The prescription of Qidong Yixin oral liquid is derived from the experience of national medical master Ren Jixue in treating viral myocarditis （VMC）. It has the functions of tonifying Qi， nourishing the heart，calming the mind， and relieving palpitations. It is used to treat VMC and angina pectoris of coronary heart disease caused by deficiency of both Qi and Yin. However，the understanding of its efficacy evidence， advantageous aspects， dosage and administration， and medication safety remains insufficient in clinical practice. Therefore，the development of the Expert Consensus on the Clinical Application of Qidong Yixin Oral Liquid （hereinafter referred to as consensus） was initiated. Consensus strictly followed the process and methods of the expert consensus on the clinical application of Chinese patent medicines of the China Association of Chinese Medicine，successively completing multiple tasks such as the consensus project initiation，determination of clinical problems，evidence search and evaluation，formation of recommendation opinions and consensus suggestions，solicitation of opinions，peer review， submission for review and release， and so on. Consensus formed a total of 10 recommendation opinions and 12 consensus suggestions，clarifying the clinical positioning，efficacy advantages，syndrome differentiation，dosage and administration，combination therapy，timing of medication，adverse reactions，contraindications， and precautions of Qidong Yixin oral liquid，indicating that it has good clinical advantages and safety in the treatment of VMC and angina pectoris of coronary heart disease，providing norms and references for physicians to safely and rationally apply Qidong Yixin oral liquid. Consensus was reviewed and approved for release by the Standardization Office of the China Association of Chinese Medicine on December 23， 2024. Standard number：GSCACM-376-2024.

Homo- or heterodimeric compounds that affect dimeric protein function through interaction between monomeric moieties and protein subunits can serve as valuable sources of potent and selective drug candidates. Here, we screened an in-house dimeric natural product collection, and panepocyclinol A (PecA) emerged as a selective and potent STAT3 inhibitor with profound anti-tumor efficacy. Through cross-linking C712/C718 residues in separate STAT3 monomers with two distinct Michael receptors, PecA inhibits STAT3 DNA binding affinity and transcription activity. Molecular dynamics simulation reveals the key conformation changes of STAT3 dimers upon the di-covalent binding with PecA that abolishes its DNA interactions. Furthermore, PecA exhibits high efficacy against anaplastic large T cell lymphoma in vitro and in vivo, especially those with constitutively activated STAT3 or STAT3^Y640F. In summary, our study describes a distinct and effective di-covalent modification for the dimeric compound PecA to disrupt STAT3 function.

Intervention in chronically activated microglia-mediated neuroinflammation is a novel approach to treat Alzheimer's disease (AD). The low permeability of the blood‒brain barrier (BBB) and non-selective distribution in the brain severely restrict AD drugs' disease-modifying efficacy. Here, an immunosuppressant TREM2-lowing antisense oligonucleotides (ASOs) and resveratrol co-loaded cationic liposome is developed as an immune reprogramming nanomodulator modified by acid-cleavable BBB-targeting peptide and microglia-targeting peptide (Res@TcMNP/ASO) for AD management. Res@TcMNP/ASO can enter brain endothelial cells via D-T7 peptides. Then D-T7 undergoes an acid-responsive cleavage, facilitating the escape of Res@MNP/ASO from endo/lysosomes to cross the BBB. The detached Res@MNP/ASO specifically targets M1-phenotype microglia via exposed MG1 peptides to prompt the simultaneous delivery of two drugs into activated microglia. This nanomodulator can not only restore the immune function of microglia through TREM2-lowing ASO but also mitigate the immune stimulation to microglia caused by reactive oxygen species (ROS) through resveratrol, thereby synergistically inhibiting the chronic activation of microglia to alleviate neuroinflammation in AD. Our results indicate that this combination treatment can achieve significant behavioral and cognitive improvements in late APP/PS1 mice.

Fifty whitespotted bamboo sharks (Chiloscyllium playgiosum) of both sexes were used to establish a large capacity variable domain of the new antigen receptor (VNAR) library with a total capacity of over 10⁹ colony-forming units (CFU). It was applied to screen VNARs against human serum albumin (HSA) and human transcription factor EB (TFEB), respectively. Meanwhile, VNAR libraries specific to HSA and TFEB with capacities above 10⁸ CFU were obtained following conventional immunization. These two approaches were systematically studied in terms of VNAR yield and composition. By comparing the VNAR sequences obtained from naïve and antigen-immunized libraries, we found that the complementary-determining region 3 (CDR3) of the former differs in composition from that of the latter. It shares a higher degree of homology with the naïve library. Meanwhile, the binding efficiency assessed by ELISA is also different between the naïve and antigen-immunized libraries. The binding of VNARs from the TFEB-immunized library appeared to surpass that observed with the naïve libraries, whereas the performance of VNARs from the HSA-immunized library indicated that both the immunized and naïve libraries for HSA had positive binding responses in polyclonal and monoclonal ELISA. The results are useful to develop novel diagnostic and therapeutic products based on shark VNARs.

