Is coffee, tea, and red wine consumption beneficial for individuals with hypertension?

OBJECTIVE: To investigate the associations of tea, coffee, and red wine intakes with health risks among individuals with hypertension. METHODS: This prospective cohort study included participants with hypertension from the UK Biobank cohort. Study exposures included self-reported intakes of coffee, tea, and red wine. The primary outcome was all-cause mortality, and the secondary outcomes were cardiovascular mortality and cardiovascular disease. The associations of beverage intake with outcomes were analyzed using Cox regression models. The hazard ratios and 95% confidence intervals were estimated. RESULTS: A total of 187 708 participants with hypertension were included. The median follow-up period was 13.8 years. In individuals with hypertension, drinking one to two cups/day of coffee or three to four cups/day of tea was significantly associated with the lowest risk of all-cause mortality compared with less than one cup/day [hazard ratio for coffee, 0.943 (95% confidence interval, 0.908-0.979); hazard ratio for tea, 0.882 (95% confidence interval, 0.841-0.924)]. Red wine intake was inversely associated with all-cause mortality risk. Dose-response analysis revealed that high coffee intake (approximately greater than or equal to six cups/day) was significantly associated with increased risks of cardiovascular mortality and cardiovascular disease, but high tea and red wine intakes were not. Furthermore, replacing plain water with tea, but not coffee, significantly reduced the risks of all-cause mortality and cardiovascular disease. Replacing other alcoholic beverages with red wine also significantly reduced the risks of all three outcomes. CONCLUSIONS: These findings suggest that tea and red wine, but not coffee, can be part of a healthy diet for the hypertensive population.

FedFSA: Hybrid and federated framework for functional status ascertainment across institutions.

INTRODUCTION: Patients' functional status assesses their independence in performing activities of daily living, including basic ADLs (bADL), and more complex instrumental activities (iADL). Existing studies have discovered that patients' functional status is a strong predictor of health outcomes, particularly in older adults. Depite their usefulness, much of the functional status information is stored in electronic health records (EHRs) in either semi-structured or free text formats. This indicates the pressing need to leverage computational approaches such as natural language processing (NLP) to accelerate the curation of functional status information. In this study, we introduced FedFSA, a hybrid and federated NLP framework designed to extract functional status information from EHRs across multiple healthcare institutions. METHODS: FedFSA consists of four major components: 1) individual sites (clients) with their private local data, 2) a rule-based information extraction (IE) framework for ADL extraction, 3) a BERT model for functional status impairment classification, and 4) a concept normalizer. The framework was implemented using the OHNLP Backbone for rule-based IE and open-source Flower and PyTorch library for federated BERT components. For gold standard data generation, we carried out corpus annotation to identify functional status-related expressions based on ICF definitions. Four healthcare institutions were included in the study. To assess FedFSA, we evaluated the performance of category- and institution-specific ADL extraction across different experimental designs. RESULTS: ADL extraction performance ranges from an F1-score of 0.907 to 0.986 for bADL and 0.825 to 0.951 for iADL across the four healthcare sites. The performance for ADL extraction with impairment ranges from an F1-score of 0.722 to 0.954 for bADL and 0.674 to 0.813 for iADL across four healthcare sites. For category-specific ADL extraction, laundry and transferring yielded relatively high performance, while dressing, medication, bathing, and continence achieved moderate-high performance. Conversely, food preparation and toileting showed low performance. CONCLUSION: NLP performance varied across ADL categories and healthcare sites. Federated learning using a FedFSA framework performed higher than non-federated learning for impaired ADL extraction at all healthcare sites. Our study demonstrated the potential of the federated learning framework in functional status extraction and impairment classification in EHRs, exemplifying the importance of a large-scale, multi-institutional collaborative development effort.

Application of membrane separation technology in the purification of pharmaceutical components.

