Performance comparison between multi-level gene expression data in cancer subgroup classification.

Cancer is a serious disease that can affect various parts of the body such as breast, colon, lung or stomach. Each of these cancers has their own treatment dependent historical subgroups. Hence, the correct identification of cancer subgroup has almost same importance as the timely diagnosis of cancer. This is still a challenging task and a system with highest accuracy is essential. Current researches are moving towards analyzing the gene expression data of cancer patients for various purposes including biomarker identification and studying differently expressed genes, using gene expression data measured in a single level (selected from different gene levels including genome, transcriptome or translation). However, previous studies showed that information carried by one level of gene expression is not similar to another level. This shows the importance of integrating multi-level omics data in these studies. Hence, this study uses tumor gene expression data measured from various levels of gene along with the integration of those data in the subgroup classification of nine different cancers. This is a comprehensive analysis where four different gene expression data such as transcriptome, miRNA, methylation and proteome are used in this subgrouping and the performances between models are compared to reveal the best model.

Deciphering the molecular landscape of ionising radiation-induced eye damage with the help of genomic data mining.

Even at low levels, exposure to ionising radiation can lead to eye damage. However, the underlying molecular mechanisms are not yet fully understood. We aimed to address this gap with a comprehensive in silico approach to the issue. For this purpose we relied on the Comparative Toxicogenomics Database (CTD), ToppGene Suite, Cytoscape, GeneMANIA, and Metascape to identify six key regulator genes associated with radiation-induced eye damage (ATM, CRYAB, SIRT1, TGFB1, TREX1, and YAP1), all of which have physical interactions. Some of the identified molecular functions revolve around DNA repair mechanisms, while others are involved in protein binding, enzymatic activities, metabolic processes, and post-translational protein modifications. The biological processes are mostly centred on response to DNA damage, the p53 signalling pathway in particular. We identified a significant role of several miRNAs, such as hsa-miR-183 and hsamiR-589, in the mechanisms behind ionising radiation-induced eye injuries. Our study offers a valuable method for gaining deeper insights into the adverse effects of radiation exposure.

RNA-binding protein AZGP1 inhibits epithelial cell proliferation by regulating the genes of alternative splicing in COPD.

BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is characterized by high morbidity, disability, and mortality rates worldwide. RNA-binding proteins (RBPs) might regulate genes involved in oxidative stress and inflammation in COPD patients. Single-cell transcriptome sequencing (scRNA-seq) offers an accurate tool for identifying intercellular heterogeneity and the diversity of immune cells. However, the role of RBPs in the regulation of various cells, especially AT2 cells, remains elusive. MATERIALS AND METHODS: A scRNA-seq dataset (GSE173896) and a bulk RNA-seq dataset acquired from airway tissues (GSE124180) were employed for data mining. Next, RNA-seq analysis was performed in both COPD and control patients. Differentially expressed genes (DEGs) were identified using criteria of fold change (FC ≥ 1.5 or ≤ 1.5) and P value ≤ 0.05. Lastly, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and alternative splicing identification analyses were carried out. RESULTS: RBP genes exhibited specific expression patterns across different cell groups and participated in cell proliferation and mitochondrial dysfunction in AT2 cells. As an RBP, AZGP1 expression was upregulated in both the scRNA-seq and RNA-seq datasets. It might potentially be a candidate immune biomarker that regulates COPD progression by modulating AT2 cell proliferation and adhesion by regulating the expression of SAMD5, DNER, DPYSL3, GBP5, GBP3, and KCNJ2. Moreover, AZGP1 regulated alternative splicing events in COPD, particularly DDAH1 and SFRP1, holding significant implications in COPD. CONCLUSION: RBP gene AZGP1 inhibits epithelial cell proliferation by regulating genes participating in alternative splicing in COPD.

CRCDB: A comprehensive database for integrating and analyzing multi-omics data of early-onset and late-onset colorectal cancer.