Acute respiratory distress syndrome (ARDS) is the leading cause of respiratory failure with high morbidity and mortality. Pulmonary surfactant (PS)-based complementary therapies have exhibited potential for ARDS healing and applied as an adjunctive therapy strategy. Coacervate (Coac) has the characteristics of softness, deformability and excellent molecular enrichment properties, and has attracted extensive attention in the biomedical field. Here PS and coacervate were combined for the potential ARDS treatment. The Coac, fabricated from polyallylamine hydrochloride (PAH) and adenosine triphosphate (ATP) by simple mixing, exhibited soft droplet property and high enrichment for dexamethasone sodium phosphate (DSP). To avoid the fusion effect of membraneless coacervate and endow it with biological functions of PS, liposomes with PS-biomimetic lipid components (PS-lipo) were further introduced to construct PS-biomimetic membranized coacervate (DSP@PS-Coac). The DSP@PS-Coac demonstrated high lung targeting effect and significant penetration efficiency after intravenous injection. Furthermore, PS-lipo replenished the endogenous PS pool and facilitated the distribution of DSP in inflammatory cells in the lung. In the ARDS mouse model, PS-Coac and DSP exerted synergetic anti-inflammatory functions, via reducing the recruitment of inflammatory neutrophils and modulating macrophages into anti-inflammatory phenotype. The overall results confirmed that DSP@PS-Coac may provide a promising delivery option for the treatment of ARDS.

OBJECTIVES: We propose a multi-feature fusion model based on manually extracted features and deep learning features from endoscopic images for grading rebleeding risk of peptic ulcers. METHODS: Based on the endoscopic appearance of peptic ulcers, color features were extracted to distinguish active bleeding (Forrest I) from non-bleeding ulcers (Forrest II and III). The edge and texture features were used to describe the morphology and appearance of the ulcers in different grades. By integrating deep features extracted from a deep learning network with manually extracted visual features, a multi-feature representation of endoscopic images was created to predict the risk of rebleeding of peptic ulcers. RESULTS: In a dataset consisting of 3573 images from 708 patients with Forrest classification, the proposed multi-feature fusion model achieved an accuracy of 74.94% in the 6-level rebleeding risk classification task, outperforming the experienced physicians who had a classification accuracy of 59.9% (P<0.05). The F1 scores of the model for identifying Forrest Ib, IIa, and III ulcers were 90.16%, 75.44%, and 77.13%, respectively, demonstrating particularly good performance of the model for Forrest Ib ulcers. Compared with the first model for peptic ulcer rebleeding classification, the proposed model had improved F1 scores by 5.8%. In the simplified 3-level risk (high-risk, low-risk, and non-endoscopic treatment) classification task, the model achieved F1 scores of 93.74%, 81.30%, and 73.59%, respectively. CONCLUSIONS The proposed multi-feature fusion model integrating deep features from CNNs with manually extracted visual features effectively improves the accuracy of rebleeding risk classification for peptic ulcers, thus providing an efficient diagnostic tool for clinical assessment of rebleeding risks of peptic ulcers.
Humans ; Deep Learning ; Peptic Ulcer ; Risk Assessment ; Peptic Ulcer Hemorrhage ; Recurrence