Traditional Chineae medicine (TCM) is often composed of a variety of natural medicines. Its composition is complex, and many of its components can not be analyzed and identified. The first step in the rational application of TCM is to successfully separate the effective components which is also a great inspiration for the development of new drugs. Among the many separation technologies of TCM, the traditional heating concentration separation technology has high energy consumption and low efficiency. As a new separation technology, membrane separation technology has the characteristics of simple operation, high efficiency, environment-friendly and so on. The separation effect of high molecular weight difference solution is better. The applications of several main membrane separation technologies such as microfiltration, nanofiltration, ultrafiltration and reverse osmosis are reviewed, the methods of restoring membrane flux after membrane fouling are discussed, and their large-scale industrial applications in the future are prospected and summarized.

Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records.

OBJECTIVE: The accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, covariate imbalance and delayed diagnosis when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. The goal of this study is to investigate the extent to which the aforementioned issues influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. METHODS: We evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration. RESULTS: We developed a novel backward masking scheme to deal with the issue of delayed diagnosis which is very common in EHR data analysis and evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training. CONCLUSIONS: The strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.

Oxygenation of Phenols with Water as the Oxygen Source and Oxoammonium Salt as the Oxidant.

Aromatic C-H oxygenation is important in both industrial production and organic synthesis. Here we report a metal-free approach for phenol oxygenation with water as the oxygen source using oxoammonium salts as the renewable oxidant. Employing this protocol, various alkyl-substituted phenols were converted into benzoquinones in yields of 59-98%. On the basis of 18O-labeling and kinetic studies, the hydroxy-oxoammonium adduct was proposed to attack the aromatic ring similarly to electrophilic aromatic substitution. We suppose that the findings described here not only provide an efficient and highly selective protocol for aromatic C-H oxygenation but also may encourage further developments of possible transition-metal-free catalytic methods.

Multiomics and blood-based biomarkers of moyamoya disease: protocol of Moyamoya Omics Atlas (MOYAOMICS).

BACKGROUND: Moyamoya disease (MMD) is a rare and complex cerebrovascular disorder characterized by the progressive narrowing of the internal carotid arteries and the formation of compensatory collateral vessels. The etiology of MMD remains enigmatic, making diagnosis and management challenging. The MOYAOMICS project was initiated to investigate the molecular underpinnings of MMD and explore potential diagnostic and therapeutic strategies. METHODS: The MOYAOMICS project employs a multidisciplinary approach, integrating various omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, to comprehensively examine the molecular signatures associated with MMD pathogenesis. Additionally, we will investigate the potential influence of gut microbiota and brain-gut peptides on MMD development, assessing their suitability as targets for therapeutic strategies and dietary interventions. Radiomics, a specialized field in medical imaging, is utilized to analyze neuroimaging data for early detection and characterization of MMD-related brain changes. Deep learning algorithms are employed to differentiate MMD from other conditions, automating the diagnostic process. We also employ single-cellomics and mass cytometry to precisely study cellular heterogeneity in peripheral blood samples from MMD patients. CONCLUSIONS: The MOYAOMICS project represents a significant step toward comprehending MMD's molecular underpinnings. This multidisciplinary approach has the potential to revolutionize early diagnosis, patient stratification, and the development of targeted therapies for MMD. The identification of blood-based biomarkers and the integration of multiple omics data are critical for improving the clinical management of MMD and enhancing patient outcomes for this complex disease.

Expression levels of ADAMTS 5, 9, and 12 in endometrial polyps and their predictive value for the diagnosis and recurrence of endometrial polyps.

