ObjectiveTo delineate imaging findings using an imaging platform and investigate the correlation between MRI characteristics of spinal cord atrophy and clinical diagnosis in children with spinal cord injury (SCI). MethodsImaging data of 150 children with SCI admitted to Beijing Bo'ai Hospital, China Rehabilitation Research Center, from January, 2002 to March, 2024 were collected and imported into the imaging platform. The anteroposterior and transverse diameters of the middle part of the spinal cord at the cross-section with the most severe atrophy were measured, and the relevant indicators of the previous normal spinal cord segment were measured as controls; the radiomic features were extracted. Clinical data of the children including gender, age, cause of injury, sensory level, motor level, spinal cord injury level, injury severity and disease course were collected. ResultsSpinal cord atrophy was identified in 81 cases (54%), among which 78 cases (96%) were American Spinal Injury Association Impairment Scale (AIS) grade A and 3 cases (4%) were AIS grade C. The upper boundary of the spinal cord atrophy site strongly correlated with the injury level, motor level and sensory level (r > 0.8, P < 0.001). ConclusionMore than half of children with SCI may develop secondary spinal cord atrophy, the vast majority of whom suffer from complete spinal cord injury; the upper boundary of spinal cord atrophy is correlated with the injury level.

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.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Objective To explore the pathogenesis of Alzheimer's disease by examining the effects of dihydroartemisinin(DHA)on cognitive behavior,hippocampal,cerebral cortex and retinal cell morphology,β-amyloid(Aβ)and autophagy-related proteins in 5×FAD mice.Methods Twenty 5×FAD mice and 5 wild type(WT)mice were selected,all of which were female.The 5×FAD mice were randomly divided into model(M)group,donepezil(D)group,low-dose DHA(DHA-L)group,and high-dose DHA(DHA-H)group.The WT and M groups were not treated,and the D group was given donepezil 0.1 mg/kg per day.DHA-L group and DHA-H group were given 10 mg/kg and 20 mg/kg DHA per day,respectively.Group D,group DHA-L and group DHA-H were given intragastric administration once a day for 3 months.The changes of in cognitive behavior were measured by Morris experiment.HE staining was used to observe the arrangement and morphology of nerve cells in cerebral cortex,hippocampus and retina.The expressions of Aβ protein in cerebral cortex,hippocampus and retina were detected by immunohistochemistry.Western blotting detected the expression of autophagy related proteins(LC3-Ⅰ,LC3-Ⅱ,Beclin-1,P62,β-actin).Results The DHA-H group and the D group exhibited more frequent adoption of both linear and trending exploration routes.Compared to the model group,significant differences in the contents of Aβ in the hippocampal CA1,cerebral cortex S1,and retinal were observed(P＜0.0001)in the other four groups.The analysis also showed significant differences in autophagy-associated proteins between the DHA-L,DHA-H,and model groups(P＜0.01).Conclusion DHA improves cognitive function and increases the number of nerve cells in mice.It also reduces Aβ content in the cerebral cortex,hippocampus,and retina,along with improving autophagy-associated protein deposition in mice.

