Appropriate statistical test selection is essential for producing valid and reproducible findings in healthcare research. However, many investigators encounter difficulties when determining the most suitable statistical methods for their study design and dataset. Recently, artificial intelligence-based large language models (LLMs) have emerged as potential tools capable of assisting researchers with methodological decisions, including statistical analysis. The objective of this study was to evaluate whether artificial intelligence-based LLMs can accurately and reliably recommend appropriate statistical tests for healthcare research studies. Four LLM platforms (ChatGPT (OpenAI, San Francisco, California, USA), Google Gemini (Google LLC, Mountain View, California, USA), Microsoft Copilot (Microsoft Corporation, Redmond, Washington, USA), and Grok (xAI, Palo Alto, California, USA)) and two traditional search engines (Google (Google LLC, Mountain View, California, USA) and Bing (Microsoft Corporation, Redmond, Washington, USA)) were evaluated. Forty published research articles were selected across four common study designs: systematic reviews, randomized controlled trials, cohort studies, and case-control studies (10 articles per category). Each article's primary objective and statistical analysis were used to generate a standardized prompt describing the study scenario. These prompts were submitted to each LLM and search engine. Model responses were recorded and compared with the statistical tests used in the original studies. Accuracy was defined as agreement between the model's recommendation and the statistical test used in the published article. LLMs demonstrated moderate-to-high accuracy in recommending appropriate statistical tests. Grok and Microsoft Copilot achieved the highest accuracy (34/40 correct recommendations), followed by Google Gemini (32/40) and ChatGPT (30/40). In contrast, traditional search engines (Google and Bing) did not provide statistical recommendations that matched the statistical tests used in the selected studies. LLMs demonstrated promising capability in identifying appropriate statistical tests for healthcare research scenarios and outperformed traditional search engines in this task. While these findings suggest that LLMs may serve as useful decision-support tools for researchers, their recommendations should be interpreted cautiously and verified by individuals with statistical expertise. Continued evaluation of AI-based tools will be necessary to ensure their responsible integration into scientific research workflows.
Treatment goal limitations, such as "do not resuscitate", "do not escalate" and "comfort terminal care" are frequent in acute hospital care for patients with advanced illness, multimorbidity or impending death. Their ethical defensibility depends on transparent documentation of medical indications, proportionality and, where relevant, patient will. To examine whether documentation made medical rationale, patient will, advance directives, relatives' involvement or ethics consultation visible. We retrospectively analyzed documentation in an Austrian internal medicine department with intensive care over 12 months. Documents were identified in the hospital information system and descriptively analyzed. Deceased patients with documented limitations underwent manual review. Data were anonymized and analyzed using Microsoft Excel for Microsoft 365 (Microsoft Corporation, Redmond, WA, USA) and IBM SPSS Statistics, version 29 (IBM Corp., Armonk, NY, USA). Across 3998 inpatient stays involving 1829 patients, 277 patients had a documented treatment goal limitation (15.1%). Among 174 deceased patients, 111 had a documented limitation at death (63.8%). The most frequent order was combined do not resuscitate/do not escalate (75/111; 67.6%), followed by isolated do not resuscitate (23/111; 20.7%). Complete records were available for 101 cases; in 10 the rationale was not clearly traceable. Among the 101 manually reviewed cases, an explicit or presumed patient will was documented in 7 cases (6.9%), involvement of relatives in 13 cases (12.9%) and ethics consultation in 1 case (1.0%); medical rationale or physician decision was documented in 90 of 91 cases with traceable rationale (98.9%). Treatment goal limitations were common and usually medically justified in the records. The findings do not imply inappropriate decisions or lack of communication but show limited reconstruction of patient-centered reasoning. Documentation should make clearer how benefit, care, respect for autonomy and justice informed treatment limitation.
Aim: To analyze the development and implementation of the new framework for assessing the functional status of disability in Ukraine, and to evaluate the results of the first year of the new system's operation. Materials and Methods: Sociological method was used. The Ministry of Health has published an analytical report on the results of the first year of work by the expert teams assessing a person's daily functioning from January 1, 2025. Before the reform, the system was based on 328 commissions and 1267 doctors. In contrast, after the reform, more than 7000 physicians were involved in all regions of Ukraine. More than 1,300 expert teams have been formed, making more than 554000 decisions in a yearStatistical analysis included descriptive statistics of indicators. Statistical processing and graphical visualisation of the data were performed using Microsoft Excel (Microsoft Excel for Microsoft 365 MSO, version 2603, build 16.0.19822.20086, 64-bit). Results: A new system focused on assessing functioning has been introduced into clinical practice. A comprehensive legal framework was developed, and the Center for Assessment of the Functional State of a Person was established in 2025. The system includes over 1300 expert teams and approximately 7000 doctors, operates nationwide, and is highly digitized via the Diia platform. Initial results show increased transparency, accessibility, and user satisfaction. Conclusions: The reform aligns with international standards and represents a systemic step toward rights-based disability assessment. However, further improvements are needed in regulatory support, institutional capacity, digital infrastructure, and consistent law enforcement.
