Frame composite imaging method based on time-sharing latency excitation for ultrasound shear wave elastography.

Ultrasound shear wave elastography is an imaging modality that noninvasively assesses mechanical properties of tissues. The results of elastic imaging are obtained by accurately estimating the propagation velocity of shear wave fronts. However, the acquisition rate of the shear wave acquisition device is limited by the hardware of the system. Therefore, increasing the collection rate of shear waves can directly improve the quality of shear wave velocity images. In addition, the problem of velocity reconstruction with relatively small elastic inclusions has always been a challenge in elastic imaging and a very important and urgent issue in early disease diagnosis. For the problem of elastography detection of the shape and boundary of inclusions in tissues, Time-sharing latency excitation frame composite imaging (TS-FCI) method is proposed for tissue elasticity measurement. The method fuses the shear wave motion data generated by time sharing and latency excitation to obtain a set of composite shear wave motion data. Based on the shear wave motion data, the local shear wave velocity image is reconstructed in the frequency domain to obtain the elastic information of the tissue. The experimental results show that the TS-FCI method has a velocity estimation error of 11 % and a contrast to noise ratio (CNR) of 3.81 when estimating inclusions with smaller dimensions (2.53 mm). Furthermore, when dealing with inclusions with small elastic changes (10 kPa), the velocity estimation error is 3 % and the CNR is 3.21. Compared to conventional time-domain and frequency-domain analysis methods, the proposed method has advantages. Results and analysis have shown that this method has potential promotional value in the quantitative evaluation of organizational elasticity.

Increasing the frequency of plant-based food intake in daily diets reduces the risk of cardiovascular disease among elderly Chinese: a cohort study.

Objective: There is limited research on the relationship between the frequency of plant-based food intake and the risk of cardiovascular disease (CVD) among elderly Chinese. This study aims to evaluate the association between plant-based dietary index (PDI) and CVD risks, providing evidence for elderly Chinese to reduce CVD risks by increasing the frequency of plant-based food consumption. Methods: This study analyzed data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2011-2018, employing a multivariate modified Poisson regression model, trend tests, and restricted cubic spline (RCS) analysis to assess the linear and non-linear relationship between the PDI and CVD risks. Subgroup analyses and interaction tests were conducted to evaluate the robustness and population-specificity of the results. Results: This study included a total of 1,414 elderly Chinese, and at the end of follow-up, 487 participants had developed CVD. The multivariate modified Poisson regression model revealed a negative association between PDI and CVD risks [RR = 0.983, 95%CI = (0.970, 0.997)]. Similarly, the multivariate trend test (p = 0.031) and RCS analysis (P for nonlinear = 0.600) indicated a linear relationship between PDI and CVD risks. Subgroup analyses showed that the relationship between PDI and CVD risk was not influenced by gender, BMI, smoking, alcohol use, or exercise. Conclusion: The PDI was negatively correlated with CVD risks, indicating that increasing the frequency of plant-based food intake in the diet may reduce CVD risks among elderly Chinese.

Integrating high-throughput phenotyping and genome-wide association studies for enhanced drought resistance and yield prediction in wheat.

Drought, especially terminal drought, severely limits wheat growth and yield. Understanding the complex mechanisms behind the drought response in wheat is essential for developing drought-resistant varieties. This study aimed to dissect the genetic architecture and high-yielding wheat ideotypes under terminal drought. An automated high-throughput phenotyping platform was used to examine 28 392 image-based digital traits (i-traits) under different drought conditions during the flowering stage of a natural wheat population. Of the i-traits examined, 17 073 were identified as drought-related. A genome-wide association study (GWAS) identified 5320 drought-related significant single-nucleotide polymorphisms (SNPs) and 27 SNP clusters. A notable hotspot region controlling wheat drought tolerance was discovered, in which TaPP2C6 was shown to be an important negative regulator of the drought response. The tapp2c6 knockout lines exhibited enhanced drought resistance without a yield penalty. A haplotype analysis revealed a favored allele of TaPP2C6 that was significantly correlated with drought resistance, affirming its potential value in wheat breeding programs. We developed an advanced prediction model for wheat yield and drought resistance using 24 i-traits analyzed by machine learning. In summary, this study provides comprehensive insights into the high-yielding ideotype and an approach for the rapid breeding of drought-resistant wheat.

