Intelligent drug delivery-wound healing integrated hydrogel dressing for Type 2 Diabetes Mellitus wounds with wound microenvironment modulation.

Level of agreement with common low back pain misconceptions among students in DPT and nursing programs, and other non-healthcare undergraduate majors: An observational study.

Performance evaluation of intelligent hybrid approach and ant colony optimisation for early-stage diabetes prediction in e-Health applications

Densification pretreatment using ternary phenolic DES enables efficient biomass fractionation and selective lignin arylation modification.

Background Clinical documentation is a major contributor to clinician workload and burnout, with physicians spending more than half of their workday on electronic health record (EHR) tasks. Artificial intelligence (AI)-based speech recognition (ASR) tools promise to reduce this burden by generating draft notes from dictated or conversational clinical encounters. Despite rapid adoption, concerns remain about their real-world accuracy, reliability, and ability to capture clinically relevant information. To systematically map the breadth of published evidence reporting on the accuracy, reliability, efficiency, and clinical information capture of ASR systems used in healthcare settings for clinical documentation. The scoping review employed the methodology developed by Arksey and O'Malley in 2005 and further expanded by Levac and Colquhoun 2010. Four databases (PubMed, Scopus, Web of Science, and MEDLINE) were searched for studies published between 2008 and 2025. All findings were reported according to PRISMA guidelines for scoping reviews. Of 3,520 records, thirty-two met the inclusion criteria, using benchmarking studies, controlled comparisons, qualitative methods, and retrospective reviews. Across settings, ASR showed substantial accuracy limitations, with word error rates ranging from moderate in dictated notes to very high in conversational and emergency contexts. Common errors included deletions, substitutions, and misrecognition of medication names or brief utterances. Although some studies reported reduced typing burden and improved workflow efficiency, systems frequently missed clinically relevant details. Evidence for improvements in note completeness was mixed, and little research linked system accuracy to patient safety or diagnostic outcomes. ASR can reduce typing and improve documentation efficiency, sometimes capturing richer narrative detail. However, frequent and clinically significant errors shaped by linguistic complexity, context, and speaker variation make unsupervised use unsafe. Human oversights remains essential, and continued refinement, rigorous evaluation, and attention to workflow, cognitive burden, and equity are required.

Efficient fractionation of lignocellulosic biomass with high sugar yields and adequate lignin integrity has become a crucial challenge for biomass upgradation, especially via environmentally benign ways. Screening of a series of choline chloride (ChCl)-based ternary deep eutectic solvents (DESs) indicated that additional glycerol in acidic DESs generally promoted enzymatic hydrolysis of pretreated wheat straw. Pretreatment by a natural deep eutectic solvent (NADES) composed of ChCl, lactic acid, and glycerol performed noticeably and was examined with regard to key process factors. The pretreatment achieved 71.2% delignification, 55.3% xylan removal, and a glucose yield of 77.2% by enzymatic hydrolysis upon optimization. Structural characterization indicated modified surface morphology, increased crystallinity, disrupted lignocellulosic structures, and enlarged specific surface area. Extracted lignin exhibited a high content of β-O-4 linkages (45.3 per 100 aromatic units), suggesting its good structural integrity. Mechanisms pertaining to biomass fractionation and lignin protection were tentatively proposed. More than 80% of the DES can be recovered after five cycles while maintaining an enzymatic glucose yield above 51.0%. This work revealed the characteristics and potential of a natural acidic polyol-based DES in biomass fractionation comprised of commonly available and eco-friendly components, which may have implications in establishing an efficient and sustainable biorefinery strategy.

Medication discrepancies (MDs) are common medication errors that can lead to adverse drug events (ADEs) and suboptimal treatment. Medication reconciliation (MR) programs are implemented in many countries, including Iran, to identify, resolve, and prevent MDs and other medication-related problems (MRPs). In the psychiatric setting, the importance of identifying and addressing MRPs is even greater due to the high prevalence of polypharmacy, limited patient cooperation, and other factors. This study aimed to comprehensively investigate the prevalence of MDs and MRPs in psychiatric patients. This study included 302 psychiatric patients receiving at least two systemic medications daily. Their medications were reviewed at discharge using the Pharmaceutical Society of Australia (PSA) guidelines to identify MRPs. The collected data were analyzed statistically. In this study, patients were taking a total of 1421 medications (4.71 medications per patient). A total of 493 MDs and 392 MRPs were identified. The most common MRPs were, in decreasing order, medication underuse or non-adherence by the patient (175 cases, 44.64% of all MRPs) and drug interactions (168 cases, 42.85%). The number of drug interactions significantly increased with the increasing number of underlying diseases and the number of medications used (P < 0.001 in both cases). The findings of this study indicate a high prevalence of medication-related problems (MRPs) in psychiatric settings, highlighting the critical role of MR programs in mitigating such risks. Among the challenges identified, non-adherence to treatment and potential drug interactions emerged as the most significant issues affecting psychiatric patients.

