搜索结果：MySQL

The SUS Regulation System is strategic for organizing access to health services. In Manaus, it is managed by the Regulatory Complex. Traditionally, communication with users was conducted via SMS an expensive method that reached only 30% of those with scheduled appointments. The Municipal Health Secretariat (SEMSA) designed and implemented, as a nationwide first, a digital online inquiry solution to modernize communication and expand access. This study describes the development of the information system and analyzes its impacts. A mixed-methods approach was adopted: quantitative analysis of booking, confirmation, and no-show data (2021-2024) using Python, Matplotlib, and Seaborn, along with statistical projections; and qualitative analysis through interviews with managers. The system was developed in PHP and MySQL, using hybrid practices that combined agile and traditional methods. Results show growth in bookings (from 215,000 to 557,000), a reduction in the no-show rate, and a threefold increase in confirmations. Replacing SMS generated estimated savings of R$ 705,000. In 2025, the portal reached 80% of scheduled users, with an average of 10,000 authorizations per day. This experience reinforces that digital solutions yield concrete advances in management and access to public health care. O Sistema de Regulação do SUS é estratégico para organizar o acesso aos serviços de saúde. Em Manaus, sua gestão é realizada pelo Complexo Regulador. Tradicionalmente, a comunicação com os usuários era feita via SMS, recurso oneroso e com alcance de 30% do público agendado. A Secretaria Municipal de Saúde (SEMSA) desenvolveu e implantou, de forma pioneira no Brasil, uma solução digital de consulta on-line, visando modernizar a comunicação e ampliar o acesso. Este estudo descreve o desenvolvimento do sistema de informação e analisa seus impactos. Adotou-se abordagem mista, com análise quantitativa dos dados de marcação, confirmação e absenteísmo (2021-2024), utilizando Python, Matplotlib e Seaborn, além de projeções estatísticas. A análise qualitativa incluiu entrevistas com gestores. O sistema foi desenvolvido em PHP e MySQL, com práticas híbridas entre métodos ágeis e tradicionais. Os resultados indicam crescimento nas marcações (215 mil para 557 mil), redução do absenteísmo e triplicação das confirmações. A substituição do SMS gerou economia estimada de R$ 705 mil. Em 2025, o portal atingiu 80% dos usuários agendados, com média de 10 mil autorizações diárias. A experiência reforça que soluções digitais promovem avanços concretos na gestão e no acesso à saúde pública. El Sistema de Regulación del SUS es estratégico para organizar el acceso a los servicios de salud. En Manaus, está gestionado por el Complejo Regulador. Tradicionalmente, la comunicación con los usuarios se realizaba por SMS, un método costoso que alcanzaba solo al 30 % de las personas con citas programadas. La Secretaría Municipal de Salud (SEMSA) diseñó e implementó, como primera iniciativa en el país, una solución digital de consulta en línea para modernizar la comunicación y ampliar el acceso. Este estudio describe el desarrollo del sistema de información y analiza sus impactos. Se adoptó un enfoque mixto: análisis cuantitativo de los datos de citas, confirmaciones y ausencias (2021-2024) utilizando Python, Matplotlib y Seaborn, junto con proyecciones estadísticas; y análisis cualitativo mediante entrevistas con gestores. El sistema fue desarrollado en PHP y MySQL, utilizando prácticas híbridas que combinaron métodos ágiles y tradicionales. Los resultados muestran crecimiento en las citas (de 215.000 a 557.000), reducción en la tasa de ausentismo y un aumento de tres veces en las confirmaciones. La sustitución del SMS generó un ahorro estimado de R$ 705.000. En 2025, el portal alcanzó al 80 % de los usuarios con citas programadas, con un promedio de 10.000 autorizaciones diarias. Esta experiencia refuerza que las soluciones digitales generan avances concretos en la gestión y el acceso a la salud pública.

