Tumor-related factor complement Clq/TNF-related protein 6 affects the development of digestive system tumors through the phosphatidylinositol 3-kinase pathway.

In this editorial, we review the work of Razali et al published in World J Gastroenterology, with a particular focus on the effect of rs10889677 variation in the phosphatidylinositol 3-kinase (PI3K) pathway and buparlisib on colitis-associated cancer. The role of PI3K in promoting cancer progression has been widely recognized, as it is involved in regulating the survival, differentiation, and proliferation of cancer cells. The complement Clq/TNF-related protein 6 (CTRP6) is a newer tumor-associated factor. Recent studies have revealed the pro-tumor effect of CTRP6 in gastric cancer, hepatocellular carcinoma, colorectal cancer, and other gastrointestinal tumors through the PI3K pathway. This article attempts to reveal the mechanism through which the CTRP6 affects the development of digestive system tumors through the PI3K pathway by summarizing recent research.

Przewaquinone A inhibits Angiotensin II-induced endothelial diastolic dysfunction activation of AMPK.

BACKGROUND: Endothelial dysfunction (ED), characterized by markedly reduced nitric oxide (NO) bioavailability, vasoconstriction, and a shift toward a proinflammatory and prothrombotic state, is an important contributor to hypertension, atherosclerosis, and other cardiovascular diseases. Adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) is widely involved in cardiovascular development. Przewaquinone A (PA), a lipophilic diterpene quinone extracted from Salvia przewalskii Maxim, inhibits vascular contraction. PURPOSE: Herein, the goal was to explore the protective effect of PA on ED in vivo and in vitro, as well as the underlying mechanisms. METHODS: A human umbilical vein endothelial cell (HUVEC) model of ED induced by angiotensin II (AngII) was used for in vitro observations. Levels of AMPK, endothelial nitric oxide synthase (eNOS), vascular cell adhesion molecule-1 (VCAM-1), nitric oxide (NO), and endothelin-1 (ET-1) were detected by western blotting and ELISA. A mouse model of hypertension was established by continuous infusion of AngII (1000 ng/kg/min) for 4 weeks using osmotic pumps. Following PA and/or valsartan administration, NO and ET-1 levels were measured. The levels of AMPK signaling-related proteins in the thoracic aorta were evaluated by immunohistochemistry. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) were measured using the tail cuff method. Isolated aortic vascular tone measurements were used to evaluate the vasodilatory function in mice. Molecular docking, molecular dynamics, and surface plasmon resonance imaging (SPRi) were used to confirm AMPK and PA interactions. RESULTS: PA inhibited AngII-induced vasoconstriction and vascular adhesion as well as activated AMPK signaling in a dose-dependent manner. Moreover, PA markedly suppressed blood pressure, activated vasodilation in mice following AngII stimulation, and promoted the activation of AMPK signaling. Furthermore, molecular simulations and SPRi revealed that PA directly targeted AMPK. AMPK inhibition partly abolished the protective effects of PA against endothelial dysfunction. CONCLUSION: PA activates AMPK and ameliorates endothelial dysfunction during hypertension.

The Role of Mindfulness Therapy in the Treatment of Chronic Pain.

PURPOSE OF REVIEW: Mindfulness therapy is a widely used treatment for many diseases and has been shown to improve pain-related functions. There is growing support for the use of psychotherapy in the treatment of chronic pain. While studies have shown a positive effect of mindfulness therapy, it is important to consider psychosocial factors as there are still a small number of studies that question its effectiveness. RECENT FINDINGS: Based on current studies, mindfulness therapy involves cognitive factors related to chronic pain, both in terms of cognitive production and its impact on cognitive control. Psychological and neurobasic studies were reviewed to provide a deeper understanding of these components, which include thought inhibition, attention deficit, pain catastrophizing, and self-efficacy. Mindfulness therapy has the potential to normalize psychology and nerves, and increase internal and external connectivity to work networks related to stress perception, cognition, and emotion. However, further research is needed to fully understand its effects. By exploring the relationship between mindfulness therapy and chronic pain. This review provides a new avenue for future research in psychotherapy for patients with chronic pain.

Subjective Cognitive Decline Plus and Longitudinal Assessment and Risk for Cognitive Impairment.

