Malignant tumors represent a major threat to global health. Conventional anti-tumor pharmacotherapy often encounters challenges such as drug resistance, highlighting an urgent need for the development of novel therapeutic strategies. Fatty acid synthase (FASN), the key enzyme catalyzing de novo fatty acid synthesis, is subject to precise regulation at multiple levels, including transcriptional control, various post-translational modifications such as ubiquitination and phosphorylation, as well as modulation by diverse signaling pathways. Recent studies have revealed that FASN is aberrantly overexpressed in various malignant tumors and is closely associated with tumor progression and poor patient prognosis. FASN is a homodimer composed of seven functional domains that catalyzes the NADPH-dependent condensation of acetyl-CoA and malonyl-CoA to generate saturated fatty acids, primarily palmitic acid. Its stability is regulated by multiple ubiquitin ligases and deubiquitinating enzymes. Additionally, FASN is subject to upstream regulation via neural precursor cell-expressed developmentally downregulated 8 (Nedd8) modification and the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway, thereby establishing a metabolic-signaling positive feedback loop. As a core executor of metabolic reprogramming, FASN promotes tumorigenesis through dual mechanisms. First, its fatty acid synthesis product, palmitate, participates in membrane phospholipid synthesis, lipid raft formation, and protein palmitoylation, thereby activating several key oncogenic signaling pathways, including PI3K/AKT/mTOR, wingless-type MMTV integration site family member (Wnt)/β‑catenin, and signal transducer and activator of transcription 3 (STAT3)/matrix metalloproteinase (MMP), leading to tumor development and progression. Second, FASN plays a pivotal role in modulating the anti-tumor functions of immune cells and remodeling the tumor immune microenvironment. Specifically, FASN enhances immune checkpoint inhibition by inducing programmed death-ligand 1 (PD-L1) palmitoylation, suppresses the activation of cytotoxic T lymphocytes and natural killer cells, and promotes the polarization of M2-type macrophages, consequently facilitating tumor immune evasion and malignant progression. Precisely due to its significant overexpression in tumor cells, its critical functional role, and its differential expression compared to normal cells, FASN has emerged as a highly promising target for anti-tumor drug development. Highly selective small-molecule inhibitors, notably represented by TVB-2640, have advanced to clinical trial stages and demonstrated favorable anti-tumor activity. Furthermore, the combination of FASN inhibitors with other chemotherapeutic agents or targeted drugs can overcome the limitations of monotherapy through synergistic effects or by resensitizing tumor cells to conventional drugs, achieving a “1+1>2” therapeutic outcome. With the advancement of modern traditional Chinese medicine (TCM), numerous active ingredients derived from TCM have been confirmed to exert anti-tumor effects by modulating FASN-related pathways. This integrated approach leverages the precision of Western medicine while simultaneously harnessing the holistic regulatory benefits of TCM to alleviate the side effects of radiotherapy and chemotherapy. Despite the promising prospects of FASN-targeted therapies, challenges remain, including tumor cell metabolic plasticity, tumor context-dependent responses, and heterogeneity. This review systematically summarizes the molecular structure, physiological functions, and mechanisms of FASN in tumorigenesis, as well as recent advances in targeted therapies. Future directions—including the precise identification of responsive patient populations using spatial transcriptomics, the development of novel combination regimens, and the active exploration of integrative strategies combining traditional Chinese and Western medicine—will facilitate the clinical translation of FASN-targeted therapies and open new avenues for improving the quality of life and prognosis of cancer patients.

