Pre-crash scenarios for safety testing of autonomous vehicles: A clustering method for in-depth crash data.

Autonomous vehicles (AVs) provide an opportunity to enhance traffic safety. However, AVs market penetration is still restricted due to their safety concerns and dependability. For widespread adoption, it is crucial to thoroughly assess the safety response of AVs in various high-risk scenarios. To achieve this objective, a clustering method was used to construct typical testing scenarios based on the China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database. Initially, 222 car-to-powered two-wheelers (PTWs) crashes and 180 car-to-car crashes were reconstructed from CIMSS-TA database. Second, six variables were extracted and analyzed, including the motion of the two vehicles involved, relative movement, lighting condition, road condition, and visual obstruction. Third, these variables were clustered using the k-medoids algorithm, identifying five typical pre-crash scenarios for car-to-PTWs and seven for car-to-car. Additionally, we extracted the velocities and surrounding environmental information of the crash-involved parties to enrich the scenario description. The approach used in this study used in-depth case review and thus provided more insightful information for identifying and quantifying representative high-risk scenarios than prior studies that analyzed overall descriptive variables from Chinese crash databases. Furthermore, it is crucial to separately test car-to-car scenarios and car-to-PTWs scenarios due to their distinct motion characteristics, which significantly affect the resulting typical scenarios.

Tree frogs Polypedates Dennysi landing on horizontal perches: the effects of perch diameters.

Secure landing is indispensable for both leaping animals and robotics. Tree frogs, renowned for their adhesive capabilities, can effectively jump across intricate 3D terrain and land safely. Compared to jumping, the mechanisms underlying their landing technique, particularly in arboreal environments, have remained largely unknown. In this study, we focused on the landing patterns of the tree frogs Polypedates Dennysi on horizontally placed perches, explicitly emphasizing the impact of perch diameters. Tree frogs demonstrated diverse landing postures, including the utilization of (1) single front foot, (2) double front feet, (3) anterior bellies, (4) middle bellies, (5) posterior bellies, (6) single hind foot, or (5) double hind feet. Generally, tree frogs favour bellies on slimmer targets but double front feet on large perches. Analysis of limb-trunk relationships revealed their adaptability to modifying postures, including body positions and limb orientations, for successful landing. The variations in the initial landing postures affect the succeeding landing procedures and, consequently, the dynamics. As the initial contact position was switched from front foot to hind foot, the stabilization time decreased first, reaching the minimum in middle belly landings, and then increased. The maximum vertical forces changed in an inverse trend, whereas the maximum fore-aft forces continuously increased as the initial contact position switched. As the perch diameter rose, the time expenses dropped, whereas the maximum impact increased. These findings not only added to our understanding of frog landings but also highlighted the necessity of considering perch diameters and landing styles when studying the biomechanics of arboreal locomotion.

Novel Multifunctional Meta-Surface Plasmon Resonance Chip Microplate for High-Throughput Molecular Screening.

The utilization of surface plasmon resonance (SPR) sensors for real-time label-free molecular interaction analysis is already being employed in the fields of in vitro diagnostics and biomedicine. However, the widespread application of SPR technology is hindered by its limited detection throughput and high cost. To address this issue, our study introduces a novel multifunctional MetaSPR high-throughput microplate biosensor featuring 3D nanocup microarrays, aiming to achieve high-throughput screening at a reduced cost and with enhanced speed. Different types of MetaSPR sensors and analytical detection methods have been developed for accurate subtype identification, epitope binding, affinity determination, antibody collocation, and other applications that have greatly promoted the screening and analysis of early antibody drugs. The MetaSPR platform combined with nano-enhanced particles amplified the detection signal and improved the detection sensitivity, making it more convenient, sensitive, and efficient than traditional ELISA. Our findings demonstrated that the MetaSPR biosensor is a new practical technology detection platform that can improve the efficiency of biomolecular interaction studies with unlimited potential for new drug development. This article is protected by copyright. All rights reserved.

Zhen-wu-tang protects against myocardial fibrosis by inhibiting M1 macrophage polarization via the TLR4/NF-κB pathway.

