Von Hippel Lindau tumor suppressor controls m6A-dependent gene expression in renal tumorigenesis.

N6-Methyladenosine (m6A) is the most abundant posttranscriptional modification, and its contribution to cancer evolution has recently been appreciated. Renal cancer is the most common adult genitourinary cancer, approximately 85% of which is accounted for by the clear cell renal cell carcinoma (ccRCC) subtype characterized by VHL loss. However, it is unclear whether VHL loss in ccRCC affects m6A patterns. In this study, we demonstrate that VHL binds and promotes METTL3/METTL14 complex formation while VHL depletion suppresses m6A modification, which is distinctive from its canonical E3 ligase role. m6A RNA immunoprecipitation sequencing (RIP-Seq) coupled with RNA-Seq allows us to identify a selection of genes whose expression may be regulated by VHL-m6A signaling. Specifically, PIK3R3 is identified to be a critical gene whose mRNA stability is regulated by VHL in a m6A-dependent but HIF-independent manner. Functionally, PIK3R3 depletion promotes renal cancer cell growth and orthotopic tumor growth while its overexpression leads to decreased tumorigenesis. Mechanistically, the VHL-m6A-regulated PIK3R3 suppresses tumor growth by restraining PI3K/AKT activity. Taken together, we propose a mechanism by which VHL regulates m6A through modulation of METTL3/METTL14 complex formation, thereby promoting PIK3R3 mRNA stability and protein levels that are critical for regulating ccRCC tumorigenesis.

Novel non-invasive method for urine mapping: Deep-learning-enabled SERS spectroscopy for the rapid differential detection of kidney allograft injury.

The kidney allograft has been under continuous attack from diverse injuries since the very beginning of organ procurement, leading to a gradual decline in function, chronic fibrosis, and allograft loss. It is vital to routinely and precisely monitor the risk of injuries after renal transplantation, which is difficult to achieve because the traditional laboratory tests lack sensitivity and specificity, and graft biopsies are invasive with the risk of many complications and time-consuming. Herein, a novel method for the diagnosis of graft injury is demonstrated, using deep learning-assisted surface-enhanced Raman spectroscopy (SERS) of the urine analysis. Specifically, we developed a hybrid SERS substrate composed of gold and silver with high sensitivity to the urine composition under test, eliminating the need for labels, which makes measurements easy to perform and meanwhile results in extremely abundant and complex Raman vibrational bands. Deep learning algorithms were then developed to improve the interpretation of the SERS spectral fingerprints. The deep learning model was trained with SERS signals of urine samples of recipients with different injury types including delayed graft function (DGF), calcineurin-inhibitor toxicity (CNIT), T cell-mediated rejection (TCMR), antibody-mediated rejection (AMR), and BK virus nephropathy (BKVN), which explored the features of these types and achieved the injury differentiation with an overall accuracy of 93.03%. The results highlight the potential of combining label-free SERS spectroscopy with deep learning as a method for liquid biopsy of kidney allograft injuries, which can provide great potential to diagnose and evaluate allograft injuries, and thus extend the life of kidney allografts.

Therapeutic potential of targeting Nrf2 by panobinostat in pituitary neuroendocrine tumors.

We aimed to identify the druggable cell-intrinsic vulnerabilities and target-based drug therapies for PitNETs using the high-throughput drug screening (HTS) and genomic sequencing methods. We examined 9 patient-derived PitNET primary cells in HTS. Based on the screening results, the potential target genes were analyzed with genomic sequencing from a total of 180 PitNETs. We identified and verified one of the most potentially effective drugs, which targeted the Histone deacetylases (HDACs) both in in vitro and in vivo PitNET models. Further RNA sequencing revealed underlying molecular mechanisms following treatment with the representative HDACs inhibitor, Panobinostat. The HTS generated a total of 20,736 single-agent dose responses which were enriched among multiple inhibitors for various oncogenic targets, including HDACs, PI3K, mTOR, and proteasome. Among these drugs, HDAC inhibitors (HDACIs) were, on average, the most potent drug class. Further studies using in vitro, in vivo, and isolated PitNET primary cell models validated HDACIs, especially Panobinostat, as a promising therapeutic agent. Transcriptional surveys revealed substantial alterations to the Nrf2 signaling following Panobinostat treatment. Moreover, Nrf2 is highly expressed in PitNETs. The combination of Panobinostat and Nrf2 inhibitor ML385 had a synergistic effect on PitNET suppression. The current study revealed a class of effective anti-PitNET drugs, HDACIs, based on the HTS and genomic sequencing. One of the representative compounds, Panobinostat, may be a potential drug for PitNET treatment via Nrf2-mediated redox modulation. Combination of Panobinostat and ML385 further enhance the effectiveness for PitNET treatment.

