With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

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.

OBJECTIVES: To explore the expression profile of circular RNAs (circRNAs) and their potential roles in prognosis and progression of Wilms' tumor (WT). METHODS: Four pairs of WT and adjacent tissues were collected for high-throughput circRNA sequencing to identify the differentially expressed circular RNAs. RT-qPCR was used to verify the expression levels of the top 6 candidate circRNAs in the clinical samples. hsa_circ_0001900 was selected for analysis of its correlation with clinicopathological features and prognosis in 34 patients with WT. Sanger sequencing and RNase R digestion experiments were used to verify the cycling site and structural stability of hsa_circ_0001900 molecule. RESULTS: A total of 23 978 circular RNA molecules were identified in WT tissues by high-throughput circular RNA sequencing, and among them 614 were differentially expressed in WT. hsa_circ_0001900 showed the highest expression level among the differentially expressed circRNAs, which was consistent with the findings in clinical tumor samples and the sequencing results. Correlation analysis showed that hsa_circ_0001900 expression level was positively correlated with WT volume, and the children with high hsa_circ_0001900 expression had a lowered recurrence-free survival rate. The results of Sanger sequencing verified the circular splice site sequence of the molecule, and Rnase R digestion assay confirmed its stable covalent structure. CONCLUSIONS This study presents a comprehensive expression profile of circular RNAs in WT, and the expression level of hsa_circ_0001900 is related to the size of WT and the patients' prognosis, suggesting its possible role as a key driving gene in WT progression.
Humans ; RNA, Circular ; Wilms Tumor/pathology* ; Prognosis ; High-Throughput Nucleotide Sequencing ; Kidney Neoplasms/genetics* ; Sequence Analysis, RNA ; Male ; Female

Episodic memory, our ability to recall past experiences, is supported by structures in the medial temporal lobe (MTL) particularly the hippocampus, and its interactions with fronto-parietal brain regions. Understanding how these brain regions coordinate to encode, consolidate, and retrieve episodic memories remains a fundamental question in cognitive neuroscience. Non-invasive brain stimulation (NIBS) methods, especially transcranial magnetic stimulation (TMS), have advanced episodic memory research beyond traditional lesion studies and neuroimaging by enabling causal investigations through targeted magnetic stimulation to specific brain regions. This review begins by delineating the evolving understanding of episodic memory from both psychological and neurobiological perspectives and discusses the brain networks supporting episodic memory processes. Then, we review studies that employed TMS to modulate episodic memory, with the aim of identifying potential cortical regions that could be used as stimulation sites to modulate episodic memory networks. We conclude with the implications and prospects of using NIBS to understand episodic memory mechanisms.
Humans ; Memory, Episodic ; Transcranial Magnetic Stimulation/methods* ; Brain/physiology* ; Nerve Net/physiology* ; Mental Recall/physiology* ; Neural Pathways/physiology*

Glutamine provides carbon and nitrogen to support the proliferation of cancer cells. However, the precise reason why cancer cells are particularly dependent on glutamine remains unclear. In this study, we report that glutamine modulates the tumor suppressor F-box and WD repeat domain-containing 7 (FBW7) to promote cancer cell proliferation and survival. Specifically, lysine 604 (K604) in the sixth of the 7 substrate-recruiting WD repeats of FBW7 undergoes glutaminylation (Gln-K604) by glutaminyl tRNA synthetase. Gln-K604 inhibits SCFFBW7-mediated degradation of c-Myc and Mcl-1, enhances glutamine utilization, and stimulates nucleotide and DNA biosynthesis through the activation of c-Myc. Additionally, Gln-K604 promotes resistance to apoptosis by activating Mcl-1. In contrast, SIRT1 deglutaminylates Gln-K604, thereby reversing its effects. Cancer cells lacking Gln-K604 exhibit overexpression of c-Myc and Mcl-1 and display resistance to chemotherapy-induced apoptosis. Silencing both c-MYC and MCL-1 in these cells sensitizes them to chemotherapy. These findings indicate that the glutamine-mediated signal via Gln-K604 is a key driver of cancer progression and suggest potential strategies for targeted cancer therapies based on varying Gln-K604 status.
Glutamine/metabolism* ; Myeloid Cell Leukemia Sequence 1 Protein/genetics* ; Humans ; Proto-Oncogene Proteins c-myc/genetics* ; Cell Proliferation ; Signal Transduction ; Neoplasms/pathology* ; F-Box-WD Repeat-Containing Protein 7/genetics* ; Cell Survival ; Cell Line, Tumor ; Apoptosis

