The risk of diabetic cardiovascular complications is closely linked to the length of hyperglycemia exposure. Mitophagy plays a significant role in vascular endothelial injury. However, the specific mechanisms by which mitophagy contributes to endothelial injury during sustained hyperglycemia remain unclear. In diabetic ApoE-/- mice and human umbilical vein endothelial cell (HUVEC) models, mitophagy is enhanced following short-term and long-term high-glucose exposure. Short-term high-glucose exposure promotes Parkin-mediated mitophagy and upregulates mitochondrial fission protein 1 (Fis1) expression, whereas long-term high-glucose exposure suppresses Parkin-mediated mitophagy and downregulates Fis1. With prolonged high-glucose exposure, Syntaxin 17 (STX17) translocates from the endoplasmic reticulum to the mitochondria, activating STX17-mediated mitophagy. Silencing STX17 alleviates mitochondrial degradation, decreases reactive oxygen species (ROS) levels, enhances endothelial nitric oxide synthase (eNOS) phosphorylation, and reduces apoptosis. Silencing Fis1 accelerates the switching to STX17-mediated mitophagy, worsening endothelial dysfunction, whereas Fis1 overexpression prevents this switching, reducing ROS and apoptosis and enhancing eNOS phosphorylation. In summary, these findings suggest that the switching from Parkin-mediated to STX17-mediated mitophagy drives vascular endothelial injury following long-term hyperglycemic exposure, providing valuable insights into therapeutic strategies for diabetic cardiovascular complications. © 2025 The Author(s). Advanced Science published by Wiley‐VCH GmbH.

Ursodeoxycholic acid (UDCA) is a first-line clinical drug for the treatment of liver diseases. U12, a derivative of UDCA, showed effective anti-hepatoma activities in previous works. However, the low polarity and large doses limited the druglikeness of U12. In this study, the structural modification and optimization of U12 were further investigated and twelve U12 derivatives were synthesized by substitution, esterification and amidation reactions. The evaluation of the cytotoxicity of synthetic derivatives against hepatoma cell lines (HepG2) indicated that U12-I, U12a-d and U12h showed more effective cytotoxic effects on the growth of HepG2 cells than U12, and the preliminary structure-activity relationship was discussed. Among them, U12a exhibited the most potent anti-hepatocellular carcinoma activity. Mechanism studies indicated that U12a inhibited HepG2 cell proliferation by arresting the G0/G1 phase, and suppressed the activation of the PI3K/AKT/mTOR pathway. Further studies showed that U12a induced HepG2 cells apoptosis through activating the caspase signaling pathway. Furthermore, U12a evidently inhibits the growth of HepG2-derived tumor xenografts in vivo without observable adverse effects. Thus, U12a might be considered as a promising candidate for the treatment of hepatocellular carcinoma.

Alzheimer's disease (AD) is age-related progressive neurological dysfunction. Limited clinical benefits for current treatments indicate an urgent need for novel therapeutic strategies. Previous transcriptomic analysis showed that DMP1 expression level was increased in AD model animals whereas it can induce cell-cycle arrest in several cell lines. However, whether the cell-cycle arrest of neural progenitor cell induced by DMP1 affects cognitive function in Alzheimer-like mice still remains unknown. The objective of our study is to explore the issue. We found that DMP1 is correlated with cognitive function based on the clinical genomic analysis of ADNI database. The negative role of DMP1 on neural progenitor cell (NPC) proliferation was revealed by silencing and overexpressing DMP1 in vitro. Furthermore, silencing DMP1 could increase the number of NPCs and improve cognitive function in Alzheimer-like mice, through decreasing P53 and P21 levels, which suggested that DMP1-induced cell-cycle arrest could influence cognitive function.

