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Despite significant advancements in total knee arthroplasty (TKA), managing postoperative pain remains a substantial clinical challenge. With advances in surgical techniques and pharmacology, TKA perioperative analgesic strategies continue to evolve, necessitating ongoing reassessment of current data. Multimodal analgesia has become the standard for perioperative pain management in TKA; however, variations in its specific components and applications across studies create challenges in selecting the optimal analgesic approach. Evaluating these pain management strategies is essential for understanding their benefits, limitations, and appropriate use, allowing the development of individualized perioperative analgesic plans. This review aims to summarize current research on perioperative pain control in TKA and assess the effectiveness of different analgesic interventions.

Activated microglia can suppress neurite outgrowth and synapse recovery in the acute stage following traumatic brain injury (TBI). However, the underlying mechanism has not been clearly elucidated. Exosomes derived from microglia have been reported to play a critical role in microglia-neuron interaction in healthy and pathological brains. Here, we aimed to investigate the role of microglia-derived exosomes in regulating neurite outgrowth and synapse recovery following TBI. In our study, exosomes derived from microglia were co-cultured with stretch-injured neurons in vitro and intravenously injected into mice that underwent fluid percussion injury (FPI) by tail vein injection in vivo. The results showed that microglia-derived exosomes could be absorbed by neurons in vitro and in vivo. Moreover, exosomes derived from stretch-injured microglia decreased the protein levels of GAP43, PSD-95, GluR1, and Synaptophysin and dendritic complexity in stretch-injured neurons in vitro, and reduced GAP43+ NEUN cell percentage and apical dendritic spine density in the pericontusion region in vivo. Motor coordination was also impaired in mice treated with stretch-injured microglia-derived exosomes after FPI. A microRNA microarray showed that the level of miR-5121 was decreased most greatly in exosomes derived from stretch-injured microglia. Overexpression of miR-5121 in stretch-injured microglia-derived exosomes partly reversed the suppression of neurite outgrowth and synapse recovery of neurons both in vitro and in vivo. Moreover, motor coordination in miR-5121 overexpressed exosomes treated mice was significantly improved after FPI. Following mechanistic study demonstrated that miR-5121 might promote neurite outgrowth and synapse recovery by directly targeting RGMa. In conclusion, our finding revealed a novel exosome-mediated mechanism of microglia-neuron interaction that suppressed neurite outgrowth and synapse recovery of neurons following TBI.

The shaking table test has been widely recognized as one of the most reliable methods for assessing the dynamic response of structures and systems when exposed to a range of vibrations. Replicating these vibrations, recorded as acceleration signals, necessitates precise controller designs. However, traditional controller designs primarily rely on linear system assumptions, leading to complications in incorporating parametric uncertainty and uncertain non‐linearity. This paper presents a novel approach to the acceleration trajectory control problem of single‐axis hydraulic shaking table systems from a non‐linear perspective. This strategy employs a controller combined with an extended state observer (ESO) and adaptive control, offering a design that more closely reflects real‐world scenarios and requires less parameter adjustment. The ESO estimates the uncertain non‐linearity using the displacement signal, while the parametric uncertainty is tackled through adaptive control. The proposed controller's effectiveness is validated through theoretical proof using Lyapunov analysis. The analysis demonstrates that asymptotic tracking performance is guaranteed when the uncertain non‐linearity is time‐invariant. Moreover, with time‐variant uncertain non‐linearity, the controller can also ensure both prescribed transient tracking performance and final tracking accuracy. Comparative experiment results underscore the superior performance of the proposed controller. This paper presents a novel approach to the acceleration trajectory control problem of single‐axis hydraulic shaking table systems from a non‐linear perspective. This strategy employs a controller combined with an extended state observer and adaptive control, offering a design that more closely reflects real‐world scenarios and requires less parameter adjustment. Both theoretical analysis and experimental results underscore the superior performance of the proposed controller.

BRD4, a major tandem-bromodomain-containing transcription regulator, has two isoforms. The long isoform (BRD4L) has an extended C terminus that binds transcription cofactors, while the short isoform (BRD4S) lacks this C-terminal extension. Unlike BRD4L, the role of BRD4S in gene transcription remains unclear. Here, we report that, in human cancer cells, BRD4S forms nuclear puncta that possess liquid-like properties and that colocalize with BRD4L, MED1 and sites of histone H3 lysine 27 acetylation. BRD4 puncta are correlated with BRD4S but not BRD4L expression levels. BRD4S knockdown reduces BRD4S condensation, and ectopic expression promotes puncta formation and target gene transcription. BRD4S nuclear condensation is mediated by its intrinsically disordered regions and binding of its bromodomains to DNA and acetylated chromatin, respectively, and BRD4S phosphorylation diminishes BRD4 condensation. Our study illuminates a previously unappreciated role of BRD4S in organizing chromatin and transcription factors through phase separation to sustain gene transcription in chromatin for cancer cell proliferation.A combination of cellular, in vitro phase separation and functional assays shows that the intrinsically disordered regions and bromodomains of the BRD4 short isoform induce formation of liquid-like condensates in cancer cell nuclei and enhance transcriptional activity.

