Appendicitis is an inflammation caused by obstruction of the appendiceal lumen or termination of blood supply leading to appendiceal necrosis followed by secondary bacterial infection. The relationship between TYROBP gene and the nursing of appendicitis remains unclear. The appendicitis dataset GSE9579 profile was downloaded from the gene expression omnibus database generated from GPL571. Differentially expressed genes were screened, followed by weighted gene co-expression network analysis, functional enrichment analysis, gene set enrichment analysis, construction and analysis of protein-protein interaction network, Comparative Toxicogenomics Database analysis, and immune infiltration analysis. Heatmaps of gene expression levels were plotted. A total of 1570 differentially expressed genes were identified. According to gene ontology analysis, they were mainly enriched in organic acid metabolic process, condensed chromosome kinetochore, oxidoreductase activity. In Kyoto Encyclopedia of Gene and Genome analysis, they mainly concentrated in metabolic pathways, P53 signaling pathway, PPAR signaling pathway. The soft threshold power in weighted gene co-expression network analysis was set to 12. Through the construction and analysis of protein-protein interaction network, 5 core genes (FCGR2A, IL1B, ITGAM, TLR2, TYROBP) were obtained. Heatmap of core gene expression levels revealed high expression of TYROBP in appendicitis samples. Comparative Toxicogenomics Database analysis found that core genes (FCGR2A, IL1B, ITGAM, TLR2, TYROBP) were closely related to abdominal pain, gastrointestinal dysfunction, fever, and inflammation occurrence. TYROBP gene is highly expressed in appendicitis, and higher expression of TYROBP gene indicates worse prognosis. TYROBP may serve as a molecular target for appendicitis and its nursing.

Video action recognition based on skeleton nodes is a highlighted issue in the computer vision field. In real application scenarios, the large number of skeleton nodes and behavior occlusion problems between individuals seriously affect recognition speed and accuracy. Therefore, we proposed a lightweight multi-stream feature cross-fusion (L-MSFCF) model to recognize abnormal behaviors such as fighting, vicious kicking, climbing over the wall, et al., which could obviously improve recognition speed based on lightweight skeleton node calculation, and improve recognition accuracy based on occluded skeleton node prediction analysis in order to effectively solve the behavior occlusion problem. The experiments show that our proposed All-MSFCF model has a video action recognition average accuracy rate of 92.7% for eight kinds of abnormal behavior recognition. Although our proposed lightweight L-MSFCF model has an 87.3% average accuracy rate, its average recognition speed is 62.7% higher than the full-skeleton recognition model, which is more suitable for solving real-time tracing problems. Moreover, our proposed Trajectory Prediction Tracking (TPT) model could real-time predict the moving positions based on the dynamically selected core skeleton node calculation, especially for the short-term prediction within 15 frames and 30 frames that have lower average loss errors.

Heart failure is associated with a significant mortality rate, and an elevated prevalence of this condition has been noted among hypertensive patients. The identification of predictive factors for heart failure progression in hypertensive individuals is crucial for early intervention and improved patient outcomes. In this study, we aimed to identify these predictive factors by utilizing medical diagnosis records for hypertension patients from the MIMIC-IV database. In particular, we employed only diagnostic history prior to hypertension to enable patients to anticipate the onset of heart failure at the moment of hypertension diagnosis. In the methodology, chi-square tests and XGBoost modeling were applied to examine age-specific predictive factors across four groups: AL (all ages), G1 (0 to 65 years), G2 (65 to 80 years), and G3 (over 80 years). As a result, the chi-square tests identified 34, 28, 20, and 10 predictive factors for the AL, G1, G2, and G3 groups, respectively. Meanwhile, the XGBoost modeling uncovered 19, 21, 27, and 33 predictive factors for these respective groups. Ultimately, our findings reveal 21 overall predictive factors, encompassing conditions such as atrial fibrillation, the use of anticoagulants, kidney failure, obstructive pulmonary disease, and anemia. These factors were assessed through a comprehensive review of the existing literature. We anticipate that the results will offer valuable insights for the risk assessment of heart failure in hypertensive patients.

