ObjectiveThis study aims to develop and validate a novel multimodal predictive model， termed criss-cross network（CCNet）-directed graph neural network（DGNN）（CGN）， for accurate assessment of pulmonary nodule progression in high-risk individuals for lung cancer， by integrating longitudinal chest computed tomography（CT） imaging with both traditional Chinese and western clinical evaluation data. MethodsA cohort of 4 432 patients with pulmonary nodules was retrospectively analyzed. A twin CCNet was employed to extract spatiotemporal representations from paired sequential CT scans. Structured clinical assessment and imaging-derived features were encoded via a multilayer perceptron， and a similarity-based alignment strategy was adopted to harmonize multimodal imaging features across temporal dimensions. Subsequently， a DGNN was constructed to integrate heterogeneous features， where nodes represented modality-specific embeddings and edges denoted inter-modal information flow. Finally， model optimization was performed using a joint loss function combining cross-entropy and cosine similarity loss， facilitating robust classification of nodule progression status. ResultsThe proposed CGN model demonstrated superior predictive performance on the held-out test set， achieving an area under the receiver operating characteristic curve（AUC） of 0.830， accuracy of 0.843， sensitivity of 0.657， specificity of 0.712， Cohen's Kappa of 0.417， and F₁ score of 0.544. Compared with unimodal baselines， the CGN model yielded a 36%-48% relative improvement in AUC. Ablation studies revealed a 2%-22% increase in AUC when compared to simplified architectures lacking key components， substantiating the efficacy of the proposed multimodal fusion strategy and modular design. Incorporation of traditional Chinese medicine （TCM）-specific symptomatology led to an additional 5% improvement in AUC， underscoring the complementary value of integrating TCM and western clinical data. Through gradient-weighted activation mapping visualization analysis， it was found that the model's attention predominantly focused on nodule regions and effectively captured dynamic associations between clinical data and imaging-derived features. ConclusionThe CGN model， by synergistically combining cross-attention encoding with directed graph-based feature integration， enables effective alignment and fusion of heterogeneous multimodal data. The incorporation of both TCM and western clinical information facilitates complementary feature enrichment， thereby enhancing predictive accuracy for pulmonary nodule progression. This approach holds significant potential for supporting intelligent risk stratification and personalized surveillance strategies in lung cancer prevention.

Recent studies have shown that an imbalance in iron metabolism can affect the composition and microstructural changes of bone, disrupting bone homeostasis and leading to osteoporosis(OP). The imbalance in iron metabolism, along with its induced local abnormal microenvironment and cellular iron death, has become a new focal point in OP research, drawing increasing attention from the academic community regarding the regulation of iron metabolism to prevent and manage OP. From the perspective of traditional Chinese medicine(TCM), iron metabolism imbalance has potential connections to TCM theories regarding internal organs, as well as treatments aimed at tonifying the kidney, strengthening the spleen, and activating blood circulation. Evidence is continually emerging that TCMs and effective components that tonify the kidney, strengthen the spleen, and activate blood circulation can prevent and manage OP by regulating iron metabolism. This article analyzes the relationship between iron and bone, as well as the effects of TCM formulations on improving iron metabolism and influencing bone metabolism, from the perspectives of iron metabolism mechanisms and TCM interventions, aiming to broaden existing clinical strategies for prevention and treatment and inject new momentum into the field of OP as it moves into a new era.
Osteoporosis/drug therapy* ; Humans ; Iron/metabolism* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Medicine, Chinese Traditional ; Bone and Bones/drug effects*