Background Mammary gland development is a critical process in mammals, crucial for their reproductive success and offspring nourishment. However, the functional roles of key candidate genes associated with teat number, including ABCD4 , VRTN , PROX2 , and DLST , in this developmental process remain elusive. To address this gap in knowledge, we conducted an in-depth investigation into the dynamic expression patterns, functional implications, and regulatory networks of these candidate genes during mouse mammary gland development. Results In this study, the spatial and temporal patterns of key genes were characterized in mammary gland development. Using time-series single-cell data, we uncovered differences in the expression of A bcd4 , Vrtn , Prox2 , and Dlst in cell population of the mammary gland during embryonic and adult stages, while Vrtn was not detected in any cells. We found that only overexpression and knockdown of Abcd4 could inhibit proliferation and promote apoptosis of HC11 mammary epithelial cells, whereas Prox2 and Dlst had no significant effect on these cells. Using RNA-seq and qPCR, further analysis revealed that Abcd4 can induce widespread changes in the expression levels of genes involved in mammary gland development, such as Igfbp3 , Ccl5 , Tlr2 , and Prlr , which were primarily associated with the MAPK, JAK-STAT, and PI3K-AKT pathways by functional enrichment. Conclusions These findings revealed ABCD4 as a candidate gene pivotal for regulating mammary gland development and lactation during pregnancy by influencing PRLR expression.

The shaking table has been used extensively in the structure test field to verify the structure's performance against various vibrations, for example earthquakes. In order to replicate the vibrations, which are measured by the acceleration signal, the model of the shaking table should be thoroughly constructed to design the controller. However, parametric uncertainty and strong nonlinearity, such as the nonlinear friction, make it an obstacle to obtaining an accurate model. A neural network‐based controller, specifically a long short‐term memory neural network‐based neural network controller, is designed in this paper to address this issue. The nonlinear systems are estimated by the neural network's universal approximation characteristics, and a long short‐term memory neural network is utilized to optimize the time‐series‐related errors. Furthermore, a robust sliding mode controller is utilized to compensate for the residual error of the neural network and other uncertainties. The semi‐global asymptotic stability of the controller is proved by Lyapunov analysis. Comparative experimental results indicate the superiority of the proposed controller. The nonlinear system model of the shaking table is estimated by the neural network's universal approximation characteristics and an LSTM network is utilized to optimize the time‐series‐related errors. Furthermore, a robust sliding mode controller is used to compensate for the residual error of the neural network and other uncertainties. The semi‐global asymptotic stability of the controller is proved by Lyapunov analysis and comparative experimental results indicate the superiority of the proposed controller.

Background Leukocyte immunoglobulin-like receptor B4 (LILRB4) plays a significant role in regulating immune responses. LILRB4 in microglia might influence the infiltration of peripheral T cells. However, whether and how LILRB4 expression aggravates brain damage after acute ischemic stroke remains unclear. This study investigates the role of LILRB4 in modulating the immune response and its potential protective effects against ischemic brain injury in mice. Methods and results Microglia-specific LILRB4 conditional knockout (LILRB4-KO) and overexpression transgenic (LILRB4-TG) mice were constructed by a Cre-loxP system. Then, they were used to investigate the role of LILRB4 after ischemic stroke using a transient middle cerebral artery occlusion (tMCAO) mouse model. Spatial transcriptomics analysis revealed increased LILRB4 expression in the ischemic hemisphere. Single-cell RNA sequencing (scRNA-seq) identified microglia-cluster3, an ischemia-associated microglia subcluster with elevated LILRB4 expression in the ischemic brain. Flow cytometry and immunofluorescence staining showed increased CD8 + T cell infiltration into the brain in LILRB4-KO-tMCAO mice. Behavioral tests, cortical perfusion maps, and infarct size measurements indicated that LILRB4-KO-tMCAO mice had more severe functional deficits and larger infarct sizes compared to Control-tMCAO and LILRB4-TG-tMCAO mice. T cell migration assays demonstrated that LILRB4-KD microglia promoted CD8 + T cell recruitment and activation in vitro, which was mitigated by CCL2 inhibition and recombinant arginase-1 addition. The scRNA-seq and spatial transcriptomics identified CCL2 was predominantly secreted from activated microglia/macrophage and increased CCL2 expression in LILRB4-KD microglia, suggesting a chemokine-mediated mechanism of LILRB4. Conclusion LILRB4 in microglia plays a crucial role in modulating the post-stroke immune response by regulating CD8 + T cell infiltration and activation. Knockout of LILRB4 exacerbates ischemic brain injury by promoting CD8 + T cell recruitment. Overexpression of LILRB4, conversely, offers neuroprotection. These findings highlight the therapeutic potential of targeting LILRB4 and its downstream pathways to mitigate immune-mediated damage in ischemic stroke.

