[Chemical composition analysis of Ganoderma lucidum based on UPLC-Orbitrap-HRMS and virtual screening of FXR activators for treatment of liver fibrosis].

The chemical composition of Ganoderma lucidum ethanol extracts was systematically analyzed and identified by ultra-high performance liquid chromatography-quadrupole electrostatic field orbitrap high-resolution mass spectrometry(UPLC-Orbitrap-HRMS). The fragmentation pattern of the representative chemical compounds was summarized, and the potential anti-liver fibrosis active compounds of G. lucidum acting on the farnesoid X receptor(FXR) target were studied to elucidate its pharmacodynamic substance basis. Preliminarily, 95 chemical constituents of G. lucidum ethanol extracts were identified, including 24 ganoderic acids, 9 ganoderenic acids, 13 lucidenic acids, 3 ganolucidic acids, 1 ganoderma lactone, 40 other triterpenoids, 4 fatty acids, and 1 other constituent. In addition, the fragmentation patterns of the representative compounds were also analyzed. The structural characteristics of ganoderic acids and ganoderenic acids were the C30 skeleton, containing free-COOH and-OH groups, which could easily lose H_2O and CO_2 to form fragment ions. The D-ring was mostly a five-membered ring, which was prone to breakage. Lucidenic acids were the lanosterolane-type of the C27 skeleton, and the side-chain structure became shorter and contained the same free-COOH and-OH compared with ganoderic acids, which had been reduced from 8 to 5 cartons and prone to lose H_2O and CO_2. Then, six reported FXR receptor agonists were selected to form a training set for establishing a pharmacophore model based on FXR ligands. The 95 identified chemical constituents of G. lucidum were matched with the pharmacophore, and the optimal pharmacophore model 02(sensitivity=0.750 00, specificity=0.555 56, ROC=0.750) was selected for the virtual screening of the G. lucidum compound library through the validation of the test set. Finally, 31 potential G. lucidum active constituents were screened and chosen to activate the FXRs. The ADMET results showed that ganoderic acid H and lucidenic acid J had less than 90% plasma protein binding rate and no hepatotoxicity, which could be used as FXR activators for developing clinical drugs for the treatment of liver fibrosis, either alone or in combination.

GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network.

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti-schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer-aided methods. To address this issue, we proposed GNN-DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti-schistosome (DDAS) small molecules. Initially, a multi-layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti-schistosome small molecules. Experimental results showed that GNN-DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real-world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN-DDAS. Overall, GNN-DDAS provided a promising solution for discovering new and active anti-schistosome small molecules.

MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.

BACKGROUND: Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. RESULTS: We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. CONCLUSIONS: During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

Update on aquaporin-4 antibody detection: the early diagnosis of neuromyelitis optica spectrum disorders.

Neuromyelitis optica spectrum disorder (NMOSD) is an autoimmune-mediated primary inflammatory myelinopathy of the central nervous system that primarily affects the optic nerve and spinal cord. The aquaporin 4 antibody (AQP4-Ab) is a specific autoantibody marker for NMOSD. Most patients with NMOSD are seropositive for AQP4-Ab, thus aiding physicians in identifying ways to treat NMOSD. AQP4-Ab has been tested in many clinical and laboratory studies, demonstrating effectiveness in diagnosing NMOSD. Recently, novel assays have been developed for the rapid and accurate detection of AQP4-Ab, providing further guidance for the diagnosis and treatment of NMOSD. This article summarizes the importance of rapid and accurate diagnosis for treating NMOSD based on a review of the latest relevant literature. We discussed current challenges and methods for improvement to offer new ideas for exploring rapid and accurate AQP4-Ab detection methods, aiming for early diagnosis of NMOSD.

Response Properties of Electrorheological Composite Hydrophilic Elastomers Based on Different Morphologies of Magnesium-Doped Strontium Titanate.

As smart materials, electrorheological elastomers (EREs) formed by pre-treating active electrorheological particles are attracting more and more attention. In this work, four Mg-doped strontium titanate (Mg-STO) particles with spherical, dendritic, flake-like, and pinecone-like morphologies were obtained via hydrothermal and low-temperature co-precipitation. XRD, SEM, Raman, and FT-IR were used to characterize these products. The results showed that Mg-STOs are about 1.5-2.0 µm in size, and their phase structures are dominated by cubic crystals. These Mg-STOs were dispersed in a hydrogel composite elastic medium. Then, Mg-STO/glycerol/gelatin electrorheological composite hydrophilic elastomers were obtained with or without an electric field. The electric field response properties of Mg-doped strontium titanate composite elastomers were investigated. We concluded that dendritic Mg-STO composite elastomers are high-performance EREs, and the maximum value of their energy storage was 8.70 MPa. The significant electrorheological performance of these products is helpful for their applications in vibration control, force transducers, smart structures, dampers, and other fields.

