Patient use of artificial intelligence (AI) chatbots for dermatologic information is increasing, but their performance on psoriasis-related questions across clinically distinct domains remains unclear. We compared ChatGPT (GPT-5.3 Instant), Gemini (Gemini 3 Flash), and Microsoft Copilot using a multidimensional scoring framework. Fifty-four psoriasis-related questions were submitted to each model across diagnostic (n = 12), treatment (n = 12), and patient-question (n = 30) categories. Three board-certified dermatologists independently scored responses for accuracy, evidence consistency, completeness, and clinical safety (maximum score, 8). Interrater agreement was substantial to almost perfect (κ = 0.743 for ChatGPT, 0.830 for Gemini, and 0.844 for Copilot). Overall mean scores differed significantly: 7.36 ± 0.97, 7.77 ± 0.77, and 7.05 ± 1.09, respectively (Friedman P < 0.001). No difference was observed for diagnostic questions (P = 0.37). Gemini outperformed both models in treatment (8.00 ± 0.00) and patient questions (P < 0.001), while ChatGPT and Copilot did not differ in treatment. Differences were driven by completeness and accuracy, not clinical safety or evidence consistency. Gemini also had the highest rate of high-reliability responses (92.6%). All models showed high clinical safety, but Gemini provided the most complete and highest-quality responses. The observation that models may provide accurate yet clinically incomplete responses, particularly for treatment content, emphasizes the need for physician oversight when AI-generated information is used in dermatological practice.
MAPMAKER is one of the most widely used computer software package for constructing genetic linkage maps.However, the PC version, MAPMAKER 3.0 for PC, could not draw the genetic linkage maps that its Macintosh version, MAPMAKER 3.0 for Macintosh,was able to do. Especially in recent years, Macintosh computer is much less popular than PC. Most of the geneticists use PC to analyze their genetic linkage data. So a new computer software to draw the same genetic linkage maps on PC as the MAPMAKER for Macintosh to do on Macintosh has been crying for. Microsoft Excel,one component of Microsoft Office package, is one of the most popular software in laboratory data processing. Microsoft Visual Basic for Applications (VBA) is one of the most powerful functions of Microsoft Excel. Using this program language, we can take creative control of Excel, including genetic linkage map construction, automatic data processing and more. In this paper, a Microsoft Excel macro called MapDraw is constructed to draw genetic linkage maps on PC computer based on given genetic linkage data. Use this software,you can freely construct beautiful genetic linkage map in Excel and freely edit and copy it to Word or other application. This software is just an Excel format file. You can freely copy it from ftp://211.69.140.177 or ftp://brassica.hzau.edu.cn and the source code can be found in Excel's Visual Basic Editor.
 Brain tumors cause high morbidity and mortality and decrease patients' quality of life and autonomy. While their clinical and epidemiological characteristics have been described worldwide, data are scarce in resource-limited areas, where diagnosis and treatment are often difficult.  This study aimed to characterize the clinical and epidemiological profile of patients with primary brain tumors aged 20-65 years admitted to the Neurosurgery Department of the Regional Hospital of the West (Guatemala) between January 2018 and January 2022, and, as exploratory secondary aims, to assess the associations between sex and tumor classification, World Health Organization grade and postoperative complications, and tumor classification and postoperative functional autonomy.  A retrospective, single-center observational study was conducted with descriptive analyses and exploratory inferential testing. The full cohort (n = 28) was analyzed for epidemiological and clinical variables; analyses of discharge status and postoperative functional autonomy (Karnofsky Performance Status (KPS)) were restricted to 23 patients, after excluding five who left against medical advice before hospital discharge. KPS was retrospectively documented at discharge from clinical descriptors in the medical record. Categorical variables are presented as frequencies, n (%), and continuous variables as mean ± standard deviation or median (interquartile range). Missing data were handled case-wise. Analyses were performed using Microsoft Excel 2024 (Microsoft Corporation, Redmond, WA) and Python 3.11 with SciPy (Python Software Foundation, Fredericksburg, VA; Fisher's exact and Mann-Whitney U tests; p < 0.05).  It was found that 57.2% (n = 16) of tumors occurred in patients aged 51-65 years, 53.6% (n = 15) were female, 46.4% (n = 13) were from Quetzaltenango, and 71.4% (n = 20) were Ladino/Mestizo. Meningiomas accounted for 46.4% (n = 13); mortality was 26.1% (n = 6). Exploratory analyses showed nonsignificant trends: higher malignancy in men (risk ratio (RR) = 1.54, 95% confidence interval (CI) = 0.42-5.64; p = 0.670), more postoperative complications with high-grade tumors (RR = 1.68, 95% CI = 0.62-4.58; p = 0.603), and lower postoperative KPS in malignant vs. benign tumors (median 30 vs. 70; Mann-Whitney U = 60.5; p = 0.256).  Primary brain tumors in this cohort were more frequent in female patients, with meningiomas as the most common histopathology and most patients residing in the hospital's department. Unlike studies typically reporting median ages above 70 years, patients here were younger (51-65 years) with a higher mortality rate. Exploratory analyses showed nonsignificant trends consistent with the international literature; given the descriptive, retrospective, single-center design and small sample size, these findings should be interpreted as hypothesis-generating only and do not support causal inference. No prior Guatemalan study reported prevalence by ethnic group, establishing a precedent for future research.
