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This paper investigates the anisotropic yielding and distorted hardening behaviors of AA6061-T6. Yoon 2014 model was used to describe the anisotropic plastic behaviors of material and two strategies (i.e. different equivalent plastic strain intervals at two levels) in describing the distorted hardening were used. At the integration point level, a program utilizing the forward Euler method and regression mapping algorithms has been developed. This program accepts strain as input and provides corresponding stress as output. Results found that the prediction ability of constitutive model strongly depend on the strain interval of parameters identification. Excessive interval might result in the unexpected fluctuations of stress-strain curve. Such fluctuations primarily come from the deviation of identified parameters and interpolation data. The former deviation is found caused by the changes in initial value during identifying and the latter mainly because the interpolation data departures from the actual values. In addition, a detailed analysis about parameters effects is also conducted for in-depth understanding of parameters identification.

Contact stress is the important cause of wear and failure of knee joint prostheses implanted in the human body during movement. The calculation of contact stress on joint prostheses is often inaccurate due to the deviation of joint position during activity. This paper proposes a correction method for contact stress based on finite element results. Firstly, the contact stress and contact radius between femoral and tibial prostheses of knee joint were calculated and analyzed by Hertz contact theory. Then, individualized finite element models based on the individualized geometry of the joint prosthesis and individualized loading were constructed, and the finite element contact stress was analyzed. The finite element results were calibrated utilizing the Hertz contact theory. Additionally, this study investigated the impact of three gait patterns (normal gait, medial thrust gait, and bouncing gait) on the contact stress experienced by the tibial insert. The research methods and results can provide ideas for theoretical analysis and finite element simulation of prosthetic joint contact, as well as offer theoretical support and analysis methods for evaluating the lifespan of prosthetic joints.

Phosphorylation site prediction is one of the key processes in protein post-transcriptional modification. It is an important research direction in the field of bioinformatics and is of great significance for understanding protein function and signal transduction. Since it is time-consuming and error-prone to perform site determination through experiments, the application of artificial intelligence is very necessary. This study introduces a novel deep neural network named PhosHSGN designed to identify and examine protein post-translational modification (PTM) sites. The model predicts phosphorylation by extracting the local sequence of the protein and incorporating global spatial information. To effectively combine sequence and spatial information for prediction, a graph neural network is introduced with residuals. This network integrates the Alphafold protein structure prediction module to construct a protein residue contact graph. Additionally, a pre-trained protein language model is employed to generate base extraction graph embeddings. Simultaneously, PhosHSGN incorporates a one-dimensional residual network to explore the sequence information of proteins. Experimental data were collected from PhosphoSitePlus, UniProt, GPS 5.0, and Phospho.ELM. Comparing the experimental results of PhosHSGN with Phosidn and other state-of-the-art models on different datasets reveals that PhosHSGN outperforms sequence-based methods in all metrics with a sensitivity of 96.18%, accuracy of 93.72%, and Mcc value of 84.19% on the dataset S/T. On dataset Y, the F1 score was 94.42% and the AUC value was 96.19%.

The primary objective of this paper is to establish a dependable constitutive model for pressure-insensitive metals, with a particular emphasis on but not limited to the Cantor alloy. This model takes into account the effect of distortional hardening on deformation response of the material. It successfully establishes a quantitative relationship between the strength differential coefficient and the effective plastic strain. In order to acquire the distortional hardening parameters, the elastoplastic behavior of the Cantor alloy is determined through compressive, tensile, and shear tests. The comparison between DIC and finite element simulations confirms the accuracy of the proposed model in describing the deformation behavior of the Cantor alloy. The presented results and analyses offer a precise computational model for studying the deformation behavior of pressure-insensitive metals under various stress states. Additionally, this model can also be utilized for further research and analysis of the mechanical properties and performance of the Cantor alloy in practical applications.