Purpose: The identification of optimal drug administration schedules to overcome the emergence of resistance that causes treatment failure is a major challenge in cancer research. We report the outcomes of a computational strategy to assess the dynamics of tumor progression as a function of time under different treatment regimens. Methods: We developed an evolutionary game theory model that combined Lotka-Volterra equations and pharmacokinetic properties with 2 competing cancer species: nivolumab-response cells and Janus kinase (JAK1/2) mutation cells. We selected 3 therapeutic schemes that have been tested in the clinical trials: 3 mg/kg Q2w, 10 mg/kg Q2w, and 480 mg Q4w. The simulation was performed under the intervals of 75, 125, and 175 days, respectively, for each regimen. The data sources of the pharmacokinetic parameters used in this study were collected from previous published clinical trials. Other parameters in the evolutionary model come from the existing references. Findings: Predictions under various dose schedules indicated a strong selection for nivolumab-independent cells. Under the 3 mg/kg dose strategy, the reproduction rate of JAK mutation cells was highest, with strongest tumor elimination ability at a 75-day interval between treatments. Prolonged drug intervals to 125 or 175 days delayed tumor evolution but accelerated tumor recurrence. Although 10 mg/kg Q2w had an obvious clinical effect in a short time, it further promotes the progress of resistant population compared with the 3 mg/kg dose. Our model suggests that 480 mg Q4w would be more valuable in terms of clinical efficacy, but complete resistant occurs earlier regardless the interval. Implications: The results of this study emphasize that increasing the dose or shortening the interval between doses accelerates the evolution of heterogeneous populations, although the short-term effect is significant. In practice, the therapeutic regimen should be balanced according to the evolutionary principle. (Clin Ther. 2021;43:2088-2103.) (c) 2021 Elsevier Inc.

Meteorological factors, which are periodic and regular in a long run, have an unignorable impact on human health. Accurate health risk prediction based on meteorological factors is essential for optimal allocation of resource in healthcare units. However, due to the non-stationary and non-linear nature of the original hospitalization sequence, traditional methods are less robust in predicting it. This study aims to investigate hospital admission prediction models using time series pre-processing algorithms and deep learning approach based on meteorological factors. Using the electronic medical record data from Panyu Central Hospital and meteorological data of Panyu district from 2003 to 2019, 46,089 eligible patients with lower respiratory tract infections (LRTIs) and four meteorological factors were identified to build and evaluate the prediction models. A novel hybrid model, Cascade GAM-CEEMDAN-LSTM Model (CGCLM), was established in combination with generalized additive model (GAM), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and long-short term memory (LSTM) networks for predicting daily admissions of patients with LRTIs. The experimental results show that CGCLM multistep method proposed in this paper outperforms single LSTM model in the prediction of health risk time series at different time window sizes. Moreover, our results also indicate that CGCLM has the best prediction performance when the time window is set to 61 days (RMSE = 1.12, MAE = 0.87, R-2 = 0.93). Adequate extraction of exposure-response relationships between meteorological factors and diseases and suitable handling of sequence pre-processing have an important role in time series prediction. This hybrid climate-based model for predicting LRTIs disease can also be extended to time series prediction of other epidemic disease.

Background: The relationship between air pollution and meteorological factors on diseases has become a research hotspot recently. Nevertheless, few studies have touched the inferences of nitrogen dioxide (NO2) and atmospheric pressure (AP) on hospitalization risk for chronic obstructive pulmonary disease (COPD).Objectives: To investigate the short-term impact of particulate air pollutants and meteorology factors on hospitalizations for COPD and quantify the corresponding risk burden of hospital admission.Methods: In our study, COPD cases were collected from Guangzhou Panyu Central Hospital (n = 11,979) from Dec of 2013 to Jun 2019. The 24-h average temperature, relative humidity (RH), wind speed (V), AP and other meteorological data were obtained from Guangzhou Meteorological Bureau. Air pollution data were collected from Guangzhou Air Monitoring Station. The influence of different NO2 and AP values on COPD risk was quantified by a distributed lag nonlinear model (DLNM) combined with Poisson Regression and Time Series analysis.Results: We found that NO2 had a non-linear relationship with the incidence of COPD, with an approximate "M" type, appearing at the peaks of 126 mu g/m(3) (RR = 1.32, 95%CI, 1.07 to 1.64) and 168 mu g/m(3) (RR = 1.21, 95%CI, 0.94 to 1.55), respectively. And the association between AP and COPD incidence exhibited an approximate Jshape with a peak occurring at 1035 hPa (RR = 1.16, 95% CI, 1.02 to 1.31).Conclusions: The nonlinear relationship of NO2 and AP on COPD admission risk in different periods of lag can be used to establish an early warning system for diseases and reduce the possible outbreaks and burdens of COPD in a sensitive population.

