Objective To investigate the feasibility, safety, and clinical value of endoscopic-assisted skin-sparing mastectomy combined with immediate implant-based breast reconstruction performed as day surgery for breast cancer, aiming to provide a reference for major hospitals seeking to implement a day surgery model for breast cancer treatment. Methods We retrospectively analyzed the patients who underwent endoscopic-assisted skin-sparing mastectomy combined with immediate implant-based breast reconstruction for breast cancer at West China Hospital of Sichuan University from June 2021 to December 2022, and they were divided into a day surgery group and a conventional inpatient group based on their admission model. The operative indicators, Breast-Q scores, preoperative waiting time, length of hospital stay, hospitalization costs and complications of the two groups were analyzed. Results Except for intraoperative bleeding (P=0.007), the difference between the two groups in comparison of the rest of the operative indicators was not statistically significant (all P>0.05); there was no significant difference between the two groups in preoperative and postoperative Breast-Q scores (all P>0.05); the preoperative waiting time and length of stay in hospital of the day surgery group were 4.0 (3.0, 11.0) days and 1.0 (1.0, 1.0) days, respectively, which were significantly shorter than that of the conventional inpatient group; the postoperative pain score in the day surgery group [1.0 (1.0, 1.0) points] was lower than that in the conventional inpatient group [3.0 (3.0, 3.0) points], with a statistically significant difference between the two groups (P<0.001). Additionally, the total hospitalization costs for the day surgery group and conventional inpatient group were 50 656.5 (48 145.3, 62 597.3) RMB and 53 689.3 (50 469.1, 64 826.5) RMB, respectively.The total hospitalization cost in the day surgery group was significantly lower than that in the conventional inpatient group, with a statistically significant difference between the two groups (P=0.001). There was no statistically significant difference in complications between the two groups (all P>0.05). Conclusion Endoscopic-assisted skin-sparing mastectomy combined with immediate implant-based breast reconstruction in day surgery is feasible and safe. Without increasing postoperative complications, it effectively reduces hospitalization costs and shortens medical care time, demonstrating significant clinical value.

Objective To explore the application of the GPT-4 large language model in simplifying lung cancer radiology reports to enhance patient comprehension and doctor–patient communication efficiency. Methods A total of 362 radiology reports of non-small cell lung cancer (NSCLC) patients were collected from two hospitals between September and December 2024. Interpretive radiology reports (IRRs) were generated using GPT-4. Original reports (ORRs) and IRRs were compared through radiologist consistency evaluation and volunteer-based assessments of reading time, comprehension scores, and simulated communication duration. Results The average word count of ORRs was (459.83±55.76) words, compared with (625.42±41.59) words for IRRs (P<0.001). No significant differences were observed in expert consistency scores between ORRs and IRRs across dimensions of image interpretation accuracy, report detail completeness, explanatory depth and insight, and clinical practicality. Compared with reading ORRs, volunteers (simulated patient) read IRRs with shorter time [(346.88±29.15) s versus (409.01 ±102.40) s], with higher comprehension scores [(7.83±1.04) points versus (5.53±0.94) points] and shorter doctor-patient communication times [(317.31±57.81) s versus (714.20±56.67) s]. All differences were statistically significant (all P<0.001). Conclusion GPT-4 generated IRRs significantly improve patient comprehension and shorten communication time while maintaining medical accuracy. These findings suggest a new approach to optimizing radiology report management and enhancing healthcare service quality.

Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.

Objective To investigate the effect of ubiquitin specific peptidase 22 (USP22) on the occurrence and development of esophageal squamous cell carcinoma (ESCC) under hypoxic conditions, and its regulatory relationship with hypoxia-inducible factor-1α (HIF-1α). Methods Western blotting and quantitative polymerase chain reaction were used to detect the differences in USP22 protein and mRNA expression between normal esophageal epithelial cells HEEC and ESCC cell lines KYSE30, KYSE150, EC9706, and TE-1 under normoxic (5% CO2, 20% O2, 75% N2) and hypoxic (5% CO2, 1% O2, 94% N2) conditions. By transfecting USP22 plasmid or siUSP22, ESCC cells were divided into a normoxia control group, a normoxia+USP22 group, a normoxia+siUSP22 group, a hypoxia control group, a hypoxia+USP22 group, and a hypoxia+siUSP22 group. The proliferation and migration abilities of cells in each group were detected. The expression of USP22 and HIF-1α under hypoxic conditions after up-regulating or down-regulating USP22 was detected, and their regulatory relationship was verified. The interaction between USP22 and HIF-1α was verified by co-immunoprecipitation (Co-IP) technique. Results Compared with HEEC cells, the expression of USP22 in ESCC cells was significantly increased (P<0.05). Up-regulation of USP22 expression promoted the proliferation and migration of ESCC cells, while silencing USP22 inhibited the proliferation and migration of ESCC cells (P<0.05). Under hypoxic conditions, the expression of USP22 and HIF-1α increased, and with the up-regulation of USP22 expression, the expression of HIF-1α also significantly increased (P<0.05). Co-IP experiment confirmed the binding between USP22 and HIF-1α. Conclusion Up-regulation of USP22 expression promotes the proliferation and migration of ESCC cells. Hypoxia microenvironment can induce the increase of USP22 expression in ESCC. USP22 may participate in the regulation of the occurrence and development of ESCC by directly binding to HIF-1α.

