Background: Malignant pleural effusion (MPE), a common complication of advanced cancers, is associated with poor prognosis and reduced quality of life. Although host–tumor interactions are known to drive MPE development, the associated immune dynamics during disease progression remain unclear. Using a Lewis lung carcinoma-induced MPE model in C57BL/6JNidfc mice, we systematically evaluated general parameters and immune cell changes at two-day intervals throughout disease progression. Results: The day of Lewis lung carcinoma cell injection into the pleural space was designated as day 0. By day 10 post-injection (p.i.), MPE-bearing mice exhibited ~ 10% body weight loss, marking the experimental endpoint. Pleural tumor mass and pleural effusion volume were minimal up to day 4 p.i. but increased sharply from day 6 onward.CD45⁺ immune cell counts rose over time, and days 6, 8, and 10 p.i. marked key stages of MPE progression. On day 6, B cells, T cells, and natural killer cells, but not macrophages and neutrophils, increased significantly compared to earlier timepoints. By day 8, all immune cell subsets except T cells exceeded day 6 levels, and at day 10, natural killer cell numbers declined while others continued to increase. Besides, the numbers of CD8⁺ T cells, Th1 cells, regulatory T cells, and M2 macrophages progressively increased from day 6 to 10. Based on these data, days 6 and 10 were defined as early and advanced MPE stages, respectively, with distinct immune phenotypes. In advanced MPE, CD8⁺ T cells displayed reduced IFN-γ, TNF-α, Granzyme B, Perforin, FasL, and Ki-67, but upregulated PD-1 and CTLA-4 relative to early stage. Similarly, Th1 cells showed decreased IFN-γ, TNF-α, and IL-2 production along with reduced Ki-67 expression. Advanced-stage M2 macrophages exhibited lower MHC-II levels and impaired phagocytosis, but higher PD-L1 and IL-10 production, while neutrophils showed reduced TNF-α release and phagocytic activity. Conclusions Our findings characterize the temporal immune dynamics associated with MPE progression in a mouse model, revealing a transition from an early immunostimulatory state to a late immunosuppressive state. This study enhances our understanding of MPE immunopathogenesis and provides a foundation for developing precise, stagespecific therapeutic strategies.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To propose a framework for risk assessment and stepwise management of pulmonary nodules based on existing guidelines and key studies, and to clarify the boundaries for triage, workup escalation, and referral in different detection scenarios. Methods A problem-oriented evidence synthesis approach was used, prioritizing Chinese expert consensus, Fleischner Society Guidelines, British Thoracic Society (BTS) Guidelines, and Lung-RADS. This was supplemented by studies on risk models, imaging measurements, liquid biopsy, tissue sampling, and surgical treatments. This paper is not presented as a formal consensus or a validated clinical pathway. Results A three-tiered framework consisting of basic non-invasive assessment, advanced assessment, and invasive diagnosis was established. It specified the applicable populations, boundaries for the use of risk models, imaging review, management of conflicting results, evaluation of histopathological consistency, and post-intervention follow-up. Liquid biopsy, genetic testing, and artificial intelligence were defined as investigational or adjunctive tools. Conclusion This framework can serve as a preliminary reference for multidisciplinary teams in developing local protocols. It cannot replace applicable guidelines, pathological diagnosis, shared decision-making with patients, or real-world validation.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Diabetes is a very complex endocrine disease whose common feature is the increase in blood glucose concentration. Persistent hyperglycemia can lead to blindness, kidney and heart disease, neurodegeneration, and many other serious complications that have a significant impact on human health and quality of life. The number of people with diabetes is increasing yearly. The global diabetes prevalence in 20-79 year olds in 2021 was estimated to be 10.5% (536.6 million), and it will rise to 12.2% (783.2 million) in 2045. The main modes of intervention for diabetes include medication, dietary management, and exercise conditioning. Medication is the mainstay of treatment. Marketed diabetes drugs such as metformin and insulin, as well as GLP-1 receptor agonists, are effective in controlling blood sugar levels to some extent, but the preventive and therapeutic effects are still unsatisfactory. Peptide drugs have many advantages such as low toxicity, high target specificity, and good biocompatibility, which opens up new avenues for the treatment of diabetes and other diseases. Currently, insulin and its analogs are by far the main life-saving drugs in clinical diabetes treatment, enabling effective control of blood glucose levels, but the risk of hypoglycemia is relatively high and treatment is limited by the route of delivery. New and oral anti-diabetic drugs have always been a market demand and research hotspot. Inhibitor cystine knot (ICK) peptides are a class of multifunctional cyclic peptides. In structure, they contain three conserved disulfide bonds (C3-C20, C7-C22, and C15-C32) form a compact “knot” structure, which can resist degradation of digestive protease. Recent studies have shown that ICK peptides derived from legume, such as PA1b, Aglycin, Vglycin, Iglycin, Dglycin, and aM₁, exhibit excellent regulatory activities on glucose and lipid metabolism at the cellular and animal levels. Mechanistically, ICK peptides promote glucose utilization by muscle and liver through activation of IR/AKT signaling pathway, which also improves insulin resistance. They can repair the damaged pancrease through activation of PI3K/AKT/Erk signaling pathway, thus lowering blood glucose. The biostability and hypoglycemic efficacy of the ICK peptides meet the requirements for commercialization of oral drugs, and in theory, they can be developed into natural oral anti-diabetes peptide drugs. In this review, the structural properties, activity and mechanism of ICK pattern peptides in regulating glucose and lipid metabolism were summaried, which provided a reference for the development of new oral peptides for diabetes.

