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.

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.

Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database.

BACKGROUND: The use of machine learning in medical diagnosis and treatment has grown significantly in recent years with the development of computer-aided diagnosis systems, often based on annotated medical radiology images. However, the lack of large annotated image datasets remains a major obstacle, as the annotation process is time-consuming and costly. This study aims to overcome this challenge by proposing an automated method for annotating a large database of medical radiology images based on their semantic similarity. RESULTS: An automated, unsupervised approach is used to create a large annotated dataset of medical radiology images originating from the Clinical Hospital Centre Rijeka, Croatia. The pipeline is built by data-mining three different types of medical data: images, DICOM metadata and narrative diagnoses. The optimal feature extractors are then integrated into a multimodal representation, which is then clustered to create an automated pipeline for labelling a precursor dataset of 1,337,926 medical images into 50 clusters of visually similar images. The quality of the clusters is assessed by examining their homogeneity and mutual information, taking into account the anatomical region and modality representation. CONCLUSIONS: The results indicate that fusing the embeddings of all three data sources together provides the best results for the task of unsupervised clustering of large-scale medical data and leads to the most concise clusters. Hence, this work marks the initial step towards building a much larger and more fine-grained annotated dataset of medical radiology images.

Using Thermal Signature to Evaluate Heat Stress Levels in Laying Hens with a Machine-Learning-Based Classifier.

Infrared thermography has been investigated in recent studies to monitor body surface temperature and correlate it with animal welfare and performance factors. In this context, this study proposes the use of the thermal signature method as a feature extractor from the temperature matrix obtained from regions of the body surface of laying hens (face, eye, wattle, comb, leg, and foot) to enable the construction of a computational model for heat stress level classification. In an experiment conducted in climate-controlled chambers, 192 laying hens, 34 weeks old, from two different strains (Dekalb White and Dekalb Brown) were divided into groups and housed under conditions of heat stress (35 °C and 60% humidity) and thermal comfort (26 °C and 60% humidity). Weekly, individual thermal images of the hens were collected using a thermographic camera, along with their respective rectal temperatures. Surface temperatures of the six featherless image areas of the hens' bodies were cut out. Rectal temperature was used to label each infrared thermography data as "Danger" or "Normal", and five different classifier models (Random Forest, Random Tree, Multilayer Perceptron, K-Nearest Neighbors, and Logistic Regression) for rectal temperature class were generated using the respective thermal signatures. No differences between the strains were observed in the thermal signature of surface temperature and rectal temperature. It was evidenced that the rectal temperature and the thermal signature express heat stress and comfort conditions. The Random Forest model for the face area of the laying hen achieved the highest performance (89.0%). For the wattle area, a Random Forest model also demonstrated high performance (88.3%), indicating the significance of this area in strains where it is more developed. These findings validate the method of extracting characteristics from infrared thermography. When combined with machine learning, this method has proven promising for generating classifier models of thermal stress levels in laying hen production environments.

Sequential Behavior of Broiler Chickens in Enriched Environments under Varying Thermal Conditions Using the Generalized Sequential Pattern Algorithm: A Proof of Concept.

Behavior analysis is a widely used non-invasive tool in the practical production routine, as the animal acts as a biosensor capable of reflecting its degree of adaptation and discomfort to some environmental challenge. Conventional statistics use occurrence data for behavioral evaluation and well-being estimation, disregarding the temporal sequence of events. The Generalized Sequential Pattern (GSP) algorithm is a data mining method that identifies recurrent sequences that exceed a user-specified support threshold, the potential of which has not yet been investigated for broiler chickens in enriched environments. Enrichment aims to increase environmental complexity with promising effects on animal welfare, stimulating priority behaviors and potentially reducing the deleterious effects of heat stress. The objective here was to validate the application of the GSP algorithm to identify temporal correlations between heat stress and the behavior of broiler chickens in enriched environments through a proof of concept. Video image collection was carried out automatically for 48 continuous hours, analyzing a continuous period of seven hours, from 12:00 PM to 6:00 PM, during two consecutive days of tests for chickens housed in enriched and non-enriched environments under comfort and stress temperatures. Chickens at the comfort temperature showed high motivation to perform the behaviors of preening (P), foraging (F), lying down (Ld), eating (E), and walking (W); the sequences <{Ld,P}>; <{Ld,F}>; <{P,F,P}>; <{Ld,P,F}>; and <{E,W,F}> were the only ones observed in both treatments. All other sequential patterns (comfort and stress) were distinct, suggesting that environmental enrichment alters the behavioral pattern of broiler chickens. Heat stress drastically reduced the sequential patterns found at the 20% threshold level in the tested environments. The behavior of lying laterally "Ll" is a strong indicator of heat stress in broilers and was only frequent in the non-enriched environment, which may suggest that environmental enrichment provides the animal with better opportunities to adapt to stress-inducing challenges, such as heat.