Tennis ball cord combined with endoscopy for giant gastric phytobezoar: A case report.

BACKGROUND: Due to the specificity of Chinese food types, gastric phytobezoars are relatively common in China. Most gastric phytobezoars can be removed by chemical enzyme lysis and endoscopic fragmentation, but the treatment for large phytobezoars is limited, and surgical procedures are often required for this difficult problem. CASE SUMMARY: For giant gastric phytobezoars that cannot be dissolved and fragmented by conventional treatment, we have invented a new lithotripsy technique (tennis ball cord combined with endoscopy) for these phytobezoars. This non-interventional treatment was successful in a patient whose abdominal pain was immediately relieved, and the gastroscope-induced ulcer healed well 3 d after lithotripsy. The patient was followed-up for 8 wk postoperatively and showed no discomfort such as abdominal pain. CONCLUSION: The combination of tennis ball cord and endoscopy for the treatment of giant gastric phytobezoars is feasible and showed high safety and effectiveness, and can be widely applied in hospitals of all sizes.

Insights into phylogenetic positions and distribution patterns: Complete mitogenomes of two sympatric Asian horned toads in Boulenophrys (Anura: Megophryidae).

Boulenophrys sangzhiensis and Boulenophrys tuberogranulata, two narrow-distributed toad species within the Megophryidae family in southern China, are experiencing population declines due to habitat loss and degradation. Despite their critical conservation status, the two species remain largely overlooked in public and scientific spheres. This study presented the first sequencing, assembly, and annotation of the complete mitogenomes of both species using next-generation sequencing. The mitogenome of B. sangzhiensis was 16,950 bp, while that of B. tuberogranulata was 16,841 bp, each comprising 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), two ribosomal RNA genes (rRNAs), and a noncoding control region (D-loop). The gene content, nucleotide composition, and evolutionary rates of each mitogenome were analyzed. Both mitogenomes exhibited negative AT skew and GC skew with high A + T content. ATP8 exhibited the highest evolutionary rate, while COI had the lowest. A phylogenetic analysis based on 28 mitogenomes revealed two major clades of Megophryidae, supporting the classification of two subfamilies, Megophryinae and Leptobrachiinae. Within the subfamily Megophryinae, the genus Boulenophrys was divided into two species groups. Intriguingly, despite coexisting in Zhangjiajie City, B. sangzhiensis and B. tuberogranulata exhibited distinct origins from the two different species groups, underscoring the unique role of the coexisting area Zhangjiajie in driving their speciation and preserving their current populations. A parallel pattern was also identified in the Leptobrachiinae genus Leptobrachium within the same region. This study provided valuable data references and enhanced our understanding of the molecular characteristics of these threatened amphibian species.

Actin-bundling protein fimbrin serves as a new auxin biosynthesis orchestrator in Arabidopsis root tips.

Plants delicately regulate endogenous auxin levels through the coordination of transport, biosynthesis, and inactivation, which is crucial for growth and development. While it is well-established that the actin cytoskeleton can regulate auxin levels by affecting polar transport, its potential role in auxin biosynthesis has remained largely unexplored. Using LC-MS/MS-based methods combined with fluorescent auxin marker detection, we observed a significant increase in root auxin levels upon deletion of the actin bundling proteins AtFIM4 and AtFIM5. Fluorescent observation, immunoblotting analysis, and biochemical approaches revealed that AtFIM4 and AtFIM5 affect the protein abundance of the key auxin synthesis enzyme YUC8 in roots. AtFIM4 and AtFIM5 regulate the auxin synthesis enzyme YUC8 at the protein level, with its degradation mediated by the 26S proteasome. This regulation modulates auxin synthesis and endogenous auxin levels in roots, consequently impacting root development. Based on these findings, we propose a molecular pathway centered on the 'actin cytoskeleton-26S proteasome-YUC8-auxin' axis that controls auxin levels. Our findings shed light on a new pathway through which plants regulate auxin synthesis. Moreover, this study illuminates a newfound role of the actin cytoskeleton in regulating plant growth and development, particularly through its involvement in maintaining protein homeostasis via the 26S proteasome.

