Objective To analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

OBJECTIVE To compare the risk of pneumonitis and interstitial lung disease （ILD） associated with different antibody-drug conjugates （ADC） in the treatment of breast cancer. METHODS CNKI， VIP， PubMed， Embase and other Chinese and English databases， and ClinicalTrials.gov were searched from the inception to June 15， 2025. Randomized controlled trials （RCT） about pneumonitis and ILD associated with ADC （T-DM1， T-DXd， SG， Dato-DXd， SHR-A1811，ARX788， and T-Duo） in the treatment of breast cancer were included. After literature screening， data extraction， and quality assessment， a network meta-analysis was conducted using Stata 17.0 software， and the surface under the cumulative ranking curve（SUCRA） of all interventions were ranked. RESULTS A total of 19 RCTs involving 10 556 patients were included. The overall incidence of pneumonitis with ARX788 and T-DXd was significantly higher than that with T-DM1， T-DM1 plus TPC（T-DM1combined with pertuzumab or atezolizumab）， TPC（treatment of primary care）， and SG （ P ＜0.05）， for grade 1-2 pneumonitis， ARX788 and T-DXd showed significantly higher incidence than T-DM1， T-DM1 plus TPC， and TPC （ P ＜0.05）. For both indicators， ARX788 and T-Duo were ranked as the top two by SUCRA. For the incidence of grade ≥3 pneumonitis， T-DXd and T-DM1 were significantly higher than SG （ P ＜0.05）， T-Duo and Dato-DXd were ranked as the top two by SUCRA. For overall incidence of ILD， ARX788 was significantly higher than T-DM1， SHR-A1811， TPC， and SG （ P ＜0.05）， for the incidence of grade 1-2 ILD， ARX788 was significantly higher than T-DM1， SHR-A1811， and TPC （ P ＜0.05）， for the incidence of grade ≥3 ILD， ARX788 was significantly higher than TPC （ P ＜0.05）. For three indicators above， ARX788 and T-DXd combined with pertuzumab were ranked as the top two by SUCRA. CONCLUSIONS Compared with other ADCs， ARX788 and T-Duo are associated with a higher risk of pneumonitis and ILD in patients with breast cancer.

Background: High-mobility group box protein 1 (HMGB1) is a chromatin-binding protein involved in arthritis, ischemia, sepsis, atherosclerosis, neurodegenerative disorders, meningitis, and cancer. HMGB1 exhibits dual roles in cancer, acting as either a tumor suppressor or oncoprotein depending on context. Objective: This research aimed to elucidate HMGB1’s functional significance in cutaneous squamous cell carcinoma (cSCC). Methods: We overexpressed HMGB1 in cSCC cell lines using recombinant adenovirus and examined its effects on cell proliferation, colony formation, and cell migration. Results: Immunohistochemical analysis revealed elevated HMGB1 expression levels in cSCC tissue relative to normal epidermis. To assess the influence of HMGB1, we employed recombinant adenoviruses expressing HMGB1 to transduce SCC cell lines (SCC12 and SCC13). Enhanced HMGB1 expression significantly promoted cellular proliferation and colony formation capacity.Notably, HMGB1 overexpression elevated the levels of proliferation regulators, including P63, SOX2, CDK4 and CDK6. Furthermore, HMGB1 overexpression substantially enhanced tumor invasiveness, accompanied by upregulation of epithelial-mesenchymal transition (EMT) biomarkers. Mechanistically, overexpression of HMGB1 enhanced transforming growth factor-β signaling by increasing phosphorylation of SMAD2/3, the key mediators of EMT. Conclusion These data imply that HMGB1 acts as a tumor-promoting factor in cSCC.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

Background: Primary dysmenorrhea is the most prevalent gynecological complaint among women of reproductive age, often manifesting as intense cramping pain in the lower abdomen during menstruation. Although pharmaceutical interventions such as nonsteroidal anti-inflammatory drugs (NSAIDs) and oral contraceptives are commonly used to manage symptoms, their temporary effects and frequent side effects, including gastrointestinal issues and hormonal imbalances, limit long-term use. As a result, there is growing interest in complementary and alternative therapies. Huolongguan moxibustion, a traditional Chinese medical technique involving the burning of mugwort (Artemisia vulgaris) near specific acupoints, is believed to enhance energy (qi) flow, promote blood circulation, reduce pain, and restore the balance of body functions. Aim: This study aims to investigate the clinical effectiveness of moxibustion in alleviating menstrual pain and improving overall well-being in patients with primary dysmenorrhea. Additionally, the study seeks to evaluate the feasibility and relevance of applying this traditional technique within contemporary medical practice. Materials and Methods: The study used a non-randomized clinical trial, double-blind, placebo-controlled design with treatment (n=30) and control (n=30) groups. Pain intensity will be assessed using the Visual Analogue Scale (VAS), while quality of life will be measured using the EQ-5D questionnaire. Psychological status, including anxiety and depression levels, will be evaluated using Cox mentrual symptom scale (CMSS). Data will be collected at baseline and post-treatment, followed by statistical analysis to determine treatment efficacy. Result: In the experimental group that received sealed cupping moxibustion therapy, the COX score decreased by approximately 99% after 3 months compared with the baseline value, whereas in the control group it decreased by approximately 27%. In addition, the VAS and EQ-5D-5L scores in the experimental group showed a statistically significant reduction as the duration of treatment increased (P<0.001), indicating that sealed cupping moxibustion therapy effectively reduced pain and related symptoms. Conclusion The results of this study demonstrate that cupping moxibustion therapy significantly reduces pain and associated symptoms in patients with primary dysmenorrhea and improves their quality of life. The therapeutic effect increased with longer treatment duration, and the outcomes in the experimental group were significantly better than those in the control group. Therefore, cupping moxibustion therapy may be considered an effective traditional therapeutic approach for the management of primary dysmenorrhea.