PURPOSE: Endometrial polyps (EPs) are common gynecological disorders for which no clear etiology has been found. ADAMTS have been associated with a variety of diseases. This study aimed to investigate the potential correlation between serologic levels of ADAMTS 5, 9, and 12 in patients with EPs. METHODS: A total of 88 patients were categorized into two groups: the EPs group, consisting of recurrent EPs and first occurrence EPs, and a control group. The study compared the general information and serum levels of ADAMTS 5, 9, and 12 between the groups. RESULTS: Regarding the general data, a statistically significant age difference (p < 0.05) was observed, while no significant differences were found in the other variables. After considering age as a confounding factor, the previously observed statistical significance in the differences of ADAMTS5 and 9 between the groups diminished. However, it was found that the concentrations of ADAMTS12 in both the EPs group and the recurrent EPs group were significantly higher compared to the control group and the first occurrence EPs group (p < 0.05). ROC curves were generated to determine the critical values of ADAMTS12 for predicting EPs and recurrent EPs, which were found to be 0.6962 ng/ml (sensitivity: 100 %, specificity: 39.5 %) and 0.8768 ng/ml (sensitivity: 75.0 %, specificity: 76.3 %), respectively. CONCLUSION: Our findings revealed elevated serologic levels of ADAMTS12 in the EPs group, particularly in the recurrent EPs group. Furthermore, ADAMTS-12 was identified as a valuable biomarker for assisting in the diagnosis and prediction of EPs recurrence.

Dietary Niacin Intake and Mortality Among Individuals With Nonalcoholic Fatty Liver Disease.

Importance: Evidence regarding the effect of dietary niacin intake on the risk of mortality among patients with nonalcoholic fatty liver disease (NAFLD) is scarce. Objective: To examine the association of dietary niacin intake with all-cause mortality and cardiovascular disease (CVD) mortality among individuals with NAFLD. Design, Setting, and Participants: This cohort study used data from the National Health and Nutrition Examination Survey (2003-2018). In total, 4315 adults aged 20 years or older with NAFLD were included, with NAFLD defined using the United States Fatty Liver Index. Exposure: Dietary niacin intake levels. Main Outcomes and Measures: Weighted Cox proportional hazards models and restricted cubic splines were used to estimate hazard ratios and 95% CIs for all-cause and CVD mortality. Data were analyzed March 1 to September 1, 2023. Results: This cohort study included data from 4315 participants in the analysis (mean [SD] age, 52.5 [16.2] years; 1670 participants ≥60 years [weighted, 30.9%]; 2351 men [weighted, 55.0%]). During a median (IQR) follow-up of 8.8 (4.6-11.8) years, 566 deaths were recorded, of which 197 were attributed to CVD. Compared with participants with a niacin intake of 18.4 mg or lower (the lowest tertile), the multivariable-adjusted hazard ratios for participants with a niacin intake of 26.7 mg or higher (the highest tertile) were 0.70 (95% CI, 0.50-0.96) for all-cause mortality (P = .03 for trend) and 0.65 (95% CI, 0.35-1.20) for CVD mortality (P = .16 for trend). Conclusions and Relevance: Findings from this cohort study suggest that higher dietary niacin intake may be associated with lower risk of all-cause mortality among individuals with NAFLD. There was no evident inverse association between dietary niacin intake and the risk of CVD mortality.

Develop and Validate a Computable Phenotype for the Identification of Alzheimer's Disease Patients Using Electronic Health Record Data.

INTRODUCTION: Alzheimer's Disease (AD) are often misclassified in electronic health records (EHRs) when relying solely on diagnostic codes. This study aims to develop a more accurate, computable phenotype (CP) for identifying AD patients by using both structured and unstructured EHR data. METHODS: We used EHRs from the University of Florida Health (UF Health) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UT Health) and the University of Minnesota (UMN). RESULTS: Our best-performing CP is " patient has at least 2 AD diagnoses and AD-related keywords " with an F1-score of 0.817 at UF, and 0.961 and 0.623 at UT Health and UMN, respectively. DISCUSSION: We developed and validated rule-based CPs for AD identification with good performance, crucial for studies that aim to use real-world data like EHRs.

Improving large language models for clinical named entity recognition via prompt engineering.