Introduction: The impact of mitochondrial haplogroups on Alzheimer's disease (AD) risk has not been fully elucidated and warrants further investigation at the subgroup level.Objectives: The aim of this research is to evaluate the association between mitochondrial haplogroups and AD risk in subgroups level.Methods: In total, 809 AD Neuroimaging Initiative subjects were assessed using mtDNA sequencing, the AD Assessment Scale-Cognitive Subscale (ADAS-cog), hippocampal volume measurements, the hypometabolic convergence index (HCI), and MCI-to-AD conversion proportion measurements.Results: The frequency of haplogroup J was significantly higher than that of other haplogroups in the AD group (p = 0.013). According to the correlation between haplogroup J-specific SNPs and ADAS-cog, haplogroup J was divided into four subgroups harboring exacerbating SNPs, protective SNPs, both exacerbating and protective SNPs, or irrelevant SNPs. The subgroups harboring exacerbating SNPs exhibited higher AD risk represented by the levels of ADAS-cog, hippocampal volume, HCI, and MCI-to-AD conversion proportion than other subgroups.Conclusion: Heterogeneity existed among the subgroups of haplogroup J, which suggested that different subgroups exhibited different levels of AD risk. This study provides novel insights into the correlation between mitochondrial haplogroups and AD risk. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Cairo University.

Emerging evidence shows that mitochondria fusion/fission imbalance is related to the occurrence of hyperglycemia-induced vascular injury. To study the temporal dynamics of mitochondrial fusion and fission, we observed the alteration of mitochondrial fusion/fission proteins in a set of different high-glucose exposure durations, especially in the early stage of hyperglycemia. The in vitro results show that persistent cellular apoptosis and endothelial dysfunction can be induced rapidly within 12 hours' high-glucose pre-incubation. Our results show that mitochondria maintain normal morphology and function within 4 hours' high-glucose pre-incubation; with the extended high-glucose exposure, there is a transition to progressive fragmentation; once severe mitochondria fusion/fission imbalance occurs, persistent cellular apoptosis will develop. In vitro and in vivo results consistently suggest that mitochondrial fusion/fission homeostasis alterations trigger high-glucose-induced vascular injury. As the guardian of mitochondria, AMPK is suppressed in response to hyperglycemia, resulting in imbalanced mitochondrial fusion/fission, which can be reversed by AMPK stimulation. Our results suggest that mitochondrial fusion/fission's staged homeostasis may be a predictive factor of diabetic cardiovascular complications.

The chemical investigation on endophytic fungusAnnulohypoxylon cf. stygiumin leaves ofAnoectochilus roxburghii(Wall.) Lindl. has been performed. Sixteen compounds were isolated and their structures were identified as (-)-notoamide A, (-)-notoamide B, (+)-versicolamide B, notoamide C, notoamide D, stephacidin A, sterigmatocystin, dihydrosterigmatocystin, secosterigmatocystin, versiconol, averufanin, kipukasin D, kipukasin E, diorcinal, palmarumycin CP2 and (-)-(3R)-mellein methyl ether, respectively, by spectroscopic analysis and comparison with literature data. All the compounds were isolated fromAnnulohypoxylongenus for the first time. Sterigmatocystin and palmarumycin CP2 showed selective cytotoxic activities against HepG2, HeLa, MCF-7 and HT-29.

Adverse events caused by drug-drug interaction (DDI) not only pose a serious threat to health, but also increase additional medical care expenditure. However, despite the emergence of many excellent text mining-based DDI classification methods, achieving a balance between using simpler method and better model performance is still unsatisfactory. In this article, we present a deep learning method of stacked bidirectional Gated Recurrent Unit (GRU)- convolutional neural network (SGRU-CNN) model which apply stacked bidirectional GRU (BiGRU) network and convolutional neural network (CNN) on lexical information and entity position information respectively to conduct DDIs extraction task. Furthermore, SGRU-CNN model assigns the weights of each word feature to improve performance with one attentive pooling layer. On the condition that other values are not inferior to other algorithms, experimental results on the DDI Extraction 2013 corpus show that our model achieves a 1.54% improvement in recall value. And the proposed SGRU-CNN model reaches great performance (F1-score: 0.75) with the fewest features, indicating an excellent balance between avoiding redundant preprocessing task and higher accuracy in relation extraction on biomedical literature using our method.
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