GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Cyanogel-Induced Facile Synthesis of Palladium Hydride for Electrocatalytic Oxygen Reduction.

Palladium hydride (PdHx) is one of the well-known electrocatalytic materials, yet its synthesis is still a challenge through an energy-efficient and straightforward method. Herein, we propose a new and facile cyanogel-assisted synthesis strategy for the preparation of PdH0.649 at a mild environment with NaBH4 as the hydrogen source. Unlike traditional inorganic Pd precursors, the unique Pd-CN-Pd bridge in Pdx[Pd(CN)4]y·aH2O cyanogel offers more favourable spatial sites for insertion of H atoms. The characteristic three-dimensional backbone of cyanogel also acts as a support scaffold resulting in the interconnected network structure of PdH0.649. Due to the incorporation of H atoms and interconnected network structure, the PdH0.649 achieves a high half-wave potential of 0.932 V, a high onset potential of 1.062 V, and a low activation energy, as well as a long-term lifetime for oxygen reduction reaction. Theoretical calculation demonstrates a downshift of the d-band centre of Pd in PdH0.649 owing to the dominant Pd-H incorporation that weakens the binding energies of the *OH intermediate species. Zn-air batteries (ZAB) based on PdH0.649 exhibits high power density, competitive open circuit voltage, and good stability, exceeding that of commercial Pt black.

A flexible self-supported electrochemical sensor Co-NC/PS@CC for real-time detection of cell-released H2O2.

BACKGROUND: Hydrogen peroxide (H2O2) is an important reactive oxygen species (ROS) molecule involved in cell metabolism regulation, transcriptional regulation, and cytoskeleton remodeling. Real-time monitoring of H2O2 levels in live cells is of great significance for disease prevention and diagnosis. RESULTS: We utilized carbon cloth (CC) as the substrate material and employed a single-atom catalysis strategy to prepare a flexible self-supported sensing platform for the real-time detection of H2O2 secreted by live cells. By adjusting the coordination structure of single-atom sites through P and S doping, a cobalt single-atom nanoenzyme Co-NC/PS with excellent peroxidase-like activity was obtained. Furthermore, we explored the enzyme kinetics and possible catalytic mechanism of Co-NC/PS. Due to the excellent flexibility, high conductivity, strong adsorption performance of carbon cloth, and the introduction of non-metallic atom-doped active sites, the developed Co-NC/PS@CC exhibited ideal sensing performance. Experimental results showed that the linear response range for H2O2 was 1-17328 µM, with a detection limit (LOD) of 0.1687 µM. Additionally, the sensor demonstrated good reproducibility, repeatability, anti-interference, and stability. SIGNIFICANCE: The Co-NC/PS@CC prepared in this study has been successfully applied for detecting H2O2 secreted by MCF-7 live cells, expanding the application of single-atom nanoenzymes in live cell biosensing, with significant implications for health monitoring and clinical diagnostics.

PA28γ induces dendritic cell maturation and activates T-cell immune responses in oral lichen planus.

Oral lichen planus (OLP) is a common chronic inflammatory disease of the oral mucosa, the mechanism of its inflammatory progression has not yet been fully elucidated. PA28γ plays a significant role in a variety of immune-related diseases. However, the exact role of PA28γ in the pathogenesis of OLP remains unclear. Here, we demonstrated that PA28γ is overexpressed in epithelial cells and inflammatory cells of OLP tissues but has no significant relationship with OLP subtypes. Functionally, keratinocytes with high PA28γ expression could induce dendritic cell (DC) maturation and promote the T-cell differentiation into Th1 cells in response to the immune response. In addition, we found that a high level of PA28γ expression is associated with high numbers of infiltrating mature DCs and activated T-cells in OLP tissues. Mechanistically, keratinocytes with high PA28γ expression could promote the secretion of C-C motif chemokine (CCL)5, blocking CCL5 or/and its receptor CD44 could inhibit the induction of T-cell differentiation by keratinocytes with high PA28γ expression. In conclusion, we reveal that keratinocytes with high expression of PA28γ in OLP can induce DC maturation and promote T-cell differentiation through the CCL5-CD44 pathway, providing previously unidentified mechanistic insights into the mechanism of inflammatory progression in OLP.