To develop a multi-task deep learning model for the automated quality control of periapical radiographs, integrating identification of technical errors with standardized quality grading. A dataset of 3510 periapical radiographs was curated from two centers for model development and external validation. We developed a multi-task deep learning model to simultaneously perform: (1) multi-label classification of six common technical errors (positioning, processing, exposure, and angulation issues); and (2) hierarchical quality grading according to two established standards: the National Radiological Protection Board (NRPB: Grades 1-3) and the College of General Dentistry (CGDent: Ranks A-B). Model performance was evaluated using ROC-AUC, sensitivity, specificity, precision, accuracy, and F1-score on both internal and external test sets. Furthermore, the model's efficiency gain in daily quality control workflows was quantified. Among five widely-adopted Convolutional Neural Networks (CNNs) and transformer architectures evaluated, EfficientNet was selected as the optimal backbone. The final model identified technical errors with high performance on the external test set (sensitivity: 0.690-0.905; specificity: 0.965-0.995; AUC: 0.842-0.978). For quality grading, it achieved strong results for both trichotomous NRPB grading (sensitivity: 0.835-0.920; specificity: 0.881-0.989; AUC: 0.940-0.960) and binary ranking (sensitivity: 0.835-0.989; specificity: 0.835-0.989; AUC: 0.959).The system demonstrated superior computational efficiency, reducing the cumulative assessment time for a typical daily workload from over 32 minutes (manual review) to approximately 1.1 seconds. Grad-CAM visualizations confirmed the model's focus was aligned with technical errors, supporting clinical interpretability. This multi-task model efficiently automates radiographic quality control by identifying technical errors and assigning quality grades. It offers significant potential to standardize quality assurance and streamline dental workflows.

A dual-mode sensing platform for the detection of glucose based on the Au NPs@Fe-MOF with dual enzyme activity.

People who are incarcerated face significantly higher health risks than the general population, yet deaths in custody remain underreported and poorly monitored by public health systems. Although the federal Death in Custody Reporting Act requires reporting of all deaths in correctional facilities to the US Department of Justice, reporting has been inconsistent, delayed, and often publicly inaccessible. Consequently, researchers have turned to press releases issued by correctional agencies as one of the few timely sources of information on deaths in custody. However, these press releases vary widely in content and structure, making standardized data extraction difficult. Crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) may offer a faster, low-cost method for gathering data, but their utility in this setting remains untested. This pilot study evaluated whether MTurk could be used to extract structured information from press releases about deaths in custody. We selected 144 press releases describing deaths between 2000 and 2023 from state prison systems and Immigration and Customs Enforcement. Each press release was assigned to 3 MTurk crowd workers (who were required to be English speaking and located in the United States), resulting in 432 individual responses. Workers were informed in advance that the task involved reviewing sensitive content related to deaths in custody. Crowd workers completed a 16-question form aligned with Death in Custody Reporting Act variables, including age, race and ethnicity, date of death, and facility location. Data quality was assessed using strict concordance (all 3 responses matched), 2-way concordance (2 of 3 responses matched), and qualitative review of common errors. Task completion time was also recorded. Sampling included complete subsets of selected press releases and a stratified subset from systems with more complex reporting formats. All 144 entries were completed within 48 hours. However, agreement across crowd workers was low: strict concordance was 14.2% (20/144) for age, 12.3% (18/144) for race or ethnicity, and 11.4% (16/144) for date of birth. Qualitative review identified frequent errors, missing data, and inattentive or automated responses. Crowd workers often misinterpreted system-specific terminology or copied placeholders instead of extracting information from the source. The low agreement indicated that this baseline MTurk configuration produced insufficient data quality for more resource-intensive use. MTurk enabled rapid task completion but produced low-quality results when applied to extracting structured data from carceral press releases. These findings suggest that general crowdsourcing platforms are poorly suited to complex data abstraction tasks without additional training or oversight. With improved task design or support from artificial intelligence tools, crowdsourcing may help address gaps in the surveillance of deaths in custody. Long-term improvements will require consistent, transparent, and standardized reporting practices across correctional institutions.

