NOD-like receptor family pyrin domain-containing 3 (NLRP3) and absent in melanoma 2 (AIM2) inflammasomes pathways were involved in an experimental model of fibroblast activation named nemosis, which was used to mimic circumstances without bacteria stimulation.

Methods

Nemosis of human dental pulp fibroblast (DPFs) was induced by three-dimensional culture in U-shaped 96-well plates and investigated by scanning electron microscopy (SEM). DPFs monolayers were used as control. Annexin V-FITC/7-AAD apoptosis assay was performed on the DPFs spheroids by flowcytometry. Caspase-1 activity detection assay was conducted on the DPFs spheroids. Quantitative real-time polymerase chain reaction (qRT-PCR), cytokine measurements, Western blot and the effect of COX-2 inhibitor on spheroids was studied.

Results

SEM study observed human dental pulp fibroblast clusters and cell membranes damage on the surface of DPFs spheroids. The percentages of necrotic cells from DPFs spheroids gradually increased as the incubation time increased. A statistically significant increase in caspase-1 activity was observed after DPFs spheroids formation. DPFs spheroids displayed significant amounts of NLRP3, AIM2 mRNA and protein expression, caspase-1 mRNA expression and cleaved Caspase-1 protein expression and high IL-1β concentrations (P < 0.05) than DPFs monolayers. Specific COX-2 inhibitor (NS-398) decreased NLRP3 mRNA and protein expression, cleaved Caspase-1 protein expression, Caspase-1 activity and IL-1β mRNA expression and IL-1β concentrations (P < 0.05). However, Specific COX-2 inhibitor had no impact on AIM2 mRNA and protein expression, caspase-1 mRNA expression and pro-Caspase-1 protein expression.

Conclusions

In conclusion, clustering human DPFs spontaneously activated NLRP3 and AIM2 inflammasomes and induced IL-1β secretion which could be partially attenuated by COX-2 inhibitor. Thus, nemosis could become a powerful model for studying mechanisms underlying aseptic pulpitis.

Mesenchymal stem cells reverse thymus aging by reprogramming the DNA methylation of thymic epithelial cells

Background

A decrease in the number and activity of thymic epithelial cells (TECs) is an important factor in thymic degeneration. Mesenchymal stem cells (MSCs) treating thymic ageing is a promising strategy, but the DNA methylation modification mechanism in TECs remains unclear.

Methods

Aged rhesus monkeys were treated with MSCs to establish a thymic senescence model, and hematoxylin-eosin (HE) staining, immunofluorescence staining, and ELISA were performed to observe the structure and function of the thymus. TEC aging model and MSCs co-culture system were established to detect DNA methylation modification and transcriptomic changes, correlation analysis between transcription factor methylation and mRNA expression, and q-PCR, immunofluorescence staining, and Western blot were used to identified key genes.

Results

MSCs improved the structure and function of thymus in elderly macaque monkeys; reduced the expression levels of β-Gal, P16, and P21; and increased the activity of aging TECs. There were 501 genes with increased methylation in the promoter region in the treated group compared with the untreated group, among which 23 genes were involved in the negative regulation of cell growth, proliferation and apoptosis, while 591 genes had decreased methylation, among which 37 genes were associated with promoting cell growth and proliferation and inhibiting apoptosis. Furthermore, 66 genes showed a negative correlation between promoter methylation levels and gene transcription; specifically, PDE5A, DUOX2, LAMP1 and SVIL were downregulated with increased methylation, inhibiting growth and development, while POLR3G, PGF, CHTF18, KRT17, FOXJ1, NGF, DYRK3, LRP8, CDT1, PRELID1, F2R, KNTC1 and TRIM3 were upregulated with decreased methylation, promoting cell growth.

Conclusion

MSCs improve the structure and function of aged thymus, which involves the regulation of DNA methylation profiles and a decrease in the methylation level of the transcription factor NGF to specifically upregulate KRT17 and FOXJ1 to promote the proliferation of TECs.

Physical therapy exercises for improving intermittent exotropia control post-strabismus surgery: A randomized controlled study

Background

Exotropia control is deteriorated by post-strabismus surgery in many cases. Improving this control is considered as an important factor for success of the strabismus surgery like ocular motor alignment.

Objective

To determine the therapeutic effect of postoperative eye exercises on exodeviation eye control after the strabismus surgery.

Methods

Forty patients suffering from intermittent exotropia after strabismus surgery were randomly divided into experimental group and control groups. Both groups received the usual medical care, while the experimental group received different types of eye exercises using physiotherapy modalities in 24 sessions over three months (two sessions a week). The measurement of exotropia control for near target and far target using the office-based scale was done before and after the treatment.

