Changes in vaccine coverage and incidence of acute gastroenteritis and severe rotavirus gastroenteritis in children <5 years in Shibata City, Niigata Prefecture, Japan

Rotavirus (RV) vaccines were first introduced in 2011 and adopted for universal vaccination in 2020 in Japan. However, the effectiveness of RV vaccines after being adopted for universal vaccination in 2020 has not been reported. Because of the easy accessibility of clinics in Japan, many children are not usually hospitalized for RV gastroenteritis (RVGE). Therefore, in order to evaluate the impact of the RV vaccine since 2008, we investigated the incidence of hospitalization for RVGE as well as the frequency of children aged < 5 years who received medical treatment for severe RVGE at clinics in Shibata City, Japan. The RV vaccine coverage rate was 94.0% (1,046/1,113) in Shibata City after universal vaccination in 2020; this was a significant increase from previous rates. The incidence per 1000 person - years for RVGE hospitalization and severe RVGE at clinics were significantly higher among children aged < 3 years than in previous time periods. The incidence in children with all acute gastroenteritis (AGE) decreased significantly after universal vaccination during the COVID-19 pandemic. The proportion of severe RVGE among all AGE cases also decreased significantly after universal vaccination among children aged < 3 years (0.0%) and those aged 3-4 years (0.6%). There were significant differences in the distribution of RV genotypes isolated from the feces of children with RVGE between different eras divided by RV vaccination rates, especially G1P[8], which was the major genotype before it recently almost disappeared. Further studies are warranted to assess the impact of the COVID-19 pandemic.

DeepFusion: A deep bimodal information fusion network for unraveling protein-RNA interactions using in vivo RNA structures

RNA-binding proteins (RBPs) are key post-transcriptional regulators, and the malfunctions of RBP-RNA binding lead to diverse human diseases. However, prediction of RBP binding sites is largely based on RNA sequence features, whereas in vivo RNA structural features based on high-throughput sequencing are rarely incorporated. Here, we designed a deep bimodal information fusion network called DeepFusion for unraveling protein-RNA interactions by incorporating structural features derived from DMS-seq data. DeepFusion integrates two sub-models to extract local motif-like information and long-term context information. We show that DeepFusion performs best compared with other cutting-edge methods with only sequence inputs on two datasets. DeepFusion's performance is further improved with bimodal input after adding in vivo DMS-seq structural features. Furthermore, DeepFusion can be used for analyzing RNA degradation, demonstrating significantly different RBP-binding scores in genes with slow degradation rates versus those with rapid degradation rates. DeepFusion thus provides enhanced abilities for further analysis of functional RNAs. DeepFusion's code and data are available at http://bioinfo.org/deepfusion/.

scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

Transcriptomic profiling of Dip2a in the neural differentiation of mouse embryonic stem cells

Introduction

The disconnected-interacting protein 2 homolog A (DIP2A), a member of disconnected-interacting 2 protein family, has been shown to be involved in human nervous system-related mental illness. This protein is highly expressed in the nervous system of mouse. Mutation of mouse DIP2A causes defects in spine morphology and synaptic transmission, autism-like behaviors, and defective social novelty [5], [27], indicating that DIP2A is critical to the maintenance of neural development. However, the role of DIP2A in neural differentiation has yet to be investigated.

Objective

To determine the role of DIP2A in neural differentiation, a neural differentiation model was established using mouse embryonic stem cells (mESCs) and studied by using gene-knockout technology and RNA-sequencing-based transcriptome analysis.

Results

We found that DIP2A is not required for mESCs pluripotency maintenance, but loss of DIP2A causes the neural differentiation abnormalities in both N2B27 and KSR medium. Functional knockout of Dip2a gene also decreased proliferation of mESCs by perturbation of the cell cycle and profoundly inhibited the expression of a large number of neural development-associated genes which mainly enriched in spinal cord development and postsynapse assembly.

Conclusions

The results of this report demonstrate that DIP2A plays an essential role in regulating differentiation of mESCs towards the neural fate.

TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding

Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3β chain information performs significantly better than those trained on data with only CDR3β, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.

A deep learning model to predict Ki-67 positivity in oral squamous cell carcinoma

Anatomical pathology is undergoing its third revolution, transitioning from analogical to digital pathology and incorporating new artificial intelligence technologies into clinical practice. Aside from classification, detection, and segmentation models, predictive models are gaining traction since they can impact diagnostic processes and laboratory activity, lowering consumable usage and turnaround time. Our research aimed to create a deep-learning model to generate synthetic Ki-67 immunohistochemistry from Haematoxylin and Eosin (H&E) stained images. We used 175 oral squamous cell carcinoma (OSCC) from the University Federico II's Pathology Unit's archives to train our model to generate 4 Tissue Micro Arrays (TMAs). We sectioned one slide from each TMA, first stained with H&E and then re-stained with anti-Ki-67 immunohistochemistry (IHC). In digitised slides, cores were disarrayed, and the matching cores of the 2 stained were aligned to construct a dataset to train a Pix2Pix algorithm to convert H&E images to IHC. Pathologists could recognise the synthetic images in only half of the cases in a specially designed likelihood test. Hence, our model produced realistic synthetic images. We next used QuPath to quantify IHC positivity, achieving remarkable levels of agreement between genuine and synthetic IHC. Furthermore, a categorical analysis employing 3 Ki-67 positivity cut-offs (5%, 10%, and 15%) revealed high positive-predictive values. Our model is a promising tool for collecting Ki-67 positivity information directly on H&E slides, reducing laboratory demand and improving patient management. It is also a valuable option for smaller laboratories to easily and quickly screen bioptic samples and prioritise them in a digital pathology workflow.

Examining technology use and mental health among parents with newborns in the intensive care unit during the COVID-19 pandemic: A cross-sectional study

Objectives

To investigate the relationship between pandemic-related stressors, mental health, and technology use among parents of hospitalized infants during the COVID-19 pandemic.

Methods

A cross-sectional study of 47 participants who had an infant in the Neonatal Intensive Care Unit (NICU) during the pandemic was completed. Participants ranked several statements on a Likert scale to assess mental health, technology use, and COVID-19-related stress during their infant's stay in the NICU.

Results

Mental health wellness scores were negatively associated with COVID-19-related stress (rs - 0.40, p = .015). The most prevalent stressor was hospital visitation restriction. Higher COVID-19-related stress was associated with greater use of text and video chat [(rs0.35, p = 0.016) and (rs0.33, p = .025)]. Enjoyment of technology use and access to technology were positively associated with higher mental health wellness scores [(rs0.42, p = .003) and (rs0.38, p = .009)].

Conclusions

Social uses of technology were valuable in a cohort of parents with infants hospitalized during the COVID-19 pandemic.

Innovation

Technology is a tool that can help parents cope with the stress of having a hospitalized infant. Digital literacy and technology access should be promoted in the post-pandemic landscape to help parents of infants in the NICU attain more benefit from these resources.

Australian and Canadian financial wellbeing policy landscape during COVID-19: An equity-informed policy scan

Background

This targeted and comprehensive policy scan examined how different levels of governments in Australia and Canada responded to the financial crisis brought on by the COVID-19 pandemic. We mapped the types of early policy responses addressing financial strain and promoting financial wellbeing. We also examined their equity considerations.