IMPORTANCE: The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets. OBJECTIVES: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance. MATERIALS AND METHODS: We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, and tests from clinical notes in the MTSamples corpus, following the 2010 i2b2 concept extraction shared task, and (2) to identify nervous system disorder-related adverse events from safety reports in the vaccine adverse event reporting system (VAERS). To improve the GPT models' performance, we developed a clinical task-specific prompt framework that includes (1) baseline prompts with task description and format specification, (2) annotation guideline-based prompts, (3) error analysis-based instructions, and (4) annotated samples for few-shot learning. We assessed each prompt's effectiveness and compared the models to BioClinicalBERT. RESULTS: Using baseline prompts, GPT-3.5 and GPT-4 achieved relaxed F1 scores of 0.634, 0.804 for MTSamples and 0.301, 0.593 for VAERS. Additional prompt components consistently improved model performance. When all 4 components were used, GPT-3.5 and GPT-4 achieved relaxed F1 socres of 0.794, 0.861 for MTSamples and 0.676, 0.736 for VAERS, demonstrating the effectiveness of our prompt framework. Although these results trail BioClinicalBERT (F1 of 0.901 for the MTSamples dataset and 0.802 for the VAERS), it is very promising considering few training samples are needed. DISCUSSION: The study's findings suggest a promising direction in leveraging LLMs for clinical NER tasks. However, while the performance of GPT models improved with task-specific prompts, there's a need for further development and refinement. LLMs like GPT-4 show potential in achieving close performance to state-of-the-art models like BioClinicalBERT, but they still require careful prompt engineering and understanding of task-specific knowledge. The study also underscores the importance of evaluation schemas that accurately reflect the capabilities and performance of LLMs in clinical settings. CONCLUSION: While direct application of GPT models to clinical NER tasks falls short of optimal performance, our task-specific prompt framework, incorporating medical knowledge and training samples, significantly enhances GPT models' feasibility for potential clinical applications.

Dynamic Prognosis Prediction for Patients on DAPT After Drug-Eluting Stent Implantation: Model Development and Validation.

BACKGROUND: The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug-eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. METHODS AND RESULTS: We developed and validated a new AI-based pipeline using retrospective data of drug-eluting stent-treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de-identified Clinformatics Data Mart Database (n=9978). The 36 months following drug-eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI-DAPT model. The AI-DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%-92%] for ischemia and 84% [95% CI, 82%-87%] for bleeding predictions. CONCLUSIONS: Our AI-DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.

Histone lactylation inhibits RARγ expression in macrophages to promote colorectal tumorigenesis through activation of TRAF6-IL-6-STAT3 signaling.

Macrophages are phenotypically and functionally diverse in the tumor microenvironment (TME). However, how to remodel macrophages with a protumor phenotype and how to manipulate them for therapeutic purposes remain to be explored. Here, we show that in the TME, RARγ is downregulated in macrophages, and its expression correlates with poor prognosis in patients with colorectal cancer (CRC). In macrophages, RARγ interacts with tumor necrosis factor receptor-associated factor 6 (TRAF6), which prevents TRAF6 oligomerization and autoubiquitination, leading to inhibition of nuclear factor κB signaling. However, tumor-derived lactate fuels H3K18 lactylation to prohibit RARγ gene transcription in macrophages, consequently enhancing interleukin-6 (IL-6) levels in the TME and endowing macrophages with tumor-promoting functions via activation of signal transducer and activator of transcription 3 (STAT3) signaling in CRC cells. We identified that nordihydroguaiaretic acid (NDGA) exerts effective antitumor action by directly binding to RARγ to inhibit TRAF6-IL-6-STAT3 signaling. This study unravels lactate-driven macrophage function remodeling by inhibition of RARγ expression and highlights NDGA as a candidate compound for treating CRC.

Intestinal flora study reveals the mechanism of Danggui Shaoyao San and its decomposed recipes to improve cognitive dysfunction in the rat model of Alzheimer's disease.

