Enhancement of motor functional recovery in thoracic spinal cord injury: voluntary wheel running versus forced treadmill exercise.

JOURNAL/nrgr/04.03/01300535-202503000-00028/figure1/v/2024-06-17T092413Z/r/image-tiff Spinal cord injury necessitates effective rehabilitation strategies, with exercise therapies showing promise in promoting recovery. This study investigated the impact of rehabilitation exercise on functional recovery and morphological changes following thoracic contusive spinal cord injury. After a 7-day recovery period after spinal cord injury, mice were assigned to either a trained group (10 weeks of voluntary running wheel or forced treadmill exercise) or an untrained group. Bi-weekly assessments revealed that the exercise-trained group, particularly the voluntary wheel exercise subgroup, displayed significantly improved locomotor recovery, more plasticity of dopaminergic and serotonin modulation compared with the untrained group. Additionally, exercise interventions led to gait pattern restoration and enhanced transcranial magnetic motor-evoked potentials. Despite consistent injury areas across groups, exercise training promoted terminal innervation of descending axons. In summary, voluntary wheel exercise shows promise for enhancing outcomes after thoracic contusive spinal cord injury, emphasizing the role of exercise modality in promoting recovery and morphological changes in spinal cord injuries. Our findings will influence future strategies for rehabilitation exercises, restoring functional movement after spinal cord injury.

Continuance intention and digital health resources from the perspective of elaboration likelihood model and DART model: a structural equation modeling analysis.

Background: Internet hospitals, online health communities, and other digital health APPs have brought many changes to people's lives. However, digital health resources are experiencing low continuance intention due to many factors, including information security, service quality, and personal characteristics of users. Methods: We used cross-sectional surveys and structural equation modeling analysis to explore factors influencing user willingness to continue using digital health resources. Results: Information quality (ß = 0.31, p < 0.05), service quality (ß = 0.19, p < 0.05), platform reputation (ß = 0.34, p < 0.05), and emotional support (ß = 0.23, p < 0.05) have significant positive effects on user value co-creation behavior. Additionally, user trust and perceived usefulness could mediate the association between user value co-creation behavior and continuance intention, with mediation effects of 0.143 and 0.125, respectively. User involvement can positively moderate the association between user value co-creation behavior and user trust (ß = 0.151, t = 2.480, p < 0.001). Also, user involvement can positively moderate the association between value co-creation behavior and perceived usefulness (ß = 0.103, t = 3.377, p < 0.001). Conclusion: The keys to solving the problem of low continuance intention are improving the quality and service level of digital health resources, and promoting users' value co-creation behavior. Meanwhile, enterprises should build a good reputation, create a positive communication atmosphere in the community, and enhance user participation and sense of belonging.

Development of a colloidal gold immunochromatographic strip for the simultaneous detection of porcine epidemic diarrhea virus and transmissible gastroenteritis virus.

In recent years, porcine diarrhea-associated viruses have caused significant economic losses globally. These viruses present similar clinical symptoms, such as watery diarrhea, dehydration, and vomiting. Co-infections with porcine epidemic diarrhea virus (PEDV) and transmissible gastroenteritis virus (TGEV) are common. For the rapid and on-site preliminary diagnosis on the pig farms, this study aimed to develop a colloidal gold immunochromatography assay (GICA) strip for the detection of PEDV and TGEV simultaneously. The GICA kit showed that there was no cross-reactivity with the other five common porcine viruses. With visual observation, the lower limits were approximately 104 TCID50/mL and 104 TCID50/mL for PEDV and TGEV, respectively. The GICA strip could be stored at 4°C or 25°C for 12 months without affecting its efficacy. To validate the GICA strip, 121 clinical samples were tested. The positive rates of PEDV and TGEV were 42.9 and 9.9%, respectively, and the co-infection rate of the two viruses was 5.8% based on the duplex GICA strip. Thus, the established GICA strip is a rapid, specific, and stable tool for on-site preliminary diagnosis of PEDV- and TGEV-associated diarrhea.

Non-coding RNAs as regulators of autophagy in chondrocytes: Mechanisms and implications for osteoarthritis.