Methods

Through a systematic search, snowballing, and manual search, we identified Canadian and Australian policies at all government levels related to financial strain or financial wellbeing enacted or amended in 2019-2020. Using a deductive-inductive approach, policies were categorized by jurisdiction level, focal areas, and target population groups.

Results

In total, 213 and 97 policies in Canada and Australia, respectively, were included. Comparisons between Canadian and Australian policies indicated a more diversified and equity-targeted policy landscape in Canada. In both countries, most policies focused on individual and family finances, followed by housing and employment areas.

Conclusions

The policy scan identified gaps and missed opportunities in the early policies related to financial strain and financial wellbeing. While fast, temporary actions addressed individuals' immediate needs, we recommend governments develop a longer-term action plan to tackle the root causes of financial strain and poor financial wellbeing for better health and non-health crisis preparedness.

Statement on Ethics and Informed Consent

This research reported in this paper did not require ethical clearance or patient informed consent as the data sources were published policy documents. This study did not involve data collection with humans (or animals), nor any secondary datasets involving data provided by humans (or from animal studies).

Assessing the effect of selective serotonin reuptake inhibitors in the prevention of post-acute sequelae of COVID-19

Background

Post-acute sequelae of COVID-19 (PASC) produce significant morbidity, prompting evaluation of interventions that might lower risk. Selective serotonin reuptake inhibitors (SSRIs) potentially could modulate risk of PASC via their central, hypothesized immunomodulatory, and/or antiplatelet properties although clinical trial data are lacking.

Materials and Methods

This retrospective study was conducted leveraging real-world clinical data within the National COVID Cohort Collaborative (N3C) to evaluate whether SSRIs with agonist activity at the sigma-1 receptor (S1R) lower the risk of PASC, since agonism at this receptor may serve as a mechanism by which SSRIs attenuate an inflammatory response. Additionally, determine whether the potential benefit could be traced to S1R agonism. Presumed PASC was defined based on a computable PASC phenotype trained on the U09.9 ICD-10 diagnosis code.

Results

Of the 17,908 patients identified, 1521 were exposed at baseline to a S1R agonist SSRI, 1803 to a non-S1R agonist SSRI, and 14,584 to neither. Using inverse probability weighting and Poisson regression, relative risk (RR) of PASC was assessed.A 29% reduction in the RR of PASC (0.704 [95% CI, 0.58-0.85]; P = 4 ×10-4) was seen among patients who received an S1R agonist SSRI compared to SSRI unexposed patients and a 21% reduction in the RR of PASC was seen among those receiving an SSRI without S1R agonist activity (0.79 [95% CI, 0.67 - 0.93]; P = 0.005).Thus, SSRIs with and without reported agonist activity at the S1R were associated with a significant decrease in the risk of PASC.

Robotic mirror therapy for stroke rehabilitation through virtual activities of daily living

Mirror therapy is a standard technique of rehabilitation for recovering motor and vision abilities of stroke patients, especially in the case of asymmetric limb function. To enhance traditional mirror therapy, robotic mirror therapy (RMT) has been proposed over the past decade, allowing for assisted bimanual coordination of paretic (affected) and contralateral (healthy) limbs. However, state-of-the-art RMT platforms predominantly target mirrored motions of trajectories, largely limited to 2-D motions. In this paper, an RMT platform is proposed, which can facilitate the patient to practice virtual activities of daily living (ADL) and thus enhance their independence. Two similar (but mirrored) 3D virtual environments are created in which the patients operate robots with both their limbs to complete ADL (such as writing and eating) with the assistance of the therapist. The recovery level of the patient is continuously assessed by monitoring their ability to track assigned trajectories. The patient's robots are programmed to assist the patient in following these trajectories based on this recovery level. In this paper, the framework to dynamically monitor recovery level and accordingly provide assistance is developed along with the nonlinear controller design to ensure position tracking, force control, and stability. Proof-of-concept studies are conducted with both 3D trajectory tracking and ADL. The results demonstrate the potential use of the proposed system to enhance the recovery of the patients.

Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma

In this study, we present a deep-learning-based multimodal classification method for lymphoma diagnosis in digital pathology, which utilizes a whole slide image (WSI) as the primary image data and flow cytometry (FCM) data as auxiliary information. In pathological diagnosis of malignant lymphoma, FCM serves as valuable auxiliary information during the diagnosis process, offering useful insights into predicting the major class (superclass) of subtypes. By incorporating both images and FCM data into the classification process, we can develop a method that mimics the diagnostic process of pathologists, enhancing the explainability. In order to incorporate the hierarchical structure between superclasses and their subclasses, the proposed method utilizes a network structure that effectively combines the mixture of experts (MoE) and multiple instance learning (MIL) techniques, where MIL is widely recognized for its effectiveness in handling WSIs in digital pathology. The MoE network in the proposed method consists of a gating network for superclass classification and multiple expert networks for (sub)class classification, specialized for each superclass. To evaluate the effectiveness of our method, we conducted experiments involving a six-class classification task using 600 lymphoma cases. The proposed method achieved a classification accuracy of 72.3%, surpassing the 69.5% obtained through the straightforward combination of FCM and images, as well as the 70.2% achieved by the method using only images. Moreover, the combination of multiple weights in the MoE and MIL allows for the visualization of specific cellular and tumor regions, resulting in a highly explanatory model that cannot be attained with conventional methods. It is anticipated that by targeting a larger number of classes and increasing the number of expert networks, the proposed method could be effectively applied to the real problem of lymphoma diagnosis.

In vivo dynamics of hard tissue-forming cell origins: Insights from Cre/loxP-based cell lineage tracing studies

Bone tissue provides structural support for our bodies, with the inner bone marrow (BM) acting as a hematopoietic organ. Within the BM tissue, two types of stem cells play crucial roles: mesenchymal stem cells (MSCs) (or skeletal stem cells) and hematopoietic stem cells (HSCs). These stem cells are intricately connected, where BM-MSCs give rise to bone-forming osteoblasts and serve as essential components in the BM microenvironment for sustaining HSCs. Despite the mid-20th century proposal of BM-MSCs, their in vivo identification remained elusive owing to a lack of tools for analyzing stemness, specifically self-renewal and multipotency. To address this challenge, Cre/loxP-based cell lineage tracing analyses are being employed. This technology facilitated the in vivo labeling of specific cells, enabling the tracking of their lineage, determining their stemness, and providing a deeper understanding of the in vivo dynamics governing stem cell populations responsible for maintaining hard tissues. This review delves into cell lineage tracing studies conducted using commonly employed genetically modified mice expressing Cre under the influence of LepR, Gli1, and Axin2 genes. These studies focus on research fields spanning long bones and oral/maxillofacial hard tissues, offering insights into the in vivo dynamics of stem cell populations crucial for hard tissue homeostasis.

Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Smad4 regulates TGF-β1-mediated hedgehog activation to promote epithelial-to-mesenchymal transition in pancreatic cancer cells by suppressing Gli1 activity

Pancreatic cancer (PC) is an aggressive and metastatic gastrointestinal tumor with a poor prognosis. Persistent activation of the TGF-β/Smad signaling induces PC cell (PCC) invasion and infiltration via epithelial-to-mesenchymal transition (EMT). Hedgehog signaling is a crucial pathway for the development of PC via the transcription factors Gli1/2/3. This study aimed to investigate the underlying molecular mechanisms of action of hedgehog activation in TGF-β1-triggered EMT in PCCs (PANC-1 and BxPc-3). In addition, overexpression and shRNA techniques were used to evaluate the role of Smad4 in TGF-β1-treated PCCs. Our data showed that TGF-β1 promoted PCC invasion and infiltration via Smad2/3-dependent EMT. Hedgehog-Gli signaling axis in PCCs was activated upon TGF-β1 stimulation. Inhibition of hedgehog with cyclopamine effectively antagonized TGF-β1-induced EMT, thereby suggesting that the hedgehog signaling may act as a downstream cascade signaling of TGF-β1. As a key protein that assists the nuclear translocation of Smad2/3, Smad4 was highly expressed in PANC-1 cells, but not in BxPc-3 cells. Conversely, Gli1 expression was low in PANC-1 cells, but high in BxPc-3 cells. Furthermore, knockdown of Smad4 in PANC-1 cells by shRNA inhibited TGF-β1-mediated EMT and collagen deposition. Overexpression of Smad4 did not affect TGF-β1-mediated EMT due to the lack of significant increase in nuclear expression of Smad4. Importantly, Gli1 activity was upregulated by Smad4 knockdown in PANC-1 cells and downregulated by Smad4 overexpression in BxPc-3 cells, indicating that Gli1 may be a negative target protein downstream of Smad4. Thus, Smad4 regulates TGF-β1-mediated hedgehog activation to promote EMT in PCCs by suppressing Gli1 activity.

Designing eHealth interventions for children with complex care needs requires continuous stakeholder collaboration and co-creation

Objective

Hospital-to-home (H2H) transitions challenge families of children with medical complexity (CMC) and healthcare professionals (HCP). This study aimed to gain deeper insights into the H2H transition process and to work towards eHealth interventions for its improvement, by applying an iterative methodology involving both CMC families and HCP as end-users.

Methods

For 20-weeks, the Dutch Transitional Care Unit consortium collaborated with the Amsterdam University of Applied Sciences, HCP, and CMC families. The agile SCREAM approach was used, merging Design Thinking methods into five iterative sprints to stimulate creativity, ideation, and design. Continuous communication allowed rapid adaptation to new information and the refinement of solutions for subsequent sprints.

Results

This iterative process revealed three domains of care - care coordination, social wellbeing, and emotional support - that were important to all stakeholders. These domains informed the development of our final prototype, 'Our Care Team', an application tailored to meet the H2H transition needs for CMC families and HCP.

Conclusion

Complex processes like the H2H transition for CMC families require adaptive interventions that empower all stakeholders in their respective roles, to promote transitional care that is anticipatory, rather than reactive.

Innovation

A collaborative methodology is needed, that optimizes existing resources and knowledge, fosters innovation through collaboration while using creative digital design principles. This way, we might be able to design eHealth solutions with end-users, not just for them.

Frequently used extraoral maxillofacial prosthetic materials and their longevity - A comprehensive review

The longevity of an extraoral prosthesis depends on its physical and mechanical properties and user maintenance. Faced with multiple outcome measures, researchers find it difficult to determine the most appropriate extraoral prosthetic material. This comprehensive review evaluates the most used extraoral prosthesis materials and qualitatively assesses their longevity and function. The study aims to identify and interpret the results of current updates on the factors that affect longevity and functionality. This comprehensive review summarizes and evaluates differences in the properties of commonly used extraoral maxillofacial prosthetic materials. The review was planned to focus on all factors related to the longevity and function of the extraoral maxillofacial prosthetics. An electronic search covered English articles in PubMed, Scopus, Google Scholar, Web of Science, and grey literature. Manual searching was also performed. Six authors participated in the screening. Search engines extracted 1107 records, and 88 studies were included for qualitative and bias assessments. Silicones are the most frequently used extraoral maxillofacial prosthetic materials. Heat-cured silicones are more color-stable than those cured at room temperature. Additional ingredients and processing techniques affect prosthesis longevity.

"Active carbon" is more advantageous to the bacterial community in the rice rhizosphere than "stable carbon"

Carbon materials are commonly used for soil carbon sequestration and fertilization, which can also affect crop growth by manipulating the rhizosphere bacterial community. However, the comparison of the differences between active carbon (e.g., organic fertilizers) and stable carbon (e.g., biochar) on rhizosphere microdomains is still unclear. Hence, a trial was implemented to explore the influence of control (CK, no fertilizer; NPK, chemical fertilizer), organic fertilizer (CF-O, organic fertilizer; CF-BO, biochar-based organic fertilizer) and biochar material (CF-B, perishable garbage biochar; CF-PMB, pig manure biochar) on the diversity, composition, and interaction of rice rhizosphere bacterial community through 16 S rRNA gene high-throughput sequencing. Our results demonstrate that organic fertilizer increases bacterial alpha-diversity compared to no-carbon supply treatment to the extend, whereas biochar has the opposite effect. The rhizosphere bacterial community composition showed pronounced variations among the various fertilization treatments. The relative abundance in Firmicutes decreased with organic fertilizer application, whereas that in Chloroflexi and Actinobacteria decreased with biochar application. Bacterial network analysis demonstrate that organic fertilizer enhances the complexity and key taxa of bacterial interactions, while biochar exhibits an opposing trend. The findings of our study indicate that organic fertilizer may contribute to a positive and advantageous impact on bacterial diversity and interaction in rice rhizosphere, whereas the influence of biochar is not as favorable and constructive. This study lays the foundation for elucidating the fate of the rhizosphere bacterial community following different carbon material inputs in the context of sustainable agricultural development.

Design, proof-of-concept of single robotic hair transplant mechanisms for both harvest and implant of hair grafts

The design and development of a prototype for a singular robotic hair transplant system capable of harvesting and implanting hair grafts were executed in this study. To establish a proof-of-concept for hair transplant procedures involving harvesting and implantation, a test system using a spherical phantom of the scalp was selected. The developed prototype of the robotic hair transplant system demonstrates the potential to reduce the duration that grafts remain without a blood supply, thereby minimizing hair graft damage. Additionally, the overall operation time for follicular unit extraction is comparatively shorter than that of conventional systems. Results from the robot vision tests indicate an 89.6% accuracy for hair graft detection with a 4 mm hair length phantom and 97.4% for a 2 mm hair length phantom. In the robot position control test, the root mean square error was found to be 1.268°, with a standard error of the mean of 0.203°. These outcomes suggest that the proposed system performs effectively under the conditions of a spherical phantom with a 2 mm hair length and a 5 mm distance between harvests.

Development and refinement of a communication guide to help young adults in Texas reconnect and reduce loneliness

Objective

Loneliness among young people is a contemporary public health crisis exacerbated by the COVID-19 pandemic. The present research examined the development of a modest yet meaningful health communication intervention resource that would become an asset in a larger health campaign: a communication guide aimed at helping young people reach out and reconnect with others.

Methods

Study One established the need for a loneliness intervention in Texas with survey data (N = 795). A communication guide was developed based on research and theory. Study Two employed focus group interviews with potential audience members (N = 31) to critically assess and inform revisions to the communication guide.

Results

Study One results indicated that a substantial proportion of young adult Texans felt as though their social connection had decreased and their loneliness had increased since the onset of the pandemic. Themes in focus group responses from Study Two suggested several strengths of the communication guide and some opportunities for revision.

