Objective To analyze the application effects of artificial intelligence (AI) software and Mimics software in preoperative three-dimensional (3D) reconstruction for thoracoscopic anatomical pulmonary segmentectomy. Methods A retrospective analysis was conducted on patients who underwent thoracoscopic pulmonary segmentectomy at the Second People's Hospital of Huai'an from October 2019 to March 2024. Patients who underwent AI 3D reconstruction were included in the AI group, those who underwent Mimics 3D reconstruction were included in the Mimics group, and those who did not undergo 3D reconstruction were included in the control group. Perioperative related indicators of each group were compared. Results A total of 168 patients were included, including 73 males and 95 females, aged 25-81 (61.61±10.55) years. There were 79 patients in the AI group, 53 patients in the Mimics group, and 36 patients in the control group. There were no statistical differences in gender, age, smoking history, nodule size, number of lymph node dissection groups, postoperative pathological results, or postoperative complications among the three groups (P>0.05). There were statistical differences in operation time (P<0.001), extubation time (P<0.001), drainage volume (P<0.001), bleeding volume (P<0.001), and postoperative hospital stay (P=0.001) among the three groups. There were no statistical differences in operation time, extubation time, bleeding volume, or postoperative hospital stay between the AI group and the Mimics group (P>0.05). There was no statistical difference in drainage volume between the AI group and the control group (P=0.494), while there were statistical differences in operation time, drainage tube retention time, bleeding volume, and postoperative hospital stay (P<0.05). Conclusion For patients requiring thoracoscopic anatomical pulmonary segmentectomy, preoperative 3D reconstruction and preoperative planning based on 3D images can shorten the operation time, postoperative extubation time and hospital stay, and reduce intraoperative bleeding and postoperative drainage volume compared with reading CT images only. The use of AI software for 3D reconstruction is not inferior to Mimics manual 3D reconstruction in terms of surgical guidance and postoperative recovery, which can reduce the workload of clinicians and is worth promoting.

In recent years, the worldwide incidence rate of peripheral arterial and aortic diseases has increased year by year, significantly increasing the cardiovascular mortality and incidence rate of the whole population. In the past, peripheral arterial and aortic diseases were often more prone to missed diagnosis and delayed treatment compared to coronary artery disease. The 2024 ESC guidelines for the management of peripheral arterial and aortic diseases for the first time combines peripheral arterial and aortic diseases, integrating and updating the 2017 guidelines for peripheral arterial disease and the 2014 guidelines for aortic disease. The aim is to provide standardized recommendations for the management of systemic arterial diseases, ensuring that patients can receive coherent and comprehensive diagnosis and treatment, thereby improving prognosis. This article interprets the main content of the guideline in order to provide reference and assistance for the clinical diagnosis and treatment of peripheral arterial and aortic diseases in China at the current stage.

According to the "Regulations on clinical application management of medical technologies", physicians intending to carry out restricted technologies must undergo standardized training and pass assessments in accordance with the clinical application management standards for the respective technology. As ventricular assist technology is classified as a nationally restricted technology, standardized training is one of the essential conditions for its application. This paper primarily explores the standardized training for the clinical application of ventricular assist technology in Shanghai, in light of its background, clinical application, and current training status. It proposes the training requirements for ventricular assist technology, animal training assessment standards, and clinical practice assessment standards in Shanghai, aiming to promote the standardized development and high-quality advancement of ventricular assist technology in Shanghai.

Objective To evaluate the safety and feasibility of remote robot-assisted thoracoscopic surgery utilizing 5G technology. Methods Clinical data from five patients who underwent 5G remote robot-assisted thoracoscopic surgery at the Thoracic Surgery Center of Gansu Provincial People's Hospital from May to October 2024 were retrospectively analyzed. Results Finally, five patients were included. There were 2 males and 3 females at median age of 50 (42-63) years. All five surgeries (including 1 patient of lobectomy, 3 patients of partial lung resection and 1 patient of mediastinal lesion resection) were successfully completed without conversion to thoracotomy, complications, or mortality. The median intraoperative signal delay across the patients was 39 (37-42) ms. The median psychological load score for the surgeons was 9 (3-13). The median operation time was 100 (80-122) minutes with a median intraoperative blood loss of 100 (30-200) mL. Catheter drainage lasted a median of 4 (3-5) days, and the median drainage volumes on the first, second, and third postoperative day were 200 (100-300) mL, 150 (60-220) mL, and 80 (30-180) mL, respectively. The median postoperative hospital stay was 4 (3-7) days, and the median pain scores on the third postoperative day were 3 (1-4), 3 (0-3), and 1 (0-3), respectively. Conclusion 5G remote robot-assisted thoracoscopic surgery is safe and effective, with good surgical experience, smooth operation and small intraoperative delay.

