Multidimensional Electrochemistry Decodes the Operando Mechanism of Hydrogen Oxidation.

Being an efficient approach to the utilization of hydrogen energy, the hydrogen oxidation reaction (HOR) is of particular significance in the current carbon-neutrality time. Yet the mechanistic picture of the HOR is still blurred, mostly because the elemental steps of this reaction are rapid and highly entangled, especially when deviating from the thermodynamic equilibrium state. Here we report a strategy for decoding the HOR mechanism under operando conditions. In addition to the wide-potential-range I-V curves obtained using gas diffusion electrodes, we have applied the AC impedance spectroscopy to provide independent and complementary kinetic information. Combining multidimensional data sources has enabled us to fit, in mathematical rigor, the core kinetic parameter set in a 5-D data space. The reaction rate of the three elemental steps (Tafel, Heyrovsky, and Volmer reactions), as a function of the overpotential, can thus be distilled individually. Such an undocumented kinetic picture unravels, in detail, how the HOR is controlled by the elemental steps on polarization. For instance, at low polarization region, the Heyrovsky reaction is relatively slow and can be ignored; but at high polarization region, the Heyrovsky reaction will surpass the Tafel reaction. Additionally, the Volmer reaction has been the fastest within overpotentials of interest. Our findings not only offer a better understanding of the HOR mechanism, but also lay the foundation for the development of improved hydrogen energy utilization systems.

Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty.

BACKGROUND: In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. METHODS: The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. RESULTS: The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. CONCLUSION: In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis.

INTRODUCTION: Ankylosing spondylitis (AS) is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS mainly affects the axial bone, sacroiliac joint, hip joint, spinal facet, and adjacent ligaments. We used machine learning (ML) methods to construct diagnostic models based on blood routine examination, liver function test, and kidney function test of patients with AS. This method will help clinicians enhance diagnostic efficiency and allow patients to receive systematic treatment as soon as possible. METHODS: We consecutively screened 348 patients with AS through complete blood routine examination, liver function test, and kidney function test at the First Affiliated Hospital of Guangxi Medical University according to the modified New York criteria (diagnostic criteria for AS). By using random sampling, the patients were randomly divided into training and validation cohorts. The training cohort included 258 patients with AS and 247 patients without AS, and the validation cohort included 90 patients with AS and 113 patients without AS. We used three ML methods (LASSO, random forest, and support vector machine recursive feature elimination) to screen feature variables and then took the intersection to obtain the prediction model. In addition, we used the prediction model on the validation cohort. RESULTS: Seven factors-erythrocyte sedimentation rate (ESR), red blood cell count (RBC), mean platelet volume (MPV), albumin (ALB), aspartate aminotransferase (AST), and creatinine (Cr)-were selected to construct a nomogram diagnostic model through ML. In the training cohort, the C value and area under the curve (AUC) value of this nomogram was 0.878 and 0.8779462, respectively. The C value and AUC value of the nomogram in the validation cohort was 0.823 and 0.8232055, respectively. Calibration curves in the training and validation cohorts showed satisfactory agreement between nomogram predictions and actual probabilities. The decision curve analysis showed that the nonadherence nomogram was clinically useful when intervention was decided at the nonadherence possibility threshold of 1%. CONCLUSION: Our ML model can satisfactorily predict patients with AS. This nomogram can help orthopedic surgeons devise more personalized and rational clinical strategies.

AS is a chronic progressive inflammatory disease of the spine and its affiliated tissues. AS starts gradually, and its early symptoms are mild. Some hospitals lack HLA-B27 and related imaging instruments to assist in the diagnosis of AS. There are relatively few studies on liver function and kidney function of patients with AS. We used ML methods to construct diagnostic models. Our model can satisfactorily predict patients with AS. This diagnostic model can help orthopedic surgeons devise more personalized and rational clinical strategies.

Blockage of PD-L1 by FERMT3-mediated Wnt/ß-catenin signalling regulates chemoresistance and immune evasion of colorectal cancer cells.

