Objective To explore a method for rapid and differential diagnosis of rejection after kidney transplantation through urine hyperspectral imaging technology. Methods Hyperspectral data information from urine samples of 118 recipients after kidney transplantation was collected, and a deep learning model was constructed to diagnose and classify the types of rejection. Results A deep learning diagnostic model based on the 34-layer residual network (ResNet-34) was constructed, and 118 patients were included and divided into the training set and the test set. Based on the pathological results of the transplanted kidney puncture, the urine samples of the patients were classified into five groups: the non-rejection group, the T-cell-mediated rejection group, the antibody-mediated rejection group, the mixed rejection group and the nephropathy recurrence group. The results showed that the diagnostic sensitivities of the model for the above five groups were 0.960, 0.980, 0.930, 0.940 and 0.943 respectively, and the diagnostic specificities were 0.983, 0.993, 0.997, 0.989 and 0.989 respectively. The overall diagnostic accuracy rate reached 95.7%. Conclusions The study provides a non-invasive, rapid and accurate auxiliary diagnostic method for the differential diagnosis of rejection after kidney transplantation.

Abstract To promote adolescents active participation in physical activity and improve sleep quality, the article analyzes the relationship of adolescent physical activity with subjective sleep satisfaction, sleep latency, sleep continuity, sleep efficiency, and sleep duration. It explores potential mechanisms underlying the link between physical activity and sleep quality, including physiological mechanisms (circadian rhythms, body temperature, neuroendocrine systems, and immune function), and psychological mechanisms (stress relief, improvement of negative emotions, and promotion of mental relaxation). Based on existing research, it is recommended that adolescents engage in moderate to vigorous physical activity daily to promote improved sleep quality.

Dietary interventions such as fasting are gaining increasing attention for their synergistic effects in anti-tumor therapy, yet the precise underlying mechanisms remain incompletely understood. Recent research has unveiled a novel mode of cell death named “mitoxyperilysis”, providing a fresh perspective on the molecular mechanisms by which fasting may interfere with tumor treatment. This form of death is primarily triggered by the synergy between metabolic dysfunction and innate immune activation. Its mechanism involves the mTORC2 signaling pathway mediating prolonged abnormal contact between damaged mitochondria and the plasma membrane. This leads to massive local release of reactive oxygen species (ROS), which further induces lipid peroxidation of the plasma membrane, ultimately resulting in the physical rupture and death of the cell. The most significant distinction between mitoxyperilysis and classical cell death pathways lies in its independence from caspases and GSDMD. This comment aims to systematically elucidate the process, molecular mechanisms, and differences from other classical cell death pathways of mitoxyperilysis, while also exploring its potential for clinical translation in oncological diseases. Targeting induction of mitoxyperilysis may enhance the efficacy of existing anti-tumor drugs and overcome chemotherapy resistance. However, intervention protocols require further optimization to achieve an optimal balance between safety and therapeutic effectiveness in clinical application.

Objective To analyze the relationship of protein intake level with high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) in patients with diabetic kidney disease (DKD) and its influence on prognosis. Methods A total of 325 patients with DKD admitted from June 2021 to June 2024 were included and classified into a low protein group and a high protein group. The levels of hs-CRP, IL-6, and TNF-α were compared between the two groups of patients before follow-up and after 1 year of follow-up. The correlation was analyzed by linear mixed model. The incidence of endpoint events during follow-up and disease progression-free survival time were compared between the two groups. Results The inflammatory indicators exhibited no statistical differences between the two groups before follow-up (P>0.05). After 1 year of follow-up, the levels of hs-CRP, IL-6, and TNF-α in the two groups were higher than those before follow-up, and the levels in the high protein group were higher than those in the low protein group (P<0.05). Linear mixed model analysis suggested that the protein intake level, time, and the interaction term of protein intake level × time were correlated with the changes of hs-CRP, IL-6 and TNF-α levels (P<0.05). The incidence rate of endpoint events in the high-protein group during follow-up was 24.84% (40/161), which was significantly higher than 12.80% (21/164) in the low-protein group (χ²=7.724, P=0.005). The disease progression-free survival time was longer in the low-protein group than that in the high-protein group (Log Rank χ²=9.007, P=0.003). Conclusion The level of protein intake in patients with diabetic kidney disease is closely related to inflammatory response and prognosis.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

