Trigonelline hydrochloride attenuates silica-induced pulmonary fibrosis by orchestrating fibroblast to myofibroblast differentiation.

BACKGROUND: Silicosis represents a paramount occupational health hazard globally, with its incidence, morbidity, and mortality on an upward trajectory, posing substantial clinical dilemmas due to limited effective treatment options available. Trigonelline (Trig), a plant alkaloid extracted mainly from coffee and fenugreek, have diverse biological properties such as protecting dermal fibroblasts against ultraviolet radiation and has the potential to inhibit collagen synthesis. However, it's unclear whether Trig inhibits fibroblast activation to attenuate silicosis-induced pulmonary fibrosis is unclear. METHODS: To evaluate the therapeutic efficacy of Trig in the context of silicosis-related pulmonary fibrosis, a mouse model of silicosis was utilized. The investigation seeks to elucidated Trig's impact on the progression of silica-induced pulmonary fibrosis by evaluating protein expression, mRNA levels and employing Hematoxylin and Eosin (H&E), Masson's trichrome, and Sirius Red staining. Subsequently, we explored the mechanism underlying of its functions. RESULTS: In vivo experiment, Trig has been demonstrated the significant efficacy in mitigating SiO2-induced silicosis and BLM-induced pulmonary fibrosis, as evidenced by improved histochemical staining and reduced fibrotic marker expressions. Additionally, we showed that the differentiation of fibroblast to myofibroblast was imped in Trig + SiO2 group. In terms of mechanism, we obtained in vitro evidence that Trig inhibited fibroblast-to-myofibroblast differentiation by repressing TGF-ß/Smad signaling according to the in vitro evidence. Notably, our finding indicated that Trig seemed to be safe in mice and fibroblasts. CONCLUSION: In summary, Trig attenuated the severity of silicosis-related pulmonary fibrosis by alleviating the differentiation of myofibroblasts, indicating the development of novel therapeutic approaches for silicosis fibrosis.

MTMol-GPT: De novo multi-target molecular generation with transformer-based generative adversarial imitation learning.

De novo drug design is crucial in advancing drug discovery, which aims to generate new drugs with specific pharmacological properties. Recently, deep generative models have achieved inspiring progress in generating drug-like compounds. However, the models prioritize a single target drug generation for pharmacological intervention, neglecting the complicated inherent mechanisms of diseases, and influenced by multiple factors. Consequently, developing novel multi-target drugs that simultaneously target specific targets can enhance anti-tumor efficacy and address issues related to resistance mechanisms. To address this issue and inspired by Generative Pre-trained Transformers (GPT) models, we propose an upgraded GPT model with generative adversarial imitation learning for multi-target molecular generation called MTMol-GPT. The multi-target molecular generator employs a dual discriminator model using the Inverse Reinforcement Learning (IRL) method for a concurrently multi-target molecular generation. Extensive results show that MTMol-GPT generates various valid, novel, and effective multi-target molecules for various complex diseases, demonstrating robustness and generalization capability. In addition, molecular docking and pharmacophore mapping experiments demonstrate the drug-likeness properties and effectiveness of generated molecules potentially improve neuropsychiatric interventions. Furthermore, our model's generalizability is exemplified by a case study focusing on the multi-targeted drug design for breast cancer. As a broadly applicable solution for multiple targets, MTMol-GPT provides new insight into future directions to enhance potential complex disease therapeutics by generating high-quality multi-target molecules in drug discovery.

Structural insight into the subclass B1 metallo-ß-lactamase AFM-1.

The emergence of drug-resistant bacteria, facilitated by metallo-beta-lactamases (MBLs), presents a significant obstacle to the effective use of antibiotics in the management of clinical drug-resistant bacterial infections. AFM-1 is a MBL derived from Alcaligenes faecalis and shares 86% homology with the NDM-1 family. Both AFM-1 and NDM-1 demonstrate the ability to hydrolyze ampicillin and other ß-lactam antibiotics, however, their substrate affinities vary, and the specific reason for this variation remains unknown. We present the high-resolution structure of AFM-1. The active center of AFM-1 binds two zinc ions, and the conformation of the key amino acid residues in the active center is in accordance with that of NDM-1. However, the substrate-binding pocket of AFM-1 is considerably smaller than that of NDM-1. Additionally, the mutation of amino acid residues in the Loop3 region, as compared to NDM-1, results in the formation of a dense hydrophobic patch comprised of hydrophobic amino acid residues in this area, which facilitates substrate binding. Our findings lay the foundation for understanding the molecular mechanism of AFM-1 with a high affinity for substrates and provide a novel theoretical foundation for addressing the issue of drug resistance caused by B1 MBLs.

