Association of MCP-4, NRTN, and PD-L1 with the risk of hepatic fibrosis: A Mendelian randomization study.

Previous studies have confirmed the affiliation between specific inflammatory cytokines and Hepatic fibrosis (HF); however, contradictions remain in the causality. The study implemented a bidirectional two-sample Mendelian randomization (MR) analysis with published statistics derived from Genome-wide Association Studies (GWAS) to investigate casualties between inflammatory cytokines and HF. Additionally, MR analysis was also introduced to consider if 1400 blood metabolites act as the key mediators in this process. Single nucleotide polymorphisms (SNPs) with strong correlations to inflammatory factors were selected for multiple MR analyses in this study. The inverse variance weighted method (IVW) was chosen as the principal analysis, and the others as the supportive. Besides, sensitivity tests were involved to identify potential heterogeneity and pleiotropic level. IVW methods revealed that a relatively high level of prediction-based monocyte chemoattractant protein-4 (MCP-4) (95% CI: 1.014-3.336, P = .045), along with neurturin (NRTN) (95% CI: 1.204-4.004, P = .010), may increase the risk of HF; while programmed cell death 1 ligand 1 (PD-L1) (95% CI: 0.223-0.928, P = .030), showed a protective effect on HF. No significant statistical differences were detected on any other inflammatory cytokines, nor did the impact of HF genetic predisposition on the 91 circulating inflammatory cytokines-related characteristics.

Combining Clinical-Radiomics Features With Machine Learning Methods for Building Models to Predict Postoperative Recurrence in Patients With Chronic Subdural Hematoma: Retrospective Cohort Study.

BACKGROUND: Chronic subdural hematoma (CSDH) represents a prevalent medical condition, posing substantial challenges in postoperative management due to risks of recurrence. Such recurrences not only cause physical suffering to the patient but also add to the financial burden on the family and the health care system. Currently, prognosis determination largely depends on clinician expertise, revealing a dearth of precise prediction models in clinical settings. OBJECTIVE: This study aims to use machine learning (ML) techniques for the construction of predictive models to assess the likelihood of CSDH recurrence after surgery, which leads to greater benefits for patients and the health care system. METHODS: Data from 133 patients were amassed and partitioned into a training set (n=93) and a test set (n=40). Radiomics features were extracted from preoperative cranial computed tomography scans using 3D Slicer software. These features, in conjunction with clinical data and composite clinical-radiomics features, served as input variables for model development. Four distinct ML algorithms were used to build predictive models, and their performance was rigorously evaluated via accuracy, area under the curve (AUC), and recall metrics. The optimal model was identified, followed by recursive feature elimination for feature selection, leading to enhanced predictive efficacy. External validation was conducted using data sets from additional health care facilities. RESULTS: Following rigorous experimental analysis, the support vector machine model, predicated on clinical-radiomics features, emerged as the most efficacious for predicting postoperative recurrence in patients with CSDH. Subsequent to feature selection, key variables exerting significant impact on the model were incorporated as the input set, thereby augmenting its predictive accuracy. The model demonstrated robust performance, with metrics including accuracy of 92.72%, AUC of 91.34%, and recall of 93.16%. External validation further substantiated its effectiveness, yielding an accuracy of 90.32%, AUC of 91.32%, and recall of 88.37%, affirming its clinical applicability. CONCLUSIONS: This study substantiates the feasibility and clinical relevance of an ML-based predictive model, using clinical-radiomics features, for relatively accurate prognostication of postoperative recurrence in patients with CSDH. If the model is integrated into clinical practice, it will be of great significance in enhancing the quality and efficiency of clinical decision-making processes, which can improve the accuracy of diagnosis and treatment, reduce unnecessary tests and surgeries, and reduce the waste of medical resources.

Development of a System for Predicting Hospitalization Time for Patients With Traumatic Brain Injury Based on Machine Learning Algorithms: User-Centered Design Case Study.