Osteoarthritis (OA) is a chronic degenerative joint disease with multiple causative factors such as aging, mechanical injury, and obesity. Autophagy is a complex dynamic process that is involved in the degradation and modification of intracellular proteins and organelles under different pathophysiological conditions. Autophagy, as a cell survival mechanism under various stress conditions, plays a key role in regulating chondrocyte life cycle metabolism and cellular homeostasis. Non-coding RNAs (ncRNAs) are heterogeneous transcripts that do not possess protein-coding functions, but they can act as effective post-transcriptional and epigenetic regulators of gene and protein expression, thus participating in numerous fundamental biological processes. Increasing evidence suggests that ncRNAs, autophagy, and their crosstalk play crucial roles in OA pathogenesis. Therefore, we summarized the complex role of autophagy in OA chondrocytes and focused on the regulatory role of ncRNAs in OA-associated autophagy to elucidate the complex pathological mechanisms of the ncRNA-autophagy network in the development of OA, thus providing new research targets for the clinical diagnosis and treatment of OA.

Impact of Esophageal Motility on Microbiome Alterations in Symptomatic Gastroesophageal Reflux Disease Patients With Negative Endoscopy: Exploring the Role of Ineffective Esophageal Motility and Contraction Reserve.

Background/Aims: Ineffective esophageal motility (IEM) is common in patients with gastroesophageal reflux disease (GERD) and can be associated with poor esophageal contraction reserve on multiple rapid swallows. Alterations in the esophageal microbiome have been reported in GERD, but the relationship to presence or absence of contraction reserve in IEM patients has not been evaluated. We aim to investigate whether contraction reserve influences esophageal microbiome alterations in patients with GERD and IEM. Methods: We prospectively enrolled GERD patients with normal endoscopy and evaluated esophageal motility and contraction reserve with multiple rapid swallows during high-resolution manometry. The esophageal mucosa was biopsied for DNA extraction and 16S ribosomal RNA gene V3-V4 (Illumina)/full-length (Pacbio) amplicon sequencing analysis. Results: Among the 56 recruited patients, 20 had normal motility (NM), 19 had IEM with contraction reserve (IEM-R), and 17 had IEM without contraction reserve (IEM-NR). Esophageal microbiome analysis showed a significant decrease in microbial richness in patients with IEM-NR when compared to NM. The beta diversity revealed different microbiome profiles between patients with NM or IEM-R and IEM-NR (P = 0.037). Several esophageal bacterial taxa were characteristic in patients with IEM-NR, including reduced Prevotella spp. and Veillonella dispar, and enriched Fusobacterium nucleatum. In a microbiome-based random forest model for predicting IEM-NR, an area under the receiver operating characteristic curve of 0.81 was yielded. Conclusions: In symptomatic GERD patients with normal endoscopic findings, the esophageal microbiome differs based on contraction reserve among IEM. Absent contraction reserve appears to alter the physiology and microbiota of the esophagus.

The effect of fasting plasma glucose on in-hospital mortality after acute myocardial infarction in patients with and without diabetes: findings from a prospective, nationwide, and multicenter registry.

OBJECTIVES: To evaluate the predictive value of fasting plasma glucose (FPG) for in-hospital mortality in patients with acute myocardial infarction (AMI) with different glucose metabolism status. METHODS: We selected 5,308 participants with AMI from the prospective, nationwide, multicenter CAMI registry, of which 2,081 were diabetic and 3,227 were nondiabetic. Patients were divided into high FPG and low FPG groups according to the optimal cutoff values of FPG to predict in-hospital mortality for diabetic and nondiabetic cohorts, respectively. The primary endpoint was in-hospital mortality. RESULTS: Overall, 94 diabetic patients (4.5%) and 131 nondiabetic patients (4.1%) died during hospitalization, and the optimal FPG thresholds for predicting in-hospital death of the two cohorts were 13.2 mmol/L and 6.4 mmol/L, respectively. Compared with individuals who had low FPG, those with high FPG were significantly associated with higher in-hospital mortality in diabetic cohort (10.1% vs. 2.8%; odds ratio [OR] = 3.862, 95% confidence interval [CI]: 2.542-5.869) and nondiabetic cohort (7.4% vs. 1.7%; HR = 4.542, 95%CI: 3.041-6.782). After adjusting the potential confounders, this significant association was not changed. Furthermore, FPG as a continuous variable was positively associated with in-hospital mortality in single-variable and multivariable models regardless of diabetic status. Adding FPG to the original model showed a significant improvement in C-statistic and net reclassification in diabetic and nondiabetic cohorts. CONCLUSIONS: This large-scale registry indicated that there is a strong positive association between FPG and in-hospital mortality in AMI patients with and without diabetes. FPG might be useful to stratify patients with AMI.