Results

There was a significant improvement in near eye control post-treatment (0 . 45 ± 0 . 61 ) in the experimental group compared with control group (3 . 65 ± 0 . 88 ) as the mean difference was -3.20 and 95% CI of the difference between them was -3.645--2.755 with P < 0 . 01 . Also, far eye control improved post-treatment in the experimental group (0 . 75 ± 0 . 64 ) compared with control group (4 . 00 ± 0 . 86 ) with mean difference of -3.250 and 95% CI of the difference between them was -3.727--2.773 with P < 0 . 01 .

Conclusions

For patients with intermittent exotropia who had undergone strabismus surgery, the addition of physiotherapy exercises for the eye provided significant improvements in exotropia control for near target and far target.

PS-GO parametric protein search engine

With the explosive growth of protein-related data, we are confronted with a critical scientific inquiry: How can we effectively retrieve, compare, and profoundly comprehend these protein structures to maximize the utilization of such data resources? PS-GO, a parametric protein search engine, has been specifically designed and developed to maximize the utilization of the rapidly growing volume of protein-related data. This innovative tool addresses the critical need for effective retrieval, comparison, and deep understanding of protein structures. By integrating computational biology, bioinformatics, and data science, PS-GO is capable of managing large-scale data and accurately predicting and comparing protein structures and functions. The engine is built upon the concept of parametric protein design, a computer-aided method that adjusts and optimizes protein structures and sequences to achieve desired biological functions and structural stability. PS-GO utilizes key parameters such as amino acid sequence, side chain angle, and solvent accessibility, which have a significant influence on protein structure and function. Additionally, PS-GO leverages computable parameters, derived computationally, which are crucial for understanding and predicting protein behavior. The development of PS-GO underscores the potential of parametric protein design in a variety of applications, including enhancing enzyme activity, improving antibody affinity, and designing novel functional proteins. This advancement not only provides a robust theoretical foundation for the field of protein engineering and biotechnology but also offers practical guidelines for future progress in this domain.

Crossing the Andes: Challenges and opportunities for digital pathology in Latin America

The most widely accepted and used type of digital pathology (DP) is whole-slide imaging (WSI). The USFDA granted two WSI system approvals for primary diagnosis, the first in 2017. In Latin America, DP has the potential to reshape healthcare by enhancing diagnostic capabilities through artificial intelligence (AI) and standardizing pathology reports. Yet, we must tackle regulatory hurdles, training, resource availability, and unique challenges to the region. Collectively addressing these hurdles can enable the region to harness DP's advantages-enhancing disease diagnosis, medical research, and healthcare accessibility for its population. Americas Health Foundation assembled a panel of Latin American pathologists who are experts in DP to assess the hurdles to implementing it into pathologists' workflows in the region and provide recommendations for overcoming them. Some key steps recommended include creating a Latin American Society of Digital Pathology to provide continuing education, developing AI models trained on the Latin American population, establishing national regulatory frameworks for protecting the data, and standardizing formats for DP images to ensure that pathologists can collaborate and validate specimens across the various DP platforms.

Public involvement and public engagement: An example of convergent evolution? Findings from a conceptual qualitative review of patient and public involvement, and public engagement, in health and scientific research

Objective

Patient and public involvement (PPI) in health research is now well-established, whilst science, technology, engineering and mathematics (STEM) research has shifted from a focus on science communication alone to public engagement (PE) within its research processes. Despite frequently drawing on similar theoretical influences, and practical motivations, there is often a lack of dialogue between such settings meaning shared learning, practice and evidence from these two sectors are rarely pooled.

Methods

In this article, we examine findings from a conceptual review of literature gathered between 1996 and 2019.

Results

Analyzing 142 peer-reviewed articles, we ascertain shared definitions and concepts in patient and public involvement and public engagement, identifying key differences and similarities.

Conclusion

The literature we review supports the notion that, in terms of origins, there are two distinct traditions, one based in science communication and one based in what we describe as public involvement in shared decision-making.

Innovation

We find evidence that the two traditions are converging but our work also calls for the need for further conversations between these two settings, which are exploring intersecting issues but from parallel pathways.

Downregulation of ABLIM3 confers to the metastasis of neuroblastoma via regulating the cell adhesion molecules pathway

