Prenatal inflammation exposure-programmed hypertension exhibits multi-generational inheritance via disrupting DNA methylome.

The multi-generation heredity trait of hypertension in human has been reported, but the molecular mechanisms underlying multi-generational inheritance of hypertension remain obscure. Recent evidence shows that prenatal inflammatory exposure (PIE) results in increased incidence of cardiovascular diseases, including hypertension. In this study we investigated whether and how PIE contributed to multi-generational inheritance of hypertension in rats. PIE was induced in pregnant rats by intraperitoneal injection of LPS or Poly (I:C) either once on gestational day 10.5 (transient stimulation, T) or three times on gestational day 8.5, 10.5, and 12.5 (persistent stimulation, P). Male offspring was chosen to study the paternal inheritance. We showed that PIE, irrespectively induced by LPS or Poly (I:C) stimulation during pregnancy, resulted in multi-generational inheritance of significantly increased blood pressure in rat descendants, and that prenatal LPS exposure led to vascular remodeling and vasoconstrictor dysfunction in both thoracic aorta and superior mesenteric artery of adult F2 offspring. Furthermore, we revealed that PIE resulted in global alteration of DNA methylome in thoracic aorta of F2 offspring. Specifically, PIE led to the DNA hypomethylation of G beta gamma (Gßγ) signaling genes in both the F1 sperm and the F2 thoracic aorta, and activation of PI3K/Akt signaling was implicated in the pathologic changes and dysregulated vascular tone of aortic tissue in F2 LPS-P offspring. Our data demonstrate that PIE reprogrammed DNA methylome of cells from the germline/mature gametes contributes to the development of hypertension in F2 PIE offspring. This study broadens the current knowledge regarding the multi-generation effect of the cumulative early life environmental factors on the development of hypertension.

A compact motorized end-effector for ankle rehabilitation training.

This paper introduces a compact end-effector ankle rehabilitation robot (CEARR) system for addressing ankle range of motion (ROM) rehabilitation. The CEARR features a bilaterally symmetrical rehabilitation structure, with each side possessing three degrees of freedom (DOF) driven by three independently designed actuators. The working intervals of each actuator are separated by a series connection, ensuring they operate without interference to accommodate the dorsiflexion/plantarflexion (DO/PL), inversion/eversion (IN/EV), and adduction/abduction (AD/AB) DOF requirements for comprehensive ankle rehabilitation. In addition, we integrated an actuator and foldable brackets to accommodate patients in varied postures. We decoded the motor intention based on the surface electromyography (sEMG) and torque signals generated by the subjects' ankle joints in voluntary rehabilitation. Besides, we designed a real-time voluntary-triggered control (VTC) strategy to enhance the rehabilitation effect, in which the root mean square (RMS) of sEMG was utilized to trigger and adjust the CEARR rehabilitation velocity support. We verified the consistency of voluntary movement with CEARR rehabilitation support output for four healthy subjects on a nonlinear sEMG signal with an R 2 metric of approximately 0.67. We tested the consistency of triggering velocity trends with a linear torque signal for one healthy individual with an R 2 metric of approximately 0.99.

Deep Learning Combined with Radiologist's Intervention Achieves Accurate Segmentation of Hepatocellular Carcinoma in Dual-Phase Magnetic Resonance Images.