Sample Size Determination and Study Design Impact on Dose-Scale Pharmacodynamic Bioequivalence: a Case Study Using Orlistat.

Dose-scale pharmacodynamic bioequivalence is recommended for evaluating the consistency of generic and innovator formulations of certain locally acting drugs, such as orlistat. This study aimed to investigate the standard methodology for sample size determination and the impact of study design on dose-scale pharmacodynamic bioequivalence using orlistat as the model drug. A population pharmacodynamic model of orlistat was developed using NONMEM 7.5.1 and utilized for subsequent simulations. Three different study designs were evaluated across various predefined relative bioavailability ratios of test/reference (T/R) formulations. These designs included Study Design 1 (2×1 crossover with T1 60 mg, R1 60 mg, and R2 120 mg), Study Design 2 (2×1 crossover with T2 120 mg, R1 60 mg, and R2 120 mg), and Study Design 3 (2×2 crossover with T1 60 mg, T2 120 mg, R1 60 mg, and R2 120 mg). Sample sizes were determined using a stochastic simulation and estimation approach. Under the same T/R ratio and power, Study Design 3 required the minimum sample size for bioequivalence, followed by Study Design 1, while Study Design 2 performed the worst. For Study Designs 1 and 3, a larger sample size was needed on the T/R ratio < 1.0 side for the same power compared to that on the T/R ratio > 1.0 side. The opposite asymmetry was observed for Study Design 2. We demonstrated that Study Design 3 is most effective for reducing the sample size for orlistat bioequivalence studies, and the impact of T/R ratio on sample size shows asymmetry.

Using Human Resources Data to Predict Turnover of Community Mental Health Employees: Prediction and Interpretation of Machine Learning Methods.

This study used machine learning (ML) to predict mental health employees' turnover in the following 12 months using human resources data in a community mental health centre. The data contain 621 employees' information (e.g., demographics, job information and client information served by employees) hired between 2011 and 2021 (56.5% turned over during the study period). Six ML methods (i.e., logistic regression, elastic net, random forest [RF], gradient boosting machine [GBM], neural network and support vector machine) were used to predict turnover, along with graphical and statistical tools to interpret predictive relationship patterns and potential interactions. The result suggests that RF and GBM led to better prediction according to specificity, sensitivity and area under the curve (>0.8). The turnover predictors (e.g., past work years, work hours, wage, age, exempt status, educational degree, marital status and employee type) were identified, including those that may be unique to the mental health employee population (e.g., training hours and the proportion of clients with schizophrenia diagnosis). It also revealed nonlinear and nonmonotonic predictive relationships (e.g., wage and employee age), as well as interaction effects, such that past work years interact with other variables in turnover prediction. The study indicates that ML methods showed the predictability of mental health employee turnover using human resources data. The identified predictors and the nonlinear and interactive relationships shed light on developing new predictive models for turnover that warrant further investigations.

Densely sampled stimulus-response map of human cortex with single pulse TMS-EEG and its relation to whole brain neuroimaging measures.

Large-scale networks underpin brain functions. How such networks respond to focal stimulation can help decipher complex brain processes and optimize brain stimulation treatments. To map such stimulation-response patterns across the brain non-invasively, we recorded concurrent EEG responses from single-pulse transcranial magnetic stimulation (i.e., TMS-EEG) from over 100 cortical regions with two orthogonal coil orientations from one densely-sampled individual. We also acquired Human Connectome Project (HCP)-styled diffusion imaging scans (six), resting-state functional Magnetic Resonance Imaging (fMRI) scans (120 mins), resting-state EEG scans (108 mins), and structural MR scans (T1- and T2-weighted). Using the TMS-EEG data, we applied network science-based community detection to reveal insights about the brain's causal-functional organization from both a stimulation and recording perspective. We also computed structural and functional maps and the electric field of each TMS stimulation condition. Altogether, we hope the release of this densely sampled (n=1) dataset will be a uniquely valuable resource for both basic and clinical neuroscience research.

