Nevin Manimala

Carbon Balance Manag. 2026 Jul 2. doi: 10.1186/s13021-026-00479-5. Online ahead of print.

ABSTRACT

Rising anthropogenic carbon emissions are a major driver of climate change and pose a critical challenge to global sustainable development. As a rapidly advancing technology, artificial intelligence (AI) has shown strong potential to enhance carbon emissions management. This review provides a critical and comprehensive synthesis of recent advances in AI-enabled approaches for carbon emissions monitoring, prediction, and reduction. For monitoring, it explores the integration of satellite remote sensing, sensor networks, and machine learning (ML) algorithms, which can improve multi-scale, high-resolution, and near-real-time monitoring capabilities. For prediction, it categorizes prediction models into three groups, namely deep learning (DL), ensemble learning, and statistical learning, to facilitate the selection of appropriate technical approaches based on varying data characteristics and prediction requirements. For reduction, it examines the practical effectiveness of AI in industrial process optimization, energy structure transformation, transportation scheduling and management, construction energy efficiency improvement, and carbon capture, utilization, and storage (CCUS). We further reveal core challenges and potential solutions across the data layer, model layer, and application layer in AI deployment, including data availability and quality, model generalization and interpretability, and engineering and governance barriers that hinder the translation of AI methods into real-world applications. Furthermore, future research directions are discussed to promote the development of more reliable and scalable AI methods that can better support decision-making and practical governance in carbon emissions management. Overall, distinct from previous reviews that mainly focus on single tasks, specific model types, or sectoral applications, this review represents, to our knowledge, one of the first review-level attempts to develop a policy-relevant and interdisciplinary AI framework for carbon emissions management across the full process of monitoring, prediction, and reduction. By integrating unified evaluation metrics, evidence matrices, deployment-constraint analysis, and a technology readiness level (TRL)-based assessment, this framework links methodological performance, application readiness, and governance needs. It provides an integrated methodological foundation for fine-grained emissions sensing, predictive analysis, and emissions reduction decision support, while supporting quantifiable, verifiable, and actionable carbon balance and management.

PMID:42390761 | DOI:10.1186/s13021-026-00479-5

Can J Public Health. 2026 Jul 2. doi: 10.17269/s41997-026-01226-6. Online ahead of print.

ABSTRACT

OBJECTIVES: This study examined temporal trends in co-infection associated with sexually transmitted and blood-borne infections (STBBIs) in Ontario and assessed associations with behavioural, social, and structural determinants within a syndemic framework.

METHODS: A retrospective, population-based approach was used to analyse 269,814 positive test records for chlamydia, gonorrhea, syphilis, and HIV from Ontario’s public health notifiable diseases database across seven Public Health Units (PHUs) between 2013 and 2023. Co-infection was defined as ≥ 2 STBBIs diagnosed within 14 days. Descriptive statistics, Chi-square, and multivariable logistic regression were used to identify behavioural, social, and structural predictors of co-infection. Area-level marginalization was assessed using ON-Marg indices.

RESULTS: STBBI rates steadily increased across all PHUs over the study period, with Toronto, Northwestern, and Thunder Bay District showing the highest burden. Co-infections accounted for 5.71% of infection episodes and increased significantly over time, with a 10.20% annual increase estimated using negative binomial regression (p = 0.010). Male and transgender individuals had higher odds ratios of co-infection compared to females. Behavioural factors (anonymous sex, multiple partners, condomless sex, substance use) and social vulnerabilities (underhoused/homeless, survival sex, sex work) were associated with co-infection. ON-Marg dimensions in housing, material resources, and racialized populations were also associated with higher co-infection rates while age/labour force marginalization showed an inverse relationship.

CONCLUSION: The findings provide evidence consistent with syndemic patterns among STBBIs in Ontario, shaped by behavioural, social, and structural inequities. Integrated, equity-focused interventions and improved integration of co-infection data within disease surveillance systems may support more effective prevention strategies as co-infection rates rise.

PMID:42390728 | DOI:10.17269/s41997-026-01226-6

Dig Dis Sci. 2026 Jul 2. doi: 10.1007/s10620-026-10087-5. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Patients increasingly use the internet and artificial intelligence chatbots to obtain health information, yet the readability, quality, understandability, and actionability of AI-generated gastrointestinal patient education remain unclear. This study compared gastrointestinal patient education from a professional society website with content generated by ChatGPT using validated health literacy instruments.

