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.

Elderly patients with lung cancer have a significantly increased risk of perioperative cardiopulmonary complications due to physiological decline, high incidence of complications and reduced surgical tolerance, which directly affects postoperative recovery and long-term survival. Although the concepts of minimally invasive surgery and enhanced recovery after surgery have improved clinical outcomes, early recognition and intervention of postoperative complications in elderly patients remains a significant challenge in the field of thoracic surgery. By integrating recent literature and clinical practice, this paper systematically analyzes the pathophysiological mechanism and risk factors of perioperative cardiopulmonary complications in elderly patients with lung cancer, and discusses individualized intervention strategies based on risk stratification and multidisciplinary team, in order to provide theoretical basis and practical guidance for optimizing perioperative management and improving postoperative prognosis in elderly patients.

Objective: To evaluate the diagnostic performance of magnetic resonance spectroscopy (MRS) in discriminating fumarate hydratase-deficient (FH-d) uterine leiomyomas (ULs) from FH-preserved ULs. Materials and Methods: This study consisted of three stages, with independent cohorts recruited for each stage: 1) sample-size estimation was retrospectively performed on UL specimens (diameter ≥3 cm; age, 20–40 years) from our database with immunohistochemistry (IHC) for 2-succinocysteine (2-SC) as the reference, without genetic testing, 2) MRS sequence optimization in confirmed FH germline mutation participants with ultrasound-detected ULs (diameter ≥3 cm), without IHC analysis, and 3) prospective diagnostic test accuracy was evaluated in consecutive participants with ultrasound-detected ULs (diameter ≥3 cm;age, 20–40 years), using IHC for 2-SC for determining the FH status and subsequent genetic testing in those with positive 2-SC results to identify whether FH mutations were germline or somatic in origin. The choline and fumarate peaks in MRS were classified as positive, negative, or technical failure (TF). TFs were analyzed separately and excluded from the primary diagnostic accuracy calculations. T1-, T2-, and diffusion-weighted images were interpreted as hyperintense or hypointense. The enhancement rate and apparent diffusion coefficient were also acquired. Diagnostic performance was compared between MRS and various magnetic resonance imaging (MRI) features. Results: The optimal MRS parameters for the fumarate peak were echo time (TE) = 140 ms and an average of 256. Among the 360 prospective participants, 37 were confirmed to have FH-dULs. MRS showed positive fumarate peaks in 35 of 37 FH-dULs.After excluding six TFs, the positive fumarate peak on MRS showed 94.6% (35/37) sensitivity, 99.7% (316/317) specificity, and 99.2% (351/354) accuracy, all of which were significantly superior to those of other MRI features (P ≤ 0.002). Conclusion A positive fumarate peak on MRS may be a useful imaging biomarker for diagnosing FH-dULs.

