Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Septic shock is the primary cause of mortality in sepsis, with its core pathophysiological mechanism being severe ischemia and hypoxia in critical units—composed of microcirculation and the mitochondria of functional cells—resulting from disruptions in blood flow and oxygen flow following a dysregulated host response. Due to the systemically convergent yet clinically heterogeneous nature of the host response, current understanding and management strategies for hemodynamics remain inconsistent, often leading to inadequate resuscitation or overtreatment. To improve the quality of care, based on a systematic review of the "blood flow-oxygen flow" theory, an expert panel emphasizes reevaluating septic shock from an integrated perspective of blood flow and oxygen flow, and has formulated the Expert Consensus on Blood Flow and Oxygen Delivery Phenotyping and Clinical Management of Septic Shock (2025). The consensus proposes that clinical typing of blood flow-oxygen flow should comprehensively consider cardiac function, vascular tone, oxygen flow utilization status in critical units, and disease trajectory, while optimizing subtype identification by integrating host response phenotypes and artificial intelligence technologies. It advocates establishing a continuous assessment system through multi-site oxygen flow monitoring for organ perfusion, peripheral perfusion monitoring, and critical care ultrasound. Under the framework of the "critical care triangle", the consensus promotes the implementation of individualized, bundled management strategies, providing guidance for multiple management points to restore blood flow-oxygen flow matching, reduce the risk of organ failure, and decrease patient mortality.

ObjectiveTo systematically assess the public health governance capacity in Zhejiang Province, to conduct an in-depth analysis of its strengths and weaknesses, so as to provide scientific basis and strategic recommendations for further enhancement. MethodsA systematic collection of policy documents, public information reports, and research literature related to public health governance capacity in Zhejiang Province from 2002 to 2023 was conducted (encompassing a total of 1 263 policy documents, 138 pieces of information reports and 631 research articles). Based on the evaluation criteria suitable for public health systems previously developed by the research team, the basic status and magnitude of change in public health governance capacity in Zhejiang Province was evaluated. Additionally, normative gap analyses were employed to identify the strengths and weaknesses. ResultsZhejiang Province ranked 4th nationwide in terms of public health governance capacity with a score of 733.4 points (1 000.0-point maximum). The province has effectively implemented the principle of health first (scoring 698.5 points in the assessment of health-first strategy implementation) and attached sufficient importance to health-related goals (scoring 658.2 points in the scientific rationality of goal setting). However, the implementation of inter-departmental coordination and incentive mechanisms only scored 178.7 points, the feasibility of management and monitoring mechanisms scored even lower at only 144.0 points, and the coverage of incentive mechanisms scored 286.0 points. ConclusionZhejiang Province has effectively implemented its health first strategy and attached great importance to health targets, but still needs to strengthen cross-departmental coordination mechanisms and health-oriented incentives.

To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

ObjectiveGastric hemorrhage is one of the most common and life-threatening emergencies of the upper digestive tract. Early identification and continuous monitoring are essential for reducing rebleeding rates and mortality, particularly within the critical early hours after onset. Although endoscopy and radiological imaging can accurately localize bleeding sites, these approaches are invasive, resource-intensive, and unsuitable for continuous bedside monitoring. Electrical impedance tomography (EIT), as a noninvasive and radiation-free functional imaging technique, offers real-time visualization of conductivity distribution and has the potential for detecting intragastric bleeding based on the electrical contrast between blood and surrounding gastric tissues. In this study, a three-dimensional gastric EIT (3D-gEIT) framework is proposed to achieve noninvasive, real-time, and dynamic monitoring of gastric hemorrhage, with emphasis on spatial localization and quantitative volume assessment. MethodsA three-dimensional upper-abdominal simulation model incorporating the stomach, gastric wall, gastric contents, and surrounding tissues was established. Three electrode configurations, namely the dual layer ring, the four layer staggered ring, and the opposed dual plane array, were designed and systematically compared to evaluate their influence on depth sensitivity and spatial resolution. Based on the Tikhonov-Noser hybrid regularization scheme, a region-clustering constraint was introduced to develop the TK-Noser-RCC algorithm. This approach aggregates spatially adjacent elements with similar conductivity variations, thereby enhancing structural continuity and suppressing isolated noise artifacts. To validate the proposed framework, an upper-abdominal physical phantom was constructed using agar to simulate background tissue conductivity. Hemispherical high-conductivity inclusions with volumes ranging from 10 ml to 50 ml were attached to the inner gastric wall to mimic localized bleeding under different gastric filling states. Boundary voltages were acquired under a 120 kHz excitation current and reconstructed using the TK-Noser-RCC algorithm. Furthermore, an in vivo animal experiment was performed using a porcine model with adult-scale abdominal dimensions. A total of 100 ml of autologous blood was injected incrementally into the stomach to simulate progressive gastric hemorrhage, and time-difference EIT reconstruction was conducted at each injection stage to assess the dynamic system response under physiological conditions. ResultsSimulation results demonstrated that the opposed dual-plane electrode array achieved superior depth sensitivity distribution and spatial resolution. For a 40 ml hemorrhage model, the average ICC and SSIM improved by 55.9% and 38.8% compared with the dual-layer ring configuration, and by 64.0% and 39.5% compared with the four-layer staggered configuration. The proposed region-clustering constraint significantly enhanced reconstruction stability. Under added Gaussian noise of 40 dB and 30 dB, ICC values remained approximately 0.85, indicating effective artifact suppression and preservation of boundary integrity. In physical phantom experiments, reconstructed hemorrhage volumes increased approximately linearly with the preset hemispherical volumes, and the reconstructed high-conductivity regions closely matched the actual bleeding locations. Both empty-stomach and full-stomach conditions were evaluated, demonstrating that the opposed dual-plane configuration maintained stable imaging performance across varying gastric contents. In the animal experiment, reconstructed low-impedance regions expanded progressively with increasing injected blood volume. The spatial localization of the hemorrhage remained stable throughout the procedure, and no significant artifacts were observed. Quantitative analysis showed that reconstructed volume and average conductivity variation exhibited an approximately linear growth trend with injected blood volume, confirming the sensitivity of the system to dynamic intragastric conductivity changes. ConclusionThe proposed 3D-gEIT framework enables quantitative reconstruction of gastric hemorrhage volume and spatial distribution with improved depth sensitivity, structural continuity, and noise robustness compared with conventional EIT approaches. By integrating optimized electrode configuration and a region-clustering-constrained reconstruction algorithm, the system provides stable dynamic monitoring under both controlled phantom conditions and in vivo physiological environments. This method offers a noninvasive, real-time, and low-cost imaging strategy for early diagnosis, postoperative monitoring, and bedside surveillance of gastric bleeding.

