With the aging population and the widespread implementation of lung cancer screening programs, an increasing number of elderly patients are undergoing curative lung resection. Due to diminished physiological reserve and complex comorbidities, this demographic exhibits a significantly higher incidence of severe perioperative complications (defined as Clavien-Dindo grade≥Ⅲ), which adversely affects both perioperative safety and long-term prognosis. In recent years, the research paradigm has shifted from univariate analysis toward multidimensional risk integration and the development of predictive models. This article systematically reviews the key risk factors for severe perioperative complications in elderly lung cancer patients, encompassing biological aging processes, frailty and sarcopenia, cardiopulmonary and renal functional reserves, inflammatory-immune and coagulation status, and perioperative interventions. Furthermore, it traces the evolution of risk assessment tools from traditional regression models to machine learning models that integrate multimodal data. The review also discusses common challenges in this field, including the standardization of outcome definitions, external validation, calibration assessment, and clinical translation. Future efforts should prioritize the deep integration of predictive tools with clinical decision support systems to establish a closed-loop care pathway from risk identification to stratified intervention, thereby effectively reducing complication rates and enhancing surgical outcomes for elderly lung cancer patients.

OBJECTIVE: To investigate the effects of acupuncture on the neurotransmitter levels and gut microbiota in a mouse model of Tourette syndrome (TS). METHODS: Thirty-six male C57/BL6 mice were randomly divided into 4 groups using a random number table method: 3,3'-iminodipropionitrile (IDPN) group, control group, acupuncture group, and tiapride group, with 9 mice in each group. In the IDPN group, acupuncture group, and tiapride group, mice received daily intraperitoneal injections of IDPN (300 mg/kg body weight) for 7 consecutive days to induce stereotyped behaviors. Subsequently, in the acupuncture intervention group, standardized acupuncture treatment was administered for 14 consecutive days to IDPN-induced TS model mice. The selected acupoints included Baihui (DU 20), Yintang (DU 29), Waiguan (SJ 5), and Zulinqi (GB 41). In the tiapride group, mice were administered tiapride (50 mg/kg body weight) via oral gavage daily for 14 consecutive days. The control group, IDPN group, and acupuncture group received the same volume of saline orally for 14 consecutive days. Stereotypic behaviors were quantified through behavioral assessments. Neurotransmitter levels, including dopamine (DA), glutamate (Glu), and aspartate (ASP) in striatal tissue were measured using enzyme-linked immunosorbent assay. Dopamine transporter (DAT) expression levels were additionally quantified through quantitative polymerase chain reaction (qPCR). Gut microbial composition was analyzed through 16S ribosomal RNA gene sequencing, while metabolic profiling was conducted using liquid chromatography-mass spectrometry (LC-MS). RESULTS: Acupuncture administration significantly attenuated stereotypic behaviors, concurrently reducing striatal levels of DA, Glu and ASP concentrations while upregulating DAT expression compared with untreated TS controls (P<0.05 or P<0.01). Comparative analysis identified significant differences in Muribaculaceae (P=0.001), Oscillospiraceae (P=0.049), Desulfovibrionaceae (P=0.001), and Marinifilaceae (P=0.014) following acupuncture intervention. Metabolomic profiling revealed alterations in 7 metabolites and 18 metabolic pathways when compared to the TS mice, which involved various amino acid metabolisms associated with DA, Glu, and ASP. CONCLUSIONS Acupuncture demonstrates significant modulatory effects on both central neurotransmitter systems and gut microbial ecology, thereby highlighting its dual therapeutic potential for TS management through gut-brain axis regulation.
Animals ; Tourette Syndrome/metabolism* ; Gastrointestinal Microbiome ; Neurotransmitter Agents/metabolism* ; Acupuncture Therapy ; Male ; Mice, Inbred C57BL ; Mice

