Identification of a novel lactylation-related gene signature predicts the prognosis of multiple myeloma and experiment verification.

Multiple myeloma (MM) is an incurable hematological malignancy with poor survival. Accumulating evidence reveals that lactylation modification plays a vital role in tumorigenesis. However, research on lactylation-related genes (LRGs) in predicting the prognosis of MM remains limited. Differentially expressed LRGs (DELRGs) between MM and normal samples were investigated from the Gene Expression Omnibus database. Univariate Cox regression and LASSO Cox regression analysis were applied to construct gene signature associated with overall survival. The signature was validated in two external datasets. A nomogram was further constructed and evaluated. Additionally, Enrichment analysis, immune analysis, and drug chemosensitivity analysis between the two groups were investigated. qPCR and immunofluorescence staining were performed to validate the expression and localization of PFN1. CCK-8 and flow cytometry were performed to validate biological function. A total of 9 LRGs (TRIM28, PPIA, SOD1, RRP1B, IARS2, RB1, PFN1, PRCC, and FABP5) were selected to establish the prognostic signature. Kaplan-Meier survival curves showed that high-risk group patients had a remarkably worse prognosis in the training and validation cohorts. A nomogram was constructed based on LRGs signature and clinical characteristics, and showed excellent predictive power by calibration curve and C-index. Moreover, biological pathways, immunologic status, as well as sensitivity to chemotherapy drugs were different between high- and low-risk groups. Additionally, the hub gene PFN1 is highly expressed in MM, knocking down PFN1 induces cell cycle arrest, suppresses cell proliferation and promotes cell apoptosis. In conclusion, our study revealed that LRGs signature is a promising biomarker for MM that can effectively early distinguish high-risk patients and predict prognosis.

The safety of Pfannenstiel incision for specimen extraction in laparoscopic colorectal surgery for colorectal cancer: a systematic review and meta-analysis.

Introduction: The Pfannenstiel incision is often used in gynecological Cesarean section; however, there is limited research on the use of the Pfannenstiel incision for specimen extraction in laparoscopic surgery for the treatment of colorectal cancer. Aim: To evaluate the safety of using the Pfannenstiel incision for specimen extraction in laparoscopic surgery for colorectal cancer patients. Material and methods: PubMed, Embase, Web of Science, Cochrane Library, CNKI, VIP and WanFangData were searched for studies published up to March 10, 2023; a random-effects model (RCT) and a fixed-effect model were used to evaluate the safety. Operative time, length of extraction skin incision, overall complications, superficial wound infection, organ/space surgical site infection and incisional hernia were evaluated. Results: A total of 5 studies were included in this research. There were no significant advantages in operation time, length of the incision, overall complications, superficial wound infection and organ/space surgical site in the Pfannenstiel group compared to the no Pfannenstiel group. However, the Pfannenstiel incision has a tendency to increase the length of the incision (SMD = 0.05; 95% CI = -0.22 to 0.33; p = 0.71) and the results of the remaining five (operative time,overall complications,incisional hernia, incisional infection and organ/space surgical site infection) are slightly skewed toward the Pfannenstiel incision. It is worth mentioning that incisional hernia (IH) may have an advantage in the Pfannenstiel group compared to the no Pfannenstiel group. Four studies were not at clear risk of bias and two studies were at risk of bias. Conclusions: Our study concludes that the Pfannenstiel incision has a good safety record and it is a good option for extracting specimens during laparoscopic surgery for colon cancer. The Pfannenstiel incision used for laparoscopic surgical specimen extraction has a significantly lower incidence of incisional hernia over no Pfannenstiel.

Unveiling the Mechanism of Phenamacril Resistance in F. graminearum: Computational and Experimental Insights into the C423A Mutation in FgMyoI.

Phenamacril (PHA) is a highly selective fungicide for controlling fusarium head blight (FHB) mainly caused by F. graminearum and F. asiaticum. However, the C423A mutation in myosin I of F. graminearum (FgMyoI) leads to natural resistance to PHA. Here, based on the computational approaches and biochemical validation, we elucidate the atomic-level mechanism behind the natural resistance of F. graminearum to the fungicide PHA due to the C423A mutation in FgMyoI. The mutation leads to a rearrangement of pocket residues, resulting in increased size and flexibility of the binding pocket, which impairs the stable binding of PHA. MST experiments confirm that the mutant protein FgMyoIC423A exhibits significantly reduced affinity for PHA compared to wild-type FgMyoI and the nonresistant C423K mutant. This decreased binding affinity likely underlies the development of PHA resistance in F. graminearum. Conversely, the nonresistant C423K mutant retains sensitivity to PHA due to the introduction of a strong hydrogen bond donor, which facilitates stable binding of PHA in the pocket. These findings shed light on the molecular basis of PHA resistance and provide new directions for the creation of new myosin inhibitors.