Cellular and molecular mechanisms of fibrosis and resolution in bleomycin-induced pulmonary fibrosis mouse model revealed by spatial transcriptome analysis.

The bleomycin-induced pulmonary fibrosis mouse model is commonly used in idiopathic pulmonary fibrosis research, but its cellular and molecular changes and efficiency as a model at the molecular level are not fully understood. In this study, we used spatial transcriptome technology to investigate the cellular and molecular changes in the lungs of bleomycin-induced pulmonary fibrosis mouse models. Our analyses revealed cell dynamics during fibrosis in epithelial cells, mesenchymal cells, immunocytes, and erythrocytes with their spatial distribution available. We confirmed the differentiation of the alveolar type II (AT2) cell type expressing Krt8, and we inferred their trajectories from both the AT2 cells and club cells. In addition to the fibrosis process, we also noticed evidence of self-resolving, especially to identify possible self-resolving related genes, including Prkca. Our findings provide insights into the cellular and molecular mechanisms underlying fibrosis resolution and represent the first spatiotemporal transcriptome dataset of the bleomycin-induced fibrosis mouse model.

Pmf-cpi: assessing drug selectivity with a pretrained multi-functional model for compound-protein interactions.

Compound-protein interactions (CPI) play significant roles in drug development. To avoid side effects, it is also crucial to evaluate drug selectivity when binding to different targets. However, most selectivity prediction models are constructed for specific targets with limited data. In this study, we present a pretrained multi-functional model for compound-protein interaction prediction (PMF-CPI) and fine-tune it to assess drug selectivity. This model uses recurrent neural networks to process the protein embedding based on the pretrained language model TAPE, extracts molecular information from a graph encoder, and produces the output from dense layers. PMF-CPI obtained the best performance compared to outstanding approaches on both the binding affinity regression and CPI classification tasks. Meanwhile, we apply the model to analyzing drug selectivity after fine-tuning it on three datasets related to specific targets, including human cytochrome P450s. The study shows that PMF-CPI can accurately predict different drug affinities or opposite interactions toward similar targets, recognizing selective drugs for precise therapeutics.Kindly confirm if corresponding authors affiliations are identified correctly and amend if any.Yes, it is correct.

MBD2 facilitates tumor metastasis by mitigating DDB2 expression.

Despite past extensive studies, the pathoetiologies underlying tumor metastasis remain poorly understood, which renders its treatment largely unsuccessful. The methyl-CpG-binding domain 2 (MBD2), a "reader" to interpret DNA methylome-encoded information, has been noted to be involved in the development of certain types of tumors, while its exact impact on tumor metastasis remains elusive. Herein we demonstrated that patients with LUAD metastasis were highly correlated with enhanced MBD2 expression. Therefore, knockdown of MBD2 significantly attenuated the migration and invasion of LUAD cells (A549 and H1975 cell lines) coupled with attenuated epithelial-mesenchymal transition (EMT). Moreover, similar results were observed in other types of tumor cells (B16F10). Mechanistically, MBD2 selectively bound to the methylated CpG DNA within the DDB2 promoter, by which MBD2 repressed DDB2 expression to promote tumor metastasis. As a result, administration of MBD2 siRNA-loaded liposomes remarkably suppressed EMT along with attenuated tumor metastasis in the B16F10 tumor-bearing mice. Collectively, our study indicates that MBD2 could be a promising prognostic marker for tumor metastasis, while administration of MBD2 siRNA-loaded liposomes could be a viable therapeutic approach against tumor metastasis in clinical settings.

Neoadjuvant radiotherapy for resectable retroperitoneal sarcoma: a meta-analysis.