Background: En bloc resection is recommended for the treatment of malignant and aggressive benign spinal tumors; however, it often requires a combined anterior-posterior approach, which is usually accompanied by longer surgical duration, increased blood loss, larger trauma, and surgical complexity. The present study describes a novel rotation-reversion technique for en bloc resection of the thoracic and upper lumbar spinal tumors using a posterior-only approach and evaluate its safety and efficacy. Methods: Thirteen patients with thoracic and upper lumbar (L1-L3) spinal tumors were treated with en bloc resection using the rotation-reversion technique through a posterior-only approach at our institution between 2015 and 2023. The clinical characteristics and surgical results of the patients were reviewed and analyzed. Results: Posterior-only en bloc resection was performed successfully in all 13 patients using the rotation-reversion technique, with a median follow-up of 30.4 months (range, 6–74 months). The average maximum size of these 13 tumors was 5.7 × 5.8 × 4.8 cm.The mean operation time and blood loss were 458.5 minutes (range, 220–880 minutes) and 3,146.2 mL (range, 1,000–6,000 mL), respectively, with 4 of the 13 patients (30.8%) experiencing perioperative complications. Negative margins were achieved in all the 13 patients (100%). One patient experienced local recurrence (7.7%) and 1 patient experienced instrumentation failures. Interbody fusion was confirmed in 11 of the 13 patients (84.6%), with a median fusion time of 6.9 months. All of the 13 patients experienced varying degrees of mild postoperative neurological deficits owing to resection of the nerve roots affected by tumor invasion of the vertebrae. No vessel injury or postoperative neurological paralysis occurred, except 1 patient who had been completely paralyzed before surgery. Conclusions The rotation-reversion technique is an effective procedure for en bloc resection of selected thoracic and upper lumbar spinal tumors through the posterior-only approach.

The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF risk prediction method based on multimodal feature fusion. Additionally, the study utilized four different feature selection methods and Pearson correlation analysis to determine the optimal multimodal feature set, and employed random forest for PAF risk assessment. The proposed method achieved accuracy of (92.3 ± 2.1)% and F1 score of (91.6 ± 2.9)% in a public dataset. In a clinical dataset, it achieved accuracy of (91.4 ± 2.0)% and F1 score of (90.8 ± 2.4)%. The method demonstrates generalization across multi-center datasets and holds promising clinical application prospects.
Humans ; Atrial Fibrillation/diagnosis* ; Machine Learning ; Deep Learning ; Risk Assessment/methods*

