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Chaotic dynamics has been the subject of both theoretical and empirical research in epidemiology, with the most recent research strongly focusing on SARS-CoV-2. However, few empirical studies have been undertaken with respect to influenza, even though evidence of chaos has also been found in influenza surveillance data. Furthermore, empirical studies on chaos are focused on reconstructing hidden attractors in epidemiological time series to filter out noise; however, dynamical noise affecting chaotic dynamics can have relevant epidemiological features that are, in this way, left unresearched and that can be used for epidemiological surveillance and risk analysis by capturing the main underlying nonlinear processes associated with epidemiological dynamics. This study aimed to reinforce empirical research on chaotic dynamics in influenza surveillance and the study of the dynamical noise affecting that chaotic dynamics, addressing the consequences for epidemiological risk analysis and surveillance. Working with the weekly share of positive influenza tests for the Northern Hemisphere from January 2009 to March 2025 compiled by Our World in Data using FluNet data from the World Health Organization, we applied a recent method based on topological data analysis for reconstructing underlying attractors from time series and decomposing the dynamics down to independent and identically distributed noise. We adapted the method to the epidemiological context so that it can be used for predictive decomposition with direct application to epidemiological risk analysis and surveillance. We found evidence of a low-dimensional chaotic attractor in the researched surveillance data, with a fractal dimension between 1 and 2, and a positive statistically significant largest Lyapunov exponent. The chaotic dynamics had power law scaling associated with long-wave influenza outbreaks, and it is affected by a stochastic component that is nonstationary in variance, leading to turbulent bursts in the outbreak dynamics. Testing different machine learning algorithms using the attractor as input for prediction and a 10-week rolling window, we found the following largest R2 scores for the prediction of the target series: 92.11% (1 week ahead), 85.95% (2 weeks ahead), 81.75% (3 weeks ahead), 77.59% (4 weeks ahead), and 73.35% (5 weeks ahead). The main results reinforce previous theoretical and empirical studies on chaos in epidemiology. Our findings showed that there is a 2-dimensional chaotic attractor that can support up to a 1-month prediction of the target surveillance series with high prediction scores and that the attractor plus noise can be modeled in a way that supports uncertainty quantification and epidemiological risk analysis.

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The IT sector is growing and encompasses all professions, from leisure and recreation to hospitals and emergency response groups. IT professionals are experiencing increased threats (eg, ransomware attacks), but little is known about the relationship between these IT profession-specific stressors and the professionals' mental health. This study aimed to (1) estimate the associations between IT profession-specific stressors and anxiety, depression, and stress, and (2) examine the role of mental health literacy (MHL) as a mediator of the relationship between depression, anxiety, stress, and help-seeking. Between February and May 2023, IT professionals working in the United States were surveyed online. Participants (n=357) reported demographic characteristics, MHL, mental health symptoms, and help-seeking intentions with the following scales: Mental Health Literacy in the Workplace (MHL-W), Center for Epidemiological Studies Depression-10 (CESD-10), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale-10 (PSS-10), and the Mental Help Seeking Intention Scale (MHSIS). Descriptive statistics, regression models, and mediation analyses were conducted for CESD-10, GAD-7, and PSS-10. Respondents who had experienced ransomware attacks in the past year reported significantly higher symptoms of depression (odds ratio [OR] 1.85, 95% CI 1.07-3.22; P=.03). Past-year exposure to balancing security and usability was associated with lower odds of reported anxiety (OR 0.48, 95% CI 0.28-0.82; P=.008). Having made critical technology decisions with limited information in the past year was associated with higher perceived stress by 2.02 points on the PSS-10 scale (SE 0.84, 95% CI 0.37-3.66; P=.02), and working with limited resources in the past year increased perceived stress by 1.70 points (SE 0.84, 95% CI 0.04-3.35; P=.04) after adjusting for the covariates. MHL was found to partially mediate the relationship between depression and help-seeking, but not between anxiety or stress and help-seeking. These findings provide insight into the workplace stressors that pose a greater psychological health risk for IT professionals. These results emphasize the important role of MHL in helping facilitate the connection between depressive symptoms and help-seeking.