Electronic Medical Records (EMRs) are crucial to modern healthcare. However, traditional relational databases fail to fulfill increased expectations for integrity, auditability, and compliance in regulated environments. This paper proposes a Hybrid Blockchain Migration Framework that integrates a conventional MySQL-based EMR system (OpenMRS) with a permissioned blockchain network (Hyperledger Fabric). Sensitive data fields are selectively mirrored to the blockchain, ensuring tamper-evident logging while retaining the high performance of SQL for routine operations. A middleware layer, implemented using Java Spring Boot, monitors changes in the EMR and commits cryptographic hashes and metadata to the blockchain in near real-time. We evaluate the hybrid system against both standalone MySQL and full-blockchain implementations using controlled benchmarks, analyzing latency, throughput, resource utilization, and auditability. Results show that the hybrid architecture sustains near-native responsiveness (median 2.1 ms versus 1.6 ms for pure MySQL and 60.5 ms for Fabric) and delivers 480 Transaction Per Second (TPS), while incurring only modest overhead (47% of i7-9750H CPU, 1.15 GB RAM) and enhancing data integrity and compliance with regulations such as Oman's Personal Data Protection Law (PDPL). The framework is extensible to multi-institutional deployments and supports regulatory alignment, making it a viable pathway for blockchain adoption in clinical settings.

Gut dysbiosis is widely recognized as a contributor to autoimmune diseases, as it can lead to the expression of microbial antigens that disrupt immune regulation through specific molecular mechanisms. However, existing resources do not systematically link gut microbial antigen sequences to the specific autoimmune mechanisms through which they act. Here, we present GUTAID (Gut Microbes in Autoimmune Disorders), a literature-curated database of gut microbial antigens annotated with experimentally supported autoimmune mechanisms. Peer-reviewed studies published from October 1970 to September 2024 were manually screened, yielding 73 potential antigens that operate through nine molecular mechanisms, including protein citrullination, epitope spreading, molecular mimicry, and immune modulation, amongst others. The corresponding protein sequences were retrieved from UniProtKB, and redundancy was removed with MMseqs2. For the database implementation, data were delivered through a lightweight LAMP (Linux-Apache-MySQL/MariaDB-PHP) stack with server-side HTML/Bootstrap rendering, MySQL indexing, and HTTPS-secured downloads. Users can browse, keyword-search, or bulk-download sequence archives via a five-tab interface (Home, Downloads, Search, Team, and About). GUTAID thus enables mechanism-oriented exploration of gut microbial antigens and supports downstream biomarker and therapeutic discovery in autoimmune research. Database URL:  https://gutaid.mgdiscoverylab.com/.

