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.

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.

Objective: To assess the current status of knowledge, attitude, and practice (KAP) regarding health protection against air pollution among primary school students in China, and to analyze the impacts of fine particulate matter (PM 2.5 ) exposure, family, and individual factors on KAP levels, so as to provide empirical basis for implementing targeted interventions. Methods: Using stratified cluster random sampling method, a questionnaire survey was conducted among 19 297 primary school students from third to fifth grade across 30 provinces (autonomous regions and municipalities directly under the central government) in China in 2024. The questionnaire was developed based on the Technical Guidelines for the Assessment of Civic Environmental and Health Literacy (Trial), and encompasses three dimensions:knowledge, attitude, and practice. Spearman correlation analysis, Chi-square test, multivariate Logistic regression, and Bootstrap mediation effect test was applied for data analysis. Results: The overall mean KAP score for health protection against air pollution among primary school students was (79.73±13.53), with an adequacy rate of 80.47% ( n =15 528). The dimension scores for knowledge, attitude, and practice were (41.34±8.01, 16.43± 2.61 ), and (21.97±5.71), respectively. Knowledge was positively correlated with attitude and practice, and attitude was positively correlated with practice ( r =0.51, 0.35, 0.32, all P <0.01). Multivariate Logistic regression showed that the northeast region ( OR =2.36, 95% CI =2.02-2.75), having parents with a bachelor s degree or higher ( OR =4.76, 95% CI =2.71-8.37), living with both parents ( OR =1.33, 95% CI =1.15-1.55), being in the fifth grade ( OR =1.22, 95% CI =1.11-1.33), outdoor PM 2.5 level exceeding standard limits at the survey site ( OR =1.25, 95% CI =1.16-1.34), history of allergy ( OR =1.47, 95% CI =1.31-1.64), and wearing masks during heavily polluted weather ( OR =1.19, 95% CI =1.07-1.31) were promoting factors for KAP adequacy rate (all P <0.05). Conversely, obesity was a detrimental factor ( OR =0.78, 95% CI =0.69-0.88, all P <0.05). Conclusions Primary school students in China exhibit a favorable KAP level regarding health protection against air pollution, though regional, familial, and individual disparities persist. Exposure to PM 2.5 levels exceeding standard limits exerts a direct positive effect on enhancing health protection literacy. Future efforts should focus on the western region, families with lower educational attainment children to implement tailored, stratified interventions.

OBJECTIVE: To explore a predictive model that can better predict the prognosis of patients with diffuse large B-cell lymphoma (DLBCL), and validate its clinical value. METHODS: Clinical data of 134 newly treated DLBCL patients were collected from Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 2015 to January 2020. Several risk factors of the patients were screened and analyzed, a novel prognostic model were then established based on this, and its clinical application potential was validated. RESULTS: In the novel model, predicting progression-free survival (PFS) based on the age at initial treatment, albumin level, Hans classification, Ann Arbor stage, and BCL2 expression showed better predictive performance than International Prognostic Index (IPI) score (AUC: 0.788 vs 0.620，P <0.001). Predicting overall survival (OS) based on the age at initial treatment, albumin level, lactate dehydrogenase (LDH) level, and expressions of BCL2 and MUM1 proteins also showed better predictive performance for mortality risk than IPI score (AUC: 0.817 vs 0.624，P <0.001). CONCLUSION This novel prognostic model can better predict the survival prognosis of DLBCL patients compared to the IPI scoring system.
Humans ; Lymphoma, Large B-Cell, Diffuse/diagnosis* ; Prognosis ; Proto-Oncogene Proteins c-bcl-2/metabolism* ; Risk Factors ; Male ; Female ; Middle Aged

OBJECTIVE: To retrospectively analyze the characteristics and influencing factors of COVID-19 infection in patients with multiple myeloma (MM) who underwent autologous hematopoietic stem cell transplantation (AHSCT). METHODS: The clinical data of MM patients who underwent AHSCT in Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from May 26, 2021 to December 26, 2022 were collected. The onset of COVID-19 infection, corresponding symptoms and laboratory tests were followed up in outpatient or by the means of telephone contact and online questionnaires. Related analysis was then performed. RESULTS: This study included 96 patients, and 72 cases among them were infected with COVID-19 while 24 cases were uninfected. Logistic regression analysis showed that vaccination did not significantly reduce the risk of COVID-19 infection, but patients who received two doses of the vaccine had a lower risk of developing moderate and severe disease than those who did not receive or received one dose (OR =0.06, P =0.029). Patients who received daratumumab before had a higher risk of COVID-19 infection (OR =5.78, P =0.039), while those with a history of immunomodulatory drugs (IMiDs) had the opposite effect (OR =0.31, P =0.028). The use of both drugs did not affect the severity of COVID-19 infection. CONCLUSION For MM patients undergoing AHSCT as first-line chemotherapy, COVID-19 vaccination does not significantly reduce the infection rate, but it plays a role in preventing moderate and severe cases. The application of antineoplastic drugs with different mechanisms has a certain impact on the susceptibility to the COVID-19, which should be considered comprehensively when creating treatment plans.
Humans ; Multiple Myeloma/complications* ; COVID-19/epidemiology* ; Hematopoietic Stem Cell Transplantation ; Transplantation, Autologous ; Retrospective Studies ; Risk Assessment ; Risk Factors ; Male ; Female ; Middle Aged ; SARS-CoV-2 ; Adult ; Antibodies, Monoclonal

