Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.

AIM:To explore the effectiveness of predictive models based on quantitative characteristics of multimodal fundus images in assessing the risk of progression of non-proliferative diabetic retinopathy(NPDR).METHODS:A single-center retrospective cohort study was conducted. Patients diagnosed with moderate to severe NPDR between January 2021 and December 2023 in Wuhan Ninth Hospital were selected. Baseline color fundus photography(CFP), optical coherence tomography(OCT), and fundus autofluorescence(FAF)images were collected. Microaneurysm count, hard exudate area, central retinal thickness, retinal nerve fiber layer thickness, and total area of hyperautofluorescent spots were extracted and quantified. All patients were followed up regularly. The primary endpoint was the progression to treatment-requiring proliferative diabetic retinopathy(PDR)or clinically significant diabetic macular edema(DME)during the follow-up period. According to the occurrence of endpoint events during follow-up, the patients were divided into the progression group and the non-progression group. Combined with clinical significance and inter-group comparison results, candidate quantitative imaging parameters were included in the multivariate logistic regression analysis to construct a prediction model. The model was visualized using a Nomogram. The discriminative ability and calibration degree of the model were evaluated via receiver operating characteristic(ROC)curves and calibration curves.RESULTS:A total of 300 patients(300 eyes)completed follow-up, with 93 in the progression group, including 47 males and 46 females, with an average age of 59.02±8.21 y. There were 207 cases in the non-progression group, including 105 males and 102 females, with an average age of 58.01±8.10 y. Multivariate logistic regression showed that the total area of FAF hyperautofluorescent spots(OR=1.580, 95%CI: 1.061-1.718, P=0.002),OCT retinal nerve fiber layer thickness(OR=1.520, 95%CI: 1.307-6.212, P=0.013), and CFP microaneurysm count(OR=1.459, 95%CI: 1.089-2.095, P=0.015)were risk factors for DR progression. The AUC of the Nomogram for predicting progression risk during the follow-up period was 0.889(95%CI: 0.826-0.952), and the calibration curve showed good consistency between predicted probability and actual risk.CONCLUSION: The prediction model integrating quantitative imaging features of CFP, OCT and FAF can effectively identify NPDR patients with high progression risk and exhibits good predictive efficacy, which can serve as a quantitative tool for individualized clinical risk management.

Coronavirus disease 2019(COVID-19)is a respiratory syndrome caused by severe acute respiratory syn-drome coronavirus 2(SARS-CoV-2).Once SARS-CoV-2 was detected in Wuhan(Hubei Province,China),it rap-idly spread widely across China and the world,posing a serious threat to global health and the economy.With the development of Artificial Intelligence(AI)technology and the openness and application of open-source software,tremendous progress has been achieved in the quantitative study of chest radiology for COVID-19,enabling the quantitative dataization of radiological image data,which adds a powerful indicator to the prognosis study of COV-ID-19.Therefore,we reviewed the current literature on the factors predicting the severity and prognosis of COV-ID-19,and summarized demographic,laboratory and radiological factors to help with risk stratification and prog-nosis assessment for COVID-19 patients and assist in their clinical management and treatment.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

The aryl hydrocarbon receptor (AhR) plays a crucial role in regulating many physiological processes. Activating the AhR-CYP1A1 axis has emerged as a novel therapeutic strategy against various inflammatory diseases. Here, a practical in situ cell-based fluorometric assay was constructed to screen AhR-CYP1A1 axis modulators, via functional sensing of CYP1A1 activities in live cells. Firstly, a cell-permeable, isoform-specific enzyme-activable fluorogenic substrate for CYP1A1 was rationally constructed for in-situ visualizing the dynamic changes of CYP1A1 function in living systems, which was subsequently used for discovering the efficacious modulators of the AhR-CYP1A1 axis. Following screening of a compound library, LAC-7 was identified as an efficacious activator of the AhR-CYP1A1 axis, which dose-dependently up-regulated the expression levels of both CYP1A1 and AhR in multiple cell lines. LAC-7 also suppressed macrophage M1 polarization and reduced the levels of inflammatory factors in LPS-induced bone marrow-derived macrophages. Animal tests showed that LAC-7 could significantly mitigate DSS-induced ulcerative colitis and LPS-induced acute lung injury in mice, and markedly reduced the levels of multiple inflammatory factors. Collectively, an optimized fluorometric cell-based assay was devised for in situ functional imaging of CYP1A1 activities in living systems, which strongly facilitated the discovery of efficacious modulators of the AhR-CYP1A1 axis as novel anti-inflammatory agents.