Neuroblastoma (NB) is the most prevalent extracranial solid tumor in pediatric patients, and its treatment failure often associated with metastasis. In this study, LASSO, SVM-RFE, and random forest tree algorithms, was used to identify the pivotal gene involved in NB metastasis. NB cell lines (SK-N-AS and SK-N-BE2), in conjunction with NB tissue were used for further study. ABLIM3 was identified as the hub gene and can be an independent prognostic factor for patients with NB. The immunohistochemical analysis revealed that ABLIM3 is negatively correlated with the metastasis of NB. Patients with low expression of ABLIM3 had a poor prognosis. High ABLIM3 expression correlated with APC co-stimulation and Type1 IFN response, and TIDE analysis indicated that patients with low ABLIM3 expression exhibited enhanced responses to immunotherapy. Downregulation of ABLIM3 by shRNA transfection increased the migration and invasion ability of NB cells. Gene Set Enrichment Analysis (GSEA) revealed that genes associated with ABLIM3 were primarily enriched in the cell adhesion molecules (CAMs) pathway. RT-qPCR and western blot analyses demonstrated that downregulation of ABLIM3 led to decreased expression of ITGA3, ITGA8, and KRT19, the key components of CAMs. This study indicated that ABLIM3 can be an independent prognostic factor for NB patients, and CAMs may mediate the effect of ABLIM3 on the metastasis of NB, suggesting that ABLIM3 is a potential therapeutic target for NB metastasis, which provides a novel strategy for future research and treatment strategies for NB patients.

Comprehensive pan-cancer analysis unveils the significant prognostic value and potential role in immune microenvironment modulation of TRIB3

TRIB3, a pseudokinase, was previously studied within only some specific cancer types, leaving its comprehensive functions in pan-cancer contexts largely unexplored. Here, we performed an integrated analysis of TRIB3 expression, prognosis, genetic alterations, functional enrichment and tumor immune-related characteristics in 33 cancer types. Our results showed that TRIB3 exhibits high expression levels across 24 different cancer types and correlates closely with unfavorable prognoses. Meanwhile, TRIB3 shows mutations in a wide spectrum of 22 distinct cancer types, with the predominant mutation types being missense mutations and gene amplifications, and significant changes in DNA methylation levels in 14 types of cancer. We further discovered that TRIB3 expression is significantly associated with cancer immune-related genome mutations, such as tumor mutational burden (TMB), microsatellite instability (MSI) and DNA mismatch repair (MMR), and infiltration of immunosuppressive cells, such as CD4+ Th2 cells and myeloid-derived suppressor cells (MDSCs), into the tumor microenvironment. These results indicated that the expression of TRIB3 might reshape the tumor immune microenvironment (TIME) and lead to immunosuppressive "cold" tumors. In addition, our results confirmed that the loss of function of TRIB3 inhibits cell proliferation, promotes apoptosis, and leads to significant enrichment of "hot" tumor-related immune pathways, at least in breast cancer cells, which further supports the important role of TRIB3 in cancer prognosis and TIME regulation. Together, this pan-cancer investigation provided a comprehensive understanding of the critical role of TRIB3 in human cancers, and suggested that TRIB3 might be a promising prognostic biomarker and a potential target for cancer immunotherapy.

Transitioning insomnia patients from zolpidem to lemborexant: A multicenter, open-label study evaluating a next-dose transition approach to insomnia pharmacotherapy

Objective

Few clinical studies have assessed real-world abrupt transitioning between insomnia medications. This study assessed strategies for directly transitioning patients from zolpidem tartrate (ZOL) immediate/extended release to the dual orexin receptor antagonist, lemborexant (LEM).

Methods

This randomized, open-label, multicenter study (Study 312; E2006-A001-312) enrolled 53 adults age ≥18 years with insomnia disorder and ≥1-month history of intermittent (3-4 nights/week) or frequent (≥5 nights/week) ZOL use. Subjects recorded their ZOL use in a 3-week Pretreatment Phase, followed by a 2-week Treatment Phase (TRT; Titration) during which ZOL was discontinued. Intermittent ZOL users transitioned to LEM 5 mg (LEM5), Cohort 1, and frequent ZOL users were randomized 1:1 to LEM5, Cohort 2A, or LEM 10 mg (LEM10), Cohort 2B. One dose adjustment was permitted during the TRT. Subjects completing the TRT could continue LEM in the 12-week Extension Phase (EXT). The primary outcome was proportion of subjects who successfully transitioned and remained on LEM at the end of the TRT.

Results

Most subjects (43 [81.1 %]) successfully transitioned to LEM (9 [90 %], 17 [81.0 %], and 17 [77.3 %] in Cohorts 1, 2A, and 2B, respectively). By the end of the EXT, 66.7 % in Cohort 1 and 60.0 % in Cohort 2A up-titrated to LEM10, whereas 41.2 % in Cohort 2B down-titrated to LEM5; 61.0 % were receiving LEM10 at study end. At the end of the TRT, more subjects taking LEM reported that it helped them return to sleep after waking, compared with those taking ZOL (71.7 % vs. 49.1 %). There were no important differences between treatments regarding how subjects reported feeling as they fell asleep. Most of the treatment-emergent adverse events with LEM were mild in severity.

Conclusions

Most subjects transitioned successfully to LEM from ZOL (intermittent or frequent use). LEM was well tolerated.

