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.

Nonobstructive azoospermia (NOA), one of the most severe types of male infertility, etiology often remains unclear in most cases. Therefore, this study aimed to detect four biallelic detrimental variants (0.5%) in the minichromosome maintenance domain containing 2 ( MCMDC2 ) genes in 768 NOA patients by whole-exome sequencing (WES). Hematoxylin and eosin (H&E) demonstrated that MCMDC2 deleterious variants caused meiotic arrest in three patients (c.1360G>T, c.1956G>T, and c.685C>T) and hypospermatogenesis in one patient (c.94G>T), as further confirmed through immunofluorescence (IF) staining. The single-cell RNA sequencing data indicated that MCMDC2 was substantially expressed during spermatogenesis. The variants were confirmed as deleterious and responsible for patient infertility through bioinformatics and in vitro experimental analyses. The results revealed four MCMDC2 variants related to NOA, which contributes to the current perception of the function of MCMDC2 in male fertility and presents new perspectives on the genetic etiology of NOA.
Humans ; Male ; Azoospermia/genetics* ; Meiosis/genetics* ; Spermatogenesis/genetics* ; Adult ; Exome Sequencing ; Microtubule-Associated Proteins/genetics* ; Alleles ; Infertility, Male/genetics*

ObjectiveTo establish a rat model of osteoarthritis and study the anti-inflammatory effects and mechanisms of fraxetin. MethodsEighteen 8-week-old male SPF-grade SD rats were randomly divided into three groups: Rats in the blank group received a right articular cavity injection of 50 μL of normal saline for 1 week; the model and intervention groups were injected with monosodium iodoacetate (MIA) into the right joint cavity to induce osteoarthritis, while the intervention group subsequently received fraxetin (5 mg·kg^-1·d^-1) for 1 week. Four weeks after drug intervention, abdominal aortic blood was collected. The animals were then euthanized, and knee joint cartilage were collected. The cartilage samples were stained with hematoxylin-eosin, safranin O-fast green, and toluidine blue for histopathological examination and scoring using the Mankin and OARSI scoring systems. The trabecular bone volume/total volume (Tb.BV/TV), trabecular bone surface density/total volume (Tb.BS/TV), and trabecular number (Tb.N) of each group were compared and analyzed using a micro-CT scanning system. The expression levels of various inflammatory factors [tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6)], and cartilage oligomeric matrix protein (COMP) were measured using enzyme-linked immunosorbent assay (ELISA). The expression levels of mitogen-activated protein kinase p38 (p38 MAPK), phosphorylation-p38 MAPK (p-p38 MAPK), c-Jun N-terminal kinase (JNK), and phosphorylation-JNK (p-JNK) were measured by western blotting. ResultsThe staining of cartilage sections of rat knee joints showed that the articular surface defects in the model group were severe, while the cartilage destruction in the intervention group was relatively reduced. Micro-CT results showed that Tb.BV/TV, Tb.BS/TV and Tb.N in the intervention group were significantly higher than those in the model group (P < 0.05); the Mankin score in the model group was significantly higher than that in the blank group (P < 0.05), the Mankin score in the intervention group was significantly lower than that in the model group (P < 0.05); while the OARSI score in the intervention group was significantly lower than that in the model group (P < 0.05). The results of the enzyme-linked immunosorbent assay showed that the serum levels of TNF-α, IL-1β, IL-6, and COMP in the model group were significantly higher than those in the blank group (all P < 0.05), while those in the intervention group were significantly lower than in the model group (P < 0.05). Western blot results showed that the expression levels of p-p38 MAPK and p-JNK in the knee cartilage tissue were significantly lower in the intervention group than in the model group (both P < 0.05), and significantly higher in the model group than in the blank group (both P < 0.05). ConclusionFraxetin may play a therapeutic role in a monosodium iodoacetate-induced rat model of osteoarthritis through the p38 MAPK pathway.

