Search

Abstract Background Neonatal hypoxic-ischemic encephalopathy (HIE) is one of the most common neurological problems occurring in the perinatal period. However, there still is not a promising approach to reduce long-term neurodevelopmental outcomes of HIE. Recently, itaconate has been found to exhibit anti-oxidative and anti-inflammatory effects. However, the therapeutic efficacy of itaconate in HIE remains inconclusive. Therefore, this study attempts to explore the pathophysiological mechanisms of oxidative stress and inflammatory responses in HIE as well as the potential therapeutic role of a derivative of itaconate, 4-octyl itaconate (4OI). Methods We used 7-day-old mice to induce hypoxic-ischemic (HI) model by right common carotid artery ligation followed by 1 h of hypoxia. Behavioral experiments including the Y-maze and novel object recognition test were performed on HI mice at P60 to evaluate long-term neurodevelopmental outcomes. We employed an approach combining non-targeted metabolomics with transcriptomics to screen alterations in metabolic profiles and gene expression in the hippocampal tissue of the mice at 8 h after hypoxia. Immunofluorescence staining and RT-PCR were used to evaluate the pathological changes in brain tissue cells and the expression of mRNA and proteins. 4OI was intraperitoneally injected into HI model mice to assess its anti-inflammatory and antioxidant effects. BV2 and C8D1A cells were cultured in vitro to study the effect of 4OI on the expression and nuclear translocation of Nrf2. We also used Nrf2-siRNA to further validate 4OI-induced Nrf2 pathway in astrocytes. Results We found that in the acute phase of HI, there was an accumulation of pyruvate and lactate in the hippocampal tissue, accompanied by oxidative stress and pro-inflammatory, as well as increased expression of antioxidative stress and anti-inflammatory genes. Treatment of 4OI could inhibit activation and proliferation of microglial cells and astrocytes, reduce neuronal death and relieve cognitive dysfunction in HI mice. Furthermore, 4OI enhanced nuclear factor erythroid-2-related factor (Nfe2l2; Nrf2) expression and nuclear translocation in astrocytes, reduced pro-inflammatory cytokine production, and increased antioxidant enzyme expression. Conclusion Our study demonstrates that 4OI has a potential therapeutic effect on neuronal damage and cognitive deficits in HIE, potentially through the modulation of inflammation and oxidative stress pathways by Nrf2 in astrocytes.

Abstract Idiopathic short stature (ISS) is a common childhood condition with largely unknown underlying causes. Recent research highlights the role of circulating exosomes in the pathogenesis of various disorders, but their connection to ISS remains unexplored. In the experiments, human chondrocytes are cocultured with plasma exosomes from ISS patients, leading to impaired chondrocyte growth and bone formation. Elevated levels of a specific long non‐coding RNA (lncRNA), ISSRL, are identified as a distinguishing factor in ISS, boasting high specificity and sensitivity. Silencing ISSRL in ISS plasma exosomes reverses the inhibition of chondrocyte proliferation and bone formation. Conversely, overexpression of ISSRL in chondrocytes impedes their growth and bone formation, revealing its mechanism of action through the miR‐877‐3p/GZMB axis. Subsequently, exosomes (CT‐Exo‐siISSRL‐oeGH) with precise cartilage‐targeting abilities are engineered, loaded with customized siRNA for ISSRL and growth hormone. This innovative approach offers a therapeutic strategy to address ISS by rectifying abnormal non‐coding RNA expression in growth plate cartilage and delivering growth hormone with precision to promote bone growth. This research provides valuable insights into ISS diagnosis and treatment, highlighting the potential of engineered exosomes.

High-power diesel engine turbochargers predominantly utilize floating ring bearings as their primary supporting components. To further enhance their load capacity, multi-lobe noncircular bearings have been progressively employed. This study focuses on the investigation of noncircular three-lobe SFRBs (semi-floating ring-bearing structures) in marine turbochargers. Employing the half-step center Finite Difference Method (FDM) and the Newton–Raphson iterative procedure, the impact of operational parameters such as the journal speed, external load, oil supply pressure, and oil supply temperature on the static and dynamic characteristics of the inner oil film is analyzed. Subsequently, the accuracy of the theoretical model is validated through a comparative analysis of simulation results obtained from Dyrobes and Fluent. The findings indicate that as the oil supply pressure and temperature increase, the temperature rise and maximum oil film pressure of the three-lobe SFRBs gradually decrease, while the oil film thickness progressively increases, thereby significantly improving the lubrication state. The load capacity of the three-lobe SFRBs is primarily sustained by the bottom tile, where wall friction is most likely to occur. Additionally, within the actual speed range, the stiffness and damping of the three-lobe SFRBs exhibit noticeable nonlinear characteristics.