Inaccurate blood product ordering can have a negative impact on patient care by leading to delays in transfusion initiation and inadequate use of hospital resources. The pediatric hematology/oncology patient population is at an increased risk of blood product ordering errors due to the numerous modifications, weight-based dosing, and special processing that are often required to keep transfusions safe. Our SMART Aim was to decrease the percentage of incorrectly ordered blood products at our center by 50% over a 9-month time period. At baseline, we found that there was a 60% median error rate in blood product ordering. The most common error in blood product ordering at our institution was the utilization of cytomegalovirus-negative blood products when it was not required institutionally due to our standardized leukoreduction process. Educating providers on the blood product ordering process, appropriate modifications required for a given clinical scenario, and weight-based dosing was associated with a reduction in the median percentage of incorrectly ordered blood products to 30% over the course of the project. In the future, we plan to implement improvements in blood product ordering across the entirety of the children's hospital at our institution.

Metabolic engineering of Saccharomyces cerevisiae to enhance resistance toward microbial toxicity in agave extracts.

Medical research often employs regression models. However, many clinicians are not confident in their ability to interpret these studies, which can lead to misinterpretation and negatively affect clinical practice. This article aims to provide a basic framework for understanding regression models in clinical research. In this article, we explain what regression models are, the purposes they serve, and some of the most common regression models, with examples illustrating how to interpret them in plain language. We discuss common errors and shortcomings in regression models.

Home-based fitness training requires automated systems for exercise quality assessment and real-time posture correction without professional supervision. We develop an AI-powered virtual fitness system using spatiotemporal skeleton analysis for automatic exercise quality assessment. We propose Anatomical-Prior Sparse Attention (APSA), integrating biomechanical constraints into graph neural networks. APSA hierarchically models spatial and temporal dependencies through anatomically-informed attention, focusing on kinematically meaningful joint interactions while pruning implausible connections. The system processes 3D skeletal keypoints from standard RGB cameras. Evaluation on two public datasets (IntelliRehabDS and Kimore) shows APSA achieves 76.9% accuracy in quality classification and 0.789 F1-score in error detection, outperforming recent 2024 methods by 1.1-8.5 percentage points while maintaining real-time performance (8.2ms per sequence). The system identifies common errors including knee valgus (84.3% accuracy) and excessive trunk lean. The biomechanically-informed attention mechanism enhances accuracy and interpretability, enabling scalable deployment in home-based training and remote rehabilitation.

To assess the ability of former professional association football players in England to recall their playing careers. Self-reported data regarding playing careers were available from former professional football players participating in the Health and Ageing Data IN the Game of football (HEADING) study. We compared the self-reported data from 141 participants regarding the teams, positions, periods, and leagues they played for against the data available in publicly available records, compiled with the assistance of the English Professional Footballers' Association. Self-reporting of career histories occurred a mean of 28.5 years since the players had their last competitive season. Overall, 20 discrepancies between self-reported and register-based data were observed in the career histories of 18 (12.8%) individual players. This is in line with data from earlier reliability studies on the self-reporting of occupational histories. The most common error observed was incorrect recording of years of play in certain clubs, occurring in 9 instances. Missing teams in self-reports were observed in another 8 cases. The results suggest that former professional association football players can provide plausible information over long periods regarding the general characteristics of their professional career. This information could form the basis for a reliable exposure assessment within epidemiological analyses. Further work is required to assess recall of the amount of heading and other head impacts during training and play.

BackgroundLarge language models (LLMs) are increasingly used to summarize clinical documents; yet, automated metrics often inadequately capture clinical relevance and safety. In the initial phase of the “Framework and Implementation of AI Tools,” an expert-driven, cocreated evaluation methodology was established to assess LLM-generated discharge letter summaries, combining prompt design considerations with intuitive expert appraisal.ObjectiveThis study aimed to quantify expert agreement and interrater reliability on LLM summaries of discharge letters, identify frequent and clinically relevant errors, and evaluate practical implications for integrating LLMs into documentation workflows.MethodsThirty expert-curated synthetic Dutch discharge letters were summarized. Thirty-one clinicians from Flemish care settings (1 university hospital, 2 private hospitals, and 2 general practice circles) evaluated the summaries. The evaluation framework consisted of 61 binary layout items assessing whether required sections and formatting were correctly present, 33 content items (correct or complete vs incorrect, subcategorizing missing, irrelevant, and hallucinated information), a 4-point global quality rating, and an open comment. Statistical analyses included descriptive statistics, mixed effects ordinal regression on the global score, consensus (agreement per question or letter) percentages, interrater reliability (Cohen κ, intraclass correlation coefficient [ICC], Fleiss κ, and prevalence index), and thematic synthesis of comments.ResultsLayout adherence was high (88%), especially in the conclusion section. The positive response rate for content was overall moderate (78%), with the best performance observed in the medical history section and the lowest performance observed in the medication section, which also showed the highest rate of hallucinations and the weakest interrater consensus. Across all sections, missing information was the most common error. Nearly 70% of global ratings were “good” or “very good.” Higher positive response rates for content predicted better global scores (β=.079; P<.001), while layout and participant specialty were not relevant to global scoring. Consensus was high for the layout questions (median 96.8%, IQR 90.2%-100%) and somewhat lower for content (median 83.9%, IQR 67.7-96.8), with the lowest agreement in the medication section. Interrater agreement was moderate (median Cohen κ=0.36, IQR 0.29-0.43; range 0.07‐0.56), but overall reliability was high (ICC 0.945, 95% CI 0.942-0.948), indicating strong consistency at the global level despite interrater variability. The prevalence index demonstrated that high ICC values were partly driven by the strong prevalence of affirmative responses in layout items, while content items showed more balanced distributions and lower agreement.ConclusionsOur framework offers a robust approach for evaluating LLM-generated discharge summaries, balancing usability and clinical relevance. Semantic integrity, especially regarding medication details, was identified as a key vulnerability. Perceived overall quality was driven by a positive response rate for content. High ICC values for global score, with lower item-level agreement lead toward the need for clearer, context-specific prompts and standardized evaluation criteria to reduce interrater variability. Human oversight and targeted automated checks for omissions and hallucinations are essential for safe clinical deployment.