Importance: Subjective cognitive decline (SCD) is recognized to be in the Alzheimer disease (AD) cognitive continuum. The SCD Initiative International Working Group recently proposed SCD-plus (SCD+) features that increase risk for future objective cognitive decline but that have not been assessed in a large community-based setting. Objective: To assess SCD risk for mild cognitive impairment (MCI), AD, and all-cause dementia, using SCD+ criteria among cognitively normal adults. Design, Setting, and Participants: The Framingham Heart Study, a community-based prospective cohort study, assessed SCD between 2005 and 2019, with up to 12 years of follow-up. Participants 60 years and older with normal cognition at analytic baseline were included. Cox proportional hazards (CPH) models were adjusted for baseline age, sex, education, APOE ε4 status, and tertiles of AD polygenic risk score (PRS), excluding the APOE region. Data were analyzed from May 2021 to November 2023. Exposure: SCD was assessed longitudinally using a single question and considered present if endorsed at the last cognitively normal visit. It was treated as a time-varying variable, beginning at the first of consecutive, cognitively normal visits, including the last, at which it was endorsed. Main Outcomes and Measures: Consensus-diagnosed MCI, AD, and all-cause dementia. Results: This study included 3585 participants (mean [SD] baseline age, 68.0 [7.7] years; 1975 female [55.1%]). A total of 1596 participants (44.5%) had SCD, and 770 (21.5%) were carriers of APOE ε4. APOE ε4 and tertiles of AD PRS status did not significantly differ between the SCD and non-SCD groups. MCI, AD, and all-cause dementia were diagnosed in 236 participants (6.6%), 73 participants (2.0%), and 89 participants (2.5%), respectively, during follow-up. On average, SCD preceded MCI by 4.4 years, AD by 6.8 years, and all-cause dementia by 6.9 years. SCD was significantly associated with survival time to MCI (hazard ratio [HR], 1.57; 95% CI, 1.22-2.03; P <.001), AD (HR, 2.98; 95% CI, 1.89-4.70; P <.001), and all-cause dementia (HR, 2.14; 95% CI, 1.44-3.18; P <.001). After adjustment for APOE and AD PRS, the hazards of SCD were largely unchanged. Conclusions and Relevance: Results of this cohort study suggest that in a community setting, SCD reflecting SCD+ features was associated with an increased risk of future MCI, AD, and all-cause dementia with similar hazards estimated in clinic-based settings. SCD may be an independent risk factor for AD and other dementias beyond the risk incurred by APOE ε4 and AD PRS.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.

White Roman Goose Feather-Inspired Unidirectionally Inclined Conical Structure Arrays for Switchable Anisotropic Self-Cleaning.

White Roman goose (Anser anser domesticus) feathers, comprised of oriented conical barbules, are coated with gland-secreted preening oils to maintain a long-term nonwetting performance for surface swimming. The geese are accustomed to combing their plumages with flat bills in case they are contaminated with oleophilic substances, during which the amphiphilic saliva spread over the barbules greatly impairs their surface hydrophobicities and allows the trapped contaminants to be anisotropically self-cleaned by water flows. Particularly, the superhydrophobic behaviors of the goose feathers are recovered as well. Bioinspired by the switchable anisotropic self-cleaning functionality of white Roman geese, superhydrophobic unidirectionally inclined conical structures are engineered through the integration of a scalable colloidal self-assembly technology and a colloidal lithographic approach. The dependence of directional sliding properties on the shape, inclination angle, and size of conical structures is systematically investigated in this research. Moreover, their switchable anisotropic self-cleaning functionalities are demonstrated by Sudan blue II/water (0.01%) separation performances. The white Roman goose feather-inspired coatings undoubtedly offer a new concept for developing innovative applications that require directional transportation and the collection of liquids.

DNA cytosine methyltransferases differentially regulate genome-wide hypermutation and interhomolog recombination in Trichoderma reesei meiosis.