BACKGROUND: Primary pulmonary mucoepidermoid carcinoma (PMEC) is an exceedingly rare malignancy originating from bronchial submucosal glands, accounting for <0.2% of lung cancers. Histologically characterized by a triphasic composition of mucinous, epidermoid, and intermediate cells, PMEC is classified into low-grade (favorable prognosis) and high-grade (aggressive behavior) subtypes. This study aimed to investigate the clinicopathological characteristics and prognostic indicators of PMEC. METHODS: Clinicopathological, radiological, molecular, and survival data from 26 PMEC patients were retrospectively analyzed, including immunohistochemical profiles and MAML2 rearrangement status, supplemented by literature review. RESULTS: The cohort comprised 14 males and 12 females (mean age: 55.6 years). Eight patients (30.8%) were smokers, and 19 (73.1%) presented with symptoms. Central tumors predominated (n=19, 73.1%) versus peripheral lesions (n=7, 26.9%). Computed tomography (CT) imaging consistently revealed hypo-to-isodense masses/nodules. Pathologically, 19 cases were low-grade and 7 high-grade. Immunohistochemically, the tumor cells were positive for CK7, P40, P63 and CK5/6, and the Ki-67 index ranged from 2% to 70%. MAML2 rearrangement was detected in 52.4% (11/21) of tested cases. Clinical staging distribution: stage I (n=14), stage II (n=8), stage III (n=3), stage IV (n=1). Treatment modalities: radical surgery alone (n=13), surgery with adjuvant chemotherapy (n=11), chemoradiotherapy (n=1), and conservative management (n=1). With a median follow-up of 57 months, 6 patients (23.1%) died. Prognostic analysis demonstrated: (1) Significantly inferior survival in high-grade versus low-grade groups (P<0.05); (2) Lymph node metastasis, advanced stage, Ki-67>20%, and high-grade histology significantly correlated with reduced overall survival (P<0.05); (3) Lymph node metastasis constituted an independent poor prognostic factor (HR=12.73, 95%CI: 1.22-132.96). CONCLUSIONS PMEC exhibits distinct clinicopathological features, with MAML2 rearrangement present in approximately half of cases. Lymph node metastasis, advanced stage, high Ki-67 proliferation index, and high-grade histology are key determinants of poor prognosis, with lymph node metastasis serving as an independent risk factor.
Humans ; Male ; Female ; Middle Aged ; Carcinoma, Mucoepidermoid/mortality* ; Lung Neoplasms/mortality* ; Trans-Activators/genetics* ; Prognosis ; Adult ; Gene Rearrangement ; Aged ; Retrospective Studies ; Transcription Factors/genetics* ; DNA-Binding Proteins/genetics*

The relationship between gut microbiota and depressive disorder has become a research focus in recent years. Within the microbiota-gut-brain axis, the gut microbiota influences the onset and progression of depressive disorder primarily through the tryptophan metabolic pathway. Tryptophan, an essential amino acid in humans, is subject to dual regulation by intestinal microorganisms, which modulate its metabolic balance via inflammatory stimulation and microbial metabolite production. In depression, excessive activation of the kynurenine branch of tryptophan metabolism leads to the accumulation of proinflammatory and neurotoxic metabolites, thereby exacerbating neuroinflammation in the brain. Intervention studies indicate that the antidepressant-like effects of probiotics and traditional Chinese medicine are associated with remodeling of the gut microbiota, restoration of tryptophan metabolic balance, and alleviation of neuroinflammation. Furthermore, targeted inhibition of kynurenine 3-monooxygenase can mitigate neuroinflammation by regulating microglial activity, thus improving depressive-like behaviors. In summary, the metabolite-inflammation axis represents a central node in the interaction regulation between tryptophan metabolism and the microbiota-gut-brain axis. This provides a theoretical foundation for developing novel therapeutic strategies targeting depression through modulation of gut microbiota-mediated tryptophan metabolism.
Tryptophan/metabolism* ; Gastrointestinal Microbiome/physiology* ; Humans ; Depressive Disorder/microbiology* ; Probiotics/therapeutic use* ; Brain/metabolism* ; Kynurenine/metabolism* ; Metabolic Networks and Pathways ; Animals ; Medicine, Chinese Traditional