Purpose: Segmentation of hepatocellular carcinoma (HCC) is crucial; however, manual segmentation is subjective and time-consuming. Accurate and automatic lesion contouring for HCC is desirable in clinical practice. In response to this need, our study introduced a segmentation approach for HCC combining deep convolutional neural networks (DCNNs) and radiologist intervention in magnetic resonance imaging (MRI). We sought to design a segmentation method with a deep learning method that automatically segments using manual location information for moderately experienced radiologists. In addition, we verified the viability of this method to assist radiologists in accurate and fast lesion segmentation. Method: In our study, we developed a semiautomatic approach for segmenting HCC using DCNN in conjunction with radiologist intervention in dual-phase gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid- (Gd-EOB-DTPA-) enhanced MRI. We developed a DCNN and deep fusion network (DFN) trained on full-size images, namely, DCNN-F and DFN-F. Furthermore, DFN was applied to the image blocks containing tumor lesions that were roughly contoured by a radiologist with 10 years of experience in abdominal MRI, and this method was named DFN-R. Another radiologist with five years of experience (moderate experience) performed tumor lesion contouring for comparison with our proposed methods. The ground truth image was contoured by an experienced radiologist and reviewed by an independent experienced radiologist. Results: The mean DSC of DCNN-F, DFN-F, and DFN-R was 0.69 ± 0.20 (median, 0.72), 0.74 ± 0.21 (median, 0.77), and 0.83 ± 0.13 (median, 0.88), respectively. The mean DSC of the segmentation by the radiologist with moderate experience was 0.79 ± 0.11 (median, 0.83), which was lower than the performance of DFN-R. Conclusions: Deep learning using dual-phase MRI shows great potential for HCC lesion segmentation. The radiologist-aided semiautomated method (DFN-R) achieved improved performance compared to manual contouring by the radiologist with moderate experience, although the difference was not statistically significant.

Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives.

Introduction: Inter- and intra-subject variability are caused by the variability of the psychological and neurophysiological factors over time and across subjects. In the application of in Brain-Computer Interfaces (BCI), the existence of inter- and intra-subject variability reduced the generalization ability of machine learning models seriously, which further limited the use of BCI in real life. Although many transfer learning methods can compensate for the inter- and intra-subject variability to some extent, there is still a lack of clear understanding about the change of feature distribution between the cross-subject and cross-session electroencephalography (EEG) signal. Methods: To investigate this issue, an online platform for motor-imagery BCI decoding has been built in this work. The EEG signal from both the multi-subject (Exp1) and multi-session (Exp2) experiments has been analyzed from multiple perspectives. Results: Firstly we found that with the similar variability of classification results, the time-frequency response of the EEG signal within-subject in Exp2 is more consistent than cross-subject results in Exp1. Secondly, the standard deviation of the common spatial pattern (CSP) feature has a significant difference between Exp1 and Exp2. Thirdly, for model training, different strategies for the training sample selection should be applied for the cross-subject and cross-session tasks. Discussion: All these findings have deepened the understanding of inter- and intra-subject variability. They can also guide practice for the new transfer learning methods development in EEG-based BCI. In addition, these results also proved that BCI inefficiency was not caused by the subject's unable to generate the event-related desynchronization/synchronization (ERD/ERS) signal during the motor imagery.

To explore the mechanism of tobacco addiction using structural and functional MRI: a preliminary study of the role of the cerebellum-striatum circuit.

Previous studies have found that the striatum and the cerebellum played important roles in nicotine dependence, respectively. In heavy smokers, however, the effect of resting-state functional connectivity of cerebellum-striatum circuits in nicotine dependence remained unknown. This study aimed to explore the role of the circuit between the striatum and the cerebellum in addiction in heavy smokers using structural and functional magnetic resonance imaging. The grey matter volume differences and the resting-state functional connectivity differences in cerebellum-striatum circuits were investigated between 23 heavy smokers and 23 healthy controls. The cigarette dependence in heavy smokers and healthy controls were evaluated by using Fagerström Test. Then, we applied mediation analysis to test whether the resting-state functional connectivity between the striatum and the cerebellum mediates the relationship between the striatum morphometry and the nicotine dependence in heavy smokers. Compared with healthy controls, the heavy smokers' grey matter volumes decreased significantly in the cerebrum (bilateral), and increased significantly in the caudate (bilateral). Seed-based resting-state functional connectivity analysis showed significantly higher resting-state functional connectivity among the bilateral caudate, the left cerebellum, and the right middle temporal gyrus in heavy smokers. The cerebellum-striatum resting-state functional connectivity fully mediated the relationship between the striatum morphometry and the nicotine dependence in heavy smokers. Heavy smokers showed abnormal interactions and functional connectivity between the striatum and the cerebellum, which were associated with the striatum morphometry and nicotine dependence. Such findings could provide new insights into the neural correlates of nicotine dependence in heavy smokers.

