Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model.

AIMS: Although there are various model-based approaches to individualized vancomycin (VCM) administration, few have been reported for adult patients with periprosthetic joint infection (PJI). This work attempted to develop a machine learning (ML)-based model for predicting VCM trough concentration in adult PJI patients. METHODS: The dataset of 287 VCM trough concentrations from 130 adult PJI patients was split into a training set (229) and a testing set (58) at a ratio of 8:2, and an independent external 32 concentrations were collected as a validation set. A total of 13 covariates and the target variable (VCM trough concentration) were included in the dataset. A covariate model was respectively constructed by support vector regression, random forest regression and gradient boosted regression trees and interpreted by SHapley Additive exPlanation (SHAP). RESULTS: The SHAP plots visualized the weight of the covariates in the models, with estimated glomerular filtration rate and VCM daily dose as the 2 most important factors, which were adopted for the model construction. Random forest regression was the optimal ML algorithm with a relative accuracy of 82.8% and absolute accuracy of 67.2% (R2 =.61, mean absolute error = 2.4, mean square error = 10.1), and its prediction performance was verified in the validation set. CONCLUSION: The proposed ML-based model can satisfactorily predict the VCM trough concentration in adult PJI patients. Its construction can be facilitated with only 2 clinical parameters (estimated glomerular filtration rate and VCM daily dose), and prediction accuracy can be rationalized by SHAP values, which highlights a profound practical value for clinical dosing guidance and timely treatment.

Comprehensive analysis of a TNF family based-signature in diffuse gliomas with regard to prognosis and immune significance.

BACKGROUND: Several studies have shown that members of the tumor necrosis factor (TNF) family play an important role in cancer immunoregulation, and trials targeting these molecules are already underway. Our study aimed to integrate and analyze the expression patterns and clinical significance of TNF family-related genes in gliomas. METHODS: A total of 1749 gliomas from 4 datasets were enrolled in our study, including the Cancer Genome Atlas (TCGA) dataset as the training cohort and the other three datasets (CGGA, GSE16011, and Rembrandt) as validation cohorts. Clinical information, RNA expression data, and genomic profile were collected for analysis. We screened the signature gene set by Cox proportional hazards modelling. We evaluated the prognostic value of the signature by Kaplan-Meier analysis and timeROC curve. Gene Ontology (GO) and Gene set enrichment analysis (GSEA) analysis were performed for functional annotation. CIBERSORT algorithm and inflammatory metagenes were used to reveal immune characteristics. RESULTS: In gliomas, the expression of most TNF family members was positively correlated. Univariate analysis showed that most TNF family members were related to the overall survival of patients. Then through the LASSO regression model, we developed a TNF family-based signature, which was related to clinical, molecular, and genetic characteristics of patients with glioma. Moreover, the signature was found to be an independent prognostic marker through survival curve analysis and Cox regression analysis. Furthermore, a nomogram prognostic model was constructed to predict individual survival rates at 1, 3 and 5 years. Functional annotation analysis revealed that the immune and inflammatory response pathways were enriched in the high-risk group. Immunological analysis showed the immunosuppressive status in the high-risk group. CONCLUSIONS: We developed a TNF family-based signature to predict the prognosis of patients with glioma. Video abstract.

The soybean plasma membrane-localized cation/H+ exchanger GmCHX20a plays a negative role under salt stress.

Cation/H+ -exchanger (CHX) perform diverse functions in plants, including being a part of the protective mechanisms to cope with salt stress. GmCHX1 has been previously identified as the causal gene in a major salt-tolerance quantitative trait locus (QTL) in soybean, but little is known about another close paralog, GmCHX20a, found in the same QTL. In this study, GmCHX20a was characterized along with GmCHX1. The expression patterns of the two genes and the direction of Na+ flux directed by overexpression of these two transporters are different, suggesting that they are functionally distinct. The ectopic expression of GmCHX20a led to an increase in salt sensitivity and osmotic tolerance, which was consistent with its role in increasing Na+ uptake into the root. Although this seems counter-intuitive, it may in fact be part of the mechanism by which soybean could counter act the effects of osmotic stress, which is commonly manifested in the initial stage of salinity stress. On the other hand, GmCHX1 from salt-tolerant soybean was shown to protect plants via Na+ exclusion under salt stress. Taken together these results suggest that GmCHX20a and GmCHX1 might work complementally through a concerted effort to address both osmotic stress and ionic stress as a result of elevated salinity.

