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Background: Transoral esophagogastroduodenoscopy (EGD) requires sedation and monitored anesthesia care, while transnasal esophagoscopy (TNE) can be performed without sedation. Despite this benefit, TNE is performed significantly less than EGD in the United States. Objectives: The current project was designed to compare the differential prevalence of TNE and EGD with and without concurrent anesthesia billing among Medicare beneficiaries during 2018-2021. Materials and Methods: The Public Use Files data for TNE, EGD, and anesthesia for EGD billed nationally among Medicare beneficiaries was exported from the Centers for Medicare & Medicaid Services (CMS) website for 2018-2021 and presented as cumulative data. Projections were made to quantify healthcare cost savings if the number of TNE services were at least 9% of the number of TNE+EGD services and at most 34% of the number of TNE+EGD services from 2018-2021. Results: The key finding was that TNE was rarely performed and billed among Medicare beneficiaries when compared to EGD. In addition, roughly one-third of EGDs among Medicare beneficiaries might have been performed without concurrent anesthesia for EGD billing. Conclusion: Secondary to the need for sedation and monitored anesthesia care, transoral upper gastrointestinal endoscopy is significantly more expensive than transnasal upper gastrointestinal endoscopy. Medicare could save millions of dollars annually if TNE replaced EGD.

Background: Antimicrobial resistance (AMR) is a growing public health concern globally, and misuse of antibiotics is a major contributor. Objective: This study investigated antibiotic utilisation patterns before and after the COVID-19 pandemic in Tanzania using data from the Tanzania Medicines and Medical Devices Authority (TMDA). Methods: This retrospective longitudinal study analysed secondary data. The study compared antibiotics consumption in defined daily doses (DDD) per 1000 inhabitants per day (DID) in two distinct eras: 2018-2019 as the pre-COVID-19 era and 2020-2021 as the post-COVID-19 era. Data was reorganised using Microsoft Power BI, and statistical analysis was conducted using SPSS software. Results: The study analysed 10,614 records and found an overall increase in antibiotics consumption from 2018 to 2021. When we divided the consumption of antibiotics into a pre- and post-COVID time period, with the pre-COVID period being 2018 and 2019 and the post-COVID period being 2020 and 2021, we found that the consumption was 61.24 DID in the post-COVID era and 50.32 DID in the pre-COVID era. Levofloxacin had the highest percentage increase in use, with a 700% increase in DID after the pandemic. Azithromycin had a 163.79% increase, while cefotaxime had a 600% increase. In contrast, some antibiotics exhibited a decrease in usage after the pandemic, such as nalidixic acid, which had a 100% decrease, and cefpodoxime, 66.67% decrease. Conclusion: The increase in antibiotic consumption during the COVID-19 pandemic highlights the importance of implementing effective antimicrobial stewardship strategies to prevent AMR, especially during pandemics.

Purpose: Invasive mechanical ventilation is utilized in over 50% of Australian Intensive Care Unit patients, with rates increasing world-wide. Prolonged mechanical ventilation is associated with increased length of hospital stay and in-hospital mortality compared with patients ventilated under the time threshold. Previous studies have highlighted mortality rates, but much remains unknown regarding the long-term physiological, functional, and psychiatric effects of prolonged mechanical ventilation. With a greater understanding of these outcomes, models of care can be formulated to reduce long-term morbidity. Methods: Medline, CINAHL and the Cochrane Library were searched between 1998 and March 2019, for PMV, patients in ICU and long-term outcomes. Included studies had patients, that received greater than or equal to 14 days of IMV. Primary outcome was mortality rates with secondary outcomes clustered into physiological, functional, and psychiatric outcomes. Results: 1057 studies were identified, with 24 included. 73% of PMV patients were discharged from ICU, with mortality rates of 57% and 69% at 12 and 48 months. 30.2% were discharged home, 25% developed new onset ventilator acquired pneumonia and up to 39% experienced psychiatric complications. Conclusion: Despite a high proportion of patients surviving to hospital discharge, subsequent outcomes are extremely poor for patients that require PMV.

