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.

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.

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.

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.

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.

[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.

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.

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.

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).

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.

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.

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/ .

[Acute heart failure in a neonate]. / æ–°ç”Ÿå„¿æ€¥æ€§å¿ƒåŠŸèƒ½ä¸å ¨1例.

The male patient, one day old, was admitted to the hospital due to hypoglycemia accompanied by apnea appearing six hours after birth. The patient had transient hypoglycemia early after birth, and acute heart failure suddenly occurred on the eighth day after birth. Laboratory tests showed significantly reduced levels of adrenocorticotropic hormone and cortisol, and pituitary magnetic resonance imaging was normal. Genetic testing results showed that the patient had probably pathogenic compound heterozygous mutations of the TBX19 gene (c.917-2A>G+c.608C>T), inherited respectively from the parents. The patient was conclusively diagnosed with congenital isolated adrenocorticotropic hormone deficiency caused by mutation of the TBX19 gene. Upon initiating hydrocortisone replacement therapy, cardiac function rapidly returned to normal. After being discharged, the patient continued with the hydrocortisone replacement therapy. By the 18-month follow-up, the patient was growing and developing well. In neonates, unexplained acute heart failure requires caution for possible endocrine hereditary metabolic diseases, and timely cortisol testing and genetic testing should be conducted.

Lactiplantibacillus plantarum RS20D Alleviates Male Reproductive Toxicity Induced by Pubertal Exposure to Di-n-butyl Phthalate and Mono-n-butyl Phthalate.

Phthalate acid esters (PAEs) and their metabolites, such as di-n-butyl phthalate (DBP) and mono-n-butyl phthalate (MBP), are known to cause male reproductive damage. Lactiplantibacillus plantarum RS20D has demonstrated the ability to remove both DBP and MBP in vitro, suggesting its potential as a detoxifying agent against these compounds. This study aimed to investigate the protective effects of RS20D on DBP or MBP-induced male reproductive toxicity in adolescent rats. Oral administration of RS20D significantly mitigated the histological damage to the testes caused by MBP or DBP, restored sperm concentration, morphological abnormalities, and the proliferation index in MBP-exposed rats, and partially reversed spermatogenic damage in DBP-exposed rats. Furthermore, RS20D restored serum levels of estradiol (E2) and testosterone, and superoxide dismutase (SOD) activity in DBP-exposed rats, significantly increased testosterone levels in MBP-exposed rats, and restored copper (Cu) concentrations in the testes after exposure to DBP or MBP. Additionally, RS20D effectively modulated the intestinal microbiota in DBP-exposed rats and partially ameliorated dysbiosis induced by MBP, which may be associated with the alleviation of reproductive toxic effects induced by DBP or MBP. In conclusion, this study demonstrates that RS20D administration can alleviate male reproductive toxicity and gut dysbacteriosis induced by DBP or MBP exposure, providing a dietary strategy for the bioremediation of PAEs and their metabolites.

Molecular strategies of the pygmy grasshopper Eucriotettix oculatus adapting to long-term heavy metal pollution.