Glomerular Filtration Rate Is a Major Predictor of Clearance of Oxcarbazepine Active Metabolite in Adult Chinese Epileptic Patients: A Population Pharmacokinetic Analysis.

BACKGROUND: Oxcarbazepine (OXC) is almost completely metabolized to its10-monohydroxy derivative (MHD), which is responsible for the pharmacological effects of the drug. Several studies have described the population pharmacokinetics (PPK) of MHD in pediatric patients, but little is known about its pharmacokinetics in adult patients. In addition, no study to date has proposed a model to investigate the influence of genetic polymorphisms on MHD pharmacokinetics. The aim of this study was to establish a PPK model of MHD to investigate the effects of genetic polymorphisms in UGT2B7, UGT1A9, ABCB1, and ABCB2 in adult Chinese patients with epilepsy and to develop a new dosage guideline for OXC. METHODS: Data were prospectively collected from 187 adult patients with epilepsy who were taking OXC. MHD trough concentrations were detected by enzyme-multiplied immunoassay. Patients were genotyped for 4 single nucleotide polymorphisms (UGT2B7 802T>C, UGT1A9 I399C>T, ABCB1 3435C>T, and ABCB2 1249G>A). Other covariates included sex, age, body weight (BW), hepato-renal function, and concomitant medications. Data were analyzed using the nonlinear mixed effects modelling software. RESULTS: The apparent clearance (CL) of MHD was significantly influenced by glomerular filtration rate and BW, and was unrelated to other covariates such as genetic polymorphisms and coadministration with levetiracetam, lamotrigine, and topiramate. Moreover, a new dosage guideline was proposed based on the final model to individualize OXC regimens for adult patients with varying BW and renal function. CONCLUSIONS: Glomerular filtration rate was first found as an important covariate influencing MHD CL. A PPK model was established to estimate the individual MHD CL for adult patients taking OXC and may be applied for individualizing doses in the target population.

Population pharmacokinetics of oxcarbazepine active metabolite in Chinese paediatric epilepsy patients and its application in individualised dosage regimens.

PURPOSE: Oxcarbazepine (OXC) is an antiepileptic drug metabolised to active 10-monohydroxy derivative (MHD) following oral administration. There are no MHD population pharmacokinetic (PPK) models that describe the influence of genetic factors on MHD pharmacokinetics (PK). We developed a PPK model of MHD to investigate gene polymorphism of enzymes associated with MHD PK in Chinese paediatric epilepsy patients and evaluated its utility for dose individualisation. METHODS: Data were prospectively collected from 141 paediatric epilepsy patients (aged ≤ 14 years) who received OXC therapy at the First Affiliated Hospital of Fujian Medical University. The trough concentrations at steady state were determined by enzyme-multiplied immunoassay. Patients were genotyped for four single nucleotide polymorphisms (UGT2B7 802T>C, UGT1A9 I399C>T, ABCB1 3435C>T, and ABCB2 1249G>A). Patient gender, age, body weight (BW), hepatorenal function, and co-administrations were recorded. The PPK model was developed using nonlinear mixed-effects modelling software. The clinical performance of the final model was evaluated by including additional paediatric patients (n = 20) in the validation group. RESULTS: Oral clearance of MHD was significantly influenced by BW. The MHD PK was unrelated to the other covariates, such as the four single nucleotide polymorphisms and co-administration with new-generation antiepileptic drugs. The final BW-dependent exponent model showed the best fit with our data and predicted the trough concentrations in the validation group more accurately than the basic model. A new dosing strategy combining the dosage guideline and Bayesian method is proposed to individualise OXC regimens. CONCLUSION: A PPK model was established to estimate individual MHD clearance in paediatric patients taking OXC to develop individualised OXC dosing regimens for Chinese paediatric epilepsy patients.

Structural insight into the serotonin (5-HT) receptor family by molecular docking, molecular dynamics simulation and systems pharmacology analysis.