Background Total hip arthroplasty (THA) is an excellent treatment for the end-stage hip disease, and perioperative blood management strategies have been effectively applied to this procedure. However, many patients still experience anemia after the operation, which is usually overlooked by orthopedic surgeons due to the hidden blood loss (HBL) in the perioperative period. Therefore, the objective of this study was to evaluate HBL in patients undergoing primary THA using the posterior approach and to explore its influencing factors. Methods A retrospective analysis of 707 patients who underwent primary THA through the posterior approach was conducted in our hospital from January 2020 to January 2022. By applying Gross’s and Nadler’s formula, the HBL was calculated. Six quantitative variables (age, body mass index, surgical duration, albumin loss, preoperative hemoglobin, and hemoglobin loss) as well as four qualitative variables (gender, American Society of Anesthesiologists class, major preoperative diagnosis, and hypertension) of patients were analyzed using multivariate linear regression. Results The HBL was recorded at 700.39 ± 368.59 mL. As a result of multivariate linear regression analysis, it was determined that body mass index, surgical duration, and hemoglobin loss were all significant risk factors for HBL, whereas preoperative hemoglobin was considered a protective factor. It has been demonstrated that HBL is not significantly correlated with age, albumin loss, gender, ASA class, or major preoperative diagnosis, but it also did not differ from HBL by hypertension. Conclusions Hidden blood Loss (HBL) in patients after primary total hip arthroplasty (THA) using the posterior approach is large and significant. When optimizing the perioperative management of THA, orthopedic surgeons should keep in mind HBL and its influencing factors, especially for patients with high body mass indexes, long surgical durations, and low preoperative hemoglobin levels. Trial registration This study was registered in the Chinese Clinical Trial Registry (ChiCTR2100053888) in 02/12/2021, http://www.chictr.org.cn .

As the second generation of biometric technology, finger vein recognition has become a research hotspot due to its advantages such as high security, and living body recognition. In recent years, the global pandemic has promoted the development of contactless identification. However, the unconstrained finger vein acquisition process will introduce more uneven illumination, finger image deformation, and some other factors that may affect the recognition, so it puts forward higher requirements for the acquisition speed, accuracy and other performance. Considering the universal, obvious, and stable characteristics of the original finger vein imaging, we proposed a new Region Of Interest (ROI) extraction method based on the characteristics of finger vein image, which contains three innovative elements: a horizontal Sobel operator with additional weights; an edge detection method based on finger contour imaging characteristics; a gradient detection operator based on large receptive field. The proposed methods were evaluated and compared with some representative methods by using four different public datasets of finger veins. The experimental results show that, compared with the existing representative methods, our proposed ROI extraction method is 1/10th of the processing time of the threshold-based methods, and it is similar to the time spent for coarse extraction in the mask-based methods. The ROI extraction results show that the proposed method has better robustness for different quality images. Moreover, the results of recognition matching experiments on different datasets indicate that our method achieves the best Equal Error Rate (EER) of 0.67% without the refinement of feature extraction parameters, and all the EERs are significantly lower than those of the representative methods.

Finger vein recognition has been widely studied due to its advantages, such as high security, convenience, and living body recognition. At present, the performance of the most advanced finger vein recognition methods largely depends on the quality of finger vein images. However, when collecting finger vein images, due to the possible deviation of finger position, ambient lighting and other factors, the quality of the captured images is often relatively low, which directly affects the performance of finger vein recognition. In this study, we proposed a new model for finger vein recognition that combined the vision transformer architecture with the capsule network (ViT-Cap). The model can explore finger vein image information based on global and local attention and selectively focus on the important finger vein feature information. First, we split-finger vein images into patches and then linearly embedded each of the patches. Second, the resulting vector sequence was fed into a transformer encoder to extract the finger vein features. Third, the feature vectors generated by the vision transformer module were fed into the capsule module for further training. We tested the proposed method on four publicly available finger vein databases. Experimental results showed that the average recognition accuracy of the algorithm based on the proposed model was above 96%, which was better than the original vision transformer, capsule network, and other advanced finger vein recognition algorithms. Moreover, the equal error rate (EER) of our model achieved state-of-the-art performance, especially reaching less than 0.3% under the test of FV-USM datasets which proved the effectiveness and reliability of the proposed model in finger vein recognition.