Numerical simulation of bubble dynamics in a micro-channel under a nonuniform electric field.

A numerical method is used to simulate the motion and coalescence of air bubbles in a micro-channel under a nonuniform electric field. The channel is equipped with arrays of electrodes embedded in its wall and voltages are applied on the electrodes to generate a specified electric field gradient in the longitudinal direction. In the study, the Navier-Stokes equations are solved by using the level set method handling the deformable/moving interfaces between the bubbles and the ambient liquid. Both the polarization Coulomb force and the dielectrophoresis force are considered as the force source of the Navier-Stokes equations by solving the Maxwell's equations. The flow field equations and the electric field equations are coupled and solved by using the finite element method. The electric field characteristics and the dynamic behavior of a bubble are analyzed by studying the distributions of the electric field and the force, the deformation and the moving velocity of the air bubble. The result suggests that the model of dispersed drops suspended in the immiscible dielectric liquid and driven by a nonuniform electric field is an effective method for the transportation and coalescence of micro-drops.

Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study.

BACKGROUND: Traditional scoring systems for patients' outcome prediction in intensive care units such as Oxygenation Saturation Index (OSI) and Oxygenation Index (OI) may not reliably predict the clinical prognosis of patients with acute respiratory distress syndrome (ARDS). Thus, none of them have been widely accepted for mortality prediction in ARDS. This study aimed to develop and validate a mortality prediction method for patients with ARDS based on machine learning using the Medical Information Mart for Intensive Care (MIMIC-III) and Telehealth Intensive Care Unit (eICU) Collaborative Research Database (eICU-CRD) databases. METHODS: Patients with ARDS were selected based on the Berlin definition in MIMIC-III and eICU-CRD databases. The APPS score (using age, PaO2/FiO2, and plateau pressure), Simplified Acute Physiology Score II (SAPS-II), Sepsis-related Organ Failure Assessment (SOFA), OSI, and OI were calculated. With MIMIC-III data, a mortality prediction model was built based on the random forest (RF) algorithm, and the performance was compared to those of existing scoring systems based on logistic regression. The performance of the proposed RF method was also validated with the combined MIMIC-III and eICU-CRD data. The performance of mortality prediction was evaluated by using the area under the receiver operating characteristics curve (AUROC) and performing calibration using the Hosmer-Lemeshow test. RESULTS: With the MIMIC-III dataset (308 patients, for comparisons with the existing scoring systems), the RF model predicted the in-hospital mortality, 30-day mortality, and 1-year mortality with an AUROC of 0.891, 0.883, and 0.892, respectively, which were significantly higher than those of the SAPS-II, APPS, OSI, and OI (all P<0.001). In the multi-source validation (the combined dataset of 2,235 patients in MIMIC-III and 331 patients in eICU-CRD), the RF model achieved an AUROC of 0.905 and 0.736 for predicting in-hospital mortality for the MIMIC-III and eICU-CRD datasets, respectively. The calibration plots suggested good fits for our RF model and these scoring systems for predicting mortality. The platelet count and lactate level were the strongest predictive variables for predicting in-hospital mortality. CONCLUSIONS: Compared to the existing scoring systems, machine learning significantly improved performance for predicting ARDS mortality. Validation with multi-source datasets showed a relatively robust generalisation ability of our prediction model.

Analysis of the primary presenting symptoms and hematological findings of COVID-19 patients in Bangladesh.

