Evaluating the safety and efficiency impacts of forced lane change with negative gaps based on empirical vehicle trajectories.

A lane-changing (LC) maneuver may cause the follower in the target lane (new follower) to decelerate and give up space, potentially affecting crash risk and traffic flow efficiency. In congested flow, a more aggressive LC maneuver occurs where the lane changer is partially next to the new follower and creates negative gaps, namely negative gap forced LC (NGFLC). Although NGFLC forms the foundation of sideswipe crashes, little has been done to address its impacts and the contributing factors. To tackle this issue, a total of 15,810 LC trajectory samples are extracted from three drone videos at different locations. These samples are categorized into NGFLC and normal LC groups for comparative analysis. Five commonly used conflict indicators are extended into two-dimensional to evaluate the crash risk of LC maneuver. The change of time gaps during LC maneuver are examined to quantify the impact of LC on traffic flow efficiency. We find that NGFLCs significantly increase crash risk, reflected by the number of hazardous LC events and potential crash areas compared to normal LC. Additionally, results reveal that both the lane changer and the new follower tend to maintain a larger time gap after NGFLCs. Factors including time headway, relative speed, and historical gaps in the target lane significantly affect NGFLC incidence. Once the movement of the leader in the original lane is taken into account, the prediction accuracy improves from 81% to 91%. The transferability tests indicate that the findings about the negative impact of NGFLC and the accuracy of its prediction model are consistent across different locations. These findings hold implications for driving assistance systems to better predict and mitigate NGFLCs.

Multicomponent Synthesis of Imidazole-Based Ionizable Lipids for Highly Efficient and Spleen-Selective Messenger RNA Delivery.

The spleen emerges as a pivotal target for mRNA delivery, prompting a continual quest for specialized and efficient lipid nanoparticles (LNPs) designed to enhance spleen-selective transfection efficiency. Here we report imidazole-containing ionizable lipids (IMILs) that demonstrate a pronounced preference for mRNA delivery into the spleen with exceptional transfection efficiency. We optimized IMIL structures by constructing and screening a multidimensional IMIL library containing multiple heads, tails, and linkers to perform a structure-activity correlation analysis. Following high-throughput in vivo screening, we identified A3B7C2 as a top-performing IMIL in spleen-specific mRNA delivery via the formulated LNPs, achieving a remarkable 98% proportion of splenic transfection. Moreover, A3B7C2-based LNPs are particularly potent in splenic dendritic cell transfection. Comparative analyses revealed that A3B7C2-based LNPs achieved a notable 2.8-fold and 12.9-fold increase in splenic mRNA transfection compared to SM102 and DLin-MC3-DMA lipid formulations, respectively. Additionally, our approach yielded an 18.3-fold enhancement in splenic mRNA expression compared to the SORT method without introducing additional anionic lipids. Collectively, these IMILs highlight promising avenues for further research in spleen-selective mRNA delivery. This work offers valuable insights for the swift discovery and rational design of ionizable lipid candidates tailored for spleen-selective transfection, thereby facilitating the application of mRNA therapeutics in spleen-related interventions.

Non-equilibrium compression achieving high sensitivity and linearity for iontronic pressure sensors.

Flexible pressure sensors with high sensitivity and linearity are highly desirable for robot sensing and human physiological signal detection. However, the current strategies for stabilizing axial microstructures (e.g., micro-pyramids) are mainly susceptible to structural stiffening during compression, thereby limiting the realization of high sensitivity and linearity. Here, we report a bending-induced non-equilibrium compression process that effectively enhances the compressibility of microstructures, thereby crucially improving the efficiency of interfacial area growth of electric double layer (EDL). Based on this principle, we fabricate an iontronic flexible pressure sensor with vertical graphene (VG) array electrodes. Ultra-high sensitivity (185.09 kPa-1) and linearity (R2 = 0.9999) are realized over a wide pressure range (0.49 Pa-66.67 kPa). It also exhibits remarkable mechanical stability during compression and bending. The sensor is successfully employed in a robotic gripping task to recognize the targets of different materials and shapes based on a multilayer perception (MLP) neural network. It opens the door to realizing haptic sensing capabilities for robotic hands and prosthetic limbs.

Helper Lipid-Enhanced mRNA Delivery for Treating Metabolic Dysfunction-Associated Fatty Liver Disease.