Background The initial recommended treatment at the start of the COVID-19 pandemic in India included isolation, symptomatic treatment, oxygen support, empirical antibiotics, and hydroxychloroquine prophylaxis. However, evolving guidelines and limited evidence on antiviral efficacy highlighted a gap in evidence-based treatment approaches. Recognizing this gap, a study was planned to assess the antiviral impact at our institute. We aimed to study the prescription pattern of antivirals in hospitalized COVID-19 patients over four months and analyse the influence of age, gender, antiviral use, comorbidities, and oxygen requirement on outcomes: clinical improvement and hospital stay duration. Methods This retrospective observational single-centre study included reverse transcription-polymerase chain reaction (RT-PCR)-confirmed COVID-19 patients who were hospitalized and received remdesivir, favipiravir, ivermectin, or oseltamivir. Descriptive statistics were analysed using Microsoft Excel 365 (Microsoft® Corp., Redmond, WA). Multiple linear regression and logistic regression models were used with JASP 0.16.3 software. Results Among 400 patient prescriptions, 5,172 drugs were recorded: 542 repurposed antivirals and 4,630 concomitant drugs. Ivermectin (376, 69.37%) was the most frequently prescribed antiviral, followed by remdesivir (97, 17.9%), favipiravir (59, 10.89%), and oseltamivir (10, 1.85%). Nutritional supplements (1536, 33.1%) were the most common concomitant drug class, with vitamin C being the most prescribed. Logistic regression showed that male gender and oxygen therapy were positively associated with clinical improvement. Linear regression revealed that older age and higher disease severity correlated with longer hospital stays. Antiviral use showed no significant association with either disease improvement or duration of stay. Conclusion Ivermectin and remdesivir were the two most frequently used repurposed antivirals. While male gender and oxygen therapy were linked with clinical improvement, older age and severe disease predicted longer hospitalization. Antivirals themselves did not significantly affect outcomes, underlining the complexity of COVID-19 management.
The aim of the research is to develop a method of registering spinopelvic balance parameters employing shadow moiré topography (SMT) and to use this diagnostic method while clinical observation of adolescents with functional and spine disorders. The subject of the study was a group of adolescents aged 12-17 (n = 50) with the symptoms of mild or moderate scoliosis (according to Cobb). SMT was employed as a clinical diagnostic method. The obtained values were compared with the values provided by the analysis of the standard thoracic and lumbar spine radiographs. The Mann-Whitney U-test was used to evaluate the differences between the values provided by radiography and SMT (due to a small number of subjects). The differences were considered significant at the criterion level α = 0,05, α = 0,01. The data were processed using built-in functions and small self-documenting codes for the Microsoft Office Excel 2013 program. Follow-up data analysis showed no significant differences with the probability of 95% (α = 0,05) in thoracolumbar kyphosis, pelvic incidence, and sagittal vertical axis values, whereas the differences are significant with the probability of 99% (α = 0,01) in the Cobb angle, Lumbar lordosis (LL1), and sacral slope values and with the probability of 95% (α = 0,05) in thoracic kyphosis values. The research found that SMT employed as a diagnostic method in vertebrogenic pathology is reliable and valid and can be employed as a screening, diagnostic, and monitoring tool in the vertebral column state while medical rehabilitation of adolescents with scoliosis. Résumé Contexte:L’objectif de cette recherche est de développer une méthode d’enregistrement des paramètres de l’équilibre spinopelvien en utilisant la topographie moirée par ombre (Shadow Moiré Topography, SMT) et d’utiliser cette méthode diagnostique lors du suivi clinique des adolescents présentant des troubles fonctionnels et des déformations de la colonne vertébrale.Matériels et Méthodes:L’étude a porté sur un groupe d’adolescents âgés de 12 à 17 ans (n = 50) présentant des signes de scoliose légère ou modérée (selon la classification de Cobb). La SMT a été utilisée comme méthode diagnostique clinique. Les valeurs obtenues ont été comparées à celles issues de l’analyse des radiographies standard du rachis thoracique et lombaire. Le test de Mann–Whitney U a été utilisé pour évaluer les différences entre les valeurs obtenues par radiographie et par SMT (en raison du faible nombre de sujets). Les