Lung cancer， with persistently high incidence and mortality rates， remains a significant global health challenge. By taking the study on the experience in diagnosis and treatment of lung cancer with phlegm-dampness and blood stasis syndrome by distinguished traditional Chinese medicine physicians of the Sichuan School as an example， the diagnosis and treatment system for lung cancer with phlegm-dampness and blood stasis syndrome， which was formed in response to the humid and foggy environment of the Sichuan Basin， possesses unique value. However， traditional inheritance modes face challenges such as fragmentation， lack of standardization， and insufficient quantification， which hinder the promotion and application of this experience. This research focused on how to leverage multimodal data and artificial intelligence-generated content （AIGC） to achieve precise analysis， intelligent inheritance， and clinical innovation of the experience in diagnosis and treatment of lung cancer with phlegm-dampness and blood stasis syndrome by distinguished traditional Chinese medicine physicians of the Sichuan School. By integrating multimodal data （encompassing four diagnostic methods of traditional Chinese medicine， modern medical imaging， clinical laboratory tests， molecular biology， and regional environmental information）， a precise diagnosis and treatment system integrating macro and micro perspectives for the "disease， syndrome， and pathogenesis" was constructed. The research yielded the following results： （1） In precise syndrome differentiation， the objective quantification of the phlegm-dampness and blood stasis syndrome was achieved. By constructing a "four diagnostic methods， imaging， and molecule" correlation model， the study revealed intrinsic links between tongue and pulse parameters and the tumor microenvironment， as well as between regional climatic factors and syndrome characteristics， enabling real-time dynamic monitoring of efficacy. （2） In elucidating patterns， the study systematically explored the syndrome differentiation thoughts of Sichuan School physicians， such as the timing of purgation and tonification. A "pathogenesis， syndrome complex， and prescriptions and herb" network model was constructed， which accurately elucidated the synergistic action mechanisms of core herb pairs and quantified the dynamic compatibility patterns of reinforcing healthy Qi and eliminating pathogenic factors. （3） In intelligent empowerment， an auxiliary system integrating intelligent syndrome differentiation， treatment plan generation， and efficacy evaluation was built. This system can fuse regional characteristics with individual data， dynamically generate and optimize personalized prescriptions aligned with the experience of Sichuan School， and predict efficacy trends and potential adverse reactions. The integration of multimodal data and AIGC can effectively facilitate the structured inheritance and clinical translation of distinguished physicians' experience. The established intelligent diagnosis and treatment model integrating traditional Chinese medicine and Western medicine demonstrates clear potential in prolonging patients' progression-free survival， alleviating symptoms， and reducing adverse reactions to treatment. This study provides a referential methodological framework for the traditional Chinese medicine experience in diagnosis and treatment of lung cancer， especially the empirical inheritance and modernized development of regional academic schools. It contributes to advancing clinical diagnosis and treatment toward greater precision and personalization.

Background: High-mobility group box protein 1 (HMGB1) is a chromatin-binding protein involved in arthritis, ischemia, sepsis, atherosclerosis, neurodegenerative disorders, meningitis, and cancer. HMGB1 exhibits dual roles in cancer, acting as either a tumor suppressor or oncoprotein depending on context. Objective: This research aimed to elucidate HMGB1’s functional significance in cutaneous squamous cell carcinoma (cSCC). Methods: We overexpressed HMGB1 in cSCC cell lines using recombinant adenovirus and examined its effects on cell proliferation, colony formation, and cell migration. Results: Immunohistochemical analysis revealed elevated HMGB1 expression levels in cSCC tissue relative to normal epidermis. To assess the influence of HMGB1, we employed recombinant adenoviruses expressing HMGB1 to transduce SCC cell lines (SCC12 and SCC13). Enhanced HMGB1 expression significantly promoted cellular proliferation and colony formation capacity.Notably, HMGB1 overexpression elevated the levels of proliferation regulators, including P63, SOX2, CDK4 and CDK6. Furthermore, HMGB1 overexpression substantially enhanced tumor invasiveness, accompanied by upregulation of epithelial-mesenchymal transition (EMT) biomarkers. Mechanistically, overexpression of HMGB1 enhanced transforming growth factor-β signaling by increasing phosphorylation of SMAD2/3, the key mediators of EMT. Conclusion These data imply that HMGB1 acts as a tumor-promoting factor in cSCC.

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.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