Colorectal cancer (CRC) constitutes a major public health problem because of the high rate of morbidity and mortality. Chemotherapy and immunotherapy are the major and promising strategies for cancer patients including CRC; nevertheless, chemoresistance and immune escape limit the final efficacy of the above approaches. FERMT3 has proven to exert a critical role in the immune system and has contradictive effects on cancer progression. In this study, bioinformatics database analysis and clinical specimen detection both corroborated the downregulation of FERMT3 in CRC tissues and cells. Of interest, overexpression of FERMT3 suppressed CRC cell invasion and sensitized cells to 5-fluorouracil (5-FU) by reducing cell viability and increasing cell apoptosis and caspase 3 activity. Noticeably, FERMT3 upregulation enhanced natural killer (NK) cells activation by increasing secretions of interferon γ (IFN-γ) and tumour necrosis factor α (TNF-α) when NK cells were co-cultured with CRC cells. Importantly, upregulation of FERMT3 promoted NK cell-mediated killing of CRC cells. Mechanically, FERMT3 inhibited the aberrant activation of Wnt/ß-catenin signalling and the subsequent programmed death-ligand 1 (PD-L1) expression in CRC cells. Moreover, knockdown of PD-L1 suppressed CRC cell invasion, 5-FU resistance and NK cells-mediated tumour killing. Additionally, reactivating the Wnt/ß-catenin signalling with a specific WNT agonist CAS 853220-52-7 overturned the efficacy of FERMT3 overexpression against CRC cell invasion, 5-FU chemoresistance and cell susceptibility to NK cell-mediated cytotoxicity. Therefore, the current findings substantiate that FERMT3 elevation may attenuate CRC cell chemoresistance and NK cell-mediated immune response to tumour cells by inhibiting Wnt/ß-catenin-PD-L1 signalling. Therefore, FERMT3 elevation may be a promising therapeutic approach to overcome chemoresistance and immune evasion in CRC.

Electronic Modulation of Ru Nanosheet by d-d Orbital Coupling for Enhanced Hydrogen Oxidation Reaction in Alkaline Electrolytes.

The alkaline polymer electrolyte fuel cells (APEFCs) hold great promise for using nonnoble metal-based electrocatalysts toward the cathodic oxygen reduction reaction (ORR), but are hindered by the sluggish anodic hydrogen oxidation reaction (HOR) in alkaline electrolytes. Here, a strategy is reported to promote the alkaline HOR performance of Ru by incorporating 3d-transition metals (V, Fe, Co, and Ni), where the conduction band minimum (CBM) level of Ru can be rationally tailored through strong d-d orbital coupling. As expected, the obtained RuFe nanosheet exhibits outstanding HOR performance with the mass activity of 233.46 A gPGM -1 and 23-fold higher than the Ru catalyst, even threefold higher than the commercial Pt/C. APEFC employing this RuFe as anodic catalyst gives a peak power density of 1.2 W cm-2 , outperforming the documented Pt-free anodic catalyst-based APEFCs. Experimental results and density functional theory calculations suggest the enhanced OH-binding energy and reduced formation energy of water derived from the downshifted CBM level of Ru contribute to the enhanced HOR activity.

Upregulated of ANXA3, SORL1, and Neutrophils May Be Key Factors in the Progressionof Ankylosing Spondylitis.