Lipid nanoparticles (LNPs) represent the forefront of mRNA delivery platforms, yet achieving precise delivery to specific cells remains a challenge. The current targeting strategies complicate the formulation and impede the regulatory approval process. Here, through a straightforward regulation of helper lipids within LNPs, we introduce an engineered LNP designed for targeted delivery of mRNA into hepatocytes for metabolic dysfunction-associated fatty liver disease (MAFLD) treatment. The optimized LNP, supplied with POPC as the helper lipid, exhibits a 2.49-fold increase in mRNA transfection efficiency in hepatocytes compared to that of FDA-approved LNPs. CTP:phosphocholine cytidylyltransferase α mRNA is selected for delivery to hepatocytes through the optimized LNP system for self-calibration of phosphatidylcholine levels to prevent lipid droplet expansion in MAFLD. This strategy effectively regulates lipid homeostasis, while demonstrating proven biosafety. Our results present a mRNA therapy for MAFLD and open a new avenue for discovering potent lipids enabling mRNA delivery to specific cells.

3,3'-((3,4,5-trifluoropHenyl)methylene)bis(4-hydroxy-2H-chromen-2-one) inhibit lung cancer cell proliferation and migration.

Lung cancer is a major public health challenge and, despite therapeutic improvements, is the first leading cause of cancer worldwide. The current cure rate from advanced cancer treatment is excessively low. Therefore, it is of great importance to identify novel, potent and less toxic anticancer agents for the treatment of lung cancer. The aim of our research is to synthesize a new biscoumarin 3,3'-((3,4,5-trifluorop -phenyl)methylene)bis(4-hydroxy-2H-chromen-2-one) (C35) as an anticancer agent. C35 was simply prepared by 4-hydroxycoumarin and 3,4,5-trifluorobenzaldehyde under ethanol and its structure was analyzed by spectroscopic analyses. The anti-proliferation effect of C35 was detected using CCK-8 assay. Migration abilities were measured by Transwell assay. The expression of correlated proteins was determined by Western blot. The results showed that C35 displayed strong cytostatic effects on lung cancer cell proliferation. In addition, C35 possessed a significant inhibition of migration by reducing the expression of matrix metalloproteinases-2 (MMP-2) and MMP-9 in lung cancer cells. Furthermore, C35 treatment suppressed the phosphorylation of p38 in lung cancer cells. Moreover, in vivo experiments were carried out, in which we treated Lewis tumor-bearing C57 mice via intraperitoneal injection of C35. Results showed that C35 inhibited tumor growth in vivo. In conclusion, our study demonstrated the anticancer activity of C35 via suppression of lung cancer cell proliferation and migration, which is possibly involved with the inhibition of the p38 pathway.

Evaluating the performance of traffic conflict measures in real-time crash risk prediction using pre-crash vehicle trajectories.

The primary objective of this study was to evaluate the performance of traffic conflict measures for real-time crash risk prediction. Drone recordings were collected from a freeway section in Nanjing, China, over a year. Twenty rear-end crashes and their associated trajectories were obtained. Vehicle trajectories preceding the crash were segmented based on different time periods to represent varying crash conditions. The Extreme Value Theory (EVT) approach combined with a block maxima sampling method was then employed to investigate the generalized extreme value (GEV) distributions of extremely risky events under non-crash and crash conditions. The prediction performance was demonstrated by the differences in GEV distributions under these two conditions. Within the proposed modeling framework, the performances of Time-to-Collision (TTC), Deceleration Rate to Avoid a Crash (DRAC), and Absolute value of Derivative of Instantaneous Acceleration (ADIA) were examined and compared. The results revealed a decreasing trend in the prediction performances as the preceding time window before a crash increased. For any given length of crash conditions, TTC consistently outperformed DRAC and ADIA. Notably, TTC's reliability in crash risk prediction became more uncertain when forecasting crashes more than 2 s in advance. This study provided the optimal thresholds for TTC and ADIA for practical application in crash early warning. The methods and results in this study have the potential to be used for crash risk assessments in autonomous vehicles.

Accurate object localization facilitates automatic esophagus segmentation in deep learning.