Background: Alzheimer's disease (AD), characterized by a severe decline in cognitive function, significantly impacts patients' quality of life. Traditional Chinese Medicine (TCM) presents notable advantages in AD treatment, closely linked to its regulation of intestinal flora. Nevertheless, a comprehensive exploration of the precise role of intestinal flora in AD remains lacking. Methods: We induced an AD model through bilateral intracerebroventricular injection of streptozotocin in rats. We divided 36 rats randomly into 6 groups: sham-operated, model, Danggui Shaoyao San (DSS), and 3 DSS decomposed recipes groups. Cognitive abilities were assessed using water maze and open field experiments. Nissl staining examined hippocampal neuron integrity. Western blot analysis determined synaptoprotein expression. Additionally, 16S rDNA high-throughput sequencing analyzed intestinal flora composition. Results: DSS and its decomposed recipe groups demonstrated improved learning and memory in rats (P<0.01). The open field test indicated increased central zone residence time and locomotor activity distance in these groups (P<0.05). Furthermore, the DSS and decomposed recipe groups exhibited reduced hippocampal neuronal damage and increased expression levels of synapsin I (P<0.05) and PSD95 (P<0.01) proteins. Alpha and Beta diversity analyses showed that the intestinal flora species richness and diversity in the DSS and decomposed recipe groups were similar to those in the sham-operated group, signifying a significant restorative effect (P<0.05). Conclusion: The combination of DSS and its decomposed recipes can reduce the abundance of harmful gut microbiota, leading to improvements in cognitive and learning abilities.

Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records.

Background: Deep learning models showed great success and potential when applied to many biomedical problems. However, the accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, and covariate imbalance when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. Objective: The goal of this study is to investigate the extent to which time-varying covariates, rare incidence, and covariate imbalance influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Methods: We evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration. Results: We developed a novel backward masking scheme to evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training. Conclusions: Devising proper strategies to address challenges from time-varying covariates, lack of data, and covariate imbalance can be key to counteracting data bias and accurately predicting disease occurrence using deep learning models. The novel strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.

Indications for Magnetic Resonance Imaging in Patients With Behcet Uveitis.

BACKGROUND: Behcet disease is a systemic vasculitis, which may involve the eyes and central nervous system. The true prevalence of neurological involvement is not precisely known but may be associated with ocular involvement. This study investigates the association between Behcet uveitis and neuro-Behcet disease. METHODS: A retrospective single-center analysis was conducted for consecutive patients with Behcet uveitis at the Massachusetts Eye Research and Surgery Institution. Uveitis characteristics, neurological symptoms, fluorescein fundus angiography, and MRI results were recorded. RESULTS: Our population included 108 patients with Behcet uveitis, and 26 (24.1%) were found to have neurological involvement associated with Behcet disease. Optic nerve leakage on fundus angiography and neurological symptoms were associated with an increased risk of neurological involvement. Three cases (11.5%) were nonparenchymal, while 23 (88.5%) were parenchymal with lesions in the cortex, subcortical white matter, thalamus, basal ganglia, and brainstem. CONCLUSIONS: There is a high comorbidity between ocular and neurological involvement in Behcet disease. Careful assessment of neurological symptoms and baseline fluorescein fundus angiography are recommended for patients with Behcet disease. MRI has a high diagnostic yield and should be pursued if there is concern for progressive or pre-existing neurological involvement.

Inhibition of SK Channels in VTA Affects Dopaminergic Neurons to Improve the Depression-Like Behaviors of Post-Stroke Depression Rats.

