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.

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.

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.

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.

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.

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.

Treatment of Superior Mesenteric Vein Thrombus by Catheter-Directed Thrombolysis.

Contrast-enhanced computed tomography (CT) greatly improves the diagnosis of superior mesenteric vein (SMV) thrombosis, which presents as the unspecific symptom of abdominal pain. Prothrombotic states or thrombophilia and local intra-abdominal infections are major causes of SMV thrombosis. A 37-year-old Chinese woman was diagnosed with SMV and portal vein thrombosis. The patient was initially given 40 mg of heparin sodium every 12 hr and 80,0000 U/day of urokinase using superior mesenteric artery angiography. The abdominal pain was not relieved after treatment. The patient then underwent open surgery, where an ileal branch of the SMV was punctured, a 4F sheath was introduced into the vein toward the portal vein, and a 20-cm Unifuse catheter was placed inside the thrombus for further thrombolysis. Both heparin sodium and urokinase were infused through catheter-directed thrombolysis. The patient's symptoms then gradually resolved.

Effects of electromagnetic fields exposure on plasma hormonal and inflammatory pathway biomarkers in male workers of a power plant.

PURPOSE: The potential health risks of electromagnetic fields (EMFs) have currently raised considerable public concerns. The aim of this study was to evaluate the effects of EMF exposure on levels of plasma hormonal and inflammatory pathway biomarkers in male workers of an electric power plant. METHODS: Seventy-seven male workers with high occupational EMF exposure and 77 male controls with low exposure, matched by age, were selected from a cross-sectional study. Moreover, high EMF exposure group was with walkie-talkies usage and exposed to power frequency EMF at the work places for a longer duration than control group. A questionnaire was applied to obtain relevant information, including sociodemographic characteristics, lifestyle factors, and EMF exposures. Plasma levels of testosterone, estradiol, melatonin, NF-κB, heat-shock protein (HSP) 70, HSP27, and TET1 were determined by an enzyme-linked immunosorbent assay. RESULTS: EMF exposure group had statistically significantly lower levels of testosterone (ß = -0.3 nmol/L, P = 0.015), testosterone/estradiol (T/E2) ratio (ß = -15.6, P = 0.037), and NF-κB (ß = -20.8 ng/L, P = 0.045) than control group. Moreover, joint effects between occupational EMF exposure and employment duration, mobile phone fees, years of mobile phone usage, and electric fees on levels of testosterone and T/E2 ratio were observed. Nevertheless, no statistically significant associations of EMF exposures with plasma estradiol, melatonin, HSP70, HSP27, and TET1 were found. CONCLUSIONS: The findings showed that chronic exposure to EMF could decrease male plasma testosterone and T/E2 ratio, and it might possibly affect reproductive functions in males. No significant associations of EMF exposure with inflammatory pathway biomarkers were found.

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.

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.

Effect of nano-hydroxyapatite suspension on cell proliferation and cycle in human periodontal ligament cells.

The study was aimed to provide insights into the effect of nano-hydroxyapatite (nHA) suspension on cell proliferation and cycle of human periodontal ligament cells, offering the evidence for nHA being used in periodontal therapy. Human periodontal ligament cells (HPDLCs) were cultured in different concentrations of nano-hydroxyapatite/sodium carboxymethyl cellulose (nHA-CMCNa) suspension in vitro. After that, cell proliferation ability was examined by MTT [3(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay and flow cytometry. MTT assay demonstrated that the Relative Proliferation Rate (RPR) of 0.5% nHA-CMCNa group was significantly higher than other groups (p <0.05), which means that nHA-CMCNa might increase cell proliferation ability. Flow cytometry showed that cells in G1 phase decreased, whilst cells in S phase increased after cultured in nHA-CMCNa suspension for 48 h. The result suggested that part of cells finished G1 phase in advance and get into S phase earlier, which speed up the cell proliferation, nHA-CMCNa suspension had great effect on cell proliferation. The high concentration of nHA-CMCNa could shorten the time in G1 phase, impel part of cells into S phase, and accelerate proliferation rate of HPDLCs.

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/.

The joint association of REST and NFKB1 polymorphisms on the risk of colorectal cancer.

Due to the high morbidity and mortality of colorectal cancer (CRC), this study aims to determine the joint association of RE-1-silencing transcription factor (REST) and nuclear factor-κB 1 (NFKB1) genes with CRC in a population-based study. A well-matched case-control study including 390 controls and 388 patients with CRC was enrolled in China. The selected single nucleotide polymorphisms (SNPs) in the REST and NFKB1 genes were genotyped by Illumina SnapShot Chip. After adjustment for important covariates, the associations of SNPs and joint association of REST and NFKB1 with CRC were evaluated by multiple logistic regression models. The subjects with the rs2228991 AA genotype of the REST gene had a decreased risk for CRC (OR = 0.38; 95%CI: 0.19-0.74), compared with the GG genotype. There were no significant associations between three SNPs in the NFKB1 gene, their haplotype and CRC risk. However, a significant combined effect of rs3774959 and rs3774964 in the NFKB1 gene with rs2228991 in the REST gene on CRC risk was observed. In conclusion, the present study found that mutation in the REST gene rather than the NFKB1 gene was associated with the risk of CRC. Furthermore, significant REST-NFKB1 joint association was observed for CRC, colon cancer and rectal cancer risk.