Introduction: Sharing research findings with participants is important but challenging. We evaluated a plan to share findings with participants of a COVID-19 seroprevalence project. Methods: An electronic survey was distributed to participants after completion of the project to determine reach of planned coverage of the findings. Results: Most respondents (80% n=428) had not seen the coverage; but nearly all (90%, n=388) wanted to see the findings. Participants identified a brief visual report as their preferred avenue. A second follow-up survey found that 99% of those who read the report approved. Discussion: When researchers alone create a plan to share project findings, efforts may not reach nor be in the format desired by participants. Conclusions: This work can serve as a model for collaborating with community to disseminate public health data.

Fundus angiography, including fundus fluorescein angiography (FFA) and indocyanine green angiography (ICGA), are essential examination tools for visualizing lesions and changes in retinal and choroidal vasculature. However, the interpretation of angiography images is labor-intensive and time-consuming. In response to this, we are organizing the third APTOS competition for automated and interpretable angiographic report generation. For this purpose, we have released the first angiographic dataset, which includes over 50,000 images labeled by retinal specialists. This dataset covers 24 conditions and provides detailed descriptions of the type, location, shape, size and pattern of abnormal fluorescence to enhance interpretability and accessibility. Additionally, we have implemented two baseline methods that achieve an overall score of 7.966 and 7.947 using the classification method and language generation method in the test set, respectively. We anticipate that this initiative will expedite the application of artificial intelligence in automatic report generation, thereby reducing the workload of clinicians and benefiting patients on a broader scale.

Objectives To examine changes in volume of and amount of sugar in purchases of soft drinks according to household income and composition, at 19 months following the implementation of the UK Soft drinks industry levy (SDIL). Design: Controlled interrupted time series analysis Setting: Representative households (mean weekly number of households =21,908) across Great Britain Participants: Members of the Kantar Fast Moving Consumer Goods panel, a market research panel which collects data on weekly household purchases (eg: drinks, confectionery etc) between March 2014 to November 2019. Interventions: The SDIL, is a two-tiered tax (announced in March 2016 and implemented in April 2018) on manufacturers of soft drinks. Drinks containing [≥]8g sugar /100mls and [≥]5 to <8g sugar/ 100mls are taxed at 0.24 GBP/litre and 18 GBP/litre, respectively. Soft drinks containing <5g sugar/100ml are not subject to the levy. Levy exempt drinks, irrespective of sugar content, include milk and milk-based drinks, no-added-sugar fruit juice and powder used to make drinks. Main Outcome measures: Absolute and relative differences in the volume of and amount of sugar in non-alcoholic soft drinks, confectionery and alcohol purchased weekly by household income (<20,000, 20-50,000 or >50,000 GBP) and composition (presence of children [<16years] in the household (yes or no), 19 months after SDIL-implementation, compared to the counterfactual scenario based on pre-announcement trends and using a control group (toiletries). Results: By November 2019, overall purchased weekly sugar in soft drinks fell by 7.46g (95%CI: 12.05, 2.87) per household but volumes of drinks purchased remained unchanged, compared to the counterfactual based on pre-announcement trends. In low-income households, weekly sugar purchased in soft drinks decreased by 14.0% (95%CI: 12.1,15.9) compared to the counterfactual but in high income households increased by 3.4% (1.07,5.75). Similarly, among households with children, sugar purchased decreased by 13.7% (12.1, 15.3) compared to the counterfactual but increased in households without children by 5.0% (3.0,7.0). Low-income households and those with children also reduced their weekly volume of soft drinks purchased by 5.7% (3.7, 7.7) and 8.5% (6.8, 10.2) respectively. There was no evidence of substitution to confectionary or alcohol. Conclusion: In the second year following implementation of the SDIL, there were sustained reductions in sugar derived from soft drink purchases, but no change in volume of soft drinks purchased. Effects on sugar purchased were greatest in those with the highest pre-SDIL purchasing levels (low-income households and those with children). The SDIL may contribute to reducing dietary inequalities.

