[摘 要] 目的：明确线粒体DNA（mtDNA）部分缺失对结肠上皮细胞NCM460的影响。方法：通过溴化乙锭（EB）诱导建立mtDNA部分缺失细胞模型（mtDNA-reduced）及恢复模型（reverted），采用CCK-8、活性氧（ROS）及线粒体膜电位检测评估线粒体功能，通过葡萄糖、乳酸及糖酵解关键酶活性检测评估代谢表型，通过Transwell实验及WB法检测细胞侵袭及上皮-间质转化（EMT），通过polyHEMA包被培养检测失巢凋亡抵抗能力；检测PI3K/Akt/mTOR信号通路及程序性死亡配体1（PD-L1）表达，并通过PI3K抑制剂LY294002验证。结果：GEPIA数据库分析显示mtDNA编码基因在结直肠癌中低表达（P < 0.05）；组织芯片显示COX1蛋白在正常结肠组织高表达，在癌组织中低表达（P < 0.05）。mtDNA-reduced NCM460细胞线粒体功能受损（生长速率下降、ROS生成减少、线粒体膜电位降低，P < 0.05），呈现Warburg效应（葡萄糖摄取增加、乳酸分泌增多、糖酵解关键酶活性增强，P < 0.05），侵袭能力增强（P < 0.05）且发生EMT，并抵抗失巢凋亡（P < 0.05）。该细胞PI3K/Akt/mTOR通路激活且PD-L1表达上调；经LY294002处理后，葡萄糖含量有降低趋势（P > 0.05）、细胞活性下降（P < 0.05），PD-L1表达下调（P < 0.05）。结论：结肠上皮细胞NCM460中mtDNA部分缺失可诱导其恶性转化及免疫逃逸。

Natural product-based drug combinations (NPDCs) present distinctive advantages in treating complex diseases. While high-throughput screening (HTS) and conventional computational methods have partially accelerated synergistic drug combination discovery, their applications remain constrained by experimental data fragmentation, high costs, and extensive combinatorial space. Recent developments in artificial intelligence (AI), encompassing traditional machine learning and deep learning algorithms, have been extensively applied in NPDC identification. Through the integration of multi-source heterogeneous data and autonomous feature extraction, prediction accuracy has markedly improved, offering a robust technical approach for novel NPDC discovery. This review comprehensively examines recent advances in AI-driven NPDC prediction, presents relevant data resources and algorithmic frameworks, and evaluates current limitations and future prospects. AI methodologies are anticipated to substantially expedite NPDC discovery and inform experimental validation.
Artificial Intelligence ; Biological Products/chemistry* ; Humans ; Drug Combinations ; Drug Discovery/methods* ; Machine Learning ; Algorithms

Peri-implant keratinized mucosa (PIKM) augmentation refers to surgical procedures aimed at increasing the width of PIKM. Consensus reports emphasize the necessity of maintaining a minimum width of PIKM to ensure long-term peri-implant health. Currently, several surgical techniques have been validated for their effectiveness in increasing PIKM. However, the selection and application of PIKM augmentation methods may present challenges for dental practitioners due to heterogeneity in surgical techniques, variations in clinical scenarios, and anatomical differences. Therefore, clear guidelines and considerations for PIKM augmentation are needed. This expert consensus focuses on the commonly employed surgical techniques for PIKM augmentation and the factors influencing their selection at second-stage surgery. It aims to establish a standardized framework for assessing, planning, and executing PIKM augmentation procedures, with the goal of offering evidence-based guidance to enhance the predictability and success of PIKM augmentation.
Humans ; Consensus ; Dental Implants ; Mouth Mucosa/surgery* ; Keratins

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/.