Trichoderma reesei is an economically important enzyme producer with several unique meiotic features. spo11, the initiator of meiotic double-strand breaks (DSBs) in most sexual eukaryotes, is dispensable for T. reesei meiosis. T. reesei lacks the meiosis-specific recombinase Dmc1. Rad51 and Sae2, the activator of the Mre11 endonuclease complex, promote DSB repair and chromosome synapsis in wild-type and spo11Δ meiosis. DNA methyltransferases (DNMTs) perform multiple tasks in meiosis. Three DNMT genes (rid1, dim2 and dimX) differentially regulate genome-wide cytosine methylation and C:G-to-T:A hypermutations in different chromosomal regions. We have identified two types of DSBs: type I DSBs require spo11 or rid1 for initiation, whereas type II DSBs do not rely on spo11 and rid1 for initiation. rid1 (but not dim2) is essential for Rad51-mediated DSB repair and normal meiosis. rid1 and rad51 exhibit a locus heterogeneity (LH) relationship, in which LH-associated proteins often regulate interconnectivity in protein interaction networks. This LH relationship can be suppressed by deleting dim2 in a haploid rid1Δ (but not rad51Δ) parental strain, indicating that dim2 and rid1 share a redundant function that acts earlier than rad51 during early meiosis. In conclusion, our studies provide the first evidence of the involvement of DNMTs during meiotic initiation and recombination.

Evaluation of body composition and bone mineral density in patients with and without idiopathic condylar resorption of temporomandibular disorders by dual energy X-ray absorptiometry.

Background/purpose: The impact of temporomandibular joint (TMJ) osseous destruction on bone mineral density (BMD) remains unclear due to controversial findings. Besides, no previous study has explored the relationship between idiopathic condylar resorption (ICR) and body composition. This study aimed to investigate the relationship between ICR and BMD or body composition. Materials and methods: Between July 2018 and August 2022, patients evaluated by an experienced dentist and diagnosed with temporomandibular disorders (TMDs) were referred to our center. They were recruited while they received the magnetic resonance image (MRI) examination, BMD and body composition completely. Patients were further categorized into TMDs with or without ICR groups according to MRI findings. One-way analysis of variance was used to compare the variables of BMD and body composition in the two groups. Results: In total, 67 patients were included in the analysis, with 42 categorized as TMDs with ICR and 25 as TMDs without ICR. Patients with ICR had a significantly higher lean mass percentage and lower fat mass percentage; lower android/gynoid fat ratio, and visceral adipose tissue area than those without ICR (P < 0.05). Besides, patients above age 30 with ICR had lower Z scores (P = 0.017) compared with subjects without ICR. Conclusion: TMDs patients with ICR show a relationship with body composition and affect the lean and fat mass distribution, especially android/gynoid fat ratio. The pathophysiological mechanism remains unclear. Further researches to investigate teeth binding, malocclusion and dietary habits are important to understand the association of ICR, BMD and body composition.

Cerebral Microbleeds in Different Brain Regions and Their Associations With the Digital Clock-Drawing Test: Secondary Analysis of the Framingham Heart Study.

BACKGROUND: Cerebral microbleeds (CMB) increase the risk for Alzheimer disease. Current neuroimaging methods that are used to detect CMB are costly and not always accessible. OBJECTIVE: This study aimed to explore whether the digital clock-drawing test (DCT) may provide a behavioral indicator of CMB. METHODS: In this study, we analyzed data from participants in the Framingham Heart Study offspring cohort who underwent both brain magnetic resonance imaging scans (Siemens 1.5T, Siemens Healthcare Private Limited; T2*-GRE weighted sequences) for CMB diagnosis and the DCT as a predictor. Additionally, paper-based clock-drawing tests were also collected during the DCT. Individuals with a history of dementia or stroke were excluded. Robust multivariable linear regression models were used to examine the association between DCT facet scores with CMB prevalence, adjusting for relevant covariates. Receiver operating characteristic (ROC) curve analyses were used to evaluate DCT facet scores as predictors of CMB prevalence. Sensitivity analyses were conducted by further including participants with stroke and dementia. RESULTS: The study sample consisted of 1020 (n=585, 57.35% female) individuals aged 45 years and older (mean 72, SD 7.9 years). Among them, 64 (6.27%) participants exhibited CMB, comprising 46 with lobar-only, 11 with deep-only, and 7 with mixed (lobar+deep) CMB. Individuals with CMB tended to be older and had a higher prevalence of mild cognitive impairment and higher white matter hyperintensities compared to those without CMB (P<.05). While CMB were not associated with the paper-based clock-drawing test, participants with CMB had a lower overall DCT score (CMB: mean 68, SD 23 vs non-CMB: mean 76, SD 20; P=.009) in the univariate comparison. In the robust multiple regression model adjusted for covariates, deep CMB were significantly associated with lower scores on the drawing efficiency (ß=-0.65, 95% CI -1.15 to -0.15; P=.01) and simple motor (ß=-0.86, 95% CI -1.43 to -0.30; P=.003) domains of the command DCT. In the ROC curve analysis, DCT facets discriminated between no CMB and the CMB subtypes. The area under the ROC curve was 0.76 (95% CI 0.69-0.83) for lobar CMB, 0.88 (95% CI 0.78-0.98) for deep CMB, and 0.98 (95% CI 0.96-1.00) for mixed CMB, where the area under the ROC curve value nearing 1 indicated an accurate model. CONCLUSIONS: The study indicates a significant association between CMB, especially deep and mixed types, and reduced performance in drawing efficiency and motor skills as assessed by the DCT. This highlights the potential of the DCT for early detection of CMB and their subtypes, providing a reliable alternative for cognitive assessment and making it a valuable tool for primary care screening before neuroimaging referral.