The incidence of early-onset colorectal cancer (EOCRC) has increased significantly worldwide. Uncovering biomarkers that are unique to EOCRC is of great importance to facilitate the prevention and detection of this growing cancer subtype. Although efforts have been made in the data curation about CRC, there is no integrated platform that gives access to data specifically related to young CRC patients. Here, we constructed a user-friendly open integrated resource called CRCDB (URL: http://crcdb-hust.com) which contains multi-omics data of 785 EOCRC, 4898 late-onset CRCs (LOCRC), and 1110 normal control samples from tissue, whole blood, platelets, and serum exosomes. CRCDB manages the differential analysis, survival analysis, co-expression analysis, and immune cell infiltration comparison analysis results in different CRC groups. Meta-analysis results were also provided for users for further data interpretation. Using the resource in CRCDB, we identified that genes associated with the metabolic process were less expressed in EOCRC patients, while up regulated genes most associated with the mitosis process might play an important role in the molecular pathogenesis of LOCRC. Survival-related genes were most enriched in oxidoreduction pathways in EOCRC while in immune-related pathways in LOCRC. With all the data gathered and processed, we anticipate that CRCDB could be a practical data mining platform to help explore potential applications of omics data and develop effective prevention and therapeutic strategies for the specific group of CRC patients.

Comparison of Infection Risks Between Various Inhaled and Intranasal Corticosteroids: A Pharmacovigilance Analysis Based on the FAERS Database.

Purpose: This study conducted a pharmacovigilance analysis based on the FDA Adverse Event Reporting System (FAERS) database to compare the infection risk of inhaled or nasal Beclomethasone, Fluticasone, Budesonide, Ciclesonide, Mometasone, and Triamcinolone Acetonide. Methods: We used proportional imbalance analysis to evaluate the correlation between ICS /INCs and infection events. The data was extracted from the FAERS database from April 2015 to September 2023. Further analysis was conducted on the clinical characteristics, site of infection, and pathogenic bacteria of ICS and INCs infection adverse events (AEs). We used bubble charts to display their top 5 infection adverse events. Results: We analyzed 21,837 reports of infection AEs related to ICS and INCs, with an average age of 62.12 years. Among them, 61.14% of infection reports were related to females. One-third of infections reported to occur in the lower respiratory tract with Fluticasone, Budesonide, Ciclesonidec, and Mometasone; over 40% of infections reported by Triamcinolone Acetonide were eye infections; the rate of oral infections caused by Beclomethasone were 7.39%. The reported rates of fungal and viral infections caused by beclomethasone were 21.15% and 19.2%, respectively. The mycobacterial infections caused by Budesonide and Ciclesonidec account for 3.29% and 2.03%, respectively. Bubble plots showed that the ICS group had more fungal infections, oral infections, pneumonia, tracheitis, etc. The INCs group had more eye symptoms, rhinitis, sinusitis, nasopharyngitis, etc. Conclusion: Women who use ICS and INCs are more prone to infection events. Compared to Budesonide, Fluticasone seemed to have a higher risk of pneumonia and oral candidiasis. Mometasone might lead to more upper respiratory tract infections. The risk of oral infection was higher with Beclomethasone. Beclomethasone causes more fungal and viral infections, while Ciclesonide and Budesonide are more susceptible to mycobacterial infections.

A real-world pharmacovigilance study of FDA adverse event reporting system events for atogepant.

BACKGROUND: Atogepant, an orally administered, small-molecule, calcitonin gene-related peptide (CGRP) receptor antagonist, is being investigated for the treatment of migraine. METHODS: We collected data from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database. Four algorithms (ROR, PRR, BCPNN, and EBGM) were used as measures to detect signals of atogepant-associated adverse events (AEs) in real-world data. RESULT: Of the 3,552,072 reports, 2876 expressly stated the use of atogepant. Women accounted for the majority of adverse events (AEs), with a notable age concentration of 45-65 years. The percentage of reported adverse events was the highest in the United States. Significant system organ categories (SOC) included nervous system disorders, gastrointestinal disorders, nervous system disorders, surgical and medical procedures, ear and labyrinth disorders. Notably, preferred terms (PTs) related to atogepant include migraine, constipation, nausea, vertigo, somnolence, decreased appetite, dizziness and fatigue. Unexpected adverse events such as abnormal dreams, self-injurious ideation, brain fog, tension headache, nightmare, brain neoplasm, feeling abnormal, euphoric mood, hyperacusis and post concussion syndrome were also identified. CONCLUSIONS: The present investigation has detected new and unexpected signals of atogepant-related adverse drug reactions (ADRs). In order to confirm these solve safety issues that were previously overlooked, more research is necessary.