Background: Currently, the treatment and care of patients with traumatic brain injury (TBI) are intractable health problems worldwide and greatly increase the medical burden in society. However, machine learning-based algorithms and the use of a large amount of data accumulated in the clinic in the past can predict the hospitalization time of patients with brain injury in advance, so as to design a reasonable arrangement of resources and effectively reduce the medical burden of society. Especially in China, where medical resources are so tight, this method has important application value. Objective: We aimed to develop a system based on a machine learning model for predicting the length of hospitalization of patients with TBI, which is available to patients, nurses, and physicians. Methods: We collected information on 1128 patients who received treatment at the Neurosurgery Center of the Second Affiliated Hospital of Anhui Medical University from May 2017 to May 2022, and we trained and tested the machine learning model using 5 cross-validations to avoid overfitting; 28 types of independent variables were used as input variables in the machine learning model, and the length of hospitalization was used as the output variables. Once the models were trained, we obtained the error and goodness of fit (R2) of each machine learning model from the 5 rounds of cross-validation and compared them to select the best predictive model to be encapsulated in the developed system. In addition, we externally tested the models using clinical data related to patients treated at the First Affiliated Hospital of Anhui Medical University from June 2021 to February 2022. Results: Six machine learning models were built, including support vector regression machine, convolutional neural network, back propagation neural network, random forest, logistic regression, and multilayer perceptron. Among them, the support vector regression has the smallest error of 10.22% on the test set, the highest goodness of fit of 90.4%, and all performances are the best among the 6 models. In addition, we used external datasets to verify the experimental results of these 6 models in order to avoid experimental chance, and the support vector regression machine eventually performed the best in the external datasets. Therefore, we chose to encapsulate the support vector regression machine into our system for predicting the length of stay of patients with traumatic brain trauma. Finally, we made the developed system available to patients, nurses, and physicians, and the satisfaction questionnaire showed that patients, nurses, and physicians agreed that the system was effective in providing clinical decisions to help patients, nurses, and physicians. Conclusions: This study shows that the support vector regression machine model developed using machine learning methods can accurately predict the length of hospitalization of patients with TBI, and the developed prediction system has strong clinical use.

A convolutional neural networks method for tropospheric ozone vertical distribution retrieval from Multi-AXis Differential Optical Absorption Spectroscopy measurements.

The vertical distribution of tropospheric ozone (O3) is crucial for understanding atmospheric physicochemical processes. A Convolutional Neural Networks (CNN) method for the retrieval of tropospheric O3 vertical distribution from ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements to tackle the issue of stratospheric O3 absorption interference faced by MAX-DOAS in obtaining tropospheric O3 profiles. Firstly, a hybrid model, named PCA-F_Regression-SVR, is developed to screen features sensitive to O3 inversion based on the MAX-DOAS spectra and EAC4 reanalysis O3 profiles, which incorporates Principal Component Analysis (PCA), F_Regression function, and Support Vector Regression (SVR) algorithm. Thus, these screened features for ancillary inversion include the profiles of temperature, specific humidity, fraction of cloud coverage, eastward and northward wind, the profiles of SO2, NO2, and HCHO, as well as season and time features to serve as sensitive factors. Secondly, the preprocessed MAX-DOAS spectra dataset and the sensitive factor dataset are utilized as input, while the O3 profiles of the EAC4 reanalysis dataset incorporating the surface O3 concentrations are employed as output for constructing the CNN model. The Mean Absolute Percentage Error (MAPE) decreases from 26 % to approximately 19 %. Finally, the CNN model is applied for inversion and comparison of tropospheric O3 profiles using independent input data. The CNN model effectively reproduces the O3 profiles of the EAC4 dataset, showing a Gaussian-like spatial distribution with peaks primarily around 950 hPa (550 m). Since the reanalysis data used for model training has been smoothed, the CNN model is insensitive to extreme values. This behavior can be attributed to the MAPE loss function, which evaluates Absolute Percentage Errors (APEs) of O3 concentration at all altitudes, resulting in varying retrieval accuracy across different altitudes while maintaining overall MAPE control. Temporally, the CNN model tends to overestimate surface O3 in summer by around 20 µg/m3, primarily due to the influence of the temperature feature in the sensitivity factor dataset. In conclusion, leveraging MAX-DOAS spectra enables the retrieval of tropospheric O3 vertical distribution through the established CNN model.