Given that ovarian stimulation is vital for assisted reproductive technology (ART) and results in elevated serum estrogen levels, exploring the impact of elevated estrogen exposure on oocytes and embryos is necessary. We investigated the effects of various ovarian stimulation treatments on oocyte and embryo morphology and gene expression using a mouse model and estrogen-treated mouse embryonic stem cells (mESCs). Female C57BL/6J mice were subjected to two types of conventional ovarian stimulation and ovarian hyperstimulation; mice treated with only normal saline served as controls. Hyperstimulation resulted in high serum estrogen levels, enlarged ovaries, an increased number of aberrant oocytes, and decreased embryo formation. The messenger RNA (mRNA)‍-sequencing of oocytes revealed the dysregulated expression of lysine-specific demethylase 6b (Kdm6b), which may be a key factor indicating hyperstimulation-induced aberrant oocytes and embryos. In vitro, Kdm6b expression was downregulated in mESCs treated with high-dose estrogen; treatment with an estrogen receptor antagonist could reverse this downregulated expression level. Furthermore, treatment with high-dose estrogen resulted in the upregulated expression of histone H3 lysine 27 trimethylation (H3K27me3) and phosphorylated H2A histone family member X (γ-H2AX). Notably, knockdown of Kdm6b and high estrogen levels hindered the formation of embryoid bodies, with a concomitant increase in the expression of H3K27me3 and γ-H2AX. Collectively, our findings revealed that hyperstimulation-induced high-dose estrogen could impair the demethylation of H3K27me3 by reducing Kdm6b expression. Accordingly, Kdm6b could be a promising marker for clinically predicting ART outcomes in patients with ovarian hyperstimulation syndrome.
Female ; Mice ; Demethylation/drug effects* ; Embryonic Stem Cells ; Estrogens/administration & dosage* ; Gene Expression/drug effects* ; Histones/metabolism* ; Jumonji Domain-Containing Histone Demethylases/metabolism* ; Mice, Inbred C57BL ; Oocytes ; Ovary/drug effects* ; Reproductive Techniques, Assisted ; Animals

Objective:To investigate the feasibility of areal bone mineral density (aBMD) quantification and the diagnostic performance for abnormal bone mass using the coronal spectral localizer radiography (SLR) from photon-counting detector CT (PCD-CT).Methods:This cross-sectional study prospectively enrolled 67 participants who underwent both dual-energy X-ray absorptiometry (DXA) and dual-source PCD-CT examinations within 7 days at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine between April 2024 and September 2025. The aBMD values of L1-L4 derived from DXA and calculated based on PCD-CT SLR were obtained (aBMD DXA and aBMD SLR, respectively). Participants were categorized into four groups based on body mass index (BMI): underweight (BMI<18.5 kg/m2, n=3), normal weight (18.5 kg/m2≤BMI<24 kg/m2, n=33), overweight (24 kg/m2≤BMI<28 kg/m2, n=22), and obese (BMI≥28 kg/m2, n=9) groups. Using aBMD DXA as the golden reference, T-scores were calculated, and participants were classified into normal bone mass (T-score≥-1.0, n=42), osteopenia (-2.50.05). Across the normal bone mass, osteopenia, and osteoporosis groups, significant differences were found in aBMD DXA, aBMD SLR, and AE ( P<0.001), but not in RAE ( P=0.426). Using aBMD SLR-based T-score classification, 38, 16, and 13 participants were classifying as having normal bone mass, osteopenia, and osteoporosis. For diagnosing osteoporosis, aBMD SLR achieved an accuracy of 94.0% (63/67), sensitivity of 90.9% (10/11), and specificity of 94.6% (53/56). For detecting abnormal bone mass, the accuracy, sensitivity, and specificity were 94.0% (63/67), 100% (25/25), and 90.5% (38/42), respectively. Conclusions:Quantitative bone density assessment based on PCD-CT SLR is feasible and accurate, unaffected by body habitus or bone mass status, and demonstrates high diagnostic performance for osteoporosis and abnormal bone mass.

Objective:To observe the effect of intensified heel anti-gravity posture stability training on the gait of stroke survivors.Methods:Thirty-six hemiplegic stroke survivors were randomly divided into a control group and an experimental group, each of 18. Both groups received conventional rehabilitation treatment (including limb positioning, limb movement training and conventional walking training), while the experimental group was additionally provided with 30 minutes of intense heel anti-gravity posture stability training 5 days a week for 4 weeks. Before and after the treatment, the subjects′ walking speed, stride frequency, step length, peak knee flexion angle and peak hip flexion angle were documented using three-dimensional gait analysis with a movement training system.Results:The average walking speed, stride rate, step length, peak hip flexion and peak knee flexion of both groups had improved significantly after the treatments. But the average walking speed [(0.46±0.06)m/s], step length [(85.05±6.68)cm], peak hip flexion angle [(34.80±2.80)°] and peak knee flexion angle [(40.55±3.58)°] of the treatment group were significantly better than those of the control group.Conclusions:Intensified heel anti-gravity posture stability training can significantly improve the walking speed, step length, hip flexion and knee flexion of hemiplegic stroke survivors.