BACKGROUND: Myocardial fibrosis is a risk factor that contributes to the increase in the incidence of cardiovascular disease and death, posing a significant threat to human health. Zhen-wu-tang (ZWT) is a classical Chinese medicinal recipe that has been extensively used to manage cardiovascular disorders throughout history. However, the fundamental processes involved in its effects were not clear. OBJECTIVE: This study examined the therapeutic effects of ZWT on myocardial fibrosis induced by isoproterenol (ISO) in mice, the effect of regulation and underlying mechanism on the polarization of M1 macrophage. METHODS: In vivo, a myocardial fibrosis mouse model was induced via intraperitoneal infusion of isoproterenol (ISO). ZWT or captopril (CAP) was administered intragastrically for 30 days. Cardiac function was evaluated by electrocardiogram (ECG) and echocardiography. By analysing myocardial fibrosis pathomorphologically and identifying fibrosis-related indicators, the protective effect of the ZWT on the heart was evaluated. A model of macrophage polarization was established in vitro by activating RAW264.7 cells with lipopolysaccharide (LPS). The regulatory effects of ZWT on macrophage polarization and the signalling pathways involved were examined by immunofluorescence staining, Western blotting (WB), quantitative real-time PCR (qRT-PCR) and siRNA transfection. RESULTS: ZWT improved cardiac function; reduced fibrotic deposition in cardiac tissues; decreased α-SMA, collagen I, and collagen III levels; and inhibited myocardial fibrosis in mice with ISO-induced myocardial fibrosis. Furthermore, the results showed that ZWT could suppress M1 macrophage polarization by downregulating the expression of CD86 and iNOS in vitro and in vivo. Finally, the results confirmed that ZWT could significantly reduce TLR4/NF-κB signalling pathway activation. CONCLUSION: ZWT showed therapeutic effects on ISO-induced myocardial fibrosis mice, and reduced M1 macrophages polarization through inhibiting TLR4/NF-κB pathway, suggesting that ZWT is a promising drug for myocardial fibrosis treatment.

Comparison of Effects of Propofol Combined with Different Doses of Esketamine for ECT in the Treatment of Depression: A Randomized Controlled Trial Protocol.

Objective: Major depressive disorder (MDD) is a common mood disorder. Electroconvulsive therapy (ECT) has a significant effect on treatment-resistant MDD. Esketamine may have potential advantages in improving the efficacy of ECT, and the strong affinity of this compound for NMDAR renders it a viable therapeutic option for the management of depression. This study aims to compare the effects of different doses of esketamine combined with propofol anesthesia versus propofol anesthesia alone in ECT, aiming to provide further insights for optimizing ECT and enhancing comprehensive treatment outcomes for depression. Study Design and Methods: This study was a prospective, randomized, controlled, double-blind trial involving subjects and evaluators. One hundred eleven patients scheduled for ECT were randomly assigned to three groups. In Group P, propofol at 1mg/kg was administered intravenously. In Group P+E, propofol at a dosage of 0.5mg/kg and esketamine at a dosage of 0.5mg/kg was administered intravenously. Patients in Group P+SE received propofol at a dosage of 0.75mg/kg and esketamine at a dosage of 0.25mg/kg. The same anesthesia protocol was used for the same patient until the end of the last treatment. The primary outcome measures were the Hamilton depression scale (HAMD) and the Patient Health Questionnaire-9 (PHQ-9), the Columbia-Suicide Severity Rating Scale (C-SSRS), and the Digit symbol substitution test (DSST). Secondary outcomes included length of hospital stay, readmission rate, hemodynamic status, recovery, and adverse events. Discussion: This study aimed to compare the effects of propofol combined with different doses of esketamine for ECT. The results may provide a better choice for ECT anesthesia.

An optimal method for diagnosing heart disease using combination of grasshopper evalutionary algorithm and support vector machines.

Due to the importance of accurate diagnosis and prompt treatment of this condition, the medical world is searching for a solution for its early detection and efficient treatment. Heart disease is one of the leading causes of death in modern society. With the development of computer science today, this issue can be resolved using computers. Data mining is one of the solutions for diagnosing this illness. One of the cutting-edge disciplines, data mining, can aid in better decision-making in many areas of medicine, including disease diagnosis and treatment. In order to improve diagnosis accuracy, a combination method using the evolutionary algorithms locust and support vector machine has been tested in this study. Use should be made of heart disease. Because of the hybrid nature of this approach, normalization is actually carried out in three steps: first, by using pre-processing operations to remove unknown and outlier data from the data set; second, by using the locust evolutionary algorithm to choose the best features from the available features; and third, by classifying the data set using a support vector machine. The accuracy criterion for the proposed method compared to Niobizin methods, neural networks, and J48 trees improved by 18 %, 30 %, and 24 %, respectively, after implementing it on the data set and comparing it with other algorithms used in the field of heart disease diagnosis.