Increasing evidence showed that histone deacetylase 6 (HDAC6) dysfunction is directly associated with the onset and progression of various diseases, especially cancers, making the development of HDAC6-targeted anti-tumor agents a research hotspot. In this study, artificial intelligence (AI) technology and molecular simulation strategies were fully integrated to construct an efficient and precise drug screening pipeline, which combined Voting strategy based on compound-protein interaction (CPI) prediction models, cascade molecular docking, and molecular dynamic (MD) simulations. The biological potential of the screened compounds was further evaluated through enzymatic and cellular activity assays. Among the identified compounds, Cmpd.18 exhibited more potent HDAC6 enzyme inhibitory activity (IC₅₀ = 5.41 nM) than that of tubastatin A (TubA) (IC₅₀ = 15.11 nM), along with a favorable subtype selectivity profile (selectivity index ≈ 117.23 for HDAC1), which was further verified by the Western blot analysis. Additionally, Cmpd.18 induced G2/M phase arrest and promoted apoptosis in HCT-116 cells, exerting desirable antiproliferative activity (IC₅₀ = 2.59 μM). Furthermore, based on long-term MD simulation trajectory, the key residues facilitating Cmpd.18's binding were identified by decomposition free energy analysis, thereby elucidating its binding mechanism. Moreover, the representative conformation analysis also indicated that Cmpd.18 could stably bind to the active pocket in an effective conformation, thus demonstrating the potential for in-depth research of the 2-(2-phenoxyethyl)pyridazin-3(2H)-one scaffold.

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

Gastric cancer with peritoneal metastasis (GCPM) is a common and lethal manifestation of advanced gastric cancer, with a median survival of only 5-11 months. This consensus was developed by 30 experts from Asia (China, Japan, Korea, and Singapore) using the Delphi method and the GRADE evidence grading system. A total of 29 statements were formulated, covering the diagnosis and assessment of GCPM, indications for laparoscopic exploration and NIPS (normothermic intraperitoneal and systemic treatment), treatment regimens, prevention and management of complications, criteria for conversion surgery, and postoperative intraperitoneal therapy. The consensus aims to standardize clinical practice and improve the prognosis of patients with GCPM.

Objective To explore the predictive value of fat attenuation index(FAI)and fibrous plaque index(FPI)for the prognosis of patients with acute coronary syndrome(ACS).Methods A retrospective cohort study was conducted on 334 ACS patients undergoing percutaneous coronary intervention(PCI)in the First Affiliated Hospital of Army Military Medical University and Yongchuan Hospital of Chongqing Medical University from March 2021 to July 2023.All patients received coronary computed tomography angiography(CCTA)to measure FAI and FPI.According to the occurrence of major adverse cardiovascular events(MACE)with 1 year of follow-up,they were divided into MACE group(n=108)and non-MACE group(n=226).The baseline data,CCTA data and results of laboratory tests were collected and compared between the 2 groups.Multivariate logistic regression analysis was used to analyze the relationship of FAI and FPI with the prognosis of ACS patients,and ROC curve was drawn to evaluate its predictive efficiency.Results Among the 334 ACS patients,108(32.34%)experienced MACE.When compared with the non-MACE group,the MACE group exhibited significantly larger proportions of diabetes(72.22%vs 31.86%)and left main coronary artery disease(18.52%vs 7.08%),but lower success rate of operation(79.63%vs 93.81%,P<0.05).Radiologic results showed that the proportion of severe stenosis(20.37%vs 10.62%),FAI(-80.12±6.41 HU vs-72.34±7.09 HU)and FPI(0.58±0.41 vs 0.26±0.12)were obviously increased in the MACE group than the non-MACE group(P<0.05).Laboratory tests indicated that there were statistical differences between the 2 groups in high-density lipoprotein-cholesterol(HDL-C,1.20±0.15 vs 1.09±0.16 mmol/L),miR-126(0.91±0.12 vs 0.96±0.15)and SST2(38.45±5.67 vs 34.30±4.89 ng/mL,P<0.05).Multivariate Logistic regression analysis revealed that FAI(OR=1.200,95%CI:1.136～1.268),FPI(OR=63.157,95%CI:14.126～282.374),moderate stenosis(OR=1.332,95%CI:1.024～1.859),severe stenosis(OR=1.480,95%CI:1.074～2.039),miR-126(OR=0.007,95%CI:0.001～0.077),and sST2(OR=1.192,95%CI:1.113～1.277)were independent predictors of MACE(P<0.05).ROC curve analysis displayed that stenosis degree(AUC=0.622,95%CI:0.561～0.683,P=0.001),FAI(AUC=0.790,95%CI:0.741～0.839,P=0.001)and FPI(AUC=0.700,95%CI:0.638～0.761,P=0.001),miR-126(AUC=0.646,95%CI:0.584～0.707,P=0.001),sST2(AUC=0.700,95%CI:0.638～0.761,P=0.001)had certain predictive values for ACS prognosis.Conclusion Coronary FAI and FPI can be used as independent prognostic indicators of ACS patients,and their numerical changes are closely related to plaque stability and inflammatory state.

