Mitochondrial outer membrane protein Samm50 protects against hypoxia-induced cardiac injury by interacting with Shmt2.

Cardiac remodeling is a critical process following myocardial infarction (MI), potentially leading to heart failure if untreated. The significance of mitochondrial homeostasis in MI remains insufficiently understood. Samm50 is an essential component of mitochondria. Our study aimed to investigate its role in hypoxia-induced cardiac injury and the underlying mechanisms. First, we observed that Samm50 was dynamically downregulated in mice with MI compared to the control mice. In vitro, Samm50 was also downregulated in oxygen-glucose-deprived neonatal rat cardiomyocytes and fibroblasts. Overexpression and knockdown of Samm50 mitigated and exacerbated cardiac apoptosis and fibrosis, while also improving and worsening mitochondrial homeostasis, respectively. Protein interactions with Samm50 during the protective process were identified via immune-coprecipitation/mass spectroscopy. Mechanistically, serine hydroxymethyltransferase 2 (Shmt2) interacted with Samm50, acting as a crucial element in the protective process by hindering the transfer of Bax from the cytoplasm to the mitochondria and subsequent activation of caspase-3. Inhibition of Shmt2 diminished the protective effect of Samm50 overexpression against cardiac injury. Finally, Samm50 overexpression in vivo mitigated cardiac remodeling and enhanced cardiac function in both acute and chronic MI. In conclusion, Samm50 overexpression mitigated hypoxia-induced cardiac remodeling by inhibiting apoptosis and fibrosis, with Shmt2 acting as a key regulator in this protective process. The Samm50/Shmt2 axis represents a newly discovered mitochondria-related pathway for mitigating hypoxia-induced cardiac injury.

Occupational Category and Professional Title Influencing the Knowledge, Attitudes and Practice (KAP) of Quality Training: A Cross-Sectional Survey From a Tertiary General Hospital.

Quality improvement is an international priority, and quality education and training are important parts of hospital quality management. The aim of this study was to understand the knowledge, attitudes and practices (KAP) and its influencing factors related to quality training in medical staff. A questionnaire survey was conducted by convenience sampling to assess the KAP of quality training in Taizhou Enze Medical Center. Principal component analysis was used to extract factors from the questionnaire. Descriptive statistics (frequency, median, mean), Kendall grade correlation analysis, and Mann-Whitney U tests were used to analyze the data. A total of 205 staff members participated in the questionnaire survey. For the 5 factors of the KAP scale, the highest score was factor F4, recognition and support for quality training (mean = 90.55, median = 100), followed by factor F3, perceived benefits (mean = 84.46, median = 85.65). Relatively lower scores were found for factor F2, quality knowledge learning and mastery (mean = 63.09, median = 63.89), and F5, quality management practices and sharing (mean = 82.07, median = 75.00). There was a correlation between the 5 factors. The scores of F2 (quality knowledge learning and mastery) for staff with senior professional titles were higher than those for staff with intermediate professional titles or below. The score of F3 (perceived benefits of quality training) in medical technicians and nurses was higher than in doctors and administrative personnel. Our findings showed that the respondents' attitude toward quality training was positive, but their knowledge mastery and practice behaviors should be further improved. Occupational category and professional title were the influencing factors of the quality training KAP. Therefore, hospital should conduct quality management training at a wider scope according to the competency requirements of different groups, and further optimize the improvement and innovation system.

Association of Circulating Long-Chain Free Fatty Acids and Incident Diabetes Risk Among Normoglycemic Chinese Adults: A Prospective Nested Case-Control Study.