Psychotic and psychotic-like experiences are thought to emerge from various patterns of disrupted belief updating. These include belief rigidity, overestimating the reliability of sensory information, and misjudging task volatility. Yet, these substrates have never been jointly addressed under one computational framework and it is not clear to what degree they reflect trait-like computational patterns. Here, we introduce a computational model that describes interindividual differences in how individuals update their beliefs about a volatile environment through noisy observations and use this model in a series of studies. In a test-retest study with healthy participants (N = 45, 4 sessions), we find that interclass correlations were moderate to high for session-level model parameters and excellent for averaged belief trajectories and learning rates estimated through hierarchical Bayesian inference. Across three studies (total N=590) we then demonstrate that two distinct computational patterns describe two different transdiagnostic categories. Higher uncertainty about the task volatility is related to schizotypal traits (N = 45, d = 0.687, P = 0.02, N = 437, d = 0.14, P = 0.032) and to positive symptoms in a sample of patients with schizophrenia (N = 108, d = 0.187, P = 0.039), when learning to gain rewards. In contrast, depressive-anxious traits were associated with more rigid beliefs about the underlying mean (N = 437, d = -0.125, P = 0.006) and outcome variance (N = 437, d = -0.111, P = 0.013), as were negative symptoms in patients with schizophrenia (d = -0.223, P = 0.026, d = -0.298, P = 0.003), when learning to avoid losses. These findings suggest that individuals high on schizotypal traits across the psychosis continuum are less likely to learn or utilize higher-order statistical regularities of the environment and showcase the potential of clinically relevant computational phenotypes for differentiating symptom groups in a transdiagnostic manner.

Objective: Depression is a heterogeneous disorder. The purpose of this article is to examine the contribution of age to this heterogeneity by characterizing the associations of depressive symptoms with cognitive performance and brain structure across the lifespan. Methods: The authors analyzed demographic variables (age, gender, education), affective measures (Hospital Anxiety and Depression Scale), and cognitive assessments (The Addenbrooke's Cognitive Examination Revised) from the Cambridge Centre for Ageing Neuroscience (Cam-CAN) cohort (N=2591, age 18-99). A subset of this cohort (N=647) underwent structural MRI, which was used for voxel-based brain morphometry. Results: A linear regression model revealed a significant interaction between age and depression score, indicating that depression-related cognitive dysfunction is more severe in older adults. A comparison of different cognitive domains showed that this effect was consistent across all tested domains but significantly more prominent for fluency. A complementary voxel-based morphometry analysis, based on similar regression models, revealed age by depression interactions in several brain regions, demonstrating preferential age-related reduction in grey matter volume in the left and right hippocampi in older adults. The reciprocal contrast revealed preferential reduction in grey matter in the left superior frontal gyrus, left middle frontal gyrus, and left superior parietal lobule in younger adults. Conclusions: These findings indicate that the associations of depression with cognitive performance and brain structure are age-dependent, suggesting that the neuropathological mechanisms underlying depression may differ between young and older adults. Recognizing these differences will support the development of better diagnostic tools and therapeutic interventions for depression across the lifespan.

Background Transcranial direct current stimulation (tDCS) has been proposed as a novel treatment in major depressive disorder (MDD). However, efficacy and safety of home-based tDCS treatment have not been investigated. Methods Fully remote, multisite, double-blind, placebo-controlled, randomized superiority trial of home-based tDCS treatment in MDD was conducted in UK and USA. Participants were adults 18 years or older, having MDD diagnosis based on DSM-5 criteria, in current depressive episode of at least moderate severity, measured by score > 16 on 17-item Hamilton Depression Rating Scale (HDRS), without treatment resistant depression history. Protocol was 10-week blinded phase: 5 tDCS sessions per week for 3 weeks then 3 sessions per week for 7 weeks; followed by 10-week open label phase. tDCS montage was bifrontal, 30-minute sessions, active tDCS 2 mA, and sham tDCS 0 mA with brief ramp up and down to mimic active device. Primary outcome was HDRS change at week 10 in modified intention-to-treat analysis. Results 174 MDD participants were randomized: active (n=87; mean age 37.1 + 11.1 years) and sham (n=87; mean age 38.3 + 10.9 years) treatment. Significant improvement in HDRS was observed in active (9.4 + 6.25 points) relative to sham treatment (7.1 + 6.10 points) (95% CI 0.5 to 4.0, p = 0.012), with no differences in discontinuation rates between active (n=13) and sham (n=12). Conclusions Home-based tDCS with remote supervision is a potential first line treatment for MDD that is acceptable and safe. Consideration of continuing effective safety monitoring is required. Trial registration number NCT05202119