Comparison of the Quality of Discharge Letters Written by Large Language Models and Junior Clinicians: Single-Blinded Study.

BACKGROUND: Discharge letters are a critical component in the continuity of care between specialists and primary care providers. However, these letters are time-consuming to write, underprioritized in comparison to direct clinical care, and are often tasked to junior doctors. Prior studies assessing the quality of discharge summaries written for inpatient hospital admissions show inadequacies in many domains. Large language models such as GPT have the ability to summarize large volumes of unstructured free text such as electronic medical records and have the potential to automate such tasks, providing time savings and consistency in quality. OBJECTIVE: The aim of this study was to assess the performance of GPT-4 in generating discharge letters written from urology specialist outpatient clinics to primary care providers and to compare their quality against letters written by junior clinicians. METHODS: Fictional electronic records were written by physicians simulating 5 common urology outpatient cases with long-term follow-up. Records comprised simulated consultation notes, referral letters and replies, and relevant discharge summaries from inpatient admissions. GPT-4 was tasked to write discharge letters for these cases with a specified target audience of primary care providers who would be continuing the patient's care. Prompts were written for safety, content, and style. Concurrently, junior clinicians were provided with the same case records and instructional prompts. GPT-4 output was assessed for instances of hallucination. A blinded panel of primary care physicians then evaluated the letters using a standardized questionnaire tool. RESULTS: GPT-4 outperformed human counterparts in information provision (mean 4.32, SD 0.95 vs 3.70, SD 1.27; P=.03) and had no instances of hallucination. There were no statistically significant differences in the mean clarity (4.16, SD 0.95 vs 3.68, SD 1.24; P=.12), collegiality (4.36, SD 1.00 vs 3.84, SD 1.22; P=.05), conciseness (3.60, SD 1.12 vs 3.64, SD 1.27; P=.71), follow-up recommendations (4.16, SD 1.03 vs 3.72, SD 1.13; P=.08), and overall satisfaction (3.96, SD 1.14 vs 3.62, SD 1.34; P=.36) between the letters generated by GPT-4 and humans, respectively. CONCLUSIONS: Discharge letters written by GPT-4 had equivalent quality to those written by junior clinicians, without any hallucinations. This study provides a proof of concept that large language models can be useful and safe tools in clinical documentation.

Disparities in medical recommendations from AI-based chatbots across different countries/regions.

This study explores disparities and opportunities in healthcare information provided by AI chatbots. We focused on recommendations for adjuvant therapy in endometrial cancer, analyzing responses across four regions (Indonesia, Nigeria, Taiwan, USA) and three platforms (Bard, Bing, ChatGPT-3.5). Utilizing previously published cases, we asked identical questions to chatbots from each location within a 24-h window. Responses were evaluated in a double-blinded manner on relevance, clarity, depth, focus, and coherence by ten experts in endometrial cancer. Our analysis revealed significant variations across different countries/regions (p < 0.001). Interestingly, Bing's responses in Nigeria consistently outperformed others (p < 0.05), excelling in all evaluation criteria (p < 0.001). Bard also performed better in Nigeria compared to other regions (p < 0.05), consistently surpassing them across all categories (p < 0.001, with relevance reaching p < 0.01). Notably, Bard's overall scores were significantly higher than those of ChatGPT-3.5 and Bing in all locations (p < 0.001). These findings highlight disparities and opportunities in the quality of AI-powered healthcare information based on user location and platform. This emphasizes the necessity for more research and development to guarantee equal access to trustworthy medical information through AI technologies.