BACKGROUND: Long-chain free fatty acids (FFAs) are associated with risk of incident diabetes. However, comprehensive assessment of the associations in normoglycemic populations is lacking. OBJECTIVE: Our study aims to comprehensively investigate the prospective associations and patterns of FFA profiles with diabetes risk among normoglycemic Chinese adults. METHODS: This is a prospective nested case-control study from the China Cardiometabolic Disease and Cancer Cohort (4C) study. We quantitatively measured 53 serum FFAs using targeted metabolomics approach in 1707 incident diabetes subjects and 1707 propensity score-matched normoglycemic controls. Conditional logistic regression models were employed to estimate odds ratios (ORs) for associations. Least Absolute Shrinkage and Selection Operator (LASSO) penalty regression and quantile g-computation (qg-comp) analyses were implemented to estimate the association between multi-FFA exposures and incident diabetes. RESULTS: The majority of odd-chain FFAs exhibited an inverse association with incident diabetes, wherein the ORs per SD increment of all 7 saturated fatty acids (SFAs), monounsaturated fatty acid (MUFA) 15:1 and polyunsaturated fatty acid (PUFA) 25:2 were ranging from 0.79 to 0.88 (95%CIs ranging between 0.71 and 0.97). Even-chain FFAs comprised 99.3% of total FFAs and displayed heterogeneity with incident diabetes. SFAs with 18 to 26 carbon atoms are inversely linked to incident diabetes, with ORs ranging from 0.81 to 0.86 (95%CIs ranging between 0.73 and 0.94). MUFAs 26:1 (OR[95%CI]: 0.85[0.76-0.94]), PUFAs 20:4 (0.84[0.75-0.94]) and 24:2 (0.87[0.78-0.97]) demonstrated significant associations. In multi-FFA exposure model, 24 FFAs were significantly associated with incident diabetes, most of which were consistent with univariate results. The mixture OR was 0.78 [0.61-0.99] (P= 0.04159). Differential correlation network analysis revealed pre-existing perturbations in intraclass and interclass FFA coregulation before diabetes onset. CONCLUSIONS: These findings underscore the variations in diabetes risk associated with FFAs across chain length and unsaturation degree, highlighting the importance of recognizing FFA subtypes in the pathogenesis of diabetes.

Association between dietary fiber intake and diabetic nephropathy among adult diabetes mellitus in the United States: A cross-sectional study.

Objective: There has been some evidence that dietary fiber may be associated with diabetic nephropathy (DN), but the relationship is still unclear. The purpose of this study was to examine the association between dietary fiber intake and DN. Methods: This cross-sectional study used National Health and Nutrition Examination Survey (NHANES) data collected between 2007 and 2020. Weighted multivariate logistic regression was used to examine the relation between dietary fiber intake and DN. In addition, fitted smoothed curves were used to explore potential non-linear relationships. If non-linearity was observed, inflection points were further calculated by a recursive algorithm. Results: The study finally included 5964 subjects ≥20 years of age. The mean age was 60.8 ± 13.4 years with males (52.4 %), and non-Hispanic Whites (62.4 %), and the weighted prevalence of DN was 36.7 %. Dietary fiber was negatively associated with the risk of DN after controlling for all confounding variables (OR = 0.89, 95%CI: 0.80, 0.99). Smoothed curve fit plots of the dose relationship showed that dietary fiber intake was linearly related to DN, whereas males (inflection point of 8.0 g/d) and non-Hispanic Blacks (inflection point of 14.9 g/d) followed a non-linear inverted U-shaped curve relationship. In United States adults aged 20 and older, dietary fiber intake may be associated with a reduced risk of DN. Conclusion: Appropriate increases in dietary fiber intake may offer potential benefits for DN. In conclusion, it appears that increasing dietary fiber intake may be one of the most effective strategies for the prevention and management of DN.

Nonvolatile Modulation of Bi2O2Se/Pb(Zr,Ti)O3 Heteroepitaxy.

The pursuit of high-performance electronic devices has driven the research focus toward 2D semiconductors with high electron mobility and suitable band gaps. Previous studies have demonstrated that quasi-2D Bi2O2Se (BOSe) has remarkable physical properties and is a promising candidate for further exploration. Building upon this foundation, the present work introduces a novel concept for achieving nonvolatile and reversible control of BOSe's electronic properties. The approach involves the epitaxial integration of a ferroelectric PbZr0.2Ti0.8O3 (PZT) layer to modify BOSe's band alignment. Within the BOSe/PZT heteroepitaxy, through two opposite ferroelectric polarization states of the PZT layer, we can tune the Fermi level in the BOSe layer. Consequently, this controlled modulation of the electronic structure provides a pathway to manipulate the electrical properties of the BOSe layer and the corresponding devices.

Improving the detection accuracy of the dual SERS aptasensor system with uncontrollable SERS "hot spot" using machine learning tools.