To construct a multimodal forensic pathology database based on artificial intelligence (AI) technology and explore methods for integrating pre-trained models into multimodal databases. A hybrid storage architecture consisting of MySQL, Redis and OSS was employed to manage desensitized multimodal data. Data entry was optimized using optical character recognition (OCR) and natural language processing (NLP) technologies. OCR performance was evaluated and optimized using cosine distance, character error rate (CER), and word error rate (WER) to meet practical operational requirements. An intelligent retrieval model was developed using the ChatGLM3-6B model and retrieval-augmented generation (RAG) technology. Model performance was evaluated using ranking metrics including Precision@K, Recall@K, discounted cumulative gain (DCG), and normalized discounted cumulative gain (NDCG). The database demonstrated satisfactory baseline performance. The response times were maintained within 150 ms for single-query condition and within 2 s for multiple-query conditions. The average disk read throughput reached 950 MB/s. In concurrent performance tests, the database achieved a maximum throughput of 1 200 queries per second (QPS), meeting multimodal data management demands. OCR evaluation showed high recognition accuracy; for high-quality documents, the cosine distance, CER, and WER achieved 0.02, 1.5%, and 3.2%, respectively. Intelligent retrieval results indicated that Precision@K remained consistently high (0.69-1.00), while NDCG values remained above 0.87 for all evaluations. When K=100, the NDCG surpassed 0.95 for all queries, meeting expected performance requirements. The multimodal forensic pathology database constructed in this study demonstrates good stability and operational efficiency and can meet the requirements of routine forensic practice for multimodal data storage, management, and analysis. The intelligent retrieval capabilities, based on pre-trained large language models (LLMs), can be applied to conversational information retrieval from forensic reports and related documents, providing a novel approach to the management and analysis of multimodal databases. 目的: 基于人工智能技术构建法医病理多模态数据库系统，并探索预训练模型在多模态数据库中集成的应用方法。方法: 系统采用MySQL、Redis和OSS的混合式存储架构管理经脱敏处理后的多模态数据。通过光学字符识别（optical character recognition，OCR）和自然语言处理（natural language processing，NLP）技术对数据录入过程进行优化，根据余弦距离、字符错误率和词错误率评估并优化OCR性能以满足实际业务需求。基于ChatGLM3-6B模型和检索增强生成（retrieval-augmented generation，RAG）技术构建智能检索模型，并采用精确率（Precision@K）、召回率（Recall@K）、折损累积增益（discounted cumulative gain，DCG）和归一化折损累积增益（normalized discounted cumulative gain，NDCG）等排名指标评估模型性能。结果: 数据库基准性能良好，单个查询条件的响应时间控制在150 ms内，多个查询条件在2 s内，平均磁盘读取吞吐量为950 MB/s，并发性能测试中数据库吞吐量最高可达1 200每秒查询率（queries per second，QPS），能够承担多模态数据管理的需求。OCR结果显示，系统识别精度较高;在文档质量较高时，余弦距离、字符错误率和词错误率分别达到0.02、1.5%、3.2%。智能检索结果显示，Precision@K维持在较高水平（0.69~1.00），NDCG值均保持在0.87以上;K=100时，所有查询的NDCG均超过0.95，达到预期要求。结论: 所构建的法医病理多模态数据库具有较好的稳定性和运行效率，能够满足法医日常工作中对多模态数据存储和管理分析的需求。基于预训练的大语言模型的智能检索功能可用于鉴定文书的对话式信息检索，为多模态数据库的管理分析提供新的途径。.

To extract structured injury features of knee trauma from forensic case files, and to assess knee functional impairment using a machine learning model combined with the voting method. A total of 490 forensic cases involving knee trauma were retrospectively collected and randomly divided into training and testing sets at an 8:2 ratio. Structured injury features were extracted and systematically organized and stored using a MySQL database. Six machine learning models, including support vector classification, random forest, logistic regression, gradient boosting, k-nearest neighbor, and extreme gradient boosting, were applied to select the optimal models. Using a 25% loss of joint range of motion as the threshold, a model for classifying the severity of knee functional impairment was established by combining the selected models with a voting method. The best models were first selected based on their average AUC values, and further validated using 5-fold cross-validation. The SHAP method was used to analyze and interpret the prediction results of the optimal model. In addition, 57 similar cases were collected as an external validation to evaluate the model's generalization ability. The average AUC values for support vector machine, random forest, and extreme gradient boosting all exceeded 0.9. In 5-fold cross-validation, each of the three individual models achieved an average AUC value of 0.89. After integrating these three models using the voting method, the average AUC of 5-fold cross-validation increased to 0.91. The model's performance, and the evaluation metrics on the external validation set were comparable to those from internal validation. The developed machine learning model based on structured injury features demonstrates good performance in classifying the severity of motor dysfunction following knee trauma, with high model interpretability and strong generalization capability. 目的: 基于鉴定案例档案提取结构化的膝关节外伤损伤特征，利用机器学习模型结合投票法，推断膝关节功能障碍。方法: 回顾性收集膝关节外伤行法医学鉴定案例490例，按照8∶2比例划分为训练集和测试集;提取结构化损伤特征并利用MySQL数据库进行数据的系统化组织与储存，采用支持向量分类、随机森林、逻辑回归、梯度提升、k-近邻法、极限梯度提升共6种机器学习模型筛选出的最佳模型，结合投票法，以关节活动度损失25％为界，建立膝关节功能障碍程度判别模型。根据初步筛选的平均AUC值挑选最佳模型，再采用5-折交叉验证进一步验证。采用SHAP方法分析并解释最佳模型的预测结果。另外收集57例同类案例作为外部验证集检验模型的泛化能力。结果: 支持向量分类、随机森林、极限梯度提升3种模型的平均AUC值较高，均超过0.9。在5-折交叉验证中，三个模型的平均AUC值均为0.89。使用投票法集成这3种模型后，5-折交叉验证平均AUC值提升至0.91。模型的外部验证集各项评估指标与内部测试结果相近。结论: 建立的基于结构化膝关节外伤损伤特征机器学习模型，在膝关节外伤后运动功能障碍程度判别任务中效果较好，具有良好的可解释性与较高的泛化能力。.