Objective To investigate the effects and mechanisms of metformin-induced metabolic intervention on the proliferation and differentiation of myoblasts.Methods C2C12 myoblasts cultured in growth or differentiation media were treated with 1 mmol/L metformin,with DMSO as the control.Cells in growth media were treated for 48 hours,with samples collected every 12 hours for metabolomic analysis,protein expression,mitochondrial function,and proliferation assays.Cells in differentiation media were induced to differentiate for 72 hours,and myotube formation was assessed via embryonic myosin heavy chain immunofluorescence staining.Results Metformin treatment increased intracellular levels of metabolites such as lactate,pyruvate,and adenosine monophosphate,while reducing adenosine triphosphate and thiamine pyrophosphate.Phosphorylation of AMP-activated protein kinase(AMPK)and related protein expression were significantly upregulated(P<0.05).Mitochondrial biogenesis was enhanced,and membrane potential markedly decreased(P<0.05).The proportion of S-phase cells and EdU-positive cells gradually increased(P<0.01),though overall proliferation rate slowed.During differentiation,the myoblast fusion index significantly decreased(P<0.01).Conclusion Metformin induces metabolic reprogramming in myoblasts and suppresses their proliferation and differentiation,with activation of AMPK-related signaling pathways serving as a key molecular mechanism.

Objective To investigate the effects of circular RNA APLP2(circAPLP2)on the proliferation,migra-tion,and invasion of human colorectal cancer(CRC)cell lines by regulating microRNA-455-3p(miR-455-3p)/Stathmin1(STMN1)axis.Methods RT-qPCR was used to detect the expression of circAPLP2,miR-455-3p and STMN1 in CRC and normal colorectal cell lines and the optimal cell line was screened.Proliferation,migra-tion and invasion were detected by MTT assay,scratch assay and Transwell assay,respectively.The relationship between circAPLP2 and miR-455-3p,between miR-455-3p and STMN1 was confirmed by dual Lucifer's reporter gene assay.Results CircAPLP2 and STMN1 were highly expressed in CRC cells,miR-455-3p showed a low ex-pression.Knocking down circAPLP2 resulted in a decrease in rate of survival and of scratch healing rate and in-vasion of SW620 cells,An up regulation of miR-455-3p expression,a down regulation of STMN1,cyclin D1,N-cadherin protein expression and an up regulation of E-cadherin protein expression were also found(P<0.05).Inhibition of miR-455-3p expression reversed inhibitory effect of knocking down circAPLP2 on SW620 cell prolif-eration,migration,and invasion,up-regulated the STMN1,cyclin D1 and N-cadherin protein expression,and down-regulation of E-cadherin protein expression(P<0.05).Dual luciferase reporter gene assay showed that circAPLP2 targeted at negative regulation of miR-455-3p expression,while miR-455-3p targeted at negative reg-ulation of STMN1 expression.Nude mouse transplantation experiment found that knocking down circAPLP2 affect-ed the growth of transplanted tumors,while miR-455-3p expression was up-regulated and STMN1 expression was down-regulated(P<0.05).Conclusions CircAPLP2 inhibits the proliferation,migration and invasion of colorectal cancer cell lines by regulating the miR-455-3p/STMN1 axis.

