BACKGROUND: Digital health plays a vital role in healthcare services. Governments in many countries, including China, are increasingly advocating for the appropriate use of digital technologies to address significant health system challenges. It is crucial to incorporate digital health education into the curriculum for future nurses to adapt to the changes in the digital medical system. This study aimed to evaluate the impact of an online Digital Health and Informatics Course in China on the knowledge and comprehension of key digital health and informatics topics, self-assessment of nursing informatics competencies, and satisfaction among undergraduate nursing students. The findings of this study provide recommendations for the design and implementation of future digital health education.
METHODS: This study employed a one-group, quasi-experimental mixed-methods design with pre- and post-assessments. The participants received digital health and informatics education through six three-hour online sessions in six interactive days, with online self-learning materials in between. An online quiz and focus group discussions pre- and post the course were designed to evaluate the knowledge and comprehension of key digital health and informatics topics. Also, a validated Chinese version of the Self-assessment of Nursing Informatics Competencies Scale was conducted pre- and post-course to assess self-assessment of nursing informatics competencies. Additionally, all students were invited to participate in an online survey with a performance-focused course evaluation form as well as focus group discussions to gather their feedback on the learning experience and their evaluations of the course.
RESULTS: A total of 24 undergraduate nursing students were enrolled in the course. All students completed all sessions of this course, resulting in an attendance rate of 100%. Additionally, all students completed both pre- and post-assessments. In terms of the knowledge and comprehension of key digital health and informatics topics, scores of the quiz on knowledge assessment improved from the pre-test [mean pretest score: 78.33 (SD 6.005)] to the post-test [mean post-test score: 83.17 (SD 4.86)] upon completion of the course (P < 0.001). Also, students acknowledged that the course enhanced their knowledge and comprehension of informatics and digital health, the benefits of (nursing) informatics in clinical practice, and the role of health care professionals in informatics and digital health. In terms of self-assessment of nursing informatics competencies, scores on nursing informatics attitudes demonstrated significant improvement (P < 0.001). Furthermore, students reported high satisfaction with various aspects of this course, including the opportunity to explore broad horizons in informatics for future careers, engaging in group discussions, and analyzing case studies on the use of informatics and digital health in clinical practice.
CONCLUSIONS: This Online Digital Health and Informatics education effectively improved undergraduate nursing students\' knowledge and comprehension of the key digital health and informatics topics, nursing informatics attitudes in the self-assessment of nursing informatics competency with high levels of satisfaction. In order to ensure that future education in digital health and informatics for nursing students is in line with the technological advancements in clinical settings, it is necessary to foster collaboration between medical school training and clinical practice. This collaboration should involve the use of clinical examples to illustrate advanced digital health applications and the inclusion of practical exercises on the use of digital health technology in clinical settings.

UNASSIGNED: Precise segmentation of Odontogenic Cystic Lesions (OCLs) from dental Cone-Beam Computed Tomography (CBCT) is critical for effective dental diagnosis. Although supervised learning methods have shown practical diagnostic results in segmenting various diseases, their ability to segment OCLs covering different sub-class varieties has not been extensively investigated.
UNASSIGNED: In this study, we propose a new supervised learning method termed OCL-Net that combines a Multi-Scaled U-Net model, along with an Auto-Adapting mechanism trained with a combined supervised loss. Anonymous CBCT images were collected retrospectively from one hospital. To assess the ability of our model to improve the diagnostic efficiency of maxillofacial surgeons, we conducted a diagnostic assessment where 7 clinicians were included to perform the diagnostic process with and without the assistance of auto-segmentation masks.
UNASSIGNED: We collected 300 anonymous CBCT images which were manually annotated for segmentation masks. Extensive experiments demonstrate the effectiveness of our OCL-Net for CBCT OCLs segmentation, achieving an overall Dice score of 88.84%, an IoU score of 81.23%, and an AUC score of 92.37%. Through our diagnostic assessment, we found that when clinicians were assisted with segmentation labels from OCL-Net, their average diagnostic accuracy increased from 53.21% to 55.71%, while the average time spent significantly decreased from 101s to 47s (P<0.05).
UNASSIGNED: The findings demonstrate the potential of our approach as a robust auto-segmentation system on OCLs in CBCT images, while the segmented masks can be used to further improve OCLs dental diagnostic efficiency.

UNASSIGNED: Iatrogenic blood loss is an important cause of neonatal anemia. In this study, a spreadsheet tool was developed to reduce blood collection, providing a new idea for the prevention of iatrogenic blood loss in newborns.
UNASSIGNED: Based on hematocrit, minimum test volume and dead volume, a new tool was to calculate the minimum blood collection volume and the number of containers required for the test portfolio. We collected data from October 2022 to October 2023 from Xiamen Maternal and Child Health Hospital for analysis and validation.
UNASSIGNED: During this year, there were 16,434 patients and 13,696 plasma/serological samples in the neonatology department. Among them, there were 8 test combinations of greater than 1%, and 9490 samples in total. According to the hospital manual, the recommended amount of blood collection is 27,534 ml and 9490 containers. Through the analysis of this tool, total blood collection was 8864.77 ml, marked qnantity of upward containers (closest level to the calculated blood collection volume) was 10301 ml, and the amount of containers was 8835, which decreased by 67.8%, 62.58% and 6.9% respectively. Besides, if the hematocrit information cannot be obtained in advance and the high hematocrit is calculated as 0.8, the recommended amount of blood collection is 14334.3 ml, and the marked amount of the upward container markering is 17340 ml, decreasing by 47.9% and 37.02% respectively.
UNASSIGNED: We have developed an auxiliary tool that can manage neonatal blood specimen collection in a fine and personalized way and can be applied among different laboratory instruments by parameters modification.