Objective:To quantify thoracic and abdominal movements during breathing using inertial measurement units (IMUs) and to build a machine learning model which identifies the abdominal breathing (AB) pattern.Methods:Ten rehabilitation therapists formed the study′s professional group, while 15 patients receiving AB training comprised the validation group. Two synchronized IMUs were applied to capture breathing motions during natural breathing (NB), deep breathing (DB) and AB. Six kinematic features were extracted from each respiratory cycle, and inter-group and inter-pattern differences were analyzed. Correlation analysis was also performed with manually measured changes in thoracic and abdominal circumferences. A support vector classification model for AB pattern detection was then developed using data from the professional and validation groups.Results:A total of 1113 respiratory cycles were extracted and analyzed. The breathing pattern significantly influenced all of the kinematic features studied (0.21≤partial η 2≤0.65, all P≤0.001). The ranges of the angles in medial-lateral axis of the IMUs showed strong correlation with the changes in abdominal and thoracic circumferences (ρ1=0.928, ρ2=0.807, P≤0.001 in both cases). A greater range of abdominal angles was found during AB compared to the other patterns. The best of the models achieved an F1 score of 0.970 (sensitivity: 0.983, specificity: 0.980) in validation. Conclusions:AB generates the greatest abdominal movement. Combining IMUs and machine learning can provide real-time quantification of chest movement and accurate detection of AB during breathing training.

Objective To investigate the sputum metabolic profiles of patients with occupational coal workers' pneumoconiosis (CWP) by an untargeted metabolomics method, and to identify relevant differential metabolic pathways and potential biomarkers. Methods A total of 12 male patients with stage Ⅰ CWP were selected as the CWP group, and 16 healthy male individuals were selected as the control group, using a judgmental sampling method. Sputum metabolites of individuals in both groups were detected to perform non-targeted metabolomic analysis using the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. Differential metabolites (DMs) and their pathways were screened using principal component analysis, partial least squares discriminant analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Potential biomarkers were analyzed and identified via the receiver operating characteristic curve (ROC). Results There were apparent metabolic alterations observed in sputum of CWP patients compared with healthy controls. In the positive ion mode, a total of 42 DMs were identified in sputum from CWP patients, including 19 downregulated and 23 upregulated metabolites. In the negative ion mode, a total of 25 DMs were identified in sputum from CWP patients, including 16 downregulated and 9 upregulated metabolites. KEGG enrichment analysis of sputum from CWP patients showed that seven DMs pathways were enriched in ABC transporters, histidine metabolism, phenylalanine metabolism, arachidonic acid metabolism, linoleic acid metabolism, purine metabolism, and oxidative phosphorylation, involving 26 DMs. ROC analysis indicated that 16(R)-hydroxyarachidonic acid, pyrophosphate, and 2-hydroxyphenylacetate of these 26 DMs may serve as potential biomarkers for CWP. Conclusion Sputum metabolomic profiles were altered in CWP patients compared with healthy controls. The potential biomarkers of CWP prevention and treatment are 16(R)-hydroxyarachidonic acid, pyrophosphate, and 2-hydroxyphenylacetate.

With the rapid advancement of artificial intelligence technology, chatbots have shown great potential in the healthcare sector. From personalized health advice to chronic disease management and psychological support, chatbots have demonstrated significant advantages in improving the efficiency and quality of healthcare services. As the scope of their applications expands, the relationship between technological complexity and practical application scenarios has become increasingly intertwined, necessitating a more comprehensive evaluation of both aspects. This paper, from the perspective of he althcare applications, systematically reviews the technological pathways and development of chatbots in the medical field, providing an in-depth analysis of their performance across various medical scenarios. It thoroughly examines the advantages and limitations of chatbots, aiming to offer theoretical support for future research and propose feasible recommendations for the broader adoption of chatbot technologies in healthcare.