Objective To develop a fused model based on combined intratumor-peritumor radiomics features and clinical risk factors to accurately predict the efficacy of radiotherapy for hepatocellular carcinoma(HCC).Methods A retrospective study was conducted on 124 HCC patients(95 males and 29 females)undergoing IMRT at the radiotherapy center of some hospital,who were divided into a training set and a validation set in a 7∶3 ratio.According to relevant standards for evaluating solid tumor treatment efficacy the patients at the progressive disease phase were enrolled into a non-response group,and the ones at the phases of complete response,partial response and stable disease were included into a response group.Firstly,radiomics features were extracted from pre-radiotherapy contrast-enhanced CT images,and were screened with the least absolute shrinkage and selection operator(LASSO)regression to obtain valuable radiomics features;secondly,the scores of intratumor radiomics features,peritumor radiomics features and combined intratumor-peritumor radiomics features underwent binary Logistic regression(LR)analysis by using the glmnet package in R software(version 4.2.0),so as to construct three models for intratumor radiomics features,peritumor radiomics features and combined intratumor-peritumor radiomics features;finally,a fused model was established based on the scores of combined intratumor-peritumor radiomics features and clinical risk factors,and the corresponding Nomogram,calibration curve and clinical decision curve were plotted.R software(version 4.4.1)was used for image analysis and statistical analysis.Results The fused model based on the scores of combined intratumor-peritumor radiomics features and clinical risk factors achieved an AUC of 0.87 on both the training and validation sets,outperforming the models based solely on intratumor radiomics features,peritumor radiomics features or combined intratumor-peritumor radiomics features.The Nomogram plot showed the prediction probability of the fused model for the short-term treatment response rate was 10%-90%within the total score range of 42-93 points,and the calibration and clinical decision curves indicated that the fused model gained advantages in prediction and clinical application.Conclusion The fused model based on the scores of combined intratumor-peritumor radiomics features and clinical risk factors predicts the short-term efficacy of HCC radiotherapy effectively,providing references for formulating radiotherapy plans and guiding subsequent treatment decisions.

Aim To study the difference in hepatotox-icity of psoralen on normal rats and Yin-deficiency rats from the perspective of lipid metabolism,so as to help explain the mechanism of psoralen cautiously used in patients with Yin deficiency recorded in ancient books.Methods SD rats were randomly divided into the nor-mal control group(carboxymethyl cellulose-Na,CMC-Na),normal administration group(CMC-Na+psor-alen),Yin-deficiency control group(CMC-Na+thy-roxine)and Yin-deficiency administration group(CMC-Na+thyroxine+psoralen).The model of Yin-deficiency was established by thyroxine(1 mg·kg-1)for ten days,and then psoralen(200 mg·kg-1)was given for three days.The serum indexes related to liver injury were detected by automatic biochemical analy-zer,the morphological changes of liver tissue were ob-served using HE and oil red O staining,and the relative transcription levels of lipid metabolism related enzymes and mRNA of transporter and endoplasmic reticulum stress related factors were detected using Real-time PCR.Results After intragastric administration of psoralen for three days,compared with the normal group,the levels of serum alanine aminotransferase(ALT),aspartate transaminase(AST),total bile acid(TBA)and triglyeride(TG)in Yin deficiency group increased more significantly,while TC,ALB and TP de-creased more significantly,and liver HE and oil red O staining showed more obvious lipid degeneration.TG synthesis factors adrenocortical carcinoma(ACC),fatty acid synthase(FASN)and sterolregulatory element binding protein-1(SREBP-1)were down-regulated more significantly,TG transport factors mili-total pro-tein(MTP)and lipoprotein pipase(LPL)were down-regulated more evidently,fatty acid β-oxidation related factors carnitine palmitoyltransferase 1A(CPT1A),carnitine/organic cation transporter 2(OCTN2)and peroxisome proliferators-activated receptors-alpha(PPARα)were down-regulated more apparently,TC transporter adenosine triphosphate binding cassette transporter G8(ABCG8)and bile acid receptor farne-soid X receptor(FXR)were down-regulated more ob-viously,and endoplasmic reticulum stress factor activa-ting transcription factor 4(ATF4)was up-regulated more significantly.Conclusions Psoralen can cause more severe hepatotoxicity in Yin deficiency rats than that in normal administration group,and its mechanism may be related to the disorder of hepatic lipid metabo-lism,aggravation of hepatic cholestasis and steatosis,and activation of endoplasmic reticulum stress re-sponse.

Aim To investigate the effect of Zhenwu decoction on inflammation,oxidative stress and apopto-sis of human glomerular epithelial cells(HGEC)in-duced by lipopolysaccharide(LPS)based on Nrf2/HO-1 signaling pathway,and to explore the underlying mechanism.Methods HGEC were treated with LPS(1.0 mg·L-1)for 24 h to construct an oxidative damage model.On this basis,2.5％,5％and 10％Zhenwu decoction-containing serum were added to the low,medium and high dose groups of Zhenwu decoc-tion,and a normal group was set up.The changes of cell activity were assessed by MTT method and LDH method.The contents of TNF-α,IL-6,IL-10,SOD,CAT,GSH-Px,ROS and MDA in each group were de-tected by ELISA.The apoptosis of each group was de-tected by flow cytometry.The mRNA and protein ex-pressions of Bax,Bcl-2,caspase-3,caspase-9 and Nrf2/HO-1 pathway were detected by RT-qPCR and Western blot,respectively.Results Compared to the normal group,the model group of HGEC exhibited increased levels of inflammatory cytokines,enhanced oxidative stress response and aggravated apoptosis;after inter-vention with various doses of Zhenwu decoction,the in-flammatory levels in HGEC were reduced,oxidative damage and apoptosis were effectively ameliorated,and the mRNA and protein expression levels of the Nrf2/HO-1 signaling pathway were upregulated.Conclu-sions Zhenwu decoction can protect HGEC from LPS-induced inflammation and oxidative damage and im-prove apoptosis.The mechanism may be related to the activation of Nrf2/HO-1 signaling pathway.

