Peripheral inflammatory diseases are often associated with behavioral comorbidities including anxiety, depression, and cognitive dysfunction, but the mechanism for these is not well understood. Changes in the neuronal and synaptic functions associated with neuroinflammation may underlie these behavioral abnormalities. We have used a model of colonic inflammation induced by 2,4,6-trinitrobenzenesulfonic acid in Sprague Dawley rats to identify inflammation-induced changes in hippocampal synaptic transmission. Hippocampal slices obtained 4 d after the induction of inflammation revealed enhanced Schaffer collateral-induced excitatory field potentials in CA1 stratum radiatum. This was associated with larger-amplitude mEPSCs, but unchanged mEPSC frequencies and paired-pulse ratios, suggesting altered postsynaptic effects. Both AMPA- and NMDA-mediated synaptic currents were enhanced, and analysis of AMPA-mediated currents revealed increased contributions of GluR2-lacking receptors. In keeping with this, both transcripts and protein levels of the GluR2 subunit were reduced in hippocampus. Both long-term potentiation (LTP) and depression (LTD) were significantly reduced in hippocampal slices taken from inflamed animals. Chronic administration of the microglial/macrophage activation inhibitor minocycline to the inflamed animals both lowered the level of the cytokine tumor necrosis factor α in the hippocampus and completely abolished the effect of peripheral inflammation on the field potentials and synaptic plasticity (LTP and LTD). Our results reveal profound synaptic changes caused by a mirror microglia-mediated inflammatory response in hippocampus during peripheral organ inflammation. These synaptic changes may underlie the behavioral comorbidities seen in patients.

Non-alcoholic fatty liver disease (NAFLD) is a significant public health concern worldwide with a complex etiology attributed to behavioural, environmental, and genetic causes. The worldwide prevalence of NAFLD is estimated to be 32.4% and constantly rising. Global data, however, indicate considerable heterogeneity among studies for both NAFLD prevalence and incidence. Identifying variables that affect the estimated epidemiological measures is essential to all stakeholders, including patients, researchers, healthcare providers, and policymakers. Besides helping with the research on disease etiology, it helps to identify individuals at risk of the disease, which in turn will outline the focus of the preventive measures and help to fittingly tailor individualized treatments, targeted prevention, screening, or treatment programs. Several studies suggest differences in the prevalence and severity of NAFLD by race or ethnicity, which may be linked to differences in lifestyle, diet, metabolic comorbidity profile, and genetic background, among others. Race/ethnicity research is essential as it can provide valuable information regarding biological and genetic differences among people with similar cultural, dietary, and geographical backgrounds. In this review, we examined the existing literature on race/ethnicity differences in susceptibility to NAFLD and discussed the contributing variables to such differences, including diet and physical activity, the comorbidity profile, and genetic susceptibility. We also reviewed the limitations of race/ethnicity studies in NAFLD.

Nonalcoholic fatty liver disease (NAFLD) is a rising epidemic worldwide and will be the leading cause of cirrhosis, hepatocellular carcinoma, and liver transplant within the next decade. NAFLD is considered as the hepatic manifestation of metabolic syndrome. Behaviors, such as a sedentary lifestyle and consuming a Western diet, have led to substantial challenges in managing NAFLD patients. With no curative pharmaceutical therapies, lifestyle modifications, including dietary changes and exercise, that ultimately lead to weight loss remain the only effective therapy for NAFLD. Multiple diets, including low-carbohydrate, low-fat, Dietary Approaches to Stop Hypertension (DASH), and Mediterranean (MD) diets, have been evaluated. NAFLD patients have shown better outcomes with a modified diet, such as the MD diet, where patients are encouraged to increase the consumption of fruits and vegetables, whole grains, and olive oil. It is increasingly clear that a personalized approach to managing NAFLD patients, based on their preferences and needs, should be implemented. In our review, we cover NAFLD management, with a specific focus on dietary patterns and their components. We emphasize the successful approaches highlighted in recent studies to provide recommendations that health care providers could apply in managing their NAFLD patients.

Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EHR is labour-intensive and requires extensive manual labelling of disease outcomes. This study developed an efficient strategy based on advanced large language models to identify multiple conditions from EHR clinical notes. We linked a cardiac registry cohort in 2015 with an EHR system in Alberta, Canada. We developed a pipeline that leveraged a generative large language model (LLM) to analyze, understand, and interpret EHR notes by prompts based on specific diagnosis, treatment management, and clinical guidelines. The pipeline was applied to detect acute myocardial infarction (AMI), diabetes, and hypertension. The performance was compared against clinician-validated diagnoses as the reference standard and widely adopted International Classification of Diseases (ICD) codes-based methods. The study cohort accounted for 3088 patients and 551,095 clinical notes. The prevalence was 55.4 %, 27.7 %, 65.9 % and for AMI, diabetes, and hypertension, respectively. The performance of the LLM-based pipeline for detecting conditions varied: AMI had 88 % sensitivity, 63 % specificity, and 77 % positive predictive value (PPV); diabetes had 91 % sensitivity, 86 % specificity, and 71 % PPV; and hypertension had 94 % sensitivity, 32 % specificity, and 72 % PPV. Compared with ICD codes, the LLM-based method demonstrated improved sensitivity and negative predictive value across all conditions. The monthly percentage trends from the detected cases by LLM and reference standard showed consistent patterns. The proposed LLM-based pipeline demonstrated reasonable accuracy and high efficiency in disease detection for multiple conditions. Human expert knowledge can be integrated into the pipeline to guide EHR note analysis without manually curated labels. The method could enable comprehensive real-time disease surveillance using EHRs.