Onco-critical care has emerged as an important subspecialty at the intersection of critical care medicine and oncology, attracting increasing attention in recent years. With continuous innovations in cancer therapies, patient survival has improved significantly; however, the incidence of associated critical complications has also increased. The reasons for cancer patients requiring intensive care unit admission are diverse and can be broadly categorized into three groups: progression of the underlying malignancy, treatment-related complications, and coexisting classical critical illnesses. Traditional critical care concepts and practices face limitations in addressing the multidimensional and heterogeneous challenges of onco-critical care. Based on the core mechanism of critical illness development—host/organ unregulated response (HOUR)—this article systematically elaborates on how this framework advances understanding and clinical practice into onco-critical care, with emphasis on its manifestations in neuroendocrine, immune-inflammatory, and coagulation-metabolic pathways. The review summarizes recent advances in clinical assessment and phenotyping systems for onco-critical illness and discusses a multidisciplinary, integrated management strategy centered on the "Disease Control, Host Response Modulation, Organ Support" triad. Finally, major challenges and future directions in this field are outlined. By integrating existing evidence and theoretical insights, this review aims to provide new perspectives and a theoretical foundation for the clinical management of onco-critical illness, thereby promoting its evolution toward precision and standardization.

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.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

Objective To evaluate the changes of bronchial mucosa observed by fiberoptic bronchoscopy after bronchial arterial embolization(BAE)treatment.Methods A total of 176 patients,who received BAE at the First Affiliated Hospital of Guangzhou Medical University of China from May 2019 to March 2024,were enrolled in this study.The pre-BAE and post-BAE bronchial mucosa was checked by fiberoptic bronchoscopy.Results Of the 176 patients,fiberoptic bronchoscopy showed no abnormal findings in 143 and showed abnormal findings in 33.All the abnormal findings were mucosal congestion and oedema,in some cases coexisting vascular bulge was seen,but no manifestations of ischemia or necrosis of the bronchial mucosa could be found.In 22 patients,the preoperative and postoperative 7-day fiberoptic bronchoscopy revealed that both preoperative and postoperative examinations showed no obvious abnormalities of the bronchial mucosa in 13 patients,preoperative examination had abnormalities of the bronchial mucosa in 9 patients,postoperative examination showed no obvious abnormalities of the bronchial mucosa in 3 patients,and in one patient the postoperative degree of bronchial mucosal congestion and oedema was significantly improved when compared with its preoperative degree.Conclusion BAE does not cause ischemic necrosis or shedding of bronchial mucosa,and BAE can reduce the degree of bronchial mucosal congestion in some patients.

Transfer-RNA derived small RNA(tsRNA),a re-cently discovered class of non-coding RNA,is produced by ma-ture tRNA or tRNA precursor through the mediation of specific endonucleases.By regulating gene expression at the transcrip-tional and post transcriptional levels and acting as an epigenetic regulator,tsRNA plays an important role in the physiological and pathological processes of many organisms.Therefore,it has gradually become a research hotspot in biomedicine and attracted widespread attention.Moreover,there is increasing evidence that tsRNA is involved in the occurrence and development of many neuropsychiatric diseases through participating in stress re-sponse,cell proliferation and apoptosis,neural development,synaptic plasticity,neuroinflammation and immune regulation,epigenetic regulation,RNA processing,and protein translation regulation.This article mainly discusses the generation,classifi-cation and biological functions of tsRNA,and elaborates on the role and possible mechanisms of tsRNA in neurodevelopment and neuropsychiatric disorders,thereby further revealing the poten-tial of tsRNA as a reliable biomarker and therapeutic target for neuropsychiatric disorders.

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

Objective:To investigate the essential determinants that are critical to initiating the assembly of outer membrane proteins by replacing the POTRA domains of the translocator protein FhaC and the insertase protein TtOmp85 of the Omp85 family. Methods:FhaC, TtOmp85 proteins and their recombinant chimeric proteins after replacing the POTRA domain were obtained by overexpression and purification in vitro. An in vitro reconstitution system was used to investigate the effects of the different domains on the transport efficiency of the substrate outer membrane protein FhaB and the membrane insertion efficiency of OmpA. Results:Replacing the POTRA domain of FhaC with that of TtOmp85 led to the loss of the transport function of FhaB. During the membrane insertion process of OmpA, the FhaC mutant containing the POTRA of TtOmp85 protein acquired the ability to assemble OmpA. Conclusion:The compositional differences in the POTRA domain of Omp85 family proteins determine their abilities to recognize their substrate proteins.