This research aims to engineer a specialized, high-speed database architecture tailored for intelligent video surveillance in critical healthcare environments. The primary objective is to overcome the input/output operations per second (IOPS) bottlenecks and latency issues inherent in traditional SQL and general-purpose NoSQL systems, which impede real-time clinical decision-making. We conceptualized and implemented "SubDataBase-0.91s," a task-specific Database Management System (DBMS) residing entirely in Random Access Memory (RAM). The architecture employs a direct memory access model with periodic, asynchronous synchronization to the file system to ensure persistence. Performance was rigorously benchmarked against industry standards-Microsoft SQL Server, OracleDB, MySQL, PostgreSQL, MongoDB, and Redis-utilizing Node.js automation scripts to simulate high-velocity write/read cycles typical of video analytics streams. The proposed RAM-resident architecture demonstrated a dramatic reduction in data access latency. Specifically, SubDataBase-0.91s achieved a write/read speed increase of 8.6 times compared to MySQL (the slowest control) and outperformed Redis (the fastest commercial in-memory control) by a factor of 0.78 in specific surveillance-related transactional workloads. The study confirms that stripping away universal ACID (Atomicity, Consistency, Isolation, Durability) compliance overhead in favor of a streamlined, memory-mapped architecture significantly enhances the throughput required for real-time patient monitoring. This solution provides a scalable foundation for next-generation "Smart Hospital" infrastructure.

BACKGROUND: Prolonged waiting times for outpatient registration remain one of the significant challenges in tertiary care hospitals, leading to patient dissatisfaction and operational inefficiencies. The use of traditional paper-based registration systems is one of the reasons for the delays, especially during peak hours. The aim of this study was to create a web-based outpatient registration system in order to make waiting time shorter and to improve service efficiency. METHODS: This prospective observational study was conducted in two phases at Kasturba Hospital, Udupi. Time and motion study was done on 50 patients in Phase I, and service time for 20 patients was measured using stopwatch-based observation. A structured, validated questionnaire was administered to 200 patients to capture satisfaction levels, awareness, and willingness to use a digital registration system. During Phase II, an online registration platform was developed using the waterfall model, which included planning, system design, programming, testing, and maintenance. The Django framework was used to develop the system with MySQL as the backend database. To ensure the smooth integration with the existing hospital system, a special browser plugin was created. RESULTS: The time it took to register patients averaged 17 min and 25 s in total, and it took 6 min and 2 s on average for the staff to serve the patients. Among the 200 patients surveyed, the current system was satisfactory for 74.5% of them, while 25.5% voiced their complaints mainly because of the long waiting lines. The majority, that is, 58% of patients were unaware of the online registration facility, however, 90.5% of them were ready to utilize such a system if it was introduced. Moreover, 83% of the respondents thought that the online registration system would allow them to spend less time waiting. A digital solution was created and integrated into the hospital’s system for pilot testing. CONCLUSION: This study reveals that there is both the need for and readiness to undergo a digital transformation of the outpatient registration process. The system for online registrations developed as part of this study is both technically viable and appreciated by the patients. Wider implementation and awareness-building, which help to decrease waiting times and boost operational efficiency, lead to greater patient satisfaction in tertiary care facilities.