HTRA1 interacts with SLC7A11 to modulate colorectal cancer chemosensitivity by inhibiting ferroptosis.

Chemotherapy is an important therapuetic strategy for colorectal cancer (CRC), but chemoresistance severely affects its efficacy, and the underlying mechanism has not been fully elucidated. Increasing evidence suggests that lipid peroxidation imbalance-mediated ferroptosis is closely associated with chemoresistance. Hence, targeting ferroptosis pathways or modulating the tolerance to oxidative stress might be an effective strategy to reverse tumor chemoresistance. HtrA serine protease 1 (HTRA1) was screened out as a CRC progression- and chemoresistance-related gene. It is highly expressed in CRC cells and negatively correlated with the prognosis of CRC patients. Gain- and loss-of-function analyses demonstrated a stimulatory role of HTRA1 on the proliferation of CRC cells. The enrichment analysis of HTRA1-interacting proteins indicated the involvement of ferroptosis in the HTRA1-mediated chemoresistance. Moreover, electron microscope analysis, as well as the ROS and MDA levels in CRC cells also confirmed the effect of HTRA1 on ferroptosis. We also verified that HTRA1 could interact with SLC7A11 through its Kazal structural domain and up-regulate the expression of SLC7A11, which in turn inhibited the ferroptosis and leaded to the chemoresistance of CRC cells to 5-FU/L-OHP. Hence, we propose that HTRA1 may be a potential therapeutic target and a prognostic indicator in CRC.

Gene Expression, Morphology, and Electrophysiology During the Dynamic Development of Human Induced Pluripotent Stem Cell-Derived Atrial- and Ventricular-Like Cardiomyocytes.

Background and Objectives: Gene expression, morphology, and electrophysiological combination are essential for assessing the dynamic development of human induced pluripotent stem cell-derived atrial- and ventricular-like cardiomyocytes (iPS-AM and iPS-VM, respectively). Methods: For iPS-AM/VM differentiation, we performed the small molecule-based temporal modulation of the retinoic acid and bone morphogenetic protein signaling pathways. We investigated the gene expression and morphology using immunofluorescence, quantitative real-time polymerase chain reaction, flow cytometry, and transmission electron microscopy as well as registered electrophysiological functions using a whole-cell patch clamp on days 20, 30, and 60 post-differentiations. Results: Pan-cardiomyocyte marker, including troponin T2 (TNNT2) and alpha-actinin-2 (ACTN2), expressions increased both in iPS-AMs and iPS-VMs. Similarly, the mRNA expression of both iPS-AM-specific markers, ie, natriuretic peptide A (NPPA), myosin light chain 7 (MYL7), and K+ channel Kir3.4 (KCNJ5), and iPS-VM-specific markers, ie, gap junction α-1 (GJA1), myosin light chain 2 (MYL2), and alpha-1-subunit of a voltage-dependent L-type calcium channel (CACNA1C), increased from 0 to 20 days, and then decreased from 30 to 60 days. Concerning morphology, cardiac troponin-T (cTnT) arrangement was progressively organized and developed from a disorderly myofibrillar distribution to an organized sarcomere pattern both in iPS-AMs and iPS-VMs. Mitochondrial numbers gradually increased and those of lipid droplets decreased during dynamic development. Regarding physiological function, the resting and action potential amplitudes remained statistically indifferent in both cell types, and the action potential duration was prolonged during the development. Conclusion: IPS-AMs/VMs displayed dynamic development concerning their gene expression, morphology, and electrophysiological function. The discoveries of this study could provide novel insights into heart development and encourage further research.

Enhanced patient-based real-time quality control using the graph-based anomaly detection.

OBJECTIVES: Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control. METHODS: The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison. RESULTS: The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example. CONCLUSIONS: The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

Development of an mRNA-based therapeutic vaccine mHTV-03E2 for high-risk HPV-related malignancies.