In this study, we investigated the effect of monobutyrin (MB) on the gut microbiota and intestinal health of weaned mice. MB was administered via gavage to 21-day-old weaned mice. Samples of small intestinal and ileal contents were collected on day 1, day 7, and day 21 post-administration. Seven days of MB administration enhanced the mucin layer and morphological structure of the intestine and the integrity of the intestinal brush border. Both MB and sodium butyrate (SB) accelerated tight junction development. Compared to SB, MB modulated intestinal T cells in a distinct manner. MB increased the ratio of Treg cells in the small intestine upon the cessation of weaning. After 21 days of MB administration, enhancement of the villus structure of the ileum was observed. MB increased the proportion of Th17 cells in the ileum. MB facilitated the transition of the small intestinal microbiota toward an adult microbial community structure and enhanced the complexity of the microbial community structure. An increase in Th17 cells enhanced intestinal barrier function. The regulatory effect of MB on Th17 cells may occur through the intestinal microbiota. Therefore, MB can potentially be used to promote intestinal barrier function, especially for weaning animals, with promising application prospects.

ABSTRACT Serological responses reveal recent and historical exposure to pathogens, as well as the state of autoimmune and other chronic conditions, including cancer. Serological tests either assess one or a few antigens in many people, or multiple antigens for modest sample sizes. Here, we describe a multiplexed serology method that evaluates samples at the scale of thousands. This molecular epidemiology tool operates at a population scale sufficient to evaluate broadly how various infectious exposures or autoimmune responses affect health. The method employs full-length folded proteins, is quantitative over a wide dynamic range, and performs favorably compared with commercial clinical assays [severe acute respiratory syndrome coronavirus 2 chemiluminescent IgG II assay (Beckman) and Platelia SARS-CoV-2 total Ab enzyme-linked immunosorbent assay (Bio-Rad, California, USA)]. Responses to 39 bacteria species/strains and 99 viruses in 2,400 people were evaluated. Subjects with longitudinal data showed quantitative stability of response to all antigens over the 6-month time window, enabling the detection of intervening clinical events. We expect this highly adaptable method will find broad application in immune profile tracking. IMPORTANCE Serology reveals exposure to pathogens, as well as the state of autoimmune and other clinical conditions. It is used to evaluate individuals and their histories and as a public health tool to track epidemics. Employing a variety of formats, studies nearly always perform serology by testing response to only one or a few antigens. However, clinical outcomes of new infections also depend on which previous infections may have occurred. We developed a high-throughput serology method that evaluates responses to hundreds of antigens simultaneously. It can be used to evaluate thousands of samples at a time and provide a quantitative readout. This tool will enable doctors to monitor which pathogens an individual has been exposed to and how that changes in the future. Moreover, public health officials could track populations and look for infectious trends among large populations. Testing many potential antigens at a time may also aid in vaccine development.

In this article, a novel optimal design method for segmented modification of cycloid gear tooth profile was proposed to improve transmission efficiency. Firstly, the cycloid tooth profile under segmented modification was analyzed. Secondly, the mathematical model of the meshing phase angle of the working segment of the cycloid gear with the highest transmission efficiency was established, and such optimization was achieved by the particle swarm algorithm，and the important indexes were calculated and compared with the basic segmented modification method. Finally, the tooth profile of the cycloid gear under the new design method was obtained by numerical solution. To verify the method, the processing technology of cycloid gear is designed, and the comparison of performance testing between the new proposed method and conventional basic segmented modification was conducted and analyzed by the assembled RV reducer prototype in accordingly. The results show the temperature rise of the RV reducer under the optimized method was reduced by 1.2℃ under the rated load, and the torsional stiffness and transmission efficiency were increased by 0.03N·m/" and 1.8%, respectively, compared with the basic segmented modification method, which shows that this method can effectively overcome the disadvantage of low transmission efficiency under the basic segmented modification method.