FSGT capsule inhibits IL-1ß-induced inflammation in chondrocytes and ameliorates osteoarthritis by upregulating LncRNA PACER and downregulating COX2/PGE2.

OBJECTIVE: To explore the efficacy and potential mechanism of Fengshi Gutong capsule (FSGTC) in osteoarthritis (OA) inflammation. METHODS: The impact of FSGTC on laboratory indicators of OA patients was explored using data mining technology and association rule analysis. Then, the OA cell model was constructed by inducing chondrocytes (CHs) with interleukin-1ß (IL-1ß). In the presence of FSGTC intervention, the regulatory mechanism of PACER/COX2/PGE2 in OA-CH viability and inflammatory responses was evaluated. RESULTS: Retrospective data mining showed that FSGTC effectively reduced inflammation indexes (ESR, HCRP) of OA patients. Cell experiments showed that LncRNA PACER (PACER) silencing inhibited the proliferation activity of OA-CHs, increased the level of COX2 protein, elevated the levels of PGE2, TNF-α, and IL-1ß, and decreased the levels of IL-4 and IL-10 (p < .01). On the contrary, FSGTC-containing serum reversed the effect of PACER silencing on OA-CHs (p < .01). After the addition of COX2 pathway inhibitor, the proliferation activity of OA-CHs was enhanced; the levels of PGE2, TNF-α, and IL-1ß were decreased while the levels of IL-4 and IL-10 were increased (p < .01). CONCLUSION: FSGTC inhibits IL-1ß-induced inflammation in CHs and ameliorates OA by upregulating PACER and downregulating COX2/PGE2.

Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical Diagnosis Data from the MIMIC-IV Database.

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.

Patterns and factors associated with dental service utilization among insured people: a data mining approach.

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.

Machine learning applied to the prediction of relapse, hospitalization, and suicide in bipolar disorder using neuroimaging and clinical data: A systematic review.

BACKGROUND: Bipolar disorder (BD) is associated with increased morbidity/mortality. Adverse outcome prediction might help 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 (4 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, BD type I, 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 functional connectivity 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.

Dataset on extreme thunderstorm ground enhancements registered on Aragats in 2023.

To advance high-energy atmospheric physics, studying atmospheric electric fields (AEF) and cosmic ray fluxes as an interconnected system is crucial. At Mt. Argats, simultaneous measurements of particle fluxes, electric fields, weather conditions, and lightning locations have significantly enhanced the validation of models that describe the charge structures of thunderclouds and the mechanics of internal electron accelerators. In 2023, observations of the five largest thunderstorm ground enhancements (TGEs) revealed electric fields exceeding 2.0 kV/cm at elevations just tens of meters above ground-potentially hazardous to rockets and aircraft during launch and charging operations. Utilizing simple yet effective monitoring equipment developed at Aragats, we can mitigate the risks posed by these high-intensity fields. The Mendeley dataset, comprising various measured parameters during thunderstorm activities, enables researchers to perform advanced correlation analysis and uncover complex relationships between these atmospheric phenomena. This study underscores the critical importance of integrated atmospheric studies for ensuring the safety of high-altitude operations and advancing atmospheric science.

Study of machine learning techniques for outcome assessment of leptospirosis patients.