Depression is a prevalent mental disorder associated with substantial social and familial burdens, and exercise is increasingly recognized as a promising non-pharmacological intervention; however, research is hindered by heterogeneous approaches, safety concerns, and individual variability. A structured knowledge framework may support the development of more personalized exercise prescription. This study aimed to construct PEPRKD-Depression (Personalized Exercise Prescription Recommendation Knowledge Database for Depression), a structured knowledge database of exercise interventions for depression, to systematically integrate existing literature and provide a knowledge foundation for personalized exercise prescription. Data were sourced from PubMed and included original studies on exercise interventions for depression published between 1960 and 2023. Relevant data including structured exercise programs, patient information, fitness and risk assessments, adverse events, and outcomes, were extracted, standardized, and organized. The knowledge database was developed with Vue3, hosted on Nginx, and uses MySQL for data storage. PEPRKD-Depression includes 567 studies with 769 exercise intervention protocols involving 100,794 subjects across 49 countries. It extracted 662 depression-related items (e.g., symptoms, disorders, perinatal and subthreshold depression). Exercise regimens followed the "FITT-VPP" principle: Frequency, Intensity, Time, Type, Volume, Progression, Periods of time. The knowledge database is publicly available at: https://dpa.bioinf.org.cn/. PEPRKD-Depression is a comprehensive exercise therapy knowledge database for depression that provides a structured repository of exercise intervention information that may serve as a preliminary knowledge resource for future personalized exercise recommendation research and decision-support applications.

This article is based on the form of "VR + Art Platform" and designs an art management platform that integrates display, trading, and socializing. The development of front-end work is carried out using Unity 3D engine, and modeling software such as 3ds Max is used to model the models used in the system. The backend is developed using IntelliJ IDEA platform, and the SSM framework is built and combined with MySQL database to implement the functions of each module in detail. The system also realizes the interactive connection between front-end scenes and back-end management, and achieves data transmission. When developing for users, platforms need to provide complex recommendation functions. Given that existing recommendation algorithms often perform inaccurately and efficiently when dealing with changes in user preferences and diverse interaction needs on art platforms. This article proposes a research on art management platform recommendation algorithm by combining graph neural network and exhibition design rules. The introduction of attention mechanism enables the graph neural network model to more accurately aggregate important neighbor node information, thereby improving the accuracy of node representation and personalized recommendation effect, and compensating for the shortcomings of traditional recommendation systems in aesthetics and display optimization, making the recommendation results more practical. Compared to existing virtual platforms, the proposed method explicitly highlights its advantages by significantly improving recommendation accuracy through graph neural networks, while simultaneously enhancing display aesthetics and user engagement via integrated exhibition design rules.

The Apiales order harbors a rich variety of species, including vegetables, spices, and medicinal herbs, that possess diverse morphological characteristics and are globally distributed. Apiaceae and Araliaceae, being the two principal families within the Apiales order, have been the focus of extensive attention and research efforts on account of their significant value. Although the species in Apiales are important and there is a large amount of genomic data available, there is currently a lack of large-scale genomic analysis and database platform at the Apiales order level. Therefore, this study aims to conduct a comprehensive genomic analysis and database construction for Apiales. This study performed comprehensive comparative genomic analysis by bioinformatics methods. The database was constructed with MySQL database management, the Django framework, and multiple programming languages. The present study was designed to clarify the phylogenetic associations and the phenomenon of paleo-polyploidization within Apiales species. Subsequently, an in-depth exploration was carried out to determine the ancestral karyotypes and to analyze the evolutionary pathways of chromosomes. In this research endeavor, genomic and transcriptomic data were employed to execute a comprehensive bioinformatic analysis. Eventually, through the integration of genomic data and bioinformatics findings, this study established the Apiales Genome Resources Database (TAGR), which is freely accessible at http://tagr.bio2db.com/index.html. The research project carried out a profound investigation into the genomic evolution and chromosomal karyotypes of Apiales species, unraveling their evolutionary history from a genomic perspective. In addition, this study established the TAGR, which will offer researchers convenient access to and efficient utilization of these omics data resources, facilitating genomic analysis and molecular breeding initiatives.