BackgroundOsteosarcoma is a highly aggressive type of bone cancer with a poor prognosis. In the tumor immune microenvironment, T-cell exhaustion can occur due to various factors, leading to reduced tumor-killing ability. The purpose of this study was to construct a prognostic model based on T-cell exhaustion-associated genes in osteosarcoma.MethodsPatient data for osteosarcoma were retrieved from the TARGET and GEO databases. Consensus clustering was employed to identify two novel molecular subgroups. The dissimilarities in the tumor immune microenvironment between these subgroups were evaluated using the “xCell” algorithm. GO and KEGG analyses were conducted to elucidate the underlying mechanisms of gene expression. Predictive risk models were constructed using the least absolute shrinkage and selection operator algorithm and Cox regression analysis. To validate the prognostic significance of the risk gene expression model at the protein level, immunohistochemistry assays were performed on osteosarcoma patient samples. Subsequently, functional analysis of the key risk gene was carried out through in vitro experimentation.ResultsFour gene expression signatures (PLEKHO2, GBP2, MPP1, and VSIG4) linked to osteosarcoma prognosis were identified within the TARGET-osteosarcoma cohort, categorizing patients into two subgroups. The resulting prognostic model showed strong predictive capability, with area under the receiver operating characteristic curve (AUC) values of 0.728/0.740, 0.781/0.658, and 0.788/0.642 for 1, 3, and 5-year survival in both training and validation datasets. Notably, patients in the low-risk group had significantly higher stromal, immune, and ESTIMATE scores compared to high-risk counterparts. Additionally, a nomogram was developed, exhibiting high accuracy in predicting the survival outcome of osteosarcoma patients. Immunohistochemistry, Kaplan-Meier, and time-dependent AUC analyses consistently supported the prognostic value of the risk model within our osteosarcoma patient cohort. In vitro experiments provided additional validation by demonstrating that the downregulation of GBP2 promoted the proliferation, migration, and invasion of osteosarcoma cells while inhibiting apoptosis.ConclusionThe current study established a prognostic signature associated with TEX-related genes and elucidated the impact of the pivotal gene GBP2 on osteosarcoma cells via in vitro experiments. Consequently, it introduces a fresh outlook for clinical prognosis prediction and sets the groundwork for targeted therapy investigations in osteosarcoma.

Abstract Introduction Dreaming reported after anesthesia remains a poorly understood phenomenon. At present, there is a hypothesis that dreaming occurs intraoperatively and is related to light or inadequate anesthesia; thus, in order to further verify the hypothesis, we choose elective surgery under general anesthesia to observe whether the generation of dreams is related to the dose of general anesthetics maintenance. Methods and analysis This randomized, double-blind controlled trial to observe whether the generation of dreams is related to the dose of general anesthetics maintenance in the elective surgery under general anesthesia. A total of 124 participants will be randomly allocated to a low bispectral index or high bispectral index group at a ratio of 1:1. The Hospital Anxiety and Depression Scale (HADS) is used to assess the anxiety and depression status of participants during the perioperative period. Ramsay score is used to assess patients’ sedation level after surgery in the PACU. Modified Brice questionnaire and awareness classification are used to assess whether patients experienced dreaming during the surgery. Ethics and dissemination This randomized, double-blind controlled trial received prospective ethics committee approval at the Human Research Ethical Committee of Shengjing Hospital, Shenyang, Liaoning Province, China (Institutional Review Board registration number 2021PS664K), and was compliant with the Declaration of Helsinki. Written informed consent was obtained from all subjects participating in the trial.