BACKGROUND: Simultaneous detection of food contaminants is crucial in addressing the collective health hazards arising from the presence of multiple contaminants. However, traditional multi-competitive surface-enhanced Raman scattering (SERS) aptasensors face difficulties in achieving simultaneous accurate detection of multiple target substances due to the uncontrollable SERS "hot spots". In this study, using chloramphenicol (CAP) and estradiol (E2) as two target substances, we introduced a novel approach that combines machine learning methods with a dual SERS aptasensor, enabling simultaneous high-sensitivity and accurate detection of both target substances. RESULTS: The strategy effectively minimizes the interference from characteristic Raman peaks commonly encountered in traditional multi-competitive SERS aptasensors. For this sensing system, the Au@4-MBA@Ag nanoparticles modified with sulfhydryl (SH)-CAP aptamer and Au@DTNB@Ag NPs modified with sulfhydryl (SH)-E2 aptamer were used as signal probes. Additionally, Fe3O4@Au nanoflowers integrated with SH-CAP aptamer complementary DNA and SH-E2 aptamer complementary DNA were used as capture probes, respectively. When compared to linear regression random forest, and support vector regression (SVR) models, the proposed artificial neural network (ANN) model exhibited superior precision, demonstrating R2 values of 0.963, 0.976, 0.991, and 0.970 for the training set, test set, validation set, and entire dataset, respectively. Validation with ten spectral groups reported an average error of 244 µg L-1. SIGNIFICANCE: The essence of our study lies in its capacity to address a persistent challenge encountered by traditional multiple competitive SERS aptasensors - the interference generated by uncontrollable SERS "hot spots" that hinders simultaneous quantification. The accuracy of the predictive model for simultaneous detection of two target substances was significantly improved using machine learning tools. This innovative technique offers promising avenues for the accurate and high-sensitive simultaneous detection of multiple food and environmental contaminants.

Neurological or Psychiatric Disorders After Dengue Fever.

This cohort study investigates the association between dengue fever and risk of neurological and psychiatric disorders among adults in Taiwan.

Establishment and Verification of an Artificial Intelligence Prediction Model for Children With Sepsis.

BACKGROUND: Early identification of high-risk groups of children with sepsis is beneficial to reduce sepsis mortality. This article used artificial intelligence (AI) technology to predict the risk of death effectively and quickly in children with sepsis in the pediatric intensive care unit (PICU). STUDY DESIGN: This retrospective observational study was conducted in the PICUs of the First Affiliated Hospital of Sun Yat-sen University from December 2016 to June 2019 and Shenzhen Children's Hospital from January 2019 to July 2020. The children were divided into a death group and a survival group. Different machine language (ML) models were used to predict the risk of death in children with sepsis. RESULTS: A total of 671 children with sepsis were enrolled. The accuracy (ACC) of the artificial neural network model was better than that of support vector machine, logical regression analysis, Bayesian, K nearest neighbor method and decision tree models, with a training set ACC of 0.99 and a test set ACC of 0.96. CONCLUSIONS: The AI model can be used to predict the risk of death due to sepsis in children in the PICU, and the artificial neural network model is better than other AI models in predicting mortality risk.

Monocyte-Derived Macrophages Aggravate Cardiac Dysfunction After Ischemic Stroke in Mice.

BACKGROUND: Cardiac damage induced by ischemic stroke, such as arrhythmia, cardiac dysfunction, and even cardiac arrest, is referred to as cerebral-cardiac syndrome (CCS). Cardiac macrophages are reported to be closely associated with stroke-induced cardiac damage. However, the role of macrophage subsets in CCS is still unclear due to their heterogeneity. Sympathetic nerves play a significant role in regulating macrophages in cardiovascular disease. However, the role of macrophage subsets and sympathetic nerves in CCS is still unclear. METHODS AND RESULTS: In this study, a middle cerebral artery occlusion mouse model was used to simulate ischemic stroke. ECG and echocardiography were used to assess cardiac function. We used Cx3cr1GFPCcr2RFP mice and NLRP3-deficient mice in combination with Smart-seq2 RNA sequencing to confirm the role of macrophage subsets in CCS. We demonstrated that ischemic stroke-induced cardiac damage is characterized by severe cardiac dysfunction and robust infiltration of monocyte-derived macrophages into the heart. Subsequently, we identified that cardiac monocyte-derived macrophages displayed a proinflammatory profile. We also observed that cardiac dysfunction was rescued in ischemic stroke mice by blocking macrophage infiltration using a CCR2 antagonist and NLRP3-deficient mice. In addition, a cardiac sympathetic nerve retrograde tracer and a sympathectomy method were used to explore the relationship between sympathetic nerves and cardiac macrophages. We found that cardiac sympathetic nerves are significantly activated after ischemic stroke, which contributes to the infiltration of monocyte-derived macrophages and subsequent cardiac dysfunction. CONCLUSIONS: Our findings suggest a potential pathogenesis of CCS involving the cardiac sympathetic nerve-monocyte-derived macrophage axis.