ABSTRACT Patients with 2019 coronavirus disease (COVID-19) exhibit a broad spectrum of clinical presentations. A person’s antimicrobial antibody profile, as partially shaped by past infection or vaccination, can reflect the immune system health that is critical to control and resolve the infection. We performed an explorative immunoproteomics study using microbial protein arrays displaying 318 full-length antigens from 77 viruses and 3 bacteria. We compared antimicrobial antibody profiles between 135 patients with mild COVID-19 disease and 215 patients with severe disease in 3 independent cohorts from Mexico and Italy. Severe disease patients were older with higher prevalence of comorbidities. We confirmed that severe disease patients elicited a stronger anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) response. We showed that antibodies against HCoV-229E and HcoV-NL63 but not against HcoV-HKU1 and HcoV-OC43 were also higher in those who had severe disease. We revealed that for a set of IgG and IgA antibodies targeting coronaviruses, herpesviruses, and other respiratory viruses, a subgroup of patients with the highest reactivity levels had a greater incidence of severe disease compared to those with mild disease across all three cohorts. On the contrary, fewer antibodies showed consistent greater prevalence in mild disease in all 3 cohorts. IMPORTANCE The clinical presentations of COVID-19 range from asymptomatic to critical illness that may lead to intensive care or even death. The health of the immune system, as partially shaped by past infections or vaccinations, is critical to control and resolve the infection. Using an innovative protein array platform, we surveyed antibodies against hundreds of full-length microbial antigens from 80 different viruses and bacteria in COVID-19 patients from different geographic regions with mild or severe disease. We not only confirmed the association of severe COVID-19 disease with higher reactivity of antibody responses to SARS-CoV-2 but also uncovered known and novel associations with antibody responses against herpesviruses and other respiratory viruses. Our study represents a significant step forward in understanding the factors contributing to COVID-19 disease severity. We also demonstrate the power of comprehensive antimicrobial antibody profiling in deciphering risk factors for severe COVID-19. We anticipate that our approach will have broad applications in infectious diseases.

Unsupervised anomaly detection models are crucial for the efficiency of industrial applications. However, frequent false alarms hinder the widespread adoption of unsupervised anomaly detection, especially in fault detection tasks. To this end, our research delves into the dependence of false alarms on the baseline anomaly detector by analyzing the high-response regions in anomaly maps. We introduce an SVM-based false positive classifier as a post-processing module, which identifies false alarms from positive predictions at the object level. Moreover, we devise a sample synthesis strategy that generates synthetic false positives from the trained baseline detector while producing synthetic defect patch features from fuzzy domain knowledge. Following comprehensive evaluations, we showcase substantial performance enhancements in two advanced out-of-distribution anomaly detection models, Cflow and Fastflow, across image and pixel-level anomaly detection performance metrics. Substantive improvements are observed in two distinct industrial applications, with notable instances of elevating the image-level F1-score from 46.15% to 78.26% in optimal scenarios and boosting pixel-level AUROC from 72.36% to 94.74%.

Unsupervised defect detection methods have garnered substantial attention in industrial defect detection owing to their capacity to circumvent complex fault sample collection. However, these models grapple with establishing a robust boundary between normal and abnormal conditions in intricate scenarios, leading to a heightened frequency of false-positive predictions. Spurious alerts exacerbate the work of reconfirmation and impede the widespread adoption of unsupervised anomaly detection models in industrial applications. To this end, we delve into the sole available data source in unsupervised defect detection models, the unsupervised training dataset, to introduce a solution called the False Alarm Identification (FAI) method aimed at learning the distribution of potential false alarms using anomaly-free images. It exploits a multi-layer perceptron to capture the semantic information of potential false alarms from a detector trained on anomaly-free training images at the object level. During the testing phase, the FAI model operates as a post-processing module applied after the baseline detection algorithm. The FAI algorithm determines whether each positive patch predicted by the normalizing flow algorithm is a false alarm by its semantic features. When a positive prediction is identified as a false alarm, the corresponding pixel-wise predictions are set to negative. The effectiveness of the FAI method is demonstrated by two state-of-the-art normalizing flow algorithms on extensive industrial applications.