Multiple Riemannian Kernel Hashing for Large-scale Image Set Classification and Retrieval.

Conventional image set methods typically learn from small to medium-sized image set datasets. However, when applied to large-scale image set applications such as classification and retrieval, they face two primary challenges: 1) effectively modeling complex image sets, and 2) efficiently performing tasks. To address the above issues, we propose a novel Multiple Riemannian Kernel Hashing (MRKH) method that leverages the powerful capabilities of Riemannian manifold and Hashing on effective and efficient image set representation. MRKH considers multiple heterogeneous Riemannian manifolds to represent each image set. It introduces a multiple kernel learning framework designed to effectively combine statistics from multiple manifolds, and constructs kernels by selecting a small set of anchor points, enabling efficient scalability for large-scale applications. In addition, MRKH further exploits inter- and intra-modal semantic structure to enhance discrimination. Instead of employing continuous feature to represent each image set, MRKH suggests learning hash code for each image set, thereby achieving efficient computation and storage. We present an iterative algorithm with theoretical convergence guarantee to optimize MRKH, and the computational complexity is linear with the size of dataset. Extensive experiments on five image set benchmark datasets including three large-scale ones demonstrate the proposed method outperforms state-of-the-arts in accuracy and efficiency particularly in large-scale image set classification and retrieval.

Discovery of a peptide proteolysis-targeting chimera (PROTAC) drug of p300 for prostate cancer therapy.

BACKGROUND: The E1A-associated protein p300 (p300) has emerged as a promising target for cancer therapy due to its crucial role in promoting oncogenic signaling pathways in various cancers, including prostate cancer. This need is particularly significant in prostate cancer. While androgen deprivation therapy (ADT) has demonstrated promising efficacy in prostate cancer, its long-term use can eventually lead to the development of castration-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC). Notably, p300 has been identified as an important co-activator of the androgen receptor (AR), highlighting its significance in prostate cancer progression. Moreover, recent studies have revealed the involvement of p300 in AR-independent oncogenes associated with NEPC. Therefore, the blockade of p300 may emerge as an effective therapeutic strategy to address the challenges posed by both CRPC and NEPC. METHODS: We employed AI-assisted design to develop a peptide-based PROTAC (proteolysis-targeting chimera) drug that targets p300, effectively degrading p300 in vitro and in vivo utilizing nano-selenium as a peptide drug delivery system. FINDINGS: Our p300-targeting peptide PROTAC drug demonstrated effective p300 degradation and cancer cell-killing capabilities in both CRPC, AR-negative, and NEPC cells. This study demonstrated the efficacy of a p300-targeting drug in NEPC cells. In both AR-positive and AR-negative mouse models, the p300 PROTAC drug showed potent p300 degradation and tumor suppression. INTERPRETATION: The design of peptide PROTAC drug targeting p300 is feasible and represents an efficient therapeutic strategy for CRPC, AR-negative prostate cancer, and NEPC. FUNDING: The funding details can be found in the Acknowledgements section.

Extracellular vesicles derived from "serum and glucose" deprived HUCMSCs promoted skin wound healing through enhanced angiogenesis.

Extracellular vesicles (EVs) produced from MSCs were currently considered as a novel therapeutic agent for skin tissue regeneration and repair. Preconditioning stem cells may activate more molecular pathways and release more bioactive agents. In this study, we obtained EVs from normal (N-EVs) and serum- and glucose-deprived (SGD-EVs) human umbilical cord mesenchymal stem cells (HUCMSCs), and showed that SGD-EVs promoted the migration, proliferation, and tube formation of HUVECs in vitro. In vivo experiments utilizing a rat model show that both N-EVs and SGD-EVs boosted angiogenesis of skin defects and accelerated skin wound healing, while treating wounds with SGD-EVs led to faster skin healing and enhanced angiogenesis. miRNA sequencing showed that miR-29a-3p was abundant in SGD-EVs, and overexpressing miR-29a-3p enhanced the angiogenic ability of HUVECs, while inhibiting miR-29a-3p presented the opposite effect. Further studies demonstrated that miR-29a-3p directly targeted CTNNBIP1, which mediated angiogenesis of HUCMSCs-derived EVs through inhibiting CTNNBIP1 to activate Wnt/ß-catenin signaling pathway. Taken together, these findings suggested that SGD-EVs promote angiogenesis via transferring miR-29a-3p, and activation of Wnt/ß-catenin signaling pathway played a crucial role in SGD-EVs-induced VEGFA production during wound angiogenesis. Our results offered a new avenue for modifying EVs to enhance tissue angiogenesis and augment its role in skin repair.