Synergistic enhancement of wearable biosensor through Pt single-atom catalyst for sweat analysis.

Real-time monitoring of biological markers in sweat is a valuable tool for health assessment. In this study, we have developed an innovative wearable biosensor for precise analysis of glucose in sweat during physical activities. The sensor is based on a single-atom catalyst of platinum (Pt) uniformly dispersed on tricobalt tetroxide (Co3O4) nanorods and reduced graphene oxide (rGO), featuring a unique three-dimensional nanostructure and excellent glucose electrocatalytic performance with a wide detection range of 1-800 µM. Additionally, density functional theory calculations have revealed the synergetic role of Pt active sites in the Pt single-atom catalyst (Co3O4/rGO/Pt) in glucose adsorption and electron transfer, thereby enhancing sensor performance. To enable application in wearable devices, we designed an S-shaped microfluidic chip and a point-of-care testing (POCT) device, both of which were validated for effectiveness through actual use by volunteers. This research provides valuable insights and innovative approaches for analyzing sweat glucose using wearable devices, contributing to the advancement of personalized healthcare.

Synergistic effect of mesenchymal stem cell-derived extracellular vesicle and miR-137 alleviates autism-like behaviors by modulating the NF-κB pathway.

Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder predominant in childhood. Despite existing treatments, the benefits are still limited. This study explored the effectiveness of mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) loaded with miR-137 in enhancing autism-like behaviors and mitigating neuroinflammation. Utilizing BTBR mice as an autism model, the study demonstrated that intranasal administration of MSC-miR137-EVs ameliorates autism-like behaviors and inhibits pro-inflammatory factors via the TLR4/NF-κB pathway. In vitro evaluation of LPS-activated BV2 cells revealed that MSC-miR137-EVs target the TLR4/NF-κB pathway through miR-137 inhibits proinflammatory M1 microglia. Moreover, bioinformatics analysis identified that MSC-EVs are rich in miR-146a-5p, which targets the TRAF6/NF-κB signaling pathway. In summary, the findings suggest that the integration of MSC-EVs with miR-137 may be a promising therapeutic strategy for ASD, which is worthy of clinical adoption.

Glutenin-chitosan 3D porous scaffolds with tunable stiffness and systematized microstructure for cultured meat model.

A glutenin (G)-chitosan (CS) complex (G-CS) was cross-linked by water annealing with aim to prepare structured 3D porous cultured meat scaffolds (CMS) here. The CMS has pore diameters ranging from 18 to 67 µm and compressive moduli from 16.09 to 60.35 kPa, along with the mixing ratio of G/CS. SEM showed the porous organized structure of CMS. FTIR and CD showed the increscent content of α-helix and ß-sheet of G and strengthened hydrogen-bondings among G-CS molecules, which strengthened the stiffness of G-CS. Raman spectra exhibited an increase of G concentration resulted in higher crosslinking of disulfide-bonds in G-CS, which aggrandized the bridging effect of G-CS and maintained its three-dimensional network. Cell viability assay and immuno-fluorescence staining showed that G-CS effectively facilitated the growth and myogenic differentiation of porcine skeletal muscle satellite cells (PSCs). CLSM displayed that cells first occupied the angular space of hexagon and then ring-growth circle of PSCs were orderly formed on G-CS. The texture and color of CMS which loaded proliferated PSCs were fresh-meat like. These results showed that physical cross-linked G-CS scaffolds are the biocompatible and stable adaptable extracellular matrix with appropriate architectural cues and natural micro-environment for structured CM models.

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.

Exciton polariton condensation from bound states in the continuum at room temperature.