Leptospirosis is a global disease that impacts people worldwide, particularly in humid and tropical regions, and is associated with significant socio-economic deficiencies. Its symptoms are often confused with other syndromes, which can compromise clinical diagnosis and the failure to carry out specific laboratory tests. In this respect, this paper presents a study of three algorithms (Decision Tree, Random Forest and Adaboost) for predicting the outcome (cure or death) of individuals with leptospirosis. Using the records contained in the government National System of Aggressions and Notification (SINAN, in portuguese) from 2007 to 2017, for the state of Pará, Brazil, where the temporal attributes of health care, symptoms (headache, vomiting, jaundice, calf pain) and clinical evolution (renal failure and respiratory changes) were used. In the performance evaluation of the selected models, it was observed that the Random Forest exhibited an accuracy of 90.81% for the training dataset, considering the attributes of experiment 8, and the Decision Tree presented an accuracy of 74.29 for the validation database. So, this result considers the best attributes pointed out by experiment 10: time first symptoms medical attention, time first symptoms ELISA sample collection, medical attention hospital admission time, headache, calf pain, vomiting, jaundice, renal insufficiency, and respiratory alterations. The contribution of this article is the confirmation that artificial intelligence, using the Decision Tree model algorithm, depicting the best choice as the final model to be used in future data for the prediction of human leptospirosis cases, helping in the diagnosis and course of the disease, aiming to avoid the evolution to death.

Analysis of the Steelmaking Process via Data Mining and Pearson Correlation.

The continuous improvement of the steelmaking process is a critical issue for steelmakers. In the production of Ca-treated Al-killed steel, the Ca and S contents are controlled for successful inclusion modification treatment. In this study, a machine learning technique was used to build a decision tree classifier and thus identify the process variables that most influence the desired Ca and S contents at the end of ladle furnace refining. The attribute of the root node of the decision tree was correlated with process variables via the Pearson formalism. Thus, the attribute of the root node corresponded to the sulfur distribution coefficient at the end of the refining process, and its value allowed for the discrimination of satisfactory heats from unsatisfactory heats. The variables with higher correlation with the sulfur distribution coefficient were the content of sulfur in both steel and slag at the end of the refining process, as well as the Si content at that stage of the process. As secondary variables, the Si content and the basicity of the slag at the end of the refining process were correlated with the S content in the steel and slag, respectively, at that stage. The analysis showed that the conditions of steel and slag at the beginning of the refining process and the efficient S removal during the refining process are crucial for reaching desired Ca and S contents.

How to obtain product green design requirements based on sentiment analysis and topic analysis: Using washing machine online reviews as an example.

Green design involves the entire life cycle of a product, including stages such as raw material acquisition, production and manufacturing, sales and transportation, use, recycling, and disposal. Extracting customer requirements (CRs) related to product green design (PGD) is one of the necessary conditions for achieving the dual carbon goal. However, only a few studies have evaluated CRs for PGD from a full life cycle perspective. This study obtained 20,000 online reviews of washing machines from e-commerce platforms. The customers' sentiment tendencies toward the requirements of washing machines at various stages of their life cycle are analyzed and evaluated. The CRs contained in online washing machine reviews were identified through cluster analysis. Based on the life cycle theory, the product green design requirements (PGDRs) of CRs were extracted and analyzed. This study can provide theoretical and methodological support for green product design.

A study on deep learning model based on global-local structure for crowd flow prediction.

Crowd flow prediction has been studied for a variety of purposes, ranging from the private sector such as location selection of stores according to the characteristics of commercial districts and customer-tailored marketing to the public sector for social infrastructure design such as transportation networks. Its importance is even greater in light of the spread of contagious diseases such as COVID-19. In many cases, crowd flow can be divided into subgroups by common characteristics such as gender, age, location type, etc. If we use such hierarchical structure of the data effectively, we can improve prediction accuracy of crowd flow for subgroups. But the existing prediction models do not consider such hierarchical structure of the data. In this study, we propose a deep learning model based on global-local structure of the crowd flow data, which utilizes the overall(global) and subdivided by the types of sites(local) crowd flow data simultaneously to predict the crowd flow of each subgroup. The experiment result shows that the proposed model improves the prediction accuracy of each sub-divided subgroup by 5.2% (Table 5 Cat #9)-45.95% (Table 11 Cat #5), depending on the data set. This result comes from the comparison with the related works under the same condition that use target category data to predict each subgroup. In addition, when we refine the global data composition by considering the correlation between subgroups and excluding low correlated subgroups, the prediction accuracy is further improved by 5.6-48.65%.