A-LLTO Nanoparticles Embedded Composite Solid Polymer Electrolyte for Room Temperature Operational Li-metal Batteries.

Solid-state batteries (SSBs) have the potential to revolutionize the current energy storage sector. A significant portion of the current development of electric vehicles and the electrification of various appliances relies on Lithium (Li)-ion batteries. However, future energy demands will require the development of stronger and more reliable batteries. This report presents a novel solid state electrolyte (SSE) composed of a self-healing composite solid polymer electrolyte (CSPE) matrix and aluminum-doped (Li0.33La0.56)1.005Ti0.99Al0.01O3 (A-LLTO) nanofillers. The CSPE contains Jeffamine ED-2003 monomer, Benzene-1,3,5-tricarbaldehyde (BTC) crosslinker dissolved in a 1:1 ratio of Dimethylformamide (DMF) to LiPF6, and a certain amount (x) of A-LLTO nanofillers (x = 5, 7.5, 10, 12.5%). A CSPE containing x-amount of A-LLTO fillers (referred to as CAL-x%) demonstrates excellent ion-conducting properties and stable battery performance. The CAL-10% demonstrates 1.1 × 10-3 S cm-1 of ionic conductivity at room temperature (RT). A-LLTO nanofillers dispersed uniformly within the polymer matrix form a percolation network, which is believed to improve ionic conductivity and the diffusion of Li+ ions. The CR-2032 cell, consisting of LiFePO4 (LFP)║CAL-10%║Li, at RT offers an initial discharge capacity of ≈165 mAh g-1 at 0.1C rate for 120 cycles with 98.85% coulombic efficiency (C.E.).

Seafood waste derived carbon nanomaterials for removal and detection of food safety hazards.

Nanobiotechnology and the engineering of nanomaterials are currently the main focus of many researches. Seafood waste carbon nanomaterials (SWCNs) are a renewable resource with large surface area, porous structure, high reactivity, and abundant active sites. They efficiently adsorb food contaminants through π-π conjugated, ion exchange, and electrostatic interaction. Furthermore, SWCNs prepared from seafood waste are rich in N and O functional groups. They have high quantum yield (QY) and excellent fluorescence properties, making them promising materials for the removal and detection of pollutants. It provides an opportunity by which solutions to the long-term challenges of the food industry in assessing food safety, maintaining food quality, detecting contaminants and pretreating samples can be found. In addition, carbon nanomaterials can be used as adsorbents to reduce environmental pollutants and prevent food safety problems from the source. In this paper, the types of SWCNs are reviewed; the synthesis, properties and applications of SWCNs are reviewed and the raw material selection, preparation methods, reaction conditions and formation mechanisms of biomass-based carbon materials are studied in depth. Finally, the advantages of seafood waste carbon and its composite materials in pollutant removal and detection were discussed, and existing problems were pointed out, which provided ideas for the future development and research directions of this interesting and versatile material. Based on the concept of waste pricing and a recycling economy, the aim of this paper is to outline current trends and the future potential to transform residues from the seafood waste sector into valuable biological (nano) materials, and to apply them to food safety.