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.

Data Clustering Utilization Technologies Using Medians of Current Values for Improving Arc Sensing in Unstructured Environments.

In the shipbuilding industry, welding automation using welding robots often relies on arc-sensing techniques due to spatial limitations. However, the reliability of the feedback current value, core sensing data, is reduced when welding target workpieces have significant curvature or gaps between curved workpieces due to the control of short-circuit transition, leading to seam tracking failure and subsequent damage to the workpieces. To address these problems, this study proposes a new algorithm, MBSC (median-based spatial clustering), based on the DBSCAN (density-based spatial clustering of applications with noise) clustering algorithm. By performing clustering based on the median value of data in each weaving area and considering the characteristics of the feedback current data, the proposed technique utilizes detected outliers to enhance seam tracking accuracy and responsiveness in unstructured and challenging welding environments. The effectiveness of the proposed technique was verified through actual welding experiments in a yard environment.

Classification of soft cliff dynamics using remote sensing and data mining techniques.

Coastal soft cliffs are subject to changes related to both marine and subaerial processes. It is imperative to comprehend the processes governing cliff erosion and develop predictive models for effective coastal protection. The primary objective of this study was to bridge the existing knowledge gap by elucidating the intricate relationship between changes in cliff system morphology and the driving forces behind these changes, all within the context of ongoing climate change. Therefore in this study, we employed various quantitative numerical methods to investigate the factors influencing coastal cliffs and the adjacent beaches. Our analysis involved the extraction of several morphological indicators, derived from terrestrial laser scanning data, which were then used to assess how cliffs respond to extreme weather events. The data span two winter storm seasons (2016-2018) and encompass three soft-cliff systems situated along the southern Baltic Sea, each characterized by distinct beach and cliff morphology. We conducted a detailed analysis of short-term cliff responses using various data mining techniques, revealing intricate mechanisms that govern beach and cliff changes. This comprehensive analysis has enabled the development of a classification system for soft cliff dynamics. Our statistical analysis highlights that each study area exhibits a unique conditional dependency between erosion processes and hydrometeorological conditions, both during and between storm events. Furthermore, our findings underscore the vulnerability of cliff coastlines to extreme water levels and episodes of intense precipitation.

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.

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.

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.

Integrating Data Mining with Metabolomics to Analyze the Mechanism of the "Pearl-Borneol" Pair in Promoting Healing of Diabetic Wounds.

INTRODUCTION: Chronic diabetic wounds pose a significant threat to the health of diabetic patients, representing severe and enduring complications. Globally, an estimated 2.5% to 15% of the annual health budget is associated with diabetes, with diabetic wounds accounting for a substantial share. Exploring new therapeutic agents and approaches to address delayed and impaired wound healing in diabetes becomes imperative. Traditional Chinese medicine (TCM) has a long history and remarkable efficacy in treating chronic wound healing. In this study, all topically applied proprietary Chinese medicines (pCMs) for wound healing officially approved by the National Medical Products Administration (NMPA) were collected from the NMPA TCM database. Data mining was employed to obtain a high-frequency TCM ingredients pair, Pearl-Borneol (1:1). METHOD: This study investigated the effect and molecular mechanism of the Pearl-Borneol pair on the healing of diabetic wounds by animal experiments and metabolomics. The results from animal experiments showed that the Pearl-Borneol pair significantly accelerated diabetic wound healing, exhibiting a more potent effect than the Pearl or Borneol treatment alone. Meanwhile, the metabolomics analysis identified significant differences in metabolic profiles in wounds between the model and normal groups, indicating that diabetic wounds had distinct metabolic characteristics from normal wounds. Moreover, Vaseline-treated wounds exhibited similar metabolic profiles to the wounds from the model group, suggesting that Vaseline might have a negligible impact on diabetic wound metabolism. In addition, wounds treated with Pearl, Borneol, and Pearl-Borneol pair displayed significantly different metabolic profiles from Vaseline-treated wounds, signifying the influence of these treatments on wound metabolism. Subsequent enrichment analysis of the metabolic pathway highlighted the involvement of the arginine metabolic pathway, closely associated with diabetic wounds, in the healing process under Pearl- Borneol pair treatment. Further analysis revealed elevated levels of arginine and citrulline, coupled with reduced nitric oxide (NO) in both the model and Vaseline-treated wounds compared to normal wounds, pointing to impaired arginine utilization in diabetic wounds. Interestingly, treatment with Pearl and Pearl-Borneol pair lowered arginine and citrulline levels while increasing NO content, suggesting that these treatments may promote the catabolism of arginine to generate NO, thereby facilitating faster wound closure. Additionally, borneol alone significantly elevated NO content in wounds, potentially due to its ability to directly reduce nitrates/nitrites to NO. Oxidative stress is a defining characteristic of impaired metabolism in diabetic wounds. RESULTS: The result showed that both Pearl and Pearl-Borneol pair decreased the oxidative stress biomarker methionine sulfoxide level in diabetic wounds compared to those treated with Vaseline, indicating that Pearl alone or combined with Borneol may enhance the oxidative stress microenvironment in diabetic wounds. CONCLUSION: In summary, the findings validate the effectiveness of the Pearl-Borneol pair in accelerating the healing of diabetic wounds, with effects on reducing oxidative stress, enhancing arginine metabolism, and increasing NO generation, providing a mechanistic basis for this therapeutic approach.

