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 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.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

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.

To report two cases of psychiatric symptoms associated with zonisamide, an antiepileptic drug, and raise clinical awareness of this potential adverse effect. Two male patients with epilepsy treated with zonisamide were retrospectively analyzed. Case 1 (25 years old) developed acute emotional and behavioral abnormalities (e.g., insomnia, aggression, incoherent speech) after switching from sodium valproate to zonisamide (200 mg/day). Case 2 (48 years old) had long-term zonisamide use (≥5 years) with persistent treatment-resistant psychotic symptoms (e.g., delusions, command hallucinations). Clinical courses, medication adjustments, and symptom responses were documented. In Case 1, psychiatric symptoms resolved after discontinuing zonisamide and switching to sodium valproate, with improved mood stability and reduced impulsivity. In Case 2, despite escalating antipsychotic medications (risperidone, clozapine), psychotic symptoms persisted, likely due to ongoing zonisamide use. Both cases highlighted zonisamide’s potential to exacerbate or induce psychiatric manifestations, possibly via mechanisms involving sodium/calcium channel inhibition and neurotransmitter dysregulation (e.g., dopamine, serotonin). Zonisamide can cause or worsen psychiatric symptoms, particularly in vulnerable individuals. Clinicians should monitor for mental health changes during zonisamide treatment and consider drug discontinuation or substitution with alternative antiepileptics (e.g., sodium valproate) if psychiatric adverse effects emerge. Awareness of this association is crucial to avoid misdiagnosis and optimize epilepsy management.

AIM: To investigate the repeatability of using IOL Master 700 to stratify corneal astigmatism and related biological parameters in cataract patients with different degrees of astigmatism, and evaluate its consistency with IOL Master 500.

METHODS: A cross-sectional study was conducted on preoperative cataract patientsadmitted to the hospital from February 2024 to September 2025. The patients were divided into three groups: low astigmatism group(astigmatism<0.75 D), moderate astigmatism group(0.75 D≤astigmatism≤1.50 D), and high astigmatism group(astigmatism>1.50 D). The IOL Master 700 and IOL Master 500 biometers were used by the same examiner respectively. Under the same environment, head position, and operating standards, each eye was continuously measured twice(with an interval of 3-5 min)with each instrument. Axial length(AL), anterior chamber depth(ACD), lens thickness(LT), corneal transverse diameter(WTW), flat curvature(Kf), steep curvature(Ks), mean curvature(Km), anterior corneal astigmatism(Kly), total corneal astigmatism(TK), iris-based diameter-vertical component(IBD-Ix), iris-based diameter-horizontal component(IBD-Iy)and astigmatism vectors J0 and J45 were recorded. Intraclass correlation coefficient(ICC)was used to evaluate the repeatability of IOL Master 700 measurements. Bland-Altman 95% limits of agreement(LoA)was used to evaluate the consistency of IOL Master 700 and IOL Master 500 measurements. Correlation scatter plots were used to evaluate the correlation between IOL Master 700 and IOL Master 500 measurements.

RESULTS:Totally 238 preoperative cataract patients(238 eyes)were selected as the research subjects and divided into a low astigmatism group of 88 cases, including 47 males and 41 females, with an average age of 68.14±6.25 y. The moderate astigmatism group consisted of 80 eyes, with 42 males and 38 females, with an average age of 68.13±6.98 y. The high astigmatism group consisted of 70 eyes, with 38 males and 32 females, with an average age of 69.11±6.79 y. The general information of the three groups of patients was comparable. In the low, medium, and high astigmatism groups, the ICC measured by IOL Master 700 for AL, ACD, LT, WTW, Kf, Ks, Km, Kly, TK, and IBD-Ix were all>0.90, and the ICC for IBD-Iy was>0.85. The ICC comparison of J0 and J45 patients with different degrees of astigmatism showed that the low astigmatism group was less than the moderate astigmatism group and less than the high astigmatism group. Bland Altman analysis between two devices showed that the 95% LoA width of AL, ACD, LT, WTW, Kf, Ks, Km, Kly, TK, and IBD-Ix was narrow, indicating good consistency between the two instruments. For the astigmatism vectors J0 and J45, the Bland Altman 95% LoA width between the two devices decreased with increasing astigmatism. A correlation scatter plot was drawn, which showed that the scatter points of AL, ACD, LT, WTW, Kf, Ks, Km, Kly, TK, IBD-Ix, and IBD-Iy were closely clustered on both sides of the diagonal without obvious outliers, indicating high correlation. J0 and J45 scatter points were still distributed along the diagonal, but the degree of dispersion was significantly greater than the above parameters, indicating slightly lower correlation.

CONCLUSION:IOL Master 700 exhibits goodtoexcellent repeatability in measuring parameters such as AL, ACD, LT, WTW, corneal curvature, Kly, TK, IBD-Ix,and IBD-Iy in cataract patients with different degrees of astigmatism, and shows good agreement with the IOL Master 500. However, for the astigmatism vectors J0 and J45, the repeatability of IOL Master 700 and its consistency with IOL Master 500 both decrease with decreasing astigmatism, and the difference is particularly significant in patients with low astigmatism. Therefore, astigmatism vector outcomes obtained from a single measurement should be interpreted with caution in patients with low astigmatism.

Isoliquiritigenin (ISL) is a chalcone compound isolated from licorice, known for its anti-diabetic, anti-cancer, and antioxidant properties. Our previous study has demonstrated that ISL effectively lowers blood glucose levels in type 2 diabetes mellitus (T2DM) mice and improves disturbances in glucolipid and energy metabolism induced by T2DM. This study aims to further investigate the effects of ISL on alleviating abnormal endoplasmic reticulum stress (ERS) caused by T2DM and to elucidate its molecular mechanisms. In vivo experiments were conducted using 8-week-old SPF male C57BL/6J mice. The T2DM animal model was established by high-fat and high-sugar diet combined with intraperitoneal injections of streptozotocin (STZ), in compliance with the ethical guidelines set by the Animal Welfare Committee of Beijing University of Chinese Medicine (approval number: BUCM-2022021503-1134). In vitro experiments employed human liver cancer HepG2 cells, which were induced with tunicamycin (TM) to establish the ERS cell model. Transcriptomic sequencing was used to analyze changes in gene expression in the liver samples of T2DM mice following ISL treatment. Real-time quantitative polymerase chain reaction (RT-qPCR) was employed to assess the regulatory effects of ISL on key ERS genes. Enzyme-linked immunosorbent assay (ELISA), Western blot (WB), and immunofluorescence techniques were used to evaluate ISL's effects on ERS-related proteins. Results indicate that ISL significantly downregulates the expression of ERS-related genes, reduces the level of glucose-regulated protein 78 (GRP78), and inhibits the phosphorylation of protein kinase RNA-like endoplasmic reticulum kinase (PERK), thereby alleviating abnormal ERS induced by T2DM. Additionally, ISL increases the protein levels of insulin receptor substrate (IRS) 1 and IRS2 and enhances the phosphorylation of protein kinase B (Akt), thereby improving insulin sensitivity. In conclusion, ISL is able to alleviate T2DM associated symptoms by improving abnormal ERS and enhancing insulin sensitivity.

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 evaluate the effectiveness of thermal tomography in breast cancer (BC) screening. Methods We conducted a general population-based BC screening in three regions of Hubei Province (Xiantao, Hongan, and Yangxin Districts). Participants underwent a questionnaire-based interview for baseline data collection. They then received a physical examination, thermal tomography, and ultrasound from doctors and technicians. We compared the efficacies, including sensitivity, specificity, and false-positive rates, of ultrasound and thermal tomography in BC screening. Results A total of 59 712 eligible women were included in this screening program. The BI-RADS 1, 2, 3, 4, and 5 accordance rates between the two screening methods were 0.9549, 0.8047, 0.9037, 0.3352, and 0, respectively. The overall accordance rate was 0.933 1. The kappa consistency results showed that the Cicchetti-Allison and Fleiss-Cohen kappa values were 0.7970 and 0.8583, respectively. Although the two methods had equal sensitivities, specificity was higher in thermal tomography than in ultrasound. The false-positive rate was lower in thermal tomography than in ultrasound. The area under the receiver operating characteristic (ROC) curve of thermal tomography was significantly larger than that of ultrasound. Conclusion The general consistency between thermal tomography and ultrasound in BC screening was high. Thermal tomography outperformed ultrasound in terms of specificity and diagnostic efficiency. Therefore, thermal tomography has a high application value for general population-based BC screening.