Effects of PGE1 on the ERS pathway in neonatal rats with hyperoxic lung injury.

BACKGROUND: With the increase in the number of low birth weight infants, oxygen therapy is more widely used. However, chronic high-concentration oxygen environments lead to hyperoxic lung injury in children, which in turn leads to bronchopulmonary dysplasia (BPD). PGE1 is widely used in the clinic for its ability to inhibit inflammation and improve circulation. Therefore, we further investigated whether PGE-1 has a therapeutic effect on hyperoxic lung injury. METHODS: Hyperoxic lung injury model was adopted for investigating the interventional effects and underlying mechanisms of intraperitoneal injection of prostaglandin E1 (PGE-1) on hyperoxic lung injury in newborn rats via relevant experimental techniques, such as Diff-Quick staining, lung wet dry specific gravity measurements, HE staining, TUNEL staining, ELISA, and the Western blot method. RESULTS: Inflammatory and apoptotic cells in the PGE1-treated group were significantly lower than those in the hyperoxic lung injury group (p < 0.05); and the contents of IL-1ß, IL-6 and TNF-α in the treated group were significantly lower than those in the model group (p < 0.05). Caspase-3, CHOP, GRP78 and Bcl-2/Bax protein expression in the treatment group was significantly lower than that in the model group (p < 0.05). CONCLUSION: PGE-1 has a therapeutic effect on hyperoxic lung injury in neonatal rats. IMPACT: PGE1 treatment reduces levels of inflammatory cells and pro-inflammatory cytokines and decreases apoptosis. PGE1 has a therapeutic effect on BPD through the endoplasmic reticulum stress pathway. This study offers the possibility of PGE1 for the treatment of BPD.

Advancing ecotoxicity assessment: Leveraging pre-trained model for bee toxicity and compound degradability prediction.

The prediction of ecological toxicity plays an increasingly important role in modern society. However, the existing models often suffer from poor performance and limited predictive capabilities. In this study, we propose a novel approach for ecological toxicity assessment based on pre-trained models. By leveraging pre-training techniques and graph neural network models, we establish a highperformance predictive model. Furthermore, we incorporate a variational autoencoder to optimize the model, enabling simultaneous discrimination of toxicity to bees and molecular degradability. Additionally, despite the low similarity between the endogenous hormones in bees and the compounds in our dataset, our model confidently predicts that these hormones are non-toxic to bees, which further strengthens the credibility and accuracy of our model. We also discovered the negative correlation between the degradation and bee toxicity of compounds. In summary, this study presents an ecological toxicity assessment model with outstanding performance. The proposed model accurately predicts the toxicity of chemicals to bees and their degradability capabilities, offering valuable technical support to relevant fields.

Is diet related to skin condition? A Mendelian randomization study.

Observational studies have revealed associations between various dietary factors and skin conditions. However, the causal relationship between diet and skin condition is still unknown. Data on 17 dietary factors were obtained from the UK Biobank. Data on four skin conditions were derived from the UK Biobank and another large-scale GWAS study. Genetic predictions suggested that the intake of oily fish was associated with a lower risk of skin aging (OR: 0.962, P = 0.036) and skin pigmentation (OR: 0.973, P = 0.033); Tea intake was associated with a lower risk of skin pigmentation (OR: 0.972, P = 0.024); Salad/raw vegetables intake was associated with a lower risk of keratinocyte skin cancer (OR: 0.952, P = 0.007). Coffee intake was associated with increased risk of skin aging (OR: 1.040, P = 0.028); Pork intake was associated with increased risk of skin aging (OR: 1.134, P = 0.020); Beef intake was associated with increased risk of cutaneous melanoma (OR: 1.013, P = 0.016); Champagne plus white wine intake was associated with increased risk of cutaneous melanoma (OR: 1.033, P = 0.004); Bread intake was associated with increased risk of keratinocyte skin cancer (OR: 1.026, P = 0.013). Our study results indicate causal relationships between genetically predicted intake of oily fish, tea, salad/raw vegetables, coffee, pork, beef, champagne plus white wine, and bread and skin conditions.