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

Sjögren's syndrome (SS) is an autoimmune disease characterized primarily by oral and periocular dryness. Astragalus-Salvia (AS) and Ophiopogon-Dendrobium (OD) represent two frequently utilized herb pairs in SS treatment. While the combination of AS-OD herb pairs demonstrates clinical efficacy in alleviating SS symptoms, its underlying mechanism remains unclear. This investigation sought to assess the therapeutic effects and elucidate the potential mechanisms of AS-OD in non-obese diabetic (NOD)/Ltj mice with SS. The study utilized NOD/Ltj mice as SS models, administering AS-OD treatment for 10 weeks at doses of 113.1, 226.2, and 339.3 mg·d^-1·20 g^-1. Results demonstrated that AS-OD improved SS symptoms, evidenced by enhanced salivary flow rate, decreased anti-SSA/Ro and anti-SSB/La antibody levels, increased swimming duration, and reduced lactate (LA) and blood urea nitrogen (BUN) levels in NOD/Ltj mice. AS-OD reduced lymphocyte infiltration, enhanced Aquaporin-5 (AQP5) expression in the submandibular gland, decreased inflammatory cytokine levels in the submandibular gland, and reduced the T helper type 17/regulatory T lymphocyte (Th17/Treg) cell ratio in the spleen. Transcriptomic and proteomic analyses indicated AS-OD's involvement in regulating phosphatidylinositol-3-kinase/protein kinase B (PI3K/AKT) and Janus kinase 3/signal transducer and activator of transcription 3 (JAK1/STAT3) pathways, with inhibitory effects validated in both NOD/Ltj mice submandibular gland and A-253 cells. Furthermore, AS-OD enhanced cell viability and reduced A-253 cell apoptosis through the PI3K/AKT pathway. In A-253 cells, AS-OD reduced inflammatory cytokine levels, CXC chemokine ligand 9/10 (CXCL9/10), and T-cell chemotaxis by inhibiting the JAK1/STAT3 pathway. AS-OD mitigates SS by suppressing inflammation and immune responses through the PI3K/AKT and JAK1/STAT3 pathways.
Animals ; STAT3 Transcription Factor/genetics* ; Sjogren's Syndrome/immunology* ; Mice, Inbred NOD ; Proto-Oncogene Proteins c-akt/genetics* ; Phosphatidylinositol 3-Kinases/genetics* ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Signal Transduction/drug effects* ; Janus Kinase 1/genetics* ; Humans ; Female ; Astragalus Plant/chemistry* ; Male

OBJECTIVE: This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications (PCCs) for older patients. METHODS: This secondary analysis of data from a randomized controlled trial involved 1,720 elderly participants in five tertiary hospitals in Beijing, China. Participants aged 60-90 years undergoing major non-cardiac surgery under general anesthesia. The primary outcome metric of the study was the occurrence of PCCs, according to the European Society of Cardiology and the European Society of Anaesthesiology diagnostic criteria. The analysis metrics contained 67 candidate variables, including baseline characteristics, laboratory tests, and scale assessments. RESULTS: Our feature selection process identified key variables that significantly impact patient outcomes, including the duration of ICU stay, surgery, and anesthesia; APACHE-II score; intraoperative average heart rate and blood loss; cumulative opioid use during surgery; patient age; VAS-Move-Median score on the 1st to 3rd day; Charlson comorbidity score; volumes of intraoperative plasma, crystalloid, and colloid fluids; cumulative red blood cell transfusion during surgery; and endotracheal intubation duration. Notably, our Random Forest model demonstrated exceptional performance with an accuracy of 0.9872. CONCLUSION We have developed and validated an algorithm for predicting PCCs in elderly patients by identifying key risk factors.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Cardiovascular Diseases/etiology* ; Machine Learning ; Postoperative Complications/etiology* ; Risk Factors ; Randomized Controlled Trials as Topic ; Secondary Data Analysis