ObjectiveIn traditional Chinese medicine (TCM), the foundational doctrine that the eyes reflect the essence of the internal viscera establishes ocular observation as a cornerstone of diagnostic practice. Specifically, the morphological characteristics and coloration variations of the scleral microvasculature serve as critical clinical indicators for assessing the dynamic balance of Qi and Blood, as well as the pathological status of internal organs. Historically, however, TCM eye diagnosis has relied predominantly on the subjective clinical experience and visual acuity of individual practitioners, leading to inherent challenges in standardization and reproducibility. While automated computer-aided diagnostic systems offer a promising solution, existing vessel segmentation algorithms encounter significant domain-specific bottlenecks when applied to scleral imagery. These challenges primarily stem from the highly reflective and moist nature of the ocular surface, which generates severe reflective interference. Furthermore, the inherent low contrast of fine capillary networks against complex background textures, compounded by non-uniform illumination, frequently results in high false-positive rates, misdetections, and severe vessel fragmentation. To address these critical limitations and advance the objective quantification of TCM diagnostics, this paper proposes a novel, highly robust sclera vessel segmentation framework that innovatively integrates Frangi-Sato dual-filter adaptive enhancement with pixel-level reflection detection. MethodsThe proposed methodology systematically addresses the segmentation pipeline through three synergistic stages. First, to overcome the structural limitations of single-filter approaches, a multi-scale weighted fusion strategy is meticulously designed to harness the complementary extraction capabilities of both Frangi and Sato filters. This adaptive enhancement optimally balances the preservation of main vessel trunk continuity with the heightened sensitivity required for delineating delicate, low-contrast peripheral capillaries. Second, to tackle the persistent issue of reflective highlights, a sophisticated multi-feature synergistic reflection detection module is introduced. By jointly analyzing local information entropy, gradient field variations, and intensity statistical distributions, this module achieves precise, pixel-level identification and elimination of reflective artifacts without compromising the underlying vascular structures. Finally, a dual-level adaptive thresholding strategy, featuring an innovative “core protection” mechanism, is implemented. This critical step effectively suppresses complex background noise while rigorously preserving the structural and topological integrity of the intricate vessel network, preventing the structural breaks often seen in conventional binarization methods. ResultsThe efficacy of the proposed framework was rigorously evaluated using both self-constructed clinical datasets specifically acquired for TCM research and standardized public datasets. Extensive experimental results demonstrate that the proposed method consistently outperforms state-of-the-art traditional approaches and contemporary deep learning models. Specifically, the proposed method achieves a Dice similarity coefficient of approximately 0.71 on the private clinical dataset, and secures the best performance across the majority of quantitative metrics on both datasets. Notably, the framework exhibits exceptional robustness and generalization capabilities in highly challenging scenarios characterized by intense reflective interference, low signal-to-noise ratios, and cross-domain image variations. ConclusionThis study successfully realizes the high-integrity, automated segmentation of scleral vessel networks under complex clinical imaging conditions. By overcoming the fundamental algorithmic challenges of reflection interference and micro-vessel loss, the proposed methodology provides potential support for the digitization, objective standardization, and intelligent advancement of modern TCM eye diagnosis systems.