Acute mountain sickness (AMS) is a common altitude-related condition affecting individuals ascending to high elevations, presenting with headache, nausea, fatigue, and dizziness. Although several pharmacological agents have been proposed for AMS prophylaxis, their comparative effectiveness remains insufficiently established. A systematic review and network meta-analysis were conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Major electronic databases were searched for randomized controlled trials (RCTs) assessing pharmacological interventions for AMS prophylaxis. Data from 46 eligible studies were extracted using Microsoft Excel (Microsoft Corp., Redmond, WA, USA) and analyzed with R software (R Foundation for Statistical Computing, Vienna, Austria). The primary outcome was AMS incidence. Network geometry was mapped using network plots; treatment effects were calculated as odds ratios (ORs) with 95% confidence intervals (CIs); and interventions were ranked using surface under the cumulative ranking (SUCRA) probabilities. Risk of bias was assessed, and certainty of evidence was determined by CINeMA (Confidence in Network Meta-Analysis). The analysis incorporated 46 studies evaluating multiple pharmacological agents. SUCRA rankings identified inhaled budesonide as the most efficacious agent for AMS prevention, followed by acetazolamide and theophylline. Most studies demonstrated low risk of bias. Acetazolamide exhibited narrower CIs, reflecting a more robust and reliable evidence base. CINeMA showed overall moderate certainty of evidence. Inhaled budesonide had the highest probability of being the most effective pharmacological agent for AMS prevention, with acetazolamide and theophylline also demonstrating substantial efficacy. Acetazolamide remains the best-evidenced option. Additional high-quality RCTs are required to consolidate treatment hierarchies and evaluate agents with limited current data.
Large language models (LLMs) show promise for text-based pathology tasks, yet most reported applications remain experimental, lack formal clinical validation, or operate outside secure, health system-approved environments. We developed and clinically validated a rule-guided, agent-based LLM that assists gastrointestinal (GI) biopsy reporting by automating report structuring while preserving full diagnostic authority with the pathologist. The AI agent (Microsoft 365 Copilot) ran within an enterprise-approved, HIPAA-compliant Microsoft 365 environment, configured with a fixed rule-based system configuration prompt and a quick-text knowledge base. In a prospective validation, 94 GI biopsy cases were evaluated by subspecialty GI pathologists using specimen container labels extracted from the laboratory information system and pathologist-entered shorthand diagnoses. Agent outputs were reviewed for formatting accuracy, organ and procedure identification, shorthand expansion fidelity, blank diagnosis enforcement, and diagnostic safety. The agent preserved specimen part structure and correctly identified organ, sub-organ, and procedure context in 100% of cases; shorthand expansion was accurate in all applicable cases. Minor formatting deviations occurred in 8 cases (8.5%) without affecting diagnostic meaning. Two cases (2%) showed minor diagnostic misinterpretation, in which descriptive container-label terms (e.g., "ulcer," "erosion") were incorporated into diagnostic text; no hallucinated diagnoses were identified. Repeatability testing on cases enriched for descriptive labels showed 81% identical outputs across 105 runs (19% variability), with non-reproducible semantic leakage in 3% of runs. A comparative time study showed faster AI-assisted reporting (mean 39 vs 72 seconds for speech-to-text and 76 seconds for manual typing; ∼33-37 second reductions, p < 0.05), measured across the full workflow through sign-out, with lower variability. By restricting this end-to-end, production-embedded agent to rule-guided structuring, formatting, and controlled shorthand expansion while prohibiting diagnostic inference, the system achieved high efficiency, consistency, and seamless workflow integration on real GI biopsy cases. Low-frequency, stochastic errors and minor variability remain inherent to LLMs despite strict constraints; although infrequent, they indicate such systems are best suited for non-diagnostic, clerical augmentation rather than autonomous use. All output therefore requires pathologist careful review before sign-out. These findings support constrained, agent-based LLMs to safely enhance reporting efficiency while preserving diagnostic responsibility and human oversight.
