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.

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.

Coronavirus disease 2019 (COVID-19) is an acute infectious respiratory disease that has been prevalent since December 2019. Chinese medicine (CM) has demonstrated its unique advantages in the fight against COVID-19 in the areas of disease prevention, improvement of clinical symptoms, and control of disease progression. This review summarized the relevant material components of CM in the treatment of COVID-19 by searching the relevant literature and reports on CM in the treatment of COVID-19 and combining with the physiological and pathological characteristics of the novel coronavirus. On the basis of sorting out experimental methods in vivo and in vitro, the mechanism of herb action was further clarified in terms of inhibiting virus invasion and replication and improving related complications. The aim of the article is to explore the strengths and characteristics of CM in the treatment of COVID-19, and to provide a basis for the research and scientific, standardized treatment of COVID-19 with CM.
Humans ; Drugs, Chinese Herbal/pharmacology* ; COVID-19 Drug Treatment ; SARS-CoV-2/drug effects* ; COVID-19/therapy* ; Medicine, Chinese Traditional/methods* ; Antiviral Agents/pharmacology* ; Animals

Objective:To explore the application value of P1 response threshold of cortical auditory evoked potential（CAEP） in evaluating the rehabilitation effect of cochlear implant in young children. Methods:Thirty-three young children after cochlear implantation were divided into groups according to hearing age: Group A（hearing age 1-<2 years old） 10 people; Group B（hearing age 2-<3 years old） 13 people; Group C（hearing age 3-<4 years old） 10 people. The subjective assessment was carried out using the assessment tool for hearing-impaired children- "Criteria and Methods for assessing Auditory and language ability of hearing-impaired children" and objective electrophysiological examination was carried out using CAEP to evaluate the rehabilitation effect. SPSS 25.0 software was used for statistical analysis. Results:The results of subjective assessment of auditory ability and language ability in each group showed an increasing trend with the increase of auditory age. In this study, the P1 response threshold of CAEP in CI implanted children had a significant positive correlation with the 2 kHz hearing threshold after intervention, and the P1 response threshold of CAEP was negatively correlated with many items in subjective auditory ability and language ability assessment. Conclusion:The P1 response threshold of CAEP has a stable correlation with the results of speech audiometry, which can effectively and objectively evaluate the postoperative rehabilitation effect of young children with cochlear implantation.
Humans ; Child, Preschool ; Infant ; Male ; Female ; Evoked Potentials, Auditory ; Cochlear Implantation/rehabilitation* ; Cochlear Implants ; Auditory Threshold

COMPERA 2.0 risk stratification has been demonstrated to be useful in patients with precapillary pulmonary hypertension (PH). However, its suitability for patients at risk for post-capillary PH or PH associated with left heart disease (PH-LHD) is unclear. To investigate the use of COMPERA 2.0 in patients with severe aortic stenosis (SAS) undergoing transcatheter aortic valve replacement (TAVR), who are at risk for post-capillary PH, a total of 327 eligible SAS patients undergoing TAVR at our institution between September 2015 and November 2020 were included in the study. Patients were classified into four strata before and after TAVR using the COMPERA 2.0 risk score. The primary endpoint was all-cause mortality. Survival analysis was performed using Kaplan-Meier curves, log-rank test, and Cox proportional hazards regression model. The study cohort had a median (interquartile range) age of 76 (70‒80) years and a pulmonary arterial systolic pressure of 33 (27‒43) mmHg (1 mmHg=0.133 kPa) before TAVR. The overall mortality was 11.9% during 26 (15‒47) months of follow-up. Before TAVR, cumulative mortality was higher with an increase in the risk stratum level (log-rank, both P<0.001); each increase in the risk stratum level resulted in an increased risk of death (hazard ratio (HR) 2.53, 95% confidential interval (CI) 1.54‒4.18, P<0.001), which was independent of age, sex, estimated glomerular filtration rate (eGFR), hemoglobin, albumin, and valve type (HR 1.76, 95% CI 1.01‒3.07, P=0.047). Similar results were observed at 30 d after TAVR. COMPERA 2.0 can serve as a useful tool for risk stratification in patients with SAS undergoing TAVR, indicating its potential application in the management of PH-LHD. Further validation is needed in patients with confirmed post-capillary PH by right heart catheterization.
Humans ; Aortic Valve Stenosis/complications* ; Aged ; Hypertension, Pulmonary/mortality* ; Male ; Female ; Transcatheter Aortic Valve Replacement ; Aged, 80 and over ; Risk Assessment/methods* ; Proportional Hazards Models ; Kaplan-Meier Estimate ; Retrospective Studies

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.
Epilepsy, Temporal Lobe/diagnosis* ; Animals ; Drug Resistant Epilepsy/drug therapy* ; Electroencephalography/methods* ; Rats ; Anticonvulsants/pharmacology* ; Neural Networks, Computer ; Male ; Humans ; Phenytoin/pharmacology* ; Adult ; Disease Models, Animal ; Female ; Rats, Sprague-Dawley ; Young Adult ; Convolutional Neural Networks

Non-alcoholic fatty liver disease (NAFLD) is a metabolic disease characterized by abnormal deposition of lipid in hepatocytes. If not intervened in time, NAFLD may develop into liver fibrosis or liver cancer, and ultimately threatening life. NAFLD has complicated etiology and pathogenesis, and there are no effective therapeutic means and specific drugs. Currently, insulin sensitizers, lipid-lowering agents and hepatoprotective agents are often used for clinical intervention, but these drugs have obvious side effects, and their effectiveness and safety need to be further confirmed. Adenosine monophosphate (AMP)-activated protein kinase (AMPK) plays a central role in maintaining energy homeostasis. Activated AMPK can enhance lipid degradation, alleviate insulin resistance (IR), suppress oxidative stress and inflammatory response, and regulate autophagy, thereby alleviating NAFLD. Natural herbal medicines have received extensive attention recently because of their regulatory effects on AMPK and low side effects. In this article, we reviewed the biologically active natural herbal medicines (such as natural herbal medicine formulas, extracts, polysaccharides, and monomers) that reported in recent years to treat NAFLD via regulating AMPK, which can serve as a foundation for subsequent development of candidate drugs for NAFLD.

Age-related macular degeneration (AMD) represents a predominant cause of blindness among older adults, with limited therapeutic options currently available. Oxidative stress, inflammation, and retinal pigment epithelium injury are recognized as key contributors to the pathogenesis of AMD. Regulated cell death plays a pivotal role in mediating cellular responses to stress, maintaining tissue homeostasis, and contributing to disease progression. Recent research has elucidated several regulated cell death pathways-such as apoptosis, ferroptosis, pyroptosis, necroptosis, and autophagy-that may contribute to the progression of AMD owing to cell death in the retinal pigment epithelium. These discoveries open new avenues for therapeutic interventions in patients with AMD. In this review, we provide a comprehensive summary and analysis of the latest advancements regarding the relationship between regulated cell death and AMD. Moreover, we examined the therapeutic potential of targeting regulated cell death pathways for the treatment and prevention of AMD, highlighting their roles as promising targets for future therapeutic strategies.