Objective:To investigate the anatomical characteristics of the atlantoaxial joint associated with nonuniform settlement of the C 2 lateral mass (C 2LM-NUS) and its correlation with atlantoaxial osteoarthritis. Methods:A retrospective analysis was conducted on clinical and imaging data of 522 hospitalized patients (288 males, 234 females; mean age 60.8±11.2 years; range 18-83 years) who underwent CT scans of the head/neck or cervical spine at the Second Affiliated Hospital of Army Medical University between January 1, 2022 and December 31, 2022. Multiplanar reconstruction of CT data was performed to measure the settlement of the C 2 lateral mass (C 2LMS). Patients with a difference in bilateral C 2LMS (d-C 2LMS) >1.4 mm were classified into the C 2LM-NUS group (137 cases; 71 males, 66 females; mean age 63.3±11.6 years), while the normal group included 385 patients (217 males, 168 females; mean age 59.9±11.0 years). Imaging parameters of the atlantoaxial joint were measured, including the C 1, 2 coronal inclination angle (C 1, 2 CI), atlanto-dental interval (ADI), lateral atlanto-dental interval (LADI), coronal deviation angle of the odontoid (Od-CDA), and C 1, 2 relative rotation angle (C 1, 2 RRA). Osteoarthritis prevalence was recorded. A normal C 0-C 3 finite element (FE) model was constructed using CT data from a 48-year-old female in the normal group. A C 2LM-NUS FE model was developed based on anatomical differences between the C 2LM-NUS and normal groups, and stress distribution on the C 2 lateral mass articular surface was analyzed under flexion-extension, lateral bending, and axial rotation torques. Results:The C 2LM-NUS group exhibited asymmetric atlantoaxial joint morphology, with bilateral differences in C 1, 2CI and LADI of 8.5°(5.8°, 11.3°) and 0.8(0.1, 1.4) mm, respectively, significantly greater than those in the normal group [1.7°(0.8°, 2.7°) and 0.2(0.1, 0.5) mm, P<0.05]. Od-CDA and C 1, 2RRA were 3.9°(2.0°, 5.4°) and 7.2°(5.0°, 10.0°) in the C 2LM-NUS group, exceeding the normal group's values [0°(0°, 1.0°) and 0°(0°, 5.5°), P<0.05]. The prevalence of C 2LM-NUS was 37.8% in the atlantoaxial osteoarthritis group, significantly higher than in the non-osteoarthritis group (22.8%, P<0.05). Significant differences were observed in age (68.3±9.4 vs. 58.6±10.8 years), sex distribution (50/69 vs. 238/165), and C 1, 2RRA [5.6°(0°, 8.2°) vs. 3.8°(0°, 6.2°)] between the osteoarthritis and non-osteoarthritis groups ( P<0.05). After adjusting for age, sex, and C 1, 2RRA, binary logistic regression identified C 2LM-NUS as an independent risk factor for atlantoaxial osteoarthritis [ OR=2.024, 95% CI (1.300, 3.150), P<0.001]. FE analysis demonstrated a reduced C 1, 2 range of motion in the C 2LM-NUS model, with elevated stress concentrations on the settled side lateral mass during simulated flexion-extension, lateral bending, and rotation. Conclusions:The study indicated that C 2LM-NUS is associated with asymmetric anatomical changes in the atlantoaxial joint, increasing the risk of osteoarthritis. Stress concentration on the C 2 lateral mass articular surface, caused by C 2LM-NUS, is a biomechanical contributor to this heightened risk.

AIM To investigate the effects of Congrong San on neuronal apoptosis and the Bax/Bcl-2/Caspase3 signaling pathway in a rat model of Alzheimer's disease(AD).METHODS A total of 60 2-month-old SD male rats were randomly divided into the blank group,the model group,the memantine hydrochloride group(0.025 g/kg)and low-dose,medium-dose and high-dose Congrong San groups(4.62,9.24,18.48 g/kg).All groups except the control group received stereotactic intracerebral injection of Aβ1-42 to establish AD models.Following the successful modeling,each group received its corresponding intragastric administration once daily for 28 consecutive days.After the administration,the rats had their learning and memory ability detected by the morris water maze test;their hippocampal neuronal morphology observed with HE and Nissl staining;their hippocampal neuronal apoptosis observed with TUNEL staining;and their hippocampal expressions of amyloid precursor protein(APP),β-site APP-cleaving enzyme 1(BACE1),and apoptosis-related proteins Bax,Bcl-2 and Caspase3 detected with Western blot assay.RESULTS Compared with the model group,the groups intervened with medium-dose and high-dose Congrong San exhibited improved learning and memory performance,alleviated hippocampal neuronal damage,increased Nissl body count(P＜0.01),reduced hippocampal apoptosis rate(P＜0.05,P＜0.01),decreased protein expressions of APP,BACE1,Bax and cleaved-Caspase3/Caspase3 ratio(P＜0.05,P＜0.01),and elevated Bcl-2 expression(P＜0.01).CONCLUSION Congrong San mitigates cognitive impairment,hippocampal neuronal damage,and apoptosis in AD rats,probably through inhibition of the Bax/Bcl-2/Caspase3 signaling pathway activation.