Background Long-term exposure to ambient fine particulate matter (PM_2.5) is a significant risk factor for cardiometabolic disorders. However, the mechanisms of its interaction with the endogenous circadian system remain incompletely understood. Objective To investigate whether chronic PM_2.5 exposure interferes with the rhythmic expression of the cardiac circadian clock, thereby disrupting downstream antioxidant defenses and metabolic homeostasis, and ultimately driving cardiometabolic dysfunction. Methods Seventy-two male C57BL/6 mice were randomly divided into a PM_2.5 exposure group (PM group) and a filtered air control group (FA group). Whole-body exposure was conducted for 8 weeks in a meteorological environmental animal exposure system. Samples were collected at six distinct zeitgeber time (ZT) points post-exposure. The 24 h ambulatory blood pressure and serum lipid profiles were monitored. Rhythm parameters were derived via cosinor analysis to compare differences in Midline statistic of rhythm (Mesor), amplitude, and phase between the two groups. The rhythmic expression of core circadian clock genes and antioxidant genes in the myocardium was detected by quantitative polymerase chain reaction (qPCR). Myocardial reactive oxygen species (ROS) levels and downstream pathway protein expression were analyzed by immunofluorescence and Western blot (WB), respectively. The expression changes of the clock gene retinoic acid receptor-related orphan receptor α (RORα) were assessed at both the mRNA and protein levels. Finally, Spearman correlation analysis was used to explore the relationships among myocardial RORα expression, lipid profiles, and oxidative stress indicators. Results Compared to the FA group, mice in the PM group exhibited a blunted circadian rhythm in blood pressure, characterized by sustained elevation throughout the day. Chronic PM_2.5 exposure showed a significant interaction with ZT on systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) (F-interaction=9.11, 5.70, and 6.02, respectively; P<0.05), as well as on serum triglycerides (TG), total cholesterol (T-CHO), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) (F-interaction=16.32, 11.12, 15.39, and 28.09, respectively; P<0.05). Cosinor analysis further revealed that the Mesor values of T-CHO, TG, and LDL-C were significantly increased (P<0.05), while that of HDL-C was significantly decreased in the PM group (P<0.05). The oscillation amplitudes of SBP, DBP, and MAP showed a decreasing trend, whereas those of TG and LDL-C were significantly increased (P<0.05). Furthermore, SBP, T-CHO, and HDL-C all exhibited a significant phase delay (P<0.05). Mechanistically, PM_2.5 exposure significantly suppressed the expression of the positive circadian regulator RORα in the myocardium, leading to disordered rhythmic expression of core clock genes (Bmal1, Clock, Per1/2, and Cry1/2). This exposure also inhibited the rhythmic expression of antioxidant genes (GPX1, SOD2, and CAT), resulting in increased ROS generation and elevated expression of calcium/calmodulin-dependent protein kinase II (CaMKII) and reduced nicotinamide adenine dinucleotide phosphate (NADPH) proteins. Correlation analysis further revealed that myocardial RORα expression level was negatively correlated with T-CHO, TG, and LDL-C (r=−0.55, −0.63, and −0.51, respectively; P<0.001), and positively correlated with HDL-C (r=0.37, P=0.010), and antioxidant genes GPX1, SOD2, and CAT expression (r=0.34, 0.35, and 0.56, respectively; P < 0.001). Conclusion Chronic PM_2.5 exposure induces cardiometabolic dysfunction by suppressing myocardial RORα expression. This suppression disrupts the cardiac circadian clock and the diurnal balance of oxidative stress, triggering oxidative damage and elevating expression of CaMKII/NADPH pathway proteins. Collectively, these alterations precipitate the loss of cardiac metabolic rhythms and subsequent functional impairment.

In the field of oral medicine, 3D-printed individualized titanium mesh technology is gradually becoming an important means for the treatment of severe alveolar bone defect augmentation. This article provides a comprehensive analysis of the advantages of this technology, the evaluation of osteogenic effects, and the progress of research in clinical applications. In response to the current issue of variability in bone augmentation outcomes, this paper delves into multiple factors affecting bone augmentation effects, including individualized titanium mesh design (involving the thickness, pore size, pore shape, porosity, contour shape, selection of titanium alloy materials, and 3D printing technology), intraoperative procedures (the accuracy of placement during 3D-printed individualized titanium mesh surgery), and postoperative care (including the prevention of complications, formation of pseudoperiosteum, and stability of the titanium mesh). By integrating the clinical experience and research findings of our team, we propose a series of targeted optimization strategies, including designing, manufacturing, and clinically applying self-positioning individualized titanium meshs (positioning wings + individualized titanium meshs) to improve the positioning accuracy of the titanium mesh; propose individualized treatment processes and titanium mesh design schemes based on specific conditions of alveolar bone defects and soft tissue status; and emphasize the importance of long-term stable fixation of the titanium mesh to reduce the risk of postoperative mesh loosening and displacement. In addition, we appropriately summarize the evaluation methods for the bone augmentation effects of 3D-printed individualized titanium meshes, covering the following key indicators: (1) vertical bone augmentation and horizontal bone augmentation; (2) changes in bone contour morphology; (3) bone volume increase; (4) clinical indicators (surgical success rate, titanium mesh exposure, infection rate, and postoperative recovery); (5) aesthetic effect evaluation; (6) long-term stability; (7) radiological assessment; (8) patient satisfaction; and (9) precision of surgical operation, aiming to assist doctors in comprehensively assessing and in-depth analyzing the surgical outcomes to achieve the best therapeutic effects. The purpose of this article is to provide a reference for the optimization and clinical application of 3D-printed individualized titanium mesh technology and to lay a theoretical foundation for achieving the best osteogenic effects.