Human papillomavirus (HPV) 16 and 18 infections are related to many human cancers. Despite several preventive vaccines for high-risk (hr) HPVs, there is still an urgent need to develop therapeutic HPV vaccines for targeting pre-existing hrHPV infections and lesions. In this study, we developed a lipid nanoparticle (LNP)-formulated mRNA-based HPV therapeutic vaccine (mHTV)-03E2, simultaneously targeting the E2/E6/E7 of both HPV16 and HPV18. mHTV-03E2 dramatically induced antigen-specific cellular immune responses, leading to significant CD8+ T cell infiltration and cytotoxicity in TC-1 tumors derived from primary lung epithelial cells of C57BL/6 mice expressing HPV E6/E7 antigens, mediated significant tumor regression, and prolonged animal survival, in a dose-dependent manner. We further demonstrated significant T cell immunity against HPV16/18 E6/E7 antigens for up to 4 months post-vaccination in immunological and distant tumor rechallenging experiments, suggesting robust memory T cell immunity against relapse. Finally, mHTV-03E2 synergized with immune checkpoint blockade to inhibit tumor growth and extend animal survival, indicating the potential in combination therapy. We conclude that mHTV-03E2 is an excellent candidate therapeutic mRNA vaccine for treating malignancies caused by HPV16 or HPV18 infections.

Inhibition of δ-catenin palmitoylation slows the progression of prostate cancer.

Prostate cancer (PCa) is the second leading cause of death in males. It has been reported that δ-catenin expression is upregulated during the late stage of prostate cancer. Palmitoylation promotes protein transport to the cytomembrane and regulates protein localization and function. However, the effect of δ-catenin palmitoylation on the regulation of cancer remains unknown. In this study, we utilized prostate cancer cells overexpressing mutant δ-catenin (J6A cells) to induce a depalmitoylation phenotype and investigate its effect on prostate cancer. Our results indicated that depalmitoylation of δ-catenin not only reduced its membrane expression but also promoted its degradation in the cytoplasm, resulting in a decrease in the effect of EGFR and E-cadherin signaling. Consequently, depalmitoylation of δ-catenin reduced the proliferation and metastasis of prostate cancer cells. Our findings provide novel insights into potential therapeutic strategies for controlling the progression of prostate cancer through palmitoylation-based targeting of δ-catenin.

The relationship between oxidative balance score and erectile dysfunction in the U.S. male adult population.

Oxidative stress strongly influences the pathophysiology of erectile dysfunction (ED). In this study, we used the oxidative balance score (OBS), a composite index, to measure the effects of oxidative stress triggered by diet and lifestyle factors. Here, we conducted a cross-sectional study to determine the statistical relationship between OBS and ED among adult males in the U.S. The data from 3318 participants in the National Health and Nutrition Examination Survey (NHANES) 2001-2004 were analyzed. Weighted logistic regression was used to correct for confounding factors and acquire nationwide representative estimates. Generalized additive modeling was used to explore the nonlinear relationship. We also supplemented subgroup and sensitivity analysis to examine the robustness of the main results. Multivariate logistic regression indicated a consistent negative linear association between OBS and ED across all participants [OR (95% CI) = 0.96 (0.94, 0.98)]. After categorizing OBS into tertiles, participants in the highest tertile had 43% lower odds of having ED than those in the lowest tertile [OR (95% CI) = 0.57 (0.37, 0.87)]. The generalized additive model also visualized the linear trend of this association. Furthermore, this linear relationship remained relatively consistent, regardless of whether subgroup or sensitivity analyses were performed. Our findings suggest that adopting a lifestyle and diet pattern that promotes favorable OBS may effectively protect against the development of ED, regardless of the underlying causes.

Involvement of S100A6/S100A11 in T-Cell Immune Regulatory in HCC Revealed by Single Cell RNA-seq.

Background: Immunotherapy plays a significant role in the treatment of hepatocellular carcinoma (HCC). Members of the S100 protein family (S100s) have been widely implicated in the pathogenesis and progression of tumors. However, the exact mechanism by which S100s contribute to tumor immunity remains unclear. Methods: To explore the role of S100s in HCC immune cells, we collected and comparatively analyzed single-cell RNA sequencing (scRNA-seq) data of HCC and hepatitis B virus-associated HCC. By mapping cell classification and searching for S100s binding targets and downstream targets. Results: S100A6/S100A11 was differentially expressed in tumor T cells and involved in the nuclear factor (NF) κB pathway. Further investigation of the TCGA dataset revealed that patients with low S100A6/S100A11 expression had a better prognosis. Temporal cell trajectory analysis showed that the activation of the NF-κB pathway is at a critical stage and has an important impact on the tumor microenvironment. Conclusion: Our study revealed that S100A6/S100A11 could be involved in regulating the differentiation and cellular activity of T-cell subpopulations in HCC, and its low expression was positively correlated with prognosis. It may provide a new direction for immunotherapy of HCC and a theoretical basis for future clinical applications.