Type 1 polyketides are a major class of natural products used as antiviral, antibiotic, antifungal, antiparasitic, immunosuppressive, and antitumor drugs. Analysis of public microbial genomes leads to the discovery of over sixty thousand type 1 polyketide gene clusters. However, the molecular products of only about a hundred of these clusters are characterized, leaving most metabolites unknown. Characterizing polyketides relies on bioactivity-guided purification, which is expensive and time-consuming. To address this, we present Seq2PKS, a machine learning algorithm that predicts chemical structures derived from Type 1 polyketide synthases. Seq2PKS predicts numerous putative structures for each gene cluster to enhance accuracy. The correct structure is identified using a variable mass spectral database search. Benchmarks show that Seq2PKS outperforms existing methods. Applying Seq2PKS to Actinobacteria datasets, we discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamide-actiphenol.

BACKGROUND: Insurance databases contain valuable information related to the use of dental services. This data is instrumental in decision-making processes, enhancing risk assessment, and predicting outcomes. The objective of this study was to identify patterns and factors influencing the utilization of dental services among complementary insured individuals, employing a data mining methodology.
METHODS: A secondary data analysis was conducted using a dental insurance dataset from Iran in 2022. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was employed as a data mining approach for knowledge extraction from the database. The utilization of dental services was the outcome of interest, and independent variables were chosen based on the available information in the insurance dataset. Dental services were categorized into nine groups: diagnostic, preventive, periodontal, restorative, endodontic, prosthetic, implant, extraction/surgical, and orthodontic procedures. The independent variables included age, gender, family size, insurance history, franchise, insurance limit, and policyholder. A multinomial logistic regression model was utilized to investigate the factors associated with dental care utilization. All analyses were conducted using RapidMiner Version 2020.
RESULTS: The analysis encompassed a total of 654,418 records, corresponding to 118,268 insured individuals. Predominantly, restorative treatments were the most utilized services, accounting for approximately 38% of all services, followed by diagnostic (18.35%) and endodontic (13.3%) care. Individuals aged between 36 and 60 years had the highest rate of utilization for any dental services. Additionally, families comprising three to four members, individuals with a one-year insurance history, people contracted with a 20% franchise, individuals with a high insurance limit, and insured individuals with a small policyholder, exhibited the highest rate of service usage compared to their counterparts. The regression model revealed that all independent variables were significantly associated with the use of dental services. However, the patterns of association varied among different service categories.
CONCLUSIONS: Restorative treatments emerged as the most frequently used dental services among insured individuals, followed by diagnostic and endodontic procedures. The pattern of service utilization was influenced by the characteristics of the insured individuals and attributes related to their insurance.

BACKGROUND: Increased workload, including workload related to electronic health record (EHR) documentation, is reported as a main contributor to nurse burnout and adversely affects patient safety and nurse satisfaction. Traditional methods for workload analysis are either administrative measures (such as the nurse-patient ratio) that do not represent actual nursing care or are subjective and limited to snapshots of care (eg, time-motion studies). Observing care and testing workflow changes in real time can be obstructive to clinical care. An examination of EHR interactions using EHR audit logs could provide a scalable, unobtrusive way to quantify the nursing workload, at least to the extent that nursing work is represented in EHR documentation. EHR audit logs are extremely complex; however, simple analytical methods cannot discover complex temporal patterns, requiring use of state-of-the-art temporal data-mining approaches. To effectively use these approaches, it is necessary to structure the raw audit logs into a consistent and scalable logical data model that can be consumed by machine learning (ML) algorithms.
OBJECTIVE: We aimed to conceptualize a logical data model for nurse-EHR interactions that would support the future development of temporal ML models based on EHR audit log data.
METHODS: We conducted a preliminary review of EHR audit logs to understand the types of nursing-specific data captured. Using concepts derived from the literature and our previous experience studying temporal patterns in biomedical data, we formulated a logical data model that can describe nurse-EHR interactions, the nurse-intrinsic and situational characteristics that may influence those interactions, and outcomes of relevance to the nursing workload in a scalable and extensible manner.
RESULTS: We describe the data structure and concepts from EHR audit log data associated with nursing workload as a logical data model named RNteract. We conceptually demonstrate how using this logical data model could support temporal unsupervised ML and state-of-the-art artificial intelligence (AI) methods for predictive modeling.
CONCLUSIONS: The RNteract logical data model appears capable of supporting a variety of AI-based systems and should be generalizable to any type of EHR system or health care setting. Quantitatively identifying and analyzing temporal patterns of nurse-EHR interactions is foundational for developing interventions that support the nursing documentation workload and address nurse burnout.