Exciton-polaritons (polaritons) resulting from the strong exciton-photon interaction stimulates the development of novel low-threshold coherent light sources to circumvent the ever-increasing energy demands of optical communications1-3. Polaritons from bound states in the continuum (BICs) are promising for Bose-Einstein condensation owing to their theoretically infinite quality factors, which provide prolonged lifetimes and benefit the polariton accumulations4-7. However, BIC polariton condensation remains limited to cryogenic temperatures ascribed to the small exciton binding energies of conventional material platforms. Herein, we demonstrated room-temperature BIC polariton condensation in perovskite photonic crystal lattices. BIC polariton condensation was demonstrated at the vicinity of the saddle point of polariton dispersion that generates directional vortex beam emission with long-range coherence. We also explore the peculiar switching effect among the miniaturized BIC polariton modes through effective polariton-polariton scattering. Our work paves the way for the practical implementation of BIC polariton condensates for integrated photonic and topological circuits.

Investigation of interactions between Jiuzao glutelin with resveratrol, quercetin, curcumin, and azelaic and potential improvement on physicochemical properties and antioxidant activities.

The interactions among small molecular functional components (FCTs) within a food matrix have become a focal point for enhancing their stability and bioactivities. Jiuzao glutelin (JG) is a mixed plant protein within Jiuzao (a protein-rich baijiu distillation by-product). This study aimed to explore the interactions between JG and selected FCTs, including resveratrol (RES), quercetin (QUE), curcumin (CUR), and azelaic acid (AZA), and the consequential impact on stability and antioxidant activity of the complexes. The findings conclusively demonstrated that the interactions between JG and the FCTs significantly enhanced the storage stability of the complexes. Moreover, the antioxidant activity of the complexes exhibited improvement compared to their individual counterparts. This study underscores the notion that JG and FCTs mutually reinforce, exerting positive effects on stability and antioxidant activity. This symbiotic relationship can be strategically employed to augment the quality of proteins and enhance the functional properties of bioactive components through these interactions.

New findings on the risk of hypertension from organophosphorus exposure under different glycemic statuses: The key role of lipids?

BACKGROUND AND OBJECTIVE: Considering the widespread use of organophosphorus pesticides (OPs) and the global prevalence of hypertension (HTN), as well as studies indicating that different glycemic statuses may respond differently to the biological effects of OPs. Therefore, this study, based on the Henan rural cohort, aims to investigate the association between OPs exposure and HTN, and further explores whether lipids mediate these associations. METHODS: We measured the plasma levels of OPs in 2730 participants under different glycemic statuses using gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS). A generalized linear model, Quantile g-computation (QGC), adaptive elastic net (AENET), and Bayesian kernel machine regression (BKMR) models were used to assess the impact of OPs exposure on HTN, with least absolute shrinkage and selection operator (LASSO) penalty regression identifying main OPs. Mediation models were used to evaluate the intermediary role of blood lipids in the OPs-HTN relationship. RESULTS: The detection rates for all OPs were high, ranging from 76.35 % to 99.17 %. In the normal glucose tolerance (NGT) population, single exposure models indicated that malathion and phenthoate were associated with an increased incidence of HTN (P-FDR < 0.05), with corresponding odds ratios (ORs) and 95 % confidence intervals (CIs) of 1.624 (1.167,2.260) and 1.290 (1.072,1.553), respectively. QGC demonstrated a positive association between OP mixtures and HTN, with malathion and phenthoate being the primary contributors. Additionally, the AENET model's Exposure Response Score (ERS) suggested that the risk of HTN increases with higher ERS (P < 0.001). Furthermore, BKMR revealed that co-exposure to OPs increases HTN risk, with phenthoate having a significant impact. Furthermore, triglycerides (TG) mediated 6.55 % of the association between phenthoate and HTN. However, no association was observed in the impaired fasting glucose (IFG) and type 2 diabetes mellitus (T2DM) populations. CONCLUSIONS: Our findings suggest that in the NGT population, OPs may significantly contribute to the development of HTN, proposing TG as a potential novel target for HTN prevention.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

Lifestyle behaviors and risk of cardiovascular disease and prognosis among individuals with cardiovascular disease: a systematic review and meta-analysis of 71 prospective cohort studies.