INTRODUCTION: SARS-Cov-2 infection or COVID-19 is a global pandemic. In this manuscript, we investigated the primary symptoms and basic hematological presentations of SARS-CoV-2 infection among the Bangladeshi patients. METHODOLOGY: This was a multicentre cross-sectional study done on COVID-19 patients tested positive by RT PCR in Bangladesh. Clinical features of mild to moderate degree of COVID-19 patients; hematological and biochemical admission day laboratory findings of moderate to severe degree hospitalized COVID-19 patients were analyzed. RESULTS: COVID-19 patients in Bangladesh commonly presented with fever, cough, fatigue, shortness of breath, and sore throat. But symptoms like myalgia, diarrhea, skin rash, headache, Abdominal pain/cramp, nausea, vomiting, restlessness, and a higher temperature of >100°F have a greater presentation rate and more frequent than other published studies. CRP and Prothrombin time was found to increase in all the patients. Serum ferritin, ESR, SGPT, and D-Dimer were increased among 53.85%, 80.43, 44%, and 25% patients. 17.39% of the patients had leucocytosis and neutrophilia, 28.26% presented with lymphocytopenia, and 62.52% had mild erythrocytopenia. The difference between the decrease hemoglobin count (higher in the male) and increased SGPT (higher in female) against gender was significant. CONCLUSIONS: Our study had evaluated a different expression in presenting symptoms of COVID-19 patients in Bangladesh. CRP, Prothrombin time, serum ferritin, ESR, SGPT, D-Dimer, erythrocytopenia, and lymphocytopenia can be assessments for diagnosis and prognosis of COVID-19 disease. Decrease hemoglobin count (higher in the male) and increased SGPT (higher in female) establish these two markers as a good candidate for diagnostic value against gender.

Deep Semantic Segmentation Feature-Based Radiomics for the Classification Tasks in Medical Image Analysis.

Recently, an emerging trend in medical image classification is to combine radiomics framework with deep learning classification network in an integrated system. Although this combination is efficient in some tasks, the deep learning-based classification network is often difficult to capture an effective representation of lesion regions, and prone to face the challenge of overfitting, leading to unreliable features and inaccurate results, especially when the sizes of the lesions are small or the training dataset is small. In addition, these combinations mostly lack an effective feature selection mechanism, which makes it difficult to obtain the optimal feature selection. In this paper, we introduce a novel and effective deep semantic segmentation feature-based radiomics (DSFR) framework to overcome the above-mentioned challenges, which consists of two modules: the deep semantic feature extraction module and the feature selection module. Specifically, the extraction module is utilized to extract hierarchical semantic features of the lesions from a trained segmentation network. The feature selection module aims to select the most representative features by using a novel feature similarity adaptation algorithm. Experiments are extensively conducted to evaluate our method in two clinical tasks: the pathological grading prediction in pancreatic neuroendocrine neoplasms (pNENs), and the prediction of thrombolytic therapy efficacy in deep venous thrombosis (DVT). Experimental results on both tasks demonstrate that the proposed method consistently outperforms the state-of-the-art approaches by a large margin.

[The analysis and solutions to common malfunctions of ventilators system].

This paper provides introduction and analysis of common malfunctions of ventilators System from the aspect of an engineer in the hospital, with a focus on feasible solutions to malfunctions.

Using Eye Aspect Ratio to Enhance Fast and Objective Assessment of Facial Paralysis.

A rapid and objective assessment of the severity of facial paralysis allows rehabilitation physicians to choose the optimal rehabilitation treatment regimen for their patients. In this study, patients with facial paralysis were enrolled as study objects, and the eye aspect ratio (EAR) index was proposed for the eye region. The correlation between EAR and the facial nerve grading system 2.0 (FNGS 2.0) score was analyzed to verify the ability of EAR to enhance FNGS 2.0 for the rapid and objective assessment of the severity of the facial paralysis. Firstly, in order to accurately calculate the EAR, we constructed a landmark detection model based on the face images of facial paralysis patients (FP-FLDM). Evaluation results showed that the error rate of facial feature point detection in patients with facial paralysis of FP-FLDM is 17.1%, which was significantly superior to the landmark detection model based on normal face images (NF-FLDM). Secondly, in this study, the Fréchet distance was used to calculate the difference in bilateral EAR of facial paralysis patients and to verify the correlation between this difference and the corresponding FNGS 2.0 score. The results showed that the higher the FNGS 2.0 score , the greater the difference in bilateral EAR. The correlation coefficient between the bilateral EAR difference and the corresponding FNGS 2.0 score was 0.9673, indicating a high correlation. Finally, through a 10-fold crossvalidation, we can know that the accuracy of scoring the eyes of patients with facial paralysis using EAR was 85.7%, which can be used to enhance the objective and rapid assessment of the severity of facial paralysis by FNGS 2.0.

An LSTM-Based Prediction Method for Lower Limb Intention Perception by Integrative Analysis of Kinect Visual Signal.

Recently, computer vision and deep learning technology has been applied in various gait rehabilitation researches. Considering the long short-term memory (LSTM) network has been proved an excellent performance in learn sequence feature representations, we proposed a lower limb joint trajectory prediction method based on LSTM for conducting active rehabilitation on a rehabilitation robotic system. Our approach based on synergy theory exploits that the follow-up lower limb joint trajectory, i.e. limb intention, could be generated by joint angles of the previous swing process of upper limb which were acquired from Kinect platform, an advanced computer vision platform for motion tracking. A customize Kinect-Treadmill data acquisition platform was built for this study. With this platform, data acquisition on ten healthy subjects is processed in four different walking speeds to acquire the joint angles calculated by Kinect visual signals of upper and lower limb swing. Then, the angles of hip and knee in one side which were presented as lower limb intentions are predicted by the fore angles of the elbow and shoulder on the opposite side via a trained LSTM model. The results indicate that the trained LSTM model has a better estimation of predicting the lower limb intentions, and the feasibility of Kinect visual signals has been validated as well.

Case report on a misleading case of appendiceal perforation presented with severe generalized convulsion.

Appendicitis and convulsions are two common pathologies among children. Though appendicitis has some certain symptoms, they might present with atypical symptoms in young ages. Here we present a misleading case of a perforated appendix that presented with severe generalized convulsion, no significant abdominal symptoms and had a recent history of mild gastrointestinal problems. The primary symptoms and the related examination findings guided the differential diagnosis as viral encephalitis, febrile convulsion, and Epilepsy. The initial treatment was started accordingly with an aim to prevent further convulsion. But this case was later diagnosed as a case of peritonitis following perforated appendix and was operated successfully. After surgery, the patient recovered with no further attack of convulsion even following the postoperative withdrawal of sedative therapy. He was discharged on the 7th postoperative day and there were no major complaints on his follow-ups. Such misleading cases usually lead to misdiagnosis and might cause morbidity, even endanger the life of the patient. Therefore regarding children of sudden generalized convulsion with even minute abdominal findings or recent gastrointestinal history, it is necessary to pay attention and evaluate the abdomen by a CT or MRI besides the nervous system at the first impression.

A comprehensive hierarchical classification based on multi-features of breast DCE-MRI for cancer diagnosis.

Computer-aided diagnosis (CAD) is widely used for early diagnosis of breast cancer. The commonly used morphological feature (MF), dynamic feature (DF), and texture feature (TF) from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been proved very valuable and are studied in this paper. However, previous studies ignored the prior knowledge that most of the benign lesions have clearer and smoother edges than malignant ones. Therefore, two new TFs are proposed. To obtain an optimal feature subset and an accurate classification result, feature selection is applied in this paper. Moreover, most existing CAD models with simple structure only focus on common lesions and ignore hard-to-spot lesions so that a satisfied performance can be obtained for common lesions but there are some contradictions for those hard-to-spot lesions. Therefore, in this paper, a comprehensive hierarchical model is proposed to deal with contradictions and predict all kinds of lesions. The experimental result shows that the new features obviously increase ACC of TF from 0.7788 to 0.8584 and feature selection increases ACC of DF form 0.6991 to 0.7345. More importantly, compared with the existing CAD models and deep learning method, the proposed model which provides a higher performance for both common and hard-to-spot lesions significantly increases the classification performance with sensitivity of 0.9452 and specificity of 0.9000. Graphical abstract.

Characterizing the Structural Pattern of Heavy Smokers Using Multivoxel Pattern Analysis.

Background: Smoking addiction is a major public health issue which causes a series of chronic diseases and mortalities worldwide. We aimed to explore the most discriminative gray matter regions between heavy smokers and healthy controls with a data-driven multivoxel pattern analysis technique, and to explore the methodological differences between multivoxel pattern analysis and voxel-based morphometry. Methods: Traditional voxel-based morphometry has continuously contributed to finding smoking addiction-related regions on structural magnetic resonance imaging. However, voxel-based morphometry has its inherent limitations. In this study, a multivoxel pattern analysis using a searchlight algorithm and support vector machine was applied on structural magnetic resonance imaging to identify the spatial pattern of gray matter volume in heavy smokers. Results: Our proposed method yielded a voxel-wise accuracy of at least 81% for classifying heavy smokers from healthy controls. The identified regions were primarily located at the temporal cortex and prefrontal cortex, occipital cortex, thalamus (bilateral), insula (left), anterior and median cingulate gyri, and precuneus (left). Conclusions: Our results suggested that several regions, which were seldomly reported in voxel-based morphometry analysis, might be latently correlated with smoking addiction. Such findings might provide insights for understanding the mechanism of chronic smoking and the creation of effective cessation treatment. Multivoxel pattern analysis can be efficient in locating brain discriminative regions which were neglected by voxel-based morphometry.

[Comparison of the effects of injured vertebra pedicle fixation combined with vertebroplasty and vertebra pedicle screw fixation combined with vertebroplasty in the treatment of osteoporotic thoracolumbar burst fractures].

OBJECTIVE: To assess the curative effects of injured vertebra pedicle fixation combined with vertebroplasty and short-segment pedicle screw fixation combined with vertebroplasty in treatment of osteoporotic thoracolumbar burst fractures. METHODS: Seventy patients with osteoporotic thoracolumbar burst fractures who met the inclusion criteria were collected in the study from January 2015 to December 2017. Among them, 35 patients were treated with injured vertebra pedicle fixation combined with vertebroplasty (group A), including 20 males and 15 females, aged from 55 to 74 years with an average of (64.03± 7.82) years. Twenty-six cases were type A3 and 9 cases were type A4 according to the AO typing;another 35 patients were treated with short segment pedicle screw fixation combined with vertebroplasty (group B), including 18 males and 17 females, aged from 54 to 72 years with an average of (62.78±6.40) years. Twenty-eight cases were type A3 and 7 cases were type A4 according to AO typing. Operation length, intraoperative bleeding volume, complication, imaging parameters and clinical effects were compared between the two groups. RESULTS: All the patients were followed up for at least 12 months. There were no significant differences in gender, age, injury site, preoperative VAS, Cobb angle, and injured vertebral height before surgery. There were no significant differences in operation length, intraoperative bleeding volume between two groups. In terms of VAS scores before surgery, 1 week after surgery, and at the final follow up, group A was 5.5 ±2.5, 1.8 ±0.8, 0.9 ±0.4, group B was 5.4 ± 2.3, 1.7±0.6, 1.2±1.8, respectively;injured vertebral height was (40.4±8.8)%, (92.0±4.9)%, (87.1±3.8)% in group A, and (41.2±6.6)%, (93.2±4.6)%, (80.0±4.3)% in group B;Cobb angle was (18.4±6.9) °, (2.8±2.2) °, (4.2±2.6) ° in group A, and (16.8±7.2) °, (2.7±2.5) °, (6.0±2.4) ° in group B. There were significant differences in the 3 parameters above before the operation and at the final follow up in all groups (P<0.05). There were significant differences in the Cobb angle and injured vertebral height between 1 week after operation and at the final follow up (P<0.05). At the final follow up, injured vertebral height in group A was obviously better than that in group B (P<0.05). Internal fixation failure occurred in 2 cases from the group A, and occurred in 4 cases from the group B. There were no neurological complications in both groups. CONCLUSION: For osteoporotic thoracolumbar vertebral burst fractures, injured vertebra pedicle fixation combined with vertebroplasty and vertebra pedicle screw fixation combined with vertebroplasty can achieve good clinical effects. However, injured vertebra pedicle fixation combined with vertebroplasty is better at maintaining postoperative vertebral height and sagittal arrangement, and reducing internal fixation related complications. The treatment strategy is worthy of application and promotion.