BACKGROUND: Neoadjuvant radiotherapy (NRT) for resectable retroperitoneal sarcoma (RPS) has been shown to be systematically feasible. Whether NRT has equivalent or better clinical effects compared to surgery alone for RPS patients remains controversial. METHODS: We performed a systematic literature search of PubMed, Web of Science, Embase, ASCO Abstracts, and Cochrane library databases for studies in humans with defined search terms. Articles were independently assessed by 2 reviewers, and only randomized controlled trials and cohort studies were included. The hazard ratios (HRs) of overall survival (OS), recurrence-free survival (RFS), and local recurrence (LR) were extracted from included studies. Heterogeneity among study-specific HRs was assessed by the Q statistic and I2 statistic. Overall HR was assessed by random-effects or fixed-effects models. Publication bias was tested by Begg's tests, and the quality of each study was assessed with the Newcastle Ottawa Scale. RESULTS: A total of 12 eligible studies with 7778 resectable RPS patients were finally included in this study. The pooled analysis revealed the distinct advantages of NRT as compared to surgery alone, including longer OS (HR = 0.81, P < 0.001), longer RFS (HR = 0.58, P = 0.04), and lower LR (HR = 0.70, P = 0.03). No evidence of publication bias was observed. CONCLUSION: NRT is likely to be beneficial for resectable RPS patients in terms of OS and RFS. However, more multicenter clinical trials are needed to confirm these findings.

DeepBLI: A Transferable Multichannel Model for Detecting ß-Lactamase-Inhibitor Interaction.

Pathogens producing ß-lactamase pose a great challenge to antibiotic-resistant infection treatment; thus, it is urgent to discover novel ß-lactamase inhibitors for drug development. Conventional high-throughput screening is very costly, and structure-based virtual screening is limited with mechanisms. In this study, we construct a novel multichannel deep neural network (DeepBLI) for ß-lactamase inhibitor screening, pretrained with a label reversal KIBA data set and fine-tuned on ß-lactamase-inhibitor pairs from BindingDB. First, the pairs of encoders (Conv and Att) fuse the information spatially and sequentially for both enzymes and inhibitors. Then, a co-attention module creates the connection between the inhibitor and enzyme embeddings. Finally, multichannel outputs fuse with an element-wise product and then are fed into 3-layer fully connected networks to predict interactions. Comparing the state-of-the-art methods, DeepBLI yields an AUROC of 0.9240 and an AUPRC of 0.9715, which indicates that it can identify new ß-lactamase-inhibitor interactions. To demonstrate its prediction ability, an application of DeepBLI is described to screen potential inhibitor compounds for metallo-ß-lactamase AIM-1 and repurpose rottlerin for four classes of ß-lactamase targets, showing the possibility of being a broad-spectrum inhibitor. DeepBLI provides an effective way for antibacterial drug development, contributing to antibiotic-resistant therapeutics.

A Comparative Study on Functional Recovery, Complications, and Changes in Inflammatory Factors in Patients with Thoracolumbar Spinal Fracture Complicated with Nerve Injury Treated by Anterior and Posterior Decompression.

BACKGROUND The aim of this study was to evaluate 2 methods to treat patients with thoracic lumbar spine fracture with merging spinal cord injury, including complications of surgery and the influence of inflammatory factors. MATERIAL AND METHODS Eighty patients were randomly divided into an anterior decompression group (study group) or a posterior decompression group (control group) to observe perioperative complications, evaluate preoperative and postoperative nerve function, and evaluate the 6-month injured vertebral height and Cobb angle of the vertebral bodies. The expression level of TGF-ß2 on day 1, day 7, day 15, and day 30 after treatment was detected by enzyme-linked immunosorbent assay (ELISA). RESULTS The nerve function sensation score, the height of the vertebral body, and the recovery of Cobb angle were better for the anterior decompression group than the posterior decompression group and the effect was significant (P<0.05). The complication rate for the posterior decompression group was lower than the anterior decompression group. The level of TGF-ß2 in the anterior decompression group was higher than in the posterior decompression group for the same times: after day 1, day 7, day 15, and day 30 after treatment (P<0.05). CONCLUSIONS Patients who had thoracic lumbar spine fracture with merging spinal cord injury and who had anterior fixation achieved a good fixation effect; their neurologic and vertebral injury recovery was better. However, this relatively complex and traumatic surgery must consider the clinical manifestations and fractures of the patients and select the appropriate surgical approach.