UNASSIGNED: Atogepant, an orally administered, small-molecule, calcitonin gene-related peptide (CGRP) receptor antagonist, is being investigated for the treatment of migraine.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.

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.

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.

The present study recorded indigenous knowledge of medicinal plants in Shahrbabak, Iran. We described a method using data mining algorithms to predict medicinal plants\' mode of application. Twenty-oneindividuals aged 28 to 81 were interviewed. Firstly, data were collected and analyzed based on quantitative indices such as the informant consensus factor (ICF), the cultural importance index (CI), and the relative frequency of citation (RFC). Secondly, the data was classified by support vector machines, J48 decision trees, neural networks, and logistic regression. So, 141 medicinal plants from 43 botanical families were documented. Lamiaceae, with 18 species, was the dominant family among plants, and plant leaves were most frequently used for medicinal purposes. The decoction was the most commonly used preparation method (56%), and therophytes were the most dominant (48.93%) among plants. Regarding the RFC index, the most important species are Adiantum capillus-veneris L. and Plantago ovata Forssk., while Artemisia auseri Boiss. ranked first based on the CI index. The ICF index demonstrated that metabolic disorders are the most common problems among plants in the Shahrbabak region. Finally, the J48 decision tree algorithm consistently outperforms other methods, achieving 95% accuracy in 10-fold cross-validation and 70-30 data split scenarios. The developed model detects with maximum accuracy how to consume medicinal plants.

BACKGROUND: Social media has become an increasingly popular and critical tool for users to digest diverse information and express their perceptions and attitudes. While most studies endeavor to delineate the emotional responses of social media users, there is limited research exploring the factors associated with the emergence of emotions, particularly negative ones, during news consumption.
OBJECTIVE: We aim to first depict the web coverage by news organizations on social media and then explore the crucial elements of news coverage that trigger the public\'s negative emotions. Our findings can act as a reference for responsible parties and news organizations in times of crisis.
METHODS: We collected 23,705 Facebook posts with 1,019,317 comments from the public pages of representative news organizations in Hong Kong. We used text mining techniques, such as topic models and Bidirectional Encoder Representations from Transformers, to analyze news components and public reactions. Beyond descriptive analysis, we used regression models to shed light on how news coverage on social media is associated with the public\'s negative emotional responses.
RESULTS: Our results suggest that occurrences of issues regarding pandemic situations, antipandemic measures, and supportive actions are likely to reduce the public\'s negative emotions, while comments on the posts mentioning the central government and the Government of Hong Kong reveal more negativeness. Negative and neutral media tones can alleviate the rage and interact with the subjects and issues in the news to affect users\' negative emotions. Post length is found to have a curvilinear relationship with users\' negative emotions.
CONCLUSIONS: This study sheds light on the impacts of various components of news coverage (issues, subjects, media tone, and length) on social media on the public\'s negative emotions (anger, fear, and sadness). Our comprehensive analysis provides a reference framework for efficient crisis communication for similar pandemics at present or in the future. This research, although first extending the analysis between the components of news coverage and negative user emotions to the scenario of social media, echoes previous studies drawn from traditional media and its derivatives, such as web newspapers. Although the era of COVID-19 pandemic gradually brings down the curtain, the commonality of this research and previous studies also contributes to establishing a clearer territory in the field of health crises.

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.

BACKGROUND: China and India have unique traditional medicine systems with vast territory and rich medical resources. Traditional medicines in China include traditional Chinese medicine, Tibetan medicine, Mongolian medicine, Uyghur medicine, Dai medicine, etc. In the third national survey of Chinese medicine resources, 12694 medicinal materials were identified. Traditional medicines in India include Ayurveda, Unani, Siddha, Homoeopathy, etc. There are 7263 medicinal materials in India.
OBJECTIVE: To reveal the characteristics of medicinal materials between China and India respectively, and to compare the similarities and differences in terms of properties, tastes, medicinal parts and therapeutic uses and to promote the exchange of traditional medicine between China and India and the international trade of traditional medicine industry.
METHODS: The information of medicinal materials between China and India was extracted from The Chinese Traditional Medicine Resource Records and Pharmacopoeia of the People\'s Republic of China, as well as from 71 Indian herbal monographs. The information of each medicinal material, such as types, families, genera, properties, distribution, medicinal parts, efficacy, therapeutic uses, dosage form and dosage, was recorded in Excel for statistical analysis and visual comparison.
RESULTS: A total of 12694 medicinal materials in China and 5362 medicinal materials in India were identified. The medicinal materials were mostly distributed in Southwest China and northern India. Plants were the main sources of medicinal materials. The common medicinal parts in China were whole medicinal materials, roots and rhizomes, and India used more renewable fruits, seeds and leaves. They are commonly used in the treatment of digestive system diseases. There were 1048 medicinal materials used by both China and India, which were distributed in 188 families and 685 genera. The Chinese and Indian pharmacopoeias had a total of 80 species of medicinal materials used by both China and India.
CONCLUSIONS: The characteristics of medicinal materials between China and India were somewhat different, which was conducive to provide a reference basis for traditional medicine in China or India to increase the medicinal parts and indications when using a certain medicinal material, as well as to expand the source of medicine and introduce new resources. However, there were certain similarities and shared medicinal materials, which can tap the potential of bilateral trade of medicinal materials between China and India, so as to promote the medical cultural exchange and economic and trade cooperation between the two countries.