différences ont été considérées comme significatives aux niveaux α = 0,05 et α = 0,01. Les données ont été traitées à l’aide des fonctions intégrées et de petits codes auto-documentés dans le logiciel Microsoft Office Excel 2013.Résultats:L’analyse des données de suivi n’a montré aucune différence significative avec une probabilité de 95 % (α = 0,05) pour les valeurs de cyphose thoraco-lombaire, d’incidence pelvienne et d’axe vertical sagittal. En revanche, des différences significatives ont été observées avec une probabilité de 99 % (α = 0,01) pour l’angle de Cobb, la lordose lombaire (LL1) et les valeurs de pente sacrée, ainsi qu’avec une probabilité de 95 % (α = 0,05) pour les valeurs de cyphose thoracique.Conclusion:Cette recherche a démontré que la SMT, utilisée comme méthode diagnostique dans les pathologies vertébrogènes, est fiable et valide. Elle peut être utilisée comme outil de dépistage, de diagnostic et de suivi de l’état de la colonne vertébrale au cours de la rééducation médicale des adolescents atteints de scoliose.
The expanding volume and increasing complexity of clinical research in East Africa, driven by emerging and re-emerging infectious diseases, has increased the need for strong regulatory monitoring systems. Clinical trials must be monitored to ensure regulatory compliance and participant safety. Despite strengthened regulatory and ethical frameworks in the region, gaps in trial monitoring persist due to a shortage of adequately trained monitors. This paper describes the design, implementation, and outcomes of a regional capacity-building initiative aimed at strengthening clinical trial monitoring capacity across six East African Community partner states. We implemented a multi-country capacity-building project across six East African Community partner states: Burundi, Kenya, Rwanda, South Sudan, Tanzania, and Uganda. We conducted stakeholders' consultations to identify the training needs that informed development of a tailored curriculum on clinical trial monitoring comprised of six modules, and approved by the Training Advisory Committee (TAC). We trained National Research Regulatory Authority (NRRA) personnel, Research Ethics Committee (REC) members, clinical researchers, and clinical trial monitors. The programme was implemented between February 2024 and February 2025, with one group per partner state. Each cohort completed a five-week blended learning programme comprising a two-day in-person workshop, four weeks of self-paced online learning, and a two-day virtual wrap-up session. Pre- and post-training assessments were administered to measure knowledge gain. Data were downloaded as CSV files, cleaned in Microsoft Excel, and exported to STATA 15.0 for analysis. Descriptive analyses were conducted and data summarized. We trained 200 participants, majority 30.5% (n = 61) of whom were from Uganda and 50.5% (n = 101) were female. Overall, mean knowledge scores increased significantly from 61.8% at pre-test to 84.3% at post-test (22.5% point improvement). The proportion of participants achieving the 60% pass mark increased from 37.5% (75/200) at pre-test to 95.6% (191/200) at post-test. This capacity-building project showed a marked improvement in knowledge of clinical trial monitoring among NRRA personnel, REC members, clinical researchers, and clinical trial monitors across the EAC. Further studies should evaluate whether this knowledge gain translates into enhanced clinical trial monitoring and oversight practices.
Due to limited surveying, mapping, and image data, as well as technological constraints, current efforts to restore missing components in traditional Chinese dwellings often result in mismatches with the original appearance. This study employed a mixed-methods approach to evaluate the effectiveness of using mixed reality (MR) to address these challenges. Using an MR system built with Microsoft HoloLens 2 and Trimble Connect, we conducted an on-site experiment with twelve participants focused on column base restoration in the main hall of Yanling House, a typical traditional Chinese dwelling in Jintong Village, Youxi County, Fujian Province. Quantitative questionnaire ratings were utilized to illustrate the effects of MR in this on-site experiment, while qualitative semi-structured interviews provided further elucidation and validation of these effects. The results demonstrate that MR can help participants restore the components in a way that conforms to their original appearance through immersive perceptions, accurate adjustments, and convenient communications, thus providing effective technical support for preserving the unique historical and cultural value of traditional Chinese dwellings.