Abstract Canine diabetes has been considered a potential model of human type 1 diabetes (T1D), however the detection of autoantibodies common in humans with T1D in affected dogs is inconsistent. The aim of this study was to compare autoantibody responses in diabetic and healthy control dogs using a novel nucleic acid programmable protein array (NAPPA) platform. We performed a cross-sectional study of autoantibody profiles of 30 diabetic and 30 healthy control dogs of various breeds. Seventeen hundred human proteins related to the pancreas or diabetes were displayed on NAPPA arrays and interrogated with canine sera. The median normalized intensity (MNI) for each protein was calculated, and results were compared between groups to identify candidate autoantibodies. At a specificity of 90%, six autoantibodies had sensitivity greater than 10% (range 13–20%) for distinguishing diabetic and control groups. A combination of three antibodies (anti-KANK2, anti-GLI1, anti-SUMO2) resulted in a sensitivity of 37% (95% confidence interval (CI) 0.17–0.67%) at 90% specificity and an area under the receiver operating characteristics curve of 0.66 (95% CI 0.52–0.80). While this study does not provide conclusive support for autoimmunity as an underlying cause of diabetes in dogs, future studies should consider the use of canine specific proteins in larger numbers of dogs of breeds at high risk for diabetes.

ABSTRACT In October 2020, the National Cancer Institute (NCI) Serological Sciences Network (SeroNet) was established to study the immune response to COVID-19, and “to develop, validate, improve, and implement serological testing and associated technologies” (https://www.cancer.gov/research/key-initiatives/covid-19/coronavirus-research-initiatives/serological-sciences-network). SeroNet is comprised of 25 participating research institutions partnering with the Frederick National Laboratory for Cancer Research (FNLCR) and the SeroNet Coordinating Center. Since its inception, SeroNet has supported collaborative development and sharing of COVID-19 serological assay procedures and has set forth plans for assay harmonization. To facilitate collaboration and procedure sharing, a detailed survey was sent to collate comprehensive assay details and performance metrics on COVID-19 serological assays within SeroNet. In addition, FNLCR established a protocol to calibrate SeroNet serological assays to reference standards, such as the U.S. severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology standard reference material and first WHO international standard (IS) for anti-SARS-CoV-2 immunoglobulin (20/136), to facilitate harmonization of assay reporting units and cross-comparison of study data. SeroNet institutions reported development of a total of 27 enzyme-linked immunosorbent assay (ELISA) methods, 13 multiplex assays, and 9 neutralization assays and use of 12 different commercial serological methods. FNLCR developed a standardized protocol for SeroNet institutions to calibrate these diverse serological assays to reference standards. In conclusion, SeroNet institutions have established a diverse array of COVID-19 serological assays to study the immune response to SARS-CoV-2 and vaccines. Calibration of SeroNet serological assays to harmonize results reporting will facilitate future pooled data analyses and study cross-comparisons. IMPORTANCE SeroNet institutions have developed or implemented 61 diverse COVID-19 serological assays and are collaboratively working to harmonize these assays using reference materials to establish standardized reporting units. This will facilitate clinical interpretation of serology results and cross-comparison of research data.

In this paper, three different shapes of indenters were used to perform quasi-static out-of-plane uniaxial compression and indentation tests on three commercial hexagonal aluminum honeycombs with different densities. In order to distinguish the different tearing conditions of the single-layer and double-layer aluminum honeycomb walls during indentation, a new indentation experiment was designed. By controlling the shape of pattern and the location of indentation, different tearing conditions in indentation experiment were measured separately. The deformation mode of the aluminum honeycomb when pressed was analyzed. It is found that as the size of the pattern increases, the peak stress of the indentation experiment decreases significantly. As the size of the indenter increases, the platform stress of aluminum honeycomb shows a downward trend and tends to approach the compression platform stress, while the tear strength remains basically stable within a certain scale. As the size of the indenter continues to increase, the proportion of tearing force to the total indenting force gradually decreases, and the tearing strength loses its stability. At this time, the tear strength can no longer be used as a stable mechanical parameter to measure the tearing condition during indentation.

