A compendium of mitochondrial molecular characteristics provides novel perspectives on the treatment of rheumatoid arthritis patients.

Rheumatoid arthritis (RA) is an autoimmune disease that exhibits a high degree of heterogeneity, marked by unpredictable disease flares and significant variations in the response to available treatments. The lack of optimal stratification for RA patients may be a contributing factor to the poor efficacy of current treatment options. The objective of this study is to elucidate the molecular characteristics of RA through the utilization of mitochondrial genes and subsequently construct and authenticate a diagnostic framework for RA. Mitochondrial proteins were obtained from the MitoCarta database, and the R package limma was employed to filter for differentially expressed mitochondrial genes (MDEGs). Metascape was utilized to perform enrichment analysis, followed by an unsupervised clustering algorithm using the ConsensuClusterPlus package to identify distinct subtypes based on MDEGs. The immune microenvironment, biological pathways, and drug response were further explored in these subtypes. Finally, a multi-biomarker-based diagnostic model was constructed using machine learning algorithms. Utilizing 88 MDEGs present in transcript profiles, it was possible to classify RA patients into three distinct subtypes, each characterized by unique molecular and cellular signatures. Subtype A exhibited a marked activation of inflammatory cells and pathways, while subtype C was characterized by the presence of specific innate lymphocytes. Inflammatory and immune cells in subtype B displayed a more modest level of activation (Wilcoxon test P < 0.05). Notably, subtype C demonstrated a stronger correlation with a superior response to biologics such as infliximab, anti-TNF, rituximab, and methotrexate/abatacept (P = 0.001) using the fisher test. Furthermore, the mitochondrial diagnosis SVM model demonstrated a high degree of discriminatory ability in distinguishing RA in both training (AUC = 100%) and validation sets (AUC = 80.1%). This study presents a pioneering analysis of mitochondrial modifications in RA, offering a novel framework for patient stratification and potentially enhancing therapeutic decision-making.

A random forest algorithm-based prediction model for moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia.

BACKGROUND: Postoperative pain is one of the most common complications after surgery. In order to detect early and intervene in time for moderate to severe postoperative pain, it is necessary to identify risk factors and construct clinical prediction models. This study aimed to identify significant risk factors and establish a better-performing model to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia. METHODS: Patients who underwent orthopedic surgery under general anesthesia were divided into patients with moderate to severe pain group (group P) and patients without moderate to severe pain group (group N) based on VAS scores. The features selected by Lasso regression were processed by the random forest and multivariate logistic regression models to predict pain outcomes. The classification performance of the two models was evaluated through the testing set. The area under the curves (AUC), the accuracy of the classifiers, and the classification error rate for both classifiers were calculated, the better-performing model was used to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia. RESULTS: A total of 327 patients were enrolled in this study (228 in the training set and 99 in the testing set). The incidence of moderate to severe postoperative pain was 41.3%. The random forest model revealed a classification error rate of 25.2% and an AUC of 0.810 in the testing set. The multivariate logistic regression model revealed a classification error rate of 31.3% and an AUC of 0.764 in the testing set. The random forest model was chosen for predicting clinical outcomes in this study. The risk factors with the greatest and second contribution were immobilization and duration of surgery, respectively. CONCLUSIONS: The random forest model can be used to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia, which is of potential clinical application value.

Light Control-Induced Oxygen Vacancy Generation and In Situ Surface Heterojunction Reconstruction for Boosting CO2 Reduction.

The weak adsorption of CO2 and the fast recombination of photogenerated charges harshly restrain the photocatalytic CO2 reduction efficiency. The simultaneous catalyst design with strong CO2 capture ability and fast charge separation efficiency is challenging. Herein, taking advantage of the metastable characteristic of oxygen vacancy, amorphous defect Bi2O2CO3 (named BOvC) was built on the surface of defect-rich BiOBr (named BOvB) through an in situ surface reconstruction progress, in which the CO32- in solution reacted with the generated Bi(3-x)+ around the oxygen vacancies. The in situ formed BOvC is tightly in contact with the BOvB and can prevent the further destruction of the oxygen vacancy sites essential for CO2 adsorption and visible light utilization. Additionally, the superficial BOvC associated with the internal BOvB forms a typical heterojunction promoting the interface carriers' separation. Finally, the in situ formation of BOvC boosted the BOvB and showed better activity in the photocatalytic reduction of CO2 into CO (three times compared to that of pristine BiOBr). This work provides a comprehensive solution for governing defects chemistry and heterojunction design, as well as gives an in-depth understanding of the function of vacancies in CO2 reduction.