Both humans and non-human primates are susceptible to Strongyloides fuelleborni, a grossly underappreciated parasitic zoonotic threadworm across the world, in addition to the more widely reported Strongyloides stercoralis. This semi-systematic review sought to assess the global evidence on zoonotic strongyloidiasis in baboons, Africa's most prolific non-human primate, to better understand the zoonotic threat these animals may pose to control strategies and public health goals. Using appropriate keyword terminology, PubMed, Scopus and Web of Science databases were searched for relevant articles, up to December 2025. Articles were then screened using inclusion and exclusion criteria, extracting relevant information for: infection prevalence, baboon species, threadworm species, infection setting and diagnostic methods. Publication content was summarised using Microsoft Excel with statistical analysis on R Studio. From 1588 articles [PubMed (n = 307), Scopus (n = 678) and Web of Science (n = 603)], a total of 44 were summarised. Across the six species of baboons currently recognised, infection prevalence differed significantly (P = 0.02), Papio cynocephalus with highest prevalence [68.9% (IQR 38.8-82.8)] and Papio papio with lowest [6.5% (IQR 2.5-21.8)], noting a total absence of information for Papio kindae. However, over two thirds of articles did not identify threadworm infections to species level. Although not statistically significant, infection prevalence by infection setting followed an ascending order of, research organisations [13.7% (IQR 8.0-37.3)], wild populations [26.0% (IQR 15.9-37.3)] and then zoological organisations [50.0% (IQR 31.8-75.0)]. Infection dynamics (e.g. baboon sex, age) were inadequately reported, moreover detection methods infrequently used molecular methods which hampered any precise incrimination of zoonotic transmission. Our semi-systematic review has revealed several gaps in the global epidemiology of zoonotic strongyloidiasis which may incur real consequences for its future elimination as a public health problem. Above all, we recommend improved threadworm species identification, particularly in population-level discrimination, to better identify transmission risks into humans. Narrowing these knowledge gaps should lead to improved future control strategies for strongyloidiasis globally.
Botswana's economy is highly exposed to floods, estimated to cost an average of $90 million, about 0.56% of the total annual country gross domestic product. Flooding is common in urban catchments, including Botswana's Notwane Catchment. The catchment is characterised by increased population anchored in the capital Gaborone, infrastructure expansion and altered natural drainage. The effects of floods necessitate proactive interventions that consider blended approaches for selecting, assessing and mapping flood conditioning factors (FCFs). This study aimed to demonstrate a triangulated, multicriteria approach for flood susceptibility mapping in the Notwane Catchment. The FCFs were identified through questionnaire surveys, key informant interviews and literature, and these included land-use, rainfall, elevation, flow accumulation, slope and soil type, among others. The analytical hierarchy process (AHP), supported by principal component analysis (PCA), was employed to derive the weights of factors using the Business Performance Management Singapore AHP tool in Microsoft Excel. Weighted factors were integrated into ESRI's ArcMap 10.8 for spatial analysis and mapping. Cumulative rainfall (38.79%) emerged as the most critical factor, while altered land-use (21.35%), with statistically significant clustering of risk around built-up areas and water bodies (GiP-value = 0.062), was the second-largest contributor. Approximately 57.6% (5201.2 km2) of the catchment area is at moderate risk of flooding, covering Greater Gaborone, Mochudi, Ramotswa, and Kanye. Research and promotion of triangulated and blended approaches anchored on geospatial technologies to investigate flood susceptibility will influence targeted and site-specific risk-informed development in urban catchments while addressing Goal 11 of the 2030 Agenda for Sustainable Development.