Preprints-scientific manuscripts shared publicly prior to formal peer review-are gaining momentum across academic disciplines. However, their adoption in clinical and biomedical sciences remains limited, particularly in countries where traditional publishing norms prevail. Editorial ambiguity and a lack of national policy further complicate their use. This study aimed to assess the awareness, experiences, and attitudes of medical academics at Marmara University School of Medicine toward preprints and to explore the editorial landscape through both journal editor feedback and a review of journal-level preprint policies. A cross-sectional survey was conducted with 103 medical faculty members. The questionnaire included demographic questions, Likert scale items, and multiple-choice items assessing knowledge, familiarity, and attitudes toward preprints, as well as open-ended items to explore concerns. A "preprint test score" (0-4) was developed to quantify objective knowledge. Subgroup analyses were conducted by age (<40 vs &#x2265;40 y) and academic discipline (basic vs clinical sciences). Additionally, all responses to open-ended questions from journal editors and 118 biomedical journals were manually reviewed for their stated stance on preprints and article processing charges (APCs). A convergent mixed methods design was used, combining a structured survey, thematic analysis of open-ended responses and editorial feedback, and a document-based review of biomedical journal policies. Only 42.9% (n=34) of participants reported familiarity with the concept of preprints, and 13% (n=10) had previously published on a preprint server. Misconceptions about ethics, peer review, and compatibility with journal policies were common. Subgroup analysis revealed that older participants scored higher on the "preprint test" (mean 2.20, SD 1.31 vs mean 1.97, SD 1.60) and had more experience with preprint publishing (1/40, 2.5% of younger participants; 7/29, 24.1% of older participants). Further, younger academics expressed less openness toward future use (n=7, 17.5% in the younger group; n=8, 27.6% in the older group). Clinical faculty were generally more hesitant than basic science faculty, although both groups raised concerns about the academic recognition of preprints. Editorial responses reflected a mix of cautious endorsement and skepticism. Among the 118 biomedical journals reviewed, most lacked clear preprint policies, while a small number either explicitly prohibited or permitted them. There is limited awareness and cautious engagement with preprints among medical academics and editors in T&#xfc;rkiye. Generational and discipline-based differences further influence knowledge and attitudes. The lack of clear editorial guidance from biomedical journals may reinforce academic uncertainty. Tailored educational initiatives, transparent journal policies, and institutional support will be essential to foster a more open and inclusive scientific publishing environment.

Long COVID (post-COVID-19 condition) continues to challenge primary care. To support family physicians in British Columbia, the general internal medicine (GIM) COVID-19 Rapid Access to Consultative Expertise (RACE) line was launched in August 2020 to provide real-time specialist advice. This quality improvement study aimed to evaluate the implementation and utilization of the GIM-COVID-19 Long-Term Sequelae RACE line in British Columbia. Specifically, it sought to characterize the demographics of patients involved in RACE consultations, identify the most common themes and clinical queries presented by primary care providers, and assess how usage patterns evolved over time during the COVID-19 pandemic. We conducted a retrospective descriptive analysis of 149 RACE line call summaries between August 2020 and June 2021. Six calls were excluded due to insufficient information, such as incomplete documentation or absence of a clear COVID-19-related question. Because the original extraction notes are no longer available, further details about these calls cannot be provided, leaving 143 eligible calls. Data extracted included patient age, sex, geographical location, symptom type, and timing of symptom onset post-COVID-19 infection. Calls were categorized by symptom duration (acute: <2 wk, subacute: 2-12 wk, chronic: >12 wk), thematic content (respiratory, fatigue, neurological, etc), and query type (symptom management, return-to-work, vaccination, etc). Data were coded independently by two reviewers using a standardized spreadsheet and predefined codebook. Discrepancies were resolved through discussion. Descriptive statistics summarized the findings. Many calls involved female patients (91/143, 64%), with the most common age group being 40-49 years (32/113, 28%). Most calls came from Greater Vancouver (35/83, 42%) and the Fraser Valley (29/83, 35%). Subacute symptoms (52/149, 35%) and vaccination-related concerns (29/149, 19%) were the most common inquiry types. Symptom-related inquiries accounted for 92 of 143 calls (64%), with 253 symptoms documented overall. Respiratory symptoms were most common (100/253, 40%), especially shortness of breath (35 calls), cough (26), and fatigue (23). Call volumes peaked from January to June 2021, coinciding with the provincial vaccine rollout. The GIM-COVID-19 Long-Term Sequelae RACE line served as a critical early support system for primary care providers as the long COVID landscape evolved. This quality improvement study emphasizes the value of rapid access and specialist-informed consultation tools during emerging public health challenges. The trends ascertained may inform future health system responses, particularly when designing more scalable, interdisciplinary models to support primary care in managing complex chronic conditions.