Gas-Solid Reaction Synthesis and Electrocatalytic Oxygen Reduction of Pt-Skin L10-PtFe/C.

Compared to Pt/C, the atomic ordered Pt-based intermetallic compounds can deliver higher efficiency and reliable stability, and they are considered one of the ideal cathode catalysts for the next generation of fuel cells. This work proposed a simple ferrocene atmosphere annealing method to improve commercial Pt/C and convert Pt to L10-PtFe. After further acid etching treatment, the obtained carbon-supported Pt-skin L10-PtFe (Pt-skin L10-PtFe/C) with superfine particle size (∼3.3 nm) not only was highly dispersed on the carbon but possesses a thin Pt skin, like the armor of L10-PtFe. As excepted, the ORR activity of Pt-skin L10-PtFe/C (0.375 A mg-1; 0.921 mA cm-2) is far better than that of commercial Pt/C (0.121 A mg-1; 0.260 mA cm-2), and its stability is also greatly improved. Our proposed gas-solid reaction is straightforward and has great potential in producing Pt-based intermetallic catalysts on a large scale.

Eye-brain connections revealed by multimodal retinal and brain imaging genetics.

The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.

[Development of a Prognostic Model for Overall Survival Adult Patients with Core Binding Factor Acute Myeloid Leukaemia].

OBJECTIVE: To analyze the factors affecting overall survival (OS) of adult patients with core-binding factor acute myeloid leukemia (CBF-AML) and establish a prediction model. METHODS: A total of 216 newly diagnosed patients with CBF-AML in the First Affiliated Hospital of Zhengzhou University from May 2015 to July 2021 were retrospectively analyzed. The 216 CBF-AML patients were divided into the training and the validation cohort at 7∶3 ratio. The Cox regression model was used to analyze the clinical factors affecting OS. Stepwise regression was used to establish the optimal model and the nomogram. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the model performance. RESULTS: Age(≥55 years old), peripheral blood blast(≥80%), fusion gene (AML1-ETO), KIT mutations were identified as independent adverse factors for OS. The area under the ROC curve at 3-year was 0.772 and 0.722 in the training cohort and validation cohort, respectively. The predicted value of the calibration curve is in good agreement with the measured value. DCA shows that this model performs better than a single factor. CONCLUSION: This prediction model is simple and feasible, and can effectively predict the OS of CBF-AML, and provide a basis for treatment decision.

Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study.

This study aimed to develop a deep learning model to predict the risk stratification of all-cause death for older people with disability, providing guidance for long-term care plans. Based on the government-led long-term care insurance program in a pilot city of China from 2017 and followed up to 2021, the study included 42,353 disabled adults aged over 65, with 25,071 assigned to the training set and 17,282 to the validation set. The administrative data (including baseline characteristics, underlying medical conditions, and all-cause mortality) were collected to develop a deep learning model by least absolute shrinkage and selection operator. After a median follow-up time of 14 months, 17,565 (41.5%) deaths were recorded. Thirty predictors were identified and included in the final models for disability-related deaths. Physical disability (mobility, incontinence, feeding), adverse events (pressure ulcers and falls from bed), and cancer were related to poor prognosis. A total of 10,127, 25,140 and 7086 individuals were classified into low-, medium-, and high-risk groups, with actual risk probabilities of death of 9.5%, 45.8%, and 85.5%, respectively. This deep learning model could facilitate the prevention of risk factors and provide guidance for long-term care model planning based on risk stratification.

Transcription factor StWRKY1 is involved in monoterpene biosynthesis induced by light intensity in Schizonepeta tenuifolia Briq.