A CNN-SVR model for NO2 profile prediction based on MAX-DOAS observations: The influence of Chinese New Year overlapping the 2020 COVID-19 lockdown on vertical distributions of tropospheric NO2 in Nanjing, China.

In this study, a hybrid model, the convolutional neural network-support vector regression model, was adopted to achieve prediction of the NO2 profile in Nanjing from January 2019 to March 2021. Given the sudden decline in NO2 in February 2020, the contribution of the Coronavirus Disease-19 (COVID-19) lockdown, Chinese New Year (CNY), and meteorological conditions to the reduction of NO2 was evaluated. NO2 vertical column densities (VCDs) from January to March 2020 decreased by 59.05% and 32.81%, relative to the same period in 2019 and 2021, respectively. During the period of 2020 COVID-19, the average NO2 VCDs were 50.50% and 29.96% lower than those during the pre-lockdown and post-lockdown periods, respectively. The NO2 volume mixing ratios (VMRs) during the 2020 COVID-19 lockdown significantly decreased below 400 m. The NO2 VMRs under the different wind fields were significantly lower during the lockdown period than during the pre-lockdown period. This phenomenon could be attributed to the 2020 COVID-19 lockdown. The NO2 VMRs before and after the CNY were significantly lower in 2020 than in 2019 and 2021 in the same period, which further proves that the decrease in NO2 in February 2020 was attributed to the COVID-19 lockdown. Pollution source analysis of an NO2 pollution episode during the lockdown period showed that the polluted air mass in the Beijing-Tianjin-Hebei was transported southwards under the action of the north wind, and the subsequent unfavorable meteorological conditions (local wind speed of < 2.0 m/sec) resulted in the accumulation of pollutants.

Pharmacokinetic study of five components of Fuzheng Huayu tablets in healthy human plasma.

Natural medicines play a crucial role in clinical drug applications, serving as a primary traditional Chinese medicine for the clinical treatment of liver fibrosis. Understanding the in vivo metabolic process of the Fuzheng Huayu (FZHY) formula is essential for delving into its material basis and mechanism. In recent years, there has been a growing body of research focused on the mechanisms and component analysis of FZHY. This study aimed to examine the pharmacokinetics of FZHY in healthy volunteers following oral administration. Blood samples were collected at designated time intervals after the oral intake of 9.6-g FZHY tablets. A UHPLC-Q/Exactive method was developed to assess the plasma concentrations of five components post-FZHY ingestion. The peak time for all components occurred within 10 min. The peak concentration (Cmax ) values for amygdalin, schisandrin, and schisandrin A ranged from 3.47 to 28.80 ng/mL, with corresponding AUC(0-t) values ranging from 10.63 to 103.20 ng h/mL. For schisandrin B and prunasin, Cmax values were in the range of 86.52 to 229.10 ng/mL, and their AUC(0-t) values ranged from 375.26 to 1875.54 ng h/mL, indicating significant exposure within the body. These findings demonstrate that the developed method enables rapid and accurate detection and quantification of the five components in FZHY, offering a valuable reference for its clinical study.

Construction of a predictive model based on MIV-SVR for prognosis and length of stay in patients with traumatic brain injury: Retrospective cohort study.