Trends in co-fractionation mass spectrometry: A new gold-standard in global protein interaction network discovery.

Co-fractionation mass spectrometry (CF-MS) uses biochemical fractionation to isolate and characterize macromolecular complexes from cellular lysates without the need for affinity tagging or capture. In recent years, this has emerged as a powerful technique for elucidating global protein-protein interaction networks in a wide variety of biospecimens. This review highlights the latest advancements in CF-MS experimental workflows including machine learning-guided analyses, for uncovering dynamic and high-resolution protein interaction landscapes with enhanced sensitivity, accuracy and throughput, enabling better biophysical characterization of endogenous protein complexes. By addressing challenges and emergent opportunities in the field, this review underscores the transformative potential of CF-MS in advancing our understanding of functional protein interaction networks in health and disease.

Data mining study on adverse events of tirzepatide based on FAERS database.

BACKGROUND: Tirzepatide is a novel dual gastric inhibitory polypeptide (GIP) and glucagon-like peptide-1 receptor agonist (GLP-1 RA) for type 2 diabetes or obesity. To explore the safety profile of tirzepatide in real-world clinical applications. RESEARCH DESIGN AND METHODS: A retrospective analysis of adverse events (AEs) reports associated with tirzepatide was conducted from the second quarter of 2022 through the fourth quarter of 2023, utilizing the FDA Adverse Event Reporting System (FAERS) database. Signal mining utilized the reporting odds ratio (ROR) method, and onset time was analyzed utilizing the Weibull Shape Parameter (WSP). RESULTS: We identified 25,215 AE reports related to tirzepatide, predominantly in the 65 to 85 age group. Four positive signals were found at the system organ classes level. Additionally,109 AEs at the preferred terms level with positive signals were indicated. Included among these are novel signals, such as the presence of thyroid mass, medullary thyroid carcinoma, and conditions affecting the reproductive system and breast. Most AEs occurred within the first 30 days. The WSP was 0.66, indicating a propensity for early failure type. CONCLUSIONS: This study identified several novel AE signals for tirzepatide, highlighting the need for careful monitoring, especially in the early stages of treatment.

Rules of acupoint selection in treatment of cancer-related insomnia with acupuncture and moxibustion based on data mining technology. / åŸºäºŽæ•°æ®æŒ–æŽ˜æŠ€æœ¯æŽ¢æžé’ˆç¸æ²»ç–—è‚¿ç˜¤ç›¸å ³æ€§å¤±çœ çš„é€‰ç©´è§„å¾‹.

OBJECTIVES: To analyze the rules of acupoint selection in treatment of cancer-related insomnia with acupuncture and moxibustion by data mining technology. METHODS: The articles of cancer-related insomnia treated with acupuncture and moxibustion were searched from CNKI, Wanfang, VIP, SinoMed, PubMed, WOS, Cochrane, and Embase databases, from the inception of each database to January 5, 2024. The prescription database of acupuncture and moxibustion for cancer-related insomnia was established. The descriptive analysis was conducted on the use frequency, meridian tropism and distribution of acupoints. Using SPSS Modeler 18.0 Apriori algorithm, the association rules of acupoint prescriptions were analyzed. With Cytoscape3.9.1 software used, the complex network diagram was plotted, and the cluster analysis of high-frequency acupoints was performed by SPSS26.0 software. RESULTS: Forty-one articles were included, and 67 prescriptions were extracted with 89 acupoints involved, and the total use frequency was 447 times. The top 4 acupoints of the high use frequency were Baihui (GV20), Sanyinjiao (SP6), Shenmen (HT7) and Shenting (GV24). The included meridians were the governor vessel, the spleen meridian, the bladder meridian, the conception vessel, the heart meridian and the stomach meridian. The selected acupoints were mostly distributed on the head, the neck and and the upper and lower limbs. The special acupoints of the high use frequency included the five-Shu points, the crossing points and yuan-primordial points. Regarding acupoint combination, GV24, SP6, HT7, and GV20 were highly correlated. The three effective clusters were categorized among the top 12 acupoints of the high use frequency. CONCLUSIONS: In treatment of cancer-related insomnia with acupuncture and moxibustion, the principle focuses on supporting the healthy qi, eliminating pathogens, regulating yin and yang, promoting the circulation of the governor vessel for regulating the spirit, and tranquilizing the mind. The core acupoint prescription may includes GV24, SP6, HT7 and GV20；combined with Zusanli (ST36) and Yintang (GV4+) to enhance the therapeutic effect.

