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.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Pulmonary fibrosis（PF） is a progressive and life-threatening disease with limited therapeutic options， highlighting the urgent need for innovative drug discovery strategies. To address this challenge， the authors propose the formula-originated rational intelligent screening&translation（FIRST）， a systematic framework for developing anti-fibrotic monomers derived from classical traditional Chinese medicine（TCM）. The strategy integrates three key dimensions， including tissue-oriented intelligent screening of active compounds， structural optimization based on drug-target spatial interactions and plant biosynthetic pathways， and cross-scale validation of drug. We further highlight its applications in discovering tissue-oriented novel drugs from clinically validated TCM， the development and mechanistic elucidation of anti-fibrotic therapeutics， as well as the clinical translation and secondary development of candidate drugs. This strategy paves the way for first-in-class， formula-derived monomeric drugs with defined structures， clarified mechanisms， and proven safety， offering a transformative avenue to meet the urgent therapeutic needs of PF and setting a new paradigm for TCM-based drug innovation.

Patients with lung cancer experience a heavy symptom burden during the perioperative period, which seriously affects their recovery and quality of life. Traditional symptom management models rely mainly on scheduled ward rounds during hospitalization and outpatient follow-up after discharge. However, they have limitations such as delayed symptom recognition, lack of post-discharge monitoring, and non-quantitative symptom assessment, which often lead to delayed interventions and low patient satisfaction. In recent years, the symptom management model based on electronic patient-reported outcomes (ePRO) has been increasingly valued in clinical practice. Existing high-level evidence from both domestic and international studies indicates that, through proactive monitoring, real-time alerts, and remote interventions, this model enables dynamic and continuous symptom management and helps improve patient recovery and healthcare experience. As a supplement to routine medical care, the ePRO-based symptom management model aims to enhance the quality of care rather than replace existing medical processes. To promote the standardized application of this model in perioperative lung cancer care, this consensus integrates domestic and international evidence. After multiple rounds of voting by more than 50 experts, it formulates 12 consensus statements covering the three core components, symptom monitoring, alerting, and intervention, to provide scientific and practical recommendations for clinical practice.

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.

ObjectiveFrozen shoulder (FS) is a debilitating musculoskeletal disorder characterized by persistent inflammation and progressive fibrosis of the glenohumeral joint capsule, leading to pain and severely restricted range of motion. Given the limited efficacy of current pharmacotherapies, there is an urgent need for novel anti-fibrotic agents. Guyanxiao Tincture (GYX), a traditional Chinese herbal formula, has shown clinical benefits in alleviating FS symptoms. However, its bioactive constituents and the molecular mechanisms underlying its therapeutic effects remain poorly defined. This study aimed to systematically evaluate the therapeutic efficacy of GYX against FS and to elucidate the mechanisms by which GYX attenuates capsular fibrosis. MethodsA Sprague-Dawley (SD) rat model of FS was established by immobilizing the unilateral shoulder with a plaster cast for 21 d. Following model induction, animals were administered with GYX or its serum-identified bioactive component, icariside F2. The absorbed prototype compounds of GYX in FS rat serum were profiled using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Therapeutic effects were assessed by measuring shoulder range of motion (ROM), histopathological evaluation of the capsule via hematoxylin-eosin staining, and quantification of transforming growth factor‑β1 (TGF-β1) content by ELISA. To explore the underlying mechanisms, transcriptomic profiling of the shoulder capsule was performed using RNA sequencing, and differentially expressed genes were validated by real-time quantitative PCR. Protein expression and localization were examined through immunohistochemistry, immunofluorescence, and Western blot. Protein-protein interaction networks were constructed to identify key regulatory hubs. In vitro, primary shoulder capsule fibroblasts were stimulated with TGF‑β1 to induce a fibrotic phenotype and then treated with icariside F2. The expression of alpha cardiac muscle 1 (ACTC1), α‑smooth muscle actin, and collagen I was subsequently measured. ResultsSerum pharmacochemistry analysis identified icariside F2 as the predominant circulating bioactive component of GYX in FS rats. Both GYX and icariside F2 treatment significantly improved the ROM and ameliorated histopathological lesions, including the attenuation of synovial hyperplasia, inflammatory infiltration, and excessive collagen deposition in the shoulder capsule. Consistently, elevated TGF‑β1 levels in the model group were markedly reduced after the interventions. RNA sequencing revealed that actin ACTC1 was dramatically upregulated in the fibrotic capsule, accompanied by the enrichment of fibrosis-associated pathways such as TGF‑β signaling, focal adhesion, and ECM-receptor interaction. Protein-protein interaction network analysis demonstrated that ACTC1 directly interacts with core fibrotic genes, including Col1a1, Col1a2, Thbs2, and Fn1. Critically, icariside F2 administration significantly reversed the aberrant overexpression of ACTC1 and suppressed the activation of the aforementioned fibrosis-related pathways in vivo. In TGF‑β1-stimulated fibroblasts, icariside F2 dose-dependently downregulated ACTC1 at both the mRNA and protein levels and concurrently decreased the expression of the fibrotic markers α‑smooth muscle actin and collagen I. Immunofluorescence and Western blot further confirmed that icariside F2 attenuated stress fiber formation and collagen production in vitro. ConclusionGYX and its bioactive component icariside F2 effectively alleviate the progression of frozen shoulder by inhibiting capsular fibrosis. Mechanistically, the therapeutic action is functionally linked to the suppression of ACTC1 and its downstream profibrotic signaling cascade, highlighting ACTC1 as a promising therapeutic target for the management of frozen shoulder.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

This case report explores the therapeutic potential of theta burst stimulation (TBS) for cognitive enhancement in individuals with brain injuries. The study presents a 38-year-old male suffering from an organic mental disorder attributed to a traumatic brain injury (TBI), who demonstrated notable cognitive improvements following an intensive TBS protocol targeting the left dorsal lateral prefrontal cortex. The treatment led to significant enhancements in impulse control, irritability, and verbal comprehension without adverse effects. Neuropsychological assessments and brain imaging post-intervention revealed improvements in short-term memory, abstract reasoning, list-generating fluency, and increased cerebral blood flow in the prefrontal cortex. These findings suggest that TBS, by promoting neural plasticity and reconfiguring neural networks, offers a promising avenue for cognitive rehabilitation in TBI patients. Further research is warranted to optimize TBS protocols and understand the mechanisms underlying its cognitive benefits.

This case report explores the therapeutic potential of theta burst stimulation (TBS) for cognitive enhancement in individuals with brain injuries. The study presents a 38-year-old male suffering from an organic mental disorder attributed to a traumatic brain injury (TBI), who demonstrated notable cognitive improvements following an intensive TBS protocol targeting the left dorsal lateral prefrontal cortex. The treatment led to significant enhancements in impulse control, irritability, and verbal comprehension without adverse effects. Neuropsychological assessments and brain imaging post-intervention revealed improvements in short-term memory, abstract reasoning, list-generating fluency, and increased cerebral blood flow in the prefrontal cortex. These findings suggest that TBS, by promoting neural plasticity and reconfiguring neural networks, offers a promising avenue for cognitive rehabilitation in TBI patients. Further research is warranted to optimize TBS protocols and understand the mechanisms underlying its cognitive benefits.