This paper aims to propose a noninvasive radiotherapy patient positioning system based on structured light surface imaging, and evaluate its clinical feasibility. First, structured light sensors were used to obtain the panoramic point clouds during radiotherapy positioning in real time. The fusion of different point clouds and coordinate transformation were realized based on optical calibration and pose estimation, and the body surface was segmented referring to the preset region of interest (ROI). Then, the global-local registration of cross-source point cloud was achieved based on algorithms such as random sample consensus (RANSAC) and iterative closest point (ICP), to calculate 6 degrees of freedom (DoF) positioning deviation and provide guidance for the correction of couch shifts. The evaluation of the system was carried out based on a rigid adult phantom and volunteers' body, which included positioning error, correlation analysis, and receiver operating characteristic (ROC) analysis. Using Cone Beam CT (CBCT) as the gold standard, the maximum translation and rotation errors of this system were (1.5 ± 0.9) mm along Vrt direction (chest) and (0.7 ± 0.3) ° along Pitch direction (head and neck). The Pearson correlation coefficient between results of system outputs and CBCT verification distributed in an interval of [0.80, 0.84]. Results of ROC analysis showed that the translational and rotational AUC values were 0.82 and 0.85, respectively. In the 4D freedom accuracy test on the human body of volunteers, the maximum translation and rotation errors were (2.6 ± 1.1) mm (Vrt direction, chest and abdomen) and (0.8 ± 0.4)° (Rtn direction, chest and abdomen) respectively. In summary, the positioning system based on structured light body surface imaging proposed in this article can ensure positioning accuracy without surface markers and additional doses, and is feasible for clinical application.
Humans ; Patient Positioning/methods* ; Phantoms, Imaging ; Cone-Beam Computed Tomography ; Algorithms ; Radiotherapy, Image-Guided/methods* ; Radiotherapy Planning, Computer-Assisted/methods*

To address the high computational complexity of the Transformer in the segmentation of ultrasound thyroid nodules and the loss of image details or omission of key spatial information caused by traditional image sampling techniques when dealing with high-resolution, complex texture or uneven density two-dimensional ultrasound images, this paper proposes a thyroid nodule segmentation method that integrates the receiving weighted key-value (RWKV) architecture and spherical geometry feature (SGF) sampling technology. This method effectively captures the details of adjacent regions through two-dimensional offset prediction and pixel-level sampling position adjustment, achieving precise segmentation. Additionally, this study introduces a patch attention module (PAM) to optimize the decoder feature map using a regional cross-attention mechanism, enabling it to focus more precisely on the high-resolution features of the encoder. Experiments on the thyroid nodule segmentation dataset (TN3K) and the digital database for thyroid images (DDTI) show that the proposed method achieves dice similarity coefficients (DSC) of 87.24% and 80.79% respectively, outperforming existing models while maintaining a lower computational complexity. This approach may provide an efficient solution for the precise segmentation of thyroid nodules.
Thyroid Nodule/diagnostic imaging* ; Humans ; Ultrasonography/methods* ; Algorithms ; Image Processing, Computer-Assisted/methods* ; Thyroid Gland/diagnostic imaging*

Thyroid nodules are a common endocrine disorder, and their early detection and accurate diagnosis are crucial for the prevention of thyroid cancer. However, the highly heterogeneous morphology and boundaries of thyroid nodules pose significant challenges to their precise identification and classification. Traditional diagnostic approaches rely heavily on physicians' experience, which increases the risk of misdiagnosis and missed diagnoses. With the rapid advancement of computer-aided diagnosis (CAD) technologies, applying deep learning algorithms to the analysis of thyroid nodule ultrasound images has shown great potential. This paper reviews the latest research progress on deep learning-based CAD methods for thyroid nodules, with a focus on their applications in image preprocessing, segmentation and classification. The advantages and limitations of current techniques are analyzed, and potential future directions are discussed. This review aims to highlight the potential of deep learning in thyroid nodule diagnosis and to provide a foundation for selecting feasible pathways for future clinical applications.
Humans ; Thyroid Nodule/diagnostic imaging* ; Deep Learning ; Ultrasonography/methods* ; Diagnosis, Computer-Assisted/methods* ; Algorithms ; Thyroid Neoplasms/diagnostic imaging* ; Image Processing, Computer-Assisted/methods*

ObjectiveTo investigate the effects of rhizosphere organic acids secreted by the roots of Salvia miltiorrhiza on continuous cropping obstacles. MethodsThe mixed solution of organic acids in the rhizosphere of S. miltiorrhiza in continuous cropping and rotation cropping was added to the hairy roots subcultured for 21 days， and samples were collected on days 0， 2， 4， 6， 8， and 10. The changes of biomass， effective components， primary metabolites， secondary metabolites， antioxidant enzymes， and hormones in hairy roots of S. miltiorrhiza were observed and determined. ResultsCompared with the rotation cropping group and the blank control group， the simulation of organic acid secretion from the roots of S. miltiorrhiza had a significant inhibitory effect on the growth of hairy roots and decreased the content of effective components as well as total sugar and total protein in primary metabolites. Compared with the blank control group， the rotation cropping group and the continuous cropping group showed total sugar and total protein content decreases of 33.9% and 5.1%， respectively. On the other hand， the secretion of organic acids from S. miltiorrhiza roots significantly promoted the accumulation of total phenolic acids and total tanshinone， which showed increases of 14.6% and 1.6%， respectively， in continuous cropping group and rotation cropping group compared with the blank control group. ConclusionThe organic acid environment under continuous cropping significantly inhibited the growth of hairy roots and the accumulation of primary metabolites， while promoting the synthesis and accumulation of secondary metabolites of S. miltiorrhiza.