Molecular neuroimaging techniques, like PET and SPECT, offer invaluable insights into the brain's in-vivo biology and its dysfunction in neuropsychiatric patients. However, the transition of molecular neuroimaging into diagnostics and precision medicine has been limited to a few clinical applications, hindered by issues like practical feasibility and high costs. In this study, we explore the use of normative modelling (NM) for molecular neuroimaging to identify individual patient deviations from a reference cohort of subjects. NM potentially addresses challenges such as small sample sizes and diverse acquisition protocols that are typical of molecular neuroimaging studies. We applied NM to two PET radiotracers targeting the dopaminergic system ([11C]-(+)-PHNO and [18F]FDOPA) to create a normative model to reference groups of controls. The models were subsequently utilized on various independent cohorts of patients experiencing psychosis. These cohorts were characterized by differing disease stages, treatment responses, and the presence or absence of matched controls. Our results showed that patients exhibited a higher degree of extreme deviations (~3-fold increase) than controls, although this pattern was heterogeneous, with minimal overlap in extreme deviations topology (max 20%). We also confirmed the value of striatal [18F]FDOPA signal to predict treatment response (striatal AUC ROC: 0.77-0.83). Methodologically, we highlighted the importance of data harmonization before data aggregation. In conclusion, normative modelling can be effectively applied to molecular neuroimaging after proper harmonization, enabling insights into disease mechanisms and advancing precision medicine. The method is valuable in understanding the heterogeneity of patient populations and can contribute to maximising cost efficiency in studies aimed at comparing cases and controls.

Background: Current antiretroviral therapies have increased the life expectancy of people living with HIV (PLHIV). There is, however, limited evidence regarding the health-related quality of life (HRQoL) and living conditions of older people living with HIV (OPLHIV) in Spain. Methods: We implemented a self-administered online questionnaire to identify sex differences in HRQoL and poverty risk among Spanish OPLHIV (PLHIV [≥]50 years). Participants were contacted through non-governmental organisations. We used the standardised WHOQoL-HIV BREF questionnaire and the Europe 2020 guidelines to estimate HRQoL and poverty risk respectively. The statistical analysis included multivariable generalised linear models with potential confounding variables and robust estimates. Results: The study included 247 OPLHIV (192 men and 55 women). On the WHOQoL-HIV BREF questionnaire, men scored higher on 84% of items and in all six domains. Women had significantly lower HRQoL in five domains: physical health ({beta}: -1.5; 95% CI: -2.5, -0.5; p: 0.002), psychological health ({beta}: -1.0; 95% CI: -1.9, -0.1; p: 0.036), level of independence ({beta}: -1.1; 95% CI: -1.9, -0.2; p: 0.019), environmental health ({beta}: -1.1; 95% CI: -1.8, -0.3; p: 0.008), and spirituality/personal beliefs ({beta}: -1.4; 95% CI: -2.5, -0.3; p: 0.012). No statistical differences were found in the domain of social relations. Poverty risk was considerable for both men (30%) and women (53%), but women were significantly more likely to experience it (OR: 2.9; 95% CI: 1.3, 6.5; p: 0.009). Conclusion: The aging of PLHIV is a public health concern. Our findings indicate that HRQoL and poverty risk among Spanish OPLHIV differ significantly by sex. Spain should, therefore, implement specific policies and interventions to address OPLHIV needs. The strategies must place a high priority on the reduction of sex inequalities in HRQoL and the enhancement of the structural conditions in which OPLHIV live.

Background How skin microbiota in psoriasis patients responded to systematic therapeutics remained unknown. Objectives To profile temporal shifts in transcriptionally active skin microbiota in psoriasis patients receiving systemic therapies. Methods We prospectively enrolled 61 psoriasis patients and 29 skin-healthy controls in 2015-2019. Using RNA-based 16S rRNA gene sequencing, we analyzed 969 samples from skin lesions and compared microbial abundance and diversity by therapeutic classes and disease severity. Results Lesional microbiota in patients on conventional systemics and TNF- inhibitor was different in relative abundances in Firmicutes (7.83% higher, adjusted P < 0.001) and Proteobacteria (6.98% lower, adjusted P < 0.01) from that in patients on anti-interleukin monoclonal antibodies (anti-ILAb) at baseline. The only difference during treatment was a 1.47% lower abundance in Bacteroides associated with nonbiologics use (adjusted P < 0.01). We identified no indicator taxa by disease severity at baseline yet noticed that a minor relative reduction in Corynebacterium sp. was associated with clinical responses to treatment. Compared to anti-ILAb, TNF- inhibitor and nonbiologics were associated with -0.21 lower Shannon Diversity (adjusted P < 0.01) and 0.03 higher Shannon Evenness (adjusted P < 0.01). Results of ordinated principal coordinates analysis revealed that, lesional microbiota from patients of these 3 therapeutic groups was compositionally distinct. Our work also demonstrated concurrent changes in clonal shifts in systemic T cell receptor clonotypes that were associated with systemic use of biologics. Conclusions Community abundances and diversities of skin microbiota may be useful in distinguishing skin microbiota from patients receiving different systemic therapeutics. Specifically, use of anti-ILAb and TNF- inhibitor was associated with sample-wise microbial abundances and diversities, but not richness, over time. These findings highlighted the potential utility of skin microbiota as biomarkers for personalized treatment plans in patients with moderate-to-severe psoriasis.