Potential mechanisms and drug prediction of Rheumatoid Arthritis and primary Sjögren's Syndrome: A public databases-based study.

Rheumatoid arthritis (RA) and primary Sjögren's syndrome (pSS) are the most common systemic autoimmune diseases, and they are increasingly being recognized as occurring in the same patient population. These two diseases share several clinical features and laboratory parameters, but the exact mechanism of their co-pathogenesis remains unclear. The intention of this study was to investigate the common molecular mechanisms involved in RA and pSS using integrated bioinformatic analysis. RNA-seq data for RA and pSS were picked up from the Gene Expression Omnibus (GEO) database. Co-expression genes linked with RA and pSS were recognized using weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. Then, we screened two public disease-gene interaction databases (GeneCards and Comparative Toxicogenomics Database) for common targets associated with RA and pSS. The DGIdb database was used to predict therapeutic drugs for RA and pSS. The Human microRNA Disease Database (HMDD) was used to screen out the common microRNAs associated with RA and pSS. Finally, a common miRNA-gene network was created using Cytoscape. Four hub genes (CXCL10, GZMA, ITGA4, and PSMB9) were obtained from the intersection of common genes from WGCNA, differential gene analysis and public databases. Twenty-four drugs corresponding to hub gene targets were predicted in the DGIdb database. Among the 24 drugs, five drugs had already been reported for the treatment of RA and pSS. Other drugs, such as bortezomib, carfilzomib, oprozomib, cyclosporine and zidovudine, may be ideal drugs for the future treatment of RA patients with pSS. According to the miRNA-gene network, hsa-mir-21 may play a significant role in the mechanisms shared by RA and pSS. In conclusion, we identified commom targets as potential biomarkers in RA and pSS from publicly available databases and predicted potential drugs based on the targets. A new understanding of the molecular mechanisms associated with RA and pSS is provided according to the miRNA-gene network.

The PANoptosis-related hippocampal molecular subtypes and key biomarkers in Alzheimer's disease patients.

Alzheimer's Disease (AD) is a neurodegenerative disorder, and various molecules associated with PANoptosis are involved in neuroinflammation and neurodegenerative diseases. This work aims to identify key genes, and characterize PANoptosis-related molecular subtypes in AD. Moreover, we establish a scoring system for distinguishing PANoptosis molecular subtypes and constructing diagnostic models for AD differentiation. A total of 5 hippocampal datasets were obtained from the Gene Expression Omnibus (GEO) database. In total, 1324 protein-encoding genes associated with PANoptosis (1313 apoptosis genes, 11 necroptosis genes, and 31 pyroptosis genes) were extracted from the GeneCards database. The Limma package was used to identify differentially expressed genes. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify gene modules significantly associated with AD. The ConsensusClusterPlus algorithm was used to identify AD subtypes. Gene Set Variation Analysis (GSVA) was used to assess functional and pathway differences among the subtypes. The Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were used to select the three PANoptosis-related Key AD genes (PKADg). A scoring model was constructed based on the Boruta algorithm. PANoptosis diagnostic models were developed using the RF, SVM-RFE, and Logistic Regression (LR) algorithms. The ROC curves were used to assess the model performance. A total of 48 important genes were identified by intersecting 725 differentially expressed genes and 2127 highly correlated module genes from WGCNA with 1324 protein-encoding genes related to PANoptosis. Machine learning algorithms identified 3 key AD genes related to PANoptosis, including ANGPT1, STEAP3, and TNFRSF11B. These genes had strong discriminatory capacities among samples, with Receiver Operating Characteristic Curve (ROC) analysis indicating Area Under the Curve (AUC) values of 0.839, 0.8, and 0.868, respectively. Using the 48 important genes, the ConsensusClusterPlus algorithm identified 2 PANoptosis subtypes among AD patients, i.e., apoptosis subtype and mild subtype. Apoptosis subtype patients displayed evident cellular apoptosis and severe functionality damage in the hippocampal tissue. Meanwhile, mild subtype patients showed milder functionality damage. These two subtypes had significant differences in apoptosis and necroptosis; however, there was no apparent variation in pyroptosis functionality. The scoring model achieved an AUC of 100% for sample differentiation. The RF PANoptosis diagnostic model demonstrated an AUC of 100% in the training set and 85.85% in the validation set for distinguishing AD. This study identified two PANoptosis-related hippocampal molecular subtypes of AD, identified key genes, and established machine learning models for subtype differentiation and discrimination of AD. We found that in the context of AD, PANoptosis may influence disease progression through the modulation of apoptosis and necrotic apoptosis.