Purpose: This study aimed to investigate the effect of small-conductance calcium-activated potassium channels (SK channels) on the dopaminergic (DA) neuron pathways in the ventral tegmental area (VTA) during the pathogenesis of post-stroke depression (PSD) and explore the improvement of PSD by inhibiting the SK channels. Patients and Methods: Four groups of Sprague-Dawley rats were randomly divided: Control, PSD, SK channel inhibitor (apamin) and SK channel activator (CyPPA) groups. In both control and CyPPA groups, sham surgery was performed. In the other two groups, middle cerebral arteries were occluded. The behavioral indicators related to depression in different groups were compared. Immunofluorescence was used to measure the activity of DA neurons in the VTA, while qRT-PCR was used to assess the expression of SK channel genes. Results: The results showed that apamin treatment improved behavioral indicators related to depression compared to the PSD group. Furthermore, the qRT-PCR analysis revealed differential expression of the KCNN1 and KCNN3 subgenes of the SK channels in each group. Immunofluorescence analysis revealed an increase in the expression of DA neurons in the VTA of the PSD group, which was subsequently reduced upon apamin intervention. Conclusion: This study suggests that SK channel activation following stroke contributes to depression-related behaviors in PSD rats through increased expression of DA neurons in the VTA. And depression-related behavior is improved in PSD rats by inhibiting the SK channels. The results of this study provide a new understanding of PSD pathogenesis and the possibility of developing new strategies to prevent PSD by targeting SK channels.

Association of Device-Measured Physical Activity With Cardiovascular Outcomes in Individuals With Hypertension.

BACKGROUND: There is insufficient evidence to show that the guidelines' recommendations of physical activity (PA) are associated with long-term benefits in individuals with hypertension. METHODS: This prospective cohort study included UK Biobank participants with hypertension. Time spent on vigorous-intensity PA (VPA), moderate-intensity PA (MPA), and light-intensity PA (LPA) measured by wrist-worn accelerometer were extracted. The primary outcomes were major adverse cardiovascular events (including cardiovascular death, stroke, and myocardial infarction) and all-cause mortality. The secondary outcomes were cardiovascular death, myocardial infarction, and stroke, respectively. The relationships of PA with outcomes were analyzed using Cox regression models. RESULTS: This study included 49 060 eligible participants with a median follow-up of 7.0 years. MPA was inversely associated with risks of major adverse cardiovascular events and all-cause mortality. Modest amounts of LPA or VPA were likely to be more beneficial than higher amounts of either in all-cause death or cardiovascular outcomes. Compared with the least active group, 150 to 300 min/wk of MPA or more was significantly associated with decreased risk of all-cause death (by 34%-54%) and major adverse cardiovascular events (by 23%-41%), but 75 to 150 min/wk of VPA or more was associated with few further benefits, even weakening the cardiovascular benefits. CONCLUSIONS: MPA had an inverse dose-response association with the risk of all-cause mortality and cardiovascular outcomes in individuals with hypertension. Modest amounts of LPA or VPA are also beneficial, but higher amounts may be not. MPA may be the optimal PA intensity for individuals with hypertension. Further researches are required to determine whether high levels of VPA should be recommended.

A novel machine-learning-derived four-gene signature predicts STEMI and post-STEMI heart failure.

High mortality and morbidity rates associated with ST-elevation myocardial infarction (STEMI) and post-STEMI heart failure (HF) necessitate proper risk stratification for coronary artery disease (CAD). A prediction model that combines specificity and convenience is highly required. This study aimed to design a monocyte-based gene assay for predicting STEMI and post-STEMI HF. A total of 1,956 monocyte expression profiles and corresponding clinical data were integrated from multiple sources. Meta-results were obtained through the weighted gene co-expression network analysis (WGCNA) and differential analysis to identify characteristic genes for STEMI. Machine learning models based on the decision tree (DT), support vector machine (SVM), and random forest (RF) algorithms were trained and validated. Five genes overlapped and were subjected to the model proposal. The discriminative performance of the DT model outperformed the other two methods. The established four-gene panel (HLA-J, CFP, STX11, and NFYC) could discriminate STEMI and HF with an area under the curve (AUC) of 0.86 or above. In the gene set enrichment analysis (GSEA), several cardiac pathogenesis pathways and cardiovascular disorder signatures showed statistically significant, concordant differences between subjects with high and low expression levels of the four-gene panel, affirming the validity of the established model. In conclusion, we have developed and validated a model that offers the hope for accurately predicting the risk of STEMI and HF, leading to optimal risk stratification and personalized management of CAD, thereby improving individual outcomes.

Associations between the size and duration of asymptomatic subchorionic hematoma and pregnancy outcomes in women with singleton pregnancies.