Background: Despite the importance of early cardiovascular disease (CVD) intervention, little data exists for evaluating cardiovascular risk in adults without traditional CVD risk factors (e.g., diabetes, hypertension). Methods: We included 4,544 adults from the 1999-2004 National Health and Nutrition Examination Survey without prevalent diabetes, hypertension, chronic kidney disease, or CVD. We used multi-variable adjusted Cox proportional hazards regression modeling to assess the relationship between logarithmically transformed cardiac biomarkers (high sensitivity cardiac troponin T (hs-cTnT), hs-cTnI (Abbott, Ortho, and Siemens assays), and NT-proBNP) and CVD mortality among a nationally representative cohort of relatively healthy adults. Results: The mean age was 38.2 years (SD 12.8) and 53.9% were women. 8.7% had elevated levels of hs-cTnT or NT-proBNP above previously established thresholds for subclinical CVD. In multivariable adjusted models, each doubling of hs-cTnT was associated with a 49% increased risk of CVD mortality (Hazard Ratio (HR) 1.49, 95%CI 1.02-2.17, p=0.04). Only two of the hs-cTnI assays (Abbott and Ortho) were significantly associated with CVD mortality (Abbott HR 1.48, 95%CI 1.06-2.07, p=0.02; Ortho HR 1.47, 95%CI 1.23-1.77, p=0.0001). Each doubling of NT-proBNP was associated with a 41% increased risk of CVD mortality (HR 1.38, 95%CI 1.09-1.74, p=0.008). Conclusion: Younger patients who maintain relatively good health may still carry occult CVD risk. Efforts to reduce population-wide CVD should consider novel methods for risk stratification, as standard CVD risk factors may overlook subpopulations at risk.

Misuse of antibiotics contributes to the worldwide rise in antibiotic resistance. However, it is equally important to consider inevitable situations that may contribute to a rise in antibiotic resistance and implement best practices to ensure the minimality of their resulting adverse effects. A case in point is the increasing prevalence of methicillin-resistant Staphylococcus aureus (MRSA), a strain of bacterium that has developed resistance to the antibiotic methicillin and other lactams among long-term diabetic patients as observed in this study. Diabetes is associated with immunosuppression and foot ulcers, which require the constant use of antibiotics as prophylactics, as such, persons with this condition would hold bacterial populations subject to immense selective pressure and are therefore at increased risk for MRSA colonization. Furthermore, these individuals may serve as reservoirs and disseminate these highly drug-resistant strains on return to the general populace. This warranted an investigation into the prevalence of MRSA among diabetic patients at the 37-Military Hospital. A total of 50 samples were taken from the sampled Military Hospital by swabbing the participants' skin, nose, and wounds aseptically, and 20 samples were acquired from non-diabetics to serve as controls. To culture the samples, blood, and mannitol salt agar were used. Bacteria were isolated and identified using Gram staining and other biochemical testing. The Kirby-Bauer disc diffusion method was used with Muller-Hinton agar to test for antimicrobial susceptibility. According to the study's findings, antibiotic resistance was much higher among diabetic patients than among non-diabetic patients at the 37-Military Hospital in the Accra Metropolis, Ghana.