Serotonin (5-HT) receptors are proteins involved in various neurological and biological processes, such as aggression, anxiety, appetite, cognition, learning, memory, mood, sleep, and thermoregulation. They are commonly associated with drug abuse and addiction due to their importance as targets for various pharmaceutical and recreational drugs. However, due to a high sequence similarity/identity among 5-HT receptors and the unavailability of the 3D structure of the different 5-HT receptor, no report was available so far regarding the systematical comparison of the key and selective residues involved in the binding pocket, making it difficult to design subtype-selective serotonergic drugs. In this work, we first built and validated three-dimensional models for all 5-HT receptors based on the existing crystal structures of 5-HT1B, 5-HT2B, and 5-HT2C. Then, we performed molecular docking studies between 5-HT receptors agonists/inhibitors and our 3D models. The results from docking were consistent with the known binding affinities of each model. Sequentially, we compared the binding pose and selective residues among 5-HT receptors. Our results showed that the affinity variation could be potentially attributed to the selective residues located in the binding pockets. Moreover, we performed MD simulations for 12 5-HT receptors complexed with ligands; the results were consistent with our docking results and the reported data. Finally, we carried out off-target prediction and blood-brain barrier (BBB) prediction for Captagon using our established hallucinogen-related chemogenomics knowledgebase and in-house computational tools, with the hope to provide more information regarding the use of Captagon. We showed that 5-HT2C, 5-HT5A, and 5-HT7 were the most promising targets for Captagon before metabolism. Overall, our findings can provide insights into future drug discovery and design of medications with high specificity to the individual 5-HT receptor to decrease the risk of addiction and prevent drug abuse.

Air Pollution and Children's Health in Chinese.

Children can be considered as a high-risk population to environmental stress since some of the organ systems including the immune system and respiratory system are still in development during childhood. During childhood, the impact of environmental pollutants exposure may program child growth and development that have long-term consequences on later health and disease risk. Considering the rapid economic growth in recent decades in China, the impact of ambient air pollution on children health causes concerns. Therefore, we aimed to review the current epidemiological evidence on the effects of air pollution exposure on adverse health outcome, including respiratory diseases, lung function, high blood pressure, cognitive disorder, and obesity in childhood in China. We believe that the findings summarized in our review demonstrate an unequivocal relationship between air pollutants exposure and Chinese children health and these results have large public health influences.

Population pharmacokinetics of vancomycin in adult Chinese patients with post-craniotomy meningitis and its application in individualised dosage regimens.

PURPOSE: Vancomycin (VCM) is a first-line antibacterial drug used to treat post-craniotomy meningitis (PCM). VCM pharmacokinetic parameters are altered in PCM patients, compared to those in other patients. Although VCM population pharmacokinetics (PPK) has been reported, changes in VCM PPK in adult Chinese PCM patients remain unknown. We developed a VCM PPK model in adult Chinese PCM patients and proposed a new strategy for individualising VCM administration using this model. METHODS: Data was obtained from a prospective study of 100 adult PCM patients in the Neurosurgery Department of the First Affiliated Hospital of Fujian Medical University. The trough concentrations at steady state were determined by enzyme multiplied immunoassay. Nonlinear mixed-effect model software was employed to develop the PPK model. The final model was evaluated using the bootstrap method and normalised prediction error distribution and through the testing of 20 independent adult PCM patients. RESULTS: VCM clearance in PCM patients was higher than that observed in other patients. Creatinine clearance affected VCM clearance, whereas no co-administered drugs influenced VCM pharmacokinetics. Trough concentrations were accurately predicted by the final model, while the prediction errors were less than ±32 %. Moreover, a new strategy for individualising VCM regimens using the PPK model was proposed and validated. CONCLUSIONS: A PPK model was developed to estimate the individual clearance in inpatients receiving intravenously infused VCM and could be used to develop individualised dosing of adult Chinese PCM patients.

Population pharmacokinetics of valproic acid in adult Chinese epileptic patients and its application in an individualized dosage regimen.

BACKGROUND: There are several reports describing population pharmacokinetic (PPK) models of valproic acid (VPA). However, little was known in Chinese adult patients with epilepsy. The present study aimed to establish a PPK model for VPA in Chinese adult epileptic patients and to demonstrate its use for dose individualization. METHODS: Data were obtained from a prospective study of 199 adult epileptic patients at 5 hospitals. The trough concentrations at steady state were measured by fluorescence polarization immunoassay. Data were analyzed using the Nonlinear Mixed Effects Model software. The serum trough concentrations at steady state were also measured using samples (n = 20) collected prospectively from a different hospital from those providing the data for deriving the original model. These independent samples served as an evaluation group. RESULTS: The important determinants of apparent VPA clearance were daily dose, body weight, and combination with carbamazepine, phenytoin, or phenobarbital. The final model predicted the individualized doses accurately. A total of 85% of the trough concentrations in the evaluation group were accurately predicted by the final model, whereas the prediction errors of the other patients were all < ± 31%. CONCLUSIONS: A PPK model was developed to estimate the individual clearance for patients taking VPA and could be applied for individualizing doses in the target population.