Regulation of intracellular process by two-component systems: Exploring the mechanism of plasmid-mediated conjugative transfer.

Plasmid-mediated conjugative transfer facilitates the dissemination of antibiotic resistance, yet the comprehensive regulatory mechanisms governing this process remain elusive. Herein, we established pure bacteria and activated sludge conjugation system to investigate the regulatory mechanisms of conjugative transfer, leveraging metformin as an exogenous agent. Transcriptomic analysis unveiled that substantial upregulation of genes associated with the two-component system (e.g., AcrB/AcrA, EnvZ/Omp, and CpxA/CpxR) upon exposure to metformin. Furthermore, downstream regulators of the two-component system, including reactive oxygen species (ROS), cytoplasmic membrane permeability, and adenosine triphosphate (ATP) production, were enhanced by 1.7, 1.4 and 1.1 times, respectively, compared to the control group under 0.1 mg/L metformin exposure. Moreover, flow sorting and high-throughput sequencing revealed increased microbial community diversity among transconjugants in activated sludge systems. Notably, the antibacterial potential of human pathogenic bacteria (e.g., Bacteroides, Escherichia-Shigella, and Lactobacillus) was augmented, posing a potential threat to human health. Our findings shed light on the spread of antibiotic resistance bacteria and assess the ecological risks associated with plasmid-mediated conjugative transfer in wastewater treatment systems.

Molecular Mechanism of Fusarium Fungus Inhibition by Phenazine-1-carboxamide.

Fusarium head blight caused by Fusarium graminearum is a devastating disease in wheat that seriously endangers food security and human health. Previous studies have found that the secondary metabolite phenazine-1-carboxamide produced by biocontrol bacteria inhibited F. graminearum by binding to and inhibiting the activity of histone acetyltransferase Gcn5 (FgGcn5). However, the detailed mechanism of this inhibition remains unknown. Our structural and biochemical studies revealed that phenazine-1-carboxamide (PCN) binds to the histone acetyltransferase (HAT) domain of FgGcn5 at its cosubstrate acetyl-CoA binding site, thus competitively inhibiting the histone acetylation function of the enzyme. Alanine substitution of the residues in the binding site shared by PCN and acetyl-CoA not only decreased the histone acetylation level of the enzyme but also dramatically impacted the development, mycotoxin synthesis, and virulence of the strain. Taken together, our study elucidated a competitive inhibition mechanism of Fusarium fungus by PCN and provided a structural template for designing more potent phenazine-based fungicides.

PharmaRedefine: A database server for repurposing drugs against pathogenic bacteria.

Pathogenic bacteria represent a formidable threat to human health, necessitating substantial resources for prevention and treatment. With the escalating concern regarding antibiotic resistance, there is a pressing need for innovative approaches to combat these pathogens. Repurposing existing drugs offers a promising solution. Our present work hypothesizes that proteins harboring ligand-binding pockets with similar chemical environments may be able to bind the same drug. To facilitate this drug-repurposing strategy against pathogenic bacteria, we introduce an online server, PharmaRedefine. Leveraging a combination of sequence and structure alignment and protein pocket similarity analysis, this platform enables the prediction of potential targets in representative bacteria for specific FDA-approved drugs. This novel approach holds tremendous potential for drug repositioning that effectively combat infections caused by pathogenic bacteria. PharmaRedefine is freely available at http://guolab.mpu.edu.mo/pharmredefine.

Deep Learning-Based Segmentation and Risk Stratification for Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Imaging.