OBJECTIVES: Mobile phone dependence has become increasingly prominent among university students, posing significant risks to their social functioning and mental health. Previous studies suggest that perfectionistic personality traits may be key psychological predictors of mobile phone dependence, but the underlying mechanisms remain unclear. This study aims to identify core symptoms of mobile phone dependence among university students and to examine the pattern of associations between different dimensions of perfectionism and mobile phone dependence. METHODS: A cross-sectional questionnaire survey was conducted among 1404 university students nationwide. The Mobile Phone Involvement Questionnaire (MPIQ) and the Forst Multidimensional Perfectionism Scale (FMPS) were used to assess mobile phone use and perfectionism traits. The EBIC-GLASSO network model was constructed to analyze the network structure linking perfectionism and mobile phone dependence. RESULTS: A total of 56.48% of university students in the sample met the criteria for mobile phone dependence. The total FMPS score was positively correlated with the total MPIQ score (r=0.47, P<0.001). Results of multiple linear regression controlling for demographic variables showed that dimensions of FMPS score significantly predicted MPIQ score (all P<0.05). Network analysis revealed that the central dimension in perfectionism is "organization" (expected influence=2.69) and the core symptom of mobile phone dependence was "I lose track of how much I am using my smartphone" (expected influence= 0.78). Bridge centrality analysis identified "organization" as a key bridging factor linking perfectionism and mobile phone dependence (bridge strength=1.96). Among the symptom-to-symptom connections, "parental expectations" showed the strongest positive association with "arguments have arisen with others because of my mobile phone use" (partial correlation coefficient=0.15), serving as a risk factor. In contrast, "organization" was most strongly negatively associated with the same symptom (partial correlation coefficient=-0.13), serving as a protective factor, suggesting a protective effect. CONCLUSIONS Mobile phone dependence is common among college students and is primarily characterized by a lack of self-control in phone use. Although perfectionism is generally positively associated with mobile phone dependence, its internal dimensions appear to exert a dual effect. Specifically, "parental expectations" and "doubt over actions" may increase the risk of mobile phone dependence, whereas "organization" serves as a protective factor, particularly against interpersonal conflicts related to phone dependency.
Humans ; Perfectionism ; Students/psychology* ; Cell Phone ; Universities ; Cross-Sectional Studies ; Male ; Female ; Surveys and Questionnaires ; China ; Young Adult ; Adult ; Adolescent ; Personality

Ischemic stroke (IS) is a globally life-threatening disease. Presently, few therapeutic medicines are available for treating IS, and rt-PA is the only drug approved by the US Food and Drug Administration (FDA) in the US. In fact, many agents showing excellent neuroprotection but no blood flow-improving activity in animals have not achieved ideal clinical efficacy, while thrombolytic drugs only improving blood flow without neuroprotection have limited their wider application. To address these challenges and meet the huge unmet clinical need, we have designed and identified a novel compound AAPB with dual effects of neuroprotection and cerebral blood flow improvement. AAPB significantly reduced cerebral infarction and neural function deficit in tMCAO rats, pMCAO rats, and IS rhesus monkeys, as well as displayed exceptional safety profiles and excellent pharmacokinetic properties in rats and dogs. AAPB has now entered phase I of clinical trials fighting IS in China.

OBJECTIVE To construct a predictive tool for surgical site infections(SSIs)in spinal surgery for Chi-nese population to provide evidence support for reducing SSIs.METHODS A systematic review of Chinese and English database literature was conducted for Meta-analysis to obtain pooled risk values for influencing factors,and a risk prediction scoring tool was constructed based on the logistic regression model.Patients who underwent spinal surgery and completed postoperative follow-up in a tertiary hospital in Beijing from Jan.to Dec.2021 were selected to validate the predictive effect of the tool.RESULTS The predictive model for SSIs in Chinese spinal sur-gery patients was Logit(P)=—3.47+0.63(age 60 years)+0.31 ×(patient with cardiovascular disease)+0.69 ×(rheumatoid arthritis)+1.07 ×(diabetes)+1.06 ×(operation duration＞3 h)+1.17 ×(preopera-tive albumin＜35 g/L)+0.71 ×(history of spinal surgery)+0.67 ×(carrying internal implants)+0.73 ×(blood transfusion).The total score of the predictive tool was 92,with a cutoff score of≥24.50 indicating high-risk individuals.The area under the curve was 0.733,with the sensitivity 58.30％and the specificity 79.60％.CONCLUSION The established predictive model for SSIs in Chinese spine surgery demonstrates good predictive performance and can be used as a reference assessment tool in clinical practice.