PurposeIn the present study, the authors intend to identify the spatial relationship between subdural tumors and spinal nerve fibers of cauda equina prior to operation using the coronal MRI of three-dimensional fast-field echo with water-selective excitation (CMRI).MethodsIn total, 30 case series with surgically and pathologically verified subdural tumors were enrolled in the present study. The spatial relationship between subdural tumors and spinal nerve fibers of the cauda equina was assessed via conventional MRI and CMRI by three experts prior to operation. The spatial relationship between subdural tumors and spinal nerve fibers of the cauda equina was classified using CMRI. The accuracy of imaging observation was determined via intraoperative observation.ResultsThough conventional MRI and gadolinium (Gd)-enhanced MRI (Gd MRI) cannot identify the spatial relationship between subdural tumors and spinal nerve fibers of cauda equina in all cases, CMRI can identify it prior to operation and divide the spatial relationship of spinal nerve fibers of cauda equina with subdural tumors into three types. CMRI shows higher sensitivity (97.44%) and specificity (90.47%) in identifying the spatial relationship of spinal nerve fibers of cauda equina with subdural tumors. Additionally, CMRI also showed a substantial agreement with a kappa value of 0.78.ConclusionHerein, the authors first describe a potential novel application that CMRI can successfully identify the spatial relationship between subdural tumors and spinal nerve fibers of cauda equina prior to operation, which play an essential role in making a prudent surgical plan and preventing postoperative nerve damage.SummaryIntraoperative observation confirms spinal nerve fibers of cauda equina are often wrapped into subdural tumors of the thoracolumbar and lumbar region, which can result in a high rate of sensory and motor dysfunction after the operation due to the unconscious about the wrapping of nerves into subdural tumors prior to operation. To date, there is not an effective strategy to identify the wrapping before operation.

In many practical applications, a speech emotion recognition model learned on a source (training) domain but applied to a novel target (testing) domain degenerates even significantly due to the mismatch between the two domains. Aiming at learning a better speech emotion recognition model for the target domain, the paper investigates this interesting problem, i.e., unsupervised cross-corpus speech emotion recognition (SER), in which the training and testing speech signals come from two different speech emotion corpora. Meanwhile, the training speech signals are labeled, while the label information of the testing speech signals is entirely unknown. To deal with this problem, we propose a simple yet effective method called transfer subspace learning (TRaSL). TRaSL aims at learning a projection matrix with which we can transform the source and target speech signals from the original feature space to the label space. The transformed source and target speech signals in the label space would share similar feature distributions. Consequently, the classifier learned on the labeled source speech signals can effectively predict the emotional states of the unlabeled target speech signals. To evaluate the performance of the proposed TRaSL method, we carry out extensive cross-corpus SER experiments on four speech emotion corpora including IEMOCAP, EmoDB, eNTERFACE, and AFEW 4.0. Compared with recent state-of-the-art cross-corpus SER methods, the proposed TRaSL can achieve more satisfactory overall results.

Objective: Preoperative anxiety is a psychological state that commonly occurs before surgery and may have a negative impact on postoperative outcomes. This study aimed to investigate the effects of preoperative anxiety on postoperative sleep quality and recovery outcomes among patients undergoing laparoscopic gynecological surgery. Methods: The study was conducted as a prospective cohort study. A total of 330 patients were enrolled and underwent laparoscopic gynecological surgery. After assessing the patient’s preoperative anxiety score on the APAIS scale, 100 patients were classified into the preoperative anxiety (PA) group (preoperative anxiety score > 10) and 230 patients into the non-preoperative-anxiety (NPA) group (preoperative anxiety score ≤ 10). The Athens Insomnia Scale (AIS) was assessed on the night before surgery (Sleep Pre 1), the first night after surgery (Sleep POD 1), the second night after surgery (Sleep POD2), and the third night after surgery (Sleep POD 3). Postoperative pain was evaluated by the Visual Analog Scale (VAS), and the postoperative recovery outcomes and adverse effects were also recorded. Result: The AIS score in the PA group was higher than that of the NPA group at Sleep-pre 1, Sleep POD 1, Sleep POD 2, and Sleep POD 3 (p < 0.05). The VAS score was higher in the PA group than in the NPA group within 48 h postoperatively (p < 0.05). In the PA group, the total dosage of sufentanil was significantly higher, and more rescue analgesics were required. Patients with preoperative anxiety showed a higher incidence of nausea, vomiting, and dizziness than those without preoperative anxiety. However, there was no significant difference in the satisfaction rate between the two groups. Conclusion: The perioperative sleep quality of patients with preoperative anxiety is worse than that of patients without preoperative anxiety. Moreover, high preoperative anxiety is related to more severe postoperative pain and an increased requirement for analgesia.

To study the typical failure mechanism (rotor unbalance, rotor friction, and rotor crack) and vibration characteristics of the turbocharger rotor system, a rotor system dynamics simulation model was established by an improved four-node aggregate parameter method. The geometric and physical characteristics of the rotor system under three failure states and its dynamics under operation were analyzed. Thus, a typical failure dynamics simulation model of the rotor system was established. On this basis, the output failure simulation signal was extracted using the Hu invariant moment feature extraction method to analyze the system vibration characteristics under each typical failure state of the rotor system. The results show that the model in this paper can effectively reduce the computational volume and computational time, and the errors of numerical simulation were less than 3%. When an unbalance fault occurred in the rotor system, the shaft trajectory was “0” shaped and the response spectrum was dominated by 1X. When the rotor system was frictional, the shaft trajectory was a slightly concave “8” shape, and the response spectrum was dominated by 0.5X. When the rotor system was cracked, the axial trajectory was a “vortex”, and the response spectrum was dominated by 0.5X. Thus, the study of typical failure mechanism and vibration characteristics of a turbocharger rotor system by simulation calculation is effective and has good research prospects, providing an important technical reference for dynamic analysis and fault diagnosis of the rotor system.