Objective: To investigate the mean impact value (MIV) method for discerning the most efficacious input variables for the machine learning (ML) model. Subsequently, various ML algorithms are harnessed to formulate a more accurate predictive model that can forecast both the prognosis and the length of hospital stay for patients suffering from traumatic brain injury (TBI). Design: Retrospective cohort study. Participants: The study retrospectively accrued data from 1128 cases of patients who sought medical intervention at the Neurosurgery Center of the Second Affiliated Hospital of Anhui Medical University, within the timeframe spanning from May 2017 to May 2022. Methods: We performed a retrospective analysis of patient data obtained from the Neurosurgery Center of the Second Hospital of Anhui Medical University, covering the period from May 2017 to May 2022. Following meticulous data filtration and partitioning, 70% of the data were allocated for model training, while the remaining 30% served for model evaluation. During the construction phase of the ML models, a gamut of 11 independent variables-including, but not limited to, in-hospital complications and patient age-were utilized as input variables. Conversely, the length of stay (LOS) and the Glasgow Outcome Scale (GOS) scores were designated as output variables. The model architecture was initially refined through the MIV methodology to identify optimal input variables, whereupon five distinct predictive models were constructed, encompassing support vector regression (SVR), convolutional neural networks (CNN), backpropagation (BP) neural networks, artificial neural networks (ANN) and logistic regression (LR). Ultimately, SVR emerged as the most proficient predictive model and was further authenticated through an external dataset obtained from the First Hospital of Anhui Medical University. Results: Upon incorporating the optimal input variables as ascertained through MIV, it was observed that the SVR model exhibited remarkable predictive prowess. Specifically, the Mean Absolute Percentage Error (MAPE) of the SVR model in predicting the GOS score in the test dataset is only 6.30%, and the MAPE in the external validation set is only 7.61%. In terms of predicting hospitalization time, the accuracy of the test and external validation sets were 9.28% and 7.91%, respectively. This error indicator is significantly lower than the error of other prediction models, thus proving the excellent efficacy and clinical reliability of the MIV-optimized SVR model. Conclusion: This study unequivocally substantiates that the incorporation of MIV for selecting optimal input variables can substantially augment the predictive accuracy of machine learning models. Among the models examined, the MIV-SVR model emerged as the most accurate and clinically applicable, thereby rendering it highly conducive for future clinical decision-making processes.

Conditional Goal-Oriented Trajectory Prediction for Interacting Vehicles.

Predicting future trajectories of pairwise traffic agents in highly interactive scenarios, such as cut-in, yielding, and merging, is challenging for autonomous driving. The existing works either treat such a problem as a marginal prediction task or perform single-axis factorized joint prediction, where the former strategy produces individual predictions without considering future interaction, while the latter strategy conducts conditional trajectory-oriented prediction via agentwise interaction or achieves conditional rollout-oriented prediction via timewise interaction. In this article, we propose a novel double-axis factorized joint prediction pipeline, namely, conditional goal-oriented trajectory prediction (CGTP) framework, which models future interaction both along the agent and time axes to achieve goal and trajectory interactive prediction. First, a goals-of-interest network (GoINet) is designed to extract fine-grained features of goal candidates via hierarchical vectorized representation. Furthermore, we propose a conditional goal prediction network (CGPNet) to produce multimodal goal pairs in an agentwise conditional manner, along with a newly designed goal interactive loss to better learn the joint distribution of the intermediate interpretable modes. Explicitly guided by the goal-pair predictions, we propose a goal-oriented trajectory rollout network (GTRNet) to predict scene-compliant trajectory pairs via timewise interactive rollouts. Extensive experimental results confirm that the proposed CGTP outperforms the state-of-the-art (SOTA) prediction models on the Waymo open motion dataset (WOMD), Argoverse motion forecasting dataset, and In-house cut-in dataset. Code is available at https://github.com/LiDinga/CGTP/.

A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study.