PURPOSE: To develop a deep neural network system for the automatic segmentation and risk stratification prediction of gastrointestinal stromal tumors (GISTs). METHODS: A total of 980 ultrasound (US) images from 245 GIST patients were retrospectively collected. These images were randomly divided (6:2:2) into a training set, a validation set, and an internal test set. Additionally, 188 US images from 47 prospective GIST patients were collected to evaluate the segmentation and diagnostic performance of the model. Five deep learning-based segmentation networks, namely, UNet, FCN, DeepLabV3+, Swin Transformer, and SegNeXt, were employed, along with the ResNet 18 classification network, to select the most suitable network combination. The performance of the segmentation models was evaluated using metrics such as the intersection over union (IoU), Dice similarity coefficient (DSC), recall, and precision. The classification performance was assessed based on accuracy and the area under the receiver operating characteristic curve (AUROC). RESULTS: Among the compared models, SegNeXt-ResNet18 exhibited the best segmentation and classification performance. On the internal test set, the proposed model achieved IoU, DSC, precision, and recall values of 82.1, 90.2, 91.7, and 88.8%, respectively. The accuracy and AUC for GIST risk prediction were 87.4 and 92.0%, respectively. On the external test set, the segmentation models exhibited IoU, DSC, precision, and recall values of 81.0, 89.5, 92.8, and 86.4%, respectively. The accuracy and AUC for GIST risk prediction were 86.7 and 92.5%, respectively. CONCLUSION: This two-stage SegNeXt-ResNet18 model achieves automatic segmentation and risk stratification prediction for GISTs and demonstrates excellent segmentation and classification performance.

NG2 glia protect against prion neurotoxicity by inhibiting microglia-to-neuron prostaglandin E2 signaling.

Oligodendrocyte-lineage cells, including NG2 glia, undergo prominent changes in various neurodegenerative disorders. Here, we identify a neuroprotective role for NG2 glia against prion toxicity. NG2 glia were activated after prion infection in cerebellar organotypic cultured slices (COCS) and in brains of prion-inoculated mice. In both model systems, depletion of NG2 glia exacerbated prion-induced neurodegeneration and accelerated prion pathology. Loss of NG2 glia enhanced the biosynthesis of prostaglandin E2 (PGE2) by microglia, which augmented prion neurotoxicity through binding to the EP4 receptor. Pharmacological or genetic inhibition of PGE2 biosynthesis attenuated prion-induced neurodegeneration in COCS and mice, reduced the enhanced neurodegeneration in NG2-glia-depleted COCS after prion infection, and dampened the acceleration of prion disease in NG2-glia-depleted mice. These data unveil a non-cell-autonomous interaction between NG2 glia and microglia in prion disease and suggest that PGE2 signaling may represent an actionable target against prion diseases.

A Preoperative Prediction Model for Lymph Node Metastasis in Patients with Gastric Cancer Using a Machine Learning-based Ultrasomics Approach.

OBJECTIVE: This study aimed to develop an ultrasomics model for predicting lymph node metastasis preoperative in patients with gastric cancer (GC). METHODS: This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC), sensitivity, specificity, and the corresponding 95% confidence intervals (CIs). RESULTS: A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19 radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95%CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802, 95%CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and actual lymph node metastasis status. CONCLUSION: Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering implications for personalized therapy approaches.

Active site-tuned high peroxidase-like activity nanozyme for on-the-spot detection of saliva total antioxidant capacity using smartphone devices.

Metal-Organic Framework (MOF) based nanozymes with clear structure are beneficial for exploration of structural-performance and exhibit broad prospects in improving activity. In this study, the prepared bimetallic Fe3Ni-MOF nanozyme was superior to single metal MOF in the peroxidase-like activity. Subsequently, a derivative nanozyme (Fe3Ni-MOF-Ar) was prepared by pyrolysis using Fe3Ni-MOF as the precursor in argon atomoshere with controlled temperature. The investigated of Fe3Ni-MOF-Ar revealed that the irregular macroporous state and the presence of heterovalent FeIII/FeII sites of Fe3Ni-MOF-Ar enable the retention, exposure, and electronic structure regulation of active sites, promoting the dual mechanism (the generation of •OH and electron transfer mechanism) and significantly increasing the peroxidase-like activity. Fe3Ni-MOF-Ar exhibited a strong affinity for substrate H2O2, which is higher than horseradish peroxidase. Ascorbic acid and cysteine are typical substances of antioxidants. Fe3Ni-MOF-Ar was used for sensitive colorimetric detection of ascorbic acid and cysteine, and the detection limit was as low as 150 and 60 nM. In addition, the smartphone devices was used to detection of antioxidant equivalent ascorbic acid, with a detection range of 0.5-120 µM. Fe3Ni-MOF-Ar nanozyme is feasible for sensitive detection of saliva total antioxidant capacity.

Discovery and genetic characterization of novel paramyxoviruses from small mammals in Hubei Province, Central China.