OBJECTIVE To investigate the expression characteristics and regulatory mechanisms of the circadian clock gene period circadian regulator 1(PER1)and the tumor suppressor gene serine peptidase inhibitor Kazal type 5(SPINK5)in head and neck squamous cell carcinoma(HNSCC),and to elucidate the molecular mechanism by which PER1 regulates SPINK5 transcription via the NF-κB signaling pathway.METHODS Differentially expressed genes in HNSCC were screened using The Cancer Genome Atlas(TCGA)and GSE205155 datasets.The association between SPINK5 expression and patient prognosis was assessed via the GEPIA database.mRNA and protein expression levels of SPINK5 and PER1 in 60 clinical samples were detected by RT-qPCR,immunohistochemistry,and Western blot.PER1 knockdown(using siRNA)and overexpression(via plasmid transfection)were performed in the AMC-HN-8 cell line.Wound healing and colony formation assays were applied to evaluate the effects of PER1,SPINK5,and their interaction on HNSCC cell migration and proliferation.Western blot was utilized to examine the regulatory effect of NF-κB on SPINK5.RESULTS SPINK5 and PER1 were significantly downregulated in HNSCC tissues(all P<0.01),and their low expression was correlated with poor patient prognosis(for SPINK5,HR=0.69,P=0.006 7).A significant positive correlation was observed between PER1 and SPINK5 expression(R2=0.719 2,P=0.001 0).Knockdown and overexpression of PER1 respectively resulted in synchronous alterations in SPINK5 mRNA levels(all P<0.05).PER1 knockdown enhanced cell migration and proliferation(P<0.05),whereas SPINK5 overexpression suppressed these capabilities(P<0.01).Importantly,SPINK5 overexpression reversed the phenotypic changes induced by PER1 knockdown.Mechanistically,PER1 overexpression led to concomitant changes in NF-κB expression,activating the NF-κB pathway and thereby promoting SPINK5 transcription.CONCLUSION PER1 positively regulates SPINK5 transcription via the NF-κB pathway,inhibiting HNSCC cell proliferation and migration.These findings suggest that PER1 and SPINK5 may serve as potential therapeutic targets for HNSCC.

A method for simultaneous determination of 21 kinds of Aconitum alkaloids(ATS)in biological specimens and infused liquor using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)was developed.The biological samples were pretreated with methanol-acetonitrile(1∶2,V/V)for protein precipitation,while infused liquors were diluted 100-fold with acetonitrile,followed by centrifugation,and filtration by a 0.22-μm membrane.Chromatographic separation was carried out on an EC-C18 column using gradient elution with the mixture of 10 mmol/L ammonium acetate and 0.2%formic acid as mobile phase A and acetonitrile as mobile phase B.With this method,all the analytes were separated within 9.5 min.The samples were detected in positive ESI mode with dynamic multiple reaction monitoring(MRM)and quantified via external standard calibration.The results showed that the concentrations of the analytes in the range of 2-1000 ng/mL had excellent linearity(R2>0.9992)with the peak area.The developed method was successfully used for detection of 21 kinds of aconitum alkaloids,with limits of detection of 0.5-2 ng/mL,quantification limits of 2-6 ng/mL,intra/inter-day precision≤6.0%,spiked recoveries of 89.4%-100.9%,extraction recoveries of 74.2%-104.4%,and matrix effects ranging from-11.1%to 9.2%in blood/urine.The method was applied to detection of 12 samples from 4 fatal aconite poisoning cases,and all 21 kinds of ATS with total alkaloid concentrations of 0.04-4.18 μg/mL in blood and 154.96-422.83 μg/mL in medicinal liquors were detected.Tissue distribution revealed that the order of concentrations from highest to lowest is as follows:urine(157.22 μg/mL)>gastric contents(51.37 μg/mL)>kidney(21.6 μg/g)>whole blood(4.18 μg/mL)>liver(0.03 μg/g).This method showed many advantages such as simple pretreatment,low detection limits,accurate quantification,broad analyte coverage,and superior anti-interference capability in complex matrices,proving ideal for forensic and toxicological analysis of aconitum alkaloids.