[Population pharmacokinetic/pharmacodynamic modeling of warfarin by nonlinear mixed effects model].

The study aimed to establish a population pharmacokinetic/pharmacodynamic (PPK/PD) model of warfarin. PCR-RFLP technique was used to genotype the CYP2C9 and VKORC1 polymorphisms of 73 patients. RP-HPLC-UV method was used to determine the 190 plasma concentrations of warfarin. Application of NONMEM, the clinical information and 263 international normalized ratio (INR) monitoring data were used to investigate the effect of genetic, physiological, pathological factors, other medication on clearance and anticoagulant response. The final model of warfarin PPK/PD was described as follows: CL = θCL · (WT/60)θWT · θCYP · eηCL (if CYP2C9*1/*1, θCYP = 1; if *1/*3, θCYP = 0.708); EC50 = θEC50 · θVKOR · eηEC50 (if VKORC1- 1639AA, θVKOR = 1; if GA, θVKOR = 2.01; V = θV; K(E0) = θK(E0); Emax = θEmax; E0 = θE0 · eηE0. Among them, the body weight (WT), CYP2C9 and VKORC1 genotype had conspicuous effect on warfarin PK/PD parameters. The goodness diagnosis, Bootstrap, NPDE verification showed that the final model was stable, effective and predictable. It may provide a reference for opitimizing the dose regimen of warfarin.

Modeling an evaluation of the efficacy of the novel neuroanalgesic drug mirogabalin for diabetic peripheral neuropathic pain and postherpetic neuralgia therapy.

Diabetic peripheral neuropathic pain (DPNP) and postherpetic neuralgia (PHN) are challenging and often intractable complex medical conditions, with a substantial impact on the quality of life. Mirogabalin, a novel voltage-gated Ca2+ channel α2δ ligand, was approved for the indication of DPNP and PHN. However, the time course of effects has not yet been clarified.We aimed to establish pharmacodynamic and placebo effect models of mirogabalin and pregabalin in DPNP and PHN, and to quantitatively compare the efficacy characteristics (maximum efficacy, onset time, and other pharmacodynamic parameters) and safety of mirogabalin and pregabalin. Public databases were comprehensively searched for randomized placebo-controlled clinical trials. A model-based meta-analysis (MBMA) was developed to describe the time course of drug efficacy and placebo effects. Adverse events were compared using a fixed-effects meta-analysis. Sixteen studies including 5,147 participants were eligible for this study. The placebo effect was relatively high and gradually increased with time, and it required at least eight weeks to reach a plateau. The pharmacodynamic model revealed that the maximum pure efficacy for mirogabalin and pregabalin was approximately -7.85 % and -8.86 %, respectively; the efficacy of mirogabalin to relieve DPNP and PHN was not superior to that of pregabalin, and both drugs had similar safety. While the rate constant of the onset rate of pregabalin was approximately thrice as high as that of mirogabalin. In addition, the baseline level of pain was an important factor affecting pregabalin efficacy. These findings are helpful in evaluating the clinical extension value of mirogabalin. They suggest that the high placebo effect and the baseline level of pain should be considered when grouping patients in future research and development of voltage-gated Ca2+ channel neuroanalgesic.

A Preoperative Prediction Model for Lymph Node Metastasis in Patients with Gastric Cancer Using a Machine Learning-based Ultrasomics Approach.

OBJECTIVE: This study aimed to develop an ultrasomics model for predicting lymph node metastasis preoperative in patients with gastric cancer (GC). METHODS: This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC), sensitivity, specificity, and the corresponding 95% confidence intervals (CIs). RESULTS: A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19 radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95%CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802, 95%CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and actual lymph node metastasis status. CONCLUSION: Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering implications for personalized therapy approaches.

Handling Delayed or Missed Dose of Antiseizure Medications: A Model-Informed Individual Remedial Dosing.