Alzheimer's disease (AD) is a common neurodegenerative disorder among the elderly, and BuChE has emerged as a potential therapeutic target. In this study, we reported the development of compound 8e, a selective reversible BuChE inhibitor (eqBuChE IC₅₀ = 0.049 μmol/L, huBuChE IC₅₀ = 0.066 μmol/L), identified through extensive virtual screening and lead optimization. Compound 8e demonstrated favorable blood-brain barrier permeability, good drug-likeness property and pronounced neuroprotective efficacy. Additionally, 8e exhibited significant therapeutic effects in zebrafish AD models and scopolamine-induced cognitive impairments in mice. Further, 8e significantly improved cognitive function in APP/PS1 transgenic mice. Proteomics analysis demonstrated that 8e markedly elevated the expression levels of very low-density lipoprotein receptor (VLDLR), offering valuable insights into its potential modulation of the Reelin-mediated signaling pathway. Thus, compound 8e emerges as a novel and potent BuChE inhibitor for the treatment of AD, with significant implications for further exploration into its mechanisms of action and therapeutic applications.

Notum, a negative feedback regulator of the Wnt signaling, has emerged as a promising target for treating glucocorticoid-induced osteoporosis (GIOP). This study showcases an efficient strategy for discovering the anti-Notum constituents from herbal medicines (HMs) as novel anti-GIOP agents. Firstly, a rapid-responding near-infrared fluorogenic substrate for Notum was rationally engineered for high-throughput identifying the anti-Notum HMs. The results showed that Bu-Gu-Zhi (BGZ), a known anti-osteoporosis herb, potently inhibited Notum in a competitive-inhibition manner. To uncover the key anti-Notum constituents in BGZ, an efficient strategy was adapted via integrating biochemical, phytochemical, computational, and pharmacological assays. Among all identified BGZ constituents, three furanocoumarins were validated as strong Notum inhibitors, while 5-methoxypsoralen (5-MP) showed the most potent anti-Notum activity and favorable safety profiles. Mechanistically, 5-MP acted as a competitive inhibitor of Notum via creating strong hydrophobic interactions with Trp128 and Phe268 in the catalytic cavity of Notum. Cellular assays showed that 5-MP remarkably promoted osteoblast differentiation and activated Wnt signaling in dexamethasone (DXMS)-challenged MC3T3-E1 osteoblasts. In dexamethasone-induced osteoporotic mice, 5-MP strongly elevated bone mineral density (BMD) and improved cancellous and cortical bone thickness. Collectively, this study constructs a high-efficient platform for discovering key anti-Notum constituents from HMs, while 5-MP emerges as a promising anti-GIOP agent.

Human carboxylesterase 2A (hCES2A) plays pivotal roles in prodrug activation and hydrolytic metabolism of ester-bearing chemicals. Targeted inhibition of intestinal hCES2A represents a feasible strategy to mitigate irinotecan-triggered gut toxicity (ITGT), but the orally active, selective, and efficacious hCES2A inhibitors are rarely reported. Here, a novel drug-like hCES2A inhibitor was developed via three rounds of structure-based drug design (SBDD) and structural optimization. Initially, donepezil was identified as a moderate hCES2A inhibitor from 2000 US Food and Drug Administration (FDA)-approved drugs. Following two rounds of SBDD and structural optimization, a donepezil derivative (B7) was identified as a strong reversible hCES2A inhibitor. Subsequently, nine B7 carbamates were rationally designed, synthesized and biologically assayed. Among all synthesized carbamates, C3 showed the most potent time-dependent inhibition on hCES2A (IC₅₀ = 0.56 nmol/L), excellent specificity and favorable drug-like properties. C3 could covalently modify the catalytic serine of hCES2A with high selectivity, while this agent also showed favorable safety profiles, high intestinal exposure, and impressive effects for ameliorating ITGT in both human intestinal organoids and tumor-bearing mice. Collectively, this study showcases a rational strategy for developing drug-like and serine-targeting covalent inhibitors against target serine hydrolase(s), while C3 emerges as a promising orally active drug candidate for ameliorating ITGT.