T4SEpp: A pipeline integrating protein language models to predict bacterial type IV secreted effectors

Many pathogenic bacteria use type IV secretion systems (T4SSs) to deliver effectors (T4SEs) into the cytoplasm of eukaryotic cells, causing diseases. The identification of effectors is a crucial step in understanding the mechanisms of bacterial pathogenicity, but this remains a major challenge. In this study, we used the full-length embedding features generated by six pre-trained protein language models to train classifiers predicting T4SEs and compared their performance. We integrated three modules into a model called T4SEpp. The first module searched for full-length homologs of known T4SEs, signal sequences, and effector domains; the second module fine-tuned a machine learning model using data for a signal sequence feature; and the third module used the three best-performing pre-trained protein language models. T4SEpp outperformed other state-of-the-art (SOTA) software tools, achieving ∼0.98 accuracy at a high specificity of ∼0.99, based on the assessment of an independent validation dataset. T4SEpp predicted 13 T4SEs from Helicobacter pylori, including the well-known CagA and 12 other potential ones, among which eleven could potentially interact with human proteins. This suggests that these potential T4SEs may be associated with the pathogenicity of H. pylori. Overall, T4SEpp provides a better solution to assist in the identification of bacterial T4SEs and facilitates studies of bacterial pathogenicity. T4SEpp is freely accessible at https://bis.zju.edu.cn/T4SEpp.

Network-based identification and mechanism exploration of active ingredients against Alzheimer's disease via targeting endoplasmic reticulum stress from traditional chinese medicine

Alzheimer's disease is a neurodegenerative disease that leads to dementia and poses a serious threat to the health of the elderly. Traditional Chinese medicine (TCM) presents as a promising novel therapeutic therapy for preventing and treating dementia. Studies have shown that natural products derived from kidney-tonifying herbs can effectively inhibit AD. Furthermore, endoplasmic reticulum (ER) stress is a critical factor in the pathology of AD. Regulation of ER stress is a crucial approach to prevent and treat AD. Thus, in this study, we first collected kidney-tonifying herbs, integrated chemical ingredients from multiple TCM databases, and constructed a comprehensive drug-target network. Subsequently, we employed the endophenotype network (network proximity) method to identify potential active ingredients in kidney-tonifying herbs that prevented AD via regulating ER stress. By combining the predicted outcomes, we discovered that 32 natural products could ameliorate AD pathology via regulating ER stress. After a comprehensive evaluation of the multi-network model and systematic pharmacological analyses, we further selected several promising compounds for in vitro testing in the APP-SH-SY5Y cell model. Experimental results showed that echinacoside and danthron were able to effectively reduce ER stress-mediated neuronal apoptosis by inhibiting the expression levels of BIP, p-PERK, ATF6, and CHOP in APP-SH-SY5Y cells. Overall, this study utilized the endophenotype network to preliminarily decipher the effective material basis and potential molecular mechanism of kidney-tonifying Chinese medicine for prevention and treatment of AD.

Discovery of a novel marine Bacteroidetes with a rich repertoire of carbohydrate-active enzymes

Members of the phylum Bacteroidetes play a key role in the marine carbon cycle through their degradation of polysaccharides via carbohydrate-active enzymes (CAZymes) and polysaccharide utilization loci (PULs). The discovery of novel CAZymes and PULs is important for our understanding of the marine carbon cycle. In this study, we isolated and identified a potential new genus of the family Catalimonadaceae, in the phylum Bacteroidetes, from the southwest Indian Ocean. Strain TK19036, the type strain of the new genus, is predicted to encode CAZymes that are relatively abundant in marine Bacteroidetes genomes. Tunicatimonas pelagia NBRC 107804T, Porifericola rhodea NBRC 107748T and Catalinimonas niigatensis NBRC 109829T, which exhibit 16 S rRNA similarities exceeding 90% with strain TK19036, and belong to the same family, were selected as reference strains. These organisms possess a highly diverse repertoire of CAZymes and PULs, which may enable them to degrade a wide range of polysaccharides, especially pectin and alginate. In addition, some secretory CAZymes in strain TK19036 and its relatives were predicted to be transported by type IX secretion system (T9SS). Further, to the best of our knowledge, we propose the first reported "hybrid" PUL targeting alginates in T. pelagia NBRC 107804T. Our findings provide new insights into the polysaccharide degradation capacity of marine Bacteroidetes, and suggest that T9SS may play a more important role in this process than previously believed.