Impact of acupuncture on ischemia/reperfusion injury: Unraveling the role of miR-34c-5p and autophagy activation.

We have previously reported that the expression of miR-34c-5p was up-regulated during acupuncture treatment in the setting of a cerebral ischemia/reperfusion injury (CIRI), indicating that miR-34c-5p plays an important role in healing from a CIRI-induced brain injury. This study sought to evaluate the effects of acupuncture on miR-34c-5p expression and autophagy in the forward and reverse directions using a rat focal cerebral ischemia/reperfusion model. After 120 minutes of middle cerebral artery occlusion and reperfusion, rats were treated with acupuncture at the "Dazhui" (DU20), "Baihui" (DU26) and "Renzhong" (DU14) points. Neurologic function deficit score, cerebral infarct area ratio, neuronal apoptosis and miR-34c-5p expression were evaluated 72 hr after treatment. The autophagy agonist RAPA and the antagonist 3MA were used to evaluate the neuro protective effects of autophagy-mediated acupuncture. We found that acupuncture treatment improved autophagy in the brain tissue of CIRI rats. Acupuncture reversed the negative effects of 3MA on CIRI, and acupuncture combined with RAPA further enhanced autophagy. We also found that acupuncture could increase miR-34c-5p expression in hippocampal neurons after ischemia/reperfusion. Acupuncture and a miR-34c agomir were able to enhance autophagy, improve neurologic deficits, and reduce the cerebral infarct area ratio and apoptosis rate by promoting the expression of miR-34c-5p. Silencing miR-34c resulted in a significantly reduced activating effect of acupuncture on autophagy and increased apoptosis, neurologic deficit symptoms, and cerebral infarct area ratio. This confirms that acupuncture can upregulate miR-34c-5p expression, which is beneficial in the treatment of CIRI.

Tea, coffee, and caffeine intake and risk of dementia and Alzheimer's disease: a systematic review and meta-analysis of cohort studies.

Background: Limited and conflicting evidence exists for the associations between tea, coffee, and caffeine intake and risk of dementia and Alzheimer's disease (AD). This meta-analysis aimed to elucidate these associations and quantify potential dose-response relationships. Methods: PubMed, EMBASE, and Web of Science were searched up to 11 June 2024 for cohort studies. Random effects models were used to calculate pooled relative risks (RRs) and 95% confidence intervals (CIs), with the dose-response relationship assessed using restricted cubic splines. The Grading of Recommendations Assessment Development and Evaluation (GRADE) tool was used to assess the risk of bias. Results: Our analysis encompassed 38 cohorts, totalling 751 824 participants and 13 017 dementia and 17 341 AD cases. For dementia, compared with the lowest category, the pooled RRs (95% CI) in the highest category of tea, coffee, and caffeine were 0.84 (0.74-0.96, n = 6), 0.95 (0.87-1.02, n = 9), and 0.94 (0.70-1.25, n = 5), with all rated as low certainty in GRADE. For AD, the pooled RRs (95% CI) in the highest category of tea, coffee, and caffeine compared to the lowest category were 0.93 (0.87-1.00, n = 6), 1.01 (0.90-1.12, n = 10), and 1.34 (1.04-1.74, n = 2), with certainty ratings of low, low, and very low, respectively. Dose-response analysis indicated a non-linear relationship between coffee intake (Poverall = 0.04 and Pnonlinear = 0.01) and dementia risk, showing the protective association of risk of dementia with 1 to 3 cups per day of coffee intake. There is a linear association between tea intake and risk of dementia, with a significantly decreased risk of dementia for each 1 cup per day increase in tea consumption (0.96, 95% CI 0.94-0.99, Poverall = 0.01 and Pnonlinear = 0.68). Conclusion: Increased tea consumption was associated with a decreased risk of dementia and AD, and a non-linear relationship was found between coffee and dementia, supporting public health recommendations for dementia prevention.