BACKGROUND: Healthy lifestyle behaviors (LBs) have been widely recommended for the prevention and management of cardiovascular disease (CVD). Despite a large number of studies exploring the association between combined LBs and CVD, a notable gap exists in integration of relevant literatures. We conducted a systematic review and meta-analysis of prospective cohort studies to analyze the correlation between combined LBs and the occurrence of CVD, as well as to estimate the risk of various health complications in individuals already diagnosed with CVD. METHODS: Articles published up to February 10, 2023 were sourced through PubMed, EMBASE and Web of Science. Eligible prospective cohort studies that reported the relations of combined LBs with pre-determined outcomes were included. Summary relative risks (RRs) and 95% confidence intervals (CIs) were estimated using either a fixed or random-effects model. Subgroup analysis, meta-regression, publication bias, and sensitivity analysis were as well performed. RESULTS: In the general population, individuals with the healthiest combination of LBs exhibited a significant risk reduction of 58% for CVD and 55% for CVD mortality. For individuals diagnosed with CVD, adherence to the healthiest combination of LBs corresponded to a significant risk reduction of 62% for CVD recurrence and 67% for all-cause mortality, when compared to those with the least-healthy combination of LBs. In the analysis of dose-response relationship, for each increment of 1 healthy LB, there was a corresponding decrease in risk of 17% for CVD and 19% for CVD mortality within the general population. Similarly, among individuals diagnosed with CVD, each additional healthy LB was associated with a risk reduction of 27% for CVD recurrence and 27% for all-cause mortality. CONCLUSIONS: Adopting healthy LBs is associated with substantial risk reduction in CVD, CVD mortality, and adverse outcomes among individuals diagnosed with CVD. Rather than focusing solely on individual healthy LB, it is advisable to advocate for the adoption of multiple LBs for the prevention and management of CVD. TRIAL REGISTRATION: PROSPERO: CRD42023431731.

Oral epithelial dysplasia detection and grading in oral leukoplakia using deep learning.

BACKGROUND: The grading of oral epithelial dysplasia is often time-consuming for oral pathologists and the results are poorly reproducible between observers. In this study, we aimed to establish an objective, accurate and useful detection and grading system for oral epithelial dysplasia in the whole-slides of oral leukoplakia. METHODS: Four convolutional neural networks were compared using the image patches from 56 whole-slide of oral leukoplakia labeled by pathologists as the gold standard. Sequentially, feature detection models were trained, validated and tested with 1,000 image patches using the optimal network. Lastly, a comprehensive system named E-MOD-plus was established by combining feature detection models and a multiclass logistic model. RESULTS: EfficientNet-B0 was selected as the optimal network to build feature detection models. In the internal dataset of whole-slide images, the prediction accuracy of E-MOD-plus was 81.3% (95% confidence interval: 71.4-90.5%) and the area under the receiver operating characteristic curve was 0.793 (95% confidence interval: 0.650 to 0.925); in the external dataset of 229 tissue microarray images, the prediction accuracy was 86.5% (95% confidence interval: 82.4-90.0%) and the area under the receiver operating characteristic curve was 0.669 (95% confidence interval: 0.496 to 0.843). CONCLUSIONS: E-MOD-plus was objective and accurate in the detection of pathological features as well as the grading of oral epithelial dysplasia, and had potential to assist pathologists in clinical practice.

Construction of resveratrol and quercetin nanoparticles based on folic acid targeted Maillard products between Jiuzao glutelin isolate and carboxymethyl chitosan: Improved stability and function.

Advanced targeted nanoparticles (NPs) were designed to enhance the targeted delivery of resveratrol (RES) and quercetin (QUE) by utilizing carboxymethyl chitosan (CTS) and Jiuzao glutelin isolate (JGI) conjugates. Briefly, RES and QUE were encapsuled within CTS-JGI-2 (CTS/JGI, m/m, 2:1). The carrier's targeting properties were further improved through the incorporation of folic acid (FA) and polyethylenimine (PEI). Moreover, the stability against digestion was enhanced by incorporating baker yeast cell walls (BYCWs) to construct RES-QUE/FA-PEI/CTS-JGI-2/MAT/BYCW NPs. The results demonstrated that FA-PEI/CTS-JGI-2/MAT/BYCW NPs could improve cellular uptake and targeting property of RES and QUE through endocytosis of folic acid receptors (FOLRs). Additionally, RES-QUE successfully alleviated LPS- and DSS-induced inflammation by regulating NF-κB/IkBa/AP-1 and AMPK/SIRT1signaling pathways and reducing the secretion of inflammatory mediators and factors. These findings indicate FA-PEI/CTS-JGI-2/MAT/BYCW NPs hold promise as an oral drug delivery system with targeted delivery capacities for functional substances prone to instability in dietary supplements.