ObjectiveTo establish a rat model of osteoarthritis and study the anti-inflammatory effects and mechanisms of fraxetin. MethodsEighteen 8-week-old male SPF-grade SD rats were randomly divided into three groups: Rats in the blank group received a right articular cavity injection of 50 μL of normal saline for 1 week; the model and intervention groups were injected with monosodium iodoacetate (MIA) into the right joint cavity to induce osteoarthritis, while the intervention group subsequently received fraxetin (5 mg·kg^-1·d^-1) for 1 week. Four weeks after drug intervention, abdominal aortic blood was collected. The animals were then euthanized, and knee joint cartilage were collected. The cartilage samples were stained with hematoxylin-eosin, safranin O-fast green, and toluidine blue for histopathological examination and scoring using the Mankin and OARSI scoring systems. The trabecular bone volume/total volume (Tb.BV/TV), trabecular bone surface density/total volume (Tb.BS/TV), and trabecular number (Tb.N) of each group were compared and analyzed using a micro-CT scanning system. The expression levels of various inflammatory factors [tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), interleukin-6 (IL-6)], and cartilage oligomeric matrix protein (COMP) were measured using enzyme-linked immunosorbent assay (ELISA). The expression levels of mitogen-activated protein kinase p38 (p38 MAPK), phosphorylation-p38 MAPK (p-p38 MAPK), c-Jun N-terminal kinase (JNK), and phosphorylation-JNK (p-JNK) were measured by western blotting. ResultsThe staining of cartilage sections of rat knee joints showed that the articular surface defects in the model group were severe, while the cartilage destruction in the intervention group was relatively reduced. Micro-CT results showed that Tb.BV/TV, Tb.BS/TV and Tb.N in the intervention group were significantly higher than those in the model group (P < 0.05); the Mankin score in the model group was significantly higher than that in the blank group (P < 0.05), the Mankin score in the intervention group was significantly lower than that in the model group (P < 0.05); while the OARSI score in the intervention group was significantly lower than that in the model group (P < 0.05). The results of the enzyme-linked immunosorbent assay showed that the serum levels of TNF-α, IL-1β, IL-6, and COMP in the model group were significantly higher than those in the blank group (all P < 0.05), while those in the intervention group were significantly lower than in the model group (P < 0.05). Western blot results showed that the expression levels of p-p38 MAPK and p-JNK in the knee cartilage tissue were significantly lower in the intervention group than in the model group (both P < 0.05), and significantly higher in the model group than in the blank group (both P < 0.05). ConclusionFraxetin may play a therapeutic role in a monosodium iodoacetate-induced rat model of osteoarthritis through the p38 MAPK pathway.

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 prepare clinical-grade human umbilical cord-derived mesenchymal stem cells (hUC-MSC) with high expression of hematopoietic supporting factors and evaluate their stem cell characteristics. METHODS: Fetal umbilical cord tissues were collected from healthy postpartum women during full-term cesarean section. Wharton's jelly was mechanically separated and hUC-MSCs were obtained by explant culture method and enzyme digestion method in an animal serum-free culture system with addition of human platelet lysate. The phenotypic characteristics of hUC-MSCs obtained by two methods were detected by flow cytometry. The differences in proliferation ability between the two groups of hUC-MSCs were identified through CCK-8 assay and colony forming unit-fibroblast (CFU-F) assay. The differences in multilineage differentiation potential between the two groups of hUC-MSCs were identified through induction of adipogenic, osteogenic, and chondrogenic differentiation. The mRNA expression levels of hematopoietic supporting factors such as SCF, IL-3, CXCL12, VCAM1 and ANGPT1 in the two groups of hUC-MSCs were identified by real-time fluorescence quantiative PCR(RT-qPCR). RESULTS: The results of flow cytometry showed that hUC-MSCs obtained by the two methods both expressed high levels of CD73, CD90 and CD105, while lowly expressed CD31, CD45 and HLA-DR. The results of CCK-8 and CFU-F assay showed that the proliferation ability of hUC-MSCs obtained by explant culture method was better than those obtained by enzyme digestion method. The results of the triple lineage differentiation experiment showed that there was no significant difference in multilineage differentiation potential between the two grous of hUC-MSCs. The results of RT-qPCR showed that the mRNA expression levels of hematopoietic supporting factors SCF, IL-3, CXCL12, VCAM1 and ANGPT1 in hUC-MSCs obtained by explant cultrue method were higher than those obtained by enzyme digestion method. CONCLUSION Clinical-grade hUC-MSCs with high expression levels of hematopoietic supporting factors were successfully cultured in an animal serum-free culture system.
Humans ; Mesenchymal Stem Cells/metabolism* ; Umbilical Cord/cytology* ; Cell Differentiation ; Female ; Cell Proliferation ; Cells, Cultured ; Chemokine CXCL12/metabolism* ; Angiopoietin-1/metabolism* ; Vascular Cell Adhesion Molecule-1/metabolism* ; Stem Cell Factor/metabolism* ; Flow Cytometry ; Pregnancy