A novel model based on ubiquitination-related gene to predict prognosis and immunotherapy response in hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is a common cancer that is increasingly becoming a global health problem and a major public health concern. In order to improve patient outcomes, additional biomarkers and targets must be explored. Ubiquitination-related genes (URGs), as tumor regulators, exhibit multiple functions in tumor development. Our objective was to examine the influence of URGs on the prognosis of patients with HCC. Methods: By utilizing unsupervised cluster analysis, we were able to identify URGs in the database and create a risk score profile for predicting the prognosis of patients with HCC. The model's clinical application was explored using subject operating characteristic curves, survival analysis, and correlation analysis. We additionally examined the variances in clinical traits, immune infiltration, somatic genetic alterations, and responsiveness to treatment among high- and low-risk populations identified by the prognostic model. Scores for immune cell infiltration and immune-related pathway activity were determined by performing ssGSEA enrichment analysis. Additionally, to investigate potential mechanisms, we utilized GO, KEGG and GSVA analyses. Results: We developed a risk scoring model that relies on genes associated with ubiquitination. As the risk score increased, the malignancy and prognosis of the tumor worsened. The high-risk and low-risk groups exhibited notable disparities in relation to the immune microenvironment, genes associated with immune checkpoints, sensitivity to drugs, and response to immunotherapy. Conclusion: The utilization of a risk model that relies on genes associated with ubiquitination can serve as a biomarker to assess the prognosis of patients with HCC, and aid in the selection of suitable therapeutic agents.

A few-shot learning method for tobacco abnormality identification.

Tobacco is a valuable crop, but its disease identification is rarely involved in existing works. In this work, we use few-shot learning (FSL) to identify abnormalities in tobacco. FSL is a solution for the data deficiency that has been an obstacle to using deep learning. However, weak feature representation caused by limited data is still a challenging issue in FSL. The weak feature representation leads to weak generalization and troubles in cross-domain. In this work, we propose a feature representation enhancement network (FREN) that enhances the feature representation through instance embedding and task adaptation. For instance embedding, global max pooling, and global average pooling are used together for adding more features, and Gaussian-like calibration is used for normalizing the feature distribution. For task adaptation, self-attention is adopted for task contextualization. Given the absence of publicly available data on tobacco, we created a tobacco leaf abnormality dataset (TLA), which includes 16 categories, two settings, and 1,430 images in total. In experiments, we use PlantVillage, which is the benchmark dataset for plant disease identification, to validate the superiority of FREN first. Subsequently, we use the proposed method and TLA to analyze and discuss the abnormality identification of tobacco. For the multi-symptom diseases that always have low accuracy, we propose a solution by dividing the samples into subcategories created by symptom. For the 10 categories of tomato in PlantVillage, the accuracy achieves 66.04% in 5-way, 1-shot tasks. For the two settings of the tobacco leaf abnormality dataset, the accuracies were achieved at 45.5% and 56.5%. By using the multisymptom solution, the best accuracy can be lifted to 60.7% in 16-way, 1-shot tasks and achieved at 81.8% in 16-way, 10-shot tasks. The results show that our method improves the performance greatly by enhancing feature representation, especially for tasks that contain categories with high similarity. The desensitization of data when crossing domains also validates that the FREN has a strong generalization ability.

Difference of Cerebrospinal Fluid Biomarkers and Neuropsychiatric Symptoms Profiles among Normal Cognition, Mild Cognitive Impairment, and Dementia Patient.