METHODS: In this cross-sectional comparative study, 50 gastrointestinal patient education topics from the American Gastroenterological Association patient information website were paired with ChatGPT-generated responses using standardized prompts. Readability was assessed using the Flesch-Kincaid Grade Level, quality of treatment information was evaluated using the DISCERN instrument, and understandability and actionability were assessed using the Patient Education Materials Assessment Tool; scoring was performed by two blinded reviewers. Paired t tests were used to compare mean scores between sources, and intraclass correlation coefficients (ICCs) were used to assess interrater reliability between reviewers.

RESULTS: Fifty paired topics were analyzed. The mean Flesch-Kincaid Grade Level was higher for ChatGPT than GI website materials (10.33 ± 1.5 vs 8.72 ± 1.7; mean difference, 1.61; P < .001). Differences in DISCERN scores (63.5 ± 5.7 vs 64.3 ± 5.4; mean difference, – 0.8), PEMAT understandability (87.9% ± 6.9% vs 86.5% ± 7.8%; mean difference, 1.4%; P = .33), and PEMAT actionability (78.6% ± 9.8% vs 77.9% ± 10.2%; mean difference, 0.6%; P = .73) were not statistically significant. Inter-rater reliability was excellent across all measures, with intraclass correlation coefficients of 0.97 (95% CI, 0.95-0.99) for PEMAT understandability, 0.96 (95% CI, 0.94-0.98) for PEMAT actionability, and 0.99 (95% CI, 0.98-0.99) for DISCERN.

CONCLUSION: ChatGPT-generated gastrointestinal patient education demonstrated similar quality, understandability, and actionability compared with professional society materials but was written at a significantly higher reading level. Improving readability may enhance accessibility and support the safe integration of AI-generated patient education.

PMID:42390721 | DOI:10.1007/s10620-026-10087-5

Ophthalmic Physiol Opt. 2026 Jul 2. doi: 10.1007/s44402-026-00114-3. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to determine the duration of mesopic light conditions in natural environments.

METHODS: Illuminance levels were measured at three outdoor locations (a city terrace, a forest and a shadowy park) during different seasons using a calibrated high-spec lux meter with a sensitivity threshold of 0.01 lux. Measurements were recorded from 1 h before sunset until complete darkness to capture the transition from photopic to mesopic (<40 lux) and scotopic (<0.1 lux) conditions. The duration and onset of mesopic light were analysed across locations and seasons to identify site-specific variations.

RESULTS: A gradual decline in illumination was seen from photopic light at 1 h before sunset down to mesopic and scotopic levels. Among the three measured locations, the shadowy park transitioned to mesopic conditions 15-20 min earlier than the terrace or forest. However, despite these differences in onset, the duration of the mesopic period remained consistent, lasting approximately 25-30 min across all locations during the different seasons.

CONCLUSIONS: In natural outdoor environments, mesopic light exposure during sunset represents a relatively brief and consistent transition period of approximately 25-30 min. These findings provide objective field-based baseline data on the temporal dynamics of mesopic illumination. While the present study did not assess refractive development or myopia status, the quantified environmental parameters may inform future research integrating wearable light sensors and refractive outcomes.

PMID:42390705 | DOI:10.1007/s44402-026-00114-3

BJU Int. 2026 Jul 2. doi: 10.1111/bju.70378. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate the incidence of venous thromboembolism (VTE) after penile cancer surgery using national hospital data and to assess current thromboprophylaxis practices across UK specialist centres.

SUBJECTS/PATIENTS AND METHODS: A retrospective cohort study was conducted using Secondary Uses Service (SUS) data on penile cancer surgeries performed in NHS hospitals in England between 2015 and 2024. A national survey of UK Penile Cancer Network (UK PeCaN) surgeons was undertaken to assess current thromboprophylaxis practice. The primary outcome was symptomatic VTE within 180 days of surgery, identified using International Classification of Diseases, 10th Revision (ICD-10) codes. Cumulative incidence of first postoperative VTE was analysed using patient-level time-to-event methods, with censoring at second surgery, 180 days or administrative end of follow-up. Survey responses were summarised using descriptive statistics.