Conclusion

A communication guide with tips for reconnecting could be a valuable tool to empower young people and promote social connection.

Innovation

This study involved the development and refinement of a new communication resource that was informed by a priority audience of a major health communication campaign.

Comparative evaluation of slide scanners, scan settings, and cytopreparations for digital urine cytology

Background

Acquiring well-focused digital images of cytology slides with scanners can be challenging due to the 3-dimensional nature of the slides. This study evaluates performances of whole-slide images (WSIs) obtained from 2 different cytopreparations by 2 distinct scanners with 3 focus modes.

Methods

Fourteen urine specimens were collected from patients with urothelial carcinoma. Each specimen was equally divided into 2 portions, prepared with Cytospin and ThinPrep methods and scanned for WSIs using Leica (Aperio AT2) and Hamamatsu (NanoZoomer S360) scanners, respectively. The scan settings included 3 focus modes (default, semi-auto, and manual) for single-layer scanning, along with a manual focus mode for 21 Z-layers scanning. Performance metrics were evaluated including scanning success rate, artificial intelligence (AI) algorithm-inferred atypical cell numbers and coverage rate (atypical cell numbers in single or multiple Z-layers divided by the total atypical cell numbers in 21 Z-layers), scanning time, and image file size.

Results

The default mode had scanning success rates of 85.7% or 92.9%, depending on the scanner used. The semi-auto mode increased success to 92.9% or 100%, and manual even further to 100%. However, these changes did not affect the standardized median atypical cell numbers and coverage rates. The selection of scanners, cytopreparations, and Z-stacking influenced standardized median atypical cell numbers and coverage rates, scanning times, and image file sizes.

Discussion

Both scanners showed satisfactory scanning. We recommend using semi-auto or manual focus modes to achieve a scanning success rate of up to 100%. Additionally, a minimum of 9-layer Z-stacking at 1 μm intervals is required to cover 80% of atypical cells. These advanced focus methods do not impact the number of atypical cells or their coverage rate. While Z-stacking enhances the AI algorithm's inferred quantity and coverage rates of atypical cells, it simultaneously results in longer scanning times and larger image file sizes.

Cluster-based radiomics reveal spatial heterogeneity of bevacizumab response for treatment of radiotherapy-induced cerebral necrosis

Background

Bevacizumab is used in the treatment of radiation necrosis (RN), which is a debilitating toxicity following head and neck radiotherapy. However, there is no biomarker to predict if a patient would respond to bevacizumab.

Purpose

We aimed to develop a cluster-based radiomics approach to characterize the spatial heterogeneity of RN and map their responses to bevacizumab.

Methods

118 consecutive nasopharyngeal carcinoma patients diagnosed with RN were enrolled. We divided 152 lesions from the patients into 101 for training, and 51 for validation. We extracted voxel-level radiomics features from each lesion segmented on T1-weighted+contrast and T2 FLAIR sequences of pre- and post-bevacizumab magnetic resonance images, followed by a three-step analysis involving individual- and population-level clustering, before delta-radiomics to derive five radiomics clusters within the lesions. We tested the association of each cluster with response to bevacizumab and developed a clinico-radiomics model using clinical predictors and cluster-specific features.

Results

71 (70.3%) and 34 (66.7%) lesions had responded to bevacizumab in the training and validation datasets, respectively. Two radiomics clusters were spatially mapped to the edema region, and the volume changes were significantly associated with bevacizumab response (OR:11.12 [95% CI: 2.54-73.47], P = 0.004; and 1.63[1.07-2.78], P = 0.042). The combined clinico-radiomics model based on textural features extracted from the most significant cluster improved the prediction of bevacizumab response, compared with a clinical-only model (AUC:0.755 [0.645-0.865] to 0.852 [0.764-0.940], training; 0.708 [0.554-0.861] to 0.816 [0.699-0.933], validation).

Conclusion

Our radiomics approach yielded intralesional resolution, enabling a more refined feature selection for predicting bevacizumab efficacy in the treatment of RN.

Sleep health among medical students in Abakaliki Nigeria: A descriptive study

Background

Sleep health focuses on those measurable characteristics of sleep that are most clearly associated with physical, mental, and neurobehavioral well-being, and not necessarily the absence of sleep disorder. Sleep health is characterised by subjective satisfaction, appropriate timing, adequate duration, high efficiency, and sustained alertness during waking hours. Adequate and restful sleep is particularly crucial for medical students, who face unique challenges due to the demanding nature of their academic and clinical responsibilities. There is limited data on sleep health among medical students in Nigeria.

Objectives

This research study investigated the pattern of sleep health among medical students in Abakaliki Nigeria.

Methods

This cross-sectional observational study was undertaken among the medical students of 2 public institutions in Abakaliki Nigeria from 16th to 23rd June 2023.

Results

Out of the 288 medical students (males- 53.1 %, females- 46.9 %), good sleep health was recorded in 6.6 %. The mean SATED sleep score was 4.9 ± 1.7 (male- 4.9 ± 1.8, female- 4.9 ± 1.6) (p-value = 1) and it was significantly lower among the final-year students. Age difference, sex difference, and presence of chronic headache did not significantly affect the SATED sleep score. The mean sleep duration was 6.1 ± 1.5 hours (male- 6.1 ± 1.6, female- 6.0 ± 1.4). Sleep duration (54.5 %) had the best rating while sleep efficiency (44 %) had the lowest rating among the assessed sleep domains.

Conclusion

Sleep health is poor among medical students in Abakaliki Nigeria and significantly poorer among final-year medical students.

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Comprehensive integration of single-cell RNA and transcriptome RNA sequencing to establish a pyroptosis-related signature for improving prognostic prediction of gastric cancer

Cell pyroptosis, a Gasdermin-dependent programmed cell death characterized by inflammasome, plays a complex and dynamic role in Gastric cancer (GC), a serious threat to human health. Therefore, the value of pyroptosis-related genes (PRGs) as prognostic biomarkers and therapeutic indicators for patients needs to be exploited in GC. This study integrates single-cell RNA sequencing (scRNA-seq) dataset GSE183904 with GC transcriptome data from the TCGA database, focusing on the expression and distribution of PRGs in GC at the single-cell level. The prognostic signature of PRGs was established by using Cox and LASSO analyses. The differences in long-term prognosis, immune infiltration, mutation profile, CD274 and response to chemotherapeutic drugs between the two groups were analyzed and evaluated. A tissue array was used to verify the expression of six PRGs, CD274, CD163 and FoxP3. C12orf75, VCAN, RGS2, MKNK2, SOCS3 and TNFAIP2 were successfully screened out to establish a signature to potently predict the survival time of GC patients. A webserver (https://pumc.shinyapps.io/GastricCancer/) for prognostic prediction in GC patients was developed based on this signature. High-risk score patients typically had worse prognoses, resistance to classical chemotherapy, and a more immunosuppressive tumor microenvironment. VCAN, TNFAIP2 and SOCS3 were greatly elevated in the GC while RGS2 and MKNK2 were decreased in the tumor samples. Further, VCAN was positively related to the infiltrations of Tregs and M2 TAMs in GC TME and the CD274 in tumor cells. In summary, a potent pyroptosis-related signature was established to accurately forecast the survival time and treatment responsiveness of GC patients.