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Objective: To investigate the effects of Zuogui Jiangtang Yishen Formula (左归降糖益肾方, ZGJTYSF) in regulating the nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3)/caspase-1/gasdermin D (GSDMD) signaling axis on pyroptosis in rats with diabetic kidney disease (DKD). Methods: Fifty male specific pathogen-free (SPF) grade Goto-Kakizaki (GK) rats (12 weeks old) were fed a high-fat diet for one month to establish an early DKD model. Model establishment was confirmed when fasting blood glucose (FBG) ≥ 11.1 mmol/L and urinary albumin-to-creatinine ratio (uACR) ≥ 30 mg/g. The successfully modeled early DKD rats were randomly divided by random number table into five groups (n = 10 per group): model group; dapagliflozin group (1.0 mg/kg, by gavage, served as positive control); and low-, medium-, and high-dose of ZGJTYSF groups (4.9, 9.9, and 19.9 g/kg, respectively, by gavage). Age-matched male SPF Wistar rats (n = 10) served as control group. Rats in control and model groups were gavaged with equivalent volumes of distilled water. Treatment lasted 12 weeks. Changes in uACR, FBG, and renal function were observed in all groups. Hematoxylin-eosin (HE), periodic acid-Schiff (PAS), and Masson staining were used to observe renal histopathological changes. Immunohistochemistry was performed to detect the localization and expression of caspase-1, GSDMD, and NLRP3 in rat renal tissues. Terminal deoxynucleotidyl transferase deoxyuridine triphosphate (dUTP) nick end labeling (TUNEL) was utilized to detect pyroptosis in renal tissues. Quantitative real-time polymerase chain reaction (qPCR) and Western blot were applied to detect mRNA and protein expression levels of NLRP3, caspase-1, GSDMD, interleukin (IL)-1β, and IL-18. Results: Compared with model group, all doses of ZGJTYSF showed reductions in FBG, with medium- and high-dose of ZGJTYSF groups demonstrating significant decreases at week 8 and 12 (P < 0.05). For uACR, all doses of ZGJTYSF groups exhibited a decreasing trend, with high-dose of ZGJTYSF group being significantly lower than low- and medium-dose of ZGJTYSF groups at week 12 (P < 0.05) and showing no significant difference from dapagliflozin group (P > 0.05). No significant differences in renal function parameters (serum creatinine, blood urea nitrogen, and uric acid) were observed among groups (P > 0.05). Histopathological examination revealed milder glomerular and tubular lesions in both ZGJTYSF groups and dapagliflozin group, with renal pathological changes in high-dose of ZGJTYSF group resembling those in dapagliflozin group. Immunohistochemistry demonstrated significantly reduced expression of caspase-1, GSDMD, and NLRP3 in renal tissues of dapagliflozin group and high-dose of ZGJTYSF group compared with model group (P < 0.05 or P < 0.01), while the differences in low- and medium-dose of ZGJTYSF groups were not statistically significant (P > 0.05). TUNEL assay showed significantly fewer TUNEL-positive cells in renal tissues of dapagliflozin and high-dose of ZGJTYSF groups (P < 0.01), indicating a marked reduction in pyroptotic cells. Molecular analysis revealed that compared with model group, both dapagliflozin and high-dose of ZGJTYSF groups showed significantly downregulated mRNA and protein expression levels of NLRP3, caspase-1, GSDMD, IL-1β, and IL-18 in renal tissues (P < 0.01), while low- and medium-dose of ZGJTYSF groups showed downward trends without statistical significance (P > 0.05). Conclusion ZGJTYSF may inhibit renal pyroptosis by regulating the NLRP3/caspase-1/GSDMD signaling axis, thereby preventing and treating early renal injury in DKD and delaying the onset and progression of DKD.