This paper addresses the event modeling and recognition problem in video surveillance systems using the system net on the Petri Net (PN) formalism. The single event on the foundation of prior knowledge is first modeled via arranging event schemes from the design document. Then, finite sequential runs (FSRs) determined by semantic features in training clips drive elements of the proposed single event model. Finally, the multi-event model is built automatically from single event models via a proposed integration method using multi-level features (including high-level semantic features and low-level features like numerical characters of individual trajectories). We provide a novel solution to event conflict issue through an extended system net where the resultant event type is determined by the decision tree technique. The comparison between the proposed methodology and other approaches in the literature is reported via experiments on an acknowledged public dataset.

Future trains will use more computer vision aids to help achieve fully autonomous driving. One of the most important parts of the train’s visual function is the detection of railroad obstacles. This makes it important to identify and segment the railroad region within each video frame as it allows the train to identify the driving area so that it can do effective obstacle detection. Traditional railroad detection methods rely on hand-crafted features or highly specialized equipment such as lidar, which typically require expensive equipment to be maintained and are less reliable in scene changes. RailNet is a deep learning segmentation algorithm for railroad detection for videos captured by the front-view on-board cameras. RailNet provides an end-to-end solution that combines feature extraction and segmentation. We have modified the backbone network to extract multi-convolution features and use a pyramid structure to make the features have a top-to-bottom propagation. Our model can detect the railroad without generating large numbers of regions, which greatly increases the detection speed. Tested on a railroad segmentation dataset (RSDS) which we have built, RailNet exhibits very good performance while achieving 20 frames per second processing speed.

Background: We aimed to investigate the relationship between miR-596, BCL-2, and apoptosis of gastric cancer cells, and to explore the mechanism of miR-596 in gastric cancer. Besides, this study aimed to find the target of miR-596 and explore the mechanism of action of miR-596 in gastric cancer. Methods: Eighteen samples of gastric cancer tissues and 18 samples of corresponding tumor-adjacent tissues were collected from 18 gastric cancer patients (aged from 40 to 55 yr) admitted to Zhuji People's Hospital, Zhuji, China from March 2017 to May 2018. The expression levels of miR-596 and BCL-2 were detected to verify the regulation of miR-596 on the apoptosis and proliferation of gastric cancer cell lines MKN-45 and HGC-27 and its effect on BCL-2 expression. Results: The expression level of miR-596 was notably lower in gastric cancer tissues than in adjacent tissues, and BCL-2 level was notably higher in gastric cancer tissues than in adjacent tissues. After the up-regulation of miR-596 expression, the proliferation of MKN-45 and HGC-27 cells was significantly decreased, the level of apoptosis was significantly increased (P

In this study, quasi-static compression, tensile, shear, and combined compression-shear tests are conducted on cast AZ91D magnesium (Mg) alloy to study its fracture behavior. Shear-compression specimens are applied instead of complex loading device to obtained the fracture response of Mg alloy under mixed compression/shear tests. Results show that the stress states of specimens can be controlled by changing the loading angle. The strength differential effect is observed, and the criterion with von Mises form cannot give an appropriate description of the fracture behavior of Mg alloy for its dependence on the direction of normal stress. The criterion with the form of odd function and that contains two deviatoric stress invariants is utilized to describe the fracture behavior of Mg alloy. In addition, the effect of pressure term and the direction of normal stress on the fracture strength of Mg alloy are commonly mistaken for the other, and the detailed method to distinguish these two effects has been conducted on the basis of the criteria of f =(J2,J3) and f =(I1,J2,J3).

This study presents the fundamental concepts and technical details of a U-model-based control (U-control for short) system design framework, including U-model realisation from classic model sets, control system design procedures, and simulated showcase examples. Consequently, the framework provides readers with clear understandings and practical skills for further research expansion and applications. In contrast to the classic model-based design and model-free design methodologies, this model-independent design takes two parallel formations: (1) it designs an invariant virtual controller with a specified closed-loop transfer function in a feedback control loop and (2) it determines the real controller output by resolving the inverse of the plant U-model. It should be noted that (1) this U-control provides a universal control system design platform for many existing linear/nonlinear and polynomial/state-space models and (2) it complements many existing design approaches. Simulation studies are used as examples to demonstrate the analytically developed formulations and guideline for potential applications.