Stretchable MoS2 Electromechanical Sensors with Ultrahigh Sensitivity and Large Detection Range for Skin-on Monitoring.

Although two-dimensional (2D) layered molybdenum disulfide (MoS2) has widespread electrical applications in catalysis, energy storage, display, and photodetection, very few reports are available to achieve MoS2 for electromechanical sensing. Here, we report a novel solution-processed MoS2 strain sensor by constructing nanojunctions between layered MoS2 nanosheets and high-conductivity silver nanofibers (AgNFs) inside an elastic film. Benefiting from the outstanding lubrication property of layered MoS2 nanosheets, these nanojunctions can be easily separated by strains, giving rise to excellent electromechanical response. The resulting MoS2 strain sensor for the first time exhibits ultrahigh sensitivity with a gauge factor of 3,300 in a large detection range over 10%. The pronounced strain-sensing ability, combined with fast response speed and good operational stability, enables the MoS2 sensor for real-time and skin-on monitorings of various physiological signals such as finger movements, pulse, and breath. Our results may pave the way to extend 2D materials in novel applications such as soft robotics and human-machine interfaces.

Levels of IL-37 and CCL11 in serum of pregnant women infected with group B streptococcus in late pregnancy and their predictive efficacy for maternal and infant outcomes / 国际检验医学杂志

Objective To explore the levels of interleukin-37(IL-37)and CC type modified chemokine 11(CCL11)in serum of pregnant women infected with group B streptococcus in late pregnancy and their predic-tive efficacy for maternal and infant outcomes.Methods A total of 86 pregnant women with reproductive tract B streptococcus infection in late pregnancy treated in the hospital from December 2020 to December 2022 were selected as the study group,and 76 pregnant women with normal physical examination admitted during the same period were selected as the control group.The levels of IL-37 and CCL11 in serum of all pregnant women were detected by enzyme-linked immunosorbent assay.According to whether the pregnant women with group B streptococcus infection in late pregnancy had adverse maternal and infant outcomes,they were divided into normal pregnancy outcome group(50 cases)and adverse pregnancy outcome group(36 cases).Receiver operating characteristic(ROC)curve was used to evaluate the diagnostic value of serum IL-37 and CCL11 on maternal and infant outcomes of pregnant women with reproductive group B streptococcus infection in late pregnancy.Multivariate Logistic regression was used to analyze the factors affecting maternal and in-fant outcomes of pregnant women with reproductive group B streptococcus infection in late pregnancy.Results The levels of IL-37 and CCL11 in the study group were higher than those in the control group(P<0.05).The incidence rate of adverse outcomes in the study group was significantly higher than that in the control group(P<0.05).The proportion of abortion history and the proportion of vaginal microecological disorders and levels of IL-37 and CCL11 and in adverse pregnancy outcome group were higher than those in normal pregnancy outcome group(P<0.05).ROC curve results showed that the area under the curve(AUC)of IL-37 and CCL11 for predicting adverse maternal and infant outcomes of pregnant women infected with group B streptococcus in late pregnancy were 0.876(95%CI:0.824-0.920)and 0.788(95%CI:0.748-0.830),re-spectively.The AUC of the combined prediction of adverse maternal and infant outcomes of pregnant women infected with group B streptococcus were 0.927(95%CI:0.889-0.970).Multivariate Logistic regression a-nalysis results showed that serum IL-37(OR=3.604,95%CI:2.106-6.166),CCL11(OR=4.250,95%CI:2.074-8.709),abortion history(OR=2.707,95%CI:1.688-4.342)and vaginal microecological disorders(OR=3.504,95%CI:1.993-6.162)were risk factors for adverse maternal and infant outcomes in pregnant women infected with group B streptococcus in late pregnancy(P<0.05).Conclusion The levels of IL-37 and CCL11 in serum of pregnant women infected with reproductive group B streptococcus in late pregnancy are in-creased,which are related to adverse pregnancy outcomes,and are expected to be effective indicator for predic-ting adverse pregnancy outcomes of pregnant women infected with reproductive group B streptococcus in late pregnancy.