Generative artificial intelligence models, especially reasoning large language models (LLMs), are gaining adoption in health care for diagnostic decision support and medical education. DeepSeek R1 is a reasoning LLM that generates extended chain-of-thought explanations to make its decision-making process more explicit. Traditional medical benchmarks often lack complexity and authenticity, motivating the adoption of scenario-rich datasets, such as the Massive Multitask Language Understanding Pro (MMLU-Pro) professional medicine subset, which provides multispecialty clinical vignettes for reasoning-centric evaluation. The objective of this study is to assess the diagnostic accuracy, reasoning quality, reasoning transparency, and practical usability of DeepSeek R1 and Gemini 3 Pro across closed- and open-ended clinical scenarios, with the intention of guiding their prospective application in practical clinical education and training. This evaluation was conducted by analyzing 162 diverse medical scenarios (both closed- and open-ended) from the MMLU-Pro health subset. In a 2-phase, dual-model evaluation, DeepSeek R1 and Gemini 3 Pro were applied to 162 matched clinical vignettes from the MMLU-Pro professional medicine subset spanning 21 specialties. Closed-ended, multiple-choice, and open-ended prompts were constructed for the same scenarios, and model outputs were coded for accuracy, reasoning steps, and citation behavior; descriptive statistics and the McNemar test were used to compare performance across formats. DeepSeek R1 achieved an accuracy of 86.4% (140/162 scenarios) on closed-ended tasks and 80.9% (131/162) on open-ended questions across 162 clinical scenarios, indicating modest attenuation of performance when answer cues were removed. Gemini 3 Pro demonstrated 90.7% (147/162) closed-ended and 88.9% (144/162) open-ended accuracy on the same scenarios, showing a similar pattern of decreased performance without answer options. Error analysis indicated that incorrect answers typically involved longer reasoning chains, suggesting overthinking. In a structured review of open-ended responses, DeepSeek R1 produced an average of 18.7 (range 0-52) references per case, with 5.2 unrelated references and 13.1 (range 3-67) reasoning steps, whereas Gemini 3 Pro averaged 22.5 (range 12-50) references, 1.9 (range 0-8) unrelated references, and 4.4 (range 1-10) reasoning steps per case. DeepSeek R1 demonstrated moderate-to-excellent accuracy and reasoning in evaluating both closed- and open-ended medical scenarios. In parallel, Gemini 3 Pro showed broadly comparable but distinct performance and reasoning patterns. While the closed-ended format may inflate accuracy due to cueing, the open-ended evaluation yielded richer insights into the fidelity of reasoning. Side-by-side evaluation of two large reasoning models highlights the importance of format, specialty, and citation behavior when considering clinical and educational use. Continued validation across a wider range of specialties and real-world contexts will enhance the model's trustworthiness for diagnostic and teaching applications.

Obesity is a significant global public health concern. Primary prevention and health promotion to encourage positive health behavior to address obesity could be delivered via mobile health (mHealth), but evidence of apps improving health outcomes over sufficient time frames to be clinically meaningful is limited. mHealth interventions for physical activity, healthy eating, and weight loss typically prioritize intention as the primary driver of behavior. This may limit their impact, as intention does not consistently translate into behavior. This review updates a previous systematic review on the effectiveness of mobile apps for health behavior change while narrowing the scope to weight management interventions to enable a more focused analysis. Our primary objective is to investigate the effectiveness of mHealth apps in improving health behaviors with respect to physical activity and healthy eating and to explore the inclusion of behavioral theories and behavior change techniques and the evidence for their effectiveness. This protocol follows the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) checklist, and the review will be structured using the PRISMA 2020 statement. Nine databases (PubMed, EMBASE, CINAHL, APA PsycINFO, Cochrane Library, SPORTDiscus, SCOPUS, Web of Science, and Science Direct) will be searched for studies reporting evaluation of the impact of mHealth interventions on weight loss, healthy eating, or physical activity outcomes. EndNote 21 software will be used for deduplication and initial screening, followed by manual title and abstract screening, and then full-text screening by 2 independent reviewers. Data regarding the studies, intervention characteristics, their theoretical basis (eg, use of behavior change frameworks such as the COM-B [Capability, Opportunity, Motivation-Behavior] model or the social cognitive theory), evaluation methods, and outcomes will be extracted into a predetermined form. A meta-analysis will be conducted on eligible studies (reporting control group comparisons) to synthesize evidence of their effectiveness, and the remaining quantitative data will be descriptively analyzed. The review is expected to start in December 2025 and to be submitted for publication by the end of 2026. This review will synthesize evidence on the theoretical basis underpinning mHealth interventions for enhancing physical activity, healthy eating, and weight loss and generate new insights into how particular behavior change techniques can best support intended outcomes. This will help guide the development of more impactful mobile-based interventions to support healthy behaviors that are better able to reduce the risk factors for chronic health conditions. PROSPERO CRD42024602819; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024602819. PRR1-10.2196/72664.