Abstract Background The intracellular bacterial pathogen Legionella pneumophila proliferates in human alveolar macrophages, resulting in a severe pneumonia termed Legionnaires’ disease. Throughout the course of infection, L. pneumophila remains enclosed in a specialized membrane compartment that evades fusion with lysosomes. The pathogen delivers over 300 effector proteins into the host cell, altering host pathways in a manner that sets the stage for efficient pathogen replication. The L. pneumophila effector protein AnkX targets host Rab GTPases and functions in preventing fusion of the Legionella-containing vacuole with lysosomes. However, the current understanding of AnkX’s interaction with host proteins and the means through which it exerts its cellular function is limited. Results Here, we investigated the protein interaction network of AnkX by using the nucleic acid programmable protein array (NAPPA), a high-density platform comprising 10,000 unique human ORFs. This approach facilitated the discovery of PLEKHN1 as a novel interaction partner of AnkX. We confirmed this interaction through multiple independent in vitro pull-down, co-immunoprecipitation, and cell-based assays. Structured illumination microscopy revealed that endogenous PLEKHN1 is found in the nucleus and on vesicular compartments, whereas ectopically produced AnkX co-localized with lipid rafts at the plasma membrane. In mammalian cells, HaloTag-AnkX co-localized with endogenous PLEKHN1 on vesicular compartments. A central fragment of AnkX (amino acids 491–809), containing eight ankyrin repeats, extensively co-localized with endogenous PLEKHN1, indicating that this region may harbor a new function. Further, we found that PLEKHN1 associated with multiple proteins involved in the inflammatory response. Conclusions Altogether, our study provides evidence that in addition to Rab GTPases, the L. pneumophila effector AnkX targets nuclear host proteins and suggests that AnkX may have novel functions related to manipulating the inflammatory response.

In this paper, the anisotropic mechanical properties of rolled AZ31 magnesium alloys are investigated using nanoindentation tests at room temperature. Nanoindentation was carried out at four angles, including the rolling direction (0°), diagonal direction (45°), transverse direction (90°), and vertical direction (ND). Experimental results show that hardness increases as the rolling angle increases from 0° to 90° and is lowest in the ND direction. The hardness independent of the effect of indentation depth is obtained by analyzing the indentation size effect and then converting hardness values into yield strengths. A new criterion is proposed on the basis of the Hill48 yield criterion. The data obtained through the above experiments are used to determine the parameters in the new criterion. Finally, a solution to the challenge of modeling a function that accurately describes the anisotropic yielding behavior of AZ31 magnesium alloys is proposed using the nanoindentation technique to solve the requirements of specimen size and experimental methods of the macro test.

We present a microarray nonlinear calibration (MiNC) method for quantifying antibody binding to the surface of protein microarrays that significantly increases the linear dynamic range and reduces assay variation compared with traditional approaches. A serological analysis of guinea pig Mycobacterium tuberculosis models showed that a larger number of putative antigen targets were identified with MiNC, which is consistent with the improved assay performance of protein microarrays. MiNC has the potential to be employed in biomedical research using multiplex antibody assays that need quantitation, including the discovery of antibody biomarkers, clinical diagnostics with multi-antibody signatures, and construction of immune mathematical models.