Objective To develop a decision support system for clinical nursing plans based on nursing diagno-sis and explore its application effects,in order to provide references for optimizing the clinical nursing process and improving the quality of nursing.Methods A multidisciplinary research team was established to construct a clini-cal nursing plan decision support system framework from 3 aspects,namely nursing diagnosis,nursing interventions,and outcome tracking.The system built a clinical nursing diagnosis decision knowledge base through 3 dimensions,namely basic nursing diagnoses,specialty disease nursing diagnoses,and nursing-related technical diagnoses.Deep learning-based artificial intelligence capture technology was developed to achieve intelligent matching and generate clinical nursing plan forms.Implemented in a tertiary hospital in Yichang City,Hubei Province,a control group(June to August 2024)and an experimental group(October to December 2024)were compared regarding nursing diagnosis implementation rate,nursing plan documentation accuracy,and clinical nursing quality scores.Results This research showed a significant improvements for nursing diagnosis implementation rate increased from 94.88％to 97.25％,and nursing plan documentation accuracy improved from 90.38％to 95.33％.Compared with the control group,the experimental group demonstrated statistically significant enhancements in deep vein thrombosis preven-tion,fall prevention,pressure injury management,unplanned extubation control,bloodstream infection control,catheter-related infection prevention,and key specialty nursing quality indicators(all P＜0.05).Conclusion The nursing di-agnosis-based clinical decision support system effectively improves nurses'diagnostic implementation rates,enhances documentation accuracy of nursing plans,and elevates overall clinical nursing quality.

Objective To investigate whether successful percutaneous coronary intervention(PCI)could improve symptoms and quality of life(QOL)in left main disease and/or 3-vessel disease patients with diabetes.Methods Patients with left main disease and/or 3-vessel disease who underwent PCI in the First Affiliated Hospital of Air Force Medical University from April 2018 to May 2021 were consecutively enrolled and subdivided into 2 groups:diabetes and no diabetes.Detailed baseline characteristics,symptoms,including dyspnea and angina,assessed with the Rose dyspnea scale(RDS),Seattle angina questionnaire(SAQ),the European quality of life-5 dimensions(EQ-5D)and 12-item short-form health survey(SF-12)questionnaire respectively,procedural details,and 1 month and 1 year follow-up data were collected.Results Among 440 left main disease and/or 3-vessel disease patients,disease was present in 176(40.00％),who had more hypertension,peripheral artery disease,and LCX lesion(all P＜0.05).The incidence of major adverse cardiovascular events(MACE)and all-cause mortality were similar between the two groups(both P＞0.05)at 1 month follow-up,while all-cause mortality in diabetes patients was significantly higher than those without diabetes at 1 year follow-up(P=0.013).Low left ventricular ejection fraction was an independent risk factor for MACE and all-cause mortality at 1 month and 1 year follow-up after successful revascularization(all P＜0.05).Most importantly,symptoms,including dyspnea and angina,and QOL were markedly improved regardless of diabetes both at 1 month and 1 year follow-up(all P＜0.05).Diabetes patients showed improved dyspnea and QOL at similar degree to the non-diabetes patients(all P＞0.05)and a more significantly relieved angina(P=0.013).Additionally,the number of chronic total occlusion(CTO)per patient was identified as an independent risk factor of dyspnea(OR 0.723,95％CI 0.525～0.997,P=0.048)and angina relief(OR 0.686,95％CI 0.473～0.995,P=0.047),and the contrast volume(OR 0.995,95％CI 0.992～0.999,P=0.008)as an independent risk factor of QOL improvement in diabetic patients.Conclusions Successful PCI is beneficial for relieving symptoms and improving quality of life in patients with diabetes who have left main disease and/or 3-vessel disease.