BACKGROUND: Currently, automatic esophagus segmentation remains a challenging task due to its small size, low contrast, and large shape variation. We aimed to improve the performance of esophagus segmentation in deep learning by applying a strategy that involves locating the object first and then performing the segmentation task. METHODS: A total of 100 cases with thoracic computed tomography scans from two publicly available datasets were used in this study. A modified CenterNet, an object location network, was employed to locate the center of the esophagus for each slice. Subsequently, the 3D U-net and 2D U-net_coarse models were trained to segment the esophagus based on the predicted object center. A 2D U-net_fine model was trained based on the updated object center according to the 3D U-net model. The dice similarity coefficient and the 95% Hausdorff distance were used as quantitative evaluation indexes for the delineation performance. The characteristics of the automatically delineated esophageal contours by the 2D U-net and 3D U-net models were summarized. Additionally, the impact of the accuracy of object localization on the delineation performance was analyzed. Finally, the delineation performance in different segments of the esophagus was also summarized. RESULTS: The mean dice coefficient of the 3D U-net, 2D U-net_coarse, and 2D U-net_fine models were 0.77, 0.81, and 0.82, respectively. The 95% Hausdorff distance for the above models was 6.55, 3.57, and 3.76, respectively. Compared with the 2D U-net, the 3D U-net has a lower incidence of delineating wrong objects and a higher incidence of missing objects. After using the fine object center, the average dice coefficient was improved by 5.5% in the cases with a dice coefficient less than 0.75, while that value was only 0.3% in the cases with a dice coefficient greater than 0.75. The dice coefficients were lower for the esophagus between the orifice of the inferior and the pulmonary bifurcation compared with the other regions. CONCLUSION: The 3D U-net model tended to delineate fewer incorrect objects but also miss more objects. Two-stage strategy with accurate object location could enhance the robustness of the segmentation model and significantly improve the esophageal delineation performance, especially for cases with poor delineation results.

Competition between biodetoxification fungus and lactic acid bacterium in the biorefinery processing chain for production of cellulosic L-lactic acid.

Biodetoxification fungus selectively degrades toxic inhibitors generated from pretreatment of lignocellulose without consuming fermentable sugars. However, one barrier for practical application is the sustained cell viability in the consequent fermentation step to compete the fermentable sugars with fermenting strains, resulting in sugar loss and reduced target product yield. This study investigated the competitive growth property between the biodetoxification fungus Paecilomyces variotii FN89 and the L-lactic acid bacterium Pediococcus acidilactici ZY271 under varying temperature and lactic acid osmatic stress. The results show that the L-lactic acid bacterium Ped. acidilactici ZY271 showed less thermotolerance to Pae. variotii FN89 at high temperature of 45 °C to 50 °C in both synthetic medium and wheat straw hydrolysate. In the higher temperature environment, the growth of the biodetoxification strian failed to compete with the lactic acid fermentation strain and was quickly eliminated from the fermentation system. The high temperature fermentation facilitated a fast transition from the detoxification stage to the fermentation stage for higher production of L-lactic acid.

A logistic-tent chaotic mapping Levenberg Marquardt algorithm for improving positioning accuracy of grinding robot.

The precision of workpiece machining is critically influenced by the geometric errors in the kinematics of grind robots, which directly affect their absolute positioning accuracy. To tackle this challenge, this paper introduces a logistic-tent chaotic mapping Levenberg Marquardt algorithm designed to accurately identify and compensate for this geometric error. the approach begins with the construction of a forward kinematic model and an error model specific to the robot. Then the algorithm is adopted to identify and compensate for the geometric error. The method establishes a mapping interval around the initial candidate solutions derived from iterative applications of the Levenberg Marquardt algorithm. Within this interval, the logistic-tent chaotic mapping method generates a diverse set of candidate solutions. These candidates are evaluated based on their fitness values, with the optimal solution selected for subsequent iterations. Empirical compensation experiments have validated the proposed method's precision and effectiveness, demonstrating a 6% increase in compensation accuracy and a 47.68% improvement in efficiency compared to existing state-of-the-art approaches. This process not only minimizes the truncation error inherent in the Levenberg Marquardt algorithm but also significantly enhances solution efficiency. Moreover, simulation experiments on grind processes further validate the method's ability to significantly improve the quality of workpiece machining.