Association between napping and type 2 diabetes mellitus.

As the incidence of type 2 diabetes mellitus (T2DM) is increasing rapidly and its consequences are severe, effective intervention and prevention, including sleep-related interventions, are urgently needed. As a component of sleep architecture, naps, alone or in combination with nocturnal sleep, may influence the onset and progression of T2DM. Overall, napping is associated with an increased risk of T2DM in women, especially in postmenopausal White women. Our study showed that napping >30 minutes (min) increased the risk of T2DM by 8-21%. In addition, non-optimal nighttime sleep increases T2DM risk, and this effect combines with the effect of napping. For nondiabetic patients, napping >30 min could increase the risks of high HbA1c levels and impaired fasting glucose (IFG), which would increase the risk of developing T2DM later on. For diabetic patients, prolonged napping may further impair glycemic control and increase the risk of developing diabetic complications (e.g., diabetic nephropathy) in the distant future. The following three mechanisms are suggested as interpretations for the association between napping and T2DM. First, napping >30 min increases the levels of important inflammatory factors, including interleukin 6 and C-reactive protein, elevating the risks of inflammation, associated adiposity and T2DM. Second, the interaction between postmenopausal hormonal changes and napping further increases insulin resistance. Third, prolonged napping may also affect melatonin secretion by interfering with nighttime sleep, leading to circadian rhythm disruption and further increasing the risk of T2DM. This review summarizes the existing evidence on the effect of napping on T2DM and provides detailed information for future T2DM intervention and prevention strategies that address napping.

Complete Mitochondrial Genomes of Nedyopus patrioticus: New Insights into the Color Polymorphism of Millipedes.

There has been debate about whether individuals with different color phenotypes should have different taxonomic status. In order to determine whether the different color phenotypes of Nedyopus patrioticus require separate taxonomic status or are simply synonyms, here, the complete mitochondrial genomes (mitogenomes) of two different colored N. patrioticus, i.e., red N. patrioticus and white N. patrioticus, are presented. The two mitogenomes were 15,781 bp and 15,798 bp in length, respectively. Each mitogenome contained 13 PCGs, 19 tRNAs, 2 rRNAs, and 1 CR, with a lack of trnI, trnL2, and trnV compared to other Polydesmida species. All genes were located on a single strand in two mitogenomes. Mitochondrial DNA analyses revealed that red N. patrioticus and white N. patrioticus did not show clear evolutionary differences. Furthermore, no significant divergence was discovered by means of base composition analysis. As a result, we suggest that white N. patrioticus might be regarded as a synonym for red N. patrioticus. The current findings confirmed the existence of color polymorphism in N. patrioticus, which provides exciting possibilities for future research. It is necessary to apply a combination of molecular and morphological methods in the taxonomy of millipedes.

Pan-cancer evaluation of regulated cell death to predict overall survival and immune checkpoint inhibitor response.