Atrial fibrillation (AF) is a condition associated with increased risk of stroke. The risk of stroke can be reduced by oral anticoagulation (OAC) medication, but as AF is often asymptomatic, it can go unrecognised. Given the move away from face-to-face consultations, it is important to take advantage of any opportunities for AF detection and onward evaluation for potential OAC prescription. Emergency medical services (EMS) provide face-to-face assessments that can provide the oppportunity to identify incidental AF. However, little is known about how EMS manage patients with incidental detection of AF or what the optimum approach may be. The aim of this service evaluation was therefore to describe current practice within UK EMS for the detection of and response to incidental AF. Interview-delivered surveys were conducted by the lead researcher between June and August 2023 with participants from the large UK ambulance trusts. Participants were emailed directly to ask if they would agree to be interviewed. All interviews were conducted via Microsoft Teams. The survey was developed by the co-authors, refined during the project and included all aspects of the patient pathway. Content analysis was used by the lead researcher to analyse the interviews. There were variations in clinical aspects, such as pulse palpation, and only five trusts had a clear process about managing incidental findings. Methods for information sharing with primary care varied between in- and out-of-hours periods, according to the services that were available locally, leading to heterogeneity of care. An EMS encounter can provide an opportunity to identify incidental AF and instigate ongoing care for modification of stroke risk; however, there is variability in practice across the UK. To ensure that opportunities for stroke risk reduction are maximised, a robust mechanism for clinical information sharing with primary care regarding incidental AF is required.
This study aimed to outline and evaluate three key implementation strategies undertaken by Humber River Health (HRH) nursing leadership to support the sustained implementation of the delirium, dementia & depression Best Practice Guideline (BPG). In 2017, HRH embarked on a high-reliability journey, prioritizing consistent quality and safe care delivery by implementing the Registered Nurses' Association of Ontario (RNAO) Best Practice Guidelines (BPGs). Based on the implementation guidelines, the hospital has also adopted three measures: the use of electronic medical records "DocOpt", embedding the content of delirium into staff training, and constructing health risk blocks with hierarchical early warnings, embedded within our Command Center. From 2019 to 2025, a retrospective longitudinal assessment of hospital inpatients was conducted using process indicators, outcome indicators, and patient satisfaction. Process and outcome metrics were evaluated pre- and post-implementation using Statistical Process Control Charts in Microsoft Excel QI Macros, in which Central Lines and Upper and Lower Lines were calculated. Further, segmented regression was conducted to evaluate key time periods during the implementation of the delirium BPG. Due to the implementation of specific recommendations for delirium, the process indicators of delirium in HRH patients improved from 2019 to 2025, and the incidence of delirium in elderly patients per 1,000 patient care days decreased accordingly, by 1.4 times (accounting for 23.3 %); more than 90 % of patients and their families were satisfied with the hospital. According to the segmented regression analysis, it indicates that from the third quarter of 2019 to the third quarter of 2020, and from the first quarter of 2021 to the first quarter of 2022, there was a transitional period, which marked the period when the "DocOpt" system and health risk blocks were applied. Long-term evaluation of process and outcomes data supported HRH to achieve improved patient delirium outcomes. Future research can rely on the regular, dynamic data-monitoring system to provide more empirical evidence for the localization adaptation, consistent implementation, and continuous quality improvement of BPGs.
Increasing evidence suggests that microbiota plays important roles in the pathogenesis and progression of interstitial lung diseases (ILDs). However, the global research landscape and emerging trends in this field remain insufficiently characterized. This study aimed to systematically characterize the research landscape, evolving hotspots, and future trends in the field of host microbiota and ILDs using bibliometric and visualization approaches, and to further explore the progress of related clinical studies. Publications up to November 8, 2025 were retrieved from the Web of Science Core Collection. Concurrently, clinical trials within the same timeframe were extracted from PubMed to assess advancements in the field. Bibliometric and visual analyses were conducted using VOSviewer, CiteSpace, SCImago Graphica, and Microsoft Excel. A total of 295 publications were included, showing a marked increase in research output since 2012. China and the United States were the leading contributors, with the United States demonstrating higher academic impact and stronger international collaboration. Core institutions and authors were mainly concentrated in North America and Europe. Keyword analysis revealed a clear evolution of research focus, shifting from early exposure-related studies and hypersensitivity pneumonitis to lung microbiome dysbiosis, the gut-lung axis, and metagenomic approaches. Recent hotspots emphasize microbiome-based clinical applications, with increasing attention to host-microbiome interactions and immune regulatory mechanisms. Research on microbiota and ILDs has expanded rapidly and shows increasing interdisciplinary integration. Future studies should enhance international collaboration, clarify underlying mechanisms, and promote clinical translation of microbiome-based biomarkers and personalized therapeutic strategies.