Epidermal growth factor receptor (EGFR) is overexpressed in a variety of human malignancies, including pancreatic cancer, breast cancer, colon cancer, and non-small cell lung cancer. Overexpression of EGFR is a predictive marker of therapeutic response and several lines of evidence suggest that EGFR is an excellent target for tumor therapy. However, the effective antitumor capacity of EGFR-specific T cells against EGFR-overexpressing tumor cells has not been fully elucidated. In our previous study, we identified an anti-EGFR single-chain variable fragment (scFv) with specific and high affinity after screening by ribosome display. In this study, the anticancer potential of anti-EGFR scFv was investigated on the basis of cell-targeted therapy. A chimeric antigen receptor (CAR) targeting EGFR was constructed and expressed on the cell membrane of T lymphocytes. These CAR-modified T cells demonstrated antitumor efficacy both in vitro and in vivo. In addition, the safety evaluation showed that CAR-modified lymphocytes have no or very minimal acute systemic toxicity. Taken together, our study provided the experimental basis for clinical application of genetically engineered lymphocytes; moreover, we also evaluate a new and interesting cell therapy protocol.

Chemo-resistance to cisplatin-centered cancer therapy is a major obstacle to the effective treatment of human ovarian cancer. Previous reports indicated that arsenic trioxide (ATO) induces cell apoptosis in both drug-sensitive and -resistant ovarian cancer cells.In this study, we determined the molecular mechanism of ATO-induced apoptosis in ovarian cancer cells. Our data demonstrated that ATO induced cell apoptosis by decreasing levels of phosphorylated AKT (p-AKT) and activating caspase-3 and caspase-9. Importantly, BIM played a critical role in ATO-induced apoptosis. The inhibition of BIM expression prevented AKT dephosphorylation and inhibited caspase-3 activation during cell apoptosis. However, surprisingly, gene silencing of AKT or FOXO3A had little effect on BIM expression and phosphorylation. Moreover, the activation of caspase-3 by ATO treatment improved AKT dephosphorylation, not only by cleaving the regulatory A subunit of protein phosphatase 2A (PP2A), but also by increasing its activation. Furthermore, our data indicated that the c-Jun N-terminal kinases (JNK) pathway is involved in the regulation of BIM expression.We demonstrated the roles of BIM in ATO-induced apoptosis and the molecular mechanisms of BIM expression regulated by ATO during ovarian cancer cell apoptosis. Our findings suggest that BIM plays an important role in regulating p-AKT by activating caspase-3 and that BIM mediates the level of AKT phosphorylation to determine the threshold for overcoming cisplatin resistance in ovarian cancer cells.

Viral infections usually induce the rearrangement of cellular cytoskeletal proteins and organelle membrane structures, thus creating independent compartments [termed replication organelles (ROs)] to facilitate viral genome replication. Within the ROs, viral replicases, including polymerases, helicases, and ligases, play functional roles during viral replication. These viral replicases are pivotal in the virus life cycle, and numerous studies have demonstrated that the viral replicases could be the potential targets for drugs development. Here, we summarize primarily the key replicases within viral ROs and emphasize the advancements of antiviral drugs targeting crucial viral replicases, providing novel insights into the future development of antiviral strategies. [ABSTRACT FROM AUTHOR]

Alphaherpesviruses, categorized as viruses with linear DNA composed of two complementary strands, can potentially to induce diseases in both humans and animals as pathogens. Mature viral particles comprise of a core, capsid, tegument, and envelope. While herpesvirus infection can elicit robust immune and inflammatory reactions in the host, its persistence stems from its prolonged interaction with the host, fostering a diverse array of immunoescape mechanisms. In recent years, significant advancements have been achieved in comprehending the immunoescape tactics employed by alphaherpesviruses, including pseudorabies virus (PRV), herpes simplex virus (HSV), varicellazoster virus (VZV), feline herpesvirus (FeHV), equine herpesvirus (EHV), and caprine herpesvirus type I (CpHV-1). Researchers have unveiled the intricate adaptive mechanisms existing between viruses and their natural hosts. This review endeavors to illuminate the research advancements concerning the immunoescape mechanisms of alphaherpesviruses by delineating the pertinent proteins and genes involved in virus immunity. It aims to furnish valuable insights for further research on related mechanisms and vaccine development, ultimately contributing to virus control and containment efforts. [ABSTRACT FROM AUTHOR]