Cascade Residual Multiscale Convolution and Mamba-Structured UNet for Advanced Brain Tumor Image Segmentation.

In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model's potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning.

Impact of healthy lifestyle on the incidence and progression trajectory of mental disorders: A prospective study in the UK Biobank.

BACKGROUND: Healthier lifestyle decreased the risk of mental disorders (MDs) such as depression and anxiety. However, research on the effects of a comprehensive healthy lifestyle on their progression is lacking. METHODS: 385,704 individuals without baseline MDs from the UK Biobank cohort were included. A composite healthy lifestyle score was computed by assessing alcohol intake, smoking status, television viewing time, physical activity, sleep duration, fruit and vegetable intake, oily fish intake, red meat intake, and processed meat intake. Follow-up utilized hospital and death register records. Multistate model was used to examine the role of healthy lifestyle on the progression of specific MDs, while a piecewise Cox regression model was utilized to assess the influence of healthy lifestyle across various phases of disease progression. RESULTS: Higher lifestyle score reduced risks of transitions from baseline to anxiety and depression, as well as from anxiety and depression to comorbidity, with corresponding hazard ratios (HR) and 95 % confidence intervals (CI) of 0.94 (0.93, 0.95), 0.90 (0.89, 0.91), 0.94 (0.91, 0.98), and 0.95 (0.92, 0.98), respectively. Healthier lifestyle decreased the risk of transitioning from anxiety to comorbidity within 2 years post-diagnosis, with HR 0.93 (0.88, 0.98). Higher lifestyle scores at 2-4 years and 4-6 years post-depression onset were associated with reduced risk of comorbidity, with HR 0.93 (0.87, 0.99) and 0.92 (0.86, 0.99), respectively. LIMITATION: The generalizability to other ethnic groups is limited. CONCLUSION: This study observed a protective role of holistic healthy lifestyle in the trajectory of MDs and contributed to identifying critical progression windows.

Record-High T_{c} and Dome-Shaped Superconductivity in a Medium-Entropy Alloy TaNbHfZr under Pressure up to 160 GPa.

Superconductivity has been one of the focal points in medium and high-entropy alloys (MEAs-HEAs) since the discovery of the body-centered cubic (bcc) HEA superconductor in 2014. Until now, the superconducting transition temperature (T_{c}) of most MEA and HEA superconductors has not exceeded 10 K. Here, we report a TaNbHfZr bulk MEA superconductor crystallized in the BCC structure with a T_{c} of 15.3 K which set a new record. During compression, T_{c} follows a dome-shaped curve. It reaches a broad maximum of roughly 15 K at around 70 GPa before decreasing to 9.3 K at 157.2 GPa. First-principles calculations attribute the dome-shaped curve to two competing effects, that is, the enhancement of the logarithmically averaged characteristic phonon frequency ω_{log} and the simultaneous suppression of the electron-phonon coupling constant λ. Thus, TaNbHfZr MEA may have a promising future for studying the underlying quantum physics, as well as developing new applications under extreme conditions.

The bioelectric mechanisms of local calcium dynamics in cancer cell proliferation: an extension of the A549 in silico cell model.

Introduction: Advances in molecular targeting of ion channels may open up new avenues for therapeutic approaches in cancer based on the cells' bioelectric properties. In addition to in-vitro or in-vivo models, in silico models can provide deeper insight into the complex role of electrophysiology in cancer and reveal the impact of altered ion channel expression and the membrane potential on malignant processes. The A549 in silico model is the first computational cancer whole-cell ion current model that simulates the bioelectric mechanisms of the human non-small cell lung cancer cell line A549 during the different phases of the cell cycle. This work extends the existing model with a detailed mathematical description of the store-operated Ca2+ entry (SOCE) and the complex local intracellular calcium dynamics, which significantly affect the entire electrophysiological properties of the cell and regulate cell cycle progression. Methods: The initial model was extended by a multicompartmental approach, addressing the heterogenous calcium profile and dynamics in the ER-PM junction provoked by local calcium entry of store-operated calcium channels (SOCs) and uptake by SERCA pumps. Changes of cytosolic calcium levels due to diffusion from the ER-PM junction, release from the ER by RyR channels and IP3 receptors, as well as corresponding PM channels were simulated and the dynamics evaluated based on calcium imaging data. The model parameters were fitted to available data from two published experimental studies, showing the function of CRAC channels and indirectly of IP3R, RyR and PMCA via changes of the cytosolic calcium levels. Results: The proposed calcium description accurately reproduces the dynamics of calcium imaging data and simulates the SOCE mechanisms. In addition, simulations of the combined A549-SOCE model in distinct phases of the cell cycle demonstrate how Ca2+ - dynamics influence responding channels such as KCa, and consequently modulate the membrane potential accordingly. Discussion: Local calcium distribution and time evolution in microdomains of the cell significantly impact the overall electrophysiological properties and exert control over cell cycle progression. By providing a more profound description, the extended A549-SOCE model represents an important step on the route towards a valid model for oncological research and in silico supported development of novel therapeutic strategies.