BACKGROUND: The treatment and care of adults and children with traumatic brain injury (TBI) constitute an intractable global health problem. Predicting the prognosis and length of hospital stay of patients with TBI may improve therapeutic effects and significantly reduce societal health care burden. Applying novel machine learning methods to the field of TBI may be valuable for determining the prognosis and cost-effectiveness of clinical treatment. OBJECTIVE: We aimed to combine multiple machine learning approaches to build hybrid models for predicting the prognosis and length of hospital stay for adults and children with TBI. METHODS: We collected relevant clinical information from patients treated at the Neurosurgery Center of the Second Affiliated Hospital of Anhui Medical University between May 2017 and May 2022, of which 80% was used for training the model and 20% for testing via screening and data splitting. We trained and tested the machine learning models using 5 cross-validations to avoid overfitting. In the machine learning models, 11 types of independent variables were used as input variables and Glasgow Outcome Scale score, used to evaluate patients' prognosis, and patient length of stay were used as output variables. Once the models were trained, we obtained and compared the errors of each machine learning model from 5 rounds of cross-validation to select the best predictive model. The model was then externally tested using clinical data of patients treated at the First Affiliated Hospital of Anhui Medical University from June 2021 to February 2022. RESULTS: The final convolutional neural network-support vector machine (CNN-SVM) model predicted Glasgow Outcome Scale score with an accuracy of 93% and 93.69% in the test and external validation sets, respectively, and an area under the curve of 94.68% and 94.32% in the test and external validation sets, respectively. The mean absolute percentage error of the final built convolutional neural network-support vector regression (CNN-SVR) model predicting inpatient time in the test set and external validation set was 10.72% and 10.44%, respectively. The coefficient of determination (R2) was 0.93 and 0.92 in the test set and external validation set, respectively. Compared with back-propagation neural network, CNN, and SVM models built separately, our hybrid model was identified to be optimal and had high confidence. CONCLUSIONS: This study demonstrates the clinical utility of 2 hybrid models built by combining multiple machine learning approaches to accurately predict the prognosis and length of stay in hospital for adults and children with TBI. Application of these models may reduce the burden on physicians when assessing TBI and assist clinicians in the medical decision-making process.

Antiliver Fibrosis Formula of Fuzheng Huayu Alleviates Inflammatory Response.

Fuzheng Huayu's (FZHY) formula ameliorated liver fibrosis in clinical and experimental practice. Based on the close link between fibrosis and inflammation, its anti-inflammatory effect and related mechanisms were explored in this present study. With the aid of the inflammatory macrophage model, FZHY significantly blocked nitrite accumulation without observable cytotoxicity due to its suppression of inducible nitric oxide synthase (iNOS) gene and protein expressions in a concentration-depended manner. Proinflammatory mediators including IL-6, CD86, and CD40 were also restrained by FZHY. Interestingly, FZHY induced anti-inflammatory mediators heme oxygenase 1 (HO-1) and peroxisome proliferator-activated receptor γ (PPAR-γ) expressions simultaneously. Downregulation of iNOS and miR-155 and upregulation of PPAR-γ were also observed in CCl4-induced liver fibrosis mice upon FZHY administration. Mechanically, FZHY strikingly eliminated the phosphorylation of STAT1 and MAPK. Taken together, FZYH regulated the balance of proinflammatory and anti-inflammatory mediators partially via modulating STAT1/MAPK pathways and the miR-155/PPAR-γ axis.

Wireless Flexible Magnetic Tactile Sensor with Super-Resolution in Large-Areas.

Tactile recognition is among the basic survival skills of human beings, and advances in tactile sensor technology have been adopted in various fields, bringing benefits such as outstanding performance in manipulating objects and general human-robot interactions. However, promoting enhanced perception of the existing tactile sensors is limited by their sensor array arrangement and wire-connected design. Here we present a wireless flexible magnetic tactile sensor (FMTS) consisting of a multidirection magnetized flexible film (perception module) and a contactless Hall sensor (signal receiving module). The flexible magnetic film is composed of NdFeB microparticles and soft silicone elastomer microparticles, and it transfers the unambiguous transduction of external force position and magnitude into magnetic signals. Benefiting from the specific magnetization arrangement and clustering algorithm, only one Hall sensor is needed in FMTS to perceive the magnitude and position of the contact spot simultaneously with super-resolution (2.1 mm average error) on a large area (3600 mm2), and the effective working distance is also greatly extended (∼30 mm), allowing for the full softness and adaptability to diverse conditions. We anticipate that this design will promote the development of soft tactile sensors and their integration into human-robot interaction and humanoid robot perception.

Lucidenic acid A inhibits the binding of hACE2 receptor with spike protein to prevent SARS-CoV-2 invasion.