Postoperative nausea and vomiting remain significant concerns in surgical care, impacting patient recovery, satisfaction, and healthcare costs. This study evaluates the efficacy of 40 mg oral aprepitant in reducing postoperative nausea and vomiting incidence and severity compared with placebo. A randomized, double-blind study was conducted on patients undergoing oculoplastic procedures. Patients were assigned to receive either 40 mg oral aprepitant or placebo preoperatively. Postoperative nausea and vomiting severity was assessed postoperatively using the modified Baxter Nausea Scale, with additional data collected on rescue antiemetic and opioid use. Statistical analysis, including Fisher exact test and 95% confidence intervals, was performed using Microsoft Excel. Patients in the aprepitant group reported significantly lower nausea severity scores and a reduced incidence of vomiting compared with the placebo group. Additionally, patients receiving aprepitant required fewer postoperative doses of ondansetron and opioids. The safety profile of aprepitant was consistent with previous studies, with no significant adverse effects reported. Aprepitant demonstrated efficacy in reducing postoperative nausea and vomiting and decreasing reliance on rescue antiemetics and opioids. These findings support its potential integration into standard prophylactic regimens for postoperative nausea and vomiting prevention in oculoplastic surgery.
Microsoft Windows remains the dominant desktop operating system and therefore a frequent focus of digital forensic and incident response investigations. Windows Registry analysis is particularly valuable because it captures persistence mechanisms, execution traces, user activity, device usage, and system configuration changes that are often central to incident reconstruction. Nevertheless, modern investigations are challenged by the scale of Registry data, the fragmentation of evidence across hives and complementary sources, and the need to prioritise investigative actions under time pressure. This paper presents WinRegRL, a hybrid framework that combines a Markov Decision Process (MDP) solved by dynamic programming with bounded Reinforcement Learning (RL) refinement and Rule-based Artificial Intelligence (RB-AI) for automated Windows Registry and timeline-centred forensic analysis. Methodologically, the core planner is a finite-state dynamic-programming solver over an expert-specified model; reinforcement learning enters only as bounded, local tabular refinement for low-support state-action regions, so the framework is positioned as an MDP/dynamic-programming approach with bounded RL rather than as an end-to-end learned agent. The framework models the investigation process as a Markov Decision Process (MDP) with explicitly defined states, actions, transition dynamics, and reward design, and incorporates expert-derived policy graphs to initialise and refine the search strategy. We evaluate the framework on four heterogeneous forensic datasets spanning multiple Windows versions and incident scenarios, and we compare it against analyst-assisted baselines and controlled examiner-led workflows. Under the evaluation protocol adopted in this study, WinRegRL reduced investigation time by up to 68%, increased the number of adjudicated relevant artefacts identified by up to 35%, and achieved high artefact-level precision on the evaluated datasets. Rather than claiming universal superiority, we show that the proposed framework provides a reproducible and explainable decision-support mechanism that improves investigation efficiency while maintaining strong evidential coverage in the tested scenarios. These findings position WinRegRL as a promising decision-support framework for large-scale and time-critical Windows incident response.
Improper disposal of unused and expired medications contributes to environmental contamination and public health risks. Despite awareness efforts, disposal practices remain inconsistent and public education limited. Undergraduate students represent a key population for shaping long-term behaviors and understanding their knowledge, attitude and practice can inform educational strategies relevant to pharmacy education and public health. A cross-sectional survey was conducted using a content-validated questionnaire via Microsoft Forms. A pilot study (n = 39) preceded the main survey (n = 108) among undergraduates from diverse disciplines. Data were analyzed using Chi-square/Fisher's exact tests and Spearman's correlation coefficients in IBM SPSS Statistics. Household waste was the most common disposal method (67.6%) and 56.5% had never used a community pharmacy for safe medication disposal. Higher knowledge scores correlated with confidence in advising others (r = -0.412, p < 0.001) and frequent use of community pharmacies (r = 0.213, p = 0.027). Higher attitude scores correlated with confidence (r = -0.356, p < 0.001) but not disposal frequency (r = 0.090, p = 0.354). Knowledge and attitude scores were positively correlated (r = 0.391, p < 0.001). Knowledge level was associated with academic program, prior disposal education and curriculum content; attitude level with academic program and curriculum content only. Undergraduates demonstrate inconsistent medication disposal practices and variable confidence, influenced by knowledge levels and educational exposure. These findings highlight opportunities for pharmacy education to address gaps in medication disposal knowledge and patient counselling skills. A structured, longitudinal pharmacy curriculum, progressing from foundational awareness to applied practice, patient education and community engagement may prepare future pharmacists to promote safe and sustainable medication disposal behaviors.