Changes in Posttreatment Spleen Volume Associated with Immunotherapy Outcomes for Advanced Hepatocellular Carcinoma.

Purpose: We investigated whether spleen volume (SV) changes were associated with treatment outcomes in advanced hepatocellular carcinoma (HCC) patients who received immunotherapy or first-line sorafenib. Patients and Methods: Patients with advanced HCC who underwent immunotherapy or first-line sorafenib at our institute were retrospectively analyzed. CT was used to measure SV before and within 3 months of treatment initiation. Tumor assessment followed Response Evaluation Criteria in Solid Tumors version 1.1. The association between SV change and tumor response or progression-free survival (PFS) was analyzed. The inverse probability of treatment weighting (IPTW) was used to adjust for differences in baseline characteristics. Results: The immunotherapy group comprised 143 patients (124 men, mean age, 59.8 years ± 11.2 [standard deviation]), while the sorafenib group had 57 (47 men, mean age, 59.6 years ± 9.9). SV increased in 108 (75.5%) immunotherapy and 21 (36.8%) sorafenib patients. In the immunotherapy group, patients with increased SV were more likely than those with decreased SV to have a higher disease control rate (76.9% vs 57.1%, p = 0.024) and durable clinical benefit (52.8% vs 25.7%, p = 0.005). It was also associated with extended PFS in the immunotherapy group in both the univariate (p = 0.028) and multivariate (p = 0.014) analysis. By contrast, in the sorafenib group, an increased in SV was not associated with treatment response but was presumably associated with reduced PFS (p = 0.072) in the multivariate analysis. After IPTW adjustment, the increase in SV remained a significant predictor for DCB and PFS in the immunotherapy group. Conclusion: Most patients exhibited an increase in SV after the initiation of immunotherapy, which may be used to predict response and prognosis. However, this association was not observed in patients who received sorafenib.

The study provides significant evidence that an increase in spleen volume is associated with better treatment outcomes in advanced hepatocellular carcinoma patients undergoing immunotherapy. These findings offer oncologists a new potential biomarker for optimizing treatment strategies. Specifically, increased spleen volume could be used to predict higher rates of disease control and durable clinical benefits, allowing for more personalized care.

The Motivation for COVID-19 Vaccination and Preventive Behavior.

COVID-19, a viral infection that emerged in late 2019, induces a severe acute respiratory syndrome marked by significant clinical symptoms, and the potential for progressive respiratory failure and death. People facing the threat of COVID-19 not only feared being infected, but were also worried about the side-effects of vaccination. This conflict affected their epidemic prevention behavior. To understand this issue, the present study explored whether infection anxiety affected the psychological avoidance or approach to getting vaccinated and the intention to take epidemic prevention measures. The study implemented a cross-sectional, web-based survey. We created questionnaires using Surveycake, an online e-form questionnaire platform. We used the snowball sampling method via a social media app to recruit participants. If individuals were willing to participate in the research, we emailed the e-form questionnaire link to them to collect data. After questionnaire collection, 288 questionnaires were returned, and 277 valid questionnaires were obtained for structural equation modeling analysis. According to the statistical results, it was found that infection anxiety was positively related to avoidance-avoidance conflict, and the power of infection anxiety on avoidance conflict was 23.0%. Infection anxiety was negatively related to approach-approach conflict regarding vaccination, and the power of infection anxiety on approach-approach conflict was 22.0%. Approach-approach conflict regarding vaccination was negatively related to prevention behavior, while avoidance-avoidance conflict regarding vaccination was positively related to prevention behavior. The two conflicts explained 12.5% of the total variance in prevention behavior. The study results highlight the long-term importance of achieving vaccine goals in order to prepare for future health emergencies similar to the recent COVID-19 pandemic.

Clinical performance of automated machine learning: A systematic review.