Background Nitrosamines (NAms), emerging as disinfection by-products in drinking water, are highly carcinogenic. Given the significant NAms contamination reported in various regions of China, evaluating the contamination levels and health risks of NAms in terminal direct drinking water is of great urgency. Objective To investigate the concentration levels of NAms in piped direct drinking water at primary and secondary schools in Shanghai, and to assess the potential health risks posed to different age groups through this exposure pathway. Methods A total of 198 water samples were collected from 66 primary and secondary schools across five districts in Shanghai from May to June 2023. The mass concentrations of eight major NAms were quantified using solid-phase extraction coupled with gas chromatography-mass spectrometry (GC-MS). A carcinogenic health risk model was employed to evaluate the lifetime cancer risks associated with NAms exposure via direct drinking water for various age groups. Results Among the 198 samples, NAms were detected in 196 samples, with concentrationsranging from below the limit of detection (LOD) to 106.06 ng·L⁻¹. The average concentration of total NAms was 21.30 ng·L⁻¹, with N-nitrosodimethylamine (NDMA) exhibiting the highest detection rate at 98.5%. Significant differences in NAms concentrations were observed among water treatment systems utilizing ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO) as core processes (P<0.05). The lifetime cancer risks for different age groups ranged from 1.38×10⁻⁶ to 1.11×10⁻⁵, with NDMA contributing the most to the overall risk (82.1%). The carcinogenic risk for adults (1.08×10⁻⁵) was higher than that for children and adolescents (1.38×10⁻⁶ to 2.61×10⁻⁶). Among children and adolescents under 18 years of age, the risk decreased as age increased. Conclusion Trace levels of NAms, primarily NDMA, are detected in the piped direct drinking water at primary and secondary schools in Shanghai, with concentrations vary significantly depending on the water treatment process. The carcinogenic risks of NAms exposure via direct drinking water for all age groups are below the acceptable level (1×10⁻⁴) recommended by the U.S. Environmental Protection Agency (EPA).

Background Nitrosamines (NAms), emerging as disinfection by-products in drinking water, are highly carcinogenic. Given the significant NAms contamination reported in various regions of China, evaluating the contamination levels and health risks of NAms in terminal direct drinking water is of great urgency. Objective To investigate the concentration levels of NAms in piped direct drinking water at primary and secondary schools in Shanghai, and to assess the potential health risks posed to different age groups through this exposure pathway. Methods A total of 198 water samples were collected from 66 primary and secondary schools across five districts in Shanghai from May to June 2023. The mass concentrations of eight major NAms were quantified using solid-phase extraction coupled with gas chromatography-mass spectrometry (GC-MS). A carcinogenic health risk model was employed to evaluate the lifetime cancer risks associated with NAms exposure via direct drinking water for various age groups. Results Among the 198 samples, NAms were detected in 196 samples, with concentrationsranging from below the limit of detection (LOD) to 106.06 ng·L⁻¹. The average concentration of total NAms was 21.30 ng·L⁻¹, with N-nitrosodimethylamine (NDMA) exhibiting the highest detection rate at 98.5%. Significant differences in NAms concentrations were observed among water treatment systems utilizing ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO) as core processes (P<0.05). The lifetime cancer risks for different age groups ranged from 1.38×10⁻⁶ to 1.11×10⁻⁵, with NDMA contributing the most to the overall risk (82.1%). The carcinogenic risk for adults (1.08×10⁻⁵) was higher than that for children and adolescents (1.38×10⁻⁶ to 2.61×10⁻⁶). Among children and adolescents under 18 years of age, the risk decreased as age increased. Conclusion Trace levels of NAms, primarily NDMA, are detected in the piped direct drinking water at primary and secondary schools in Shanghai, with concentrations vary significantly depending on the water treatment process. The carcinogenic risks of NAms exposure via direct drinking water for all age groups are below the acceptable level (1×10⁻⁴) recommended by the U.S. Environmental Protection Agency (EPA).