Objective To investigate the effect of up-regulating tumor necrosis factor alpha induced protein 3(TNFAIP3)expression on hippocampal neurons in mice with cerebral ischemia-reperfusion.Methods The mice were randomly divided into 6 groups:sham group,sham empty vector group(sham-),sham TNFAIP3 high expression group(sham+),model group,model empty vector group(model-),model TNFAIP3 high expression group(model+).A mouse model of middle cerebral artery occlusion and cerebral ischemia-reperfusion was established using the suture method.After the successful establishment of the model,lentivirus was injected into the hippocampus 24 hours later.Two weeks later,samples were collected and Western blotting was used to detect the expressions of TNFAIP3 and ERK signaling pathway proteins.The changes in ischemic area were observed by 2,3,5-triphenyltetrazolium chloride(TTC)staining;HE staining was used to observe the morphological changes of hippocampal neurons,and ELISA was used to detect the expressions of lipoprotein-associated phospholipase A2(Lp-PLA2)and interleukin(IL)-8.Results The results of Western blotting indicated that the TNFAIP3 expression in the model group decreased significantly compared with the sham group(P＜0.05);Compared with the model group,there was no significant change in TNFAIP3 expression in the model-group(P＞0.05);The TNFAIP3 expression in the model+group increased significantly compared with the model group and model-group(P＜0.05).Compared with the sham group,the results of the sham+group showed that the ischemic area had no significant changes in TTC staining,and there were no significant changes in hippocampal neuronal morphology,and the expressions ERK signaling pathway proteins,Lp-PLA2 and IL-8(P＞0.05);Compared with the sham-and sham+groups,the model group showed an increase in ischemic area,significant damage to hippocampal neurons,a decrease in the number of Nissl bodies,and a significant increase in the expressions of ERK signaling pathway proteins,Lp-PLA2,and IL-8(P＜0.05);Compared to the model-group,the model+group showed a decrease in ischemic area,an increase in the number of neurons in the hippocampus and the number of Nissl bodies,and a significant decrease in the expressions of ERK signaling pathway proteins,Lp-PLA2,and IL-8(P＜0.05).Conclusion Up-regulation of TNFAIP3 may be one of the methods for repairing hippocampal neuronal damage caused by cerebral ischemia reperfusion.

Hepatocellular carcinoma(HCC)expresses abundant glycolytic enzymes and displays comprehensive glucose metabolism reprogramming.Aldolase A(ALDOA)plays a prominent role in glycolysis;however,little is known about its role in HCC development.In the present study,we aim to explore how ALDOA is involved in HCC proliferation.HCC proliferation was markedly suppressed both in vitro and in vivo following ALDOA knockout,which is consistent with ALDOA overexpression encouraging HCC prolifera-tion.Mechanistically,ALDOA knockout partially limits the glycolytic flux in HCC cells.Meanwhile,ALDOA translocated to nuclei and directly interacted with c-Jun to facilitate its Thr93 phosphorylation by P21-activated protein kinase;ALDOA knockout markedly diminished c-Jun Thr93 phosphorylation and then dampened c-Jun transcription function.A crucial site Y364 mutation in ALDOA disrupted its interaction with c-Jun,and Y364S ALDOA expression failed to rescue cell proliferation in ALDOA deletion cells.In HCC patients,the expression level of ALDOA was correlated with the phosphorylation level of c-Jun(Thr93)and poor prognosis.Remarkably,hepatic ALDOA was significantly upregulated in the promotion and progression stages of diethylnitrosamine-induced HCC models,and the knockdown of Aldoa strikingly decreased HCC development in vivo.Our study demonstrated that ALDOA is a vital driver for HCC development by activating c-Jun-mediated oncogene transcription,opening additional avenues for anti-cancer therapies.

BackgroundIn recent years， the prevalence of obstructive sleep apnea （OSA） is escalating in China， leading to a serious disease burden. However， previous studies on the influencing factors of OSA， such as obesity and sleep position， were mostly cross-sectional studies. This approach inherently hinders the identification of dynamic interaction mechanism among multiple variables， consequently obstructing the formulation of individualized intervention strategies. ObjectiveTo investigate the differences in body mass index （BMI）-oxygen saturation-sleep position-heart rate variability （HRV） network models between OSA and non-OSA populations， thereby offering a reference for the early detection and management of OSA. MethodsA total of 384 adult participants undergoing sleep monitoring at the Affiliated Hospital of Southwest Medical University from July 12， 2022 to October 11， 2023 were included. Subjects were categorized into OSA group （n=203） and control group （n=181） based on an apnea-hypopnea index （AHI） threshold of 5 events per hour. Subsequently， BMI-oxygen saturation-sleep position-HRV networks were constructed and compared between two groups. ResultsThere was no significant difference in the overall edge weight （P=0.55） and overall strength （P=0.28） of the network model between control group and OSA group. Notable differences emerged in both the node connection strength （e.g.， minimum oxygen saturation with BMI， sleep in prone position， and mean RR interval） and node centrality indices （mean oxygen saturation， minimum oxygen saturation， AHI in upright position， AHI in right lateral position and mean heart rate） within the two network models （P<0.05）. ConclusionSignificant differences are observed between the non-OSA and OSA populations in specific factors， including sleep position， heart rate and oxygen saturation.