AIMER: A SNP-independent software for identifying imprinting-like allelic methylated regions from DNA methylome

Genomic imprinting is essential for mammalian growth and embryogenesis. High-throughput bisulfite sequencing accompanied with parental haplotype-specific information allows analysis of imprinted genes and imprinting control regions (ICRs) on a large scale. Currently, although several allelic methylated regions (AMRs) detection software were developed, methods for detecting imprinted AMRs is still limited. Here, we developed a SNP-independent statistical approach, AIMER, to detect imprinting-like AMRs. By using the mouse frontal cortex methylome as input, we demonstrated that AIMER performs very well in detecting known germline ICRs compared with other methods. Furthermore, we found the putative parental AMRs AIMER detected could be distinguished from sequence-dependent AMRs. Finally, we found a novel germline imprinting-like AMR using WGBS data from 17 distinct mouse tissue samples. The results indicate that AIMER is a good choice for detecting imprinting-like (parent-of-origin-dependent) AMRs. We hope this method will be helpful for future genomic imprinting studies. The Python source code for our project is now publicly available on both GitHub (https://github.com/ZhaoLab-TMU/AIMER) and Gitee (https://gitee.com/zhaolab_tmu/AIMER).

Persons living with dementia and caregivers' communication preferences for receiving a dementia diagnosis

Objective

As the number of individuals diagnosed with dementia increases, so does the need to understand the preferences of persons living with dementia (PLWD) and caregivers for how clinicians can deliver a dementia diagnosis effectively, which can be a difficult process. This study describes the diagnostic communication preferences of PLWD and caregivers.

Methods

We conducted semi-structured individual phone interviews with two groups: PLWD who were diagnosed in the past two years (n = 11) and family caregivers of PLWD (n = 19) living in Florida. PLWD and caregivers were not recruited/enrolled as dyads.

Results

The groups' communication preferences were largely similar. Data were analyzed thematically into five themes: communicate the diagnosis clearly, meet information needs, discuss PLWD/caregiver resources, prepare for continued care, and communicate to establish and maintain relationships.

Conclusion

Participants wanted clear communication, information, and support, but differed in some details (e.g. the language used to describe the diagnosis and the amount/type of desired information). Clinicians can apply general principles but will need to tailor them to individual preferences of PLWD and caregivers.

Innovation

Limited research has elicited PLWD and caregivers' communication preferences for receiving dementia diagnoses, particularly through an individualized data collection method allowing for richer descriptions and deeper understanding.

Epidemiology of sleep patterns and circadian typology in uruguayan children: The contribution of school shifts

Healthy sleep is defined by the combination of adequate duration, good quality, and regular timing. In children, sleep thus depends on the interplay of individual, parental, organizational, community, and social variables, but only a few studies have addressed this issue in a comprehensive way nationwide. Using the Uruguayan nationally representative survey (Nutrition, Child Development, and Health Survey, Encuesta de Nutrición, Desarrollo Infantil y Salud, ENDIS), we present the first epidemiological characterization of chronobiological and sleep parameters in Latin American children. On average, Uruguayan urban children (n = 2437; 5-10-years old) showed quite late chronotypes (MSFsc = 03:53 ± 1:07), moderate misalignment (SJL = 1.0 ± 0.9 h), and adequate sleep duration (SDweek = 9.9 ± 1.0 h). Further, we show the substantial influence of school shift schedules on children's circadian typology and sleep patterns. Our results show that children attending the morning school shift have a higher risk of sleep problems than afternoon-school shift ones. The chronotype and sleep were earlier in morning-school shift children than in children attending the afternoon school shift. However, morning-school shift children had stronger misalignment, shorter sleep on school days, and a higher risk of chronic sleep deficit and non-healthy circadian misalignment (even worse in late chronotypes) than afternoon-shift children. This evidence points to the need of evaluating policies to reorganize school start times to prevent the negative effects that early schooling seems to have on children's sleep health, which has been neglected so far.

Prevalence of high-risk human papillomavirus infection and genotype distribution among Kazakhstani women with abnormal cervical cytology

AIM

This study aimed to identify the prevalence and distribution of high-risk human papillomavirus (HR-HPV) types among Kazakhstani women with abnormal cervical cytology.

METHODS

A cross-sectional study was performed from May 2019 to June 2020. Cervical samples were collected from women in the different regions of Kazakhstan.

RESULTS

A total of 316 patients' samples were analysed for HR-HPV using real-time multiplex PCR. Cervical cytology abnormalities were reported according to the Bethesda classification. HPV detection by cytology showed a statistically significant association with HPV status and the number of HPV infection types (p < .05). Among women with abnormal cervical cytology, 62.4% were positive for HPV infection of those 79.4% had low-grade squamous intraepithelial lesions (LSIL), and 20.6% had high-grade squamous intraepithelial lesions (HSIL). Among patients with LSIL, 77.4% had HPV16 and 58.8% were infected with HPV18. Among patients with HSIL, 41.2% had HPV18 and 22.6% - HPV16.

CONCLUSIONS

There is a high prevalence of HR-HPV types among Kazakhstani women with abnormal cervical cytology. The most identified types were HPV16, 18, 31, 33 and 52. There is an emergency need to implement an HPV vaccination program to prevent cervical lesion development.

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis

Background

Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis.

