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 evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Aim To study whether intravenous injec-tion of miR-129-3p mimetic(agomir)can resist bone loss caused by hind limb disuse,and to provide new i-deas for preventing bone loss in microgravity environ-ment.Methods Forty-eight C57BL/6J male mice were randomly divided into the control group(CON),tail suspension model group(TS),tail suspension+miR-129-3p agomir administration group(miRNA)and tail suspension+miR-129-3p negative sequence agomir control group(NC).The miRNA group was given 4 mg·kg-1 miR-129-3p agomir by intravenous injection into the medial canthus twice a week.The NC agomir group were consistent with those in the miR-129-3p agomir group,and the CON and TS groups were given only equal volumes of normal saline.After four weeks,all mice were sacrificed and samples were collected.Micro-CT scan of femur,three-point femur bending test,serum bone metabolism index detection,oxidative stress index detection and osteogenesis-related protein expression analysis in bone tissue were per-formed.Results After four weeks,the number of tra-becular bone in the TS group was significantly re-duced,and Tb.BMD,Tb.Th,Tb.N,Tb.BS/TV and Tb.BV/TV were significantly lower than those in the CON group(P＜0.01).While Tb.Sp TS group was significantly higher than the CON group(P＜0.05),the maximum load and flexural strength of the femur significantly decreased(P＜0.01),the content of ser-um bone formation index PINP was significantly lower than that of the CON group(P＜0.01),and the con-tent of bone resorption index CTX-I was significantly higher than that of the CON group(P＜0.01),the content of serum oxidative damage indexes 8-iso-PGF2α and 8-OHdG significantly increased(P＜0.01),and the expression of osteogenesis-related pro-teins in bone tissue markedly decreased(P＜0.01).However,the increase or decrease of all indexes in miRNA group was significantly lower than that in TS group.Conclusions miR-129-3p mimetic can signifi-cantly reduce bone loss caused by hind limb disuse.This experiment provides a new idea and method for preventing bone loss in microgravity environment.

OBJECTIVE: To explore the application value of joint detection of serum neutrophil-to-lymphocyte ratio (NLR), prostate-specific antigen (PSA) and MMP26 in differentiating prostate cancer (PCa) from benign prostatic hyperplasia (BPH). METHODS: A total of 61 PCa patients (PCa group) and 63 BPH patients (BPH group) who were treated in The Affiliated Huaian Hospital of Xuzhou Medical University from May 2022 to May 2024 were retrospectively included. The relevant clinical data of all subjects were collected with the serum NLR, PSA and MMP26 levels being detected. Multivariate logistic regression analysis was used to analyze the influencing factors in differentiating PCa from BPH. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of serum NLR, PSA and MMP26 in differentiating PCa from BPH. RESULTS: The levels of TC and LDL-C in the PCa group were higher than those in the BPH group. And the level of HDL-C in the PCa group was lower than that in the BPH group (P<0.05). The serum levels of NLR, PSA and MMP26 in the PCa group were higher than those in the BPH group (P<0.05). The results of multivariate logistic regression analysis showed that NLR, PSA and MMP26 were risk factors for the diagnosis of PCa in patients (P<0.05). The ROC results showed that the area under the curve (AUC) of NLR, PSA MMP26 and joint diagnosis in the identification of PCa was 0.804, 0.800, 0.809 and 0.905, respectively. The comparison results of AUC showed that the joint diagnosis was superior to the single diagnosis (Z=2.262, 2.177, 2.002, P<0.05). CONCLUSION The joint detection of serum NLR, PSA and MMP26 has significant application value in the differentiation of PCa and BPH, which is expected to become an effective tool for early screening and diagnosis of PCa.
Humans ; Male ; Prostatic Hyperplasia/blood* ; Prostate-Specific Antigen/blood* ; Diagnosis, Differential ; Prostatic Neoplasms/blood* ; Retrospective Studies ; Neutrophils ; Lymphocytes ; ROC Curve ; Aged ; Middle Aged