Regulated cell death (RCD) plays a pivotal role in various biological processes, including development, tissue homeostasis, and immune response. However, a comprehensive assessment of RCD status and its associated features at the pan-cancer level remains unexplored. Furthermore, despite significant advancements in immune checkpoint inhibitors (ICI), only a fraction of cancer patients currently benefit from treatments. Given the emerging evidence linking RCD and ICI efficacy, we hypothesize that the RCD status could serve as a promising biomarker for predicting the ICI response and overall survival (OS) in patients with malignant tumors. We defined the RCD levels as the RCD score, allowing us to delineate the RCD landscape across 30 cancer types, 29 normal tissues in bulk, and 2,573,921 cells from 82 scRNA-Seq datasets. By leveraging large-scale datasets, we aimed to establish the positive association of RCD with immunity and identify the RCD signature. Utilizing 7 machine-learning algorithms and 18 ICI cohorts, we developed an RCD signature (RCD.Sig) for predicting ICI response. Additionally, we employed 101 combinations of 10 machine-learning algorithms to construct a novel RCD survival-related signature (RCD.Sur.Sig) for predicting OS. Furthermore, we obtained CRISPR data to identify potential therapeutic targets. Our study presents an integrative framework for assessing RCD status and reveals a strong connection between RCD status and ICI effectiveness. Moreover, we establish two clinically applicable signatures and identify promising potential therapeutic targets for patients with tumors.

Trajectories tracking of maternal and neonatal health in eastern China from 2010 to 2021: A multicentre cross-sectional study.

Background: China's fertility policy has dramatically changed in the past decade with the successive promulgation of the partial two-child policy, universal two-child policy and three-child policy. The trajectories of maternal and neonatal health accompanied the changes in fertility policy are unknown. Methods: We obtained data of 280 203 deliveries with six common pregnancy complications and thirteen perinatal outcomes between 2010 and 2021 in eastern China. The average annual percent change (AAPC) was calculated to evaluated the temporal trajectories of obstetric characteristics and adverse outcomes during this period. Then, the autoregressive integrated moving average (ARIMA) models were constructed to project future trend of obstetric characteristics and outcomes until 2027. Results: The proportion of advanced maternal age (AMA), assisted reproduction technology (ART) treatment, gestational diabetes mellitus (GDM), anaemia, thrombocytopenia, thyroid dysfunction, oligohydramnios, placental abruption, small for gestational age (SGA) infants, and congenital malformation significantly increased from 2010 to 2021. However, the placenta previa, large for gestational age (LGA) infants and stillbirth significantly decreased during the same period. The AMA and ART treatment were identified as independent risk factors for the uptrends of pregnancy complications and adverse perinatal outcomes. The overall caesarean section rate remained above 40%. Importantly, among multiparas, a previous caesarean section was found to be associated with a significantly reduced risk of hypertensive disorders of pregnancy (HDP), premature rupture of membranes (PROM), placenta previa, placental abruption, perinatal asphyxia, LGA infants, stillbirths, and preterm births. In addition, the ARIMA time series models predicted increasing trends in the ART treatment, GDM, anaemia, thrombocytopenia, postpartum haemorrhage, congenital malformation, and caesarean section until 2027. Conversely, a decreasing trend was predicted for HDP, PROM, and placental abruption premature, LGA infants, SGA infants, perinatal asphyxia, and stillbirth. Conclusions: Maternal and neonatal adverse outcomes became more prevalent from 2010 to 2021 in China. Maternal age and ART treatment were independent risk factors for adverse obstetric outcomes. The findings offered comprehensive trajectories for monitoring pregnancy complications and perinatal outcomes in China, and provided robust intervention targets in obstetric safety. The development of early prediction models and the implementation of prevention efforts for adverse obstetric events are necessary to enhance obstetric safety.

Au- and Pd-Doped SnS2 Monolayers for Lung Cancer Biomarkers (C3H6O, C6H6, and C5H8) Detection: A Density Functional Theory Investigation.

An efficient and noninvasive method of sensing lung cancer at an early stage is through detecting its biomarkers in the patient's exhaled breath. Acetone (C3H6O), benzene (C6H6), and isoprene (C5H8) emerged as crucial biomarkers, which were significantly elevated in lung cancer patients. Here, we investigated the adsorption behaviors of the three gas molecules on pristine and transition metal (TM)-doped (Au and Pd) SnS2 monolayers using the density functional theory (DFT) method. Our findings indicate that both Au- and Pd-doped SnS2 display higher adsorption energies (-0.53 to -1.313 eV) than that of the pure SnS2 monolayer (0.031 to 0.066 eV). Specifically, Pd-SnS2 exhibits smaller adsorption energy compared to that of Au-SnS2 when capturing C3H6O, C6H6, and C5H8. The estimated recovery times for Pd-SnS2 (8.016 × 10-4 to 16.02 s) are shorter compared to those of Au-SnS2 (1.11 to 1.14 × 1010 s), indicating the superior capability of Pd-SnS2 over Au-SnS2 as a reversible sensor. Afterward, calculations of band structure, projected density of states (PDOS), and charge transfer were performed, which further substantiates the more promising potentials for Pd-doped SnS2 monolayer as gas sensors over the others. Overall, our results suggest that Pd-SnS2 is a better candidate for C3H6O, C6H6, and C5H8 detection over Au-SnS2 and pristine SnS2.