Methods

We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm.

Findings

The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line.

Conclusion

In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.

Distinct changes in serum metabolites and lipid species in the onset and progression of NAFLD in Obese Chinese

Introduction

Metabolic disturbances are major contributors to the onset and progression of non-alcoholic fatty liver disease (NAFLD), which includes a histological spectrum ranging from single steatosis (SS) to non-alcoholic steatohepatitis (NASH). This study aimed to identify serum metabolites and lipids enriched in different histological stages of NAFLD and to explore metabolites/lipids as non-invasive biomarkers in risk prediction of NAFLD and NASH in obese Chinese.

Methods

Serum samples and liver biopsies were obtained from 250 NAFLD subjects. Untargeted metabolomic and lipidomic profiling were performed using Liquid Chromatography-Mass Spectrometry. Significantly altered metabolites and lipids were identified by MaAsLin2. Pathway enrichment was conducted with MetaboAnalyst and LIPEA. WGCNA was implemented to construct the co-expression network. Logistic regression models were developed to classify different histological stages of NAFLD.

Results

A total of 263 metabolites and 550 lipid species were detected in serum samples. Differential analysis and pathway enrichment analysis revealed the progressive patterns in metabolic mechanisms during the transition from normal liver to SS and to NASH, including N-palmitoyltaurine, tridecylic acid, and branched-chain amino acid signaling pathways. The co-expression network showed a distinct correlation between different triglyceride and phosphatidylcholine species with disease severity. Multiple models classifying NAFLD versus normal liver and NASH versus SS identified important metabolic features associated with significant improvement in disease prediction compared to conventional clinical parameters.

Conclusion

Different histological stages of NAFLD are enriched with distinct sets of metabolites, lipids, and metabolic pathways. Integrated algorithms highlight the important metabolic and lipidomic features for diagnosis and staging of NAFLD in obese individuals.

Human-specific epigenomic states in spermatogenesis

Infertility is becoming increasingly common, affecting one in six people globally. Half of these cases can be attributed to male factors, many driven by abnormalities in the process of sperm development. Emerging evidence from genome-wide association studies, genetic screening of patient cohorts, and animal models highlights an important genetic contribution to spermatogenic defects, but comprehensive identification and characterization of the genes critical for male fertility remain lacking. High divergence of gene regulation in spermatogenic cells across species poses challenges for delineating the genetic pathways required for human spermatogenesis using common model organisms. In this study, we leveraged post-translational histone modification and gene transcription data for 15,491 genes in four mammalian species (human, rhesus macaque, mouse, and opossum), to identify human-specific patterns of gene regulation during spermatogenesis. We combined H3K27me3 ChIP-seq, H3K4me3 ChIP-seq, and RNA-seq data to define epigenetic states for each gene at two stages of spermatogenesis, pachytene spermatocytes and round spermatids, in each species. We identified 239 genes that are uniquely active, poised, or dynamically regulated in human spermatogenic cells distinct from the other three species. While some of these genes have been implicated in reproductive functions, many more have not yet been associated with human infertility and may be candidates for further molecular and epidemiologic studies. Our analysis offers an example of the opportunities provided by evolutionary and epigenomic data for broadly screening candidate genes implicated in reproduction, which might lead to discoveries of novel genetic targets for diagnosis and management of male infertility and male contraception.

Uncovering the potential of APOD as a biomarker in gastric cancer: A retrospective and multi-center study

Gastric cancer (GC) poses a significant health challenge worldwide, necessitating the identification of predictive biomarkers to improve prognosis. Dysregulated lipid metabolism is a well-recognized hallmark of tumorigenesis, prompting investigation into apolipoproteins (APOs). In this study, we focused on apolipoprotein D (APOD) following comprehensive analyses of APOs in pan-cancer. Utilizing data from the TCGA-STAD and GSE62254 cohorts, we elucidated associations between APOD expression and multiple facets of GC, including prognosis, tumor microenvironment (TME), cancer biomarkers, mutations, and immunotherapy response, and identified potential anti-GC drugs. Single-cell analyses and immunohistochemical staining confirmed APOD expression in fibroblasts within the GC microenvironment. Additionally, we independently validated the prognostic significance of APOD in the ZN-GC cohort. Our comprehensive analyses revealed that high APOD expression in GC patients was notably associated with unfavorable clinical outcomes, reduced microsatellite instability and tumor mutation burden, alterations in the TME, and diminished response to immunotherapy. These findings provide valuable insights into the potential prognostic and therapeutic implications of APOD in GC.