Objective To compare the clinical efficacy of typeⅡhybrid surgery versus Sun’s surgery in treating acute Stanford A aortic dissection. Methods A retrospective analysis was conducted on the clinical data of patients with acute Stanford A aortic dissection who were treated at the Central Hospital of Wuhan affiliated to Tongji Medical College, Huazhong University of Science and Technology from 2016 to 2022. According to the surgical method, patients were divided into a typeⅡhybrid group and a Sun’s surgery group, and the clinical efficacy of the two groups was compared. Results A total of 52 patients were included, with 22 in the typeⅡhybrid surgery group and 30 in the Sun’s surgery group. The typeⅡhybrid group consisted of 18 males and 4 females, with an average age of (58.18±6.00) years, while the Sun’s surgery group consisted of 22 males and 8 females, with an average age of (53.03±11.89) years. All surgeries were successfully completed. There were 4 (13.3%) perioperative deaths in the Sun’s surgery group, including 2 patients of multiple organ failure, 1 patient of paraplegia, and 1 patient of uncontrollable postoperative bleeding. There was 1 (4.5%) perioperative death in the typeⅡhybrid surgery group, who was suspected of acute coronary syndrome and took a loading dose of dual antiplatelet drugs preoperatively. The patient underwent secondary thoracotomy for hemostasis, was re-cannulated during the operation, and finally died of circulatory failure after implantation of intra-aortic balloon pumping. There was no statistical difference in perioperative mortality between the two groups (P=0.381). Compared with the Sun’s surgery group, the typeⅡhybrid surgery group had shorter cardiopulmonary bypass time [153.00 (135.00, 185.25) min vs. 182.50 (166.50, 196.75) min, P=0.013], aortic cross-clamping time [77.00 (70.50, 92.00) min vs. 102.50 (93.50, 109.75) min, P<0.001], postoperative ICU stay [4.00 (2.83, 6.00) days vs. 8.00 (6.38, 11.78) days, P<0.001], postoperative ventilator support time [72.00 (29.50, 93.25) h vs. 87.65 (39.13, 139.13) h, P=0.138], less intraoperative blood loss [(1586.82±209.41) mL vs. (1 806.00±292.62) mL, P=0.004], postoperative 24 h drainage volume [612.50 (507.50, 762.50) mL vs. 687.50 (518.75, 993.75) mL, P=0.409], and shorter postoperative hospital stay [18.00 (13.00, 20.25) days vs. 22.00 (17.00, 29.25) days, P=0.013]. There was no statistically significant difference in the incidence of other early postoperative complications such as secondary thoracotomy for hemostasis, tracheotomy, renal dysfunction requiring dialysis, stroke, and paraplegia between the two groups (P>0.05). Conclusion For patients with acute Stanford A aortic dissection, typeⅡhybrid surgery is safe and effective; compared with traditional Sun’s surgery, typeⅡhybrid surgery has relatively less trauma, lower incidence of complications, satisfactory short-term results, and further research is needed on long-term prognosis.

Medical cross-modal retrieval aims to achieve semantic similarity search between different modalities of medical cases, such as quickly locating relevant ultrasound images through ultrasound reports, or using ultrasound images to retrieve matching reports. However, existing medical cross-modal hash retrieval methods face significant challenges, including semantic and visual differences between modalities and the scalability issues of hash algorithms in handling large-scale data. To address these challenges, this paper proposes a Medical image Semantic Alignment Cross-modal Hashing based on Transformer (MSACH). The algorithm employed a segmented training strategy, combining modality feature extraction and hash function learning, effectively extracting low-dimensional features containing important semantic information. A Transformer encoder was used for cross-modal semantic learning. By introducing manifold similarity constraints, balance constraints, and a linear classification network constraint, the algorithm enhanced the discriminability of the hash codes. Experimental results demonstrated that the MSACH algorithm improved the mean average precision (MAP) by 11.8% and 12.8% on two datasets compared to traditional methods. The algorithm exhibits outstanding performance in enhancing retrieval accuracy and handling large-scale medical data, showing promising potential for practical applications.
Algorithms ; Semantics ; Humans ; Ultrasonography ; Information Storage and Retrieval/methods* ; Image Processing, Computer-Assisted/methods*