RESULTS: In this observational population-level study, 4310 patients underwent 5903 penile cancer-related procedures. A total of 143 VTE episodes were recorded over the 9-year period, corresponding to an overall crude incidence of 2.5%. In patient-level time-to-event analysis, the cumulative incidence of first postoperative symptomatic VTE was 0.21% at 30 days, 0.69% at 90 days and 1.08% at 180 days. Descriptive procedure-level analyses suggested higher unadjusted VTE rates following more extensive procedures, including lymph node dissection and total penectomy, although these estimates should be interpreted cautiously because of staged procedures and unmeasured patient-level confounding. Most VTE events occurred after hospital discharge. The survey, comprising 24 responses from 10 specialist centres, revealed substantial variation in thromboprophylaxis practice, with 71% of surgeons not using formal VTE risk assessment tools.

CONCLUSIONS: Venous thromboembolism is an important postoperative complication after penile cancer surgery, particularly after more extensive procedures and staged treatment pathways. Current prophylaxis practices are inconsistent. These findings support the further collection and analysis of disease-specific and the development of procedure-specific guidelines recommending extended thromboprophylaxis in high-risk patients.

PMID:42389899 | DOI:10.1111/bju.70378

Clin Obes. 2026 Aug;16(4):e70096. doi: 10.1111/cob.70096.

ABSTRACT

This service evaluation of a primary care-based specialist weight management service reports data from 1094 patients over 5 years (2014-2019), including weight data at 1-year post-discharge. The results show clinically and statistically significant improvements in weight, diet, physical activity, quality of life, blood pressure and blood glucose control (in people living with type 2 diabetes). Change in weight was statistically significant for all timepoints in all subgroups. At 1-year completers (n = 560) had lost a mean of 8.3 kg (SD 0.3) and 133 patients (23.8%) had lost ≥ 10% of their starting weight. Using baseline observation carried forward analysis on the whole cohort (n = 1094) the mean weight loss at the end of the 1-year programme was 4.5 kg (SD 0.2) and 144 (13.2%) had lost ≥ 10% of their starting weight. A year after discharge completers demonstrated a mean weight loss of 8.3% (SD 10.3 n = 303) and 35.6% (n = 108) of completers had maintained ≥ 10% change in body weight. Analysis showed a mean weight loss of 2.5% (SD 6.8 n = 1094) in the whole cohort using baseline observation carried forward, demonstrating maintenance of weight loss and suggesting that specialist weight management services in primary care may be effective in the longer-term.

PMID:42389898 | DOI:10.1111/cob.70096

Phlebology. 2026 Jul 2:2683555261467340. doi: 10.1177/02683555261467340. Online ahead of print.

ABSTRACT

ObjectivesGenerative artificial intelligence (AI) models capable of producing photorealistic medical images are increasingly proposed for patient education, clinical illustration, and trainee instruction. However, their ability to accurately represent anatomically distinct disease subtypes remains unclear. This study evaluated the diagnostic accuracy of a widely used generative AI model in producing images corresponding to the five anatomical lipedema types defined by the Schmeller classification.MethodsIn this prospective audit, ChatGPT’s image-generation interface was prompted to create 60 images for each lipedema type (Types I-V),yielding 300 images. Prompts were standardized and limited to the subtype label without additional descriptors. Two clinicians independently classified each image into one of the five lipedema types or as indeterminate, blinded to the original prompt; disagreements were resolved by a third clinician. Diagnostic performance was assessed using a confusion matrix and per-type sensitivity, specificity, positive predictive value(PPV), negative predictive value (NPV),F1-score,and one-vs-rest receiver operating characteristic area under the curve (ROC AUC). Overall accuracy and Cohen’s κ statistics were also calculated.ResultsAll 300 images were evaluable. The model generated anatomically consistent images for Types I,II, and III (sensitivity = 1.00 for each). Specificity was 1.00 for Types I and II but 0.50 for Type III because all images requested as Types IV and V were classified as Type III. Consequently, the model failed to generate any images consistent with Type IV(arm-predominant) or Type V(calf-isolated) lipedema (sensitivity = 0.00 for both). Overall accuracy was 0.600. Unweighted and quadratic-weighted Cohen’s κ values were 0.500 and 0.667, respectively. Micro- and macro-averaged ROC AUC were both 0.750.ConclusionThe model reproduces severity gradients within lower-extremity lipedema but systematically collapses anatomically distinct subtypes into the dominant Type III phenotype, failing to depict arm-predominant and calf-isolated disease. Current generative AI systems may therefore encode lipedema as a single visual phenotype rather than a distributed anatomical entity, limiting their reliability for medical education and clinical communication.

PMID:42389893 | DOI:10.1177/02683555261467340

Aust Endod J. 2026 Jul 2. doi: 10.1111/aej.70105. Online ahead of print.