The delineation of biomarkers and neuropsychiatric symptoms across normal cognition, mild cognitive impairment (MCI), and dementia stages holds significant promise for early diagnosis and intervention strategies. This research investigates the association of neuropsychiatric symptoms, evaluated via the Neuropsychiatric Inventory (NPI), with cerebrospinal fluid (CSF) biomarkers (Amyloid-ß42, P-tau, T-tau) across a spectrum of cognitive states to enhance diagnostic accuracy and treatment approaches. Drawing from the National Alzheimer's Coordinating Center's Uniform Data Set Version 3, comprising 977 individuals with normal cognition, 270 with MCI, and 649 with dementia. To assess neuropsychiatric symptoms, we employed the NPI to understand the behavioral and psychological symptoms associated with each cognitive category. For the analysis of CSF biomarkers, we measured levels of Amyloid-ß42, P-tau, and T-tau using the enzyme-linked immunosorbent assay (ELISA) and Luminex multiplex xMAP assay protocols. These biomarkers are critical in understanding the pathophysiological underpinnings of Alzheimer's disease and its progression, with specific patterns indicative of disease stage and severity. This study cohort consists of 1896 participants, which is composed of 977 individuals with normal cognition, 270 with MCI, and 649 with dementia. Dementia is characterized by significantly higher NPI scores, which are largely reflective of mood-related symptoms (p < 0.001). In terms of biomarkers, normal cognition shows median Amyloid-ß at 656.0 pg/mL, MCI at 300.6 pg/mL, and dementia at 298.8 pg/mL (p < 0.001). Median P-tau levels are 36.00 pg/mL in normal cognition, 49.12 pg/mL in MCI, and 58.29 pg/mL in dementia (p < 0.001). Median T-tau levels are 241.0 pg/mL in normal cognition, 140.6 pg/mL in MCI, and 298.3 pg/mL in dementia (p < 0.001). Furthermore, the T-tau/Aß-42 ratio increases progressively from 0.058 in the normal cognition group to 0.144 in the MCI group, and to 0.209 in the dementia group (p < 0.001). Similarly, the P-tau/Aß-42 ratio also escalates from 0.305 in individuals with normal cognition to 0.560 in MCI, and to 0.941 in dementia (p < 0.001). The notable disparities in NPI and CSF biomarkers among normal, MCI and Alzheimer's patients underscore their diagnostic potential. Their combined assessment could greatly improve early detection and precise diagnosis of MCI and dementia, facilitating more effective and timely treatment strategies.

The regional disparities in liver disease comorbidity among elderly Chinese based on a health ecological model: the China Health and Retirement Longitudinal Study.

PURPOSE: This study aimed to investigate the risk factors for liver disease comorbidity among older adults in eastern, central, and western China, and explored binary, ternary and quaternary co-morbid co-causal patterns of liver disease within a health ecological model. METHOD: Basic information from 9,763 older adults was analyzed using data from the China Health and Retirement Longitudinal Study (CHARLS). LASSO regression was employed to identify significant predictors in eastern, central, and western China. Patterns of liver disease comorbidity were studied using association rules, and spatial distribution was analyzed using a geographic information system. Furthermore, binary, ternary, and quaternary network diagrams were constructed to illustrate the relationships between liver disease comorbidity and co-causes. RESULTS: Among the 9,763 elderly adults studied, 536 were found to have liver disease comorbidity, with binary or ternary comorbidity being the most prevalent. Provinces with a high prevalence of liver disease comorbidity were primarily concentrated in Inner Mongolia, Sichuan, and Henan. The most common comorbidity patterns identified were "liver-heart-metabolic", "liver-kidney", "liver-lung", and "liver-stomach-arthritic". In the eastern region, important combination patterns included "liver disease-metabolic disease", "liver disease-stomach disease", and "liver disease-arthritis", with the main influencing factors being sleep duration of less than 6 h, frequent drinking, female, and daily activity capability. In the central region, common combination patterns included "liver disease-heart disease", "liver disease-metabolic disease", and "liver disease-kidney disease", with the main influencing factors being an education level of primary school or below, marriage, having medical insurance, exercise, and no disabilities. In the western region, the main comorbidity patterns were "liver disease-chronic lung disease", "liver disease-stomach disease", "liver disease-heart disease", and "liver disease-arthritis", with the main influencing factors being general or poor health satisfaction, general or poor health condition, severe pain, and no disabilities. CONCLUSION: The comorbidities associated with liver disease exhibit specific clustering patterns at both the overall and local levels. By analyzing the comorbidity patterns of liver diseases in different regions and establishing co-morbid co-causal patterns, this study offers a new perspective and scientific basis for the prevention and treatment of liver diseases.

Decreased connexin 40 expression of the sinoatrial node mediates ischemic stroke-induced arrhythmia in mice.