Allergic rhinitis is a common condition affecting up to 40% of people worldwide, with a notably high prevalence in South Asia. The primary treatment for moderate to severe allergic rhinitis is intranasal corticosteroid sprays (INCS), the use of which is typically demonstrated to patients by registered pharmacists. However, many patients do not use these sprays correctly. This study evaluated the proficiency of pharmacists in demonstrating the correct technique for using INCS and the factors contributing to proper technique. In a cross-sectional survey of 365 registered pharmacists in the Kathmandu Valley, Nepal, a trained observer used a standardized 12-step checklist to assess each pharmacist's technique for using INCS. The 12-step checklist was created after studying international guidelines, studies conducted in Nepal, international research articles, and instructional pamphlets. Simple random sampling was done to collect the data from community pharmacies in Kathmandu district. Demographics, education, experience, previous training, and instructional materials use were recorded. A total of 12 marks were awarded for all 12 steps, with one mark given for each step. Proficiency was classified as "adequate" if more than 6 marks were obtained. Out of 365 pharmacists, 239 (65.5%) were male and 126 (34.5%) were female. Overall, 216 pharmacists (59.2%) were aged 26 years or younger and 235 pharmacists (69.9%) held a diploma in pharmacy. We found that 193 (52.9%) pharmacists demonstrated inadequate technique, while only 172 (47.1%) showed adequate skill overall. However, only 22 pharmacists (6%) demonstrated all 5 critical steps. The likelihood of providing appropriate counseling on the use of INCS was significantly correlated with multiple independent factors. Those with a diploma in pharmacy had a 97% lower likelihood of providing appropriate counseling compared with those with a bachelor's degree in pharmacy and above (P<.001). Pharmacists who perform counseling sessions 1-4 times per week had 11-fold greater odds of doing so correctly compared with those who do not (P=.002). Pharmacists who do not use educational leaflets were 96% less likely to provide adequate counseling (P= .005) . Similarly, pharmacists under the age of 26 are 89% less likely than older pharmacists to provide adequate counseling (P=.001). It is interesting to note that men were found to have almost 2.3 times higher odds of providing appropriate counseling than women (P=.02). More than half of the registered pharmacists in Nepal demonstrated inadequate technique when using INCS. The inadequate patient counseling on INCS use can significantly increase the risk of adverse drug reactions and reduce the efficacy of the therapy. Thus, there is a strong need for educational interventions and policy change for improved proficiency.

The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use published the estimand framework in 2019. The estimand framework aims to clearly define a treatment effect for a clinical question through construction of estimands, and it has been widely applied in clinical trials in the pharmaceutical industry. The estimand framework proposes 5 attributes for an estimand: treatments, variables, target populations, population-level summaries, and intercurrent events. It also proposes the treatment policy strategy, the hypothetical strategy, the composite variable strategy, the while on treatment strategy, and the principal stratum strategy to handle intercurrent events. When people give clear definitions for these 5 attributes, they clearly define an estimand that represents a treatment effect. From a statistical perspective, a genuine or causal treatment effect is defined through a causal inference framework. This article aims to interpret the estimand framework using a causal inference framework and help researchers understand the differences between estimands and causal treatment effects. From a causal inference framework based on potential outcomes, an individual treatment effect (ITE) is defined by comparison of individual potential outcomes with experimental or control treatments, and the average treatment effect (ATE) of the experimental treatment versus the control treatment is defined as an average of all ITEs. The statistical presentation of the ATE is not equivalent to an estimand. It has the same treatments, variables, target populations, and population-level summaries as an estimand, but intercurrent events are not part of it. Intercurrent events modify the statistical presentation of the ATE through treatments, variables, and target populations, whose impact can be controlled by intercurrent event strategies. I propose that the estimand attributes can be mapped onto the statistical presentation of the ATE, and that intercurrent events act as mediation mechanisms in the attribute mapping process, which provides a novel way to incorporate the causal inference framework into the estimand framework. If the estimand framework is combined with a causal inference framework, it will gain a stronger theoretical foundation. The interpretation of the estimand framework from a causal inference perspective is useful for both industrial and academic clinical trials. Observational studies may also find useful information on causal inference theories in this article.