Based on the interleukin-17(IL-17) signaling pathway, this study explores the effect and mechanism of Bufei Decoction on Klebsiella pneumoniae pneumonia in rats. SD rats were randomly divided into the control group, model group, Bufei Decoction low-dose group(6.68 g·kg~(-1)·d~(-1)), Bufei Decoction high-dose group(13.36 g·kg~(-1)·d~(-1)), and dexamethasone group(1.04 mg·kg~(-1)·d~(-1)), with 10 rats in each group. A pneumonia model was established by tracheal drip injection of K. pneumoniae. After successful model establishment, the improvement in lung tissue damage was observed following drug administration. Core targets and signaling pathways were screened using transcriptomics techniques. Real-time fluorescence quantitative polymerase chain reaction was used to detect the mRNA expression of core targets interleukin-6(IL-6), interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and chemokine CXC ligand 6(CXCL6). Western blot was used to assess key proteins in the IL-17 signaling pathway, including interleukin-17A(IL-17A), nuclear transcription factor-κB activator 1(Act1), tumor necrosis factor receptor-associated factor 6(TRAF6), and downstream phosphorylated p38 mitogen-activated protein kinase(p-p38 MAPK), and phosphorylated nuclear factor-κB p65(p-NF-κB p65). Apoptosis of lung tissue cells was detected by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling(TUNEL). The results showed that, compared with the control group, the model group exhibited significant pathological damage in lung tissue. The mRNA expression of IL-6, IL-1β, TNF-α, and CXCL6, as well as the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly increased, and the number of apoptotic cells was notably higher, indicating successful model establishment. Compared with the model group, both low-and high-dose groups of Bufei Decoction showed reduced pathological damage in lung tissue. The mRNA expression levels of IL-6, IL-1β, TNF-α, and CXCL6, and the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly decreased, with a significant reduction in apoptotic cells in the high-dose group. In conclusion, Bufei Decoction can effectively improve lung tissue damage and reduce inflammation in rats with K. pneumoniae. The mechanism may involve the regulation of the IL-17 signaling pathway and the reduction of apoptosis.
Animals ; Interleukin-17/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Rats ; Male ; Klebsiella pneumoniae/physiology* ; Klebsiella Infections/immunology* ; Humans ; Lung/drug effects*

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

Chronic kidney disease(CKD)commonly used dietary assessments including 24-hour dietary recall(24 h DR)/3-day dietary recall(3DDR),food frequency questionnaire(FFQ),dietary records,and estimation of dietary protein intake based on nitrogen balance.Given the high prevalence of CKD patients in Asian population and the scarcity of research using FFQ method,it is crucial to develop an FFQ suitable for Chinese CKD patients.This review summarizes the advantages and disadvantages of dietary assessment methods for CKD,the current research status,and the content and steps involved in establishing an FFQ,with the aim of providing reference for the modification of FFQ for Chinese CKD patients.

Objective To propose a method for classifying small bowel capsule endoscopy images by combining the Swin Transformer network with an improved Adapt-RandAugment data augmentation approach,aiming to enhance the accuracy and efficiency of small bowel lesion classification and recognition.Methods An Adapt-RandAugment data augmentation approach was formulated based on the RandAugment data enhancement sub-strategy and the principles of no feature loss and no distortion when enhancing small bowel capsule endoscopy images.In the publicly available Kvasir-Capsule dataset of small bowel capsule endoscopic images,the Adapt-RandAugment data augmentation approach was trained based on the Swin Transformer network,and the convolutional neural networks ResNet152 and DenseNet161 were used as the benchmarks to validate the combined Swin Transformer network and Adapt-RandAugment data augmentation approach for small bowel capsule endoscopy image classification.Results The proposed algorithm gained advantages over ResNet152 and DenseNet161 networks in the indicators,which had the macro average precision(MAC-PRE),macro average recall(MAC-REC),macro average F1 score(MAC-Fi-S)being 0.383 2,0.314 8 and 0.290 5 respectively,the micro average precision(MIC-PRE),micro average recall(MIC-REC)and micro average F1 score(MIC-Fi-S)all being 0.755 3,and the Matthews correlation coe-fficient(MCC)being 0.452 3.Conclusion The proposed small bowel capsule endoscopy image classification method based on Swin Transformer network and Adapt-RandAugment data augmentation approach behaves well in classified recognition efficiency and accuracy.[Chinese Medical Equipment Journal,2024,45(6):9-16]