BACKGROUND: Arrhythmia is the most common cardiac complication after ischemic stroke. Connexin 40 is the staple component of gap junctions, which influences the propagation of cardiac electrical signals in the sinoatrial node. However, the role of connexin 40 in post-stroke arrhythmia remains unclear. METHODS: In this study, a permanent middle cerebral artery occlusion model was used to simulate the occurrence of an ischemic stroke. Subsequently, an electrocardiogram was utilized to record and assess variations in electrocardiogram measures. In addition, optical tissue clearing and whole-mount immunofluorescence staining were used to confirm the anatomical localization of the sinoatrial node, and the sinoatrial node tissue was collected for RNA sequencing to screen for potential pathological mechanisms. Lastly, the rAAV9-Gja5 virus was injected with ultrasound guidance into the heart to increase Cx40 expression in the sinoatrial node. RESULTS: We demonstrated that the mice suffering from a permanent middle cerebral artery occlusion displayed significant arrhythmia, including atrial fibrillation, premature ventricular contractions, atrioventricular block, and abnormal electrocardiogram parameters. Of note, we observed a decrease in connexin 40 expression within the sinoatrial node after the ischemic stroke via RNA sequencing and western blot. Furthermore, rAAV9-Gja5 treatment ameliorated the occurrence of arrhythmia following stroke. CONCLUSIONS: In conclusion, decreased connexin 40 expression in the sinoatrial node contributed to the ischemic stroke-induced cardiac arrhythmia. Therefore, enhancing connexin 40 expression holds promise as a potential therapeutic approach for ischemic stroke-induced arrhythmia.

MicroRNAs in hepatocellular carcinoma treatment: Charting the path forward.

MicroRNAs (miRNAs) are recognized for their involvement in the regulation of gene expression and exhibit significant potential in both the prognostic assessment and treatment of hepatocellular carcinoma (HCC). HCC, like other tumors, seldom occurs in isolation; instead, it evolves within a microenvironment featuring oncogenic and tumor-suppressive elements. When combined with suitable delivery vehicles, miRNA technology provides the capability to directly engage with these elements, thereby hindering tumor formation and progression. Ongoing research in this domain holds the promise of enabling a more efficacious and multi-modal treatment approach for HCC in the near future.

[Effect of printing orientation on physical and mechanical properties of 3D printing prosthodontic base resin materials].

OBJECTIVE: To analyze the influence of forming direction on the surface characteristics, elastic modulus, bending strength and fracture toughness of printed parts and the relationship between forming direction and force direction, and to provide scientific basis and guidance for the clinical application of oral denture base resin materials. METHODS: The 3D printing technology was used to print denture base resin samples. The shape and size of the samples referred to the current standard for testing conventional denture base materials. The samples used for physical performance testing were cylindrical (with a diameter of 15 mm and a thickness of 1 mm) and printed at different angles along the Z axis (0°, 45°, 90°). Scanning electron microscope was used to observe the microscopic topography of the different samples. The color stability of different samples was observed by color stabilizer. The surface roughness of the samples was analyzed by using surface roughness tester. The Vickers hardness was measured to analyze the hardness of the samples. The samples used for mechanical performance testing were rectangular (elastic modulus and bending strength: A length of 64 mm, a width of 10 mm, and a height of 3.3 mm; fracture toughness: A length of 39 mm, a width of 8 mm, and a height of 4 mm), divided into two groups: W group and H group. The W group was printed from the bottom up along the Z axis with the length × width as the bottom surface parallel to the X, Y axis plane, while the H group printed from the bottom up along the Z axis with the length × height as the bottom surface parallel to the X, Y axis plane. The forming angles of both groups were equally divided into 0°, 45°, and 90°. The elastic modulus, bending strength and fracture toughness of different samples were studied through universal mechanical testing machine. SPSS 22.0 software was used for statistical analysis. RESULTS: The microscopic topography and roughness of different samples were closely related to the printing direction, with significant differences between the 0°, 45°, and 90° specimens. The 0° specimens had the smoothest surface (roughness < 1 µm). The surface of the 45° specimen was the roughest (roughness>3 µm). The microhardness of the 0° sample was the best [(196.13±0.20) MPa], with a significant difference compared with the 90° sample [(186.62±4.81) MPa, P < 0.05]. The mechanical properties of different samples were also closely related to the printing direction. The elastic modulus, bending strength, and fracture toughness of the 45° samples in the W group were the highest compared with the other groups. The results of elastic modulus showed that in the H group, the 45° specimens had the highest elastic mo-dulus, which was significantly different from the 0° and 90° specimens (P < 0.05). The elastic modulus of 0° and 45° specimens in the W group were higher than those in 90° specimens (P < 0.05). The bending strength results showed that there was no significant difference between the specimens from dif-ferent angles in the H group. The bending strength of the 90° specimens in the W group was the smallest, and there was a significant difference between 90° and the 0° and 45° specimens (P < 0.05); And the bendind strength of the 0° and 45° specimens in the W group was significantly higher than that of the 0° and 45° specimens in the H group (P < 0.05). The fracture toughness results showed that the fracture toughness of the H group specimens was lower than 1.9 MPa m1/2, which was specified in the denture base standard. The 45° samples in the W group were the highest, with significant differences compared with the 0° and 90° samples (P < 0.05). And the 90° samples of the W group specimens were lower than 1.9 MPa m1/2. And the fracture toughness of the 45° specimen in the W group was significantly higher than that of all the specimens in the H group (P < 0.05). CONCLUSION: The 0° samples had relatively better physical properties. The 45° samples had the best mechanical properties. But the fracture toughness of specimens (H group and 90° samples of W group) did not yet meet clinical requirements. That indicated that the characteristics of the 3D printing denture base resin were affected by the printing direction. Only when the performance of the printed samples in all directions met the minimum requirements of the standard, they could be used in clinical practice.