A pharmacovigilance study of drug-reduced male semen quality based on the Food and Drug Administration adverse event reporting system database.

BACKGROUND: Real-world big data studies on drug-reduced male semen quality are few and far between, with most studies based on animal trials, small scale retrospective studies, or a limited number of pre-market clinical trials. METHODS: This study aimed to identify culprit drugs that reduced male semen quality based on the United States Food and Drug Administration adverse event reporting system. The Medical Dictionary for Regulatory Activities preferred terms and standardized Medical Dictionary for Regulatory Activities queries were used to define reduced male semen quality. Adverse events related to drug-reduced male semen quality were then analyzed by disproportionality analysis using the United States Food and Drug Administration adverse event reporting system data between 2004 and 2023. RESULTS: At the preferred term level, 59 drugs with risk signals were detected to be associated with drug-reduced male semen quality, with the three most frequently reported second-level Anatomical Therapeutic Chemical groups being antineoplastic agents (n = 16, 27.12%), psychoanaleptics (n = 9, 15.25%), and psycholeptics (n = 6, 10.17%). At the standardized Medical Dictionary for Regulatory Activities queries level, the five drugs with the greatest number of cases were finasteride (845 cases, IC025 = 7.72), dutasteride (163 cases, IC025 = 7.22), tamsulosin (148 cases, IC025 = 5.99), testosterone (101 cases, IC025 = 4.08), and valproic acid (54 cases, IC025 = 2.44). Additionally, clinical information about drug-reduced male semen quality is absent from the Summary of Product Characteristics of 41 drugs in our study. CONCLUSIONS: Using the United States Food and Drug Administration adverse event reporting system database, we offer a list of drugs with risk signals for reducing male semen quality. In the future, there is still a need for more studies on drugs whose effects on male semen quality are not fully understood.

Exploring the rules of related parameters in acupuncture for post-stroke dysphagia based on data mining.

Background: Post-stroke dysphagia (PSD) affects the efficacy and safety of swallowing, causing serious complications. Acupuncture is a promising and cost-effective treatment for PSD; however, as the number of randomized controlled trials increases, scientific analysis of the parameters and acupoint prescription is required. Therefore, this study aimed to explore the effects of acupuncture on parameters related to post-stroke dysphagia (PSD). Methods: We searched the Cochrane Library, PubMed, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang Database, Chinese Biomedical Literature, and Chongqing VIP Database for randomized controlled trials of acupuncture for PSD in the last 15 years and relevant parameters were analyzed using data mining techniques. Results: In total, 3,205 records were identified, of which 3,507 patients with PSD were included in 39 studies. The comprehensive analysis demonstrated that the closest parameter combinations of acupuncture on PSD were 0.25 mm × 40 mm needle size, 30 min retention time, five treatments per week, and a 4-week total course of treatment. Additionally, the gallbladder and nontraditional meridians, crossing points, and head and neck sites are the most commonly used acupoint parameters. The core acupoints identified were GB20, RN23, EX-HN14, Gongxue, MS6, SJ17, EX-HN12, EX-HN13, and the commonly used combination of EX-HN12, EX-HN13, GB20, and RN23. Conclusion: This study analyzed the patterns of PSD-related needling and acupoint parameters to provide evidence-based guidelines for clinical acupuncturists in treating PSD, potentially benefitting affected patients.

[Structural Topic Modeling Analysis of Patient Safety Interest among Health Consumers in Social Media].