Analysis of Haemonchus embryos at single cell resolution identifies two eukaryotic elongation factors as intervention target candidates

Advances in single cell technologies are allowing investigations of a wide range of biological processes and pathways in animals, such as the multicellular model organism Caenorhabditis elegans - a free-living nematode. However, there has been limited application of such technology to related parasitic nematodes which cause major diseases of humans and animals worldwide. With no vaccines against the vast majority of parasitic nematodes and treatment failures due to drug resistance or inefficacy, new intervention targets are urgently needed, preferably informed by a deep understanding of these nematodes' cellular and molecular biology - which is presently lacking for most worms. Here, we created the first single cell atlas for an early developmental stage of Haemonchus contortus - a highly pathogenic, C. elegans-related parasitic nematode. We obtained and curated RNA sequence (snRNA-seq) data from single nuclei from embryonating eggs of H. contortus (150,000 droplets), and selected high-quality transcriptomic data for > 14,000 single nuclei for analysis, and identified 19 distinct clusters of cells. Guided by comparative analyses with C. elegans, we were able to reproducibly assign seven cell clusters to body wall muscle, hypodermis, neuronal, intestinal or seam cells, and identified eight genes that were transcribed in all cell clusters/types, three of which were inferred to be essential in H. contortus. Two of these genes (i.e. Hc-eef-1A and Hc-eef1G), coding for eukaryotic elongation factors (called Hc-eEF1A and Hc-eEF1G), were also demonstrated to be transcribed and expressed in all key developmental stages of H. contortus. Together with these findings, sequence- and structure-based comparative analyses indicated the potential of Hc-eEF1A and/or Hc-eEF1G as intervention targets within the protein biosynthesis machinery of H. contortus. Future work will focus on single cell studies of all key developmental stages and tissues of H. contortus, and on evaluating the suitability of the two elongation factor proteins as drug targets in H. contortus and related nematodes, with a view to finding new nematocidal drug candidates.

Network-based analysis of heterogeneous patient-matched brain and extracranial melanoma metastasis pairs reveals three homogeneous subgroups

Melanoma, the deadliest form of skin cancer, can metastasize to different organs. Molecular differences between brain and extracranial melanoma metastases are poorly understood. Here, promoter methylation and gene expression of 11 heterogeneous patient-matched pairs of brain and extracranial metastases were analyzed using melanoma-specific gene regulatory networks learned from public transcriptome and methylome data followed by network-based impact propagation of patient-specific alterations. This innovative data analysis strategy allowed to predict potential impacts of patient-specific driver candidate genes on other genes and pathways. The patient-matched metastasis pairs clustered into three robust subgroups with specific downstream targets with known roles in cancer, including melanoma (SG1: RBM38, BCL11B, SG2: GATA3, FES, SG3: SLAMF6, PYCARD). Patient subgroups and ranking of target gene candidates were confirmed in a validation cohort. Summarizing, computational network-based impact analyses of heterogeneous metastasis pairs predicted individual regulatory differences in melanoma brain metastases, cumulating into three consistent subgroups with specific downstream target genes.

Multi-omics analysis reveals promiscuous O-glycosyltransferases involved in the diversity of flavonoid glycosides in Periploca forrestii (Apocynaceae)

Flavonoid glycosides are widespread in plants, and are of great interest owing to their diverse biological activities and effectiveness in preventing chronic diseases. Periploca forrestii, a renowned medicinal plant of the Apocynaceae family, contains diverse flavonoid glycosides and is clinically used to treat rheumatoid arthritis and traumatic injuries. However, the mechanisms underlying the biosynthesis of these flavonoid glycosides have not yet been elucidated. In this study, we used widely targeted metabolomics and full-length transcriptome sequencing to identify flavonoid diversity and biosynthetic genes in P. forrestii. A total of 120 flavonoid glycosides, including 21 C-, 96 O-, and 3 C/O-glycosides, were identified and annotated. Based on 24,123 full-length coding sequences, 99 uridine diphosphate sugar-utilizing glycosyltransferases (UGTs) were identified and classified into 14 groups. Biochemical assays revealed that four UGTs exhibited O-glycosyltransferase activity toward apigenin and luteolin. Among them, PfUGT74B4 and PfUGT92A8 were highly promiscuous and exhibited multisite O-glycosylation or consecutive glycosylation activities toward various flavonoid aglycones. These four glycosyltransferases may significantly contribute to the diversity of flavonoid glycosides in P. forrestii. Our findings provide a valuable genetic resource for further studies on P. forrestii and insights into the metabolic engineering of bioactive flavonoid glycosides.

Developing a narrative communication intervention in the context of HPV vaccination

Objective

We outline the development of a narrative intervention guided by the Common-Sense Model of Self-Regulation (CSM) to promote Human Papillomavirus (HPV) vaccination in a diverse college population.

Methods

We adapted the Obesity-Related Behavioral Intervention Trials (ORBIT) model to guide the development, evaluation, and refinement of a CSM-guided narrative video. First, content experts developed a video script containing information on HPV, HPV vaccines, and HPV-related cancers. The script and video contents were evaluated and refined, in succession, utilizing the think-aloud method, open-ended questions, and a brief survey during one-on-one interviews with university students.