A Multimorbidity Analysis of Hospitalized Patients With COVID-19 in Northwest Italy: Longitudinal Study Using Evolutionary Machine Learning and Health Administrative Data.

BACKGROUND: Multimorbidity is a significant public health concern, characterized by the coexistence and interaction of multiple preexisting medical conditions. This complex condition has been associated with an increased risk of COVID-19. Individuals with multimorbidity who contract COVID-19 often face a significant reduction in life expectancy. The postpandemic period has also highlighted an increase in frailty, emphasizing the importance of integrating existing multimorbidity details into epidemiological risk assessments. Managing clinical data that include medical histories presents significant challenges, particularly due to the sparsity of data arising from the rarity of multimorbidity conditions. Also, the complex enumeration of combinatorial multimorbidity features introduces challenges associated with combinatorial explosions. OBJECTIVE: This study aims to assess the severity of COVID-19 in individuals with multiple medical conditions, considering their demographic characteristics such as age and sex. We propose an evolutionary machine learning model designed to handle sparsity, analyzing preexisting multimorbidity profiles of patients hospitalized with COVID-19 based on their medical history. Our objective is to identify the optimal set of multimorbidity feature combinations strongly associated with COVID-19 severity. We also apply the Apriori algorithm to these evolutionarily derived predictive feature combinations to identify those with high support. METHODS: We used data from 3 administrative sources in Piedmont, Italy, involving 12,793 individuals aged 45-74 years who tested positive for COVID-19 between February and May 2020. From their 5-year pre-COVID-19 medical histories, we extracted multimorbidity features, including drug prescriptions, disease diagnoses, sex, and age. Focusing on COVID-19 hospitalization, we segmented the data into 4 cohorts based on age and sex. Addressing data imbalance through random resampling, we compared various machine learning algorithms to identify the optimal classification model for our evolutionary approach. Using 5-fold cross-validation, we evaluated each model's performance. Our evolutionary algorithm, utilizing a deep learning classifier, generated prediction-based fitness scores to pinpoint multimorbidity combinations associated with COVID-19 hospitalization risk. Eventually, the Apriori algorithm was applied to identify frequent combinations with high support. RESULTS: We identified multimorbidity predictors associated with COVID-19 hospitalization, indicating more severe COVID-19 outcomes. Frequently occurring morbidity features in the final evolved combinations were age>53, R03BA (glucocorticoid inhalants), and N03AX (other antiepileptics) in cohort 1; A10BA (biguanide or metformin) and N02BE (anilides) in cohort 2; N02AX (other opioids) and M04AA (preparations inhibiting uric acid production) in cohort 3; and G04CA (Alpha-adrenoreceptor antagonists) in cohort 4. CONCLUSIONS: When combined with other multimorbidity features, even less prevalent medical conditions show associations with the outcome. This study provides insights beyond COVID-19, demonstrating how repurposed administrative data can be adapted and contribute to enhanced risk assessment for vulnerable populations.

Analysis of post-market adverse events of tafamidis base on the FDA adverse event reporting system.

Tafamidis is the world's first and only oral drug approved to treat the rare disease transthyretin amyloid cardiomyopathy (ATTR-CM). Medicines are known to have different adverse reactions during the course of treatment. However, the current limited clinical studies did not identify significant adverse drug reactions to tafamidis. Tafamidis has been on the market for 5 years now, a large number of adverse drug event (ADE) reports with tafamidis as the primary suspected drug have been reported in the United Food and Drug Administration's adverse event reporting system (FAERS). We retrieved 8170 adverse event reports in FAERS with tafamidis as the first suspected drug, and mined these reports for positive signals to perform risk warnings for potentially possible adverse events with tafamidis. We found that a large number of adverse events associated with the primary disease were reported due to insufficient awareness of ATTR among the reporters, leading to a large number of positive signals reported in the cardiac disorders system. We also found that tafamidis has the potential to cause an adverse event risks of ear and labyrinth disorders system and urinary tract infection bacterial, which deserve continued clinical attention.

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%.

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.

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.