Optimizing transseptal puncture guided by three-dimensional mapping: the role of a unipolar electrogram in a needle tip.

AIMS: A three-dimensional electroanatomic mapping system-guided transseptal puncture (3D-TSP), without fluoroscopy or echocardiography, has been only minimally reported. Indications for 3D-TSP remain unclear. Against this background, this study aims to establish a precise technique and create a workflow for validating and selecting eligible patients for fluoroless 3D-TSP. METHODS AND RESULTS: We developed a new methodology for 3D-TSP based on a unipolar electrogram derived from a transseptal needle tip (UEGM tip) in 102 patients (the derivation cohort) with intracardiac echocardiography (ICE) from March 2018 to February 2019. The apparent current of injury (COI) was recorded at the muscular limbus of the foramen ovalis (FO) on the UEGM tip (sinus rhythm: 2.57 ± 0.95 mV, atrial fibrillation: 1.92 ± 0.77 mV), which then disappeared or significantly reduced at the central FO. Changes in the COI, serving as a major criterion to establish a 3D-TSP workflow, proved to be the most valuable indicator for identifying the FO in 99% (101/102) of patients compared with three previous techniques (three minor criteria) of reduction in atrial unipolar or bipolar potential and FO protrusion. A total of 99.9% (1042/1043) patients in the validation cohort underwent successful 3D-TSP through the workflow from March 2019 to July 2023. Intracardiac echocardiography guidance was required for 6.6% (69/1042) of patients. All four criteria were met in 740 patients, resulting in a 100% pure fluoroless 3D-TSP success rate. CONCLUSION: In most patients, fluoroless 3D-TSP was successfully achieved using changes in the COI on the UEGM tip. Patients who met all four criteria were considered suitable for 3D-TSP, while those who met none required ICE guidance.

Burden of depression in adolescents in the Western Pacific Region from 1990 to 2019: An age-period-cohort analysis of the Global Burden of Disease study.

BACKGROUND: Depression is a highly prevalent and disabling mental health condition among adolescents. The epidemiology of depression in adolescents has been changing over time, reflecting changes in risk factors as well as disease concepts and diagnosis. However, few studies have characterized the longitudinal epidemiology of depression in adolescents. Understanding trends of disease burden provides key insights to improve resource allocation and design targeted interventions for this vulnerable population. The Western Pacific Region (WPR) is home to over 1.3 billion people with tremendous diversity in culture and socioeconomic development. The epidemiology of adolescent depression in WPR remains largely unknown. In this study, we aimed to estimate trends of disease burden attributable to depressive disorders among adolescents aged 10-24 years in WPR countries between 1990 and 2019, and to investigate period and cohort effects using the Global Burden of Disease (GBD) study database. METHODS: The study utilized data from the Global Burden of Disease, Injuries, and Risk Factors Study 2019, concentrating on adolescents aged 10 to 24 years with depression. We conducted an in-depth analysis of depression, including its age-standardized prevalence, incidence, and Disability-Adjusted Life Years (DALYs), across diverse demographics such as regions, ages, genders, and socio-demographic indexes, spanning from 1990 to 2019. RESULTS: The analysis found decreasing trends in the prevalence, incidence, and DALYs of adolescent depression in the WPR between 1990-2019, although some countries like Australia and Malaysia showed increases. Specifically, the prevalence of adolescent depression in the region decreased from 9,347,861.6 cases in 1990 to 5,551,341.1 cases in 2019. The incidence rate declined from 2,508.6 per 100,000 adolescents in 1990 to 1,947.9 per 100,000 in 2019. DALYs decreased from 371.9 per 100,000 in 1990 to ASR 299.7 per 100,000 in 2019. CONCLUSION: This study found an overall decreasing trend in adolescent depression burden in the Western Pacific Region between 1990 and 2019, with heterogeneity across countries. For 30 years, the 20-24 age group accounted for the majority of depression among adolescents Widening inequality in depression burden requires policy attention. Further analysis of risk factors contributing to epidemiological trends is warranted to inform prevention strategies targeting adolescent mental health in the region.

Insights into the role of RNA m6A modification in the metabolic process and related diseases.