Results

Script and video content analyses led to significant revisions that enhanced quality, informativeness, and relevance to the participants. We highlight the critical issues that were revealed and revised in the iterative process.

Conclusions

We developed and refined a CSM guided narrative video for diverse university students. This framework serves as a guide for developing health communication interventions for other populations and health behaviors.

Innovation

This project is the first to apply the ORBIT framework to HPV vaccination and describe a process to develop, evaluate, and refine comparable CSM guided narrative interventions that are tailored to specific audiences.

Erratum regarding previously published articles

[This corrects the article DOI: 10.1016/j.pecinn.2022.100097.][This corrects the article DOI: 10.1016/j.pecinn.2022.100087.].

SERT-StructNet: Protein secondary structure prediction method based on multi-factor hybrid deep model

Protein secondary structure prediction (PSSP) is a pivotal research endeavour that plays a crucial role in the comprehensive elucidation of protein functions and properties. Current prediction methodologies are focused on deep-learning techniques, particularly focusing on multi-factor features. Diverging from existing approaches, in this study, we placed special emphasis on the effects of amino acid properties and protein secondary structure propensity scores (SSPs) on secondary structure during the meticulous selection of multi-factor features. This differential feature-selection strategy results in a distinctive and effective amalgamation of the sequence and property features. To harness these multi-factor features optimally, we introduced a hybrid deep feature extraction model. The model initially employs mechanisms such as dilated convolution (D-Conv) and a channel attention network (SENet) for local feature extraction and targeted channel enhancement. Subsequently, a combination of recurrent neural network variants (BiGRU and BiLSTM), along with a transformer module, was employed to achieve global bidirectional information consideration and feature enhancement. This approach to multi-factor feature input and multi-level feature processing enabled a comprehensive exploration of intricate associations among amino acid residues in protein sequences, yielding a Q 3 accuracy of 84.9% and an Sov score of 85.1%. The overall performance surpasses that of the comparable methods. This study introduces a novel and efficient method for determining the PSSP domain, which is poised to deepen our understanding of the practical applications of protein molecular structures.

Co-creation of a health literate-sensitive training and conversation aid to support shared decision-making in maternity care

Background

Maternity care increasingly aims to achieve Shared Decision-making (SDM), yet seemingly not to the benefit of clients with low health literacy (HL). We developed an SDM training for healthcare professionals (HCPs) and a conversation aid to support HL-sensitive SDM in maternity care.

Methods

The training and conversation aid were based on previous needs assessments and expert consultation, and were developed in co-creation with clients (n = 15) and HCPs (n = 7). Usability, acceptability and comprehension of the conversation aid were tested among new clients (n = 14) and HCPs (n = 6). Acceptability of the training was tested among midwifery students (n = 5).

Results

In the co-creation sessions, clients reported to expect that their midwife becomes acquainted with their general values, priorities and daily context. Clients also emphasized wanting to be supported in their preferred decisional role. User test interviews showed that clients and HCPs were positive towards using the conversation aid, but also apprehensive about the time it required. The user test of the training showed that more attention was needed for recognizing and adapting information provision to clients' HL level.

Conclusion and innovation

The newly developed conversation aid and training have potential to support HCPs and clients in HL-sensitive SDM.

Multiscale calculations reveal new insights into the reaction mechanism between KRASG12C and α, β-unsaturated carbonyl of covalent inhibitors

Utilizing α,β-unsaturated carbonyl group as Michael acceptors to react with thiols represents a successful strategy for developing KRASG12C inhibitors. Despite this, the precise reaction mechanism between KRASG12C and covalent inhibitors remains a subject of debate, primarily due to the absence of an appropriate residue capable of deprotonating the cysteine thiol as a base. To uncover this reaction mechanism, we first discussed the chemical reaction mechanism in solvent conditions via density functional theory (DFT) calculation. Based on this, we then proposed and validated the enzymatic reaction mechanism by employing quantum mechanics/molecular mechanics (QM/MM) calculation. Our QM/MM analysis suggests that, in biological conditions, proton transfer and nucleophilic addition may proceed through a concerted process to form an enolate intermediate, bypassing the need for a base catalyst. This proposed mechanism differs from previous findings. Following the formation of the enolate intermediate, solvent-assisted tautomerization results in the final product. Our calculations indicate that solvent-assisted tautomerization is the rate-limiting step in the catalytic cycle under biological conditions. On the basis of this reaction mechanism, the calculated kinact/ki for two inhibitors is consistent well with the experimental results. Our findings provide new insights into the reaction mechanism between the cysteine of KRASG12C and the covalent inhibitors and may provide valuable information for designing effective covalent inhibitors targeting KRASG12C and other similar targets.