Artificial intelligence (AI) chatbots or large language models (LLMs) are adept at generating language, but their increasing use in the healthcare field, including endodontics, raises concerns about their accuracy. The potential of LLMs to assist clinicians in their decision-making processes regarding vital pulp therapy (VPT) is worth exploring. This study aims to evaluate and compare the responses provided by OpenAI GPT-5.1 Instant, DeepSeek-R1, Claude, Google Gemini, Comet, and Perplexity to clinically relevant questions related to VPT according to the guidelines set by the American Association of Endodontists, European Society of Endodontics, and Indian Endodontic Society. Twenty-three open-ended questions covering various aspects of VPT were developed and presented to OpenAI GPT-5.1 Instant, DeepSeek-R1, Claude, Google Gemini, Comet, and Perplexity. Two experienced endodontists, who were blinded to the different chatbots, evaluated the answers on a 3-point Likert scale. To assess the reproducibility of these answers, the same questions were presented again after 1 month and subsequently saved in a separate Microsoft Word file. The findings were recorded in an Microsoft Excel Sheet, and then statistical analysis was performed. All the LLMs were able to answering all the questions on VPT with almost similar reproducibility across two different intervals. Most tested LLMs, regardless of whether they are free or subscription-based, demonstrated high accuracy and reproducibility when evaluated on guidelines-based questions related to VPT.
Despite extensive evidence regarding the significant impact of the COVID-19 pandemic on population health in Iran, its effects at the sub-national level and comparisons across different geographic regions have garnered less attention. This study aimed to estimate key disease burden indicators, including incidence rate, mortality rate, case fatality rate, and disability-adjusted life years, across Iranian provinces during 2020 and 2021. We obtained and analyzed hospital billing records of beneficiaries from the Iran Health Insurance Organization (IHIO) who were admitted for COVID-19 across Iran from the onset of the pandemic until December 2021. We identified cases using the WHO-recommended ICD-10 codes U07.1 and U07.2. In addition to reporting provincial-level rates of incidence, mortality, and case fatality rates, we also assessed disease burden by aggregating Years of Life Lost and Years Lived with Disability. Data management and analysis were conducted using Microsoft Excel Office 19 (Microsoft Corporation). Substantial interprovincial disparities were observed. Semnan (3,331) and Yazd (3,171) recorded the highest incidence rates per 100,000 people, whereas Qom (224) and Alborz (212) reported the highest death rates. In 2020, the highest case fatality rate (18.59%) was recorded in Razavi Khorasan province, while in 2021, the peak rate (12.52%) was observed in Qom province, both rates exceeding the national average. The burden of disease peaked in Qom (4,287) and the central provinces, while the lowest burden was observed in Kohgiluyeh and Boyer-Ahmad (1,042). This study indicates geographic, age, and sex-based disparities in the burden of COVID-19 in Iran. Therefore, future pandemic preparedness policies must account for regional differences and allocate resources equitably based on the needs of various population subgroups. Further research on health inequalities and comprehensive data on social determinants of health is essential.This provides stronger evidence for policymakers to address disparities in COVID-19 case fatality and mortality rates.
To explore gender disparity in the development of clinical practice guidelines (CPGs) in Peru. A descriptive and observational study was conducted. An electronic search for Peruvian CPGs published from 2015 until July 30, 2024, was performed. Microsoft Excel was used for data collection and RStudio for statistical analysis. 177 CPGs published between 2015 and 2024 were analyzed that reported information related to the CPG development group (CDG). In general, the CDGs included 1527 men (58.8%) and 1070 women (41.2%) among clinical experts and methodologists. Men predominated as clinical experts, especially in specialties such as internal medicine and surgery; while women predominated as methodologists. The specialty with the highest male predominance was internal medicine (61.8%). The study revealed lower female participation in the development of CPGs in Peru, as well as greater gender disparity in participation in the surgical field and in public institutions. The low participation of women in leadership positions influences this gap. Strategies are required to improve gender equity. Explorar la disparidad de género en la elaboración de guías de práctica clínica (GPC) en Perú. Se realizó un estudio descriptivo y observacional. Se realizó una búsqueda electrónica de las GPC peruanas publicadas desde el 2015 hasta el 30 de julio del 2024. Se empleó Microsoft Excel para la recolección de datos y RStudio para el análisis estadístico. Se analizaron 177 GPC publicadas entre el 2015 y el 2024 que reportaban información relacionada al grupo elaborador de la GPC (GEG). En general, los GEG incluyeron a 1527 hombres (58,8%) y 1070 mujeres (41,2%) entre expertos clínicos y metodólogos. Los hombres predominaron como expertos clínicos, sobre todo en especialidades como medicina interna y cirugía; mientras que las mujeres predominaron como metodólogas. La especialidad con mayor predominio masculino fue medicina interna (61,8%). El estudio reveló una menor participación femenina en el desa-rrollo de las GPC en Perú, así como una mayor disparidad de género en la participación en el ámbito quirúrgico y en instituciones públicas. La poca participación de mujeres en puestos de liderazgo influye en esta brecha. Se requieren estrategias para mejorar la equidad de género.