BACKGROUND AND OBJECTIVES: Delayed or missed antiseizure medications (ASMs) doses are common during long-term or lifelong antiepilepsy treatment. This study aims to explore optimal individualized remedial dosing regimens for delayed or missed doses of 11 commonly used ASMs. METHODS: To explore remedial dosing regimens, Monte Carlo simulation was used based on previously identified and published population pharmacokinetic models. Six remedial strategies for delayed or missed doses were investigated. The deviation time outside the individual therapeutic range was used to evaluate each remedial regimen. The influences of patients' demographics, concomitant medication, and scheduled dosing intervals on remedial regimens were assessed. RxODE and Shiny in R were used to perform Monte Carlo simulation and recommend individual remedial regimens. RESULTS: The recommended remedial regimens were highly correlated with delayed time, scheduled dosing interval, and half-life of the ASM. Moreover, the optimal remedial regimens for pediatric and adult patients were different. The renal function, along with concomitant medication that affects the clearance of the ASM, may also influence the remedial regimens. A web-based dashboard was developed to provide individualized remedial regimens for the delayed or missed dose, and a user-defined module with all parameters that could be defined flexibly by the user was also built. DISCUSSION: Monte Carlo simulation based on population pharmacokinetic models may provide a rational approach to propose remedial regimens for delayed or missed doses of ASMs in pediatric and adult patients with epilepsy.

[Application of Wechat public platform in teaching reform of prosthodontics].

PURPOSE: To explore the law of development and existing problems in prosthodontics teaching in the new era, through flipped classroom teaching based on WeChat public platform. METHODS: WeChat public number was applied and WeChat groups were set up among the students of grade 2013, and 93 students were divided into 20 WeChat groups, with 4 to 5 students in each group. The main platform on reform of prosthodontics teaching was WeChat app, supplemented by platforms of Lediaocha and Youkaoshi. Teachers published courseware and learning resources with WeChat public number and WeChat groups. Two-dimensional codes on questionnaires and tests which were generated by Lediaocha and Youkaoshi could be published with WeChat and the final learning effectiveness was compared using SPSS 19.0 software package for t test. RESULTS: The results showed that 59.1% students admitted WeChat teaching effective, only 5.4% students believed ineffective. Based upon partial chapters of prosthodontics, the score of classroom test (91.35±4.45) was significantly higher than that of pre-class test(90.14±5.03, P＜0.05). CONCLUSIONS: The reform of flipped classroom based on WeChat platform in prosthodontics have some advantages, such as more flexibility in teaching form and time arrangement, promoting students' learner autonomy, and increasing students' motivation and effects of learning prosthodontics.

Fabrication of PEBA/HZIF-8 Pervaporation Membranes for High Efficiency Phenol Recovery.

Phenol and its chemical derivatives serve as essential chemical materials are indispensable for the synthesis of many kinds of polymers. However, they are highly toxic, carcinogenic, difficult to be degraded biologically, and often found in aqueous effluents. Recovery of hazardous phenol from wastewater remains a daunting challenge. Herein, we prepared a hybrid membrane containing polyether block amide (PEBA) matrix and HZIF-8 fillers. To improve the compatibility between ZIF-8 and PEBA, ZIF-8 was modified by using polystyrene (PS) as a template to prepare porous HZIF-8. ZIF-8, composed of zinc nodes linked by the imidazole ring skeleton, is a kind of inorganic material with high hydrothermal stability, ordered pores, and hydrophobic microporous surfaces, which has a wide range of applications in membrane separation. The separation performance of the PEBA/HZIF-8 based membranes for phenol/water is improved due to the presence of PS on the surface of HZIF-8 and the imidazole ring skeleton in ZIF-8, which enhance the π-π interaction between HZIF-8 and phenol molecules. The effects of HZIF-8 content, feed phenol concentration, and feed temperature on the pervaporation performance of PEBA/HZIF-8 membranes were further investigated. The results showed that the pervaporation performance of the PEBA/HZIF-8-10 membrane was promising with a separation factor of 80.89 and permeate flux of 247.70 g/m2·h under the feed phenol concentration of 0.2 wt % at 80 °C. In addition, the PEBA/HZIF-8-10 membrane presented excellent stability, which has great prospect for practical application in phenol recovery from waste water.

Diagnostic performance evaluation of adult Chiari malformation type I based on convolutional neural networks.