Biological characteristics of Muse cells derived from MenSCs and their application in acute liver injury and intracerebral hemorrhage diseases

The increasing interest in multilineage differentiating stress-enduring (Muse) cells within the field of regenerative medicine is attributed to their exceptional homing capabilities, prolonged viability in adverse conditions, and enhanced three-germ-layer differentiate ability, surpassing their parent mesenchymal stem cells. Given their abundant sources, non-invasive collection procedure, and periodic availability, human menstrual blood-derived endometrium stem cells (MenSCs) have been extensively investigated as a potential resource for stem cell-based therapies. However, there is no established modality to isolate Muse cells from MenSCs and disparity in gene expression profiles between Muse cells and MenSCs remain unknown. In this study, Muse cells were isolated from MenSCs by long-time trypsin incubation method. Muse cells expressed pluripotency markers and could realize multilineage differentiation in vitro. Compared with MenSCs, Muse cells showed enhanced homing ability and superior therapeutic efficacy in animal models of acute liver injury (ALI) and intracerebral hemorrhage (ICH). Furthermore, the RNA-seq analysis offers insights into the mechanism underlying the disparity in trypsin resistance and migration ability between Muse and MenSCs cells. This research offers a significant foundation for further exploration of cell-based therapies using MenSCs-derived Muse cells in the context of various human diseases, highlighting their promising application in the field of regenerative medicine.

Parents' information needs during the first year at home with their very premature born child; a qualitative study

Objective

To obtain insights into parents' information needs during the first year at home with their very preterm (VP) born infant.

Methods

We conducted semi-structured interviews with parents of VP infants participating in a post-discharge responsive parenting intervention (TOP program). Online interviews were audiotaped and transcribed verbatim. Inductive thematic analysis was performed by two independent coders.

Results

Ten participants were interviewed and had various and changing information needs during the developmental trajectory of their infant. Three main themes emerged; (1) Help me understand and cope, (2) Be fully responsible for my baby, and (3) Teach me to do it myself. Available and used sources, such as the Internet, did not meet their information needs. Participants preferred their available and knowledgeable healthcare professionals for reassurance, tailored information, and practical guidance.

Conclusion

This study identified parents' information needs during the first year at home with their VP infant and uncovered underlying re-appearing needs to gain confidence in child-caring abilities and autonomy in decision-making about their infants' care.

Innovation

This study provides valuable information for healthcare professionals and eHealth developers to support parental self-efficacy during the first year after preterm birth.

MKG-GC: A multi-task learning-based knowledge graph construction framework with personalized application to gastric cancer

Over the past decade, information for precision disease medicine has accumulated in the form of textual data. To effectively utilize this expanding medical text, we proposed a multi-task learning-based framework based on hard parameter sharing for knowledge graph construction (MKG), and then used it to automatically extract gastric cancer (GC)-related biomedical knowledge from the literature and identify GC drug candidates. In MKG, we designed three separate modules, MT-BGIPN, MT-SGTF and MT-ScBERT, for entity recognition, entity normalization, and relation classification, respectively. To address the challenges posed by the long and irregular naming of medical entities, the MT-BGIPN utilized bidirectional gated recurrent unit and interactive pointer network techniques, significantly improving entity recognition accuracy to an average F1 value of 84.5% across datasets. In MT-SGTF, we employed the term frequency-inverse document frequency and the gated attention unit. These combine both semantic and characteristic features of entities, resulting in an average Hits@ 1 score of 94.5% across five datasets. The MT-ScBERT integrated cross-text, entity, and context features, yielding an average F1 value of 86.9% across 11 relation classification datasets. Based on the MKG, we then developed a specific knowledge graph for GC (MKG-GC), which encompasses a total of 9129 entities and 88,482 triplets. Lastly, the MKG-GC was used to predict potential GC drugs using a pre-trained language model called BioKGE-BERT and a drug-disease discriminant model based on CNN-BiLSTM. Remarkably, nine out of the top ten predicted drugs have been previously reported as effective for gastric cancer treatment. Finally, an online platform was created for exploration and visualization of MKG-GC at https://www.yanglab-mi.org.cn/MKG-GC/.

A conceptual model of the nurse's role as primary palliative care provider in goals of care communication

Objective

Nurses have opportunities to engage in goals of care conversations that can promote palliative care communication. The purpose of this study was to describe nurses' experiences in goals of care communication as summarized in the literature and to present a conceptual model of communication pathways for nurses.

Methods

An integrative review of the literature (2016-2022) addressing nurses' experiences in goals of care communication was conducted using PubMed, CINAHL, and PsychInfo databases. A total of 92 articles were retrieved. A total of 12 articles were included for this review after applying the inclusion and exclusion criteria.

Results

Of the 12 articles, the majority were qualitative studies (n = 8). Qualitative analysis of findings from all articles revealed three dominant themes: nurses' ambiguous role responsibilities, goals of care as end-of-life communication, and the need for nurse communication training.

Conclusion

This article suggests an innovative conceptual model for advancing nurse communication about goals of care to facilitate primary palliative care.

Innovation

The framework characterizes two communication pathways for Advanced Practice Nurses who direct goals of care discussions and Registered Nurses who support goals of care communication. The model informs future communication training aimed at supporting primary palliative care.

The bidirectional association between diabetes and periodontitis, from basic to clinical

The prevalence and severity of periodontitis are increased and advanced in diabetes. Severe periodontitis elicits adverse effects on diabetes by impairing insulin actions due to systemic microinflammation. Recent studies unveil the emerging findings and molecular basis of the bidirectional relationship between periodontitis and diabetes. In addition to conventional mechanisms such as hyperglycemia, hyperlipidemia, and chronic inflammation, deficient insulin action may play a pathogenic role in the progression of periodontitis under diabetes. Epidemiologically, from the viewpoint of the adverse effect of periodontitis on diabetes, recent studies have suggested that Asians including Japanese and Asian Americans with diabetes and mild obesity (BMI <25 kg/m2) should pay more attention to their increased risk for cardiovascular diseases. In this review, we summarize recent findings on the effect of diabetes on periodontitis from the viewpoint of abnormalities in metabolism and insulin resistance with novel mechanisms, and the influence of periodontitis on diabetes mainly focused on micro-inflammation related to mature adipose tissue and discuss future perspectives about novel approaches to interrupt the adverse interrelationship.

Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation

Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.

Are circadian rhythms in disarray in patients with chronic critical illness?

Aim

The aim of our study is to assess circadian rhythms in patients with chronic critical illness due to severe brain injury in intensive care unit by establishing the relation between melatonin and cortisol secretion, considering astronomical time and the sleep-wake cycle in chronic critical illness.

Materials and methods

The study included 54 adult patients with chronic critical illness who resided in the intensive care unit for at least 30 days. The level of consciousness was determined using the CRS-R scale. We did the continuous electroencephalographic (EEG) monitoring with polygraphic leads for 24 h. Also, we determined the serum levels of cortisol and melatonin using the tandem mass spectrometry method with ultra-performance liquid chromatography.

Results

90.74 % of patients had one acrophase in melatonin secretion curve, which suggests the preservation of the rhythmic secretion of melatonin. These acrophases of the melatonin rhythm occurred during the night time in 91.8 % of patients. Most of the patients (69.3 %) slept during the period from 2:00 to 4:00 a.m. The evening levels of cortisol and melatonin had an inverse relation (rs=0.61, p<0.05), i.e., a decrease in the level of cortisol secretion accompanies an increase in melatonin.