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Cardiac implantable electronic devices (CIEDs) are crucial in managing various cardiac conditions, but their monitoring poses considerable challenges. Algorithm-enabled remote monitoring of these devices has emerged as a promising solution to enhance patient outcomes and potentially reduce health care expenditures; however, its economic impact remains underexplored. This systematic review protocol aims to review and synthesize the existing evidence on the cost-effectiveness and cost-utility of algorithm-enabled remote monitoring for CIEDs in patients with or at risk of heart failure. The search of literature will be performed in MEDLINE, Embase, Scopus, Web of Science, and the Cochrane Library, with supplementary searches in the National Health Service Economic Evaluation Database, the National Institute for Health and Care Excellence, the Canadian Agency for Drugs and Technologies in Health, the International Network of Agencies for Health Technology Assessment, and the National Institute for Health and Care Research. This protocol is reported in accordance with the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) 2015 statement, and the completed review will be reported following the PRISMA 2020 statement. Following database searching and deduplication, 3108 records were retrieved; 731 (23.5%) duplicates were removed, leaving 2377 (76.5%) records for title and abstract screening. The review will identify and synthesize economic evaluations of algorithm-enabled remote monitoring in adults with CIEDs, summarizing reported costs, outcomes, and cost-effectiveness results. Methodological quality, risk of bias, and sources of heterogeneity across studies will be assessed. The findings of this review may help inform health care providers, policymakers, and other stakeholders by clarifying the current economic evidence on these monitoring systems, informing adoption decisions, and identifying areas requiring further research.

Consumer-level drug recalls usually require action by individual patients. The Food and Drug Administration (FDA) has public-facing outlets to inform the public about drug safety information, including all recalls, but individual consumers may not be aware of them. And there is no system in place to notify individual prescribers which of their patients are affected by a specific recall. We aimed to leverage the FDA's Healthy Citizen prototype web-based software platform, which provides users with information about recalls, to automatically notify patients of relevant recalls. We developed and evaluated an electronic notification system in the primary care and cardiology practices at a large, urban, academic medical center. The health care portal scanned the FDA Healthy Citizen application programming interface nightly to detect new recalls, identified patients who had those medications in their electronic health record (EHR) medication list, and sent them a message through the EHR patient portal with a link to a customized FDA information display. Using structured interviews, we assessed qualitative feedback on the system and portal messaging from a convenience sample of 9 patients. The system was technically functional, but it was not possible to trace a medication prescription from the EHR to specific lot numbers dispensed to that patient by a community pharmacy. The qualitative feedback obtained from patients showed convergence of topics. Lack of an accurate electronic audit trail from prescription to dispensed medication precludes clinical deployment of automated drug recall notification.

The existence of the variable component of the systematic error (VCSE) was known from the beginning. Still, it is a kind of taboo: it does not have a definition in the International Vocabulary of Metrology and is not present in equations, as it is considered transformed over time into random error. This theoretical study aims to reevaluate the role and significance of the VCSE in quality control (QC). Assuming three quintessential principles-(1) a parameter must be determined under the same conditions under which it is used, (2) a calibration cannot correct smaller biases than the calibration error, and (3) a constant cannot correct a variable-it was deduced that the source of the VCSE is bias drift caused by reagent instability and the shifts caused by human interventions. Both phenomena are mentioned in the literature. The two causes were confirmed by two series of computer simulations using 1000 normally distributed values with an SD of 1 to simulate random error and appropriately chosen bias values to simulate (1) drifts with different slopes and (2) variable shifts. Real-life examples from day-to-day QC, using Roche reagents on Cobas 6000 and Cobas PRO analyzers, confirmed the computer simulations. "The bias" is a definitional uncertainty because bias is time-variable. The causes of the cyclic variations are reagent instability and human intervention, confirmed by computer simulation and real-life QC data. Making a clear distinction between bias measured under repeatability and reproducibility within laboratory conditions, as in the case of SDs, and also separating constant and variable subcomponents of the systematic error, 2 sets of error parameters are obtained, each set being consistent with the measurement conditions. The link between them is the time-variable VCSE function. More properties of the VCSE(t) impose a distinction from random error component: predictability and corrigibility in the short term and non-Gaussian distribution. Its transformation into random phenomena is a myth based on confusion between random and variable error components. The accurate determination of the VCSE(t) function is possible, but it has an excessively high cost-effectiveness ratio. Because it is hidden in the bias measured in repeatability and in the SD in reproducibility within laboratory conditions, it helps us to avoid the redundant use in total measurement error and MU equations. Several false assumptions behind the Westgard rules were uncovered. The new error model aims to serve as the foundation of a new QC system. Internal QC decisions are only consistent with graphs designed using SD measured in repeatability conditions; therefore, they are not consistent with the actual Westgard rules. Alarms should be avoided in cases of incorrigible biases. Immediately after calibration, constant biases, gradually increasing biases, and unexpected shifts in bias represent distinct situations, each requiring a unique strategy.