BACKGROUND: Parkinson's disease (PD) is a leading cause of death and disability worldwide, and is associated with a significant Global Burden of Disease (GBD). We analyzed the trends in PD incidence, mortality, and disability-adjusted life year (DALY) burden in China, and compared them with global data. METHODS: Estimates and 95% uncertainty intervals (UIs) for incidence, mortality, DALYs, years lived with disability (YLDs), and years of life lost (YLLs) for PD were extracted from the GBD, Injuries, and Risk Factors Study 2021. We describe the epidemiology of PD at global and Chinese levels, analyze trends in incidence and mortality from 1990 to 2021 by joinpoint regression models, and decompose PD burden according to population size, age structure, and epidemiological changes. RESULTS: GBD 2021 estimated 508,378 (95% UI: 430,499-592,748) incident cases of PD, 92,035 (95% UI: 75,908-108,133) deaths, and 2,159,514 (95% UI: 1,826,196-2,521,344) DALYs in China, with the higher age-standardized rate (ASR) in incidence, mortality and DALYs than the global levels. The DALY burden of PD in China increased slightly from 1990 to 2021, consistent with the global upward trend. Joinpoint regression analysis indicated that the ASR of incidence in China increased faster than the global average, while the ASR of mortality decreased, with the fastest decline in 2004-2014. Decomposition analysis revealed that men and the middle sociodemographic index (SDI) quintile (32.82%) were responsible for the most significant DALYs, whose changes were primarily driven by population growth and aging. CONCLUSIONS The burden of PD showed an overall increasing trend from 1990 to 2021, which was primarily driven by population growth and aging. This study highlights the significant challenges in controlling and managing PD, including the increase in cases and gender differences, which may provide guidance for comprehensive strategies to address the changing profiles of PD in China.
Humans ; Parkinson Disease/mortality* ; China/epidemiology* ; Global Burden of Disease ; Male ; Incidence ; Female ; Disability-Adjusted Life Years ; Middle Aged ; Aged ; Adult ; Quality-Adjusted Life Years ; Aged, 80 and over ; Cost of Illness ; Adolescent ; Pattern Analysis, Machine

This study aimed to explore the molecular mechanism of rubioncolin C(RuC) in inhibiting gastric cancer(GC). AGS and MGC803 cell lines were selected as cellular models. After treating the cells with RuC at different concentrations, the effects of RuC on the proliferation ability of GC cells were assessed using the CCK-8 method, real-time cellular analysis(RTCA), and colony formation assays. Transmission electron microscopy was used to observe subcellular structural changes. Immunofluorescence was applied to detect LC3 fluorescent foci. Acridine orange staining was used to evaluate the state of intracellular lysosomes. Western blot was employed to detect the expression of autophagy-related proteins LC3Ⅱ, P62, and lysosomal cathepsin D(CTSD). The SuperPred online tool was used to predict the target proteins that bound to RuC, and molecular docking analysis was conducted to identify the interaction sites between RuC and CTSD. The drug affinity responsive target stability(DARTS) assay was performed to detect the direct binding interaction between RuC and CTSD. The results showed that RuC significantly inhibited the proliferation and colony formation of GC cells at low concentrations, with 24-hour half-maximal inhibitory concentrations(IC_(50)) of 3.422 and 2.697 μmol·L~(-1) for AGS and MGC803 cells, respectively. After 24 hours of treatment with RuC at concentrations of 1, 2, and 3 μmol·L~(-1), the colony formation rates for AGS cells were 61.0%±1.5%, 28.0%±0.5%, and 18.2%±0.5%, respectively, while the rates for MGC803 cells were 56.0%±0.5%, 23.3%±1.0%, and 11.8%±1.0%, all of which were significantly reduced. Transmission electron microscopy revealed that RuC promoted an increase in autophagosome formation in GC cells. Immunofluorescence detection showed that LC3 fluorescent foci of GC cells increased with the increase in RuC dose. RuC up-regulated the expression of autophagy-related proteins LC3Ⅱ and P62 in GC cells. Acridine orange staining indicated that RuC altered the acidic environment of lysosomes. SuperPred online prediction identified CTSD as a potential target protein of RuC. Western blot analysis revealed that RuC induced the up-regulation of the inactive precursor of CTSD in GC cells. CTSD activity assays indicated that RuC reduced the activity of CTSD. Molecular docking simulations found that RuC bound to the substrate-binding region of CTSD, forming hydrogen bonds with the Tyr205 and Asp231 residues. Microscale thermophoresis and DARTS assays further confirmed that RuC directly bound to CTSD. In summary, RuC inhibits lysosomal activity by targeting and down-regulating the expression of CTSD, thereby inducing autophagosome accumulation in GC cells.
Humans ; Stomach Neoplasms/enzymology* ; Cathepsin D/chemistry* ; Cell Line, Tumor ; Molecular Docking Simulation ; Cell Proliferation/drug effects* ; Autophagosomes/metabolism* ; Autophagy/drug effects*