Medicinal plants and phytocompounds targeting skeletal muscle wasting in humans are under-represented in the majority of databases reporting plant/herb-diseases association. However, a large body of literature exists wherein plant extracts or active pharmaceutical ingredients thereof demonstrate potential benefit in skeletal muscle wasting diseases across model organisms. Underscoring the relevance of a repertoire documenting such medicinal plants, we introduce PDMD (Plants Database for Muscle Wasting Diseases), a manually curated plants database reported for muscle wasting diseases such as cachexia, sarcopenia, muscle atrophy, muscle frailty, impaired muscle regeneration, and muscle fatigue. PDMD was developed through systematic manual collection and curation of published studies from PubMed, Science Direct, etc, retrieving literature on plants conferring pharmacological efficacy against muscle wasting across experimental model organisms. Phytochemical and taxonomic information were extracted via tools like ClassyFire, PubChem. To handle the storage of an annotated listing of plants, MS-Excel and MySQL were used. Frontend was designed in Visual Studio Code and HTML/CSS. An Apache/PHP server was used to integrate MS-Excel data and charts. PDMD encompasses 206 medicinal plants, 230 API reported across 18 model organisms, offering taxonomical information, phytochemical classes, SMILES structure, geographical distribution, and other bioactivity indications. PDMD is cross-referenced with standard databases such as PubChem and PubMed for enhanced functionality. PDMD highlights overlooked plant-muscle links, bridging ethnopharmacology and botany gaps, and can aid hypothesis generation for novel therapies. PDMD highlights overlooked plant-muscle links, bridging ethnopharmacology and botany gaps, and can aid hypothesis generation. PDMD is freely available at https://www.jiit.ac.in/biotechhighlightes/Research-Databases/PDMD/index.html, and was last updated in September 2025.

Real-time visual recognition systems integrated with culturally adaptive reasoning are urgently demanded in globalized culinary scenarios. An agent-oriented framework, Agent-based Gastronomy Recommender Enhanced Engine with YOLO (AGREE-YOLO), is proposed in this study, which integrates an optimized lightweight YOLOv13 detector and vision language model (VLM)-driven agents for cross-cultural seafood recipe recommendation. The improved YOLOv13 is equipped with group shuffle convolution (GSConv) modules and Wise-IoU (WIoU) loss, which is validated on a refined underwater seafood dataset targeting sea cucumbers, sea urchins and scallops. It achieves 91.2% precision and 87.3% recall, with 3.9% and 4.2% increments over the baseline model, and maintains 2.0 ms inference speed. Detection outputs are structured and stored in a MySQL database, and a novel ChatFlow pipeline is constructed in the Dify platform to support natural language database querying. VLM-powered agents retrieve structured data and generate culturally tailored recipes and dish images automatically. Operational validation verifies that the end-to-end pipeline realizes seamless conversion from seafood images to personalized cross-cultural recommendations. This work provides an integrated solution for intelligent, culturally adaptive gastronomy in food informatics.

INTRODUCTION: Mobile health tools are evolving rapidly across healthcare sectors, particularly for enhancing patient self-care in trauma care. This research focuses on developing a self-care application specifically for chest trauma patients, aiming to improve their well-being. The study will also evaluate the application&#x2019;s usability and effectiveness in supporting patient recovery. METHOD: The study, conducted in 2024, involved three phases: identifying application specifications through literature review and expert interviews; developing the application using programming languages like Laravel Node.js, JavaScript, and MySQL; and evaluating usability and effectiveness with 21 chest trauma patients. The assessment utilized the USE questionnaire for usability and a researcher-developed questionnaire to measure effectiveness before and after application use. RESULTS: The educational elements for patients were divided into five categories: fractures, physical activities, respiratory activities, diet, medication use, and emergencies. Functional requirements included 25 items across eight categories, such as communication between doctor and patient, patient information recording, notifications, discussion forums, care teams, performance dashboards, news updates, and useful information. A web-based application was developed, achieving an 80.04% usability rating from patients. Data analysis revealed a significant improvement (p&#x2009;<&#x2009;0.05) in self-care behaviors&#x2014;specifically in physical activity, diet, respiratory exercises, and medication adherence&#x2014;after using the application. CONCLUSION: Considering the results of this study, it can be said that the self-care application for chest trauma patients has performed well and has succeeded in gaining user satisfaction and proving effective. This health tool can be utilized to enhance the self-care performance of chest trauma patients at a national level. CLINICAL TRIAL NUMBER: Not applicable.