Idiopathic granulomatous mastitis (IGM), also known as granulomatous lobular mastitis, is a rare, benign, chronic inflammatory breast disease that primarily affects women of reproductive age and frequently mimics breast carcinoma clinically and radiologically, making diagnosis challenging. Its aetiology remains poorly understood, and treatment is not standardised. This systematic narrative review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and included English-language studies published between January 2009 and March 2026 identified through PubMed, Ovid MEDLINE, Scopus, Google Scholar, and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Studies addressing the aetiology, clinical features, diagnosis, management, or outcomes of IGM were eligible, and data were extracted using a standardised Microsoft Excel form (Microsoft Corp., Redmond, WA, USA). A total of 271 studies were included, with publication trends demonstrating increasing research activity over time and the highest volume of studies published between 2023 and 2025. Most studies were retrospective observational studies (29.7%; n = 81) or case reports (22.3%; n = 61), while prospective studies (8.8%; n = 24) and interventional trials (4.8%; n = 13) were limited. Thematic analysis showed that treatment and therapeutics were the most frequently studied areas (31%; n = 120), followed by diagnosis and diagnostic methods (25%; n = 95). Recurring findings included diagnostic uncertainty, a lack of standardised treatment protocols, variable recurrence risk, and inconsistent predictors of treatment escalation, while common limitations included small sample sizes, retrospective study designs, single-centre data, and methodological heterogeneity. Overall, IGM remains a diagnostically difficult condition with uncertain aetiology and heterogeneous management; despite growing research interest, the overall level of evidence remains low, highlighting the need for multicentre prospective studies and standardised diagnostic and treatment guidelines to improve evidence-based care and patient outcomes.
Severe asthma in children is challenging to manage due to high exacerbation rates and significant impacts on quality of life. Despite corticosteroids and bronchodilators, many patients remain symptomatic, highlighting the need for alternative treatments. Biologic therapies, such as omalizumab and mepolizumab, offer potential to reduce exacerbations and oral corticosteroid use. This study aimed to evaluate the efficacy of novel biologic therapies in managing severe pediatric asthma. A systematic review was conducted following the PRISMA protocol and registered on PROSPERO (CRD42025634956). Studies published between January 2019 and December 2024 were included. Eligible articles were randomized clinical trials, cohort, and case-control studies involving biologic therapies, excluding those with adults, animals, or unrelated topics. Data extraction was performed in Microsoft Excel, and bias was assessed using the Newcastle-Ottawa tool and the Jadad scale. Most trials analyzed omalizumab, with only one evaluating mepolizumab. These therapies significantly reduced asthma exacerbations, with effects sustained up to 6 years. Reductions in hospitalizations, PICU admissions, and ED visits were noted. Most studies used manufacturer-recommended doses, while some adjusted doses based on weight and/or IgE levels. Pulmonary function improved in seven studies (increased FEV1), while three showed no significant changes. A reduction in inhaled and oral corticosteroid use, FeNO, and eosinophil levels was observed, suggesting reduced airway inflammation. Most patients tolerated therapies well, with mild adverse events reported. Biologic therapies, particularly omalizumab, show promising clinical benefits for managing severe pediatric asthma. Further studies are needed to assess their long-term safety and effectiveness.