Physiological and transcriptomic analysis reveals the toxicological mechanisms of polystyrene micro- and nano-plastics in Chlamydomonas reinhardtii.

With the accumulation of plastic waste in the environment, the toxicity of micro- and nano-plastics (MNPs) to microalgae has attracted increasing attention. However, the underlying toxic mechanisms of MNPs remain to be elucidated. In this study, we synthesized micro- and nano-scale of polystyrene MNPs (PS MNPs) to investigate their toxicity and toxic mechanisms in Chlamydomonas reinhardtii. We found that PS MNPs significantly inhibit the production of photosynthetic pigments and increase soluble protein content. The detailed analysis of results shows that both materials affect photosynthetic efficiency by damaging the donor side, reaction center, and electron transfer of photosystem II. Moreover, compared to PS MPs, PS NPs have a greater negative impact on algal cells. Analyzing the transcriptome of cells suggests that the most sensitive metabolic pathways in response to PS MNPs involve oxidative phosphorylation, biosynthesis of secondary metabolites, and photosynthesis. Especially, genes related to photosynthesis and oxidative phosphorylation showed significant changes in expression after exposure to PS MNPs. This study provided molecular-level insights into the toxic mechanisms of PS MNPs on microalgae.

Experimental study on the biomechanical stability of complex acetabular fractures in the quadrilateral area: application of a dynamic anterior titanium-plate screw system.

BACKGROUND AND OBJECTIVE: Complex acetabular fractures involving quadrilateral areas are more challenging to treat during surgery. To date, there has been no ideal internal fixation for these acetabular fractures. The purpose of this study was to evaluate the biomechanical stability of complex acetabular fractures using a dynamic anterior titanium-plate screw system of the quadrilateral area (DAPSQ) by simulating the standing and sitting positions of pelvic specimens. MATERIALS AND METHODS: Eight formal in-preserved cadaveric pelvises aged 30-50 years were selected as the research objects. First, one hip of the normal pelvises was randomly used as the control model (group B) for measurement, and then one hip of the pelvises was randomly selected to make the fracture model in the 8 intact pelvises as the experimental model (group A) for measurement. In group A, acetabular both-column fractures in the quadrilateral area were established, and the fractures were fixed by DAPSQ. The biomechanical testing machine was used to load (simulated physiological load) from 400 N to 700 N at a 1 mm/min speed for 30 s in the vertical direction when the specimens were measured at random in simulated standing or sitting positions in groups. The horizontal displacement and longitudinal displacement of the acetabular fractures in the quadrilateral area were measured in both the standing and sitting simulations. RESULTS: As the load increased, no dislocation or internal fixation breakage occurred during the measurements. In the standing position, the horizontal displacement of the quadrilateral area fractures in group A and group B appeared to be less than 1 mm with loads ranging from 400 N to 700 N, and there was no significant difference between group A and group B (p > 0.05). The longitudinal displacement appeared to be greater than 1 mm with a load of 700 mm in group A (700 N, 2 cases), and the difference was significant between group A and group B (p < 0.05). In the sitting position, the horizontal and longitudinal displacements of the quadrilateral areas were within 0.5 mm in group A and group B, and there was no significant difference between group A and group B (p > 0.05). CONCLUSION: For complex acetabular fractures in the quadrilateral area, DAPSQ fixation may provide early sitting stability, but it is inappropriate for patients to stand too early.

The spatio-temporal accumulation of 6 PPD-Q in greenbelt soils and its effects on soil microbial communities.