In different stages of schizophrenia (SZ), alterations in gray matter volume (GMV) of patients are normally regulated by various pathological mechanisms. Instead of analyzing stage-specific changes, this study employed a multivariate structural covariance model and sliding-window approach to investigate the illness duration-related developmental trajectory of GMV in SZ. The trajectory is defined as a sequence of brain regions activated by illness duration, represented as a sparsely directed matrix. By applying this approach to structural magnetic resonance imaging data from 145 patients with SZ, we observed a continuous developmental trajectory of GMV from cortical to subcortical regions, with an average change occurring every 0.208 years, covering a time window of 20.176 years. The starting points were widely distributed across all networks, except for the ventral attention network. These findings provide insights into the neuropathological mechanism of SZ with a neuroprogressive model and facilitate the development of process for aided diagnosis and intervention with the starting points.
Humans ; Schizophrenia/pathology* ; Gray Matter/pathology* ; Magnetic Resonance Imaging ; Disease Progression ; Male ; Female ; Brain/pathology* ; Cerebral Cortex/pathology* ; Adult

Online hashing methods are receiving increasing attention in cross modal medical image retrieval research. However, existing online methods often lack the learning ability to maintain semantic correlation between new and existing data. To this end, we proposed online semantic similarity cross-modal hashing (OSCMH) learning framework to incrementally learn compact binary hash codes of medical stream data. Within it, a sparse representation of existing data based on online anchor datasets was designed to avoid semantic forgetting of the data and adaptively update hash codes, which effectively maintained semantic correlation between existing and arriving data and reduced information loss as well as improved training efficiency. Besides, an online discrete optimization method was proposed to solve the binary optimization problem of hash code by incrementally updating hash function and optimizing hash code on medical stream data. Compared with existing online or offline hashing methods, the proposed algorithm achieved average retrieval accuracy improvements of 12.5% and 14.3% on two datasets, respectively, effectively enhancing the retrieval efficiency in the field of medical images.
Semantics ; Humans ; Algorithms ; Information Storage and Retrieval/methods* ; Diagnostic Imaging ; Image Processing, Computer-Assisted/methods*

Flexible conductive fibers have been widely applied in wearable flexible sensing. However, exposed wearable flexible sensors based on liquid metal (LM) are prone to abrasion and significant conductivity degradation. This study presented a high-sensitivity LM conductive fiber with integration of strain sensing, electrical heating, and thermochromic capabilities, which was fabricated by coating eutectic gallium-indium (EGaIn) onto spandex fibers modified with waterborne polyurethane (WPU), followed by thermal curing to form a protective polyurethane sheath. This fiber, designated as Spandex/WPU/EGaIn/Polyurethane (SWEP), exhibits a four-layer coaxial structure: spandex core, WPU modification layer, LM conductive layer, and polyurethane protective sheath. The SWEP fiber had a diameter of (458.3 ± 10.4) μm, linear density of (2.37 ± 0.15) g/m, and uniform EGaIn coating. The fiber had excellent conductivity with an average value of (3 716.9 ± 594.2) S/m. The strain sensing performance was particularly noteworthy. A 5 cm × 5 cm woven fabric was fabricated using polyester warp yarns and SWEP weft yarns. The fabric exhibited satisfactory moisture permeability [(536.06 ± 33.15) g/(m ²·h)] and maintained stable thermochromic performance after repeated heating cycles. This advanced conductive fiber development is expected to significantly promote LM applications in wearable electronics and smart textile systems.
Wearable Electronic Devices ; Polyurethanes/chemistry* ; Electric Conductivity ; Gallium/chemistry* ; Metals/chemistry*

Small animal multimodal biomedical imaging refers to the integration of multiple imaging techniques within the same system or device to acquire comprehensive physiological and pathological information of small animals, such as mice and rats. With the continuous advancement of biomedical research, this cutting-edge technology has attracted extensive attention. Multimodal imaging techniques, based on diverse imaging principles, overcome the limitations of single-modal imaging through information fusion, significantly enhancing the overall system's sensitivity, temporal/spatial resolution, and quantitative accuracy. In the future, the integration of new materials and artificial intelligence will further boost its sensitivity and resolution. Through interdisciplinary innovation, this technology is expected to become the core technology of personalized medicine and expand its applications to drug development, environmental monitoring, and other fields, thus reshaping the landscape of biomedical research and clinical practice. This review summarized the progress on the application and investigation of multimodal biomedical imaging techniques, and discussed its development in the future.
Animals ; Multimodal Imaging/trends* ; Rats ; Mice ; Artificial Intelligence ; Diagnostic Imaging/methods* ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed