Twenty-three medication-taking traits and stroke: A comprehensive Mendelian randomization study

BackgroundCertain medication categories may increase the risk of stroke. Nonetheless, the evidence regarding the causal relationship of medication-taking in promoting stroke and subtypes is deficient.MethodsWe evaluated the causal effect of a genetic predisposition for certain medication categories on stroke and subtypes (ischemic and hemorrhagic categories) by a two-sample Mendelian randomization (MR) analysis. Data for 23 medication categories were gathered from a genome-wide association study (GWAS) involving 318,177 patients. The Medical Research Council Integrative Epidemiology Unit Open GWAS database and the FinnGen consortium were used to gather GWAS data for stroke and subtypes. Inverse variance weighted, MR-Egger, and weighted median were used for the estimation of causal effects. Cochran's Q test, MR-Egger intercept test, and leave-one-out analysis were used for sensitivity analyses.ResultsTen medication categories were linked to a high stroke risk. Nine categories were linked to a high-risk ischemic stroke. Five categories were associated with small vessel ischemic stroke. Nine categories were positively associated with large artery atherosclerotic ischemic stroke. Three categories causally increased the possibility of cardioembolic ischemic stroke. Four categories were associated with intracerebral hemorrhage. Four categories were associated with nontraumatic intracranial hemorrhage. Three categories were causally associated with subarachnoid hemorrhage (SAH). Four categories were associated with the combination of SAH, unruptured cerebral aneurysm, and aneurysm operations SAH.ConclusionsThis study confirms that some medication categories lead to a greater risk of strokes. Meanwhile, it has an implication for stroke screening as well as direct clinical significance in the design of conduction of future randomized controlled trials

KD_ConvNeXt: knowledge distillation-based image classification of lung tumor surgical specimen sections

Introduction: Lung cancer is currently among the most prevalent and lethal cancers in the world in terms of incidence and fatality rates. In clinical practice, identifying the specific subtypes of lung cancer is essential in diagnosing and treating lung lesions.Methods: This paper aims to collect histopathological section images of lung tumor surgical specimens to construct a clinical dataset for researching and addressing the classification problem of specific subtypes of lung tumors. Our method proposes a teacher-student network architecture based on a knowledge distillation mechanism for the specific subtype classification of lung tumor histopathological section images to assist clinical applications, namely KD_ConvNeXt. The proposed approach enables the student network (ConvNeXt) to extract knowledge from the intermediate feature layers of the teacher network (Swin Transformer), improving the feature extraction and fitting capabilities of ConvNeXt. Meanwhile, Swin Transformer provides soft labels containing information about the distribution of images in various categories, making the model focused more on the information carried by types with smaller sample sizes while training.Results: This work has designed many experiments on a clinical lung tumor image dataset, and the KD_ConvNeXt achieved a superior classification accuracy of 85.64% and an F1-score of 0.7717 compared with other advanced image classification method

miR-33a Mediates the Anti-Tumor Effect of Lovastatin in Osteosarcoma by Targeting CYR61

Background/Aims: Preventing cell metastasis is an effective therapeutic strategy to treat osteosarcoma and improve prognosis. Statins have been found to have anticancer effects in addition to their cholesterol-lowering action. As a new target of statins, cysteine-rich 61 (CYR61) was recently identified to promote cell migration and metastasis in osteosarcoma. However, the underlying mechanisms mediating the regulation of CYR61 expression by statins remain unknown. Methods: Human osteosarcoma cell lines MG63 and SaOS2 were used to clarify the effect of lovastatin on CYR61 expression. Real-time PCR was performed to detect mRNA or microRNA (miRNA) levels and western blot was performed to detect protein levels. Cell invasive ability was determined using Transwell assays. Lentivirus encoding CYR61 cDNA or sterol regulatory element-binding protein 2 (SREBP-2) shRNA was used to upregulate CYR61 expression or downregulate SREBP-2 expression. Binding of the CYR61 3’ untranslated region (UTR) and miR-33a was analyzed by luciferase reporter assay. Results: We found that lovastatin treatment decreased CYR61 expression, inhibited cell invasion and altered epithelial-to-mesenchymal-transition (EMT)-related protein expression, while CYR61 overexpression abolished the effect of lovastatin. Moreover, lovastatin increased the expression of SREBP-2 and miR-33a, which were then downregulated by SREBP-2 silencing. Bioinformatics analysis indicated that the CYR61 3′UTR harbored a potential miR-33a binding site and luciferase reporter assay demonstrated that CYR61 was a target of miR-33a in osteosarcoma cells. Furthermore, miR-33a could inhibit cell invasion and alter EMT-related protein expression. SREBP-2 silencing or miR-33a inhibitor upregulated CYR61 expression and reversed the effects of lovastatin on cell invasion and EMT-related proteins. Conclusion: Our findings suggest lovastatin suppresses osteosarcoma cell invasion through the SREBP-2/miR-33a/CYR61 pathway

Enhanced Electrochemical Performance of PEO-Based Composite Polymer Electrolyte with Single-Ion Conducting Polymer Grafted SiO₂ Nanoparticles

In order to enhance the electrochemical performance and mechanical properties of poly(ethylene oxide) (PEO)-based solid polymer electrolytes, composite solid electrolytes (CSE) composed of single-ion conducting polymer-modified SiO2, PEO and lithium salt were prepared and used in lithium-ion batteries in this work. The pyridyl disulfide terminated polymer (py-ss-PLiSSPSI) is synthesized through RAFT polymerization, then grafted onto SiO2 via thiol-disulfide exchange reaction between SiO2-SH and py-ss-PLiSSPSI. The chemical structure, surface morphology and elemental distribution of the as-prepared polymer and the PLiSSPSI-g-SiO2 nanoparticles have been investigated. Moreover, CSEs containing 2, 6, and 10 wt% PLiSSPSI-g-SiO2 nanoparticles (PLi-g-SiCSEs) are fabricated and characterized. The compatibility of the PLiSSPSI-g-SiO2 nanoparticles and the PEO can be effectively improved owing to the excellent dispersibility of the functionalized nanoparticles in the polymer matrix, which promotes the comprehensive performances of PLi-g-SiCSEs. The PLi-g-SiCSE-6 exhibits the highest ionic conductivity (0.22 mS·cm−1) at 60 °C, a large tLi+ of 0.77, a wider electrochemical window of 5.6 V and a rather good lithium plating/stripping performance at 60 °C, as well as superior mechanical properties. Hence, the CSEs containing single-ion conducting polymer modified nanoparticles are promising candidates for all-solid-state lithium-ion batteries

Reconstructing the regulatory circuit of cell fate determination in yeast mating response.

Massive technological advances enabled high-throughput measurements of proteomic changes in biological processes. However, retrieving biological insights from large-scale protein dynamics data remains a challenging task. Here we used the mating differentiation in yeast Saccharomyces cerevisiae as a model and developed integrated experimental and computational approaches to analyze the proteomic dynamics during the process of cell fate determination. When exposed to a high dose of mating pheromone, the yeast cell undergoes growth arrest and forms a shmoo-like morphology; however, at intermediate doses, chemotropic elongated growth is initialized. To understand the gene regulatory networks that control this differentiation switch, we employed a high-throughput microfluidic imaging system that allows real-time and simultaneous measurements of cell growth and protein expression. Using kinetic modeling of protein dynamics, we classified the stimulus-dependent changes in protein abundance into two sources: global changes due to physiological alterations and gene-specific changes. A quantitative framework was proposed to decouple gene-specific regulatory modes from the growth-dependent global modulation of protein abundance. Based on the temporal patterns of gene-specific regulation, we established the network architectures underlying distinct cell fates using a reverse engineering method and uncovered the dose-dependent rewiring of gene regulatory network during mating differentiation. Furthermore, our results suggested a potential crosstalk between the pheromone response pathway and the target of rapamycin (TOR)-regulated ribosomal biogenesis pathway, which might underlie a cell differentiation switch in yeast mating response. In summary, our modeling approach addresses the distinct impacts of the global and gene-specific regulation on the control of protein dynamics and provides new insights into the mechanisms of cell fate determination. We anticipate that our integrated experimental and modeling strategies could be widely applicable to other biological systems

Table3_Twenty-three medication-taking traits and stroke: A comprehensive Mendelian randomization study.xlsx

BackgroundCertain medication categories may increase the risk of stroke. Nonetheless, the evidence regarding the causal relationship of medication-taking in promoting stroke and subtypes is deficient.MethodsWe evaluated the causal effect of a genetic predisposition for certain medication categories on stroke and subtypes (ischemic and hemorrhagic categories) by a two-sample Mendelian randomization (MR) analysis. Data for 23 medication categories were gathered from a genome-wide association study (GWAS) involving 318,177 patients. The Medical Research Council Integrative Epidemiology Unit Open GWAS database and the FinnGen consortium were used to gather GWAS data for stroke and subtypes. Inverse variance weighted, MR-Egger, and weighted median were used for the estimation of causal effects. Cochran's Q test, MR-Egger intercept test, and leave-one-out analysis were used for sensitivity analyses.ResultsTen medication categories were linked to a high stroke risk. Nine categories were linked to a high-risk ischemic stroke. Five categories were associated with small vessel ischemic stroke. Nine categories were positively associated with large artery atherosclerotic ischemic stroke. Three categories causally increased the possibility of cardioembolic ischemic stroke. Four categories were associated with intracerebral hemorrhage. Four categories were associated with nontraumatic intracranial hemorrhage. Three categories were causally associated with subarachnoid hemorrhage (SAH). Four categories were associated with the combination of SAH, unruptured cerebral aneurysm, and aneurysm operations SAH.ConclusionsThis study confirms that some medication categories lead to a greater risk of strokes. Meanwhile, it has an implication for stroke screening as well as direct clinical significance in the design of conduction of future randomized controlled trials.</p

Reconstructing the regulatory circuit of cell fate determination in yeast mating response

<div><p>Massive technological advances enabled high-throughput measurements of proteomic changes in biological processes. However, retrieving biological insights from large-scale protein dynamics data remains a challenging task. Here we used the mating differentiation in yeast <i>Saccharomyces cerevisiae</i> as a model and developed integrated experimental and computational approaches to analyze the proteomic dynamics during the process of cell fate determination. When exposed to a high dose of mating pheromone, the yeast cell undergoes growth arrest and forms a shmoo-like morphology; however, at intermediate doses, chemotropic elongated growth is initialized. To understand the gene regulatory networks that control this differentiation switch, we employed a high-throughput microfluidic imaging system that allows real-time and simultaneous measurements of cell growth and protein expression. Using kinetic modeling of protein dynamics, we classified the stimulus-dependent changes in protein abundance into two sources: global changes due to physiological alterations and gene-specific changes. A quantitative framework was proposed to decouple gene-specific regulatory modes from the growth-dependent global modulation of protein abundance. Based on the temporal patterns of gene-specific regulation, we established the network architectures underlying distinct cell fates using a reverse engineering method and uncovered the dose-dependent rewiring of gene regulatory network during mating differentiation. Furthermore, our results suggested a potential crosstalk between the pheromone response pathway and the target of rapamycin (TOR)-regulated ribosomal biogenesis pathway, which might underlie a cell differentiation switch in yeast mating response. In summary, our modeling approach addresses the distinct impacts of the global and gene-specific regulation on the control of protein dynamics and provides new insights into the mechanisms of cell fate determination. We anticipate that our integrated experimental and modeling strategies could be widely applicable to other biological systems.</p></div

High-resolution profiling of protein expression and synthesis rates.

<p>(A) Protein expression, growth rate and protein synthesis rates for the investigated genes in yeast cells in response to a high concentration of pheromone. The order of the genes in the heatmap is determined via hierarchical clustering of the gene expression profiles. Values are normalized and transformed to a z-score. (B) The normalized values of protein expression, growth rate and protein synthesis rate for genes in yeast cells exposed to the intermediate concentration of pheromone.</p