OBJECTIVES: To address the challenge of managing diverse multisource data in dental implantology, this study aimed to construct and validate a patient-centered, time series-based data governance framework. MATERIALS AND METHODS: We established a dental implant time series model and defined a unified dental implantation standard dataset. A MySQL-based governance platform was developed with a user-friendly interface. Usability was evaluated through a within-subject crossover study involving 8 implant dentists performing four representative clinical tasks and comparing the platform against conventional tools, with outcomes measured by task completion time and System Usability Scale (SUS) scores. Additionally, the framework&#x2019;s utility was assessed through its application in research and education. RESULTS: The standard dental implantation dataset contained 715 common data elements for interpreting multisource heterogeneous dental implant data. The dental implant platform allowed users to perform various patient data management tasks, effectively organizing multimodal data along the dental implant time series. Usability testing demonstrated significant efficiency and user satisfaction gains: task completion time decreased by 21.9&#x2013;38.1% across all the scenarios (P&#x2009;<&#x2009;0.05), with mean SUS scores of 76.88&#x2009;&#xb1;&#x2009;4.77, indicating high clinical acceptability. In addition, we explored the research utility of the data in evidence-based epidemiological studies and artificial intelligence applications. CONCLUSION: This study establishes a validated, standardized framework for clinical data governance that significantly improves clinical data management efficiency and user satisfaction, offering a replicable model for specialty-specific medical data governance. CLINICAL RELEVANCE: The platform enhances real-world data accessibility providing high-quality structured data to support clinical decision-making, education, and future AI applications in implant dentistry.

Tirzepatide, the first dual glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptor agonist with rapidly expanding clinical use, requires detailed post-marketing pharmacovigilance to monitor emerging safety signals. This study aims to identify and characterize specific adverse events (AEs) associated with tirzepatide utilizing FDA Adverse Event Reporting System (FAERS). The datasets were cleaned and standardized using Python, a programming language for data processing, and MySQL, a database management system, to ensure accuracy and consistency before analysis. Subsequently, AE signals were detected via four quantitative disproportionality algorithms, sorted and categorized by demographics, gender, and clinical prioritization, with a modified Weibull model developed to analyze AE onset timing. A total of 67,305 cases (75.83% female) and 137,583 adverse events were identified related to tirzepatide. One hundred and forty-four AE signals showed statistically significant signals suggesting a potential association with tirzepatide, with several new including postmenopausal haemorrhage and menstrual disorder (implying regulatory interference on sex hormones), Wernicke's encephalopathy and sleep disorder (malnutrition caused by low intake). Pancreatitis, impaired gastric emptying, dehydration and cholelithiasis carried higher risks with serious clinical outcomes. Sleep disorder, delayed gastric emptying, and medullary thyroid cancer are more common in males; starvation ketoacidosis and incorrect injection site, in females. The median time-to-onset (TTO) was 6.36 days (Interquartile Range (IQR) 0.85-31.2) with the Weibull shape parameter (&#x3b2;) of 0.44, indicating an early failure profile. This study uncovered new risks of tirzepatide, including AEs associated with skin, menstruation, psychiatric and nervous system. Median TTO was corrected to within a week, highlighting the need for early monitoring before clinicians prescribe tirzepatide, and special attention should be given to patients who have pre-existing digestive dysfunction, malnutrition, or a family history of thyroid disease.