Timely and highly accurate diagnoses by physicians play a crucial role in improving the quality and effectiveness of patient treatment outcomes. Currently, the use of artificial intelligence capabilities in this area has garnered the attention of many health science researchers. Therefore, the main goal of this preliminary study was to compare the text-based diagnostic reasoning performance of emergency medicine physicians and large language models in definitive and differential diagnoses using standardized clinical vignettes. This descriptive comparative study evaluated the diagnostic accuracy of 10 emergency medicine physicians and 4 large language models (LLMs)-ChatGPT (GPT-5.2), Gemini 3, Microsoft Copilot (GPT-4), and Claude Opus 4.1-using 10 standardized clinical vignettes. All LLMs were accessed via official web interfaces. Clinical vignettes were developed from emergency department presentations, validated by an expert panel, and presented as text-only inputs to all evaluators. Diagnostic accuracy was assessed using a standardized scoring protocol: definitive diagnoses required an exact match with expert-derived reference standards, while differential diagnoses required ≥ 3 matches. Overall diagnostic accuracy was the primary outcome. Data were analyzed using Pearson's Chi-square test, McNemar's test, and Generalized Estimating Equation (GEE) logistic regression with Bonferroni correction (SPSS version 28). A total of 280 diagnostic evaluations (10 clinical cases assessed by 14 evaluators across 2 diagnosis types) were analyzed. Overall diagnostic accuracy was 61.79%. AI models demonstrated significantly higher overall accuracy (73.75%) compared to emergency medicine physicians (57.00%, p = 0.014). Across all evaluators, definitive diagnoses were more accurate than differential diagnoses (70.71% vs. 52.86%). Generalized Estimating Equation (GEE) analysis revealed a significant interaction between evaluator group and diagnosis type (p = 0.036). Specifically, physicians experienced a significant decline in accuracy when providing differential diagnoses compared to definitive diagnoses (45.0% vs. 69.0%, p = 0.003; remained significant after Bonferroni correction). In contrast, AI models maintained consistently high accuracy across both diagnosis types, with no significant difference between definitive (75.0%) and differential (72.5%) diagnoses (p = 1.000). Large language models outperformed emergency medicine physicians in overall diagnostic accuracy and demonstrated superior consistency across different types of diagnostic tasks. While human physicians struggled significantly with differential diagnoses, AI models maintained high and stable performance regardless of the diagnostic complexity. These findings indicate that AI possesses robust pattern-recognition and reasoning capabilities, suggesting it could serve as a highly reliable clinical decision support tool, particularly in complex scenarios requiring differential diagnostic reasoning. Due to study limitations, such as the small number of clinical scenarios and assessors, these findings should be interpreted with significant caution.
Antimicrobial resistance (AMR) is a global health challenge. Clinical trials are essential for evaluating novel and alternative therapeutic strategies. However, conducting randomized clinical trials (RCTs) in infectious diseases faces regulatory, logistical, and financial barriers, as well as challenges in patient recruitment. Collaborative research networks have demonstrated their effectiveness in overcoming these limitations. This study evaluates the capacity of Spanish medical centers to participate in RCTs on infectious diseases and AMR. A structured feasibility survey was developed to assess infrastructure, research experience, patient recruitment potential, and willingness to collaborate in Spanish hospitals. Responses were collected using the REDCap platform and analyzed using Microsoft Excel, with results expressed as percentages. Of 90 centers contacted, 65 centers from 17 autonomous communities responded. The majority (78.5%) had dedicated infectious disease departments or units, and 36.9% reported over 1000 annual hospital admissions for infectious diseases. Research experience was strongest in antimicrobial-resistant Gram-negative (84.6%) and Gram-positive bacteria (78.5%). Phase II-IV RCTs were the most conducted trials, predominantly funded by private sources. While 75.4% of centers reported available human resources for research, specialized roles such as project managers (18.5%) and monitors (12.3%) were less common. Notably, 90% of centers expressed willingness to participate in a collaborative network for conducting unfunded RCTs. The results highlight Spain's strong research infrastructure and expertise in infectious diseases, providing a solid foundation for establishing a national collaborative clinical trials network. However, variations in specialized research personnel and the low frequency of academic trials indicate areas for improvement.