A perovskite solar cell-photothermal-thermoelectric tandem system for enhanced solar energy utilization.

Photovoltaic-thermoelectric (PV-TE) tandem system has been considered as an effective way to fully utilize the solar spectrum, and has been demonstrated in a perovskite solar cell (PSC)-thermoelectric (TE) configuration. However, the conventional PSC-TE tandem architecture cannot convert infrared light transmitted through the upper PSC into heat effectively, impeding the heat-electricity conversion of TE devices. Herein, a semi-transparent PSC-photothermal-TE tandem system is designed for improved photothermal utilization. Through optimizing the buffer layer of the back transparent electrode, semi-transparent PSC with a power conversion efficiency (PCE) of 13% and an average transmittance of 53% in the range of 800-1500 nm was obtained. On this basis, a photothermal thin film was introduced between the semi-transparent PSC and the TE device, which increased the efficiency contribution ratio of the TE device from 14% to 19%, showing enhanced utilization of AM 1.5 G solar spectrum and improved photo-thermal-electric conversion efficiency.

We have constructed a semi-transparent perovskite solar cell-photothermal-thermoelectric tandem system through the optimization of transparent back electrode and the introduction of photothermal thin-film, realizing enhanced utilization of solar energy.

Effect of 5:2 Regimens: Energy-Restricted Diet or Low-Volume High-Intensity Interval Training Combined With Resistance Exercise on Glycemic Control and Cardiometabolic Health in Adults With Overweight/Obesity and Type 2 Diabetes-A Three-Arm Randomized Controlled Trial.

OBJECTIVE: We aimed to examine the effects of a 5:2 regimens diet (2 days per week of energy restriction by formula diet) or an exercise (2 days per week of high-intensity interval training and resistance training) intervention compared with routine lifestyle education (control) on glycemic control and cardiometabolic health among adults with overweight/obesity and type 2 diabetes. RESEARCH DESIGN AND METHODS: This two-center, open-label, three-arm, parallel-group, randomized controlled trial recruited 326 participants with overweight/obesity and type 2 diabetes and randomized them into 12 weeks of diet intervention (n = 109), exercise intervention (n = 108), or lifestyle education (control) (n = 109). The primary outcome was the change of glycemic control measured as glycated hemoglobin (HbA1c) between the diet or exercise intervention groups and the control group after the 12-week intervention. RESULTS: The diet intervention significantly reduced HbA1c level (%) after the 12-week intervention (-0.72, 95% CI -0.95 to -0.48) compared with the control group (-0.37, 95% CI -0.60 to -0.15) (diet vs. control -0.34, 95% CI -0.58 to -0.11, P = 0.007). The reduction in HbA1c level in the exercise intervention group (-0.46, 95% CI -0.70 to -0.23) did not significantly differ from the control group (exercise vs. control -0.09, 95% CI -0.32 to 0.15, P = 0.47). The exercise intervention group was superior in maintaining lean body mass. Both diet and exercise interventions induced improvements in adiposity and hepatic steatosis. CONCLUSIONS: These findings suggest that the medically supervised 5:2 energy-restricted diet could provide an alternative strategy for improving glycemic control and that the exercise regimen could improve body composition, although it inadequately improved glycemic control.