According to the latest consensus, many traditional diseases are considered metabolic diseases, such as cancer, type 2 diabetes, obesity, and cardiovascular disease. Currently, metabolic diseases are increasingly prevalent because of the ever-improving living standards and have become the leading threat to human health. Multiple therapy methods have been applied to treat these diseases, which improves the quality of life of many patients, but the overall effect is still unsatisfactory. Therefore, intensive research on the metabolic process and the pathogenesis of metabolic diseases is imperative. N6-methyladenosine (m6A) is an important modification of eukaryotic RNAs. It is a critical regulator of gene expression that is involved in different cellular functions and physiological processes. Many studies have indicated that m6A modification regulates the development of many metabolic processes and metabolic diseases. In this review, we summarized recent studies on the role of m6A modification in different metabolic processes and metabolic diseases. Additionally, we highlighted the potential m6A-targeted therapy for metabolic diseases, expecting to facilitate m6A-targeted strategies in the treatment of metabolic diseases.

Effect of respiratory muscle training in patients with stable chronic obstructive pulmonary disease: A systematic review and meta-analysis.

Objectives: Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disorder characterized by progressive airflow limitation. This meta-analysis aims to evaluate the effectiveness of respiratory muscle training (RMT) on key pulmonary function parameters, inspiratory muscle strength and quality of life in patients with stable COPD. Methods: A comprehensive search was conducted in the databases including PubMed, Cochrane, Web of Science, Embase, and ClinicalTrials.gov, from their inception to June 12, 2023. Randomized controlled trials (RCTs) evaluating the impact of RMT on stable COPD were included for meta-analysis. Results: In total, 12 RCTs involving 453 participants were included in the meta-analysis. RMT demonstrated a significant increase in maximal inspiratory pressure (PImax, MD, 95% CI: 14.34, 8.17 to 20.51, P < 0.001) but not on maximal expiratory pressure (PEmax). No significant improvement was observed in 6-Min walk test (6MWT), dyspnea, forced expiratory volume in 1 s (FEV1), forced vital capacity ratio (FVC) and quality of life between RMT and control groups. However, subgroup analysis revealed a significant negative effect of RMT alone on FEV1/FVC (MD, 95% CI: 2.59, -5.11 to -0.06, P = 0.04). When RMT was combined with other interventions, improvements in FEV1/FVC and FEV1 were found, although not statistically significant. Conclusion: RMT can effectively improve maximal inspiratory pressure in stable COPD patients, but the effect is slight in improving lung function, dyspnea and quality of life. It is recommended to combine with other treatment strategies to comprehensively improve the prognosis of COPD patients.

Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion.

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes.

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.

Global, regional and national burdens of depression in adolescents and young adults aged 10-24 years, from 1990 to 2019: findings from the 2019 Global Burden of Disease study.

BACKGROUND: Depression is a significant mental health concern affecting the overall well-being of adolescents and young adults. Recently, the prevalence of depression has increased among young people. Nonetheless, there is little research delving into the longitudinal epidemiology of adolescent depression over time. AIMS: To investigate the longitudinal epidemiology of depression among adolescents and young adults aged 10-24 years. METHOD: Our research focused on young people (aged 10-24 years) with depression, using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. We explored the age-standardised prevalence, incidence and disability-adjusted life-years (DALYs) of depression in different groups, including various regions, ages, genders and sociodemographic indices, from 1990 to 2019. RESULTS: The prevalence, incidence and DALYs of depression in young people increased globally between 1990 and 2019. Regionally, higher-income regions like High-Income North America and Australasia recorded rising age-standardised prevalence and incidence rates, whereas low- or middle-income regions mostly saw reductions. Nationally, countries such as Greenland, the USA and Palestine reported the highest age-standardised prevalence and incidence rates in 2019, whereas Qatar witnessed the largest growth over time. The burden disproportionately affected females across age groups and world regions. The most prominent age effect on incidence and prevalence rates was in those aged 20-24 years. The depression burden showed an unfavourable trend in younger cohorts born after 1980, with females reporting a higher cohort risk than males. CONCLUSIONS: Between 1990 and 2019, the general pattern of depression among adolescents varied according to age, gender, time period and generational cohort, across regions and nations.

Safety and efficacy of ultrasound-guided superior hypogastric plexus block combined with conscious sedation in ambulatory patients undergoing percutaneous microwave ablation of uterine myomas: Study protocol for a single-center, double-blinded, randomized controlled trial.