6 PPD-Q (6 PPD-Quinone) is an ozone-induced byproduct derived from the degradation of N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6 PPD), commonly found in road dust resulting from tire wear. However, the extent of 6 PPD-Q pollution in urban soil remains unclear. This study investigates the spatial and temporal accumulation patterns of 6 PPD-Q in greenbelt soils in Ningbo, and explores the correlation between 6 PPD-Q accumulation and soil microbial community composition and functions. Our findings indicate that 6 PPD-Q is present (ranging from 0.85 to 12.58 µg/kg) in soil samples collected from both sides of urban traffic arteries. Soil fungi exhibit higher sensitivity to 6 PPD-Q accumulation compared to bacteria, and associated fungi (Basidiomycota) may be potential biomarkers for environmental 6 PPD-Q contamination. Co-occurrence network analysis reveals that the bacterial microbial network in summer exhibits greater stability and resilience in response to 6 PPD-Q inputs than in winter. However, 6 PPD-Q accumulation disrupts the network structure of fungal communities to some extent, leading to reduced diversity in fungal microbial communities. Long-term accumulation of 6 PPD-Q weakens the nitrogen and phosphorus cycling potential within urban soil, while the enhancement of carbon cycling may further promote 6 PPD-Q degradation in urban soil. Taken together, this study provides new insights into the ecological risks of 6 PPD-Q in urban soils.

Deep learning in pulmonary nodule detection and segmentation: a systematic review.

OBJECTIVES: The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. METHODS: This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. RESULTS: After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. CONCLUSIONS: This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. CLINICAL RELEVANCE STATEMENT: Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. KEY POINTS: Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

Impact of radish seeds (Semen Raphani) on the absorption and transportation of ginsenosides in the Caco-2 cell model: a UPLC-ESI-MS analysis.

This study examined the impact of Semen raphani on the absorption of ginsenosides from Panax ginseng C.A. Meyer (ginseng) using a Caco-2 cell model and Ultra-High-Performance Liquid Chromatography-Electrospray Ionization-Tandem Mass Spectrometry (UPLC-ESI-MS). Six primary ginsenosides (Rg1, Re, Rb1, Rb2, Rc, Rd) were quantified. Results showed that Semen Raphani increased the efflux rate of ginsenosides, particularly at higher concentrations, suggesting it inhibits their absorption. The research elucidates the intestinal absorption process of ginsenosides and the antagonistic mechanism of Semen Raphani against ginseng.

Causal role of immune cells in hypertension: a bidirectional Mendelian randomization study.

Background: Although Hypertension (HTN) is considered to be a cardiovascular disease caused by multiple factors, the cause of it is still unknown. In this study, we aim to find out whether circulating immune cell characteristics have an impact on susceptibility to HTN. Methods: This study employed a comprehensive two-sample Mendelian randomization (MR) analysis to investigate the causal association between immune cell characteristics and HTN. Utilizing publicly accessible genetic data, we examined the causal relationship between HTN and the susceptibility to 731 immune cell signatures. To ensure the reliability and validity of the findings, a comprehensive sensitivity analysis was conducted to assess heterogeneity, confirm the robustness of the results and evaluate the presence of horizontal pleiotropy. Results: After FDR correction, immune phenotype had an effect on HTN. In our study, one immunophenotype was identified as being positively associated with HTN risk significance: HLA DR on CD33- HLA DR+. In addition, we examined 8 immune phenotype with no statistically significant effect of HTN, but it is worth mentioning that they had an unadjusted low P-value phenotype. Conclusions: Our MR study by genetic means demonstrated the close relationship between HTN and immune cells, thus providing guidance for future clinical prediction and subsequent treatment of HTN.

Extreme Fast Charging of Lithium Metal Batteries Enabled by a Molten-Salt-Derived Nanocrystal Interphase.

The extreme fast charging performance of lithium metal batteries (LMBs) with a long life is an important focus in the development of next-generation battery technologies. The friable solid electrolyte interphase and dendritic lithium growth are major problems. The formation of an inorganic nanocrystal-dominant interphase produced by preimmersing the Li in molten lithium bis(fluorosulfonyl)imide that suppresses the overgrowth of the usual interphase is reported. Its high surface modulus combined with fast Li+ diffusivity enables a reversible dendrite-proof deposition under ultrahigh-rate conditions. It gives a record-breaking cumulative plating/stripping capacity of >240 000 mAh cm-2 at 30 mA cm-2@30 mAh cm-2 for a symmetric cell and an extreme fast charging performance at 6 C for 500 cycles for a Li||LiCoO2 full cell with a high-areal-capacity, thus expanding the use of LMBs to high-loading and power-intensive scenarios. Its usability both in roll-to-roll production and in different electrolytes indicating the scalable and industrial potential of this process for high-performance LMBs.