Development and verification of a combined diagnostic model for primary Sjögren's syndrome by integrated bioinformatics analysis and machine learning.

Primary Sjögren's syndrome (pSS) is a chronic, systemic autoimmune disease mostly affecting the exocrine glands. This debilitating condition is complex and specific treatments remain unavailable. There is a need for the development of novel diagnostic models for early screening. Four gene profiling datasets were downloaded from the Gene Expression Omnibus database. The 'limma' software package was used to identify differentially expressed genes (DEGs). A random forest-supervised classification algorithm was used to screen disease-specific genes, and three machine learning algorithms, including artificial neural networks (ANN), random forest (RF), and support vector machines (SVM), were used to build a pSS diagnostic model. The performance of the model was measured using its area under the receiver operating characteristic curve. Immune cell infiltration was investigated using the CIBERSORT algorithm. A total of 96 DEGs were identified. By utilizing a RF classifier, a set of 14 signature genes that are pivotal in transcription regulation and disease progression in pSS were identified. Through the utilization of training and testing datasets, diagnostic models for pSS were successfully designed using ANN, RF, and SVM, resulting in AUCs of 0.972, 1.00, and 0.9742, respectively. The validation set yielded AUCs of 0.766, 0.8321, and 0.8223. It was the RF model that produced the best prediction performance out of the three models tested. As a result, an early predictive model for pSS was successfully developed with high diagnostic performance, providing a valuable resource for the screening and early diagnosis of pSS.

N, S/P co-doped hemicellulose-based carbon dots with tunable fluorescence for anti-counterfeiting.

Based on the status quo of high energy consumption and low utilization of nonfibrous components in traditional pulp and paper industry, a sustainable and facile approach was proceeded to realize the high-value utilization of hemicelluloses from papermaking waste liquor. The hemicellulose waste produced by ethanol precipitation in pre-hydrolysis liquor (PHL), was directly used to fabricate carbon dots (CDs) via a hydrothermal method. The hydrothermal carbonization and heteroatoms doping contributed to the sp2 conjugated domains and surface defect states of CDs, thus creating the bright blue (N-CDs), deep cyan (N/S-CDs), and light cyan (N/P-CDs) fluorescence under UV radiation. The XPS analysis and density functional theory (DFT) calculations demonstrated that the large sp2 conjugated system and the synergistic effect of CO, N-(C)3, CS, and PO groups promoted the narrow of band gap and the red-shift of fluorescence emission. Importantly, the prepared CDs grew in situ on cotton fibers, showed excellent fluorescent performance. The obtained CDs could be also utilized to prepare anti-counterfeiting film and ink due to their excellent optical features, verifying the great potential application as security material. The feasible strategy of the high-value conversion of biomass waste opens a window of opportunity for the practical anti-counterfeiting utilizations.

(D-Ser2) oxyntomodulin recovers hippocampal synaptic structure and theta rhythm in Alzheimer's disease transgenic mice.

In our previous studies, we have shown that (D-Ser2) oxyntomodulin (Oxm), a glucagon-like peptide 1 (GLP-1) receptor (GLP1R)/glucagon receptor (GCGR) dual agonist peptide, protects hippocampal neurons against Aß1-42-induced cytotoxicity, and stabilizes the calcium homeostasis and mitochondrial membrane potential of hippocampal neurons. Additionally, we have demonstrated that (D-Ser2) Oxm improves cognitive decline and reduces the deposition of amyloid-beta in Alzheimer's disease model mice. However, the protective mechanism remains unclear. In this study, we showed that 2 weeks of intraperitoneal administration of (D-Ser2) Oxm ameliorated the working memory and fear memory impairments of 9-month-old 3×Tg Alzheimer's disease model mice. In addition, electrophysiological data recorded by a wireless multichannel neural recording system implanted in the hippocampal CA1 region showed that (D-Ser2) Oxm increased the power of the theta rhythm. In addition, (D-Ser2) Oxm treatment greatly increased the expression level of synaptic-associated proteins SYP and PSD-95 and increased the number of dendritic spines in 3×Tg Alzheimer's disease model mice. These findings suggest that (D-Ser2) Oxm improves the cognitive function of Alzheimer's disease transgenic mice by recovering hippocampal synaptic function and theta rhythm.

Study on the Influence of Shale Storage Space Types on Shale Gas Transport.