High infection caused by mutations of SARS-CoV-2 calls for new prevention strategy. Ganoderma lucidum known as a superior immunoenhancer exhibits various antiviral effects, whether it can resist SARS-CoV-2 remains unclear. Herein, virtual screening combined with in vitro hACE2 inhibition assays were used to investigate its anti SARS-CoV-2 effect. Potential 54 active components, 80 core targets and 20 crucial pathways were identified by the component-target-pathway network. The binding characters of these components to hACE2 and its complexes with spike protein including omicron variant was analyzed by molecular docking. Lucidenic acid A was selected as the top molecule with high affinity to all receptors by forming hydrogen bonds. Molecular dynamics simulation showed it had good binding stability with the receptor proteins. Finally, in vitro FRET test demonstrated it inhibited the hACE2 activity with IC50 2 µmol/mL. Therefore, lucidenic acid A can prevent the virus invasion by blocking hACE2 binding with SARS-CoV-2.

The vertical distribution and potential sources of aerosols in the Yangtze River Delta region of China during open straw burning.

To explore the impact of open straw burning on air quality in the Yangtze River Delta (YRD) and surrounding areas, three key cities in the YRD, namely Hefei, Nanjing, and Shanghai, were selected to observe changes in aerosol characteristics. Based on Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations from May to June 2021, the spatial-temporal distribution and potential sources of aerosol were studied. During the observation period, aerosol optical depth (AOD) in Shanghai was 55.15 % and 29.50 % higher than that in Hefei and Nanjing, respectively. For Shanghai, aerosols accumulated at night, and the aerosol extinction could reach 1.3 km-1 in the morning. The aerosol variations in Hefei and Nanjing were consistent due to the relative conformity of the surrounding environmental conditions (R = 0.84). The vertical distribution of aerosol in all three cities had the same Gaussian shape. The aerosol lifted layers in Nanjing and Shanghai were higher than that in Hefei, with heights of 0.2-0.8 km and 0.2-0.6 km, respectively. The averaged aerosol extinctions for these two cities were 0.34 km-1 and 0.49 km-1, respectively. Pollution source analysis was conducted based on wind field trajectory, satellite observation, and model simulation, taking Hefei as the recipient. The results showed that western Shandong Province, northern Anhui Province, northern Jiangxi Province, central Jiangsu Province, and the central YRD were the most important aerosols sources for Hefei. The contributions of central and southern Jiangsu Province were significantly higher than those of other potential sources, with a WCWTAOD (Meteoinfo concentration weight trajectory) between 1.2 and 3.0. The influence of fine particles produced by open biomass burning inside the YRD was significantly higher than that outside the region (outside contribution: 36.6 %). Regarding the influence between YRD cities, more aerosols were transported from Shanghai to Hefei and Nanjing, with similar transport contributions between Nanjing and Hefei.

Two Myricetin-Derived Flavonols from Morella rubra Leaves as Potent α-Glucosidase Inhibitors and Structure-Activity Relationship Study by Computational Chemistry.

Diabetes mellitus (DM) is a chronic disease characterized by hyperglycemia, and oxidative stress is an important cause and therapeutic target of DM. Phytochemicals such as flavonols are important natural antioxidants that can be used for prevention and treatment of DM. In the present study, six flavonols were precisely prepared and structurally elucidated from Morella rubra leaves, which were screened based on antioxidant assays and α-glucosidase inhibitory activities of different plant tissues. Myricetin-3-O-(2″-O-galloyl)-α-L-rhamnoside (2) and myricetin-3-O-(4″-O-galloyl)-α-L-rhamnoside (3) showed excellent α-glucosidase inhibitory effects with IC50 values of 1.32 and 1.77 µM, respectively, which were hundredfold higher than those of positive control acarbose. Molecular docking simulation illustrated that the presence of galloyl group altered the binding orientation of flavonols, where it occupied the opening of the cavity pocket of α-glucosidase along with Pi-anion interaction with Glu304 and Pi-Pi stacked with His279. Pi-conjugations generated between galloyl moiety and key residues at the active site of α-glucosidase reinforced the flavonol-enzyme binding, which might explain the greatly increased activity of compounds 2 and 3. In addition, 26 flavonols were evaluated for systematic analysis of structure-activity relationship (SAR) between flavonols and α-glucosidase inhibitory activity. By using their pIC50 (-log IC50) values, three-dimensional quantitative SAR (3D-QSAR) models were developed via comparative molecular field analysis (CoMFA) and comparative similarity index analysis (CoMSIA), both of which were validated to possess high accuracy and predictive power as indicated by the reasonable cross-validated coefficient (q 2) and non-cross-validated coefficient (r 2) values. Through analyzing 3D contour maps of both CoMFA and CoMSIA models, QSAR results were in agreement with in vitro experimental data. Therefore, such results showed that the galloyl group in compounds 2 and 3 is crucial for interacting with key residues of α-glucosidase and the established 3D-QSAR models could provide valuable information for the prediction of flavonols with great antidiabetic potential.