Introduction: Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other. Method: This review adhered to a prospectively registered protocol (PROSPERO identifier CRD42022344427). The Cochrane Library, Embase, MEDLINE and Scopus were searched from inception to 11 July 2022. Two researchers screened abstracts and full texts, extracted data and conducted quality assessment. Disagreement was resolved through discussion and if required, arbitration by a third researcher. Results: There were 26 distinct autoML platforms featured in 82 studies. Brain and lung disease were the most common fields of study of 22 specialties. AutoML exhibited variable performance: area under the receiver operator characteristic curve (AUCROC) 0.35-1.00, F1-score 0.16-0.99, area under the precision-recall curve (AUPRC) 0.51-1.00. AutoML exhibited the highest AUCROC in 75.6% trials; the highest F1-score in 42.3% trials; and the highest AUPRC in 83.3% trials. In autoML platform comparisons, AutoPrognosis and Amazon Rekognition performed strongest with unstructured and structured data, respectively. Quality of reporting was poor, with a median DECIDE-AI score of 14 of 27. Conclusion: A myriad of autoML platforms have been applied in a variety of clinical contexts. The performance of autoML compares well to bespoke computational and clinical benchmarks. Further work is required to improve the quality of validation studies. AutoML may facilitate a transition to data-centric development, and integration with large language models may enable AI to build itself to fulfil user-defined goals.

Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema.

BACKGROUND: Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges. MAIN TEXT: This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications. CONCLUSION: AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.

The association between circulating CD34+CD133+ endothelial progenitor cells and reduced risk of Alzheimer's disease in the Framingham Heart Study.

Aim: Endothelial dysfunction has been associated with both cerebrovascular pathology and Alzheimer's disease (AD). However, the connection between circulating endothelial cells and the risk of AD remains uncertain. The objective was to leverage data from the Framingham Heart Study to investigate various circulating endothelial subtypes and their potential correlations with the risk of AD. Methods: The study conducted data analyses using Cox proportional hazard regression and linear regression methods. Additionally, genome-wide association study (GWAS) was carried out to further explore the data. Results: Among the eleven distinct circulating endothelial subtypes, only circulating endothelial progenitor cells (EPCs) expressing CD34+CD133+ were found to be negatively and dose-dependently associated with reduced AD risk. This association persisted even after adjusting for age, sex, years of education, apolipoprotein E (APOE) ε4 status, and various vascular diseases. Particularly noteworthy was the significant association observed in individuals with hypertension and cerebral microbleeds. Consistently, positive associations were identified between CD34+CD133+ EPCs and specific brain regions, such as higher proportions of circulating CD34+CD133+ cells correlating with increased volumes of white matter and the hippocampus. Additionally, a GWAS study unveiled that CD34+CD133+ cells influenced AD risk specifically in individuals with homozygous genotypes for variants in two stem cell-related genes: kirre like nephrin family adhesion molecule 3 (KIRREL3, rs580382 CC and rs4144611 TT) and exocyst complex component 6B (EXOC6B, rs61619102 CC). Conclusions: The findings suggest that circulating CD34+CD133+ EPCs possess a protective effect and may offer a new therapeutic avenue for AD, especially in individuals with vascular pathology and those carrying specific genotypes of KIRREL3 and EXOC6B genes.

Defining the structure-function relationship of specific lesions in early and advanced age-related macular degeneration.

The objective of this study is to define structure-function relationships of pathological lesions related to age-related macular degeneration (AMD) using microperimetry and multimodal retinal imaging. We conducted a cross-sectional study of 87 patients with AMD (30 eyes with early and intermediate AMD and 110 eyes with advanced AMD), compared to 33 normal controls (66 eyes) recruited from a single tertiary center. All participants had enface and cross-sectional optical coherence tomography (Heidelberg HRA-2), OCT angiography, color and infra-red (IR) fundus and microperimetry (MP) (Nidek MP-3) performed. Multimodal images were graded for specific AMD pathological lesions. A custom marking tool was used to demarcate lesion boundaries on corresponding enface IR images, and subsequently superimposed onto MP color fundus photographs with retinal sensitivity points (RSP). The resulting overlay was used to correlate pathological structural changes to zonal functional changes. Mean age of patients with early/intermediate AMD, advanced AMD and controls were 73(SD = 8.2), 70.8(SD = 8), and 65.4(SD = 7.7) years respectively. Mean retinal sensitivity (MRS) of both early/intermediate (23.1 dB; SD = 5.5) and advanced AMD (18.1 dB; SD = 7.8) eyes were significantly worse than controls (27.8 dB, SD = 4.3) (p < 0.01). Advanced AMD eyes had significantly more unstable fixation (70%; SD = 63.6), larger mean fixation area (3.9 mm2; SD = 3.0), and focal fixation point further away from the fovea (0.7 mm; SD = 0.8), than controls (29%; SD = 43.9; 2.6 mm2; SD = 1.9; 0.4 mm; SD = 0.3) (p ≤ 0.01). Notably, 22 fellow eyes of AMD eyes (25.7 dB; SD = 3.0), with no AMD lesions, still had lower MRS than controls (p = 0.04). For specific AMD-related lesions, end-stage changes such as fibrosis (5.5 dB, SD = 5.4 dB) and atrophy (6.2 dB, SD = 7.0 dB) had the lowest MRS; while drusen and pigment epithelial detachment (17.7 dB, SD = 8.0 dB) had the highest MRS. Peri-lesional areas (20.2 dB, SD = 7.6 dB) and surrounding structurally normal areas (22.2 dB, SD = 6.9 dB) of the retina with no AMD lesions still had lower MRS compared to controls (27.8 dB, SD = 4.3 dB) (p < 0.01). Our detailed topographic structure-function correlation identified specific AMD pathological changes associated with a poorer visual function. This can provide an added value to the assessment of visual function to optimize treatment outcomes to existing and potentially future novel therapies.

Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study.

Large language models (LLMs) underlie remarkable recent advanced in natural language processing, and they are beginning to be applied in clinical contexts. We aimed to evaluate the clinical potential of state-of-the-art LLMs in ophthalmology using a more robust benchmark than raw examination scores. We trialled GPT-3.5 and GPT-4 on 347 ophthalmology questions before GPT-3.5, GPT-4, PaLM 2, LLaMA, expert ophthalmologists, and doctors in training were trialled on a mock examination of 87 questions. Performance was analysed with respect to question subject and type (first order recall and higher order reasoning). Masked ophthalmologists graded the accuracy, relevance, and overall preference of GPT-3.5 and GPT-4 responses to the same questions. The performance of GPT-4 (69%) was superior to GPT-3.5 (48%), LLaMA (32%), and PaLM 2 (56%). GPT-4 compared favourably with expert ophthalmologists (median 76%, range 64-90%), ophthalmology trainees (median 59%, range 57-63%), and unspecialised junior doctors (median 43%, range 41-44%). Low agreement between LLMs and doctors reflected idiosyncratic differences in knowledge and reasoning with overall consistency across subjects and types (p>0.05). All ophthalmologists preferred GPT-4 responses over GPT-3.5 and rated the accuracy and relevance of GPT-4 as higher (p<0.05). LLMs are approaching expert-level knowledge and reasoning skills in ophthalmology. In view of the comparable or superior performance to trainee-grade ophthalmologists and unspecialised junior doctors, state-of-the-art LLMs such as GPT-4 may provide useful medical advice and assistance where access to expert ophthalmologists is limited. Clinical benchmarks provide useful assays of LLM capabilities in healthcare before clinical trials can be designed and conducted.

Disparities in Visual Field Testing Frequency Among Subjects With Glaucoma.

Purpose: Prior evidence suggests racial disparities in the utilization of visual field testing (VFT) for the diagnosis and monitoring of glaucoma. In this study, we considered the effect of baseline glaucoma severity and socioeconomic disadvantage along with other potential confounders such as test reliability, ancillary tests, and glaucoma surgeries on racial disparity in the frequency of VFT. Methods: The records of all subjects with a diagnosis of glaucoma who received VFT at an academic, tertiary care facility from January 2018 to December 2021 were accessed. Analysis was performed to compare VFT frequency, the total number of office visits (DoS), and the ratio of VFT frequency to DoS (VFT/DoS) across self-reported races while controlling for sex, age, socioeconomic disadvantage (Area Deprivation Index), VF reliability indicators and baseline mean deviation, optical coherence tomography frequency, and glaucoma surgeries. Results: Among the 2654 subjects (1515 White, 782 Black, and 357 Asian) included in this study, Black subjects had the worst socioeconomic status and disease severity at baseline. They also experienced a 3% lower VFT/DoS ratio compared to White subjects (P = 0.031). Asian subjects had a 5% lower VFT/DoS ratio compared to White subjects (P = 0.015). Discussion: We identified racial disparity in performing VFT in subjects with glaucoma even when multiple confounders were considered. Further investigation is necessary to identify other race-associated factors to work toward reducing racial disparities in VFT. Translational Relevance: Black and Asian subjects with glaucoma receive fewer VFT per visit compared to White subjects even when considering socioeconomic disadvantage and disease severity.

AI-based differential diagnosis of dementia etiologies on multimodal data.

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.