Microplastics are widely present in various environments such as water bodies,land,and atmosphere,which pose threats to the ecological environment and human health through transmission and accumulation in the food chain.The existing detection techniques for microplastics face challenges such as complex preparation procedure of samples,low efficiency in processing large batches of samples,and difficulties in handling complex samples.Therefore,there is an urgent need for rapid and efficient detection techniques suitable for complex microplastics samples in the field of environmental monitoring.Raman spectroscopy,known for its advantages such as rapidity,accuracy,high sensitivity,non-destructiveness,and non-contact,demonstrates great application potential in detection of microplastics.Deep learning,an artificial intelligence method known for its large-scale data processing,nonlinear modeling and automatic feature extraction capabilities,is receiving increasing attention in the analysis of Raman spectroscopy signals.The application of deep learning-based Raman spectroscopy has significantly improved performance indicators such as detection efficiency and accuracy.This article introduced the existing Raman enhancement techniques,summarized the deep learning methods applied in Raman spectroscopy signal analysis,reviewed the recent research and application progress of deep learning-based Raman spectroscopy in detection of microplastics,and finally discussed the challenges and future prospects of deep learning-based Raman spectroscopy in detection of microplastics.

Objective:To observe the safety and short-term efficacy of using an autologous fascia lata sling (AFLS) for tension-free mid-urethral sling (MUS) in the treatment of stress urinary incontinence (SUI).Methods:Between February 2022 and December 2023, 11 patients with SUI underwent AFLS-MUS. Preoperative data were recorded, including basic patient information and completion of urinary distress inventory 6 (UDI-6). During surgery, AFLS was harvested through a small incision using a tendon extractor, and used as a sling for transobturator or retropubic MUS. Perioperative indicators were recorded, including surgical approach, operation time, intraoperative blood loss, postoperative hospital stay, duration of catheterization, perioperative complications (Clavien-Dindo classification), and surgical costs. Follow-ups included outpatient physical examination at 2 months postoperatively, and telephone follow-up at 6 months, 1 year, and annually thereafter. Follow-up content included the presence or absence of urinary leakage symptoms, UDI-6, satisfaction, patient global impression of improvement (PGI-I), and complications.Results:The age of the 11 patients was (54.8±10.9) years (range: 41-72 years), with body mass index of (23.9±1.8) kg/m2 (range: 21.4-27.3 kg/m2). All patients experienced urinary leakage after coughing, sneezing and physical activity, with positive SUI provocation tests. The preoperative UDI-6 was 50.0±21.6 (range: 16.7-79.2), the result of 1-hour pad test was (18.9±12.0) g (range: 2.5-71.2 g). Four cases underwent MUS only, with operation time of (98.0±13.3) minutes (range: 86-117 minutes), and intraoperative blood loss of (17.5±5.0) ml (range: 10-20 ml); 7 cases also underwent pelvic floor repair simultaneously. The postoperative hospital stay was (3.5±2.0) days (range: 2-9 days). The duration of catheterization was (4.5±3.8) days (range: 2-11 days), with postoperative urinary retention in three cases, one of which underwent sling release surgery due to severe postoperative voiding difficulty 1 week after MUS, with no other complications of Clavien-Dindo grade 2 or above. The cost of AFLS harvest plus MUS was (2 762±293) yuan. At the 2-month outpatient follow-up, all patients were free of urinary leakage symptoms, with UDI-6 of 2.3±1.9 (range: 0-8.3); satisfaction was "very satisfied" in 10 cases and "fairly satisfied" in 1 case, with PGI-I all being "much better", and pelvic examinations were normal. Telephone follow-up showed one case lost to follow-up, and the remaining 10 cases had follow-up time of (18.6±4.9) months (range: 7-26 months), all without urinary leakage, with UDI-6 of 2.7±2.6, satisfaction rated as "very satisfied", and PGI-I all "much better".Conclusion:This modified AFLS-MUS for the treatment of SUI shows good short-term efficacy and high safety in harvest site, with the need for more data accumulation and long-term follow-up.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.