OBJECTIVES: To investigate the role of apelin in regulating proliferation, migration and angiogenesis of bladder cancer cells and the possible regulatory mechanism. METHODS: GEO database was used to screen the differentially expressed genes in bladder cancer tissues and cells. Bladder cancer and paired adjacent tissues were collected from 60 patients for analysis of apelin expressions in relation to clinicopathological parameters. In cultured bladder cancer J82 cells and human umbilical vein endothelial cells (HUVECs), the effects of transfection with an apelin-overexpressing plasmid or specific siRNAs targeting apelin, fibroblast growth factor 2 (FGF2) and fibroblast growth factor receptor 1 (FGFR1) on proliferation and migration of J82 cells and tube formation in HUVECs were examined using plate cloning assay, Transwell assay, and angiogenesis assay; the changes in FGF2 expression and FGFR1 phosphorylation were detected using Western blotting. RESULTS: The expression level of apelin was significantly higher in bladder cancer tissues than adjacent tissues, and bladder cancer cell lines (T24 and J82) also expressed higher mRNA and protein levels of apelin than SV-HUC-1 cells. Apelin expression level in bladder cancer tissues was correlated with tumor invasion, distant metastasis and advanced TNM stages. Apelin knockdown significantly suppressed proliferation and migration of J82 cells and decreased the total angiogenic length of HUVECs. In contrast, apelin overexpression significantly promoted proliferation and migration and enhanced FGFR1 phosphorylation in J82 cells, and increased the total angiogenesis length in HUVECs, but this effects were effectively mitigated by transfection of the cells with FGF2 siRNA or FGFR1 siRNA. CONCLUSIONS High expression of apelin promotes J82 cell proliferation and migration and HUVEC angiogenesis by promoting activation of the FGF2/FGFR1 pathway.
Humans ; Urinary Bladder Neoplasms/blood supply* ; Receptor, Fibroblast Growth Factor, Type 1/metabolism* ; Cell Proliferation ; Cell Movement ; Fibroblast Growth Factor 2/metabolism* ; Neovascularization, Pathologic ; Human Umbilical Vein Endothelial Cells ; Cell Line, Tumor ; Signal Transduction ; Apelin ; Intercellular Signaling Peptides and Proteins/genetics* ; Female ; Male ; Angiogenesis

Dysregulated RNA splicing is a well-recognized characteristic of colorectal cancer (CRC); however, its intricacies remain obscure, partly due to challenges in profiling full-length transcript variants at the single-cell level. Here, we employ high-depth long-read scRNA-seq to define the full-length transcriptome of colorectal epithelial cells in 12 CRC patients, revealing extensive isoform diversities and splicing alterations. Cancer cells exhibited increased transcript complexity, with widespread 3'-UTR shortening and reduced intron retention. Distinct splicing regulation patterns were observed between intrinsic-consensus molecular subtypes (iCMS), with iCMS3 displaying even higher splicing factor activities and more pronounced 3'-UTR shortening. Furthermore, we revealed substantial shifts in isoform usage that result in alterations of protein sequences from the same gene with distinct carcinogenic effects during tumorigenesis of CRC. Allele-specific expression analysis revealed dominant mutant allele expression in key oncogenes and tumor suppressors. Moreover, mutated PPIG was linked to widespread splicing dysregulation, and functional validation experiments confirmed its critical role in modulating RNA splicing and tumor-associated processes. Our findings highlight the transcriptomic plasticity in CRC and suggest novel candidate targets for splicing-based therapeutic strategies.
Humans ; Colorectal Neoplasms/metabolism* ; RNA Isoforms/metabolism* ; Single-Cell Analysis ; RNA Splicing ; Gene Expression Regulation, Neoplastic ; RNA, Neoplasm/metabolism* ; Transcriptome

Advancements in artificial intelligence (AI) and emerging technologies are rapidly expanding the exploration of chemical space, facilitating innovative drug discovery. However, the transformation of novel compounds into safe and effective drugs remains a lengthy, high-risk, and costly process. Comprehensive early-stage evaluation is essential for reducing costs and improving the success rate of drug development. Despite this need, no comprehensive tool currently supports systematic evaluation and efficient screening. Here, we present druglikeFilter, a deep learning-based framework designed to assess drug-likeness across four critical dimensions: 1) physicochemical rule evaluated by systematic determination, 2) toxicity alert investigated from multiple perspectives, 3) binding affinity measured by dual-path analysis, and 4) compound synthesizability assessed by retro-route prediction. By enabling automated, multidimensional filtering of compound libraries, druglikeFilter not only streamlines the drug development process but also plays a crucial role in advancing research efforts towards viable drug candidates, which can be freely accessed at https://idrblab.org/drugfilter/.

Drug development encompasses multiple processes, wherein protein subcellular localization is essential. It promotes target identification, treatment development, and the design of drug delivery systems. In this research, a deep learning framework called LocPro is presented for predicting protein subcellular localization. Specifically, LocPro is unique in (a) combining protein representations from the pre-trained large language model (LLM) ESM2 and the expert-driven tool PROFEAT, (b) implementing a hybrid deep neural network architecture that integrates convolutional neural network (CNN), fully connected (FC) layer, and bidirectional long short-term memory (BiLSTM) blocks, and (c) developing a multi-label framework for predicting protein subcellular localization at multiple granularity levels. Additionally, a dataset was curated and divided using a homology-based strategy for training and validation. Comparative analyses show that LocPro outperforms existing methods in sequence-based multi-label protein subcellular localization prediction. The practical utility of this framework is further demonstrated through case studies on drug target subcellular localization. All in all, LocPro serves as a valuable complement to existing protein localization prediction tools. The web server is freely accessible at https://idrblab.org/LocPro/.