Shale gas is an important unconventional natural gas resource. Studying the microstructure of shale and the gas transport law is of great significance for the development of shale gas. This paper uses the field emission scanning electron microscope to observe shale samples of the BC shale gas reservoirs in southern China. It is found that there are three types of storage spaces on the micro-nano-scale of shale samples. The storage space can be distributed either in pure organic matter or pure inorganic matter or in both organic matter and inorganic matter. They are called organic storage space, inorganic storage space, and mixed storage space of organic matter and inorganic matter, respectively. According to these types of storage spaces, an ideal conceptual model that reflects various types of storage spaces has been researched and established on the micro-nano-scale. At the same time, the transport mechanisms of slip, diffusion, adsorption, and coupling have been considered, and shale mixed storage space has also been considered in particular. On this basis, a comprehensive equation that can simulate the transport of shale gas in various types of storage spaces is derived. The equation also introduces the proportional parameters of the organic part, fractal characteristics, and water film of the inorganic part in the mixed storage space. Researchers can adjust this parameter to simulate shale gas transportation in different types of storage spaces and then use the finite element method to solve it numerically. This paper analyzes the influence of shale reservoir space types on shale gas transport. The larger the proportion of organic components in the mixed pores, the better the gas transport. The rough fractal dimension of the pores also affects the gas transport. However, when the pore diameter is less than 300 nm, the rough fractal dimension of the pores has a negligible influence on gas transport. For the water film on the inorganic wall surface of mixed pores, the gas transport of the macropore is more sensitive to the change in water film thickness.

Modeling and Simulation of Shale Fracture Attitude.

A large number of natural fractures are distributed in shale gas reservoirs. In-depth studying of the attitude of fractures is of great significance for the efficient development of shale gas. In previous studies, the complex three-dimensional discrete fracture networks (DFNs) and transport mechanisms were often not fully considered. In this study, the fully coupled multimechanism transport model and the complex discrete fracture networks (DFNs) model are developed to incorporate these complexities. The comprehensive transport model can couple multiple mechanisms such as slippage, diffusion, adsorption, and dissolution of shale gas. Moreover, the mechanisms of two-phase flow, reservoir deformation, real gas effect, and fracture closure are also considered. The three-dimensional DFN model can flexibly characterize the fracture attitudes, which means that the construction of the discrete fracture network is easier and faster. Under these frameworks, a series of partial differential equations (PDEs) were derived to describe transport mechanisms of shale gas in the shale fracture-matrix system. These PDEs were numerically discretized and solved by the finite element method. The proposed models are verified against gas production data from the field and validated against others' solutions. This study numerically simulates the influence of different fracture attitudes on shale gas transport and analyzes the sensitivity of the model. The results and sensitivity analysis reveal that both fracture dip angle and strike direction will significantly affect the gas production, and the smaller the angle between the strike direction and the flow direction, the higher the shale gas production. The length, density, area, and shape of fractures also play important roles in shale gas transport. There is an ideal fracture density in the fracture network, and the suggested excessive fracturing is not economic. The shale fracture-matrix system modeling and simulation methods can improve the development of shale gas reservoirs and increase the gas production of wells.

Trichostatin A ameliorates Alzheimer's disease-related pathology and cognitive deficits by increasing albumin expression and Aß clearance in APP/PS1 mice.

BACKGROUND: Alzheimer's disease (AD) is an intractable neurodegenerative disorder in the elderly population, currently lacking a cure. Trichostatin A (TSA), a histone deacetylase inhibitor, showed some neuroprotective roles, but its pathology-improvement effects in AD are still uncertain, and the underlying mechanisms remain to be elucidated. The present study aims to examine the anti-AD effects of TSA, particularly investigating its underlying cellular and molecular mechanisms. METHODS: Novel object recognition and Morris water maze tests were used to evaluate the memory-ameliorating effects of TSA in APP/PS1 transgenic mice. Immunofluorescence, Western blotting, Simoa assay, and transmission electron microscopy were utilized to examine the pathology-improvement effects of TSA. Microglial activity was assessed by Western blotting and transwell migration assay. Protein-protein interactions were analyzed by co-immunoprecipitation and LC-MS/MS. RESULTS: TSA treatment not only reduced amyloid ß (Aß) plaques and soluble Aß oligomers in the brain, but also effectively improved learning and memory behaviors of APP/PS1 mice. In vitro study suggested that the improvement of Aß pathology by TSA was attributed to the enhancement of Aß clearance, mainly by the phagocytosis of microglia, and the endocytosis and transport of microvascular endothelial cells. Notably, a meaningful discovery in the study was that TSA dramatically upregulated the expression level of albumin in cell culture, by which TSA inhibited Aß aggregation and promoted the phagocytosis of Aß oligomers. CONCLUSIONS: These findings provide a new insight into the pathogenesis of AD and suggest TSA as a novel promising candidate for the AD treatment.