BACKGROUND: None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered to an established framework. We aimed to develop and validate machine learning (ML)-informed subtypes for type 2 diabetes mellitus (T2D) using nationally representative data.
METHODS: In population-based electronic health records (2006-2020; Clinical Practice Research Datalink) in individuals ≥18 years with incident T2D (n=420 448), we included factors (n=3787), including demography, history, examination, biomarkers and medications. Using a published framework, we identified subtypes through nine unsupervised ML methods (K-means, K-means++, K-mode, K-prototype, mini-batch, agglomerative hierarchical clustering, Birch, Gaussian mixture models, and consensus clustering). We characterized clusters using intracluster distributions and explainable artificial intelligence (AI) techniques. We evaluated subtypes for (1) internal validity (within dataset; across methods); (2) prognostic validity (prediction for 5-year all-cause mortality, hospitalization and new chronic diseases); and (3) medication burden.
RESULTS: Development: We identified four T2D subtypes: metabolic, early onset, late onset and cardiometabolic. Internal validity: Subtypes were predicted with high accuracy (F1 score >0.98). Prognostic validity: 5-year all-cause mortality, hospitalization, new chronic disease incidence and medication burden differed across T2D subtypes. Compared with the metabolic subtype, 5-year risks of mortality and hospitalization in incident T2D were highest in late-onset subtype (HR 1.95, 1.85-2.05 and 1.66, 1.58-1.75) and lowest in early-onset subtype (1.18, 1.11-1.27 and 0.85, 0.80-0.90). Incidence of chronic diseases was highest in late-onset subtype and lowest in early-onset subtype. Medications: Compared with the metabolic subtype, after adjusting for age, sex, and pre-T2D medications, late-onset subtype (1.31, 1.28-1.35) and early-onset subtype (0.83, 0.81-0.85) were most and least likely, respectively, to be prescribed medications within 5 years following T2D onset.
CONCLUSIONS: In the largest study using ML to date in incident T2D, we identified four distinct subtypes, with potential future implications for etiology, therapeutics, and risk prediction.

BACKGROUND: The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively.
METHODS: The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology\'s scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers.
RESULTS: WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system\'s utility and effectiveness.
CONCLUSIONS: Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions.

OBJECTIVE: Our goal was to find metabolism-related lncRNAs that were associated with osteoporosis (OP) and construct a model for predicting OP progression using these lncRNAs.
METHODS: The GEO database was employed to obtain gene expression profiles. The WGCNA technique and differential expression analysis were used to identify hypoxia-related lncRNAs. A Lasso regression model was applied to select 25 hypoxia-related genes, from which a classification model was created. Its robust classification performance was confirmed with an area under the ROC curve close to 1, as verified on the validation set. Concurrently, we constructed a ceRNA network based on these genes to unveil potential regulatory processes. Biologically active compounds of STZYD were identified using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP) database. BATMAN was used to identify its targets, and we obtained OP-related genes from Malacards and DisGeNET, followed by identifying intersection genes with metabolism-related genes. A pharmacological network was then constructed based on the intersecting genes. The pharmacological network was further integrated with the ceRNA network, resulting in the creation of a comprehensive network that encompasses herb-active components, pathways, lncRNAs, miRNAs, and targets. Expression levels of hypoxia-related lncRNAs in mononuclear cells isolated from peripheral blood of OP and normal patients were subsequently validated using quantitative real-time PCR (qRT-PCR). Protein levels of RUNX2 were determined through a western blot assay.
RESULTS: CBFB, GLO1, NFKB2 and PIK3CA were identified as central therapeutic targets, and ADD3-AS1, DTX2P1-UPK3BP1-PMS2P11, TTTY1B, ZNNT1 and LINC00623 were identified as core lncRNAs.
CONCLUSIONS: Our work uncovers a possible therapeutic mechanism for STZYD, providing a potential therapeutic target for OP. In addition, a prediction model of metabolism-related lncRNAs of OP progression was constructed to provide a reference for the diagnosis of OP patients.