Warship always face the threat of underwater explosion shock, which may affect the ship stern tube bearing lubrication performance when the propeller shaft is bending. This paper builds the coupling model of shaft longitudinal vibration and bearing lubrication with consideration of shaft axial shock and misalignment. The performance of stern bearing under the influence of explosion shock was analyzed by solving the generalized Reynolds equation, shaft longitudinal vibration equation and explosion shock equation. The result shows that: the effect of underwater explosion shock on bearing lubrication performance is transient, and the effect will be more obvious when the bearing works at close quarter, larger misaligned angle, eccentricity and rotation speed. Moreover, the edge effect which caused by shaft bending will increase with the decrease of explosion distance and the increase of eccentricity.

Behavior is the first visible change in an animal species after exposure to its own or environmental stressors and is a sensitive indicator. Fish are social animals, and the abnormality of group behavior is more indicative about a particular event than individual behavior, providing more effective informeqation about environmental or group social changes. The group behavior is not only reflected in the spatial distribution, but also reflected in the temporal behavior of the group and individual movement changes under the influence of pressure factors. This paper proposes a group behavior discrimination method based on convolutional neural network and spatiotemporal information fusion, which intends to make use of the prominent performance of convolutional neural network in image recognition and state classification, and imitating the attentional mechanism of ventral channel and dorsal channel when the human brain processes visual signals. Some pressure environments are made in laboratory, the behavior states of fish shoals are recorded, and the sample database of shoals' behavior state is established by combining the spatial information of shoals' spatial distribution with the time information reflected in the movement behavior. A simple convolutional neural network is constructed to quickly identify the behavior state of fish shoals. The effects of bath size and training epoch on network training speed and recognition accuracy are discussed, and the visualization of the intermediate data of the convolutional neural network is studied. Shown from the results of experiments of this paper, different behavior states of fish shoals can be recognized and classified effectively by using the simple convolutional neural network and spatiotemporal fusion images. What's more, from the visualization of network intermediate data, it is found that the convolutional neural network has a higher discrimination power to the image edge feature than the image gray-value feature.

Time-space four-dimensional motion target localization is a fundamental and challenging task in the field of intelligent driving, and an important part of achieving the upgrade in existing target localization technologies. In order to solve the problem of the lack of localization of moving targets in a spatio-temporal four-dimensional environment in the existing spatio-temporal data model, this paper proposes an optical imaging model in the four-dimensional time-space system and a mathematical model of the object-image point mapping relationship in the four-dimensional time-space system based on the central perspective projection model, combined with the one-dimensional “time” and three-dimensional “space”. After adding the temporal dimension, the imaging system parameters are extended. In order to solve the nonlinear mapping problem of complex systems, this paper proposes to construct a time-space four-dimensional object-image mapping relationship model based on a BP artificial neural network and demonstrates the feasibility of the joint time-space four-dimensional imaging model theory. In addition, indoor time-space four-dimensional localization prediction experiments verify the performance of the model in this paper. The maximum relative error rates of the predicted motion depth values, time values, and velocity values of this localization method compared with the real values do not exceed 0.23%, 2.03%, and 1.51%, respectively

Background: Work-related stress among healthcare professionals poses a serious economic and healthcare burden. This study aimed to investigate the prevalence of burnout as well as anxiety, depression, and stress in medical residents from different majors, and assess the effects of an online psychological intervention on the mental health status of medical residents with a high degree of burnout.Methods: We conducted an online survey that collected information on the demographics, mental health, and burnout conditions of medical residents from Shengjing Hospital. The mental health condition was assessed by the Depression, Anxiety, and Stress Scale (DASS)−21. Further, burnout was assessed by the Maslach Burnout Inventory (MBI). Medical residents with a total MBI score between 50 and 75 were selected to receive online psychological intervention for 3 months.Results: Two-hundred and ten medical residents completed the questionnaire, of whom, 63 residents with an MBI score between 50 and 75 received the 3-month online psychological intervention. Anesthesia residents showed the highest level of depression, anxiety, and stress, and presented with a lower sense of personal accomplishment, higher emotional exhaustion, and higher depersonalization. Furthermore, pediatric residents had the second highest DASS and MBI scores following anesthesia residents. Following the online psychological intervention, negative emotional states and burnout levels were significantly lower among anesthesia and pediatric residents. There were no differences in the level of stress and sense of personal accomplishment pre- and post-online psychological intervention among the different majors.Conclusion: Our findings revealed high levels of burnout, as well as depression, anxiety, and stress symptoms in medical residents, with marked differences among different majors. The online psychological intervention effectively improved emotional exhaustion, and depersonalization, and relieved the psychological problems such as anxiety and depression in medical residents.