U-shaped association between serum calcium and in-hospital mortality in diabetes patients with congestive heart failure: a cohort study.

Previous studies have reported that the significant association between serum calcium and mortality substantially in patients, especially among those with intensive care unit (ICU). And In diabetes mellitus, congestive heart failure (CHF) is a significant comorbidity. We aim to evaluate the association between serum calcium levels and in-hospital mortality among patients with diabetes and congestive heart failure. The participants in this study were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. To scrutinize potential associations between serum calcium levels and in-hospital mortality, a comprehensive analysis encompassing multivariate logistic regression, cubic spline function model, threshold effect analysis, and subgroup analysis was performed. This retrospective cohort study encompassed 7063 patients, among whom the in-hospital mortality stood at 12.2%. In the multivariate logistic regression, adjusted odds ratios (ORs) were contrasted with the reference category Q6 (8.8-9.1 mg/dL) for serum calcium levels and in-hospital mortality. The adjusted ORs for Q1 (≤ 7.7 mg/dL), Q2 (7.7-8 mg/dL), and Q7 (≥ 9.1 mg/dL) were 1.69 (95% CI 1.17-2.44, p = 0.005), 1.62 (95% CI 1.11-2.36, p = 0.013), and 1.57 (95% CI 1.1-2.24, p = 0.012) respectively. The dose-response analysis uncovered a U-shaped relationship between serum calcium levels and in-hospital mortality in diabetic patients with heart failure. Subgroup analyses confirmed result stability notwithstanding the influence of diverse factors. Our investigation revealed a U-shaped correlation between serum calcium levels and in-hospital mortality in diabetes patients with congestive heart failure, pinpointing a significant inflection point at 9.05 mg/dL.

The cellular-centered view of hypoxia tumor microenvironment: Molecular mechanisms and therapeutic interventions.

Cancer is a profoundly dynamic, heterogeneous and aggressive systemic ailment, with a coordinated evolution of various types of tumor niches. Hypoxia plays an indispensable role in the tumor micro-ecosystem, drastically enhancing the plasticity of cancer cells, fibroblasts and immune cells and orchestrating intercellular communication. Hypoxia-induced signals, particularly hypoxia-inducible factor-1α (HIF-1α), drive the reprogramming of genetic, transcriptional, and proteomic profiles. This leads to a spectrum of interconnected processes, including augmented survival of cancer cells, evasion of immune surveillance, metabolic reprogramming, remodeling of the extracellular matrix, and the development of resistance to conventional therapeutic modalities like radiotherapy and chemotherapy. Here, we summarize the latest research on the multifaceted effects of hypoxia, where a multitude of cellular and non-cellular elements crosstalk with each other and co-evolve in a synergistic manner. Additionally, we investigate therapeutic approaches targeting hypoxic niche, encompassing hypoxia-activated prodrugs, HIF inhibitors, nanomedicines, and combination therapies. Finally, we discuss some of the issues to be addressed and highlight the potential of emerging technologies in the treatment of cancer.

Quantitative Expression of Latent Disease Factors in Individuals Associated with Psychopathology Dimensions and Treatment Response.

Psychiatric comorbidity is common in symptom-based diagnoses like autism spectrum disorder (ASD), attention/deficit hyper-activity disorder (ADHD), and obsessive-compulsive disorder (OCD). However, these co-occurring symptoms mediated by shared and/or distinct neural mechanisms are difficult to profile at the individual level. Capitalizing on unsupervised machine learning with a hierarchical Bayesian framework, we derived latent disease factors from resting-state functional connectivity data in a hybrid cohort of ASD and ADHD and delineated individual associations with dimensional symptoms based on canonical correlation analysis. Models based on the same factors generalized to previously unseen individuals in a subclinical cohort and one local OCD database with a subset of patients undergoing neurosurgical intervention. Four factors, identified as variably co-expressed in each patient, were significantly correlated with distinct symptom domains (r = -0.26-0.53, P < 0.05): behavioral regulation (Factor-1), communication (Factor-2), anxiety (Factor-3), adaptive behaviors (Factor-4). Moreover, we demonstrated Factor-1 expressed in patients with OCD and Factor-3 expressed in participants with anxiety, at the degree to which factor expression was significantly predictive of individual symptom scores (r = 0.18-0.5, P < 0.01). Importantly, peri-intervention changes in Factor-1 of OCD were associated with variable treatment outcomes (r = 0.39, P < 0.05). Our results indicate that these data-derived latent disease factors quantify individual factor expression to inform dimensional symptom and treatment outcomes across cohorts, which may promote quantitative psychiatric diagnosis and personalized intervention.