BACKGROUND:There is still no consensus on the optimal internal fixation for the treatment of Pauwels Ⅲ femoral neck fracture,and most of the related finite element analyses have been performed using a single simplified loading condition,and the biomechanical properties of commonly used internal fixation devices need to be further investigated. OBJECTIVE:To analyze the biomechanical characteristics of Pauwels Ⅲ femoral neck fractures treated with cannulated compression screw,dynamic hip screw,and femoral neck system by finite element method under different loading conditions of single-leg standing loads and sideways fall loads. METHODS:The DICOM data of healthy adult femur were obtained by CT scanning,imported into Mimics 15.0 software to obtain the rough model of bone tissue.The data exported from Mimics were optimized by Geomagics software,and then three internal fixation models were built and assembled with the femur model according to the parameters of the clinical application of the cannulated compression screw,dynamic hip screw,and femoral neck system by using Pro/E software.Finally,the three internal fixation models were imported into Ansys software for loading and calculation to analyze the stress distribution and displacement of the femur and the internal fixation under different working conditions of single-leg standing loads and sideways fall loads,as well as the stress characteristics of the calcar femorale and Ward's triangle. RESULTS AND CONCLUSION:(1)Under the single-leg standing load and the sideways fall load,the proximal femoral stress of the three internal fixation models was mainly distributed above the fracture end of the femoral neck.The peak stress of the proximal femoral end,fracture end,Ward triangle,and calcar femorale of the three internal fixation models were the smallest in the femoral neck system model and the largest in the cannulated compression screw model.(2)Under the single-leg standing load and the sideways fall load,the peak displacement of the proximal femur of the three internal fixation models was all located at the top of the femoral head,and the peak displacement was the smallest in the femoral neck system model and the largest in the cannulated compression screw model.(3)The peak displacement of the three internal fixation models was all located at the top of the internal fixation device under the single-leg standing and sideways fall loading conditions,and the peak displacement values were the smallest in the femoral neck system internal fixation model and the largest in the cannulated compression screw internal fixation model.(4)The internal fixation stress of the three internal fixation models was mainly distributed in the area near the fracture end of the internal fixation device under the single-leg standing and sideways fall loads,and the peak value of internal fixation stress was the smallest in the femoral neck system model and the largest in the cannulated compression screw model.(5)These results suggest that the mechanical stability of the femoral neck system is the best,but there may be a risk of stress shielding of the fracture end and calcar femorale.The stress of the internal fixation device of the femoral neck system is more dispersed,and the risk of internal fixation break is lower.

BACKGROUND: Over 20 million colonoscopies are performed in China annually. An automatic clinical decision support system (CDSS) with accurate semantic recognition of colonoscopy reports and guideline-based is helpful to relieve the increasing medical burden and standardize the healthcare. In this study, the CDSS was built under a hierarchical-label interpretable classification framework, trained by a state-of-the-art transformer-based model, and validated in a multi-center style. METHODS: We conducted stratified sampling on a previously established dataset containing 302,965 electronic colonoscopy reports with pathology, identified 2041 patients' records representative of overall features, and randomly divided into the training and testing sets (7:3). A total of five main labels and 22 sublabels were applied to annotate each record on a network platform, and the data were trained respectively by three pre-training models on Chinese corpus website, including bidirectional encoder representations from transformers (BERT)-base-Chinese (BC), the BERT-wwm-ext-Chinese (BWEC), and ernie-3.0-base-zh (E3BZ). The performance of trained models was subsequently compared with a randomly initialized model, and the preferred model was selected. Model fine-tuning was applied to further enhance the capacity. The system was validated in five other hospitals with 3177 consecutive colonoscopy cases. RESULTS: The E3BZ pre-trained model exhibited the best performance, with a 90.18% accuracy and a 69.14% Macro-F1 score overall. The model achieved 100% accuracy in identifying cancer cases and 99.16% for normal cases. In external validation, the model exhibited favorable consistency and good performance among five hospitals. CONCLUSIONS The novel CDSS possesses high-level semantic recognition of colonoscopy reports, provides appropriate recommendations, and holds the potential to be a powerful tool for physicians and patients. The hierarchical multi-label strategy and pre-training method should be amendable to manage more medical text in the future.
Humans ; Colonoscopy/methods* ; Deep Learning ; Decision Support Systems, Clinical ; Female ; Male