Conclusions

We concluded from our study that the rhythmic secretion of melatonin and cortisol is preserved in patients with chronic critical illness that resulted from severe brain injury. No statistically significant discrepancy between melatonin and cortisol secretion, day-and-night time and the sleep-wake cycle are found. We may focus our future work on finding more reliable methods to stabilize the preservation of circadian rhythms to protect vital organ functions.

Privacy-preserving federated machine learning on FAIR health data: A real-world application

Objective

This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorithms in a federated manner, enabling collaborative model-building among health data owners without sharing their datasets.

Materials and methods

Utilizing an agent-based architecture, a privacy-preserving federated ML algorithm was developed to create a global predictive model from various local models. This involved formally defining the algorithm in two steps: data preparation and federated model training on FAIR health data and constructing the architecture with multiple components facilitating algorithm execution. The solution was validated by five healthcare organizations using their specific health datasets.

Results

Five organizations transformed their datasets into Health Level 7 Fast Healthcare Interoperability Resources via a common FAIRification workflow and software set, thereby generating FAIR datasets. Each organization deployed a Federated ML Agent within its secure network, connected to a cloud-based Federated ML Manager. System testing was conducted on a use case aiming to predict 30-day readmission risk for chronic obstructive pulmonary disease patients and the federated model achieved an accuracy rate of 87%.

Discussion

The paper demonstrated a practical application of privacy-preserving federated ML among five distinct healthcare entities, highlighting the value of FAIR health data in machine learning when utilized in a federated manner that ensures privacy protection without sharing data.

Conclusion

This solution effectively leverages FAIR datasets from multiple healthcare organizations for federated ML while safeguarding sensitive health datasets, meeting legislative privacy and security requirements.

Acceptability and usability of the Planning Advance Care Together (PACT) website for improving patients' engagement in advance care planning

Objectives

Most prior advance care planning (ACP) interventions lack integration of the social context of patients' ACP process, which patients indicate is critically important. The current study developed the Planning Advance Care Together (PACT) website to foster inclusion of loved ones in the ACP process.

Methods

To provide feedback about the PACT website, patients with advanced cancer (N = 11), their caregivers (N = 11), and experts (N = 10) participated in semi-structured interviews. Patients and caregivers also completed standardized ratings of acceptability and usability.

Results

Overall, patient (n = 11) and caregiver (n = 11) ratings of acceptability and usability of the website exceeded benchmark cut-offs (≥24 on the Acceptability E-Scale and ≥ 68 on the System Usability Scale). Patients, caregivers, and experts liked the topic of ACP but felt that it could be emotionally challenging. They recommended focusing more on planning and less on end of life. They appreciated being able to include loved ones and recommended adding resources for caregivers.

Conclusions

Study findings support the preliminary usability and acceptability of the PACT website. Findings will be used to inform a modified prototype of the PACT website that is interactive and ready for field testing with patients with advanced cancer and their loved ones.

Innovation

We utilized a novel application of the shared mind framework to support patients with advanced cancer in engaging their loved ones in the ACP process.

TAK-242 improves sepsis-associated acute kidney injury in rats by inhibiting the TLR4/NF-κB signaling pathway

OBJECTIVE

This study was designed to observe the effect of toll-like receptor 4 (TLR4)/nuclear factor kappa-B (NF-κB) pathway activity on sepsis-associated acute kidney injury (SA-AKI), thereby providing new considerations for the prevention and treatment of SA-AKI.

METHODS

The rats were divided into Sham, cecal ligation and puncture (CLP), CLP + vehicle, and CLP + TAK-242 groups. Except the Sham group, a model of CLP-induced sepsis was established in other groups. After 24 h, the indicators related to kidney injury in blood samples were detected. The pathological changes in the kidneys were observed by hematoxylin-eosin staining, and tubular damage was scored. Oxidative stress-related factors, mitochondrial dysfunction-related indicators in each group were measured; the levels of inflammatory factors in serum and kidney tissue of rats were examined. Finally, the expression of proteins related to the TLR4/NF-κB signaling pathway was observed by western blot.

RESULTS

Compared with the CLP + vehicle and CLP + TAK-242 groups, the CLP + TAK-242 group reduced blood urea nitrogen (BUN), creatinine (Cr), cystatin-C (Cys-C), reactive oxygen species (ROS), malondialdehyde (MDA), and inflammatory factors levels (p < 0.01), as well as increased superoxide dismutase (SOD) activity of CLP rats (p < 0.01). Additionally, TAK-242 treatment improved the condition of CLP rats that had glomerular and tubular injuries and mitochondrial disorders (p < 0.01). Further mechanism research revealed that TAK-242 can inhibit the TLR4/NF-κB signaling pathway activated by CLP (p < 0.01). Above indicators after TAK-242 treatment were close to those of the Sham group.

CONCLUSION

TAK-242 can improve oxidative stress, mitochondrial dysfunction, and inflammatory response by inhibiting the activity of TLR4/NF-κB signaling pathway, thereby preventing rats from SA-AKI.

Cyclodextrins: Establishing building blocks for AI-driven drug design by determining affinity constants in silico

Cyclodextrins (CDs) are cyclic carbohydrate polymers that hold significant promise for drug delivery and industrial applications. Their effectiveness depends on their ability to encapsulate target molecules with strong affinity and specificity, but quantifying affinities in these systems accurately is challenging for a variety of reasons. Computational methods represent an exceptional complement to in vitro assays because they can be employed for existing and hypothetical molecules, providing high resolution structures in addition to a mechanistic, dynamic, kinetic, and thermodynamic characterization. Here, we employ potential of mean force (PMF) calculations obtained from guided metadynamics simulations to characterize the 1:1 inclusion complexes between four different modified βCDs, with different type, number, and location of substitutions, and two sterol molecules (cholesterol and 7-ketocholesterol). Our methods, validated for reproducibility through four independent repeated simulations per system and different post processing techniques, offer new insights into the formation and stability of CD-sterol inclusion complexes. A systematic distinct orientation preference where the sterol tail projects from the CD's larger face and significant impacts of CD substitutions on binding are observed. Notably, sampling only the CD cavity's wide face during simulations yielded comparable binding energies to full-cavity sampling, but in less time and with reduced statistical uncertainty, suggesting a more efficient approach. Bridging computational methods with complex molecular interactions, our research enables predictive CD designs for diverse applications. Moreover, the high reproducibility, sensitivity, and cost-effectiveness of the studied methods pave the way for extensive studies of massive CD-ligand combinations, enabling AI algorithm training and automated molecular design.

Level of self-efficacy among skill-based allied health students in the University of Santo Tomas-Enriched Virtual Mode of learning: A cross-sectional study

Background

Self-efficacy expresses a learner's perception of how well they can do in the online academic setting. Although students' academic successes can be measured by online learning self-efficacy, there is a scarcity of evidence targeted toward allied health students.

Objective

The primary aim of this study is to determine the level of online self-efficacy among students from skill-based allied health programs. The secondary aim is to correlate online self-efficacy with age, sex, year level, and program of the students.