Background: Pain is a major challenge in performing ultrasound-guided percutaneous microwave ablation (PMWA) of uterine myomas. Inadequate analgesia by local anesthetics hinders the possibility of conducting PMWA of uterine myomas in the Ambulatory Surgery Center (ASC) of the Department of Ultrasound. Objective: The superior hypogastric plexus (SHP) forms a suitable target for pain relief through the blockade, as it contains nociceptive afferent fibers from pelvic organs such as the uterus, rectum, and bladder. Superior hypogastric plexus block (SHPB) has demonstrated promise as an alternative treatment option for alleviating pelvic pain, reducing opioid consumption, and improving quality of life. This study aims to evaluate the efficacy of ultrasound-guided SHPB combined with conscious sedation as an alternative anesthesia option for ambulatory patients receiving ultrasound-guided PMWA of uterine myomas. Methods and analysis: This randomized controlled trial (RCT) will be carried out at the Department of Ultrasound, The First Affiliated Hospital of Xiamen University. Women scheduled for ultrasound-guided PMWA of uterine myomas will be eligible. 86 patients will be recruited and randomly assigned to either the intervention or control groups in a 1:1 ratio. The intervention group will undergo ultrasound-guided superior hypogastric plexus block (SHPB) combined with conscious sedation, while the control group will receive local anesthesia combined with conscious sedation. The primary outcome is the success rate of anesthesia, secondary outcomes include vasoactive drug consumption, acetaminophen consumption, sleep quality, sonographer satisfaction score, patient satisfaction score, the detained time in hospital, and adverse events. Discussions: This RCT represents the inaugural effort to specifically evaluate the safety and efficacy of ultrasound-guided SHPB combined with conscious sedation in patients undergoing ultrasound-guided PMWA of uterine myomas and will provide valuable evidence and insight into the analgesic management of this ambulatory surgery. Ethics and dissemination: This study has been approved by the Ethics Committee of the First Affiliated Hospital of Xiamen University (Scientific Research Ethics Review 2023, No. 139). The results will be submitted for publication in peer-reviewed journals.

Predictive value of coagulation function, alpha-fetoprotein and placental growth factor in patients with perilous placenta previa.

OBJECTIVE: To analyze the predictive value of coagulation function, alpha-fetoprotein (AFP) and placental growth factor (PIGF) for postpartum hemorrhage in patients with perilous placenta previa (PPP). METHODS: The clinical data of 104 PPP patients were retrospectively analyzed. The patients were divided into a hemorrhage group (n=68) and a non-hemorrhage group (n=36). A total of 55 healthy pregnant women were recruited as controls. The coagulation function, AFP and PIGF were compared between the three groups. Multivariate logistic regression was performed to determine independent risk factors for hemorrhage. RESULTS: PT, TT, APTT, FIB and AFP were significantly higher while PIGF was lower in the PPP group than the control group (all P<0.05). Placental adhesion (OR 3.924, 95% CI 1.389-11.083, P=0.01), anterior placenta (OR 4.583, 95% CI 1.589-13.22, P=0.005), AFP (OR 0.208, 95% CI 0.068-0.635, P=0.006) and PIGF (OR 3.963, 95% CI 1.385-11.34, P=0.01) were independent risk factors for hemorrhage. CONCLUSION: Coagulation function, AFP and PIGF could predict postpartum hemorrhage in PPP patients.

Modification patterns and metabolic characteristics of m6A regulators in digestive tract tumors.

M6A is essential for tumor occurrence and progression. The expression patterns of m6A regulators differ in various kinds of tumors. Transcriptomic expression statistics together with clinical data from a database were analyzed to distinguish patients with digestive tract tumors. Based on the expression patterns of diverse m6A regulators, patients were divided into several clusters. Survival analysis suggested significant differences in patient prognosis among the m6A clusters. The results showed overlapping of m6A expression patterns with energy metabolism and nucleotide metabolism. Functional analyses imply that m6A modifications in tumor cells probably drive metabolic reprogramming to sustain rapid proliferation of cancer cells. Our analysis highlights the m6A risk characterizes various kinds of metabolic features and predicts chemotherapy sensitivity in digestive tract tumors, providing evidence for m6A regulators as markers to predict patient outcomes.