Upregulated miR-146b-3p predicted rheumatoid arthritis development and regulated TNF-α-induced excessive proliferation, motility, and inflammation in MH7A cells.

BACKGROUND: Rheumatoid arthritis (RA) is a chronic immune system disease with a high disability rate threatening the living quality of patients. Identifying potential biomarkers for RA is of necessity to improve the prevention and management of RA. OBJECTIVES: This study focused on miR-146b-3p evaluating its clinical significance and revealing the underlying regulatory mechanisms. MATERIALS AND METHODS: A total of 107 RA patients were enrolled, and both serum and synovial tissues were collected. Another 78 osteoarthritis patients (OA, providing synovial tissues), and 72 healthy individuals (providing serum samples) were enrolled as the control group. The expression of miR-146b-3p was analyzed by PCR and analyzed with ROC and Pearson correlation analyses evaluating its significance in diagnosis and development prediction of RA patients. In vitro, MH7A cells were treated with TNF-α. The regulation of cell proliferation, motility, and inflammation by miR-146b-3p was assessed by CCK8, Transwell, and ELISA assays. RESULTS: Significant upregulation of miR-146b-3p was observed in serum and synovial tissues of RA patients, which distinguished RA patients and were positively correlated with the erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), anti-cyclic citrullinated peptide antibodies (anti-CCP), and rheumatoid factor (RF) of RA patients. TNF-α promoted the proliferation and motility of MH7A cells and induced significant inflammation in cells. Silencing miR-146b-3p alleviated the effect of TNF-α and negatively regulated the expression of HMGCR. The knockdown of HMGCR reversed the protective effect of miR-146b-3p silencing on TNF-α-stimulated MH7A cells. CONCLUSIONS: Increased miR-146b-3p served as a biomarker for the diagnosis and severity of RA. Silencing miR-146b-3p could suppress TNF-α-induced excessive proliferation, motility, and inflammation via regulating HMGCR in MH7A cells.

Automatic Quantitative Assessment of Muscle Strength Based on Deep Learning and Ultrasound.

Skeletal muscle is a vital organ that promotes human movement and maintains posture. Accurate assessment of muscle strength is helpful to provide valuable insights for athletes' rehabilitation and strength training. However, traditional techniques rely heavily on the operator's expertise, which may affect the accuracy of the results. In this study, we propose an automated method to evaluate muscle strength using ultrasound and deep learning techniques. B-mode ultrasound data of biceps brachii of multiple athletes at different strength levels were collected and then used to train our deep learning model. To evaluate the effectiveness of this method, this study tested the contraction of the biceps brachii under different force levels. The classification accuracy of this method for grade 4 and grade 6 muscle strength reached 98% and 96%, respectively, and the overall average accuracy was 93% and 87%, respectively. The experimental results confirm that the innovative methods in this paper can accurately and effectively evaluate and classify muscle strength.

Comparison of perioperative and functional outcomes of single-incision versus standard multi-incision robot-assisted laparoscopic radical prostatectomy: a prospective, controlled, nonrandomized trial.