Menthone-type monoterpenes are the main active ingredients of Schizonepeta tenuifolia Briq. Previous studies have indicated that light intensity influences the synthesis of menthone-type monoterpenes in S. tenuifolia, but the mechanism remains unclear. WRKY transcription factors play a crucial role in plant metabolism, yet their regulatory mechanisms in S. tenuifolia are not well understood. In this study, transcriptome data of S. tenuifolia leaves under different light intensities were analyzed, identifying 57 candidate transcription factors that influence monoterpene synthesis. Among these, 7 members of the StWRKY gene family were identified and mapped onto chromosomes using bioinformatics methods. The physicochemical properties of the proteins encoded by these StWRKY genes, their gene structures, and cis-acting elements were also studied. Comparative genomics and phylogenetic analyses revealed that Sch000013479 is closely related to AaWRKY1, AtWRKY41, and AtWRKY53, and it was designated as StWRKY1. Upon silencing and overexpressing the StWRKY1 transcription factor in S. tenuifolia leaves, changes in the expression of key genes in the menthone-type monoterpene synthesis pathway were observed. Specifically, when StWRKY1 was effectively silenced, the content of (-)-pulegone significantly decreased. These results enhance our understanding of the impact of StWRKYs on monoterpene synthesis in S. tenuifolia and lay the groundwork for further exploration of the regulatory mechanisms involved in the biosynthesis of menthone-type monoterpenes.

Multi-organ imaging-derived polygenic indexes for brain and body health.

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

Impact of telomere length and mitochondrial DNA copy number variants on survival of newborn cloned calves.

An established technology to create cloned animals is through the use of somatic cell nuclear transfer (SCNT), in which reprogramming the somatic cell nucleus to a totipotent state by enucleated oocyte cytoplasm is a necessary process, including telomere length reprogramming. The limitation of this technology; however, is that the live birth rate of offspring produced through SCNT is significantly lower than that of IVF. Whether and how telomere length play a role in the development of cloned animals is not well understood. Only a few studies have evaluated this association in cloned mice, and fewer still in cloned cows. In this study, we investigated the difference in telomere length as well as the abundance of some selected molecules between newborn deceased cloned calves and normal cows of different ages either produced by SCNT or via natural conception, in order to evaluate the association between telomere length and abnormal development of cloned cows. The absolute telomere length and relative mitochondrial DNA (mtDNA) copy number were determined by real-time quantitative PCR (qPCR), telomere related gene abundance by reverse-transcription quantitative PCR (RT-qPCR), and senescence-associated ß-galactosidase (SA-ß-gal) expression by SA-ß-gal staining. The results demonstrate that the newborn deceased SCNT calves had significantly shortened telomere lengths compared to newborn naturally conceived calves and newborn normal SCNT calves. Significantly lower mtDNA copy number, and significantly lower relative abundance of LMNB1 and TERT, higher relative abundance of CDKN1A, and aberrant SA-ß-gal expression were observed in the newborn deceased SCNT calves, consistent with the change in telomere length. These results demonstrate that abnormal telomere shortening, lower mtDNA copy number and abnormal abundance of related genes were specific to newborn deceased SCNT calves, suggesting that abnormally short telomere length may be associated with abnormal development in the cloned calves.

[A multicenter study of neonatal stroke in Shenzhen, China]. / 深圳市新生儿脑卒中多中心现况调查.

OBJECTIVES: To investigate the incidence rate, clinical characteristics, and prognosis of neonatal stroke in Shenzhen, China. METHODS: Led by Shenzhen Children's Hospital, the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022. The incidence, clinical characteristics, treatment, and prognosis of neonatal stroke in Shenzhen were analyzed. RESULTS: The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137, 1/6 060, and 1/7 704, respectively. Ischemic stroke accounted for 75% (27/36); boys accounted for 64% (23/36). Among the 36 neonates, 31 (86%) had disease onset within 3 days after birth, and 19 (53%) had convulsion as the initial presentation. Cerebral MRI showed that 22 neonates (61%) had left cerebral infarction and 13 (36%) had basal ganglia infarction. Magnetic resonance angiography was performed for 12 neonates, among whom 9 (75%) had involvement of the middle cerebral artery. Electroencephalography was performed for 29 neonates, with sharp waves in 21 neonates (72%) and seizures in 10 neonates (34%). Symptomatic/supportive treatment varied across different hospitals. Neonatal Behavioral Neurological Assessment was performed for 12 neonates (33%, 12/36), with a mean score of (32±4) points. The prognosis of 27 neonates was followed up to around 12 months of age, with 44% (12/27) of the neonates having a good prognosis. CONCLUSIONS: Ischemic stroke is the main type of neonatal stroke, often with convulsions as the initial presentation, involvement of the middle cerebral artery, sharp waves on electroencephalography, and a relatively low neurodevelopment score. Symptomatic/supportive treatment is the main treatment method, and some neonates tend to have a poor prognosis.