Paramyxoviruses are a group of single-stranded, negative-sense RNA viruses, some of which are responsible for acute human disease, including parainfluenza virus, measles virus, Nipah virus and Hendra virus. In recent years, a large number of novel paramyxoviruses, particularly members of the genus Jeilongvirus, have been discovered in wild mammals, suggesting that the diversity of paramyxoviruses may be underestimated. Here we used hemi-nested reverse transcription PCR to obtain 190 paramyxovirus sequences from 969 small mammals in Hubei Province, Central China. These newly identified paramyxoviruses were classified into four clades: genera Jeilongvirus, Morbillivirus, Henipavirus and Narmovirus, with most of them belonging to the genus Jeilongvirus. Using Illumina sequencing and Sanger sequencing, we successfully recovered six near-full-length genomes with different genomic organizations, revealing the more complex genome content of paramyxoviruses. Co-divergence analysis of jeilongviruses and their known hosts indicates that host-switching occurred more frequently in the evolutionary histories of the genus Jeilongvirus. Together, our findings demonstrate the high prevalence of paramyxoviruses in small mammals, especially jeilongviruses, and highlight the diversity of paramyxoviruses and their genome content, as well as the evolution of jeilongviruses.

The predictive and prognostic value of risk factors in patients receiving hybrid coronary revascularization with postoperative pulmonary complications.

Background: The mortality rate of coronary artery disease ranks first in developed countries, and coronary revascularization therapy is an important cornerstone of its treatment. The postoperative pulmonary complications (PPCs) in patients receiving one-stop hybrid coronary revascularization (HCR) aggravate the dysfunction of multiple organs such as the heart and lungs, therefore increasing mortality. However, the risk factors are still unclear. The objective of this study was to explore the risk factors of PPCs after HCR surgery. Methods: In this study, the perioperative data of 311 patients undergoing HCR surgery were reviewed. All patients were divided into two groups according to whether the PPCs occurred. The baseline information and surgery-related indicators in preoperative laboratory examination, intraoperative fluid management, and anesthesia management were compared between the two groups. Results: Advanced age [odds ratio (OR): 1.065, 95% confidence interval (CI): 1.030-1.101, P<0.001], high body mass index (BMI; OR: 1.113, 95% CI: 1.011-1.225, P=0.02), history of percutaneous coronary intervention (PCI) surgery (OR: 2.831, 95% CI: 1.388-5.775, P=0.004), one-lung volume ventilation (OR: 3.804, 95% CI: 1.923-7.526, P<0.001), inhalation of high concentration oxygen (OR: 3.666, 95% CI: 1.719-7.815, P=0.001), the application of positive end-expiratory pressure (PEEP; OR: 2.567, 95% CI: 1.338-4.926, P=0.005), and long one-lung ventilation time (OR: 1.015, 95% CI: 1.006-1.023, P=0.001) may be risk factors for postoperative PPCs in patients undergoing one-stop coronary revascularization surgery. Using the above seven factors to jointly predict the risk of PPCs in patients undergoing one-stop coronary revascularization surgery, the receiver operating characteristic (ROC) curve showed an area under the curve (AUC) =0.873, 95% CI: 0.835-0.911, sensitivity: 84.81%, and specificity: 75.82%; the predictive model was shown to be effective. Conclusions: Patients undergoing HCR surgery with advanced age, high BMI, a history of PCI surgery, one-lung volume ventilation, inhalation of high concentration oxygen, use of PEEP, and prolonged single lung ventilation are more prone to PPCs.

Metal-Organic Framework-Based Nanovaccine for Relieving Immunosuppressive Tumors via Hindering Efferocytosis of Macrophages and Promoting Pyroptosis and Cuproptosis of Cancer Cells.