Objective To construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.

Objective To explore the construction of heart preservation model of empty beating donor based on extracorporeal membrane oxygenation (ECMO). Methods From January 2022 to August 2023, 20 Guangxi Bama miniature pigs weighing 25-30 kg were selected, half male and half female. Under general anesthesia and heparinization, a midline thoracotomy was performed. The pericardium was cut after freeing the anterior and posterior vena cavae, and a perfusion needle was inserted near the brachiocephalic artery in the ascending aorta, connected to a blood collection bag to collect 500-600 mL of blood. The anterior and posterior vena cavae were ligated, the aorta was blocked and perfused with HTK solution to stop the heart beating. The superior and inferior vena cavae were cut off, the right pulmonary vein was decompressed, the aorta and left and right pulmonary arteries and veins were cut off, and the whole heart was removed. An ECMO device was used to continuously perfuse a cardioprotective solution mainly composed of oxygenated warm blood, maintaining the isolated pig heart beating for 8 hours, monitoring (once/hour) ECMO perfusion parameters, blood gas indicators, perfusate electrolytes, inflammatory factors, myocardial enzymes, myoglobin, and troponin levels. Myocardial tissue was taken for hematoxylin-eosin (HE) staining to observe myocardial cell damage and evaluate the quality of heart preservation. Results Among the 20 isolated beating pig hearts, 17 successfully resumed beating, 3 experienced ventricular fibrillation, resuscitated after intracardiac electrical defibrillation, and all 20 pig hearts successfully beat for 8 hours. There was no statistical difference in ECMO perfusion parameters, blood gas indicators, perfusate electrolytes, and inflammatory factors at each time point (P>0.05). There were statistical increases in myocardial enzymes, myoglobin, and troponin levels (P<0.05). HE staining results suggested that there was no severe myocardial damage. Conclusion ECMO technology can be used for pig heart preservation with good results, and this study provides experimental evidence for improving heart preservation research in clinical heart transplantation.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Objective To explore the safety and feasibility of 5G remote robot-assisted thoracoscopic anatomic lobectomy and segmentectomy. Methods A retrospective analysis was conducted on patients who underwent 5G remote robot-assisted thoracoscopic anatomic lobectomy or segmentectomy between July and September 2024. The surgeries were performed collaboratively by Shanghai Chest Hospital, Affiliated Hospital of Yangzhou University, and The First Affiliated Hospital of Nanchang University. Perioperative outcomes were recorded and analyzed. Results Ten patients were included, comprising 4 anatomic lobectomies and 6 segmentectomies. The median age was 61.0 (56.0, 72.0) years. The median intraoperative bi-directional network latency was 52.5 (39.0, 54.0) ms. There were no instances of network interruption, robot-related adverse events, or conversions to open thoracotomy. The median postoperative chest tube drainage volume was 500.0 (375.0, 600.0) mL, and the median hospital stay was 5.5 (4.0, 6.0) days. No complications of Clavien-Dindo grade Ⅱor higher occurred. All patients were recurrence-free and alive during the 6-month postoperative follow-up period. Conclusion 5G remote robot-assisted thoracoscopic surgery demonstrates high safety and operational stability. This technology shows promising potential for clinical application and is worthy of further development and utilization.

Objective To explore the clinical application effects of using non-drainage tube in mediastinal tumor resection via thoracoscopic subxiphoid approach. Methods A retrospective analysis was conducted on the clinical data of patients who underwent mediastinal tumor resection via thoracoscopic subxiphoid approach at the Fourth People's Hospital of Zigong City from January 2020 to February 2024. Patients were divided into a non-drainage tube group and a drainage tube group, and their perioperative data were compared. Results A total of 149 patients were included, and there were 111 patients of thymoma, 5 patients of teratoma, and 33 patients of cyst. There were 77 patients in the non-drainage tube group, including 40 males and 37 females, aged 28-79 (53.72±13.34) years; there were 72 patients in the drainage tube group, including 33 males and 39 females, aged 26-80 (55.60±11.06) years. The differences in postoperative pain score at 48 hours, maximum postoperative pain score, postoperative hospital stay, postoperative wound complications, and the number of temporary analgesics used after surgery between the two groups were statistically significant (P<0.05). Conclusion The use of non-drainage tube technology in mediastinal tumor resection through thoracoscopic subxiphoid approach can reduce postoperative pain and the number of temporary analgesics used, as well as decrease the incidence of wound complications.

With the rapid development of large language models and related technologies, various forms of patient modeling have emerged, including virtual patients, synthetic patients, artificial intelligence patients, and digital twin patients. Although these forms all share the core objective of patient simulation, they exhibit significant differences in theoretical ontology, data sources, modeling logic, and application orientation. Based on the abstraction levels of simulation objects and system mapping logic, this study systematically compares five paradigms—standardized patients, virtual patients, synthetic patients, AI patients, and digital twin patients—to clarify their conceptual boundaries and technical positioning. The findings indicate that these paradigms represent distinct modeling pathways: behavioral reproduction, scenario simulation, data generation, cognitive interaction, and individual system mapping, respectively. By constructing a multidimensional comparative framework, this study provides a theoretical foundation for the conceptual standardization of patient modeling and the strategic deployment of intelligent healthcare systems.