This study aimed to evaluate the effectiveness of virtual reality (VR) and binaural noise as distraction techniques for managing dental anxiety in pediatric patients. Forty-four healthy children aged 5-8 years, with mild-to-moderate anxiety (Frankle's rating 2 and 3), participated. Each child was exposed to three different distraction methods on separate visits: (1) control (no aid), (2) binaural noise, and (3) VR. Anxiety levels were measured using Venham's picture test and pulse oximetry before and after local anesthesia (LA). The values were recorded in Microsoft Excel sheets, and the data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 27 software. Analysis of the data between groups was carried out using repeated measures analysis of variance (RANOVA) to analyze the effects of intervention, Chi-square test, and paired t-test for differences between groups. Both VR and binaural noise significantly reduced anxiety compared to the control group. The VR group showed the most pronounced reduction in pulse rate and Visual Analog Scale (VAS) scores, indicating lower anxiety and stress. The binaural noise group showed moderate anxiety reduction, whereas the control group experienced a significant increase in anxiety post-LA. Statistical analysis confirmed VR's superior effectiveness in reducing anxiety. VR was the most effective in reducing dental anxiety, likely due to its immersive, multisensory experience. Binaural noise also reduced anxiety but to a lesser extent. The study suggests that nonpharmacological techniques, particularly VR, can significantly improve pediatric dental care by reducing anxiety and enhancing the treatment experience for young patients.
Infections, especially pneumonia, are among the significant causes of morbidity and mortality in transplant recipients. Pneumonia leads to a complex clinical circumstance, related not only to the infection itself but also to underlying immunosuppression, concomitant diseases, and organ dysfunction. Here, we evaluated the effect of the immunonutritional /inflammatory biomarker known as the HALP score (calculated from hemoglobin, albumin, lymphocytes, and platelets ) on prognosis in patients who developed pneumonia after kidney transplant. We retrospectively reviewed files of kidney transplant recipients treated at our hospital's chest diseases outpatient clinic between April 2020 and April 2025 to discover patients who had been diagnosed with pneumonia on thoracic computed tomography scans. We exported data (age, sex, laboratory tests at diagnosis, length of hospital stay, and mortality rates ) to a database spreadsheet file (Excel, Microsoft ) and compared HALP scores of patients who died versus survived. We analyzed disease severity according to the Pneumonia Severity Index and the CURB -65 (calculated from confusion, uremia, respiratory rate, blood pressure, and age ≥65 years ) scores. Among 178 included patients (100 female, 78 male ), 148 (83.1 %) survived and 30 (16.9 % ) died. Patients who died had HALP scores lower than patients who survived. Patients with higher Pneumonia Severity Index scores had significantly lower HALP scores. The HALP score is a biomarker based on easily calculable, low -cost, and accessible parameters and may be a potential tool to evaluate prognosis in the general patient population. Prospective and multicenter studies with larger sample groups are needed to better evaluate the role of the HALP score for prognosis determination related to infections in kidney transplant recipients.
Mycosis fungoides (MF) is the most common type of cutaneous T-cell lymphoma. MF is typically managed with narrowband ultraviolet B (NB-UVB) or psoralen plus ultraviolet A (PUVA) phototherapy. However, comparative data on their efficacy remain limited. To evaluate the treatment outcomes of NB-UVB and PUVA in MF and to identify clinical and demographic predictors of response. This was a retrospective cohort study of 33 patients with biopsy-proven MF treated with NB-UVB or PUVA between 2007 and 2024 in the dermatology department at the Sandwell and West Birmingham Hospitals National Health Service Trust. Statistical analysis was performed using Microsoft Excel version 2023 and R version 2023.03.1 + 446. Of the 33 patients (median age 58 years), 26 (79%) received NB-UVB, 5 (15%) PUVA and 2 (6%) both. Complete remission was achieved in 15 cases (45%) and partial remission in 15 (45%), with 3 (9%) showing no response. Patients with hypopigmented MF had significantly better outcomes than those with hyperpigmented MF (P = 0.02). Advanced stage at treatment initiation was associated with poorer outcomes (P = 0.009). No significant difference was observed in response by sex, age or type of phototherapy. Phototherapy was well tolerated, with adverse effects in only three patients (9%). NB-UVB and PUVA are effective and well tolerated for the treatment of MF. Hypopigmented variants and -early-disease stage predicted better outcomes.