PURPOSE: This study aimed to evaluate the diagnostic performance of convolutional neural network (CNN) models in Chiari malformation type I (CMI) and to verify whether CNNs can identify the morphological features of the craniocervical junction region between patients with CMI and healthy controls (HCs). To date, numerous indicators based on manual measurements are used for the diagnosis of CMI. However, the corresponding postoperative efficacy and prognostic evaluations have remained inconsistent. From a diagnostic perspective, CNN models may be used to explore the relationship between the clinical features and image morphological parameters. METHODS: This study included a total of 148 patients diagnosed with CMI at our institution and 205 HCs were included. T1-weighted sagittal magnetic resonance imaging (MRI) images were used for the analysis. A total of 220 and 355 slices were acquired from 98 patients with CMI and 155 HCs, respectively, to train and validate the CNN models. In addition, median sagittal images obtained from 50 patients with CMI and 50 HCs were selected to test the models. We applied original cervical MRI images (CI) and images of posterior cranial fossa and craniocervical junction area (CVI) to train the CI- and CVI-based CNN models. Transfer learning and data augmentation were used for model construction and each model was retrained 10 times. RESULTS: Both the CI- and CVI-based CNN models achieved high diagnostic accuracy. In the validation dataset, the models had diagnostic accuracy of 100% and 97% (p = 0.005), sensitivity of 100% and 98% (p = 0.016), and specificity of 100% (p = 0.929), respectively. In the test dataset, the accuracy was 97% and 96% (p = 0.25), sensitivity was 97% and 92% (p = 0.109), and specificity was 100% (p = 0.123), respectively. For patients with cerebellar subungual herniation less than 5 mm, three out of the 10 CVI-based retrained models reached 100% sensitivity. CONCLUSIONS: Our results revealed that the CNN models demonstrated excellent diagnostic performance for CMI. The models had higher sensitivity than the application of cerebellar tonsillar herniation alone and could identify features in the posterior cranial fossa and craniocervical junction area of patients. Our preliminary experiments provided a feasible method for the diagnosis and study of CMI using CNN models. However, further studies are needed to identify the morphologic characteristics of patients with different clinical outcomes, as well as patients who may benefit from surgery.

Facile fabrication of self-supporting porous CuMoO4@Co3O4 nanosheets as a bifunctional electrocatalyst for efficient overall water splitting.

Research shows that redox complementarity and synergism among the ingredients of heterogeneous catalysts can enhance the performance of the catalyst. In this research, a porous CuMoO4@Co3O4 nanosheet electrocatalyst is prepared, which is uniformly decorated on nickel foam (NF) by hydrothermal reactions and the impregnation method. The CuMoO4@Co3O4 is an efficient bifunctional catalyst with prominent electrocatalytic activity and durability. It requires overpotentials of only 54 and 251 mV to obtain current densities of 10 and 50 mA cm-2 for the cathodic hydrogen evolution reaction (HER) and the anodic oxygen evolution reaction (OER) in 1.0 mol L-1 KOH, corresponding to Tafel slope values of 98.8 and 87.4 mV dec-1, respectively. Furthermore, the CuMoO4@Co3O4 shows excellent stability of 120 h chronopotentiometry at a current density of 100 mA cm-2 for the HER/OER. Notably, an alkaline electrolyzer (with CuMoO4@Co3O4 as the HER and OER electrodes) can deliver a current density of 10 mA cm-2 at a low voltage of 1.51 V. The catalytic activity of CuMoO4@Co3O4 can be attributed to the structure of the porous nanosheets and the synergistic effect between CuMoO4 and Co3O4.

Hollow CoS2 anchored on hierarchically porous carbon derived from Pien Tze Huang for high-performance supercapacitors.

