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.

To address the growing challenge of an increasing number of elderly lung cancer patients amidst China's aging population and to fill the gap in quality control standards for surgical treatment in this special population, this study aimed to develop a standardized surgical evaluation system for elderly lung cancer patients tailored to China's national conditions. The system was established through a literature review, integrated the pathophysiological characteristics of elderly patients, and was constructed following review, feedback, and revision by experts from multiple thoracic surgery centers. Employing a 100-point scoring system, it comprises three primary domains: physical infrastructure and geriatric adaptability foundational conditions (10 points); management level and perioperative care models (20 points); and technical proficiency and clinical outcomes (70 points). The system places a strong emphasis on geriatric adaptability, proposing specific, quantifiable indicators for age-friendly facility modifications, control of elderly-specific complications, multidisciplinary collaboration, and standardized perioperative management. It provides a convenient and measurable assessment tool for quality control in the surgical treatment of elderly lung cancer in China, which is expected to promote the standardization and homogenization of diagnosis and treatment.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Objective:To explore the role of prostate health index(PHI)and its derivatives for early screening in patients with PI-RADS score 3 and prostate-specific antigen(PSA)levels of 4-20 μg/L.Methods:Clinical data of 203 patients with a score of 3 on the PI-RADS v2.1 scoring system who underwent ultrasound-guided transperineal prostate aspiration biopsy from April 2021 to April 2024 from Provincial Hospital of Shandong First Medical University,Qilu Hospital of Shandong University and Weifang People's Hospital were collected.Patients who met the inclusion criteria were divided into prostate cancer(PCa)group(62 cases)and benign prostatic hyperplasia(BPH)group(141 cases).Serum total prostate-specific antigen(tPSA),serum free prostate-specific an-tigen(fPSA),and PSA isoform 2(p2PSA)were routinely detected after admission.And prostate-specific antigen density(PSAD),prostate health index(PHI)and prostate health index density(PHID)were calculated with the basic personal information being col-lected.The efficacy of each parameter in early screening of patients with PI-RADS score of 3 and PSA level of 4-20 μg/L was as-sessed using ROC curve approach.Results:There was no statistical difference in tPSA,fPSA and fPSA/tPSA between the two groups(P＞0.05).There was a statistical difference in p2PSA,PSAD,PHI,PHID and age between the two groups(P＜0.05).PHI(AUC=0.783 7,95％CI:0.711 3-0.856 1)and PHID(AUC=0.782 3,95％CI:0.708 3-0.856 4)showed a good pre-dictive ability in the early screening of prostate cancer.In the clinically significant prostate cancer(csPCa)group,the ROC curve are-as of PHI and PHID were 0.823 0 and 0.788 5,respectively,which showed better predictive efficacy of prediction of csPCa.The ROC curves of the combined diagnostic indexes were plotted on the basis of the independent ROC curves,and the area under the curve of PHI combined with age,p2PSA and PHID(AUC=0.843 6)was the largest and had the best predictive ability among all the com-bined diagnostic indexes.Conclusion:In patients with PI-RADS score 3 and PSA level between 4 and 20 μg/L,PHI and its deriva-tives(PHI and PHID)provide a new way for early screening of prostate cancer compared with the traditional index tPSA,which has a high value of application in reducing the over-penetration.

Chronic ulcers on the body surface are non-healing wounds.Establishing a suitable animal model of chronic wounds will provide an important tool for research aimed at preventing and understanding the complexity of chronic wound formation and related pathological mechanisms in the human body.Animal wound models are usually constructed by inducing molecular abnormalities via external injury interventions to induce wound formation.Common modeling method include surgical resection,pressure ischemia,drugs,and radiation treatment.The success of model construction can then be evaluated by various monitoring method,such as natural recovery of the wound without intervention,measurement of wound size observation of physical signs,measurement of body mass,organ index,and infrared imaging.Despite the existence of numerous modeling and evaluating method,however,there is currently a lack of unified standards for animal chronic wound models.Researchers should thus choose appropriate modeling and model-evaluation method based on their actual needs,to obtain the best experimental result.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

OBJECTIVE: To reveal the molecular basis of knee osteoarthritis (KOA) with Yang deficiency and blood stasis syndrome by analyzing the gene expression profiles in synovial fluid and blood of KOA patients with this syndrome. METHODS: A total of 80 KOA patients were recruited from October 2022 to June 2024, including 40 cases in the non-Yang deficiency and blood stasis group (27 males and 13 females), with an average age of (61.75±3.45) years old;and 40 cases in the Yang deficiency and blood stasis group (22 males and 18 females), with an average age of (62.00±2.76) years old. The levels of body mass index (BMI), high-density lipoprotein (HDL), low-density lipoprotein (LDL), fibrinogen, total cholesterol, and D-dimer were recorded and summarized. Blood and synovial fluid samples from patients were collected for gene expression profile microarray sequencing, and then PCR and immunohistochemistry were used for clinical verification on the patients' synovial fluid and cartilage samples. RESULTS: Logistic regression analysis showed that compared with KOA patients with non-Yang deficiency and blood stasis syndrome, those with Yang deficiency and blood stasis syndrome had increased BMI, LDL, fibrinogen, total cholesterol, and D-dimer, and decreased HDL, with a clear correlation between the two groups. There were 562 differential genes in the blood, among which 322 were up-regulated and 240 were down-regulated;755 differential genes were found in the synovial fluid, with 350 up-regulated and 405 down-regulated. KEGG signaling pathway analysis of synovial fluid revealed changes in lipid metabolism-related pathways, including cholesterol metabolism, fatty acid metabolism, and PPARG signaling pathway. Analysis of the involved differential genes identified 6 genes in synovial fluid that were closely related to lipid metabolism, namely LRP1, LPL, ACOT6, TM6SF2, DGKK, and PPARG. Subsequently, PCR and immunohistochemical verification were performed using synovial fluid and cartilage samples, and the results were consistent with those of microarray sequencing. CONCLUSION This study explores the clinical and genomic correlation between traditional Chinese medicine syndromes and knee osteoarthritis from the perspective of lipid metabolism, and proves that abnormal lipid metabolism is closely related to KOA with Yang deficiency and blood stasis syndrome from both clinical and basic aspects.
Humans ; Male ; Female ; Middle Aged ; Synovial Fluid/metabolism* ; Osteoarthritis, Knee/metabolism* ; Yang Deficiency/complications* ; Aged

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*