The First Complete Mitochondrial Genome of the Genus Litostrophus: Insights into the Rearrangement and Evolution of Mitochondrial Genomes in Diplopoda.

This study presents the complete mitochondrial genome (mitogenome) of Litostrophus scaber, which is the first mitogenome of the genus Litostrophus. The mitogenome is a circular molecule with a length of 15,081 bp. The proportion of adenine and thymine (A + T) was 69.25%. The gene ND4L used TGA as the initiation codon, while the other PCGs utilized ATN (A, T, G, C) as the initiation codons. More than half of the PCGs used T as an incomplete termination codon. The transcription direction of the L. scaber mitogenome matched Spirobolus bungii, in contrast to most millipedes. Novel rearrangements were found in the L. scaber mitogenome: trnQ -trnC and trnL1- trnP underwent short-distance translocations and the gene block rrnS-rrnL-ND1 moved to a position between ND4 and ND5, resulting in the formation of a novel gene order. The phylogenetic analysis showed that L. scaber is most closely related to S. bungii, followed by Narceus magnum. These findings enhance our understanding of the rearrangement and evolution of Diplopoda mitogenomes.

From glioma gloom to immune bloom: unveiling novel immunotherapeutic paradigms-a review.

In tumor therapeutics, the transition from conventional cytotoxic drugs to targeted molecular therapies, such as those targeting receptor tyrosine kinases, has been pivotal. Despite this progress, the clinical outcomes have remained modest, with glioblastoma patients' median survival stagnating at less than 15 months. This underscores the urgent need for more specialized treatment strategies. Our review delves into the progression toward immunomodulation in glioma treatment. We dissect critical discoveries in immunotherapy, such as spotlighting the instrumental role of tumor-associated macrophages, which account for approximately half of the immune cells in the glioma microenvironment, and myeloid-derived suppressor cells. The complex interplay between tumor cells and the immune microenvironment has been explored, revealing novel therapeutic targets. The uniqueness of our review is its exhaustive approach, synthesizing current research to elucidate the intricate roles of various molecules and receptors within the glioma microenvironment. This comprehensive synthesis not only maps the current landscape but also provides a blueprint for refining immunotherapy for glioma, signifying a paradigm shift toward leveraging immune mechanisms for improved patient prognosis.

Single-cell transcriptomic Atlas of aging macaque ocular outflow tissues.

The progressive degradation in the trabecular meshwork (TM) is related to age-related ocular diseases like primary open-angle glaucoma. However, the molecular basis and biological significance of the aging process in TM have not been fully elucidated. Here, we established a dynamic single-cell transcriptomic landscape of aged macaque TM, wherein we classified the outflow tissue into 12 cell subtypes and identified mitochondrial dysfunction as a prominent feature of TM aging. Furthermore, we divided TM cells into 13 clusters and performed an in- depth analysis on cluster 0, which had the highest aging score and the most significant changes in cell proportions between the two groups. Ultimately, we found that the APOE gene was an important differentially expressed gene in cluster 0 during the aging process, highlighting the close relationship between cell migration and extracellular matrix regulation, and TM function. Our work further demonstrated that silencing the APOE gene could increase migration and reduce apoptosis by releasing the inhibition on the PI3K-AKT pathway and downregulating the expression of extracellular matrix components, thereby increasing the aqueous outflow rate and maintaining intraocular pressure within the normal range. Our work provides valuable insights for future clinical diagnosis and treatment of glaucoma.

Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients.

Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment. The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR. Our study provided comprehensive insights into the RCD roles in glioma, developed a robust RCD gene pair signature for predicting the prognosis of glioma patients, constructed a machine learning framework for screening the core candidates and identified the SLC43A3 as an oncogenic role and a prediction biomarker in glioblastoma.

Kinome-wide siRNA screen identifies a DCLK2-TBK1 oncogenic signaling axis in clear cell renal cell carcinoma.

TANK-binding kinase 1 (TBK1) is a potential therapeutic target in multiple cancers, including clear cell renal cell carcinoma (ccRCC). However, targeting TBK1 in clinical practice is challenging. One approach to overcome this challenge would be to identify an upstream TBK1 regulator that could be targeted therapeutically in cancer specifically. In this study, we perform a kinome-wide small interfering RNA (siRNA) screen and identify doublecortin-like kinase 2 (DCLK2) as a TBK1 regulator in ccRCC. DCLK2 binds to and directly phosphorylates TBK1 on Ser172. Depletion of DCLK2 inhibits anchorage-independent colony growth and kidney tumorigenesis in orthotopic xenograft models. Conversely, overexpression of DCLK2203, a short isoform that predominates in ccRCC, promotes ccRCC cell growth and tumorigenesis in vivo. Mechanistically, DCLK2203 elicits its oncogenic signaling via TBK1 phosphorylation and activation. Taken together, these results suggest that DCLK2 is a TBK1 activator and potential therapeutic target for ccRCC.

Copy number and selection of MHC genes in ruminants are related to habitat, average life span and diet.

The ruminants, as the main group of livestock, have been extensively studied in terms of their physiology, endocrinology, biochemistry, genetics, and nutrition. Despite the wide geographic distribution and habitat diversity of animals in this group, their ecology and evolution remain poorly understood. In this study, we analyzed the gene copy number, selection, and ecological and evolutionary processes that have affected the evolution of major histocompatibility complex (MHC) genes across ruminant lineages based on available genomic data. The 51 species analyzed represented all six families of ruminants. Our finding indicated that the architecture of the MHC region is conserved in ruminants, but with variable copy numbers of MHC-I, MHC-IIA, and MHC-IIB genes. No lineage-specific gene duplication was observed in the MHC genes. The phylogenetic generalized least squares regression (PGLS) model revealed association between ecological and biological factors (habitat and lifespan) and gene duplication in DQA and DQB, but not in DRB. The selection pressure of DQA and DQB were related with lifespan, diet, and the ratio of genetic repeat elements. These results suggest that the MHC evolution in ruminants, including copy number and selection, has been influenced by genetic repeat elements, pathogen exposure risk, and intrinsic cost of possessing multiple MHC genes.

Identification of the genetic characteristics of copy number variations in experimental specific pathogen-free ducks using whole-genome resequencing.

BACKGROUND: Specific pathogen-free ducks are a valuable laboratory resource for waterfowl disease research and poultry vaccine development. High throughput sequencing allows the systematic identification of structural variants in genomes. Copy number variation (CNV) can explain the variation of important duck genetic traits. Herein, the genome-wide CNVs of the three experimental duck species in China (Jinding ducks (JD), Shaoxing ducks (SX), and Fujian Shanma ducks (SM)) were characterized using resequencing to determine their genetic characteristics and selection signatures. RESULTS: We obtained 4,810 CNV regions (CNVRs) by merging 73,012 CNVs, covering 4.2% of the duck genome. Functional analysis revealed that the shared CNVR-harbored genes were significantly enriched for 31 gene ontology terms and 16 Kyoto Encyclopedia of Genes and Genomes pathways (e.g., olfactory transduction and immune system). Based on the genome-wide fixation index for each CNVR, growth (SPAG17 and PTH1R), disease resistance (CATHL3 and DMBT1), and thermoregulation (TRPC4 and SLIT3) candidate genes were identified in strongly selected signatures specific to JD, SM, and SX, respectively. CONCLUSIONS: In conclusion, we investigated the genome-wide distribution of experimental duck CNVs, providing a reference to establish the genetic basis of different phenotypic traits, thus contributing to the management of experimental animal genetic resources.