The Relationship Between Perioperative Use of Esketamine and Postpartum Depression Risk: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Purpose: To determine whether perioperative esketamine use decreases the risk of postpartum depression (PPD). Methods: Online search of PubMed, Web of Science, and Embase was conducted to identify relevant studies. Key words for search included, but were not limited to, postpartum depression, esketamine, and clinical trials. The mean and standard deviation of the Edinburgh Postnatal Depression Scale (EPDS) scores were extracted from the studies as primary parameters. Results: The literature search identified 226 articles, of which 5 met the criteria and were enrolled in the study. In total, 886 patients in the studies were taken into analysis. The EPDS scores in the esketamine group were lower than those of the control group at the early stage of puerperium (WMD=-2.05, 95% CI: -3.77, -0.34, p=0.019), whereas there was no significant difference at the middle and later stages (WMD=-1.41, 95% CI: -2.86, 0.04, p=0.056). The sensitivity analyses indicated that the result for the early stage was stable, whereas it was unreliable for the middle and later stages. The results of the Egger's test indicated no publication bias. Conclusion: Perioperative use of esketamine contributes to a lower risk of PPD at the early stage of puerperium but not at the middle and later stages. To further verify this conclusion, more high-quality studies are required.

Design optimization of groundwater circulation well based on numerical simulation and machine learning.

The optimal design of groundwater circulation wells (GCWs) is challenging. The key to purifying groundwater using this technique is its proficiency and productivity. However, traditional numerical simulation methods are limited by long modeling times, random optimization schemes, and optimization results that are not comprehensive. To address these issues, this study introduced an innovative approach for the optimal design of a GCW using machine learning methods. The FloPy package was used to create and implement the MODFLOW and MODPATH models. Subsequently, the formulated models were employed to calculate the characteristic indicators of the effectiveness of the GCW operation, including the radius of influence (R) and the ratio of particle recovery (Pr). A detailed collection of 3000 datasets, including measures of operational efficiency and key elements in machine learning, was meticulously compiled into documents through model execution. The optimization models were trained and evaluated using multiple linear regression (MLR), artificial neural networks (ANN), and support vector machines (SVM). The models produced by the three approaches exhibited notable correlations between anticipated outcomes and datasets. For the optimal design of circulating well parameters, machine learning methods not only improve the optimization speed, but also expand the scope of parameter optimization. Consequently, these models were applied to optimize the configuration of the GCW at a site in Xi'an. The optimal scheme for R (Q = 293.17 m3/d, a = 6.09 m, L = 7.28 m) and optimal scheme for Pr (Q = 300 m3/d, a = 3.64 m, L = 1 m) were obtained. The combination of numerical simulations and machine learning is an effective tool for optimizing and predicting the GCW remediation effect.

Numerical simulation of the influence of nasal cycle on nasal airflow.

To study the characteristics of nasal airflow in the presence of nasal cycle by computational fluid dynamics. CT scan data of a healthy Chinese individual was used to construct a three-dimensional model of the nasal cavity to be used as simulation domain. A sinusoidal airflow velocity is set at the nasal cavity entrance to reproduce the breathing pattern of a healthy human. There was a significant difference in the cross-sectional area between the two sides of the nasal cavity. Particularly, the decongested side is characterized by a larger cross-section area, and consequently, by a larger volume with respect to the congested side. The airflow velocity, pressure, and nasal resistance were higher on the congested narrow side. The temperature regulation ability on the congested narrow side was stronger than that on the decongested wider side. During the nasal cycle, there are differences in the nasal cavity function between the congested and decongested sides. Therefore, when evaluating the impact of various factors on nasal cavity function, the nasal cycle should be considered.