To compare perioperative and functional outcomes between improved (port-free) single-site robot-assisted laparoscopic radical prostatectomy (pf-ssRARP) and standard multi-port robot-assisted radical prostatectomy (MPRARP). A total of 372 consecutive patients underwent RARAP using the da Vinci Si® robotic surgical system. Group I (n = 210) included patients undergoing pf-ssRARP and Group II (n = 162) included patients undergoing MPRARP. Demographics and perioperative data including postoperative recovery outcomes were recorded and compared between the two groups. Overall mean operative time was significantly shorter with the pf-ssRARP compared to the MPRARP (p < 0.05). The length of hospitalization after the pf-ssRARP was shorter (p < 0.05). In Group I, the positive surgical margin rate was 15.2%; while in Group II, the positive margin rate was 33.3% (p < 0.05). The rate of instant urinary continence was significantly higher in Group I than in Group II (p < 0.05). The percentage of urinary continence was higher in the pf-ssRARP than in the MPRARP, at 6 months post-surgery (p < 0.05) and 9 months post-surgery (p < 0.05). There was no significant difference in the proportion of erectile function in the pf-ssRARP and MPRARP groups at the time of reaching the endpoint of this study (p > 0.05). The two groups were comparable in terms of total hospitalization costs (p < 0.05). The improved (port-free) single-site robot-assisted laparoscopic radical prostatectomy is a practical and easy technique to implement in clinical practice. Extraperitoneal implementation of the modified technique requires only a small incision, no special PORT, no additional auxiliary foramen creation, increased postoperative aesthetics and reduced hospitalization costs, and a high percentage of early postoperative urinary control recovery.

A Network Pharmacology Approach and Validation Experiments to Investigate the Mechanism of Wen-Dan Decoction in the Treatment of SINFH.

INTRODUCTION: Steroid-induced necrosis of the femoral head (SINFH) is a femoral head necrotic disease caused by prolonged use of hormones. Wen-Dan decoction is used in Chinese clinical practice for the treatment of steroid-induced necrosis of the femoral head (SINFH). However, the mechanism and active compounds of Wen-Dan decoction used to treat SINFH are not well understood. OBJECTIVES: We studied the mechanism of action of Wen-Dan decoction in treating steroidinduced necrosis of the femoral head (SINFH) via network pharmacology and in vivo experiments. METHODS: The active compounds of Wen-Dan decoction and SINFH-related target genes were identified through public databases. Then, network pharmacological analysis was conducted to explore the potential key active compounds, core targets and biological processes of Wen-Dan decoction in SINFH. The potential mechanisms of Wen-Dan decoction in SINFH obtained by network pharmacology were validated through in vivo experiments. RESULTS: We identified 608 DEGs (differentially expressed genes) (230 upregulated, 378 downregulated) in SINFH. GO analysis revealed that the SINFH-related genes were mainly involved in neutrophil activation and the immune response. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis showed that the SINFH-related genes were mainly associated with cytokine receptor interactions, lipids, atherosclerosis, and tuberculosis. We identified 147 active ingredients of Wen-Dan decoction; the core ingredient was quercetin, and licorice was an active ingredient. Moreover, 277 target genes in the treatment of SINFH with Wen-Dan decoction were identified, and NCF1, PTGS2, and RUNX2 were selected as core target genes. QRT-PCR of peripheral blood from SINFH patients showed higher levels of PGTS2 and NCF1 and showed lower levels of RUNX2 compared to controls. QRT-PCR analysis of peripheral blood and femoral bone tissue from a mouse model of SINFH showed higher levels of PGTS2 and NCF1 and lower levels of RUNX2 in the experimental animals than the controls, which was consistent with the bioinformatics results. HE, immunohistochemistry, and TUNEL staining confirmed a significant reduction in hormone-induced femoral head necrosis in the quercetintreated mice. HE, immunohistochemistry, and TUNEL staining confirmed significant improvement in hormone-induced femoral head necrosis in the quercetin-treated mice. CONCLUSION: We provide new insights into the genes and related pathways involved in SINFH and report that PTGS2, RUNX2, and NCF1 are potential drug targets. Quercetin improved SINFH by promoting osteogenesis and inhibiting apoptosis.