Primary Amoebic Meningoencephalitis (PAM), a severe lethal brain disease, is caused by a parasite, Naegleria fowleri, also known as the \"brain-eating amoeba\". The chances of a patient\'s recovery after being affected by this parasite are very low. Only 5% of people are known to survive this life-threatening infection. Despite the fact that N. fowleri causes a severe, fatal infection, there is no proper treatment available to prevent or cure it. In this context, it is necessary to formulate a potential vaccine that could be able to combat N. fowleri infection. The current study aimed at developing a multi-epitope subunit vaccine against N. fowleri by utilizing immunoinformatics techniques and reverse vaccinology approaches. The T- and B-cell epitopes were predicted by various tools. In order to choose epitopes with the ability to trigger both T- and B-cell-mediated immune responses, the epitopes were put through a screening pipeline including toxicity, antigenicity, cytokine-inductivity, and allergenicity analysis. Three vaccine constructs were designed from the generated epitopes linked with linkers and adjuvants. The modeled vaccines were docked with the immune receptors, where vaccine-1 showed the highest binding affinity. Binding affinity and stability of the docked complex were confirmed through normal mode analysis and molecular dynamic simulations. Immune simulations developed the immune profile, and in silico cloning affirmed the expression probability of the vaccine construct in Escherichia coli (E. coli) strain K12. This study demonstrates an innovative preventative strategy for the brain-eating amoeba by developing a potential vaccine through immunoinformatics and reverse vaccinology approaches. This study has great preventive potential for Primary Amoebic Meningoencephalitis, and further research is required to assess the efficacy of the designed vaccine.

OBJECTIVE: To identify the pivotal genes, specifically the STTK genes, that govern the sensitivity of tumor cells to T cell-mediated killing in Head and Neck Squamous Cell Carcinoma (HNSC).
METHODS: The differentially expressed genes (DEGs) in HNSC and STTK genes were overlapped to obtain the DE-STTK genes. Univariate and LASSO regression analyses were conducted to identify the pivotal DE-STTK genes that serve as hubs in HNSC (i.e., hub DE-STTK genes). The risk model was established to divide HNSC tumor samples into high- and low-risk groups based on the hub DE-STTK genes. Further investigations were carried out by examing the expression level, prognostic values, diagnostic values, enriched signaling pathways, correlation with tumor mutation burden (TMB), and association with tumor immune infiltration cells (TIICs).
RESULTS: A total of 71 genes were found to be overlapped between DEGs in HNSC and STTK genes. Lasso regression analysis identified 9 hub genes which were MYF6, AATF, AURKA, CXCL9, DPM2, MYO1B, NCBP2, TNFRSF12A, and TRAF1. The network analysis of hub DE-STTK genes-pathway reveals that these 9 hub genes exhibit enrichment in multiple signaling pathways, including toll-like receptor signaling, TNF signaling, NF-kappa B signaling, cytokine-cytokine receptor interaction, spliceosome, mRNA surveillance pathway, nucleocytoplasmic transport, GPI-anchor biosynthesis, as well as N-Glycan biosynthesis. The Pearson correlation analysis showed that the majority of correlations between 9 hub DE-STTK genes and immune cells were positive.
CONCLUSIONS: The 9 identified hub DE-STTK genes (MYF6, AATF, AURKA, CXCL9, DPM2, MYO1B, NCBP2, TNFRSF12A, and TRAF1) are presumptively implicated in the modulation of tumor immunity in HNSC. These genes, along with their enriched pathways, hold promise as potential personalized immunotherapeutic targets for the treatment of HNSC, thereby offering novel avenues for therapeutic intervention in this malignancy.

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Pulmonary hypertension (PH) is a syndrome characterized by marked remodeling of the pulmonary vasculature and increased pulmonary vascular resistance, ultimately leading to right heart failure and even death. The localization of Zrt/Irt-like Protein 8 (ZIP8, a metal ion transporter, encoded by SLC39A8) was abundantly in microvasculature endothelium and its pivotal role in the lung has been demonstrated. However, the role of Zip8 in PH remains unclear.
Bioinformatics analysis was employed to identify SLC39A8 expression patterns and differentially expressed genes (DEGs) between PH patients and normal controls (NC), based on four datasets (GSE24988, GSE113439, GSE117261, and GSE15197) from the Biotechnology Gene Expression Omnibus (NCBI GEO) database. Gene set enrichment analysis (GSEA) was performed to analyze signaling pathways enriched for DEGs. Hub genes were identified by cytoHubba analysis in Cytoscape. Reverse transcriptase-polymerase chain reaction was used to validate SLC39A8 and its correlated metabolic DEGs expression in PH (SU5416/Hypoxia) mice.
SLC39A8 expression was downregulated in PH patients, and this expression pattern was validated in PH (SU5416/Hypoxia) mouse lung tissue. SLC39A8-correlated genes were mainly enriched in the metabolic pathways. Within these SLC39A8-correlated genes, 202 SLC39A8-correlated metabolic genes were screened out, and seven genes were identified as SLC39A8-correlated metabolic hub genes. The expression patterns of hub genes were analyzed between PH patients and controls and further validated in PH mice. Finally, four genes (Fasn, Nsdhl, Acat2, and Acly) were downregulated in PH mice. However, there were no significant differences in the expression of the other three hub genes between PH mice and controls. Of the four genes, Fasn and Acly are key enzymes in fatty acids synthesis, Nsdhl is involved in cholesterol synthesis, and Acat2 is implicated in cholesterol metabolic transformation. Taken together, these results provide novel insight into the role of Zip8 in PH.