Acute pancreatitis is a common abdominal pathology with significant morbidity and mortality in select patients. Currently, prediction of outcome and prognosis in patients with acute pancreatitis is based on a variety of indices, scores, and criteria calculated from biochemical, clinical, and imaging parameters, but all currently available predictors of outcome have limitations. Analytic morphomics, a quantitative technique that assesses body composition from cross-sectional imaging, may offer a novel approach to outcome prediction. To evaluate whether analytic morphomics-derived parameters can predict recurrence and mortality after an index episode of acute pancreatitis. Following ethical approval, a retrospective single-centre study was conducted at Cork University Hospital. Adult patients presenting with acute pancreatitis between January 2012 and December 2013 were included and followed for 10 years. Patients with pancreatic malignancy, equivocal diagnoses, or age < 18 years were excluded. Cases of acute pancreatitis were classified as acute interstitial oedematous pancreatitis or necrotising pancreatitis. Demographic, biochemical, and clinical data were collected, including the modified Glasgow Imrie severity score. Computed tomography examinations were analysed using CoreSlicer for semi-automated segmentation of skeletal muscle and adipose tissue at the L3 vertebral level. Statistical analysis was performed using Microsoft Excel and Jamovi, including the Mann-Whitney U test, Pearson correlation, and logistic regression. Seventy-two patients were included with a median modified Glasgow Imrie severity score of 2. Wall muscle (WM) density correlated inversely with disease severity [WM density (Hounsfield units): r = -0.480, P < 0.001; psoas muscle density (Hounsfield units): r = -0.465, P < 0.001]. Higher WM density and greater subcutaneous fat (SCF) area were associated with reduced 30-day mortality (mean difference 14.4 HU, P = 0.003; 65.3 cm2, P = 0.033, respectively) and improved 10-year survival. On multivariate analysis, lower WM density and lower SCF area independently predicted short- and long-term mortality, while no morphomic variables were independently associated with recurrence. Skeletal muscle quality and SCF are protective factors in acute pancreatitis. Integration of morphomics with existing severity scores may enhance prognostication. Larger studies are needed to validate these findings.
In recent years, a considerable body of research has increasingly underscored the critical roles that protein Post-Translational Modifications (PTMs) play in the pathogenesis of Alzheimer's Disease (AD). However, a comprehensive bibliometric analysis of this field is still lacking. This study aims to systematically map research trends and hotspots and to identify promising directions for future work. The data in this study were extracted from the Web of Science Core Collection (WOSCC) and visualized using CiteSpace, VOSviewer, R-bibliometrix, and Microsoft Excel 2016 to analyze bibliometric indicators including countries, institutions, authors, journals, citations, production categories, and keywords. A collection of 1,170 articles was retrieved, spanning the publication period from January 1, 1990, to December 31, 2024. The top three countries in terms of publications were the United States, China, and Germany. The most productive institution was the University of California System in the United States, contributing 57 articles. The leading authors identified were Mitkevich Vladimir, Perry George, and Makarov Alexander A. The Journal of Alzheimer's Disease was the top-ranked journal in terms of published papers. The most frequently cited article was "The NLRP3 Inflammasome: An Overview of Mechanisms of Activation and Regulation," published in the International Journal of Molecular Sciences. Finally, the most prolific research category was neuroscience, with 432 papers published. High-frequency keywords included Alzheimer's disease, phosphorylation, tau, and neurodegeneration. The study's findings suggest that PTM research in AD continues to revolve around the core pathological hallmarks represented by Aβ and tau protein. At the same time, some studies have reported aberrant modifications of α-synuclein and its potential role in AD. By systematically cataloging diverse PTM types and the molecular mechanisms involving Aβ and tau throughout AD progression, this analysis paves the way for a reassessment of AD pathogenesis from a "modification-function-pathology" perspective and provides a basis for identifying potential PTM-related targets and intervention strategies. This bibliometric analysis highlights the growing scholarly attention devoted to the relationship between PTMs and AD. The significant contributions and emerging trends emphasize the pivotal role of PTMs in the pathogenesis of AD, which may guide future biomarker discovery.
Background and Objectives: Musculoskeletal ultrasound (MSUS) is increasingly used to assess structural changes in knee osteoarthritis (KOA), although measurement reproducibility may vary with examination protocol and operator experience. This pilot study aimed to evaluate intra- and inter-observer reliability for assessing cartilage thickness, osteophyte dimensions and meniscal displacement in supine and weight-bearing conditions in patients with KOA. Material and Methods: This prospective reliability study included patients with KOA stages 2-3 on the Kellgren-Lawrence (K-L) scale. Ultrasound (US) evaluation of cartilage thickness and osteophytes was performed in the supine position, while medial and lateral meniscal extrusion was additionally assessed in bipedal and single-leg conditions using a standardized protocol. Intra- and inter-reproducibility were evaluated through the intraclass correlation coefficient (ICC), agreement analysis and kappa statistics. The analysis was performed using Microsoft Excel (version 2019) and IBM SPSS version 26 software. Results: Both inter-reliability and intra-reliability were evaluated, with intra-rater agreement being constantly higher than inter-rater agreement. From the inter-rater reliability, ICC values ranged from 0.48 to 0.88 for quantitative ultrasound measurements, whereas osteophyte assessment showed Cohen's k values ranging from 0.71 to 0.86, indicating substantial to almost perfect agreement. Both evaluators identified a higher number of knees with medial meniscal extrusion > 3 mm under weight-bearing conditions than in the supine position. Conclusions: Standardized US evaluation demonstrated at least good reproducibility for structural abnormalities in KOA. Medial and lateral meniscal displacement increased under loading conditions, highlighting the added value of dynamic weight-bearing assessment. However, the clinical significance of load-dependent meniscal extrusion requires further investigation.