BACKGROUND: The purpose of this study was to investigate the relationship between the size and duration of asymptomatic subchorionic hematoma and pregnancy outcomes in women with singleton pregnancies. METHODS: This was a retrospective study that enrolled 701 singleton pregnant women who were diagnosed with asymptomatic subchorionic hematoma by ultrasound at 5-10 gestational weeks. The control group recruited 640 normal pregnant women without subchorionic hematoma who were matched with subchorionic hematoma group on baseline characteristics. The pregnancy outcomes were compared between the two groups, and the associations of the size and duration of subchorionic hematoma with pregnancy outcomes were analyzed by logistic regression model. RESULTS: Compared with the normal pregnancy group, the incidence of, gestational diabetes mellitus, gestational thrombocytopenia, placenta adhesion, fetal growth restriction, macrosomia in subchorionic hematoma group were higher (all P < 0.05). After adjusting for confounding factors, the hematoma size was positively associated with the occurrence of gestational hypothyroidism (adjusted OR[95%CI]: 1.029[1.004-1.054]), intrahepatic cholestasis of pregnancy (adjusted OR[95%CI]: 1.095[1.047-1.146]), term premature rupture of membranes (adjusted OR[95%CI]: 1.044[1.005-1.085]), hypertensive disorders of pregnancy (adjusted OR[95%CI]: 1.030[1.0004-1.060]), gestational thrombocytopenia (adjusted OR[95%CI]: 1.078 [1.045-1.113]), placenta adhesion (adjusted OR[95%CI]: 1.054 [1.027-1.082]), and the duration of hematoma was positively associated with the incidence of term premature rupture of membranes (adjusted OR[95%CI]: 1.070[1.027-1.115]), gestational diabetes mellitus (adjusted OR[95%CI]: 1.938 [1.886-1.993]) and fetal growth restriction (adjusted OR[95%CI]: 1.194 [1.124-1.268]). CONCLUSIONS: The presence, size and duration of a first-trimester asymptomatic subchorionic hematoma may be associated with adverse pregnancy outcomes at later gestations such as term premature rupture of membranes and fetal growth restriction.

Protective effects of Radix Stellariae extract against Alzheimer's disease via autophagy activation in Caenorhabditis elegans and cellular models.

Enhancing the clearance of proteins associated with Alzheimer's disease (AD) emerges as a promising approach for AD therapeutics. This study explores the potential of Radix Stellariae, a traditional Chinese medicine, in treating AD. Utilizing transgenic C. elegans models of AD, we demonstrated that a 75% ethanol extract of Radix Stellariae (RSE) (at 50 µg/mL) effectively diminishes Aß and Tau protein expression, and alleviates their induced impairments including paralysis, behavioral dysfunction, neurotoxicity, and ROS accumulation. Additionally, RSE enhances the stress resistance of C. elegans. Further investigations revealed that RSE promotes autophagy, a critical cellular process for protein degradation, in these models. We found that inhibiting autophagy-related genes negated the neuroprotective effects of RSE, suggesting a central role for autophagy in the actions of RSE. In PC-12 cells, we observed that RSE not only inhibited Aß fibril formation but also promoted the degradation of AD-related proteins and reduced their cytotoxicity. Mechanistically, RSE was found to induce autophagy via modulating PI3K/AKT/mTOR and AMPK/mTOR signaling pathways. Importantly, inhibiting autophagy counteracted the beneficial effects of RSE on the clearance of AD-associated proteins. Moreover, we identified Dichotomine B, a ß-carboline alkaloid, as a key active constituent of RSE in mitigating AD pathology in C. elegans at concentrations ranging from 50 to 1000 µM. Collectively, our study presents novel discoveries that RSE alleviates AD pathology and toxicity primarily by inducing autophagy, both in vivo and in vitro. These findings open up new avenues for exploring the therapeutic potential of RSE and its active component, Dichotomine B, in treating neurodegenerative diseases like AD.