Methods

This cross-sectional analytic study utilized the Online Learning Self-Efficacy Scale (OLSE) to determine students' level of self-efficacy via a survey conducted with students who underwent the Enriched Virtual Mode of learning. Descriptive statistics was used to characterize the study participants, describe the level of self-efficacy, and compare the subscales of OLSE. Inferential statistics using Spearman's rho was performed to determine the correlation.

Results

A total of 117 respondents with a mean age ± SD of 20.59 ± 0.11 years old, predominantly female (71%) were included in the study. The students from allied health programs had an online self-efficacy overall mean score of 3.83 (SD = 0 . 05 ; range 2.64-5.00) with the use of technology subscales weighted the highest mean score (4.24). Females have a higher average OLSE score (M = 3 . 92 , SD = 0 . 05 ) compared to males. Significant correlation was found between OLSE scores to sex (r = 0 . 260 , p = 0 . 005 ) and year level (r =- 0 . 199 , p = 0 . 031 ) while nonsignificant correlation was found between OLSE scores to the program (r =- 0 . 048 , p = 0 . 604 ) and age (r =- 0 . 123 , p = 0 . 185 ).

Conclusion

Students of allied health programs generally have a very good level of online self-efficacy. This study assists educators in developing proactive strategies and approaches to promote students' self-confidence across all domains and encourage them to adopt a dynamic remote learning-based approach. Educational institutions should use this opportunity to assess how well they have implemented remote learning to ensure educational continuity, especially in times of crisis.

Human umbilical cord mesenchymal stem cells protect MC3T3-E1 osteoblasts from dexamethasone-induced apoptosis via induction of the Nrf2-ARE signaling pathway

Objective

To investigate the protective effect human umbilical cord mesenchymal stem cells (hUC-MSCs) have on Dexamethasone (Dex)-induced apoptosis in osteogenesis via the Nrf2-ARE signaling pathway.

Methods

Glucocorticoid-induced osteonecrosis of the femoral head (GC-ONFH) was developed in rats through the administration of lipopolysaccharide and methylprednisolone. The incidence of femoral head necrosis, cavity notch, apoptosis of osteoblasts, and bone density were observed by HE staining, TUNEL staining, and Micro-CT. HUC-MSCs were co-cultured with mouse pre-osteoblast MC3T3-E1. The survival rate of osteoblasts was determined by CCK8, and apoptosis and ROS levels of osteoblasts were determined by flow cytometer. The viability of antioxidant enzymes SOD, GSH-Px, and CAT was analyzed by biochemistry. Nrf2 expression levels and those of its downstream proteins and apoptosis-related proteins were analyzed by Western blotting.

Results

In rats, hUC-MSCs can reduce the rates of empty bone lacuna and osteoblast apoptosis that are induced by glucocorticoids (GCs), while reducing the incidence of GC-ONFH. hUC-MSCs can significantly improve the survival rate and antioxidant SOD, GSH-Px, and CAT activity of MC3T3-E1 cells caused by Dex, and inhibit apoptosis and oxidative stress levels. In addition, hUC-MSCs can up-regulate the expression of osteoblast antioxidant protein Nrf2 and its downstream protein HO-1, NQO-1, GCLC, GCLM, and apoptosis-related protein bcl-2, while also down-regulating the expression of apoptosis-related protein bax, cleaved caspase-3, cleaved caspase-9, and cytochrome C in MC3T3-E1 cells. hUC-MSCs improve the ability of MC3T3-E1 cells to mineralize to osteogenesis. However, the promoting effects of hUC-MSCs were abolished following the blocking of the Nrf2-ARE signaling pathway for osteoblasts.

Conclusion

The results reveal that hUC-MSCs can reduce Dex-induced apoptosis in osteoblasts via the Nrf2-ARE signaling pathway.

Unraveling the hidden complexity: Exploring dental tissues through single-cell transcriptional profiling

Understanding the composition and function of cells constituting tissues and organs is vital for unraveling biological processes. Single-cell analysis has allowed us to move beyond traditional methods of categorizing cell types. This innovative technology allows the transcriptional and epigenetic profiling of numerous individual cells, leading to significant insights into the development, homeostasis, and pathology of various organs and tissues in both animal models and human samples. In this review, we delve into the outcomes of major investigations using single-cell transcriptomics to decipher the cellular composition of mammalian teeth and periodontal tissues. The recent single-cell transcriptome-based studies have traced in detail the dental epithelium-ameloblast lineage and dental mesenchyme lineages in the mouse incisors and the tooth germ of both mice and humans; unraveled the microenvironment, the identity of niche cells, and cellular intricacies in the dental pulp; shed light on the molecular mechanisms orchestrating root formation; and characterized cellular dynamics of the periodontal ligament. Additionally, cellular components in dental pulps were compared between healthy and carious teeth at a single-cell level. Each section of this review contributes to a comprehensive understanding of tooth biology, offering valuable insights into developmental processes, niche cell identification, and the molecular secrets of the dental environment.

Structural and functional prediction, evaluation, and validation in the post-sequencing era

The surge of genome sequencing data has underlined substantial genetic variants of uncertain significance (VUS). The decryption of VUS discovered by sequencing poses a major challenge in the post-sequencing era. Although experimental assays have progressed in classifying VUS, only a tiny fraction of the human genes have been explored experimentally. Thus, it is urgently needed to generate state-of-the-art functional predictors of VUS in silico. Artificial intelligence (AI) is an invaluable tool to assist in the identification of VUS with high efficiency and accuracy. An increasing number of studies indicate that AI has brought an exciting acceleration in the interpretation of VUS, and our group has already used AI to develop protein structure-based prediction models. In this review, we provide an overview of the previous research on AI-based prediction of missense variants, and elucidate the challenges and opportunities for protein structure-based variant prediction in the post-sequencing era.

Impact of a patient and family participation education program on hospital nurses' attitudes and competencies: A controlled before-after study

Objective

We designed a Patient and Family Participation Education Program (PFEP) with the aim of fostering a positive attitude and enhancing the competencies of hospital nurses required for effective patient and family participation in care.

Methods

In a Dutch university hospital, we conducted a before-after study. The PFEP comprising three courses: family conversation, supporting shared decision-making, and health literacy. We assessed nursing attitudes using the FINC-NA questionnaire and competencies with a separate questionnaire before and three months after the program. Changes in attitudes and competencies were analyzed using regression analysis.

Results

Twenty-two nurses participated in the education group, and 58 participated as controls.After three months, the change scores for the education group were statistically significantly higher on the total attitude score (FINC-NA) compared to the control group. Moreover, in six out of twelve competencies, the education group demonstrated significantly higher scores than the control group.

Conclusion

The educational program appeared effective in promoting nurses' attitudes and feelings of competencies towards patient and family-centered care.

Innovation

A blended education program focusing on patient and family has potential value for implementation in hospital care settings, especially for hospitals aiming to cultivate a more patient- and family-centered environment.

Turkish adaptation of the maternal cognition about infant sleep questionnaire

Background

Exploring early childhood sleep problems requires a detailed understanding of parental beliefs and cognitions related to infant sleep. There is a need for validated measures to investigate the cognitions of Turkish mothers about infant sleep however no scale measuring parental perceptions related to infant sleep behaviors in Turkish is available. We aimed to culturally adapt the Maternal Cognitions about Infant Sleep Questionnaire (MCISQ) in Turkish.