COVID-19 has resulted in over 645 million hospitalization and 7 million deaths globally. However, many questions still remain about neuropsychiatric complications in COVID-19 and if these complications changed with different circulating SARS-CoV-2 strains. We analyzed a 2.5-year retrospective cohort of 47,063 encounters for 21,312 acute care patients at five Central Texas hospitals and define distinct trajectory groups (TGs) with latent class mixed modeling, based on the World Health Organization COVID-19 Ordinal Scale. Using this TG framework, we evaluated the association of demographics, diagnoses, vitals, labs, imaging, consultations, and medications with COVID-19 severity and neuropsychiatric outcomes. Patients within 6 distinct TGs differed in manifestations of multi-organ disease and multiple clinical factors. The proportion of mild patients increased over time, particularly during Omicron waves. Age separated mild and fatal patients, though did not distinguish patients with severe versus critical disease. Male and Hispanic/Latino demographics were associated with more severe/critical TGs. More severe patients had a higher rate of neuropsychiatric diagnoses, consultations, and brain imaging, which did not change significantly in severe patients across SARS-CoV-2 variant waves. More severely affected patients also demonstrated an immunological signature of high neutrophils and immature granulocytes, and low lymphocytes and monocytes. Interestingly, low albumin was one of the best lab predictors of COVID-19 severity in association with higher malnutrition in severe/critical patients, raising concern of nutritional insufficiency influencing COVID-19 outcomes. Despite this, only a small fraction of severe/critical patients had nutritional labs checked (pre-albumin, thiamine, Vitamin D, B vitamins) or received targeted interventions to address nutritional deficiencies such as vitamin replacement. Our findings underscore the significant link between COVID-19 severity, neuropsychiatric complications, and nutritional insufficiency as key risk factors of COVID-19 outcomes and raise the question of the need for more widespread early assessment of a patient's neurological, psychiatric, and nutritional status in acute care settings to help identify those at risk of severe disease outcomes.

Background: Increased use of electronic health records (EHR) and patient-initiated messaging has led to inefficiency, staff shortages, and provider burnout. We evaluated the impact of a natural language processing (NLP) algorithm for message routing on communication dynamics. Methods: We developed an NLP model to accurately label inbound EHR messages from patients using a pre-trained classifier with fine-tuning based on clinician feedback. In a real-world study, the model was deployed to prospectively label and route messages sent to participating physicians at an integrated health system. A parallel control group of unrouted messages was generated for comparative analysis. The primary endpoints for assessing model performance were the time to first message interaction, the time to conversation resolution, and the total number of message interactions by staff, compared with the control group. Secondary endpoints were the precision, recall, F1 score (a measure of positive predictive value and sensitivity), and accuracy for correct message classification. Results: The model prospectively labeled and routed 469 unique conversations over 14 days from the inbaskets of participating physicians. Compared to a control group of 402 unrouted conversations from the same time period, conversations in the routed group had an 83.3% reduction in the time to first interaction (median difference [MD], -1 hour; P<0.001), an 84.4% reduction in the time to conversation resolution (MD, -22.5 hours; P<0.001), and a 40% reduction in the total number of staff interactions after application of intervention (MD, -2.0 interactions; P<0.001). The model demonstrated high precision (>97.6%), recall (>95%), and F1 scores (>96.5%) for accurate prediction of all five message classes, with a total accuracy of 97.8%. Conclusions: Real-time message routing using advanced NLP was associated with significantly reduced message response and resolution times.

Background: Evidence on the health benefits of spending time in nature has highlighted the importance of provision of blue and green spaces in peoples living environments. The potential for health benefits offered by nature exposure, however, extends beyond health promotion to health treatment. Social prescribing links people with health or social care needs to community-based, non-clinical health and social care interventions. The aim is to improve health and wellbeing. Nature-based social prescribing (NBSP) is a variant which uses the health-promoting benefits of activities carried out in natural environments, such as gardening and conservation volunteering. Much of current NBSP practice has been developed in the UK and there is increasing global interest in its implementation. This requires interventions to be adapted for different contexts, considering the needs of populations and the structure of healthcare systems. Methods: This paper presents results from an expert group participatory workshop involving 29 practitioners, researchers, and policymakers from the UK and Germany health and environmental sectors. Using the UK and Germany, two countries with different healthcare systems and in different developmental stages of NBSP practice as case studies, we analysed opportunities, challenges, and facilitators for the development and implementation of NBSP. Results: We identified five overarching themes for developing, implementing, and evaluating NBSP: Capacity Building; Universal Accessibility; Embedded and Integrated Networks and Collaborations; Standardised Implementation and Evaluation; and Sustainability. We also discuss key strengths, weaknesses, opportunities, and threats (i.e., a SWOT analysis) for each overarching theme to understand how they could be developed to support NBSP implementation. Conclusions: NBSP could offer significant public health benefits using available blue and green spaces. We offer guidance on how NBSP implementation, from wider policy support to the design and evaluation of individual programmes, could be adapted to different contexts. This research could help inform the development and evaluation of NBSP programmes to support planetary health from local and global scales.