ObjectiveTo develop the Evidence Grading Scale for Ancient classical prescriptions in Traditional Chinese medicine， assess its reliability and validity， and apply it in practice to provide multi-source evidence for clinical practice guidelines development. MethodsLiterature retrieval was conducted to extract and screen existing evaluation dimensions， then the initial items were summarized using thematic analysis. Experts in the clinical medicine， medical history and literature participated in the Delphi questionnaire survey to evaluate and refine the items. An expert consensus meeting was conducted to finalize the included items， refine the method for items evaluation and evidence grading. The evidence quality rating scale for ancient classical traditional Chinese medicine （TCM） prescriptions was then established and tested for reliability and validity. ResultsThrough literature review， extraction， screening and summarization， a total of 3 dimensions and 12 initial items were formed. Questionnaires were sent to 69 experts to evaluate the initial items， with a questionnaire response rate of 100% and an expert authority coefficient of 0.92. All 12 items were retained for they had importance scores above 4. The Evidence Grading Scale on Ancient classical prescriptions in Traditional Chinese medicine includes 3 dimensions with 12 items. The 3 dimensions includes ancient evidence， inheritance status， and modern application. Each dimension contains 4 items， and each item has a full score of 5 points. The evidence was rated as high-level， moderate-level， and low-level according to the final scores. The content validity index （CVI） of the 12 items was ＞0.9， the average CVI of the scale was 0.98， and the intraclass correlation coefficient （ICC） was 0.90. ConclusionThe Evidence Grading Scale on Ancient classical prescriptions in Traditional Chinese medicine has good reliability and validity， which is practical for use in the development of TCM clinical guidelines and can better support clinical decision-making.

OBJECTIVE: To develop a digital intelligent multimodal shift handover system for the intensive care unit (ICU) and evaluate its application effect in ICU shift handovers. METHODS: A research and development team was established, consisting of 1 department director, 1 head nurse, 3 information technology engineers, 3 nurses, and 2 doctors. Team members were assigned responsibilities including overall coordination and planning, platform design and maintenance, pre-application training, collection and organization of clinical feedback, and research investigation respectively. A digital intelligent multimodal shift handover system was developed for ICU based on the Shannon-Weaver linear transmission model. This innovative system integrated automated data collection, intelligent dynamic monitoring, multidimensional condition analysis and visual reporting functions. A cloud platform was used to gather data from multi-parameter vital signs monitors, infusion pumps, ventilators and other devices. Artificial intelligence algorithms were employed to standardize and analyze the data, providing personalized recommendations for healthcare professionals. A self-controlled before-after method was adopted. Before the application of the ICU digital intelligent multimodal shift handover system (from December 2023 to March 2024), the traditional verbal bedside handover was used; from June 2024 to March 2025, the ICU digital intelligent multimodal shift handover system was applied for shift handovers. Questionnaires before the application of the shift handover system were collected in April 2024, and those after the application were collected in April 2025. The shift handover time, handover quality (scored by the nursing handover evaluation scale), satisfaction with doctor-nurse communication (scored by the ICU doctor-nurse scale) before and after the application of the handover system were compared, and nurses' satisfaction with the shift handover system (scored by the clinical nursing information system effectiveness evaluation scale) was investigated. RESULTS: After the application of the ICU digital intelligent multimodal shift handover system, the shift handover time was significantly shorter than that before the application [minutes: 20 (15, 25) vs. 30 (22, 40)], the handover quality was significantly higher than that before the application [score: 84.0 (78.0, 88.5) vs. 71.0 (55.0, 79.0)], and the satisfaction with doctor-nurse communication was also significantly higher than that before the application (score: 84.58±6.79 vs. 74.50±11.30). All differences were statistically significant (all P < 0.05). In addition, the nurses' system effectiveness evaluation scale score was 102.30±10.56, which indicated that nurses had a very high level of satisfaction with the ICU digital intelligent multimodal shift handover system. CONCLUSIONS The application of the ICU digital intelligent multimodal shift handover system can shorten the shift handover time, improve the handover quality, and enhance the satisfaction with doctor-nurse communication. Nurses have a high level of satisfaction with this system.
Intensive Care Units ; Humans ; Patient Handoff ; Artificial Intelligence ; Algorithms