Two-layer cascaded catalytic hairpin assemblies based on locked nucleic acids for one-step and highly sensitive ctDNA detection.

Enzyme-free signal amplification of catalytic hairpin assembly (CHA) has enabled sensitive detection of circulating tumor DNA (ctDNA) in early clinical diagnosis. Conventional CHA strategies are restrained by the limited amplification efficiency of the single-stage system, and signal leakage from "breathing" influence and nuclease degradation. Here, we introduced two-layer cascaded locked nucleic acid (LNA)-assisted CHA circuits with the intelligent incorporation of LNA in the hairpins and reporter for the highly sensitive one-step detection of scarce ctDNA. The target-triggered upstream CHA reaction continuously generates hybrid products to catalyze the downstream CHA reaction for transducing the primary sensing event, and the released target and the produced hybrid product trigger the next catalytic reaction round at the same time and finally cascade to amplify the target ctDNA fluorescence output signal. Meanwhile, the stronger binding affinity of the LNA-DNA duplex endows the two-layer LNA-assisted CHA system with thermodynamic stability and nuclease resistance, and thus our designed system exhibits an excellent detection performance for target ctDNA in the range from 2 pM to 5 nM with a low detection limit of 0.6 pM. Significantly, the two-layer LNA-assisted CHA circuits have been successfully implemented for the feasible analysis of clinical samples. This two-layer cascaded LNA-assisted CHA strategy provides a promising high sensitivity tool for one-step detection of scarce ctDNA from complex clinical samples and would facilitate the reconfiguration of DNA circuit-based DNA nanotechnology for the precise analysis of other biomarkers in clinical research fields.

Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm.

PURPOSE: The objective of this study was to develop a deep learning model, using the ConvNeXt algorithm, that can effectively differentiate between ovarian endometriosis cysts (OEC) and benign mucinous cystadenomas (MC) by analyzing ultrasound images. The performance of the model in the diagnostic differentiation of these two conditions was also evaluated. METHODS: A retrospective analysis was conducted on OEC and MC patients who had sought medical attention at the Fourth Affiliated Hospital of Harbin Medical University between August 2018 and May 2023. The diagnosis was established based on postoperative pathology or the characteristics of aspirated fluid guided by ultrasound, serving as the gold standard. Ultrasound images were collected and subjected to screening and preprocessing procedures. The data set was randomly divided into training, validation, and testing sets in a ratio of 5:3:2. Transfer learning was utilized to determine the initial weights of the ConvNeXt deep learning algorithm, which were further adjusted by retraining the algorithm using the training and validation ultrasound images to establish a new deep learning model. The weights that yielded the highest accuracy were selected to evaluate the diagnostic performance of the model using the validation set. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated. Additionally, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and odds ratio were calculated. Decision curve analysis (DCA) curves were plotted. RESULTS: The study included 786 ultrasound images from 184 patients diagnosed with either OEC or MC. The deep learning model achieved an AUC of 0.90 (95 % CI: 0.85-0.95) in accurately distinguishing between the two conditions, with a sensitivity of 90 % (95 % CI: 84 %-95 %), specificity of 90 % (95 % CI: 77 %-97 %), a positive predictive value of 96 % (95 % CI: 91 %-99 %), a negative predictive value of 77 % (95 % CI: 63 %-88 %), a positive likelihood ratio of 9.27 (95 % CI: 3.65-23.56), and a negative likelihood ratio of 0.11 (95 % CI: 0.06-0.19). The DCA curve demonstrated the practical clinical utility of the model. CONCLUSIONS: The deep learning model developed using the ConvNeXt algorithm exhibits high accuracy (90 %) in distinguishing between OEC and MC. This model demonstrates excellent diagnostic performance and clinical utility, providing a novel approach for the clinical differentiation of these two conditions.

Clinical Comparison between HD-tDCS and tDCS for Improving Upper Limb Motor Function: A Randomized, Double-Blinded, Sham-Controlled Trial.