Comprehensive Multiomics Analyses Establish the Optimal Prognostic Model for Resectable Gastric Cancer : Prognosis Prediction for Resectable GC.

BACKGROUND: Prognostic models based on multiomics data may provide better predictive capability than those established at the single-omics level. Here we aimed to establish a prognostic model for resectable gastric cancer (GC) with multiomics information involving mutational, copy number, transcriptional, methylation, and clinicopathological alterations. PATIENTS AND METHODS: The mutational, copy number, transcriptional, methylation data of 268, 265, 226, and 252 patients with stages I-III GC were downloaded from the TCGA database, respectively. Alterations from all omics were characterized, and prognostic models were established at the individual omics level and optimized at the multiomics level. All models were validated with a cohort of 99 patients with stages I-III GC. RESULTS: TTN, TP53, and MUC16 were among the genes with the highest mutational frequency, while UBR5, ZFHX4, PREX2, and ARID1A exhibited the most prominent copy number variations (CNVs). Upregulated COL10A1, CST1, and HOXC10 and downregulated GAST represented the biggest transcriptional alterations. Aberrant methylation of some well-known genes was revealed, including CLDN18, NDRG4, and SDC2. Many alterations were found to predict the patient prognosis by univariate analysis, while four mutant genes, two CNVs, five transcriptionally altered genes, and seven aberrantly methylated genes were identified as independent risk factors in multivariate analysis. Prognostic models at the single-omics level were established with these alterations, and optimized combination of selected alterations with clinicopathological factors was used to establish a final multiomics model. All single-omics models and the final multiomics model were validated by an independent cohort. The optimal area under the curve (AUC) was 0.73, 0.71, 0.71, and 0.85 for mutational, CNV, transcriptional, and methylation models, respectively. The final multiomics model significantly increased the AUC to 0.92 (P < 0.05). CONCLUSIONS: Multiomics model exhibited significantly better capability in predicting the prognosis of resectable GC than single-omics models.

Intelligent Nanoplatform Integrating Macrophage and Cancer Cell Membrane for Synergistic Chemodynamic/Immunotherapy/Photothermal Therapy of Breast Cancer.

Cell membrane-coated nanoplatforms for drug delivery have garnered significant attention due to their inherent cellular properties, such as immune evasion and homing abilities, making them a subject of widespread interest. The coating of mixed membranes from different cell types onto the surface of nanoparticles offers a way to harness natural cell functions, enhancing biocompatibility and improving therapeutic efficacy. In this study, we merged membranes from murine-derived 4T1 breast cancer cells with RAW264.7 (RAW) membranes, creating a hybrid biomimetic coating referred to as TRM. Subsequently, we fabricated hybrid TRM-coated Fe3O4 nanoparticles loaded with indocyanine green (ICG) and imiquimod (R837) for combination therapy in breast cancer. Comprehensive characterization of the RIFe@TRM nanoplatform revealed the inherent properties of both cell types. Compared to bare Fe3O4 nanoparticles, RIFe@TRM nanoparticles exhibited remarkable cell-specific self-recognition for 4T1 cells in vitro, leading to significantly prolonged circulation life span and enhanced in vivo targeting capabilities. Furthermore, the biomimetic RIFe@TRM nanoplatform induced tumor necrosis through the Fenton reaction and photothermal effects, while R837 facilitated enhanced uptake of tumor-associated antigens, further activating CD8+ cytotoxic T cells to strengthen antitumor immunotherapy. Hence, RIFe@TRM nanoplatform demonstrated outstanding synergy in chemodynamic/immunotherapy/photothermal therapies, displaying significant inhibition of breast tumor growth. In summary, this study presents a promising biomimetic nanoplatform for effective treatment of breast cancer.

Metabolic Features of a Novel Trichoderma asperellum YNQJ1002 with Potent Antagonistic Activity against Fusarium graminearum.