Systematic review and meta-analysis of current evidence in endograft therapy vs medical treatment for uncomplicated type B aortic dissection.

OBJECTIVE: Best medical therapy (BMT) should be recommended for treating uncomplicated Stanford type B aortic dissection (uSTBAD), whereas thoracic aortic endovascular repair (TEVAR) has been controversial for uSTBAD. METHODS: In this paper, a meta-analysis was conducted on all available randomized controlled trials and observational studies that evaluated the relative benefits and harms of TEVAR and BMT for the management of patients suffering from uSTBAD. Primary endpoints consisted of early adverse events, long-term adverse events, and aortic remodeling. In addition, risk differences (RDs) or odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. The random-effects model or the fixed-effects model was used in accordance with the 50% heterogeneity threshold. RESULTS: Seven observational studies and two randomized controlled studies from 11 articles that contained 15,066 patients with uSTBAD (1518 TEVARs) met the inclusion criteria. For early outcomes, no significant differences were found between the TEVAR group and the BMT group in aortic rupture, retrograde dissection, paraplegia/paraparesis, reintervention, aorta-related death, and all-cause death. In the long run, the TEVAR group was found to have a significantly lower incidence of adverse events, which included aortic rupture (OR, 0.26; 95% CI, 0.16-0.42; P < .05; heterogeneity: P = .90, I2 = 0%), reintervention (OR, 0.45; 95% CI, 0.26-0.75; P < .05; heterogeneity: P = .17, I2 = 41%), aorta-related death (OR, 0.27; 95% CI, 0.18-0.42; P < .05; heterogeneity: P = .61, I2 = 0%), and all-cause death (OR, 0.52; 95% CI, 0.42-0.66; P < .05; heterogeneity: P = .05, I2 = 53%) as compared with the BMT group. Moreover, in compared with BMT, TEVAR was found to significantly contribute to the complete thrombosis of thoracic false lumen (OR, 55.34; 95% CI, 34.32-89.21; P < .05; heterogeneity: P = .97, I2 = 0%), and aortic regression (true lumen expansion and false lumen shrinkage). CONCLUSIONS: Although early endovascular repair of uSTBAD does not outperform BMT, its implementation is found to be necessary to facilitate the long-term prognosis. Accordingly, if early TEVAR is to be deferred, close follow-up is critical to allow for timely reintervention.

Simultaneous determination of eight analytes of Fuzheng Huayu recipe in beagle dog plasma by UHPLC-Q/exactive Orbitrap HRMS and its application to toxicokinetics.

Fuzheng Huayu recipe (FZHY) is a Chinese patent medicine for the treatment of liver fibrosis. This study aimed to investigate the toxicokinetics of FZHY in beagle dogs after oral administration. Blood samples were collected on days 1, 15 and 28 after oral gavage of FZHY dosages of 400 or 1,200 mg/kg body weight once a day. A UHPLC-Q-Orbitrap method was developed and validated to simultaneously determine and quantify eight components of FZHY in beagle dog plasma. The times to peak concentration for eight components were18-120 min. The peak concentrations (Cmax ) of amygdalin, genistein, daidzein and 3,4-dihydroxybenzaldehyde were 1.43-43.50 ng/ml, the areas under the concentration-time curve (AUC(0-t) ) were 2.45-6,098.25 ng min/ml, and the apparent volumes of distribution (Vd ) were 0.05-131.23 × 104 ml/kg. The values of Cmax of prunasin, schisantherin A, schisandrin A and schisandrin were 7.35-1,450.73 ng/ml, the values of AUC(0-t) were 3,642.30-330,388.65 ng min/ml, and the values of Vd were 11.15-1,087.18 × 104 ml/kg. No obvious accumulation of the eight compounds was observed in beagle dogs. The results showed that the method is rapid, accurate and sensitive, and is suitable for detecting the eight analytes of FZHY. This study provides an important basis for the assessment of FZHY safety.