Backgrounds: GPT4-Vision (GPT4V) has generated great interest across various fields, while its performance in ocular multimodal images is still unknown. This study aims to evaluate the capabilities of a GPT4V-based chatbot in addressing queries related to ocular multimodal images. Methods: A digital ophthalmologist app was built based on GPT4V. The evaluation dataset comprised various ocular imaging modalities: slit-lamp, scanning laser ophthalmoscopy (SLO), fundus photography of the posterior pole (FPP), optical coherence tomography (OCT), fundus fluorescein angiography (FFA), and ocular ultrasound (OUS). Each modality included images representing 5 common and 5 rare diseases. The chatbot was presented with ten questions per image, focusing on examination identification, lesion detection, diagnosis, decision support, and the repeatability of diagnosis. The responses of GPT4V were evaluated based on accuracy, usability, and safety. Results: There was a substantial agreement among three ophthalmologists. Out of 600 responses, 30.5% were accurate, 22.8% of 540 responses were highly usable, and 55.5% of 540 responses were considered safe by ophthalmologists. The chatbot excelled in interpreting slit-lamp images, with 42.0%, 42.2%, and 68.5% of the responses being accurate, highly usable, and no harm, respectively. However, its performance was notably weaker in FPP images, with only 13.7%, 3.7%, and 38.5% in the same categories. It correctly identified 95.6% of the imaging modalities. For lesion identification, diagnosis, and decision support, the chatbot's accuracy was 25.6%, 16.1%, and 24.0%, respectively. The average proportions of correct answers, highly usable, and no harm for GPT4V in common diseases were 37.9%, 30.5%, and 60.1%, respectively. These proportions were all higher compared to those in rare diseases, which were 23.2% (P<0.001), 15.2% (P<0.001), and 51.1% (P=0.032), respectively. The overall repeatability of GPT4-V in diagnosing ocular images was 63% (38/60). Conclusion: Currently, GPT4V lacks the reliability required for clinical decision-making and patient consultation in ophthalmology. Ongoing refinement and testing are essential for improving the efficacy of large language models in medical applications.

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean-ketogenic diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

Introduction: Acute ischemic stroke is a major subtype of acute stroke. More than 65% of stroke-related deaths occur in developing countries. Various modifiable and non-modifiable risk factors are associated with the stroke. TOAST classification categorizes ischemic stroke into five etiologic subgroups. Aims: To identify subtypes of acute ischemic stroke and compare the influence of various risk factors on them. Methodology: A prospective observational study was conducted in a tertiary care teaching hospital. 200 consecutive patients diagnosed as having acute ischemic stroke were randomly enrolled. Patients were categorized into 3 groups- Group A: 18-39 years (17%); Group B: 40-60 years (20%) and Group C: >60 years (63%). Stroke subtypes were ascertained using TOAST classification. Data analysis was done. For categorical variables, data values were represented as numbers and percentages. A chi-square test was applied to find the level of significance. p value<0.05 was considered significant. Results: The study included 136 men and 64 women. Overall male dominance (Male: Female = 2.125:1), while in Group A, strong female dominance (Male: Female = 1:4.67) was observed. The commonest subtype was embolism (29.4%) in Group A, and small vessel disease in Group B, and C ((30% & 46.03% respectively). Hypertension was the commonest risk factor (62%). A higher incidence of hypertension was found in Group A (73.02%, p = 0.002), dyslipidemia in Group B (45%, p = 0.004), valvular heart disease (29.4%, p = 0.00001), and atrial fibrillation (29.4%, p = 0.005) in Group-C. Smoking and diabetes mellitus were strongly related to the male gender (p = 0.000001, p=0.002 respectively) and valvular heart disease to the female gender (p= 0.0002). Conclusions: Awareness of risk factors and lifestyle modifications may have a bearing on stroke prevention. Cardiovascular risk factors in young patients mandate the need for robust prevention and screening. Key Words: Acute ischemic stroke, Etiology, Modifiable risk factors, Nonmodifiable risk factors, TOAST classification