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.

LncRNA 4930581F22Rik promotes myogenic differentiation by regulating the ERK/MAPK signaling pathway.

The skeletal muscle is the largest organ in mammals and is the primary motor function organ of the body. Our previous research has shown that long non-coding RNAs (lncRNAs) are significant in the epigenetic control of skeletal muscle development. Here, we observed progressive upregulation of lncRNA 4930581F22Rik expression during skeletal muscle differentiation. Knockdown of lncRNA 4930581F22Rik hindered skeletal muscle differentiation and resulted in the inhibition of the myogenic markers MyHC and MEF2C. Furthermore, we found that lncRNA 4930581F22Rik regulates myogenesis via the ERK/MAPK signaling pathway, and this effect could be attenuated by the ERK-specific inhibitor PD0325901. Additionally, in vivo mice injury model results revealed that lncRNA 4930581F22Rik is involved in skeletal muscle regeneration. These results establish a theoretical basis for understanding the contribution of lncRNAs in skeletal muscle development and regeneration.

Short-Term Effects of Ambient Air Pollution on Chronic Obstructive Pulmonary Disease Admissions in Jiuquan, China.

Recent findings indicate that air pollution contributes to the onset and advancement of chronic obstructive pulmonary disease (COPD). Nevertheless, there is insufficient research indicating that air pollution is linked to COPD in the region of inland northwest China. Daily hospital admission records for COPD, air pollutant levels, and meteorological factor information were collected in Jiuquan for this study between 1 January 2018 and 31 December 2019. We employed a distributed lag non-linear model (DLNM) integrated with the generalized additive model (GAM) to assess the association between air pollution and hospital admissions for COPD with single lag days from lag0 to lag7 and multiday moving average lag days from lag01 to lag07. For example, the pollutant concentration on the current day was lag0, and on the prior 7th day was lag7. The present and previous 7-day moving average pollutant concentration was lag07. Gender, age, and season-specific stratified analyses were also carried out. It is noteworthy that the delayed days exhibited a different pattern, and the magnitude of associations varied. For NO2 and CO, obvious associations with hospitalizations for COPD were found at lag1, lag01-lag07, and lag03-lag07, with the biggest associations at lag05 and lag06 [RR = 1.015 (95%CI: 1.008, 1.023) for NO2, RR = 2.049 (95%CI: 1.416, 2.966) for CO], while only SO2 at lag02 was appreciably linked to hospitalizations for COPD [1.167 (95%CI: 1.009, 1.348)]. In contrast, short-term encounters with PM2.5, PM10, and O3 were found to have no significant effects on COPD morbidity. The lag effects of NO2 and CO were stronger than those of PM2.5 and PM10. Males and those aged 65 years or older were more vulnerable to air pollution. When it came to the seasons, the impacts appeared to be more pronounced in the cold season. In conclusion, short-term encounters with NO2 and CO were significantly correlated with COPD hospitalization in males and the elderly (≥65).

Integrating network pharmacology and experimental validation reveals therapeutic effects of D-mannose on NAFLD through mTOR suppression.

Non-alcoholic fatty liver disease (NAFLD), a chronic liver condition and metabolic disorder, has emerged as a significant health issue worldwide. D-mannose, a natural monosaccharide widely existing in plants and animals, has demonstrated metabolic regulatory properties. However, the effect and mechanism by which D-mannose may counteract NAFLD have not been studied. In this study, network pharmacology followed by molecular docking analysis was utilized to identify potential targets of mannose against NAFLD, and the leptin receptor-deficient, genetically obese db/db mice was employed as an animal model of NAFLD to validate the regulation of D-mannose on core targets. As a result, 67 targets of mannose are predicted associated with NAFLD, which are surprisingly centered on the mechanistic target of rapamycin (mTOR). Further analyses suggest that mTOR signaling is functionally enriched in potential targets of mannose treating NAFLD, and that mannose putatively binds to mTOR as a core mechanism. Expectedly, repeated oral gavage of supraphysiological D-mannose ameliorates liver steatosis of db/db mice, which is based on suppression of hepatic mTOR signaling. Moreover, daily D-mannose administration reduced hepatic expression of lipogenic regulatory genes in counteracting NAFLD. Together, these findings reveal D-mannose as an effective and potential NAFLD therapeutic through mTOR suppression, which holds translational promise.