Comparison of Success Rate and Complications of Totally Percutaneous Decannulation in Patients With Veno-Arterial Extracorporeal Membrane Oxygenation and Endovascular Aneurysm Repair.

Background: Total percutaneous closure for the site of femoral arterial puncture using Perclose ProGlide (PP) has become prevalent post-percutaneous endovascular aortic repair (EVAR) and veno-arterial extracorporeal membrane oxygenation (VA-ECMO). Objective: To evaluate the safety and efficacy of total percutaneous closure of the femoral artery access site post-EVAR compared with VA-ECMO. Methods: This was a retrospective observational study conducted over 4 years, including 88 patients who underwent EVAR (64 patients) and VA-ECMO (24 patients). Perclose ProGlide devices were used in the femoral artery puncture sites closed percutaneously. In this study, technical success was defined as successful arterial closure of the common femoral artery (CFA) without additional surgical or endovascular procedures to prevent vessel leaking. Access site complications, including overt bleeding requiring transfusion or surgical intervention, minor bleeding, tinea cruris, pseudoaneurysm, and lymphocele, were recorded 24 h and 30 days after arterial closure. Results: Each group's technical success rates were 95.8% (VA-ECMO) and 92.2% EVAR, respectively. There were no differences in the periprocedural complications of major bleeding, pseudoaneurysm, minor bleeding, acute limb ischemia, and groin infection. Furthermore, we did not observe any complications such as arterial thrombosis, dissection, stenosis, arteriovenous fistula, hematoma, groin infection, or lymphocele at the access site by following-up an ultrasound examination. There was no significant difference in the technical success rate of percutaneous closure by the PP device in the EVAR and VA-ECMO oxygenation groups. Also, no periprocedural or 30-day complications were observed at the access site of the EVAR and VA-ECMO patients.

Multiple Spontaneous Visceral Arterial Dissections in a Patient With Tolosa-Hunt Syndrome on Corticosteroid Therapy.

Multiple spontaneous visceral arterial dissections are an infrequent occurrence. The etiology, risk factors and natural history of these dissections have not been elucidated, and the optimal therapeutic strategy has not been established. We report a rare case of multiple spontaneous visceral arterial dissections involving the celiac artery, splenic artery, superior mesenteric artery, and right renal artery in a patient with Tolosa-Hunt syndrome on short-term corticosteroid therapy. The patient was subjected to conservative treatment and endovascular repair, achieving good clinical and radiological outcomes during the long-term follow-up period.

Synergistic Antimicrobial Effectiveness of Plant Essential Oil and Its Application in Seafood Preservation: A Review.

The synergistic potential of plant essential oils (EOs) with other conventional and non-conventional antimicrobial agents is a promising strategy for increasing antimicrobial efficacy and controlling foodborne pathogens. Spoilage microorganisms are one of main concerns of seafood products, while the prevention of seafood spoilage principally requires exclusion or inactivation of microbial activity. This review provides a comprehensive overview of recent studies on the synergistic antimicrobial effect of EOs combined with other available chemicals (such as antibiotics, organic acids, and plant extracts) or physical methods (such as high hydrostatic pressure, irradiation, and vacuum-packaging) utilized to reduce the growth of foodborne pathogens and/or to extend the shelf-life of seafood products. This review highlights the synergistic ability of EOs when used as a seafood preservative, discovering the possible routes of the combined techniques for the development of a novel seafood preservation strategy.