Sarcoptic mange is a parasitic contagious disease that can lead to significant declines in wildlife populations. To identify the most used methods for diagnosing mange in free-ranging wildlife species, a systematic review was conducted across multiple databases: Scopus, PubMed, Web of Science, ProQuest Central and CABI. Citation screening was performed using the web-based platform, Covidence. A customized data extraction form, developed by the authors, was used to collect relevant information. This information was later exported and analyzed using Microsoft Excel. The objectives of this systematic review were to identify the most used detection methods for sarcoptic mange in wildlife globally over the past 30 years, classify them into invasive and non-invasive categories and provide an updated overview of related adjunct techniques and the broader scientific literature on mange detection and documentation in free-ranging animals. A total of 228 studies were analyzed and categorized into two groups-non-invasive and invasive methods. Non-invasive methods (visual observation, camera trapping, spotlighting, detector dogs and infrared thermography) do not require restraining of animals. Invasive methods (skin scraping, molecular diagnosis using mite isolation and polymerase chain reaction (PCR), serological diagnosis using Enzyme Linked ImmunoSorbent Assay (ELISA), histological analysis and immunohistochemistry) typically require taking samples from the animals and clinical or laboratory infrastructure. Across all diagnostic methods, visual observation was the most widely used technique, followed by skin scraping as the second most frequently applied method for confirming sarcoptic mange. Non-invasive approaches like spotlighting and camera traps provided valuable population-level insights without direct animal contact, though laboratory methods such as PCR, ELISA, and histological analysis offered greater accuracy at higher costs. We recommend a combined approach, starting with non-invasive methods to assess overall population health before using invasive techniques on selected animals for definitive diagnosis, with advanced technologies expected to enhance long-range detection capabilities in the future.
Pembrolizumab has become a cornerstone in the treatment of multiple tumor types. In Portugal, it is currently reimbursed across 25 indications. However, a comprehensive holistic assessment of its cumulative clinical and humanistic impact is lacking. The aim was to estimate the population-level clinical and humanistic outcomes associated with pembrolizumab use in Portugal from 2017 to 2024, and to project its potential future impact until 2030. We developed a Microsoft Excel-based tool to aggregate outputs from validated, Portugal-adapted, cost-effectiveness models across all reimbursed indications for pembrolizumab. Using a real-world number of treated patients, clinical outcomes such as incremental life years (LYs), incremental quality-adjusted life years (QALYs), and avoided deaths were estimated. Forecasts through 2030 were developed using market and epidemiological projections. Alternative scenarios evaluated the additional benefits of accelerated reimbursement decisions and expanded patient access. Between 2017 and 2024, among 13,530 treated patients, pembrolizumab was associated with estimated lifetime outcomes of 14,042 incremental QALYs, 17,914 incremental LYs, and 2021 deaths avoided. Scenario analyses showed that with accelerated reimbursement, within 180 days of market approval, and full uptake, cumulative gains could have increased by up to 14,705 QALYs (total 28,746 QALYs) and an additional 1250 deaths could have been avoided (total 3271 avoided deaths). Forecasts project continued substantial benefits for patients treated between 2025 and 2030, with lifetime outcomes estimated at approximately 31,306 QALYs, 38,274 LYs, and 4841 deaths avoided for the currently reimbursed indications. Pembrolizumab has delivered significant clinical benefits in Portugal. Our results suggest that more timely reimbursement decisions and broader access could have yielded measurable additional health gains. These findings highlight the potential impact of access timelines on health outcomes, while acknowledging that broader assessments of value require consideration of cost-effectiveness, budget impact, and opportunity costs. Pembrolizumab's benefits to the Portuguese health system is expected to grow through 2030.