Methods

Subjects were recruited from an internet sample through social media. Internal consistency was evaluated by Cronbach's alpha, and test-retest reliability was determined by Pearson's correlation test and paired t-test. For factorial validity, the principal component factor analysis was performed for the components of MCISQ.

Results

A total of 417 mothers, most aged between 25 and 29 years (47.8 %), participated in the study. Infants' age ranged between 6 and 18 months, with a mean of 10.5 ± 3.9 months. Factor analysis revealed four factors after removal of item 11: Anger, doubt, safety, limit setting. Cronbach's alpha was 0.85. A subgroup of 32 mothers completed MCISQ three weeks after the initial administration. Total mean scores showed a significantly strong correlation (p:<0.01, r:0.82). Higher scores were noted in both total and subscale scores in infants with maternally reported sleep problems (p:<0.01).

Conclusion

Findings suggest a four-factor solution for MCISQ in Turkish mothers with infants aged 6-18 months. The adapted Turkish version is composed of 19 items with good reliability. Factor structure and items included in the subscales differed from the original study, highlighting the cultural factors related to maternal perceptions about infant sleep.

Binding affinity between coronavirus spike protein and human ACE2 receptor

Coronaviruses (CoVs) pose a major risk to global public health due to their ability to infect diverse animal species and potential for emergence in humans. The CoV spike protein mediates viral entry into the cell and plays a crucial role in determining the binding affinity to host cell receptors. With particular emphasis on α- and β-coronaviruses that infect humans and domestic animals, current research on CoV receptor use suggests that the exploitation of the angiotensin-converting enzyme 2 (ACE2) receptor poses a significant threat for viral emergence with pandemic potential. This review summarizes the approaches used to study binding interactions between CoV spike proteins and the human ACE2 (hACE2) receptor. Solid-phase enzyme immunoassays and cell binding assays allow qualitative assessment of binding but lack quantitative evaluation of affinity. Surface plasmon resonance, Bio-layer interferometry, and Microscale Thermophoresis on the other hand, provide accurate affinity measurement through equilibrium dissociation constants (KD). In silico modeling predicts affinity through binding structure modeling, protein-protein docking simulations, and binding energy calculations but reveals inconsistent results due to the lack of a standardized approach. Machine learning and deep learning models utilize simulated and experimental protein-protein interaction data to elucidate the critical residues associated with CoV binding affinity to hACE2. Further optimization and standardization of existing approaches for studying binding affinity could aid pandemic preparedness. Specifically, prioritizing surveillance of CoVs that can bind to human receptors stands to mitigate the risk of zoonotic spillover.

Application of one-piece endodontic crowns fabricated with CAD-CAM system to molars

Computer-aided design-computer-aided manufacturing (CAD-CAM) systems have been widely used as a fabrication method for restorations because of their high efficiency and accuracy, which significantly reduces fabrication time. However, molars with insufficient clearance or short clinical crown lengths require retention holes or grooves on the preparation, making it difficult to replicate the shapes with the CAM milling system. In these cases, restorations using the lost-wax method are selected. This article focuses on one-piece endodontic crowns (endocrowns) fabricated with a CAD-CAM system (CAD-CAM endocrowns), in which their posts and crowns are integrated. Articles from July 2012 to August 2023 were searched in PubMed with the keyword "endocrown". This review discusses the application of CAD-CAM endocrowns to molars from the viewpoint of model experiment (fracture resistance, adaptation) and clinical research. This technique, which allows margins and internal gaps to be set within the clinically acceptable range, is reported to be an effective way of restoring molars with high survival rates in clinical research.

Splicing inhibition mediated by reduced splicing factors and helicases is associated with the cellular response of lung cancer cells to cisplatin

Lung cancer's mortality is predominantly linked to post-chemotherapy recurrence, driven by the reactivation of dormant cancer cells. Despite the critical role of these reactivated cells in cancer recurrence and metastasis, the molecular mechanisms governing their therapeutic selection remain poorly understood. In this study, we conducted an integrative analysis by combining PacBio single molecule real-time (SMRT) sequencing with short reads Illumina RNA-seq. Our study revealed that cisplatin-induced dormant and reactivated cancer cells exhibited a noteworthy reduction in gene transcripts and alternative splicing events. Particularly, the differential alternative splicing events were found to be overlapping with the differentially expression genes and enriched in genes related to cell cycle and cell division. Utilizing ENCORI database and correlation analysis, we identified key splicing factors, including SRSF7, SRSF3, PRPF8, and HNRNPC, as well as RNA helicase such as EIF4A3, DDX39A, DDX11, and BRIP1, which were associated with the observed reduction in alternative splicing and subsequent decrease in gene expression. Our study demonstrated that lung cancer cells reduce gene transcripts through diminished alternative splicing events mediated by specific splicing factors and RNA helicase in response to the chemotherapeutic stress. These findings provide insights into the molecular mechanisms underlying the therapeutic selection and reactivation of dormant cancer cells. This discovery opens a potential avenue for the development of therapeutic strategies aimed at preventing cancer recurrence following chemotherapy.

Experimental and computational biophysics to identify vasodilator drugs targeted at TRPV2 using agonists based on the probenecid scaffold

TRP channels are important pharmacological targets in physiopathology. TRPV2 plays distinct roles in cardiac and neuromuscular function, immunity, and metabolism, and is associated with pathologies like muscular dystrophy and cancer. However, TRPV2 pharmacology is unspecific and scarce at best. Using in silico similarity-based chemoinformatics we obtained a set of 270 potential hits for TRPV2 categorized into families based on chemical nature and similarity. Docking the compounds on available rat TRPV2 structures allowed the clustering of drug families in specific ligand binding sites. Starting from a probenecid docking pose in the piperlongumine binding site and using a Gaussian accelerated molecular dynamics approach we have assigned a putative probenecid binding site. In parallel, we measured the EC50 of 7 probenecid derivatives on TRPV2 expressed in Pichia pastoris using a novel medium-throughput Ca2+ influx assay in yeast membranes together with an unbiased and unsupervised data analysis method. We found that 4-(piperidine-1-sulfonyl)-benzoic acid had a better EC50 than probenecid, which is one of the most specific TRPV2 agonists to date. Exploring the TRPV2-dependent anti-hypertensive potential in vivo, we found that 4-(piperidine-1-sulfonyl)-benzoic acid shows a sex-biased vasodilator effect producing larger vascular relaxations in female mice. Overall, this study expands the pharmacological toolbox for TRPV2, a widely expressed membrane protein and orphan drug target.

Microbial functional pathways based on metatranscriptomic profiling enable effective saliva-based health assessments for precision wellness

It is increasingly recognized that an important step towards improving overall health is to accurately measure biomarkers of health from the molecular activities prevalent in the oral cavity. We present a general methodology for computationally quantifying the activity of microbial functional pathways using metatranscriptomic data. We describe their implementation as a collection of eight oral pathway scores using a large salivary sample dataset (n = 9350), and we evaluate score associations with oropharyngeal disease phenotypes within an unseen independent cohort (n = 14,129). Through this validation, we show that the relevant oral pathway scores are significantly worse in individuals with periodontal disease, acid reflux, and nicotine addiction, compared with controls. Given these associations, we make the case to use these oral pathway scores to provide molecular health insights from simple, non-invasive saliva samples, and as molecular endpoints for actionable interventions to address the associated conditions.

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.