OBJECTIVE: To analyze the Epstein-Barr virus (EBV) load in different lymphocyte subsets, as well as clinical characteristics and outcomes in patients with hematologic malignancies experiencing EBV reactivation. METHODS: Peripheral blood samples from patients were collected. B, T, and NK cells were isolated sorting with magnetic beads by flow cytometry. The EBV load in each subset was quantitated by real-time quantitative polymerase chain reaction (RT-qPCR). Clinical data were colleted from electronic medical records. Survival status was followed up through outpatient visits and telephone calls. Statistical analyses were performed using SPSS 25.0. RESULTS: A total of 39 patients with hematologic malignancies were included, among whom 35 patients had undergone allogeneic hematopoietic stem cell transplantation (allo-HSCT). The median time to EBV reactivation was 4.8 months (range: 1.7-57.1 months) after allo-HSCT. EBV was detected in B, T, and NK cells in 20 patients, in B and T cells in 11 patients, and only in B cells in 4 patients. In the 35 patients, the median EBV load in B cells was 2.19×10⁴ copies/ml, significantly higher than that in T cells (4.00×10³ copies/ml, P <0.01) and NK cells (2.85×10² copies/ml, P <0.01). Rituximab (RTX) was administered for 32 patients, resulting in EBV negativity in 32 patients with a median time of 8 days (range: 2-39 days). Post-treatment analysis of 13 patients showed EBV were all negative in B, T, and NK cells. In the four non-transplant patients, the median time to EBV reactivation was 35 days (range: 1-328 days) after diagnosis of the primary disease. EBV was detected in one or two subsets of B, T, or NK cells, but not simultaneously in all three subsets. These patients received a combination chemotherapy targeting at the primary disease, with 3 patients achieving EBV negativity, and the median time to be negative was 40 days (range: 13-75 days). CONCLUSION In hematologic malignancy patients after allo-HSCT, EBV reactivation commonly involves B, T, and NK cells, with a significantly higher viral load in B cells compared to T and NK cells. Rituximab is effective for EBV clearance. In non-transplant patients, EBV reactivation is restricted to one or two lymphocyte subsets, and clearance is slower, highlighting the need for prompt anti-tumor therapy.
Humans ; Hematologic Neoplasms/virology* ; Herpesvirus 4, Human/physiology* ; Epstein-Barr Virus Infections ; Hematopoietic Stem Cell Transplantation ; Virus Activation ; Lymphocyte Subsets/virology* ; Flow Cytometry ; Killer Cells, Natural/virology* ; Male ; Female ; B-Lymphocytes/virology* ; Viral Load ; Adult ; T-Lymphocytes/virology* ; Middle Aged

BACKGROUND: The clinical impact of post-percutaneous coronary intervention (PCI) quantitative flow ratio (QFR) in patients treated with PCI for chronic total occlusion (CTO) was still undetermined. METHODS: All CTO vessels treated with successful anatomical PCI in patients from PANDA III trial were retrospectively measured for post-PCI QFR. The primary outcome was 2-year vessel-oriented composite endpoints (VOCEs, composite of target vessel-related cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization). Receiver operator characteristic curve analysis was conducted to identify optimal cutoff value of post-PCI QFR for predicting the 2-year VOCEs, and all vessels were stratified by this optimal cutoff value. Cox proportional hazards models were employed to calculate the hazard ratio (HR) with 95% CI. RESULTS: Among 428 CTO vessels treated with PCI, 353 vessels (82.5%) were analyzable for post-PCI QFR. 31 VOCEs (8.7%) occurred at 2 years. Mean value of post-PCI QFR was 0.92 ± 0.13. Receiver operator characteristic curve analysis shown the optimal cutoff value of post-PCI QFR for predicting 2-year VOCEs was 0.91. The incidence of 2-year VOCEs in the vessel with post-PCI QFR < 0.91 (n = 91) was significantly higher compared with the vessels with post-PCI QFR ≥ 0.91 (n = 262) (22.0% vs. 4.2%, HR = 4.98, 95% CI: 2.32-10.70). CONCLUSIONS Higher post-PCI QFR values were associated with improved prognosis in the PCI practice for coronary CTO. Achieving functionally optimal PCI results (post-PCI QFR value ≥ 0.91) tends to get better prognosis for patients with CTO lesions.

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (∼10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.