Artificial intelligence (AI) chatbots are increasingly used in healthcare to provide health-related information and answer patient questions. However, their reliability in specialized dental fields such as restorative dentistry remains insufficiently evaluated. This study aimed to evaluate and compare the accuracy and consistency of responses generated by five artificial intelligence chatbot systems-ChatGPT-3.5 and ChatGPT-4 (OpenAI), Bing Chat (Microsoft), Gemini (Google), and Claude-Instant (Anthropic)-regarding dental bleaching. Fifteen frequently asked questions about dental bleaching, identified by restorative dentistry specialists, were categorized as undergraduate-or specialist-level questions. All questions were submitted to five artificial intelligence chatbots in both Turkish and English. Each question was asked three times per day over three consecutive days using standardized prompts. Responses were independently evaluated by two experts using a five-point Likert scale, and mean scores were calculated. A three-way ANOVA was conducted to assess the effects of chatbot type, knowledge level, and question language on response accuracy. Inter-rater agreement between evaluators was assessed using Cohen's kappa coefficient. Statistical significance was set at p < 0.05. Chatbot type had a significant effect on response accuracy (p < 0.001, η² = 0.405). ChatGPT-4 showed the highest accuracy, followed by Claude-Instant and GPT-3.5, whereas Gemini and especially Bing Chat demonstrated significantly lower performance (p < 0.001). Question language and knowledge level showed no significant main effects (p > 0.05). Significant interactions were observed between chatbot type and knowledge level and between chatbot type and language (p < 0.001). No significant differences were observed across days or time periods (p > 0.05). The accuracy of chatbot-generated information regarding dental bleaching depends strongly on the specific AI model used. Advanced large language models, particularly ChatGPT-4, generate more accurate and consistent responses than other evaluated systems. AI chatbots should therefore not be considered interchangeable sources of clinical information, and their outputs should be interpreted cautiously and verified with professional guidance. These findings highlight the importance of critically evaluating AI-generated health information and emphasize that chatbot responses should not replace professional clinical consultation.
The teaching of pharmacology includes a fundamental understanding of ligand binding; how it is measured and how it is calculated. We sought to modernize the techniques by which ligand affinity is determined by undergraduate students and introduce them to parameter estimation through curve fitting. We taught a NanoBRET ligand binding assay to student cohorts completing their second year of undergraduate study in Natural Sciences (71 and 61 students in 2024 and 2025 respectively). The aim was to measure the affinity of fluorescent and unlabeled ligands for the β2-adrenoceptor in live cells. Affinities were then calculated from their data using a custom Microsoft Excel spreadsheet. This series of practical classes was well received by students, with most students able to follow the protocol and successfully determine ligand affinities. Furthermore, students recognized the benefit of the practical class for their education, confirming they felt it improved their understanding of how ligand affinity is calculated. We also demonstrated that this protocol could be scaled up to accommodate larger class sizes (class of 367 students studying medicine and veterinary sciences).
Rabies is a fatal acute viral encephalitis that remains a public health problem in Iraq. There is continuous reported transmission nationwide. To describe the epidemiology of reported rabies cases from 1997 to 2024 among humans in Iraq. We collected data on all reported probable cases of rabies among humans for 1997-2024, from the Iraq national rabies surveillance database. We analysed the data using SPSS version 27 and Microsoft Excel 2019, and calculated the incidence rates per 10 million population. A total of 437 rabies cases were reported during the period, with an annual mean of 15.6 ± 6.6 cases. Cases occurred throughout the year, with a relative peak between May and October. The highest number of cases was reported in 1999 (30 cases; 6.9%) and the lowest was in 2015 (6 cases; 1.4%). Most cases occurred among children aged <15 years (60.2%), and males accounted for 82.2% of cases. The highest incidence rates per 10 million population were reported in the central and southern governorates, particularly Babylon (11.18) and Diyala (10.99). Iraq continues to report a high number of rabies cases among humans annually. To achieve the WHO target of eliminating rabies among humans by 2030, there is a need for more effective rabies control measures, including better stray dog population management, expanded animal vaccinations, improved public awareness, and continuous access to rabies vaccines and immunoglobulin.