Introduction: Genetic testing has revolutionized the clinical landscape by enabling rapid diagnosis, promoting translational research, and improving chances for actionability. With plummeting sequencing costs, Whole Genome Sequencing is becoming widely accessible and is paramount in identifying novel disorders or atypical presentations. The pipelines are now robust enough to uncover substitutions, small indels and also those which are typically not elucidated by sequencing strategies like copy number changes, repeat expansions, etc. Methods: We present two clinical cases, one with a copy number variation and another with a triplet repeat expansion, both of which were missed on the exome sequencing and fragment analysis, respectively, and eventually were diagnosed with Whole Genome Sequencing. Results: Whole Genome Sequencing may not be the primary test of choice in either case. However when limited panel tests fail despite being sensitive and specific, it might lead to unnecessary broader-spectrum testing and distress. Therefore, it is worth looking into the possibility of common errors before ordering expensive and extensive tests. Rechecking the previous data in clinical cases with classic presentations and strong family history may be cumbersome, but it is a valuable exercise before proceeding with the chase for rarer and novel genetic disorders.

ObjectiveThis review article was conducted to summarize the published literature on the most common errors and clinical consequences, and to explore technological solutions for securing the process of preparing chemotherapeutic drugs.Data sourceThe literature used in this review was identified through searches of major medical and pharmaceutical databases including (PubMed) (Science Direct). Articles were searched using the Medical Subject Headings terms: "cytotoxic drugs", "compounding", "reconstitution errors", "oncology pharmacy", "chemotherapy preparation errors "; "automated compounding"; "dilution errors ".Summary of dataThe preparation of cytotoxic drugs has generally been centralized in the hospital pharmacy for two main reasons: to ensure the safety of staff, and to provide greater protection for patients. Errors in chemotherapy compounding have been identified, which can lead to severe outcomes, including death or permanent loss of function in patients with cancer. Such errors may occur during drug reconstitution and mixing, as well as during verification and labeling of the compounded mixture. Automated compounding systems have demonstrated the ability to prepare chemotherapy drugs effectively, delivering high-quality products with productivity comparable to that manual preparation methods.ConclusionCytotoxic drug reconstitution is a strategic link in oncology pharmacy to enhance safety, it is necessary to promote harmonized practices, develop continuing education, and integrate innovative technologies into preparation units.

Diabetes mellitus (DM) has become a serious public health problem. Diabetes-related male infertility (DMI) and diabetes-related cognitive impairment (DCI) in DM patients are common and easily ignored problems in clinical practice. To explore the deep-seated connections between DMI, DCI, and DM from the neuroendocrine-immune (NEI) network. We reviewed literature related to DMI and DCI separately, and conducted an in-depth analysis of their relationship with the NEI network. Through an extensive literature review, we found that the important mechanisms involved in DMI and DCI are closely related to the NEI network, including the hypothalamic-pituitary-adrenal-thymus axis and the hypothalamic-pituitary-gonadal-thymus axis. This article elaborates on the different states of these axes in DM patients at different stages, their interrelationships, and how high sugar affects the blood-testis barrier and triggers DMI. The impact of high sugar on the hippocampus leads to a decline in recent memory and cognitive function problems, and the relationship between cognition and the catecholamine system can lead to cognitive impairment during stress states.

Understanding Mortality Data: A Step-by-Step Guide to CDC WONDER, Joinpoint Analysis, and Forecasting Models