ABSTRACT

This study aimed to evaluate the content and quality of YouTube videos on bioceramic-based root canal sealers. A total of 43 videos were assessed using the Journal of the American Medical Association (JAMA), Global Quality Score (GQS) and Modified DISCERN (mDISCERN) measurement tools. Statistical analyses were conducted using the Kruskal-Wallis test, Spearman correlation analysis and multiple linear regression (p < 0.05). Videos uploaded by dentists had significantly higher GQS, mDISCERN, JAMA and Total Content Score (TCS) scores than those from other sources (p < 0.05). Videos with rich content showed higher GQS, mDISCERN and JAMA scores than those with poor content (p < 0.05). Although the number of comments demonstrated an individual association with GQS, the overall regression model was not statistically significant (p > 0.05). In conclusion, most videos on bioceramic-based root canal sealers were of poor to moderate quality and could have limitations in reliability.

PMID:42389892 | DOI:10.1111/aej.70105

Suicide Life Threat Behav. 2026 Aug;56(4):e70124. doi: 10.1111/sltb.70124.

ABSTRACT

INTRODUCTION: Passive suicidal ideation (SI) is a well-established risk factor for suicidal behavior but has received less attention than active SI. Although recent work has leveraged intensive longitudinal data and machine learning (ML) to forecast short-term risk for active SI, passive SI remains understudied as a prediction target.

METHODS: Seventy-eight psychiatrically hospitalized youth (ages 13-17 years) completed baseline assessments and daily ratings of risk and protective factors for 28 days post-discharge. Multiple ML models were trained to predict the presence of next-day passive SI. Models with and without baseline variables were compared to assess the relative predictive value of time-varying versus baseline features.

RESULTS: ML models predicted next-day passive SI with high accuracy (AUC = 0.90). The strongest predictors were within-person 7-day moving averages of passive SI duration and frequency. Including baseline variables had negligible performance impact, even during initial days post-discharge.

CONCLUSIONS: Short-term passive SI remains an underutilized but important target for suicide prevention. Forecasting next-day passive SI using ML is feasible and highly accurate. Within-person, time-varying features outperformed baseline factors, even in early days post-discharge. Additional research on SI facets, such as duration, is needed. Integrating passive SI into personalized intervention frameworks may enhance the precision of suicide prevention efforts.

PMID:42389887 | DOI:10.1111/sltb.70124

Cancer Control. 2026 Jan-Dec;33:10732748261465064. doi: 10.1177/10732748261465064. Epub 2026 Jul 2.

ABSTRACT

IntroductionLow cancer health literacy undermines patients’ ability to interpret complex information and participate in shared decision making, and is associated with worse outcomes. Evidence for effective health literacy interventions in metastatic breast cancer (MBC) remains limited. We evaluated a patient-led, virtual health literacy program designed to strengthen MBC-specific knowledge and self-efficacy.MethodsWe conducted a mixed-methods, pre-post evaluation across two program iterations (Fall 2023; Spring 2024) delivered by a national, patient-led-MBC organization, Project Life. The five-week synchronous course called Spinning Science covered breast cancer subtyping, genetic/genomic testing, clinical trials, circulating tumor DNA, and information literacy, with small-group activities and polling. Program participants were adults living with MBC. De-identified pre/post surveys assessed (1) self-efficacy for engaging in health decisions, and (2) knowledge using items adapted from the Cancer Health Literacy Test aligned to course content. Paired two-sample t-tests examined pre-post changes (α=0.05). Open-ended responses were analyzed thematically.ResultsFifty-four people with MBC were enrolled (cohort 1, n=17; cohort 2, n=37); 46 provided matched pre- and post-surveys (14 and 32, respectively). Agreement with “I don’t know enough to make my own medical decisions” declined from 43% pre to 13% post (p<0.05), indicating improved self-efficacy. Baseline knowledge scores were high, and knowledge item gains were not statistically significant, consistent with ceiling effects. Post-program items showed >80% agreement for increased confidence in self-advocacy, improved health literacy, and sense of community. Qualitative feedback highlighted strengths like digestible content, approachable patient facilitators and flexible scheduling, as well as priorities for refinement such as continued access to materials, and more MBC-specific and numeracy content.ConclusionsA patient-developed, virtual health literacy program for people living with MBC showed meaningful improvements in self-efficacy, with actionable, participant-driven refinements between cohorts. This model offers a practical, scalable pathway for advancing self-efficacy within and beyond MBC.

PMID:42389868 | DOI:10.1177/10732748261465064