Objective: To investigate the status of drug resistance before anti-retroviral therapy among newly dignosed HIV/AIDS patients aged ≥50 years in Yangzhou City, Jiangsu Province, so as to provide the evidence for improving the anti-retroviral therapy effect of AIDS. Methods: HIV/AIDS patients aged ≥50 years who were newly dignosed in Yangzhou City from 2021 to 2024 and did not receive anti-retroviral therapy were selected. Basic information were collected through the Chinese Disease Prevention and Control Information System. Blood samples were collected to determine CD4+T lymphocyte (CD4 cell) counts and HIV-1 viral load. Following nucleic acid extraction, the pol gene region was amplified using reverse transcription and nested PCR, and subsequently subjected to Sanger sequencing. The resulting sequences were uploaded to the Stanford University HIV Drug Resistance Database to analyze drug resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), and nucleoside reverse transcriptase inhibitors (NRTIs). Results: Totally 404 blood samples from HIV/AIDS patients were collected, with successful sequencing of the pol gene region in 341 cases. Among them, 253 (74.19%) were males and 88 (25.81%) were females, with a mean age of (62.48±7.60) years. A total of 152 cases (44.57%) had CD4 cell counts below 200 cells/μL, and 296 cases (86.80%) had HIV-1 viral loads exceeding 5 000 copies/mL. A total of 87 cases exhibited drug resistance-associated mutations, corresponding to a mutation rate of 25.51%. The predominant mutation site was V179, with a mutation rate of 17.01%. A total of 29 cases exhibited resistance to at least one drug, resulting in a resistance rate of 8.50%. The resistance rates to NNRTIs, PIs, and NRTIs were 5.57%, 2.93%, and 1.17%, respectively. The HIV/AIDS patients exhibited varying degrees of resistance to 13 anti-retroviral drugs, with low- or intermediate-level drug resistance being predominant. High-level drug resistance cases were observed against NNRTIs such as nevirapine and efavirenz. Conclusions The drug resistance rate before anti-retroviral therapy among newly dignosed HIV/AIDS patients aged ≥50 years in Yangzhou City was at a moderate level. The predominant resistance mutation was observed at V179 site, with NNRTIs resistance being most prevalent, primarily demonstrating low- or intermediate-level drug resistance.

OBJECTIVE: To analyze the rules of moxibustion for low back pain by ZHOU Meisheng by using data mining and knowledge graph technology. METHODS: Taking the medical cases of moxibustion for low back pain from ZHOU Meisheng's legacy manuscripts and existing works as the research objects, information on disease types, symptoms, tongue manifestations, pulse conditions, syndrome patterns, moxibustion methods and acupoints were collected. Frequency statistics and community analysis were conducted by the ancient and modern medical record cloud platform V 2.3.7, cluster analysis of high-frequency acupoints was performed by SPSS26.0, association rule analysis of high-frequency acupoints was performed by SPSS Modeler 18.0, and the generated linked data were imported into Cytoscape 3.9.1 for complex network analysis. Knowledge graph of moxibustion for low back pain by ZHOU Meisheng was constructed based on the results of data mining. The data storage and display of knowledge graph were realized through the Neo4j 3.5.25 graph database, and the Cypher query language was used for knowledge graph retrieval and discovery. RESULTS: A total of 219 medical cases were collected, involving 14 disease types, 85 related clinical symptoms, 5 related TCM syndrome types, and 6 moxibustion methods. The acupoints were mostly attributed to the governor vessel, the bladder meridian of foot-taiyang, non-meridian and non-acupoint areas. The core prescription of acupoints derived from complex network analysis included tender points, Shenshu (BL23), Jinsuo (GV8), Yinjiao (CV7), Yaoyangguan (GV3), Yanglingquan (GB34), which were largely coincides with high-frequency acupoints. Cluster analysis obtained 4 cluster combinations. Community analysis yielded 6 communities, each corresponding to different acupoints.The constructed knowledge graph contained 187 nodes and 696 relationships, by retrieving clinical elements related to low back pain, the disease-moxibustion association graph, disease-acupoint association graph, accompanying symptom-acupoint association graph and syndrome type-matching point association graph were obtained. CONCLUSION When treating low back pain with moxibustion, ZHOU Meisheng adopts the principle of promoting circulation, distinguishing diseases to determine the treatment, selecting acupoints according to the diseases, and matching points according to the symptoms.And taking tender points, Shenshu (BL23), Jinsuo (GV8), Yinjiao (CV7), Yaoyangguan (GV3), Yanglingquan (GB34) as core acupoints, combined with tenderness point selection, acupoint selection based on meridian and zangfu syndrome differentiation, "sunshine area" acupoint selection, yin-yang acupoint matching. Additionally, he skillfully employs special points, such as Zhongzhu (KI15) and ear tips, pays attention to the reform of moxibustion tools, and innovates the moxibustion techniques, using distinctive moxibustion tools and methods to treat low back pain.
Moxibustion/methods* ; Humans ; Data Mining ; Low Back Pain/history* ; Acupuncture Points ; History, Ancient ; Female ; China ; Male ; Adult ; Middle Aged