Social care systems worldwide face increasing demographic and financial pressures. This necessitates exploring innovative technological solutions to enhance service delivery without substantially increasing costs. Conversational interfaces, including interactive voice response, chatbots, and voice assistants, have gained traction as a means to improve accessibility and efficiency in social care. The rapid development of large language models such as ChatGPT has further accelerated interest in conversational artificial intelligence (AI). These technologies can offer intuitive interactions, particularly for individuals with limited digital literacy. However, their real-world impact, usability, and ethical considerations in social care remain underexplored. This scoping review aims to synthesize existing literature on the implementation, evaluation, and impact of conversational AI systems within social care settings for older adults. The review will identify best practices, current gaps, and future directions for research and implementation. Key research questions include the following: how are conversational systems implemented on a technical level, and how do older adults and their support systems use them in a social care context?&#x202f;What methods are used to evaluate acceptability, usability, and the impact of broad well-being in the context of older adults' social care? and What are conversational technologies' acceptability, usability, and well-being impact in the context of older adults' social care? The review will follow the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and Population, Concept, and Context (PCC) frameworks. A systematic search will be conducted across five databases (IEEE, Web of Science, PubMed, ACM, and Scopus) for English-language articles published from 2019 onward. Studies will be included if they empirically examine conversational systems' implementation, evaluation, or impact for older adults (aged &#x2265;55 years) within a social care context. Two independent reviewers will screen articles and extract data. A descriptive analysis will then categorize findings across key domains such as accessibility, usability, ethical considerations, and well-being outcomes. The results will be included in the scoping review, which began in March 2025. The analysis is underway and is expected to be completed and submitted for publication by September 2025. This scoping review will provide an overview of the role of conversational AI in social care, highlighting both opportunities and challenges in implementation. By synthesizing existing research, the review will inform future developments in the use of conversational agents to improve social inclusion, engagement, and well-being among older adults. PRR1-10.2196/72310.

Alzheimer disease (AD) is a severe neurological brain disorder. While not curable, earlier detection can help improve symptoms substantially. Machine learning (ML) models are popular and well suited for medical image processing tasks such as computer-aided diagnosis. These techniques can improve the process for an accurate diagnosis of AD. In this paper, a complete computer-aided diagnosis system for the diagnosis of AD has been presented. We investigate the performance of some of the most used ML techniques for AD detection and classification using neuroimages from the Open Access Series of Imaging Studies (OASIS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets. The system uses artificial neural networks (ANNs) and support vector machines (SVMs) as classifiers, and dimensionality reduction techniques as feature extractors. To retrieve features from the neuroimages, we used principal component analysis (PCA), linear discriminant analysis, and t-distributed stochastic neighbor embedding. These features are fed into feedforward neural networks (FFNNs) and SVM-based ML classifiers. Furthermore, we applied the vision transformer (ViT)-based ANNs in conjunction with data augmentation to distinguish patients with AD from healthy controls. Experiments were performed on magnetic resonance imaging and positron emission tomography scans. The OASIS dataset included a total of 300 patients, while the ADNI dataset included 231 patients. For OASIS, 90 (30%) patients were healthy and 210 (70%) were severely impaired by AD. Likewise for the ADNI database, a total of 149 (64.5%) patients with AD were detected and 82 (35.5%) patients were used as healthy controls. An important difference was established between healthy patients and patients with AD (P=.02). We examined the effectiveness of the three feature extractors and classifiers using 5-fold cross-validation and confusion matrix-based standard classification metrics, namely, accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve (AUROC). Compared with the state-of-the-art performing methods, the success rate was satisfactory for all the created ML models, but SVM and FFNN performed best with the PCA extractor, while the ViT classifier performed best with more data. The data augmentation/ViT approach worked better overall, achieving accuracies of 93.2% (sensitivity=87.2, specificity=90.5, precision=87.6, F1-score=88.7, and AUROC=92) for OASIS and 90.4% (sensitivity=85.4, specificity=88.6, precision=86.9, F1-score=88, and AUROC=90) for ADNI. Effective ML models using neuroimaging data could help physicians working on AD diagnosis and will assist them in prescribing timely treatment to patients with AD. Good results were obtained on the OASIS and ADNI datasets with all the proposed classifiers, namely, SVM, FFNN, and ViTs. However, the results show that the ViT model is much better at predicting AD than the other models when a sufficient amount of data are available to perform the training. This highlights that the data augmentation process could impact the overall performance of the ViT model.