Current cancer vaccines face challenges due to an immunosuppressive tumor microenvironment and their limited ability to produce an effective immune response. To address the above limitations, we develop a 3-(2-spiroadamantyl)-4-methoxy-4-(3-phosphoryloxy)-phenyl-1,2-dioxetane (alkaline phosphatase substrate) and XMD8-92 (extracellular signal-regulated kinase 5 inhibitor)-codelivered copper-tetrahydroxybenzoquinone (Cu-THBQ/AX) nanosized metal-organic framework to in situ-generate therapeutic vaccination. Once inside the early endosome, the alkaline phosphatase overexpressed in the tumor cells' membrane activates the in situ type I photodynamic effect of Cu-THBQ/AX for generating •O2-, and the Cu-THBQ/AX catalyzes O2 and H2O2 to •O2- and •OH via semiquinone radical catalysis and Fenton-like reactions. This surge of ROS in early endosomes triggers caspase-3-mediated proinflammatory pyroptosis via activating phospholipase C. Meanwhile, Cu-THBQ/AX can also induce the oligomerization of dihydrolipoamide S-acetyltransferase to trigger tumor cell cuproptosis. The production of •OH could also trigger the release of XMD8-92 for effectively inhibiting the efferocytosis of macrophages to convert immunosuppressive apoptosis of cancer cells into proinflammatory secondary necrosis. The simultaneous induction of pyroptosis, cuproptosis, and secondary necrosis effectively converts the tumor microenvironment from "cold" to "hot" conditions, making it an effective antigen pool. This transformation successfully activates the antitumor immune response, inhibiting tumor growth and metastasis.

Inhibition of ACSS2-mediated histone crotonylation alleviates kidney fibrosis via IL-1ß-dependent macrophage activation and tubular cell senescence.

Histone lysine crotonylation (Kcr), as a posttranslational modification, is widespread as acetylation (Kac); however, its roles are largely unknown in kidney fibrosis. In this study, we report that histone Kcr of tubular epithelial cells is abnormally elevated in fibrotic kidneys. By screening these crotonylated/acetylated factors, a crotonyl-CoA-producing enzyme ACSS2 (acyl-CoA synthetase short chain family member 2) is found to remarkably increase histone 3 lysine 9 crotonylation (H3K9cr) level without influencing H3K9ac in kidneys and tubular epithelial cells. The integrated analysis of ChIP-seq and RNA-seq of fibrotic kidneys reveal that the hub proinflammatory cytokine IL-1ß, which is regulated by H3K9cr, play crucial roles in fibrogenesis. Furthermore, genetic and pharmacologic inhibition of ACSS2 both suppress H3K9cr-mediated IL-1ß expression, which thereby alleviate IL-1ß-dependent macrophage activation and tubular cell senescence to delay renal fibrosis. Collectively, our findings uncover that H3K9cr exerts a critical, previously unrecognized role in kidney fibrosis, where ACSS2 represents an attractive drug target to slow fibrotic kidney disease progression.

Relationships between high-concentration toxic metals in sediment and evolution of microbial community structure and carbon-nitrogen metabolism functions under long-term stress perspective.

Microorganisms are highly sensitive to toxic metal pollution and play an important role in the material cycling and energy flow of the water ecosystem. Herein, 13 sediment samples from Junchong Reservoir (Guangxi Province, China) were collected in December 2021. The spatial distribution of pollution levels for toxic metals and the effects of toxic metals on the composition, functional characteristics, and metabolism of microorganisms were investigated. The results demonstrated that the area is a proximate area to industrial zones with severity of toxic metal pollution. Their mean concentrations of As, Cu, Zn, and Pb were up to 128.79 mg/kg, 57.62 mg/kg, 594.77 mg/kg, and 97.12 mg/kg respectively. There was a strong correlation between As, Cu, Zn, and Pb, with the highest correlation coefficient reaching 0.94. As the level of toxic metal pollution increases, the diversity and abundance of microorganisms gradually decrease. Compared to those with lower pollution levels, the Shannon index in regions with higher pollution levels decreases by up to 0.373, and the Chao index decreases by up to 143.507. However, the relative abundance of Bacteroidota, Patescibacteria, and Chloroflexi increased by 23%, 20%, and 5%, respectively, indicating their higher adaptability to toxic metals. Furthermore, microbial carbon and nitrogen metabolism were also affected by the presence of toxic metals. FAPROTAX analysis demonstrated an abundant reduction of ecologically functional groups associated with carbon and nitrogen transformations under high toxic metal pollution levels. KEGG pathway analysis indicated that carbon fixation and nitrogen metabolism pathways were inhibited with increasing toxic metal concentrations. These findings would contribute to a better understanding of the effects of toxic metal pollution on sediment microbial communities and function, shedding light on the ecological consequences of toxic metal contamination.

A new paradigm for applying deep learning to protein-ligand interaction prediction.

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.