MH-STRALP: A scoring system for prognostication in patients with upper gastrointestinal bleeding.

BACKGROUND: Upper gastrointestinal bleeding (UGIB) is a common medical emergency and early assessment of its outcomes is vital for treatment decisions. AIM: To develop a new scoring system to predict its prognosis. METHODS: In this retrospective study, 692 patients with UGIB were enrolled from two centers and divided into a training (n = 591) and a validation cohort (n = 101). The clinical data were collected to develop new prognostic prediction models. The endpoint was compound outcome defined as (1) demand for emergency surgery or vascular intervention, (2) being transferred to the intensive care unit, or (3) death during hospitalization. The models' predictive ability was compared with previously established scores by receiver operating characteristic (ROC) curves. RESULTS: Totally 22.2% (131/591) patients in the training cohort and 22.8% (23/101) in the validation cohort presented poor outcomes. Based on the stepwise-forward Logistic regression analysis, eight predictors were integrated to determine a new post-endoscopic prognostic scoring system (MH-STRALP); a nomogram was determined to present the model. Compared with the previous scores (GBS, Rockall, ABC, AIMS65, and PNED score), MH-STRALP showed the best prognostic prediction ability with area under the ROC curves (AUROCs) of 0.899 and 0.826 in the training and validation cohorts, respectively. According to the calibration curve, decision curve analysis, and internal cross-validation, the nomogram showed good calibration ability and net clinical benefit in both cohorts. After removing the endoscopic indicators, the pre-endoscopic model (pre-MH-STRALP score) was conducted. Similarly, the pre-MH-STRALP score showed better predictive value (AUROCs of 0.868 and 0.767 in the training and validation cohorts, respectively) than the other pre-endoscopic scores. CONCLUSION: The MH-STRALP score and pre-MH-STRALP score are simple, convenient, and accurate tools for prognosis prediction of UGIB, and may be applied for early decision on its management strategies.

DRP1 inhibition-mediated mitochondrial elongation abolishes cancer stemness, enhances glutaminolysis, and drives ferroptosis in oral squamous cell carcinoma.

BACKGROUND: Mitochondrial dynamics play a fundamental role in determining stem cell fate. However, the underlying mechanisms of mitochondrial dynamics in the stemness acquisition of cancer cells are incompletely understood. METHODS: Metabolomic profiling of cells were analyzed by MS/MS. The genomic distribution of H3K27me3 was measured by CUT&Tag. Oral squamous cell carcinoma (OSCC) cells depended on glucose or glutamine fueling TCA cycle were monitored by 13C-isotope tracing. Organoids and tumors from patients and mice were treated with DRP1 inhibitors mdivi-1, ferroptosis inducer erastin, or combination with mdivi-1 and erastin to evaluate treatment effects. RESULTS: Mitochondria of OSCC stem cells own fragment mitochondrial network and DRP1 is required for maintenance of their globular morphology. Imbalanced mitochondrial dynamics induced by DRP1 knockdown suppressed stemness of OSCC cells. Elongated mitochondria increased α-ketoglutarate levels and enhanced glutaminolysis to fuel the TCA cycle by increasing glutamine transporter ASCT2 expression. α-KG promoted the demethylation of histone H3K27me3, resulting in downregulation of SNAI2 associated with stemness and EMT. Significantly, suppressing DRP1 enhanced the anticancer effects of ferroptosis. CONCLUSION: Our study reveals a novel mechanism underlying mitochondrial dynamics mediated cancer stemness acquisition and highlights the therapeutic potential of mitochondria elongation to increase the susceptibility of cancer cells to ferroptosis.

Immune Characteristic Genes and Neutrophil Immune Transformation Studies in Severe COVID-19.