PURPOSE: This study aimed to investigate healthcare consumers' interest in patient safety on social media using structural topic modeling (STM) and to identify changes in interest over time. METHODS: Analyzing 105,727 posts from Naver news comments, blogs, internet cafés, and Twitter between 2010 and 2022, this study deployed a Python script for data collection and preprocessing. STM analysis was conducted using R, with the documents' publication years serving as metadata to trace the evolution of discussions on patient safety. RESULTS: The analysis identified a total of 13 distinct topics, organized into three primary communities: (1) "Demand for systemic improvement of medical accidents," underscoring the need for legal and regulatory reform to enhance accountability; (2) "Efforts of the government and organizations for safety management," highlighting proactive risk mitigation strategies; and (3) "Medical accidents exposed in the media," reflecting widespread concerns over medical negligence and its repercussions. These findings indicate pervasive concerns regarding medical accountability and transparency among healthcare consumers. CONCLUSION: The findings emphasize the importance of transparent healthcare policies and practices that openly address patient safety incidents. There is clear advocacy for policy reforms aimed at increasing the accountability and transparency of healthcare providers. Moreover, this study highlights the significance of educational and engagement initiatives involving healthcare consumers in fostering a culture of patient safety. Integrating consumer perspectives into patient safety strategies is crucial for developing a robust safety culture in healthcare.

Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach.

Long non-coding RNAs (lncRNAs), which are RNA sequences greater than 200 nucleotides in length, play a crucial role in regulating gene expression and biological processes associated with cancer development and progression. Liver cancer is a major cause of cancer-related mortality worldwide, notably in Thailand. Although machine learning has been extensively used in analyzing RNA-sequencing data for advanced knowledge, the identification of potential lncRNA biomarkers for cancer, particularly focusing on lncRNAs as molecular biomarkers in liver cancer, remains comparatively limited. In this study, our objective was to identify candidate lncRNAs in liver cancer. We employed an expression data set of lncRNAs from patients with liver cancer, which comprised 40 699 lncRNAs sourced from The CancerLivER database. Various feature selection methods and machine-learning approaches were used to identify these candidate lncRNAs. The results showed that the random forest algorithm could predict lncRNAs using features extracted from the database, which achieved an area under the curve (AUC) of 0.840 for classifying lncRNAs between early (stage 1) and late stages (stages 2, 3, and 4) of liver cancer. Five of 23 significant lncRNAs (WAC-AS1, MAPKAPK5-AS1, ARRDC1-AS1, AC133528.2, and RP11-1094M14.11) were differentially expressed between early and late stage of liver cancer. Based on the Gene Expression Profiling Interactive Analysis (GEPIA) database, higher expression of WAC-AS1, MAPKAPK5-AS1, and ARRDC1-AS1 was associated with shorter overall survival. In conclusion, the classification model could predict the early and late stages of liver cancer using the signature expression of lncRNA genes. The identified lncRNAs might be used as early diagnostic and prognostic biomarkers for patients with liver cancer.

Prediction of subsequent fragility fractures: application of machine learning.

BACKGROUND: Machine learning (ML) has shown exceptional promise in various domains of medical research. However, its application in predicting subsequent fragility fractures is still largely unknown. In this study, we aim to evaluate the predictive power of different ML algorithms in this area and identify key features associated with the risk of subsequent fragility fractures in osteoporotic patients. METHODS: We retrospectively analyzed data from patients presented with fragility fractures at our Fracture Liaison Service, categorizing them into index fragility fracture (n = 905) and subsequent fragility fracture groups (n = 195). We independently trained ML models using 27 features for both male and female cohorts. The algorithms tested include Random Forest, XGBoost, CatBoost, Logistic Regression, LightGBM, AdaBoost, Multi-Layer Perceptron, and Support Vector Machine. Model performance was evaluated through 10-fold cross-validation. RESULTS: The CatBoost model outperformed other models, achieving 87% accuracy and an AUC of 0.951 for females, and 93.4% accuracy with an AUC of 0.990 for males. The most significant predictors for females included age, serum C-reactive protein (CRP), 25(OH)D, creatinine, blood urea nitrogen (BUN), parathyroid hormone (PTH), femoral neck Z-score, menopause age, number of pregnancies, phosphorus, calcium, and body mass index (BMI); for males, the predictors were serum CRP, femoral neck T-score, PTH, hip T-score, BMI, BUN, creatinine, alkaline phosphatase, and spinal Z-score. CONCLUSION: ML models, especially CatBoost, offer a valuable approach for predicting subsequent fragility fractures in osteoporotic patients. These models hold the potential to enhance clinical decision-making by supporting the development of personalized preventative strategies.