Objective: The Coronavirus Disease 2019 (COVID-19) pandemic is a public health emergency of international concern and poses a challenge to people’s psychological resilience. Students are reported to have greater psychological impacts from COVID-19. This study aimed to survey international students to better understand their traumatic effects and psychological reactions from COVID-19, to develop evidence-driven strategies to reduce adverse psychological impact during the pandemic. Method: We conducted an online survey that collected information on the demographics, economic conditions, academic conditions, and health statuses of native Chinese students attending university in the U.S. Psychological impact was assessed by the Post-traumatic Stress Disorder (PTSD) Checklist Civilian Version (PCL-C) and mental health status was assessed by the Depression, Anxiety, and Stress Scale. Results: This study included 261 Chinese international students. In total, 37.5% of respondents’ PTSD PCL-C scores measured as moderate or severe. International students who were currently in China facing job-hunting or planning to continue studying abroad, severe economic pressure, and poor self-rated health status were significantly associated with greater PTSD PCL-C scores and higher levels of stress, anxiety, and depression. Conclusion: During the COVID-19 pandemic, more than one-third of the respondents rated their PTSD PCL-C score as moderate-to-severe and nearly half of them reported moderate-to-severe anxiety. Our findings identify factors such as future academic plan, economic pressure, and health status are associated with higher levels of psychological impact and worse mental health status. These should receive attention and psychological interventions should be implemented to improve the mental health of international students during the COVID-19 pandemic.

The purpose of this article is to review (1) sleep mechanism under general anesthesia, harmful effects of postoperative sleep disturbances; (2) risk factors associated with postoperative sleep disturbances; (3) measures to prevent and improve postoperative sleep disturbances. General anesthesia changes the postoperative sleep structure especially in elderly patients after major surgery and results in a high incidence rate of sleep disturbances. Sleep disturbances produce harmful effects on postoperative patients and lead to a higher risk of delirium, more cardiovascular events, and poorer recovery. Some researchers do propose non-pharmacological treatments such as attention to environmental and psychological factors, application of electroacupuncture (EA) technology and pharmacological treatments are helpful, but larger high-quality clinical trials with longer following-up are needed to further investigate the efficacy and safety.

For some measurement and detection applications based on video (sequence images), if the exposure time of camera is not suitable with the motion speed of the photographed target, fuzzy edges will be produced in the image, and some poor lighting condition will aggravate this edge blur phenomena. Especially, the existence of noise in industrial field environment makes the extraction of fuzzy edges become a more difficult problem when analyzing the posture of a high-speed moving target. Because noise and edge are always both the kind of high-frequency information, it is difficult to make trade-offs only by frequency bands. In this paper, a noise-tolerant edge detection method based on the correlation relationship between layers of wavelet transform coefficients is proposed. The goal of the paper is not to recover a clean image from a noisy observation, but to make a trade-off judgment for noise and edge signal directly according to the characteristics of wavelet transform coefficients, to realize the extraction of edge information from a noisy image directly. According to the wavelet coefficients tree and the Lipschitz exponent property of noise, the idea of neural network activation function is adopted to design the activation judgment method of wavelet coefficients. Then the significant wavelet coefficients can be retained. At the same time, the non-significant coefficients were removed according to the method of judgment of isolated coefficients. On the other hand, based on the design of Daubechies orthogonal compactly-supported wavelet filter, rational coefficients wavelet filters can be designed by increasing free variables. By reducing the vanishing moments of wavelet filters, more high-frequency information can be retained in the wavelet transform fields, which is benefit to the application of edge detection. For a noisy image of high-speed moving targets with fuzzy edges, by using the length 8-4 rational coefficients biorthogonal wavelet filters and the algorithm proposed in this paper, edge information could be detected clearly. Results of multiple groups of comparative experiments have shown that the edge detection effect of the proposed algorithm in this paper has the obvious superiority.