To study the heavy metal accumulation and its impact on insect exterior and chromosome morphology, and reveal the molecular mechanism of insects adapting to long-term heavy metal compound pollution habitats, this study, in the Diaojiang river basin, which has been polluted by heavy metals(HMs) for nearly a thousand years, two Eucriotettix oculatus populations was collected from mining and non-mining areas. It was found that the contents of 7 heavy metals (As, Cd, Pb, Zn, Cu, Sn, Sb) in E. oculatus of the mining area were higher than that in the non-mining 1-11 times. The analysis of morphology shows that the external morphology, the hind wing type and the chromosomal morphology of E. oculatus are significant differences between the two populations. Based on the heavy metal accumulation，morphological change, and stable population density, it is inferred that the mining area population has been affected by heavy metals and has adapted to the environment of heavy metals pollution. Then, by analyzing the transcriptome of the two populations, it was found that the digestion, immunity, excretion, endocrine, nerve, circulation, reproductive and other systems and lysosomes, endoplasmic reticulum and other cell structure-related gene expression were suppressed. This shows that the functions of the above-mentioned related systems of E. oculatus are inhibited by heavy metal stress. However, it has also been found that through the significant up-regulation of genes related to the above system, such as ATP2B, pepsin A, ubiquitin, AQP1, ACOX, ATPeV0A, SEC61A, CANX, ALDH7A1, DLD, aceE, Hsp40, and catalase, etc., and the down-regulation of MAPK signalling pathway genes, can enhanced nutrient absorption, improve energy metabolism, repair damaged cells and degrade abnormal proteins, maintain the stability of cells and systems, and resist heavy metal damage so that E. oculatus can adapt to the environment of heavy metal pollution for a long time.

[Ginsenoside Re regulates mitochondrial biogenesis through Nrf2/HO-1/PGC-1α pathway to reduce hypoxia/reoxygenation injury in H9c2 cells].

This article explored the mechanism by which ginsenoside Re reduces hypoxia/reoxygenation(H/R) injury in H9c2 cells by regulating mitochondrial biogenesis through nuclear factor E2-related factor 2(Nrf2)/heme oxygenase-1(HO-1)/peroxisome prolife-rator-activated receptor gamma coactivator-1α(PGC-1α) pathway. In this study, H9c2 cells were cultured in hypoxia for 4 hours and then reoxygenated for 2 hours to construct a cardiomyocyte H/R injury model. After ginsenoside Re pre-administration intervention, cell activity, superoxide dismutase(SOD) activity, malondialdehyde(MDA) content, intracellular reactive oxygen species(Cyto-ROS), and intramitochondrial reactive oxygen species(Mito-ROS) levels were detected to evaluate the protective effect of ginsenoside Re on H/R injury of H9c2 cells by resisting oxidative stress. Secondly, fluorescent probes were used to detect changes in mitochondrial membrane potential(ΔΨ_m) and mitochondrial membrane permeability open pore(mPTP), and immunofluorescence was used to detect the expression level of TOM20 to study the protective effect of ginsenoside Re on mitochondria. Western blot was further used to detect the protein expression levels of caspase-3, cleaved caspase-3, Cyto C, Nrf2, HO-1, and PGC-1α to explore the specific mechanism by which ginsenoside Re protected mitochondria against oxidative stress and reduced H/R injury. Compared with the model group, ginse-noside Re effectively reduced the H/R injury oxidative stress response of H9c2 cells, increased SOD activity, reduced MDA content, and decreased Cyto-ROS and Mito-ROS levels in cells. Ginsenoside Re showed a good protective effect on mitochondria by increasing ΔΨ_m, reducing mPTP, and increasing TOM20 expression. Further studies showed that ginsenoside Re promoted the expression of Nrf2, HO-1, and PGC-1α proteins, and reduced the activation of the apoptosis-related regulatory factor caspase-3 to cleaved caspase-3 and the expression of Cyto C protein. In summary, ginsenoside Re can significantly reduce I/R injury in H9c2 cells. The specific mechanism is related to the promotion of mitochondrial biogenesis through the Nrf2/HO-1/PGC-1α pathway, thereby increasing the number of mitochondria, improving mitochondrial function, enhancing the ability of cells to resist oxidative stress, and alleviating cell apoptosis.

Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform.

As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summary statistics for thousands of phenotypes can be both logistically challenging and computationally demanding. In this paper, we introduce BIGA (https://bigagwas.org/), a website that aims to offer unified data analysis pipelines and processed data resources for cross-trait genetic architecture analyses using GWAS summary statistics. We have developed a framework to implement statistical genetics tools on a cloud computing platform, combined with extensive curated GWAS data resources. Through BIGA, users can upload data, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights.

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.