The mental health of children and adolescents is a growing public health concern, with increasing rates of depression and anxiety impacting their emotional, social, and academic well-being. In Japan, access to timely psychiatric care is limited, leading to extended waiting periods that can range from 3 months to a year. Artificial intelligence (AI)-driven chatbots, such as emol (Emol Inc) that integrates acceptance and commitment therapy, show potential as digital solutions to support young patients during these waiting times. The AI chatbot emol was selected based on a comprehensive review of Japanese mental health technology apps, including in-person evaluations with company representatives. This exploratory parallel-group randomized controlled trial examined the feasibility of using an AI chatbot emol with pediatric and adolescent individuals on psychiatric waiting lists. Participants aged 12-18 years were recruited from 4 hospitals in Kanagawa Prefecture and randomly assigned to either an intervention group, receiving 8 weekly chatbot sessions, or a control group, receiving standard mental health information. The primary outcome was the change in scores on the 9-item Patient Health Questionnaire from pre- to postintervention. Secondary assessments, such as voice and writing pressure analysis, provided additional engagement metrics, with data collected at baseline, during the intervention, and at week 9. Of the 96 eligible individuals on psychiatric waiting lists, 8 expressed interest and 3 provided initial consent. However, all participants subsequently withdrew or were excluded, resulting in no evaluable data for analysis. Low engagement may have been influenced by the perceived irrelevance of digital tools, complex protocols, and privacy concerns. Significant barriers to engagement suggest that digital interventions may need simpler protocols and trusted environments to improve feasibility. Future studies could test these interventions in supportive settings, like schools or community centers, to enhance accessibility and participation among youth.

The increasing integration of artificial intelligence (AI) systems into critical societal sectors has created an urgent demand for robust privacy-preserving methods. Traditional approaches such as differential privacy and homomorphic encryption often struggle to maintain an effective balance between protecting sensitive information and preserving data utility for AI applications. This challenge has become particularly acute as organizations must comply with evolving AI governance frameworks while maintaining the effectiveness of their AI systems. This paper aims to introduce and validate data obfuscation through latent space projection (LSP), a novel privacy-preserving technique designed to enhance AI governance and ensure responsible AI compliance. The primary goal is to develop a method that can effectively protect sensitive data while maintaining essential features necessary for AI model training and inference, thereby addressing the limitations of existing privacy-preserving approaches. We developed LSP using a combination of advanced machine learning techniques, specifically leveraging autoencoder architectures and adversarial training. The method projects sensitive data into a lower-dimensional latent space, where it separates sensitive from nonsensitive information. This separation enables precise control over privacy-utility trade-offs. We validated LSP through comprehensive experiments on benchmark datasets and implemented 2 real-world case studies: a health care application focusing on cancer diagnosis and a financial services application analyzing fraud detection. LSP demonstrated superior performance across multiple evaluation metrics. In image classification tasks, the method achieved 98.7% accuracy while maintaining strong privacy protection, providing 97.3% effectiveness against sensitive attribute inference attacks. This performance significantly exceeded that of traditional anonymization and privacy-preserving methods. The real-world case studies further validated LSP's effectiveness, showing robust performance in both health care and financial applications. Additionally, LSP demonstrated strong alignment with global AI governance frameworks, including the General Data Protection Regulation, the California Consumer Privacy Act, and the Health Insurance Portability and Accountability Act. LSP represents a significant advancement in privacy-preserving AI, offering a promising approach to developing AI systems that respect individual privacy while delivering valuable insights. By embedding privacy protection directly within the machine learning pipeline, LSP contributes to key principles of fairness, transparency, and accountability. Future research directions include developing theoretical privacy guarantees, exploring integration with federated learning systems, and enhancing latent space interpretability. These developments position LSP as a crucial tool for advancing ethical AI practices and ensuring responsible technology deployment in privacy-sensitive domains.

The Government of Bangladesh offers COVID-19 vaccines at no cost; however, sustaining this free vaccination program for a large population poses significant challenges. Thus, assessing the willingness to pay (WTP) for the COVID-19 vaccine is essential for understanding potential pricing strategies, subsidy requirements, and vaccine demand. This study aimed to assess the prevalence of WTP for the COVID-19 vaccine and its correlates. A cross-sectional design was used to collect data from 1497 respondents through web-based platform and face-to-face interviews. Multivariable logistic regression was used to analyze the correlates of the WTP. The results showed that 772 of 1497 (51.6%) participants were willing to pay for the COVID-19 vaccine, with a median of 300 BDT (IQR 150-500 BDT; a currency exchange rate of 1 BDT=US $0.008 is applicable). The WTP was significantly higher among individuals with a graduate degree (adjusted odds ratio [aOR] 1.98, 95% CI 1.14-3.45) or master's and MPhil or PhD-level education (aOR 1.93, 95% CI 1.07-3.48) and those with higher knowledge about the vaccine (aOR 1.09, 95% CI 1.02-1.15), positive behavioral practices (aOR 1.11, 95% CI 1.06-1.17), stronger subjective norms regarding COVID-19 vaccine (aOR 1.25, 95% CI 1.08-1.46), and higher anticipated regret of getting infected with COVID-19 (aOR 1.17, 95% CI 1.04-1.32). Conversely, WTP was lower among participants with negative attitudes toward vaccines (aOR 0.91, 95% CI 0.88-0.95) and high perceived behavioral control regarding COVID-19 vaccination (aOR 0.86, 95% CI 0.76-0.96; P=.006). With nearly half of the respondents unwilling to pay, this study highlights the need to improve vaccine-related knowledge and enhance income-based affordability to increase WTP. Health promotion efforts should focus on disseminating knowledge about vaccines and addressing negative perceptions. Additionally, a subsidized program for low-income groups can help mitigate financial barriers and promote equitable access to vaccines.

Access to contraception is a preventive measure against unplanned pregnancy and sexually transmitted infections; especially in sub-Saharan Africa where unmet need is a public health concern. This study assessed the levels and predictors of knowledge, attitudes, and practices regarding contraception among female TV studies students in Nigeria. This is a cross-sectional study conducted among female students of NTA TV College, Nigeria. Categorical sociodemographics, knowledge, attitude, and practice were presented as frequencies and proportions, while the continuous variables were presented as summary measures of central tendencies and dispersions. The primary outcome variable was the practices regarding contraception, while attitude and knowledge were secondary outcome variables, with sociodemographics as covariates. Predictors of good knowledge, attitude, and practice regarding contraception were determined by multivariable binary logistic regression, which was preceded by a bivariate regression analysis to determine candidate variables for the final model. A P value <.05 was determined to be statistically significant. There were 217 study participants with an average age of 22 (SD 2.6) years. Levels of good knowledge, attitude, and practice regarding contraception were reported in 55.3% (n=120), 47.5% (n=103), and 50.7% (n=110) of participants, respectively. The majority have had sex, used friends and the internet as their main sources of contraceptive information, and commonly used contraceptives such as condoms and oral contraceptive pills. The most common reason for not using contraceptives was fear of side effects or health risks. Being a young adult was a significant predictor (adjusted odds ratio [aOR] 2.6, 95% CI 1.0-6.7; P=.04) of good knowledge, while being a diploma student (aOR 2.4, 95% CI 1.2-4.6; P=.01), living off campus (aOR 2.1, 95% CI 1.0-4.4; P=.04), and good knowledge (aOR 3.8, 95% CI 2.1-6.9; P<.001) were significant predictors of good attitude. Being from the state's indigenous population (aOR 2.4, 95% CI 1.2-4.6; P=.01) and having engaged in sex (aOR 24.5, 95% CI 7.9-75.7; P<.001) were significant predictors of good contraception use. Our study has shown relatively low levels of good knowledge, attitude, and practice regarding contraception and their predictors. Therefore, there is an urgent need to consistently improve advocacy, curricular development, and policies to improve knowledge, attitude, and practice regarding contraception and sexual and reproductive health services among young people.