Background: Stroke is a common and frequently occurring disease among middle-aged and elderly people, with approximately 55%-75% of patients remaining with upper limb dysfunction. How to promote the recovery of motor function at an early stage is crucial to the life of the patient. Objectives: This study aimed to investigate whether high-definition transcranial direct current stimulation (HD-tDCS) of the primary motor cortex (M1) functional area in poststroke patients in the subacute phase is more effective in improving upper limb function than conventional tDCS. Methods: This randomized, sham-controlled clinical trial included 69 patients with subcortical stroke. They were randomly divided into the HD-tDCS, anodal tDCS (a-tDCS), and sham groups. Each group received 20 sessions of stimulation. The patients were assessed using the Action Research Arm Test, Fugl-Meyer score for upper extremities, Motor Function Assessment Scale, and modified Barthel index (MBI) pretreatment and posttreatment. Results: The intragroup comparison scores improved after 4 weeks of treatment. The HD-tDCS group showed a slightly greater, but nonsignificant improvement as compared to a-tDCS group in terms of mean change observed in function of trained items. The MBI score of the HD-tDCS group was maintained up to 8 weeks of follow-up and was higher than that in the a-tDCS group. Conclusion: Both HD-tDCS and a-tDCS can improve upper limb motor function and daily activities of poststroke patients in the subacute stage. This trial is registered with ChiCTR2000031314.

In Situ Electrochemical Rapid Induction of Highly Active γ-NiOOH Species for Industrial Anion Exchange Membrane Water Electrolyzer.

Limited by the strong oxidation environment and sluggish reconstruction process in oxygen evolution reaction (OER), designing rapid self-reconstruction with high activity and stability electrocatalysts is crucial to promoting anion exchange membrane (AEM) water electrolyzer. Herein, trace Fe/S-modified Ni oxyhydroxide (Fe/S-NiOOH/NF) nanowires are constructed via a simple in situ electrochemical oxidation strategy based on precipitation-dissolution equilibrium. In situ characterization techniques reveal that the successful introduction of Fe and S leads to lattice disorder and boosts favorable hydroxyl capture, accelerating the formation of highly active γ-NiOOH. The Density Functional Theory (DFT) calculations have also verified that the incorporation of Fe and S optimizes the electrons redistribution and the d-band center, decreasing the energy barrier of the rate-determining step (*O→*OOH). Benefited from the unique electronic structure and intermediate adsorption, the Fe/S-NiOOH/NF catalyst only requires the overpotential of 345 mV to reach the industrial current density of 1000 mA cm-2 for 120 h. Meanwhile, assembled AEM water electrolyzer (Fe/S-NiOOH//Pt/C-60 °C) can deliver 1000 mA cm-2 at a cell voltage of 2.24 V, operating at the average energy efficiency of 71% for 100 h. In summary, this work presents a rapid self-reconstruction strategy for high-performance AEM electrocatalysts for future hydrogen economy.

Melatonin MT1 receptors regulate the Sirt1/Nrf2/Ho-1/Gpx4 pathway to prevent α-synuclein-induced ferroptosis in Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the loss of dopaminergic (DA) neurons and aggregation of α-synuclein (α-syn). Ferroptosis, a form of cell death induced by iron accumulation and lipid peroxidation, is involved in the pathogenesis of PD. It is unknown whether melatonin receptor 1 (MT1) modulates α-syn and ferroptosis in PD. Here, we used α-syn preformed fibrils (PFFs) to induce PD models in vivo and in vitro. In PD mice, α-syn aggregation led to increased iron deposition and ferroptosis. MT1 knockout exacerbated these changes and resulted in more DA neuronal loss and severe motor impairment. MT1 knockout also suppressed the Sirt1/Nrf2/Ho1/Gpx4 pathway, reducing resistance to ferroptosis, and inhibited expression of ferritin Fth1, leading to more release of ferrous ions. In vitro experiments confirmed these findings. Knockdown of MT1 enhanced α-syn PFF-induced intracellular α-syn aggregation and suppressed expression of the Sirt1/Nrf2/Ho1/Gpx4 pathway and Fth1 protein, thereby aggravating ferroptosis. Conversely, overexpression of MT1 reversed these effects. Our findings reveal a novel mechanism by which MT1 activation prevents α-syn-induced ferroptosis in PD, highlighting the neuroprotective role of MT1 in PD.

