Vertical Greenhouse Homes

This project is intended to propose an active, attractive, and innovative lifestyle for older peoples, and motivate the interaction between nature and people

Anthocyanin attenuates oxygen-glucose deprivation/reperfusion-induced apoptosis of PC12 cells via inactivation of ASK1/JNK/p38 signaling pathway

Purpose: To investigate whether the cytoprotective effect of anthocyanin (Anc) on oxygen-glucose deprivation/reperfusion (OGD/R)-induced cell injury is related to apoptosis signal-regulating kinase 1 (ASK1)/c-Jun N-terminal kinase (JNK)/p38 signaling pathway. Methods: PC12 cells were pre-treated with various concentrations of Anc (10, 50, and 100 μg/mL) in OGD/R-induced cell injury model. The 3-(4, 5)-dimethylthiahiazo (-z-y1)-3, 5-di-phenytetrazoliumromide (MTT) assay was used to assess cell viability. Cell apoptosis was measured by lactic acid dehydrogenase (LDH) release assay and flow cytometry. Western blot was employed to determine the protein expressions of BCL-2, BAX, caspase-3, p-ASK1 (Thr845), p-JNK, and p-p38. Results: The results indicate that Anc increased the viability of PC12 cells after OGD/R exposure (p < 0.05), and also efficiently rescued OGD/R-induced apoptosis (p < 0.05). Mechanistic studies showed that these protective roles of Anc are related to the inhibition of ASK1/JNK/p38 signaling pathway. Conclusion: The results indicate Anc protects against OGD/R-induced cell injury by enhancing cell viability and inhibiting cell apoptosis. The underlying mechanism of action is partly via inactivation of ASK1/JNK/p38 signaling pathway. Thus, Anc has promise as a potential natural agent to prevent and treat cerebral ischemia-reperfusion injury

Dietary total antioxidant capacity and risk of prediabetes and diabetes mellitus: a systematic review and dose-response meta-analysis of 170,919 participants

BackgroundObservational studies have assessed the association between total antioxidant capacity of the diet and risk of diabetes mellitus. However, results from these studies were not entirely consistent. In the current systematic review and dose-response meta-analysis, we aimed to determine the association between dietary total antioxidant capacity (TAC) and the risk of prediabetes and diabetes mellitus.MethodsA systematic literature search of authentic electronic resources including PubMed/Medline, Embase, Scopus, ISI Web of Science and China National Knowledge Infrastructure (CNKI) was carried out to find the relevant articles published up to November 2024. Random-effects or fixed-effects models were used to aggregate the relative risks (RRs) and their 95% confidence intervals (CIs) where appropriate. Heterogeneity across the studies were determined using the Cochran’s Q test and I-square (I2) statistics.ResultsA total of 10 observational studies (five cohort, three case-control and two cross-sectional studies) were included in our meta-analysis. The pooled results indicated that higher dietary TAC was significantly associated with lower risk of prediabetes (RR = 0.58; 95% CI: 0.34–0.97; p = 0.039) and diabetes mellitus (RR = 0.71; 95% CI: 0.58–0.87, p = 0.001). In addition, dose-response analysis showed a linear trend association between dietary TAC and risk of diabetes mellitus (RR = 0.928; 95% CI: 0.842–1.023, pdose-response = 0.131, pnonlinearity = 0.078). Subgroup analyses showed the significant inverse association between dietary TAC and diabetes mellitus in mean age <50 and sample size <5,000 (RR = 0.26, 95% CI: 0.16–0.41, p < 0.001), and there was no evidence of heterogeneity (p = 0.939; I2 = 0.0%). Meanwhile, there was also an inverse association between dietary TAC and diabetes mellitus in Western countries (RR = 0.79; 95% CI: 0.68–0.92, p = 0.003), with less evidence of heterogeneity (p = 0.226; I2 = 36.7%).ConclusionOverall, higher dietary TAC was inversely associated with the risk of prediabetes and diabetes mellitus. Further well-designed prospective studies or randomized controlled trials are needed to validate the present findings.Systematic Review Register(PROSPERO), CRD42024611235

ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capturing complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observe a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.Comment: To appear in VLDB 2024.Code: https://github.com/17000cyh/IMDiffusion.gi

A Metabonomics Profiling Study on Phlegm Syndrome and Blood-Stasis Syndrome in Coronary Heart Disease Patients Using Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry

A metabonomics approach based on liquid chromatography/quadrupole time-of-flight mass spectrometry (LC-Q-TOF/MS) was utilized to obtain potential biomarkers of coronary heart disease (CHD) patients and investigate the ZHENG types differentiation in CHD patients. The plasma samples of 20 CHD patients with phlegm syndrome, 20 CHD patients with blood-stasis syndrome, and 16 healthy volunteers were collected in the study. 26 potential biomarkers were identified in the plasma of CHD patients and 19 differential metabolites contributed to the discrimination of phlegm syndrome and blood-stasis syndrome in CHD patients (VIP>1.5; P<0.05) which mainly involved purine metabolism, pyrimidine metabolism, amino acid metabolism, steroid biosynthesis, and arachidonic acid metabolism. This study demonstrated that metabonomics approach based on LC-MS was useful for studying pathologic changes of CHD patients and interpreting the differentiation of ZHENG types (phlegm and blood-stasis syndrome) in traditional Chinese medicine (TCM)

Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiation

BackgroundSepsis-associated acute kidney injury (S-AKI) has a significant impact on patient survival, with continuous renal replacement therapy (CRRT) being a crucial intervention. However, the optimal timing for CRRT initiation remains controversial.MethodsUsing the MIMIC-IV database for model development and the eICU database for external validation, we analyzed patients with S-AKI to compare survival rates between early and late CRRT initiation groups. Propensity score matching was performed to address potential selection bias. Subgroup analyses stratified patients by disease severity using SOFA scores (low ≤10, medium 11–15, high &gt;15) and creatinine levels (low ≤3 mg/dL, medium 3–5 mg/dL, high &gt;5 mg/dL). Multiple machine learning models were developed and evaluated to predict patient prognosis, with Shapley Additive exPlanations (SHAP) analysis identifying key prognostic factors.ResultsAfter propensity score matching, late CRRT initiation was associated with improved survival probability, but led to increased hospital and ICU stays. Subgroup analyses showed consistent trends favoring late CRRT across all SOFA categories, with the most pronounced effect in high SOFA scores (&gt;15, p = 0.058). The GBM model demonstrated robust predictive performance (average C-index 0.694 in validation and test sets). SHAP analysis identified maximum lactate levels, age, and minimum SpO2 as the strongest predictors of mortality, while CRRT timing showed relatively lower impact on outcome prediction.ConclusionWhile later initiation of CRRT in S-AKI patients was associated with improved survival, this benefit comes with increased healthcare resource utilization. The clinical parameters, rather than CRRT timing, are the primary determinants of patient outcomes, suggesting the need for a more personalized approach to CRRT initiation based on overall illness severity

The Altered Reconfiguration Pattern of Brain Modular Architecture Regulates Cognitive Function in Cerebral Small Vessel Disease

Background: Cerebral small vessel disease (SVD) is a common cause of cognitive dysfunction. However, little is known whether the altered reconfiguration pattern of brain modular architecture regulates cognitive dysfunction in SVD.Methods: We recruited 25 cases of SVD without cognitive impairment (SVD-NCI) and 24 cases of SVD with mild cognitive impairment (SVD-MCI). According to the Framingham Stroke Risk Profile, healthy controls (HC) were divided into 17 subjects (HC-low risk) and 19 subjects (HC-high risk). All individuals underwent resting-state functional magnetic resonance imaging and cognitive assessments. Graph-theoretical analysis was used to explore alterations in the modular organization of functional brain networks. Multiple regression and mediation analyses were performed to investigate the relationship between MRI markers, network metrics and cognitive performance.Results: We identified four modules corresponding to the default mode network (DMN), executive control network (ECN), sensorimotor network and visual network. With increasing vascular risk factors, the inter- and intranetwork compensation of the ECN and a relatively reserved DMN itself were observed in individuals at high risk for SVD. With declining cognitive ability, SVD-MCI showed a disrupted ECN intranetwork and increased DMN connection. Furthermore, the intermodule connectivity of the right inferior frontal gyrus of the ECN mediated the relationship between periventricular white matter hyperintensities and visuospatial processing in SVD-MCI.Conclusions: The reconfiguration pattern of the modular architecture within/between the DMN and ECN advances our understanding of the neural underpinning in response to vascular risk and SVD burden. These observations may provide novel insight into the underlying neural mechanism of SVD-related cognitive impairment and may serve as a potential non-invasive biomarker to predict and monitor disease progression

3D-structured mesoporous silica memristors for neuromorphic switching and reservoir computing

Memristors are emerging as promising candidates for practical application in reservoir computing systems that are capable of temporal information processing. Here, we experimentally implement a physical reservoir computing system using resistive memristors based on three-dimensional (3D)-structured mesoporous silica (mSiO2) thin films fabricated by a low cost, fast and vacuum-free sol–gel technique. The in situ learning capability and a classification accuracy of 100% on a standard machine learning dataset are experimentally demonstrated. The volatile (temporal) resistive switching in diffusive memristors arises from the formation and subsequent spontaneous rupture of conductive filaments via diffusion of Ag species within the 3D-structured nanopores of the mSiO2 thin film. Besides volatile switching, the devices also exhibit a bipolar non-volatile resistive switching behavior when the devices are operated at a higher compliance current level. The implementation of mSiO2 thin films opens the route to fabricate a simple and low cost dynamic memristor with a temporal information process functionality, which is essential for neuromorphic computing applications