As a disease causing a global pandemic, the progression of symptoms to severe disease in patients with COVID-19 often has adverse outcomes, but research on the immunopathology of COVID-19 severe disease remains limited. In this study, we used mRNA-seq data from the peripheral blood of COVID-19 patients to identify six COVID-19 severe immune characteristic genes (FPR1, FCGR2A, TLR4, S100A12, CXCL1, and L TF), and found neutrophils to be the critical immune cells in COVID-19 severe disease. Subsequently, using scRNA-seq data from bronchoalveolar lavage fluid from COVID-19 patients, neutrophil subtypes highly expressing the S100A family were found to be located at the end of cellular differentiation and tended to release neutrophil extracellular traps. Finally, it was also found that alveolar macrophages, macrophages, and monocytes with a high expression of COVID-19 severe disease immune characteristic genes may influence neutrophils through intercellular ligand-receptor pairs to promote neutrophil extracellular trap release. This study provides immune characteristic genes, critical immune pathways, and immune cells in COVID-19 severe disease, explores intracellular immune transitions of critical immune cells and pit-induced intercellular communication of immune transitions, and provides new biomarkers and potential drug targets for the treatment of patients with COVID-19 severe disease.

Resveratrol regulates Thoc5 to improve maternal immune activation-induced autism-like behaviors in adult mouse offspring.

Maternal infection during pregnancy is an important cause of autism spectrum disorder (ASD) in offspring, and inflammatory infiltration caused by maternal immune activation (MIA) can cause neurodevelopmental disorders in the fetus. Medicine food homologous (MFH) refers to a traditional Chinese medicine (TCM) concept, which effectively combines food functions and medicinal effects. However, no previous study has screened, predicted, and validated the potential targets of MFH herbs for treating ASD. Therefore, in this study, we used comprehensive bioinformatics methods to screen and analyze MFH herbs and drug targets on a large scale, and identified resveratrol and Thoc5 as the best small molecular ingredient and drug target, respectively, for the treatment of MIA-induced ASD. Additionally, the results of in vitro experiments revealed that resveratrol increased the expression of Thoc5 and effectively inhibited lipopolysaccharide-induced inflammatory factor production by BV2 cells. Moreover, in vivo, resveratrol increased the expression of Thoc5 and effectively inhibited placental and fetal brain inflammation in MIA pregnancy mice, and improved ASD-like behaviors in offspring.

Nickel induces epithelial-mesenchymal transition in pulmonary fibrosis in mice via activation of the oxidative stress-mediated TGF-ß1/Smad signaling pathway.

Nickel (Ni) is recognized as a carcinogenic metal, and its widespread use has led to severe environmental and health problems. Although the lung is among the main organs affected by Ni, the precise mechanisms behind this effect remain poorly understood. This study aimed to elucidate the physiological mechanisms underlying Ni-induced pulmonary fibrosis (PF), using various techniques including histopathological detection, biochemical analysis, immunohistochemistry, western blotting, and quantitative real-time PCR. Mice were treated with nickel chloride (NiCl2), which induced PF (detected by Masson staining), up-regulation of α-smooth muscle actin (α-SMA), and collagen-1 mRNA and protein expression. NiCl2 was found to induce PF by: activation of the epithelial-mesenchymal transition (EMT) and the transforming growth factor-ß1 (TGF-ß1)/Smad signaling pathway; up-regulation of protein and mRNA expression of TGF-ß1, p-Smad2, p-Smad3, vimentin, and N-cadherin; and down-regulation of protein and mRNA expression of E-cadherin. In addition, NiCl2 treatment increased malondialdehyde content while inhibiting antioxidant activity, as indicated by decreased catalase, total antioxidant capacity, and superoxide dismutase activities, and glutathione content. Co-treatment with the effective antioxidant and free radical scavenger N-acetyl cysteine (NAC) plus NiCl2 was used to study the effects of oxidative stress in NiCl2-induced PF. The addition of NAC significantly mitigated NiCl2-induced PF, and reversed activation of the TGF-ß1/Smad signaling pathway and EMT. NiCl2-induced PF was therefore shown to be due to EMT activation via the TGF-ß1/Smad signaling pathway, mediated by oxidative stress.