Composite indices provide an opportunity to measure the reach of multisector strategies as countries progress towards Sustainable Development Goals and the achievement of nutrition, health and other sectoral targets. Composite measures allow for the measurement of nutrition and health intervention coverage across sectors and can be used to benchmark progress, track nutrition goals and assess inequities of multisector nutrition interventions. Of the many composite indices in use to measure health and nutrition intervention coverage and population status as a means of assessing progress in achieving health and nutrition targets, few have been previously documented in the nutrition and health literature, nor have these indicators been described in detail. This scoping review aimed to identify composite coverage indices that capture nutrition and health intervention coverage or status, summarise their estimation methodologies and validation approaches and evaluate their strengths and weaknesses. Scoping review conducted in accordance with the Joanna Briggs Institute Reviewer Manual following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) verification list. PubMed, EMBASE, Scopus, Google Scholar and Global Health registries were searched from 1980 to 3 March 2026. We included studies of any design that described, used or developed a composite measurement of health or nutrition intervention coverage or status. One reviewer screened the titles and abstracts and full text using Covidence and abstracted data from each article using Microsoft Excel using standardised methods. We retrieved and screened a total of 7120 records of which 154 articles were included. We identified a total of 56 unique indices (25 universal healthcare coverage; 14 reproductive, maternal, newborn, child health (RMNCH) coverage; 10 nutritional status, food security or nutrition intervention coverage; 6 health service coverage and 1 combined universal healthcare, health risk factors and nutrition coverage measure). We identified three major formula construction methodologies: normative (n=34), statistical (n=13) and participatory (n=10). Together, the indices employed six different aggregation methods: weighted linear mean (n=28), geometric mean (n=13), linear mean (n=9), random-effects meta-analysis (n=6), summation (n=2) and weighted geometric mean (n=1). More than one third of the indices identified have not been validated in the literature (n=21). Our review identified a significant gap in composite nutrition intervention coverage index availability, methodological frameworks for index design and index validation. There is a need for additional resources for guiding policy and programme actors to develop validated, fit for purpose composite nutrition-specific and nutrition-sensitive coverage indices. We propose that a framework be developed for stakeholders to guide composite index construction for multisectoral nutrition intervention coverage measurement.
Transcranial magnetic stimulation (TMS), as a non-invasive neurostimulation technique, modulates neural activity by applying electromagnetic fields to specific areas of the brain. It is clinically used for several approved indications, including major depressive disorder, obsessive‑compulsive disorder, and migraine with aura, and is under active investigation for other neurological and psychiatric conditions. Accurate stimulation targeting is crucial for the effectiveness of TMS. Existing targeting methods, such as generic brain localization caps and the international 10-20 electroencephalogram (EEG) system, generally provide only rough localization, leading to significant targeting errors. In recent years, significant progress has been made in the application of mixed reality (MR) technology in medicine, particularly in surgical navigation, offering new ideas and possibilities for developing a simple, low-cost, and efficient TMS navigation system. This study proposes, for the first time, a portable MR navigation system for non-invasive neural modulation target localization. The aim is to evaluate its localization accuracy and operational efficiency in TMS through preclinical validation. This system seeks to provide a simple and high-precision localization solution for other non-invasive technologies, with the goal of improving localization accuracy and simplifying the operational workflow in clinical applications. The system is based on Microsoft HoloLens 2 and features three specifically designed interaction tools. Five different types of simulation head models were selected, and ten target points were set on each head model. CT scanning was used to obtain imaging data for each head model. Three researchers used the system to perform target localization and repeated the verification process by adjusting the head model posture (from standing to lying) to assess localization accuracy and efficiency. The validation conducted by the three researchers showed the following results: In the standing position of the simulated head model, the measurement errors were 2.4 (IQR: 1.4-2.7) mm, 2.3 (IQR: 1.7-2.7) mm, and 2.6 (IQR: 1.9-3.0) mm, respectively. In the lying position of the simulated head model, the measurement errors were 1.9 (IQR: 1.6-2.4) mm, 2.0 (IQR: 1.4-3.0) mm, and 2.5 (IQR: 1.9-2.9) mm, respectively. There was a significant difference between researchers (p < 0.05), but no significant difference within the same researcher (p > 0.05). The TMS-Guide, based on mixed reality technology, is a portable and simple navigation solution that provides higher localization accuracy than traditional manual targeting. It shows promising potential for broader applications in non-invasive neural modulation and brain-computer interface fields.