OBJECTIVE To systematically evaluate risk prediction models for chemotherapy-induced myelosuppression in breast cancer， and provide a scientific reference for clinical healthcare workers in selecting or developing effective predictive models. METHODS A systematic search was conducted for studies on predictive models of the risk of chemotherapy-induced myelosuppression in breast cancer across the CNKI， VIP， Wanfang， PubMed， Web of Science， Cochrane Library， Embase， and Scopus databases， with a time frame of the establishment of the database to May 7， 2024. Literature was independently screened by 2 investigators， data were extracted according to critical appraisal and data extraction for systematic reviews of predictive model studies， and the risk of bias evaluation tool for predictive model studies was used to analyze the risk of bias and applicability of the included studies. RESULTS There were totally 7 studies， comprising 12 models. Among them， 11 models indicated an area under the subject operating characteristic curve of 0.600-0.908； 2 models indicated calibration. The common predictor variables of the included models were age， pre-chemotherapy neutrophil count， pre-chemotherapy lymphocyte count， and pre-chemotherapy albumin. The overall risk of bias of the 7 studies was high， which was mainly attributed to the flaws in the study design， insufficient sample sizes， inappropriate treatment of variables， non-reporting of missing data， and the lack of indicators for the assessment of the models， but the applicability was good. CONCLUSIONS The predictive performance of risk predictive models for chemotherapy-induced myelosuppression in breast cancer remains to be further enhanced， and the overall risk of model bias is high. Future studies should follow the specifications of model development and reporting， then combine machine learning algorithms to develop risk predictive models with good predictive performance， high stability， and low risk of bias， so as to provide a decision-making basis for the clinic.

Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1％(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5％,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3％(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1％)were isolated from sputum and respiratory secretions,followed by urine(10.7％)and blood samples(8.1％).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1％.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4％in the patients＜18 years old to 20.6％in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.

Objective To explore the current situation of medication safety behavior of elderly cancer patients and the path relationship of various influencing factors for improving medication safety behavior.Methods A total of 340 elderly cancer patients were investigated by a demographic questionnaire,the Medication Safety Behavior Scale,the Medication Literacy Scale,the Family Care Index Questionnaire,and the Chinese version of the Empowerment Scale for Cancer Patients from August to December 2024.The multiple linear regression analysis was applied to analyze influencing factors,and data were analyzed using SmartPLS 4.0 to construct a partial least squares structural equation model with path analysis.Results A total of 307 valid questionnaires were collected.The mean medication safety behavior score was 31.89±5.38.Residential area,drug literacy,family care,and health empowerment are factors that affect medication safety in elderly cancer patients,accounting for 37.3％of the total variation.The path analysis results indicated that health empowerment(β=0.480),medication literacy(β=0.154),and family care(β=0.227)positively correlate with medication safety behavior.Health empowerment played a partial mediating role between family care and medication safety behavior,as well as between medication literacy and medication safety behavior.The mediating effects are 0.125 and 0.332(P＜0.001),accounting for 35.51％and 68.31％of the total effect,respectively.Conclusion Medication safety behaviors among elderly cancer patients are at a median level and influenced by multiple factors.By improving their levels of health empowerment,healthcare professionals can motivate patients to take an active role in medication safety management.Further,promoting education on medication knowledge and teaching relevant medical skills,and together with guiding patients to perceive family care and support,can collectively improve their overall medication safety behaviors.