Association between different insulin resistance surrogates and all-cause mortality in patients with coronary heart disease and hypertension: NHANES longitudinal cohort study.

BACKGROUND: Studies on the relationship between insulin resistance (IR) surrogates and long-term all-cause mortality in patients with coronary heart disease (CHD) and hypertension are lacking. This study aimed to explore the relationship between different IR surrogates and all-cause mortality and identify valuable predictors of survival status in this population. METHODS: The data came from the National Health and Nutrition Examination Survey (NHANES 2001-2018) and National Death Index (NDI). Multivariate Cox regression and restricted cubic splines (RCS) were performed to evaluate the relationship between homeostatic model assessment of IR (HOMA-IR), triglyceride glucose index (TyG index), triglyceride glucose-body mass index (TyG-BMI index) and all-cause mortality. The recursive algorithm was conducted to calculate inflection points when segmenting effects were found. Then, segmented Kaplan-Meier analysis, LogRank tests, and multivariable Cox regression were carried out. Receiver operating characteristic (ROC) and calibration curves were drawn to evaluate the differentiation and accuracy of IR surrogates in predicting the all-cause mortality. Stratified analysis and interaction tests were conducted according to age, gender, diabetes, cancer, hypoglycemic and lipid-lowering drug use. RESULTS: 1126 participants were included in the study. During the median follow-up of 76 months, 455 participants died. RCS showed that HOMA-IR had a segmented effect on all-cause mortality. 3.59 was a statistically significant inflection point. When the HOMA-IR was less than 3.59, it was negatively associated with all-cause mortality [HR = 0.87,95%CI (0.78, 0.97)]. Conversely, when the HOMA-IR was greater than 3.59, it was positively associated with all-cause mortality [HR = 1.03,95%CI (1.00, 1.05)]. ROC and calibration curves indicated that HOMA-IR was a reliable predictor of survival status (area under curve = 0,812). No interactions between HOMA-IR and stratified variables were found. CONCLUSION: The relationship between HOMA-IR and all-cause mortality was U-shaped in patients with CHD and hypertension. HOMA-IR was a reliable predictor of all-cause mortality in this population.

Association of sleep characteristics with cardiovascular disease risk in adults over 40 years of age: a cross-sectional survey.

Background: The relationship between sleep characteristics and cardiovascular disease (CVD) risk has yet to reach a consistent conclusion, and more research needs to be carried out. This study aimed to explore the relationship between snoring, daytime sleepiness, bedtime, sleep duration, and high-risk sleep patterns with CVD risk. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) 2015-2018 were collected and analyzed. Multivariable logistic regression was used to evaluate the relationship between snoring, daytime sleepiness, bedtime, sleep duration, high-risk sleep patterns, and CVD risk. Stratified analysis and interaction tests were carried out according to hypertension, diabetes and age. Results: The final analysis contained 6,830 participants, including 1,001 with CVD. Multivariable logistic regression suggested that the relationship between snoring [OR = 7.37,95%CI = (6.06,8.96)], daytime sleepiness [OR = 11.21,95%CI = (9.60,13.08)], sleep duration shorter than 7 h [OR = 9.50,95%CI = (7.65,11.79)] or longer than 8 h [OR = 6.61,95%CI = (5.33,8.19)], bedtime after 0:00 [OR = 13.20,95%CI = (9.78,17.80)] compared to 22:00-22:59, high-risk sleep patterns [OR = 47.73,95%CI = (36.73,62.04)] and CVD risk were statistically significant. Hypertension and diabetes interacted with high-risk sleep patterns, but age did not. Conclusions: Snoring, daytime sleepiness, excessive or short sleep duration, inappropriate bedtime, and high-risk sleep patterns composed of these factors are associated with the CVD risk. High-risk sleep patterns have a more significant impact on patients with hypertension and diabetes.