UNASSIGNED: To identify the most commonly reported drugs associated with QT interval prolongation in the FDA Adverse Event Reporting System (FAERS) and evaluate their risk for QT interval prolongation.
UNASSIGNED: We employed the preferred term (PT) \"electrocardiogram QT prolonged\" from the Medical Dictionary for Regulatory Activities (MedDRA) 26.0 to identify adverse drug events (ADEs) of QT interval prolongation in the FAERS database from the period 2004-2022. Reporting odds ratio (ROR) was performed to quantify the signals of ADEs.
UNASSIGNED: We listed the top 40 drugs that caused QT interval prolongation. Among them, the 3 drugs with the highest number of cases were quetiapine (1,151 cases, ROR = 7.62), olanzapine (754 cases, ROR = 7.92), and citalopram (720 cases, ROR = 13.63). The two most frequently reported first-level Anatomical Therapeutic Chemical (ATC) groups were the drugs for the nervous system (n = 19, 47.50%) and antiinfectives for systemic use (n = 7, 17.50%). Patients with missing gender (n = 3,482, 23.68%) aside, there were more females (7,536, 51.24%) than males (5,158, 35.07%) were involved. 3,720 patients (25.29%) suffered serious clinical outcomes resulting in deaths or life-threatening conditions. Overall, most drugs that caused QT interval prolongation had early failure types according to the assessment of the Weibull\'s shape parameter (WSP) analysis.
UNASSIGNED: Our study offered a list of drugs that frequently caused QT interval prolongation based on the FAERS system, along with a description of some risk profiles for QT interval prolongation brought on by these drugs. When prescribing these drugs in clinical practice, we should closely monitor the occurrence of ADE for QT interval prolongation.

BACKGROUND: The National Health Service (NHS) Talking Therapies program treats people with common mental health problems in England according to \"stepped care,\" in which lower-intensity interventions are offered in the first instance, where clinically appropriate. Limited resources and pressure to achieve service standards mean that program providers are exploring all opportunities to evaluate and improve the flow of patients through their service. Existing research has found variation in clinical performance and stepped care implementation across sites and has identified associations between service delivery and patient outcomes. Process mining offers a data-driven approach to analyzing and evaluating health care processes and systems, enabling comparison of presumed models of service delivery and their actual implementation in practice. The value and utility of applying process mining to NHS Talking Therapies data for the analysis of care pathways have not been studied.
OBJECTIVE: A better understanding of systems of service delivery will support improvements and planned program expansion. Therefore, this study aims to demonstrate the value and utility of applying process mining to NHS Talking Therapies care pathways using electronic health records.
METHODS: Routine collection of a wide variety of data regarding activity and patient outcomes underpins the Talking Therapies program. In our study, anonymized individual patient referral records from two sites over a 2-year period were analyzed using process mining to visualize the care pathway process by mapping the care pathway and identifying common pathway routes.
RESULTS: Process mining enabled the identification and visualization of patient flows directly from routinely collected data. These visualizations illustrated waiting periods and identified potential bottlenecks, such as the wait for higher-intensity cognitive behavioral therapy (CBT) at site 1. Furthermore, we observed that patients discharged from treatment waiting lists appeared to experience longer wait durations than those who started treatment. Process mining allowed analysis of treatment pathways, showing that patients commonly experienced treatment routes that involved either low- or high-intensity interventions alone. Of the most common routes, >5 times as many patients experienced direct access to high-intensity treatment rather than stepped care. Overall, 3.32% (site 1: 1507/45,401) and 4.19% (site 2: 527/12,590) of all patients experienced stepped care.
CONCLUSIONS: Our findings demonstrate how process mining can be applied to Talking Therapies care pathways to evaluate pathway performance, explore relationships among performance issues, and highlight systemic issues, such as stepped care being relatively uncommon within a stepped care system. Integration of process mining capability into routine monitoring will enable NHS Talking Therapies service stakeholders to explore such issues from a process perspective. These insights will provide value to services by identifying areas for service improvement, providing evidence for capacity planning decisions, and facilitating better quality analysis into how health systems can affect patient outcomes.