Objective To explore the clinical rational use evaluation method for high-value medical consumables in diagnosis related groups(DRG)using association rules in order to provide references for the supervision of clinical rational use of high-value medical consumables.Methods The cardiovascular department was taken as an example.Firstly,K-means algorithm was applied to cluster analysis of DRG cases in the department,and representative cases were selected as the research objects;secondly,Apriori algorithm was used to mine the frequent item sets of DRG patient groups,the high-value medical consumables in the department and the rules of association between DRG patient groups and medical consumables;finally,two indicators of the importance of regulation and rationality of the use of medical consumables were designed to evaluate the importance of regulation and rationality of the use of high-value medical consumables.Results There were two common DRG patient groups in the cardiovascular department,including FM39 percutaneous cardiac catheterization and FM19 percutaneous coronary stent implantation,and the frequently used medical consumables contained vascular sheath,contrast catheter,pressure monitoring kit,triple three-way stopcock and coronary guide wire in the two groups.The common combinations of medical consumables used in the FM39 DRG patient group comprised of vascular sheath,contrast catheter,pressure monitoring kit,triple three-way stopcock and coronary guide wire,which were close to that of the cardiovascular department;there were some additional consumables involved in the common combinations in the FM19 group such as occluder,coronary guide catheter,pressure pump,drug-eluting stent and coronary dilation balloon.The top three medical consumables in terms of regulatory importance were cutting balloon,coronary guide wire and drug-eluting stent;using the confidence level from January to September 2022 as a reference,from January to September 2023 the rationality of using high-value consumables in FM39 group went higher by 5.08％while that in FM19 group went lower by 9.23％.Conclusion The association rule-based evaluation method for the use of medical consumables in DRG patient groups can be used for assessing the importance of regulation and rationality of the use of high-value medical consumables,which provides references for the supervision of clinical rational use of high-value medical consumables.[Chinese Medical Equipment Journal,2024,45(11):67-71]

Objective To present the initial practice of a novel procedure for the surgical treatment of inguinal hernia-"lateral approach single-incision laparoscopic totally extraperitoneal(L-SILTEP)repair"in certain specific situations.Methods The clinical data of 110 inguinal hernia patients who underwent L-SILTEP in the First Affiliated Hospital of Ningbo University,Shanghai General Hospital affiliated to Shanghai Jiao Tong University School of Medicine,and Shanghai East Hospital affiliated to Tongji University from June 2021 to March 2024 were collected retrospectively.Patients' demographics,surgical details,length of hospital stay,and postoperative outcomes were analyzed respectively.Results All surgeries were completed successfully and there was no conversion.The median surgical time was 55(41.25,70)mins and the intraoperative blood loss was 5(2,10)mL.In surgery,inferior epigastric artery injury occurred in 5 cases(4.5%)and spermatic cord injury occurred in 1 case(0.9%).The mean visual analog scale(VAS)scores pain assessment at 6,24,and 48 h after surgery were 3.0±0.8,1.9±0.7 and 1.1±0.4,respectively.The duration of hospital stay was(3.3±0.7)days.The most common postoperative complication was seroma,which occurred in 9 cases(8.2%).Additionally,extraperitoneal hematoma occurred in 1 case(0.9%)and scrotum effusion in 1 case(0.9%).Conclusions Generally,L-SILTEP is safe,feasible and effective.However,due to its advanced technique-demand,the application of L-SILTEP should be patient-specific and surgeon-specific.The successful implementation of this surgical procedure necessitates extensive training and meticulous attention to the surgical details.

The post-translational modification of proteins is a key mechanism that imparts physiological functions to proteins,among which reversible phosphorylation modifications play a pivotal role in many biological processes.Aberrant changes in phosphorylation are often closely associated with various major disease processes.In recent years,with the aid of proteomic technologies and methods,high-throughput,high-precision qualitative and quantitative approaches for phosphorylated proteins have rapidly advanced.This article reviews the research progress of phosphorylated protein quantification and chemical proteomics analysis methods based on the"bottom-up"strategy,including phosphopeptide enrichment methods,mass spectrometry fragmentation methods,quantification analysis methods and phosphorylation site stoichiometry,and discusses the development trend of quantification and stoichiometric analysis methods for phosphorylated proteins.