Introduction: The specific pathogenesis of ankylosing spondylitis (AS) remains unclear, and our study aimed to investigate the possible pathogenesis of AS. Materials and Methods: Two datasets were downloaded from the GEO database to perform differentially expressed gene analysis, GO enrichment analysis, KEGG pathway analysis, DO enrichment analysis, GSEA analysis of differentially expressed genes, and construction of diagnostic genes using SVM and WGCNA along with Hypoxia-related genes. Also, drug sensitivity analysis was performed on diagnostic genes. To identify the differentially expressed immune genes in the AS and control groups, we analyzed the composition of immune cells between them. Then, we examined differentially expressed genes in three AS interspinous ligament specimens and three Degenerative lumbar spine specimens using high-throughput sequencing while the immune cells were examined using the neutrophil count data from routine blood tests of 1770 HLA-B27-positive samples and 7939 HLA-B27-negative samples. To assess the relationship between ANXA3 and SORL1 and disease activity, we took the neutrophil counts of the first 50 patients with above-average BASDAI scores and the last 50 patients with below-average BASDAI scores for statistical analysis. We used immunohistochemistry to verify the expression of ANXA3 and SORL1 in AS and in controls. Results: ANXA3 and SORL1 were identified as new diagnostic genes for AS. These two genes showed a significant differential expression between AS and controls, along with showing a significant positive correlation with the neutrophil count. The results of high-throughput sequencing verified that these two gene deletions were indeed differentially expressed in AS versus controls. Data from a total of 9707 routine blood tests showed that the neutrophil count was significantly higher in AS patients than in controls (p < 0.001). Patients with AS with a high BASDAI score had a much higher neutrophil count than those with a low score, and the difference was statistically significant (p < 0.001). The results of immunohistochemistry showed that the expression of ANXA3 and SORL1 in AS was significantly higher than that in the control group. Conclusion: Upregulated of ANXA3, SORL1, and neutrophils may be a key factor in the progression of Ankylosing spondylitis.

A completely precious metal-free alkaline fuel cell with enhanced performance using a carbon-coated nickel anode.

SignificanceWe present a groundbreaking advance in completely nonprecious hydrogen fuel cell technologies achieving a record power density of 200 mW/cm2 with Ni@CNx anode and Co-Mn cathode. The 2-nm CNx coating weakens the O-binding energy, which effectively mitigates the undesirable surface oxidation during hydrogen oxidation reaction (HOR) polarization, leading to a stable fuel cell operation for Ni@CNx over 100 h at 200 mA/cm2, superior to a Ni nanoparticle counterpart. Ni@CNx exhibited a dramatically enhanced tolerance to CO relative to Pt/C, enabling the use of hydrogen gas with trace amounts of CO, critical for practical applications. The complete removal of precious metals in fuel cells lowers the catalyst cost to virtually negligible levels and marks a milestone for practical alkaline fuel cells.

Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning.

Background: Tuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment. Methods: A total of 1,667 patients were finally enrolled in the study. Patients were randomly assigned to the training and validation cohorts. The training cohort included 1,268 patients: 158 patients with bone and joint TB and 1,110 patients with TB. The validation cohort included 399 patients: 48 patients with bone and joint TB and 351 patients with TB. We used three ML methods, namely logistic regression, LASSO regression, and random forest, to screen the differential variables, obtained the most representative variables by intersection to construct the prediction model, and verified the performance of the proposed prediction model in the validation group. Results: The results revealed a great difference in the blood examination results of patients with bone and joint TB and those with TB. Infectious markers such as hs-CRP, ESR, WBC, and NEUT were increased in patients with bone and joint TB. Patients with bone and joint TB were found to have higher liver function burden and poorer nutritional status. The factors screened using ML were PDW, LYM, AST/ALT, BUN, and Na, and the nomogram diagnostic model was constructed using these five factors. In the training cohort, the area under the curve (AUC) value of the model was 0.71182, and the C value was 0.712. In the validation cohort, the AUC value of the model was 0.6435779, and the C value was 0.644. Conclusion: We used ML methods to screen out the blood-specific factors-PDW, LYM, AST/ALT, BUN, and Na+-of bone and joint TB and constructed a diagnostic model to help clinicians better diagnose the disease in the future.

Comparison of novel visceral obesity indexes with traditional obesity measurements in predicting of metabolically unhealthy nonobese phenotype in hemodialysis patients.