Trichoderma, a well-known and extensively studied fungal genus, has gained significant attention for its remarkable antagonistic abilities against a wide range of plant pathogens. In this study, a total of 108 Trichoderma isolates were screened through in vitro dual antagonistic assays and culture filtrate inhibition against Fusarium graminearum. Of these, the YNQJ1002 displayed noteworthy inhibitory activities along with thermal stability. To validate the metabolic differences between YNQJ1002 and GZLX3001 (with strong and weak antagonism, respectively), UPLC-TOF-MS/MS mass spectrometry was employed to analyze and compare the metabolite profiles. We identified 12 significantly up-regulated metabolites in YNQJ1002, which include compounds like Trigoneoside, Torvoside, trans,trans-hepta-2,4,6-trienoic acid, and Chamazulene. These metabolites are known for their antimicrobial properties or signaling roles as components of cell membranes. Enriched KEGG analysis revealed a significant enrichment in sphingolipid metabolism and linoleic acid metabolism, as well as autophagy. The results demonstrated that YNQJ1002's abundance of antimicrobial substances, resulting from specific metabolic pathways, enhanced its superior antagonistic activity against F. graminearum. Finally, YNQJ1002 was identified using the ITS, tef1-1α, and rpb2 regions, with MIST system sequence matching confirming its classification within the species. Overall, we have obtained a novel strain, T. asperellum YNQJ1002, which is rich in metabolites and shows potential antagonistic activity against F. graminearum. This study has opened promising prospects for the development of innovative Trichoderma-derived antifungal compounds, featuring a unique mechanism against pathogens.

Efficient Exploration of Chemical Compound Space Using Active Learning for Prediction of Thermodynamic Properties of Alkane Molecules.

We introduce an exploratory active learning (AL) algorithm using Gaussian process regression and marginalized graph kernel (GPR-MGK) to sample chemical compound space (CCS) at minimal cost. Targeting 251,728 enumerated alkane molecules with 4-19 carbon atoms, we applied the AL algorithm to select a diverse and representative set of molecules and then conducted high-throughput molecular simulations on these selected molecules. To demonstrate the power of the AL algorithm, we built directed message-passing neural networks (D-MPNN) using simulation data as the training set to predict liquid densities, heat capacities, and vaporization enthalpies of the CCS. Validations show that D-MPNN models built on the smallest training set considered in this work, which consists of 313 molecules or 0.124% of the original CCS, predict the properties with R2 > 0.99 against the computational data and R2 > 0.94 against the experimental data. The advantage of the presented AL algorithm is that the predicted uncertainty of GPR depends on only the molecular structures, which renders it compatible with high-throughput data generation.

The status of virtual simulation experiments in medical education in China: based on the national virtual simulation experiment teaching Center (iLAB-X).

BACKGROUND: Virtual simulation experiments have been rapidly applied to medical education curricula in recent years. China constructed a national virtual simulation experimental teaching center (iLAB-X), and this platform covered almost all of the virtual simulation experiment curricula of domestic colleges or universities. We aimed to comprehensively assess the characteristics and usages of virtual simulation experiments in medical education based on iLAB-X. METHODS: A total of 480 virtual simulation experiment courses had been constructed on iLAB-X (https://www.ilab-x.com/) by December 20, 2022, and the curriculum level, type and design were all searched in this platform. We also conducted an evaluation of curriculum usage and online tests, including the page view, frequency of participation, number of participants, duration of experimental learning and passing rate of the experimental test. RESULTS: The national and provincial high-quality virtual simulation experiment curricula accounted for 33.5% (161/480) and 35.8% (172/480), respectively. The curricula were mainly set as basic practice experiments (46.5%) and synthetic designing experiments (48.8%). Significantly, forensic medicine (100%), public health and preventive medicine (83%) and basic medical sciences (66%) focused on synthetic design experiments. In terms of usage experiments, the average duration of experimental learning was 25 minutes per course, and the average number of participants was just 1257. The average passing (score ≥60) rate of online tests was 80.6%, but the average rate of score ≥ 85 was only 58.5%. In particular, the average page views, the number of participants, the duration of learning and the test passing rate of clinical medicine were relatively low. CONCLUSIONS: The curriculum design features, construction level and utilization rate varied in different medical majors. Virtual simulation experiments are particularly underutilized in clinical medicine. There is a long way for virtual simulation experiments to go to become a supplement or alternative for traditional medical education in the future.