Esophageal squamous cell carcinoma(ESCC)is a common malignant tumor with insidious early symptoms and a poor prognosis.In traditional Chinese medicine(TCM),ESCC is classified as"ye ge."Drawing on clinical experience,we believe that kidney deficiency leads to the deficiency of vital qi and immune dysfunction,providing the foundation for cancerous growth by depleting qi and damaging essence,toxic stasis and stagnation,forming a local hypoxic and acidic microenvironment that promotes tumor invasion,metastasis,and recurrence.Considering the effect of modern comprehensive treatments,the occurrence and development of ESCC are summarized as kidney deficiency being the root cause and toxic stasis being the driving force.The pathogenesis and treatment of ESCC in the preoperative,postoperative,and non-surgical treatment stages are discussed.The pathogenesis of the disease is summarized as follows:preoperatively,toxicity and stasis intertwine,depleting the kidney;postoperatively,the kidney loses its vitality,allowing various pathogenic factors to persist;during non-operative treatment,vital qi and pathogens contend,resulting in entrenched toxicity.During the preoperative neoadjuvant phase,therapy should resolve stasis,eliminate toxins,enhance kidney function,tonify essence,and support the body.During the postoperative adjuvant phase,therapy should strengthen the root and consolidate the foundation while detoxifying and expelling stasis.The non-surgical treatment stage uses"balanced interruption,"targeting tumor progression and metastasis by harmonizing yin and yang,thus preventing recurrence.This article will provide insights into the integrative Chinese-Western management of ESCC.

Objective To explore the characteritics of spontaneous brain functional activities for patients with hand dysfunction after ischemic stroke using resting state functional magnetic resonance imaging(rs-fMRI).Methods From August,2024 to June,2025,23 patients with hand dysfunction after ischemic stroke were as patients group,and ten age-matched healthy adults were recruited as healthy control group.The patients group was as-sessed with Fugl-Meyer Assessment-Upper Extremities(FMA-UE),Wolf Motor Function Test(WMFT)and modified Lindmark Scale.All subjects underwent rs-fMRI examinations,and the amplitude of low frequency fluctuations(ALFF),fractional amplitude of low frequency fluctuations(fALFF)and regional homogeneity(Re-Ho)were compared between two groups.The resting state functional network was constructed based on indepen-dent component analysis,and the functional connectivity(FC)were analyzed.The rs-fMRI indicators of the pa-tients and the clinical scale scores were analyzed.Results Compared with the healthy control group,the zALFF value decreased in the precentral gyrus(P<0.05,FWE corrected)in the patient group,as well as the values of zfALFF in Cerebelum-Crus2-R(P<0.05,FWE correct-ed),and ReHo values in the putamen(P<0.05,corrected by Alpha Sim).FC of the inferior frontal gyrus(IF-Goperc.R)in the executive control network enhanced(P<0.05,Alpha Sim corrected),while FC of the middle oc-cipital gyrus(MOG.L)in the dorsal attention network(DAN)weakened(P<0.05,Alpha Sim corrected).There was a positive correlation between FC of MOG.L in the patient's DAN and the scores of FMA-UE(r=0.439,P=0.036),WMFT(r=0.516,P=0.012),and modified Lindmark Scales(r=0.425,P=0.043).Conclusion The resting state spontaneous activity of some brain areas of the cerebrum,cerebellum and basal ganglia is reduced in patients with hand dysfunction after ischemic stroke,and some brain network connections are abnor-mal.FC of MOG.L may serve as an objective imaging marker for evaluating hand dysfunction.