BACKGROUND: Normal-weight maintenance hemodialysis (MHD) patients with abdominal obesity exhibited a more proatherogenic profile than overweight and obesity patients with abdominal obesity, highlighting the importance of early identification of metabolically unhealthy nonobese (MUNO) in this population. Visceral fat accumulation plays a crucial role in the development of MUNO. Lipid accumulation product (LAP), visceral adiposity index (VAI) have been proved as reliable visceral obesity markers. The Chinese visceral adiposity index (CVAI) and a body shape index (ABSI) are newly discovered indexes of visceral obesity and have been reported to be associated with multiple metabolic disorders. There are limited studies investigating the associations between different visceral obesity indices and risk of MUNO, especially in hemodialysis patients. Moreover, no general agreement has been reached to date regarding which of these obesity indices performs best in identifying MUNO. We aimed to investigate the prevalence of MUNO in MHD patients and compare the associations between different adiposity indices (CVAI, ABSI,VAI, LAP, body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHtR)) with MUNO risk in this population. METHODS: We conducted a multi-center cross-sectional study in Guizhou Province, Southwest China. 1302 nonobese adult MHD patients were included in our study. MUNO was defined as being nonobese and having the presence of > = 2 components of metabolic syndrome (MetS). Nonobese was defined as BMI less than 25 kg/m2. VAI, LAP, CVAI, ABSI, BMI, WC and WHtR were calculated. Logistic regression analyses and receiver operator curve (ROC) analyses were performed. Results 65.6% participants were metabolically unhealthy. The ROC curve analysis demonstrated that of the seven obesity indices tested, the VAI (AUC 0.84 for women and 0.79 for men) followed by LAP (AUC 0.78 for women and 0.72 for men) had the highest diagnostic accuracy for MUNO phenotype while ABSI exhibited the lowest AUC value for identifying MUNO phenotype CONCLUSIONS: Metabolically unhealthy is highly prevalent in nonobese MHD patients. VAI and LAP outperformed CVAI in discriminating MUNO in MHD patients. Though ABSI could be a weak predictor of MUNO, it is not better than WHtR, WC and BMI.

Dysregulation of SAA1, TUBA8 and Monocytes Are Key Factors in Ankylosing Spondylitis With Femoral Head Necrosis.

Introduction: The mechanism of ankylosing spondylitis with femoral head necrosis is unknown, and our study aimed investigate the effects of genetic and immune cell dysregulation on ankylosing spondylitis. Materials and Methods: The protein expression of all ligaments in ankylosing spondylitis with femoral head necrosis was obtained using label-free quantification protein park analysis of six pairs of specimens. The possible pathogenesis was explored using differential protein analysis, weighted gene co-expression network analysis, recording intersections with hypoxia-related genes, immune cell correlation analysis, and drug sensitivity analysis. Finally, routine blood test data from 502 AS and 162 healthy controls were collected to examine immune cell differential analysis. Results: SAA1 and TUBA8 were significantly expressed differentially in these two groups and correlated quite strongly with macrophage M0 and resting mast cells (P < 0.05). Routine blood data showed that monocytes were significantly more expressed in AS than in healthy controls (P < 0.05). SAA1 and TUBA8 were closely related to the sensitivity of various drugs, which might lead to altered drug sensitivity. Conclusion: Dysregulation of SAA1, TUBA8 and monocytes are key factors in ankylosing spondylitis with femoral head necrosis.

Breast cancer metastasis suppressor 1 modulates SIRT1-dependent p53 deacetylation through interacting with DBC1.

Breast cancer metastasis suppressor 1 (BRMS1) is a specific tumor metastasis suppressor implicated in the regulation of chromatin modification and gene transcription. However, the molecular mechanism of BRMS1 remains to be elucidated. Here, we report that DBC1 (deleted in breast cancer 1), is a novel interacting protein of BRMS1. The imperfect leucine zipper motifs of BRMS1 and the N-terminal domain of DBC1 are required for the interaction. DBC1 is identified as an important negative regulator of SIRT1's activity and genotoxic stress response. We demonstrated that BRMS1 is able to interrupt endogenous DBC1-SIRT1 association. Consistently, SIRT1-dependent p53 acetylation under genotoxic stress is also affected by BRMS1. Overall, our results identify BRMS1 as a novel regulator of DBC1-SIRT1 complex and SIRT1-dependent p53 deacetylation.