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

OBJECTIVE: Recent studies have overturned the traditional concept of the lung as a "sterile organ" revealing that pulmonary microbiota dysbiosis and abnormal surfactant proteins (SPs) expression are involved in the progression of silicosis. This study aimed to investigate the relationship between abnormal SPs expression and dysbiosis of lung microbiota in silica-induced lung fibrosis, providing insights into mechanisms of silicosis. METHODS: Lung pathology, SPs expression, and microbiota composition were evaluated in silica-exposed mice. A mouse model of antibiotic-induced microbiota depletion was established, and alveolar structure and SPs expression were assessed. The roles of the lung microbiota and SPs in silicosis progression were further evaluated in mice with antibiotic-induced microbiota depletion, both with and without silica exposure. RESULTS: Silica exposure induced lung inflammation and fibrosis, along with increased expression of SP-A expression. Antibiotics (Abx)-induced microbiota depletion elevated SP-A and SP-D expression. Furthermore, silica exposure altered lung microbiota composition, enriching potentially pathogenic taxa. However, antibiotic-induced microbiota depletion prior to silica exposure reduced silica-mediated lung fibrosis and inflammation. CONCLUSION Lung microbiota is associated with silica-induced lung injury. Overproduction of SP-A and SP-D, induced by Abx-induced microbiota depletion, may enhance the resistance of mouse lung tissue to silica-induced injury.
Animals ; Silicosis/prevention & control* ; Lung/metabolism* ; Mice ; Anti-Bacterial Agents/pharmacology* ; Microbiota/drug effects* ; Silicon Dioxide/toxicity* ; Mice, Inbred C57BL ; Male ; Pulmonary Surfactant-Associated Proteins/genetics*

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Autism spectrum disorder(ASD),characterized by unknown etiology and high heterogeneity,ne-cessitates precise diagnostic and intervention strategies.Neuroimaging techniques have shown great promise in un-covering the neural mechanisms of ASD,providing a foundation for aided diagnosis and transcranial magnetic stim-ulation(TMS)interventions.This review highlights that integrating multimodal neuroimaging and developing indi-vidualized indices with developmental specificity can significantly improve the accuracy of ASD diagnosis and clas-sification.Furthermore,TMS interventions guided by functional connectivity derived from functional magnetic reso-nance imaging(fMRI)offer a personalized approach to ASD treatment.

Objective To propose a low-dose CT image denoising method based on an improved Corediff model to recover the detailed features of the image and enhance the image quality.Methods An RS-Corediff model was established by modifying the key component U-Net network of the Corediff model.Firstly,the residual module was introduced in the network input stage for feature extraction;secondly,a new downsampling module was designed in the U-Net network encoder,which learned the semantic information of the feature map by convolution and maintained the learning state during the downsampling process so as to fully extract the image features;thirdly,the feature splicing processing was used to further enhance the learning effect during the upsampling process of the U-Net network decoder;finally,the convolutional kernel size was modified to adjust the sensory field during the convolutional process of the whole U-Net network structure so as to obtain rich features.The RS-Corediff model was compared with the residual encoder-decoder convolutional neural network(RED-CNN)model and the Corediff model on the public dataset AAPM 2016 in order to verify its effectiveness for low-dose CT image denoising.Results The RS-Corediff model gained advantages over the RED-CNN and Corediff models with a peak signal-to-noise ratio(PSNR)of 41.269 8,structural similarity(SSIM)of 0.953 4 and root mean square error(RMSE)of 17.568 7.Conclusion The proposed method effectively preserves the texture and details of low-dose CT images during the denoising process to improve the overall quality of the images.[Chinese Medical Equipment Journal,2025,46(5):9-13]

The composition and working principle of MAQUET ROTAFLOW ECMO system were introduced.Four cases of common failures of ROTAFLOW ECMO systems were analyzed in terms of the cause and elimination method.The methods for the maintenance and quality control of ECMO system were put forward.References were provided for engineering technicians to treat similar faults.[Chinese Medical Equipment Journal,2025,46(5):116-120]