BACKGROUND: Bipolar disorder (BD) is a mental disorder associated with increased morbidity/mortality. Adverse outcome prediction helps with the management of patients with BD.
METHODS: We systematically reviewed the performance of machine learning (ML) studies in predicting adverse outcomes (relapse or recurrence, hospital admission, and suicide-related events) in patients with BD. Demographic, clinical, and neuroimaging-related poor outcome predictors were also reviewed. Three databases (PubMed, Scopus, and Web of Science) were explored from inception to July 2023.
RESULTS: Eighteen studies, accounting for >30,000 patients, were included. Support vector machine, decision trees, random forest, and logistic regression were the most frequently used ML algorithms. ML models\' area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity ranged from 0.71 to 0.98, 72.7-92.8 %, and 59.0-95.2 % for relapse/recurrence prediction (5 studies (3 on relapses and 1 on recurrences). The corresponding values were 0.78-0.88, 21.4-100 %, and 77.0-99.7 % for hospital admissions (3 studies, 21,266 patients), and 0.71-0.99, 44.4-97.9 %, and 38.9-95.0 % for suicide-related events (10 studies, 5558 patients). Also, one study addressed a combination of the interest outcomes. Adverse outcome predictors included early onset BD, type I BD, comorbid psychiatric or substance use disorder, circadian rhythm disruption, hospitalization characteristics, and neuroimaging parameters, including increased dynamic amplitude of low-frequency fluctuation, decreased frontolimbic functional connectivity and aberrant dynamic FC in corticostriatal circuitry.
CONCLUSIONS: ML models can predict adverse outcomes of BD with relatively acceptable performance measures. Future studies with larger samples and nested cross-validation validation should be conducted to reach more reliable results.

Artificial intelligence has revolutionized many sectors with unparalleled predictive capabilities supported by machine learning (ML). So far, this tool has not been able to provide the same level of development in pharmaceutical nanotechnology. This review discusses the current data science methodologies related to polymeric drug-loaded nanoparticle production from an innovative multidisciplinary perspective while considering the strictest data science practices. Several methodological and data interpretation flaws were identified by analyzing the few qualified ML studies. Most issues lie in following appropriate analysis steps, such as cross-validation, balancing data, or testing alternative models. Thus, better-planned studies following the recommended data science analysis steps along with adequate numbers of experiments would change the current landscape, allowing the exploration of the full potential of ML.
[Box: see text].

The teaching of medical humanities is increasingly being integrated into medical school curricula. We developed a podcast called Le Serment d\'Augusta (Augusta\'s Oath), consisting of six episodes tackling hot topics in the modern world of healthcare related to the patient-doctor relationship, professionalism, and ethics. This podcast aimed to provide scientific content in an entertaining way, while promoting debate among medical students. The Le Serment d\'Augusta podcast was proposed as one of the various optional modules included in the second- to fifth-year curriculum at the School of Medicine of Sorbonne University (Paris). We asked students to report their lived experience of listening to the podcast. We then used a text-mining approach focusing on two main aspects: i) students\' perspective of the use of this educational podcast to learn about medical humanities; ii) self-reported change in their perception of and knowledge about core elements of healthcare after listening to the podcast. 478 students were included. Students were grateful for the opportunity to participate in this teaching module. They greatly enjoyed this kind of learning tool and reported that it gave them autonomy in learning. They appreciated the content as well as the format, highlighting that the topics were related to the very essence of medical practice and that the numerous testimonies were of great added value. Listening to the podcast resulted in knowledge acquisition and significant change of perspective. These findings further support the use of podcasts in medical education, especially to teach medical humanities, and their implementation in the curriculum.

A regular tetrahedron model was established to pierce the fractionation of dissolved organic matter (DOM) among quaternary components by using high-resolution mass spectrometry. The model can stereoscopically visualize molecular formulas of DOM to show the preference to each component according to the position in a regular tetrahedron. A classification method was subsequently developed to divide molecular formulas into 15 categories related to fractionation ratios, the relative change of which was demonstrated to be convergent with the uncertainty of mass peak area. The practicality of the regular tetrahedron model was verified by seven kinds of sludge from waste leachate treatment and sewage wastewater treatment plants by using stratification of extracellular polymeric substances coupled with Orbitrap MS as an example, presenting the DOM chemodiversity in stratified sludge flocs. Sensitivity analysis proved that classification results were relatively stable with the perturbation of four model parameters. Multinomial logistic regression analysis could further help identify the effect of molecular properties on the fractionation of DOM based on the classification results of the regular tetrahedron model. This model offers a methodology for the assessment of specificity of sequential extraction on DOM from solid or semisolid components and simplifies the complex mathematical expression of fractionation coefficients for quaternary components.