Association of genetic variants in tachykinins pathway genes with colorectal cancer risk.

PURPOSE: This study aims to explore the associations of polymorphisms in tachykinin, precursor 1 (TAC1), tachykinin receptor 1 (TACR1), and tachykinin receptor 2 (TACR2) genes and their interactions with the risk of colorectal cancer (CRC) among Chinese population. METHODS: A population-based case-control study which included 394 cases and 393 cancer-free controls was carried out. A total of 19 tagSNPs in the three genes were chosen based on HapMap and NCBI datasets and genotyped by SNPshot assay. Multiple logistic regression models were applied to evaluate the associations of SNPs with CRC after adjustment for potential covariates. Furthermore, generalized multifactor dimensionality reduction (GMDR) method was used to test the interactive effect among three genes on CRC. RESULTS: Compared with those carrying rs3755457 CC/CT or rs12477554 TT/CT genotype, individuals carrying homozygous variants had higher risk of colorectal cancer (adjusted OR = 1.80, 95 % CI = 1.03-3.13, P = 0.039 for rs3755457; adjusted OR = 1.73, 95 % CI = 1.07-2.79, P = 0.024 for rs12477554). As for rs10198644, GG genotype was associated with a 1.72-fold (95 % CI = 0.37-0.88) decreased risk when compared with the common CC genotype. Moreover, the GMDR analysis indicated that the best interactive model included five polymorphisms: rs2072100 (TAC1), rs10198644 (TACR1), rs2193409 (TACR1), rs3771810 (TACR1), and rs4644560 (TACR2). CONCLUSIONS: Our study suggests that tachykinins pathway genes may participate in the development of CRC and the potential interactions among the three genes on CRC may exist, which has to be confirmed in future larger studies.

PLA2G4A mutants modified protective effect of tea consumption against colorectal cancer.

PURPOSE: The primary aim was to respectively evaluate PLA2G4A mutants modifying protective effect of tea consumption against colorectal cancer (CRC), colon and rectal cancer. METHODS: All participants were recruited from January 2006 to April 2008. The information about tea consumption was collected by a structured questionnaire. CRC patients were diagnosed based on histology. Four single-nuclear polymorphisms (SNPs) in PLA2G4A gene were selected. Multiple logistic regression models were used for assessing the joint effects between tea consumption and SNPs on CRC, colon and rectal cancer. RESULTS: Three hundred patients with CRC and 296 controls well-matched were used in the final analyses. The significant individual associations between four SNPs (rs6666834, rs10911933, rs4650708 and rs7526089) and CRC were not observed. However, their CTAC haplotype was significantly associated with the increased risk of CRC (OR = 3.06; 95%CI = 1.52-6.19), compared with TCAC haplotype. Drinking tea was correlated with a decreased risk of CRC after adjustment for covariates (OR = 0.61; 95%CI = 0.39-0.97). Meanwhile, compared with no-tea drinkers with TT/CT genotype of rs6666834, tea drinkers with TT/CT or CC had significant lower risk of CRC (OR = 0.6, 95%CI = 0.36-1.00 for TT/CT; 0.38, 0.19-0.74 for CC). The joint effects between the remaining three SNPs and drinking tea on CRC were observed as well. Similar findings were observed on colon and rectal cancers. CONCLUSIONS: Tea consumption and haplotype of mutants in PLA2G4A gene were respectively associated with the risk of CRC. PLA2G4A mutants modified the protective effect of tea consumption against CRC, colon and rectal cancers in Chinese population.

[Analysis on anti-vascular inflammatory mechanism in vitro of total flavones from Artemisia anomala].

OBJECTIVE: To study the impact of total flavones from Artemisia anomala (TFAS) on activation of macrophages, cell oxidative stress, auto-nitration of CuZn-SOD, platelet aggregation and isolated vascular tension. METHOD: LPS and IFN-gamma induced activation of macrophages and oxidative stress in rats; H2O2 and nitrite induced auto-nitration of CuZn-SOD; ADP, AA and collagen induced platelet aggregation in vitro in mice; PE stimulates isolated vascular tension; nitrite content of macrophages was measured by Griess assay; MTT assay and FRAP assay was applied for cell viability and total cell antioxidant capacity; auto-nitration of CuZn-SOD was measured by Western blot and colorimetric methods; platelet aggregation was detected by turbidimetry; and aorta ring relaxation was recorded by isolated vascular function experience devices for rats. RESULT: TFAS demonstrated dose dependence (25, 50, 100, 200 mg x L(-1)) on inhibiting induced macrophages NO production from generating, while increasing cell viability and total anti-oxidant capacity. Auto-nitration of CuZn-SOD was suppressed by TFAS in dose dependence (0.5, 5, 50 mg x L(-1)). TFAS showed an inhibitory effect on collagen-induced platelet aggregation at 50 mg x L(-1) and an endothelium-dependent relaxation effect on PE-induced vasoconstriction at 1 g x L(-1). CONCLUSION: TFAS shows effect on anti-inflammation, anti-oxidation, anti-nitration, anti-platelet aggregation and vasodilatation in experiment in vitro, which may inhibit vascular inflammatory by regulating multiple target points. It is among material bases for promoting blood circulation and removing blood stasis.