Halophilic organisms are among the oldest microorganisms on earth which thrive in extreme saline environments through unique physiological and biochemical mechanisms. Halophiles are classified into extreme, moderate, and mild groups based on their salt tolerance. Proteins play a crucial role in their adaptation by undergoing structural modifications and cellular alterations and allowing them to maintain stability and functionality in high-saline conditions. To support research on halophilic adaptations, we have developed an advanced Halophile Protein Database 2.0 (HProtDB 2.0) which serves as a comprehensive resource for analyzing the physicochemical properties of halophilic proteins. This database provides extensive data on diverse physicochemical properties, including molecular weight, theoretical pI, amino acid composition, atomic composition, instability index, aliphatic index, extinction coefficients, estimated half-life, and the grand average of the hydropathicity index. These properties help researchers understand how halophilic proteins maintain their structure and function by influencing salt-ion interaction, solubility and protein folding. HProtDB 2.0 significantly expands the earlier version by increasing its dataset from 59,897 protein sequences (21 strains) to 777,979 protein sequences (54 strains) with enhanced precision in physicochemical properties. We developed R programs to compute physicochemical properties of halophilic proteins. Additionally, we designed the database using a three-tier web architecture, integrating HTML, CSS, and JavaScript for the front-end, PHP for server-side scripting, and MySQL for data storage. Researchers can access HProtDB 2.0&#xa0;at: http://proteindb2.iari.res.in; http://webapp.cabgrid.res.in/proteindb2.0/. This database will serve as valuable tool for researchers seeking information on the characteristics and features of proteins adapted to salt conditions.

Genomic variant data are useful in detecting and treating antibiotic-resistant bacteria. However, there are no bacterial genomic variant databases that catalogue the variations in the different genes across strains. In this work a Nextflow- and Docker-based end-to-end pipeline, BVbase, that can automate the creation of databases from raw high-throughput sequences has been created to fill this lacuna with Pseudomonas aeruginosa as a case study. Pseudomonas aeruginosa is a Gram-negative adaptable pathogen with multiple antibiotic resistances that causes various types of infections, including respiratory, urinary, and bloodstream infections. The pipeline can take multistrain genomic files, detect missense variants, and save results in a database with the help of Python and SQLite (https://github.com/bic-sastra/BVbase). Using the generated database for P. aeruginosa, a web application interface has been made using Flask and HTML that runs in a server with MySQL backend (https://bic.sastra.edu/pavardb). The web application provides supports for different types of queries to select variants by gene, geographical group, isolation country, antibiotics, and resistance phenotype. This web interface generates results as variant tables, plots, and statistics for the selected data. By enabling interactive visualizations and advanced selection, the platform supports research and clinical use through the exploration of genomic variations associated with antimicrobial resistance.

The Regional Hospital Center of Ziniare records approximately 775 prenatal consultations per year in a context where medical records are primarily paper-based, exposing clinical data to risks of loss and fragmentation. This study presents the design and pilot evaluation of a digital prenatal telemonitoring system integrating clinical data management and analysis. The system was developed using UML modeling and a client-server architecture combining HTML, CSS, JavaScript, CodeIgniter, and MySQL. It supports structured patient registration, prenatal consultation management, automated appointment reminders, and real-time clinical dashboards. A pilot study was conducted over four weeks involving 30 users. The system ensured full digital availability of medical records (0% data loss observed), while the reminder module achieved a delivery success rate exceeding 95%. Appointment attendance increased from an estimated baseline of 65% to approximately 82% during the pilot phase. In addition, access to structured medical records reduced consultation and decision-making time by approximately 15-25%. These results indicate that the proposed system improves data availability, continuity of care, and clinical workflow efficiency. This work contributes to digital health implementation in resource-limited settings by providing a practical and context-adapted telemonitoring solution integrating clinical decision-support indicators.