The development of electrode materials with a high specific capacitance, power density, and long-term stability is essential and remains a challenge for developing supercapacitors. Cobalt sulfides (CoS2) are considered one of the most promising and widely studied electrode materials for supercapacitors. Herein, CoS2 and hierarchical porous carbon derived from Pien Tze Huang waste are assembled into a cobalt sulfide/carbon (CoS2/PZH) matrix composite using a one-step hydrothermal method to resolve the challenges of supercapacitors. The resulting CoS2/PZH composite material exhibits a hierarchical porous structure with hollow CoS2 embedded in a PZH framework. The uniform dispersion of the hierarchical porous structure CoS2/PZH is achieved due to the PZH framework, while the uniform decoration of the porous PZH with the hollow CoS2 prevents the PZH from stacking easily. Moreover, the excellent synergistic effect of the hierarchical porous and hollow structure of CoS2/PZH can shorten the electron/ion diffusion channels, expose additional active sites, and provide stable structures for subsequent reactions. As a result, the CoS2/PZH composite material displays a high initial specific capacity of 447.5 F g-1 at 0.5 A g-1, a high energy density of 22.38 W h kg-1, and long-term cycling stability (a retention rate of 92.3% over 10 000 cycles at 5 A g-1).

Exercise Modifies the Transcriptional Regulatory Features of Monocytes in Alzheimer's Patients: A Multi-Omics Integration Analysis Based on Single Cell Technology.

Monocytes have been reported to be important mediators of the protective effect of exercise against the development of Alzheimer's disease (AD). This study aims explored the mechanism by which monocytes achieve this. Using single cell transcriptome analysis, results showed that CD14 + and CD16 + monocytes interacted with other cells in the circulating blood. TNF, CCR1, APP, and AREG, the key ligand-receptor-related genes, were found to be differentially expressed between exercise-treated and AD patients. The SCENIC analysis was performed to identify individual clusters of the key transcription factors (TFs). Nine clusters (M1-M9) were obtained from the co-expression network. Among the identified TFs, MAFB, HES4, and FOSL1 were found to be differentially expressed in AD. Moreover, the M4 cluster to which MAFB, HES4, and FOSL1 belonged was defined as the signature cluster for AD phenotype. Differential analysis by bulkRNA-seq revealed that the expression of TNF, CCR1, and APP were all upregulated after exercise (p < 0.05). And ATF3, MAFB, HES4, and KLF4 that were identified in M4 clusters may be the TFs that regulate TNF, CCR1, and APP in exercise prescription. After that, APP, CCR1, TNF, ATF3, KLF4, HES4, and MAFB formed a regulatory network in the ERADMT gene set, and all of them were mechanistically linked. The ERADMT gene set has been found to be a potential risk marker for the development of AD and can be used as an indicator of compliance to exercise therapy in AD patients. Using single-cell integration analysis, a network of exercise-regulating TFs in monocytes was constructed for AD disease. The constructed network reveals the mechanism by which exercise regulated monocytes to confer therapeutic benefits against AD and its complications. However, this study, as a bioinformatic research, requires further experimental validation.

The effect of denture-wearing on physical activity is associated with cognitive impairment in the elderly: A cross-sectional study based on the CHARLS database.

Background: Currently, only a few studies have examined the link between dental health, cognitive impairment, and physical activity. The current study examined the relationship between denture use and physical activity in elderly patients with different cognitive abilities. Methods: The study data was sourced from the 2018 China Health and Retirement Longitudinal Study (CHARLS) database, which included information on denture use and amount of daily physical activity undertaken by older persons. Physical activity was categorized into three levels using the International Physical Activity General Questionnaire and the International Physical Activity Scale (IPAQ) rubric. The relationship between denture use and physical activity in middle-aged and older persons with varying degrees of cognitive functioning was studied using logistic regression models. Results: A total of 5,892 older people with varying cognitive abilities were included. Denture use was linked to physical activity in the cognitively healthy 60 + age group (p = 0.004). Denture use was positively related with moderate physical activity in the population (odds ratio, OR: 1.336, 95% confidence interval: 1.173-1.520, p < 0.001), according to a multivariate logistic regression analysis, a finding that was supported by the calibration curve. Furthermore, the moderate physical activity group was more likely to wear dentures than the mild physical activity group among age-adjusted cognitively unimpaired middle-aged and older persons (OR: 1.213, 95% CI: 1.053-1.397, p < 0.01). In a fully adjusted logistic regression model, moderate physical activity population had increased ORs of 1.163 (95% CI: 1.008-1.341, p < 0.05) of dentures and vigorous physical activity population had not increased ORs of 1.016 (95% CI: 0.853-1.210, p > 0.05), compared with mild physical activity population. Conclusion: This findings revealed that wearing dentures affects physical activity differently in older persons with different cognitive conditions. In cognitively unimpaired older adults, wearing dentures was associated with an active and appropriate physical activity status.