Intravenous thrombolysis in patients with stroke warning syndrome: comparison with antiplatelet therapy / 国际脑血管病杂志

Objective:To investigate the efficacy and safety of intravenous thrombolysis and antiplatelet therapy in patients with stroke warning syndrome (SWS), as well as influencing factors of the outcome in patients with SWS.Method:Patients with SWS admitted to the 521 st Hospital of Ordnance Group from June 1, 2018 to December 31, 2023 were retrospectively included. Some patients were treated with ateplase intravenous thrombolysis, followed by oral antiplatelet therapy; some patients only received antiplatelet therapy. The main outcome measure was the modified Rankin Scale score at 90 days after onset, with a score of 0-2 defined as good outcome. Results:A total of 35 patients with SWS were included, including 26 males (74.3%) with an age of 58.29±11.06 years. Nineteen patients (54.3%) received intravenous thrombolysis, and 27 (77.1%) had good outcome at 90 days. There was no statistically significant difference in demographic, baseline data, and good outcome between the intravenous thrombolysis group and the antiplatelet therapy group. One patient had new stroke and one had transient ischemic attack in the intravenous thrombolysis group. There were statistically significant differences in ABCD2 score, systolic blood pressure, low-density lipoprotein cholesterol, fasting blood glucose, highest National Institutes of Health Stroke Scale (NIHSS) score at onset, and symptom duration between the good outcome group and the poor outcome group (all P<0.05). Conclusions:The efficacy of intravenous thrombolysis is similar to that of antiplatelet drugs alone in treating SWS. ABCD2 score, systolic blood pressure, low-density lipoprotein cholesterol, fasting blood glucose, highest NIHSS score at onset, and duration of symptoms may be influencing factors for the outcome of patients with SWS.

Analysis of cell mutation types of colorectal neuroendocrine tumors / 基础医学与临床

Objective To investigate the mutation types of colorectal neuroendocrine tumors(NETs)and better un-derstand the pathogenesis of colorectal nets.Methods Patients undergoing colorectal NETs surgery were recruited,colorectal NETs and corresponding adjacent cancerous tissues were collected,and whole genome sequencing(WGS)was performed and further deeply analyzed.Results WGS sequencing showed that the mutation types of colorectal NETs included single nucleotide mutations,insertion and deletion mutations(InDel,less than 50 bp in length),copy number variations(CNV),and large structural variations(SV,more than 50 bp in length),such as insertion(INS),deletion(DEL),intra chromosomal translocation(ITX),inter chromosomal translocation(CTX)and inversion(INV).Conclusions A large number of somatic mutations occur in colorectal NETs,especially chro-mosome translocation