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Objective The role of hypoalbuminemia throughout the course of chronic periprosthetic joint infection (PJI) remains poorly understood. This study aimed to determine the prevalence and risk factors of hypoalbuminemia in periprosthetic joint infection (PJI) patients and to explore the association between hypoalbuminemia and treatment outcomes. Methods This retrospective cohort study included 387 PJI cases who underwent two‐stage exchange arthroplasty between January 2007 and August 2020, of which 342 were reimplanted. The mean follow‐up period was 7.9 years. Multivariate logistic regression analyses were performed to identify risk factors for hypoalbuminemia and to assess the effect of hypoalbuminemia at 1st‐ and 2nd‐stage exchange on the treatment outcome. Furthermore, the impact of dynamic changes in hypoalbuminemia was investigated. Results The prevalence of hypoalbuminemia at 1st‐ and 2nd‐stage exchange was 22.2% and 4.7%, respectively. Patients with age ≥ 68 years and those with isolation of Staphylococcus aureus, Streptococcus, or Gram‐negative bacteria exhibited a higher risk of hypoalbuminemia. Hypoalbuminemia at 1st‐stage was significantly related to treatment failure (OR = 3.3), while hypoalbuminemia at 2nd‐stage raised the OR to 10.0. Patients with persistent hypoalbuminemia at both the 1st‐ and 2nd‐stage exchanges had a significantly higher rate of treatment failure than patients with hypoalbuminemia at the 1st‐stage but normal albumin levels at the 2nd‐stage exchange (55.6% vs 20.0%, p = 0.036). Conclusion One in five patients with chronic PJI exhibits hypoalbuminemia. Hypoalbuminemia is more likely to develop in patients of advanced age and those infected by specific highly virulent organisms. Also, our results highlight the close association between hypoalbuminemia and treatment outcomes.

Deterministically achieving on-chip photon storage and retrieval is a fundamental challenge for integrated photonics. Moreover, this requirement is increasingly urgent as photon storage and retrieval is crucial to realize truly scalable room-temperature quantum computing. However, most of existing quantum memories integrated on chips must either work at cryogenic temperature or else are strongly coupled with the environment, which hugely reduces the efficiency. Here, we propose an on-chip room-temperature quantum memory comprising three coupled microcavities, which presents an ideal dark state decoupled by a waveguide, thereby allowing on-demand photon storage and retrieval with high efficiency and high fidelity simultaneously. Furthermore, we demonstrate that the single-photon temporal duration can be increased or decreased by a factor of 103, thereby enabling many crucial quantum applications. Our error-robust approach highlights the potential for a solid-state photonic molecule for use as on-chip quantum memory and for optical quantum computing.

Abstract Climate warming disproportionately impacts countries in the Global South by increasing extreme heat exposure. However, geographic disparities in adaptation capacity are unclear. Here, we assess global inequality in green spaces, which urban residents critically rely on to mitigate outdoor heat stress. We use remote sensing data to quantify daytime cooling by urban greenery in the warm seasons across the ~500 largest cities globally. We show a striking contrast, with Global South cities having ~70% of the cooling capacity of cities in the Global North (2.5 ± 1.0 °C vs. 3.6 ± 1.7 °C). A similar gap occurs for the cooling adaptation benefits received by an average resident in these cities (2.2 ± 0.9 °C vs. 3.4 ± 1.7 °C). This cooling adaptation inequality is due to discrepancies in green space quantity and quality between cities in the Global North and South, shaped by socioeconomic and natural factors. Our analyses further suggest a vast potential for enhancing cooling adaptation while reducing global inequality.

The spread through air space (STAS) in lung cancer refers to the presence of tumor cells in the airway outside the main tumor body in the form of micropapillary cell clusters, solid cell nests, or individual cells. The researches of lung cancer in recent years discover the significant value of STAS for diagnosis, treatment, and prognosis, therefore the World Health Organization designated it as a new invasive pattern of lung adenocarcinoma in 2015. Predicting the presence of STAS by medicine imaging has been the research highlight in recent years. Though the feasibility of predicting STAS by medicine imaging has been confirmed, more in-depth researches are necessary for establishing more reliable and accurate prediction method in medicine imaging. This article reviews the research progresses of pathology, clinical significance and medicine imaging on STAS.

Background: The application of nanomaterials (NMs) in the treatment of periodontitis and peri-implantitis has shown multifunctional benefits, such as antibacterial properties, immune regulation, and promotion of osteogenesis. However, a comprehensive bibliometric analysis to evaluate global scientific production in this field has not yet been conducted. Method: We searched for publications related to nanomaterials in periodontitis and peri-implantitis using the WOSCC database. The contributions from institutions, journals, countries, and authors were assessed using VOSviewer, the bibliometrix R package, and Microsoft Excel 2019. Results: We identified 2275 publications from 66 countries/regions focusing on nanomaterials in periodontitis and peri-implantitis, published between 1993 and 2023. China and the USA were the top contributors in this field, with 653 and 221 publications, respectively. Key topics include antibacterial properties, delivery systems, nanoparticles, and regeneration. The research focus has evolved from traditional treatments to advanced applications of multifunctional nanomaterials. Conclusion: Significant progress has been made in the application of NMs in periodontitis and peri-implantitis from 1993 to 2023. Future research hotspots will likely focus on multifunctional nanomaterials and those adhering to good manufacturing practices (GMP).

Background Musculoskeletal (MSK) disorders, affecting billions of people worldwide, pose significant challenges to the healthcare system and require effective management models. The rapid development of digital healthcare technologies (DHTs) has revolutionized the healthcare industry. DHT-based interventions have shown promising clinical benefits in managing MSK disorders, alleviating pain, and improving functional impairment. There is, however, no bibliometric analysis of the overall trends on this topic. Methods We extracted all relevant publications from the Web of Science Core Collection (WoSCC) database until April 30, 2023. We performed bibliometric analysis and visualization using CiteSpace, VOSviewer, and R software. Annual trends of publications, countries/regions distributions, funding agencies, institutions, co-cited journals, author contributions, references, core journals, and keywords and research hotspots were analyzed. Results A total of 6810 papers were enrolled in this study. Publications have increased drastically from 16 in 1995 to 1198 in 2022, with 4067 articles published in the last five years. In all, 53 countries contributed with publications to this research area. The United States, the United Kingdom, and China were the most productive countries. Harvard University was the most contributing institution. Regarding keywords, research focuses include artificial intelligence, deep learning, machine learning, telemedicine, rehabilitation, and robotics. Conclusion The COVID-19 pandemic has further accelerated the adoption of DHTs, highlighting the need for remote care options. The analysis reveals the positive impact of DHTs on improving physician productivity, enhancing patient care and quality of life, reducing healthcare expenditures, and predicting outcomes. DHTs are a hot topic of research not only in the clinical field but also in the multidisciplinary intersection of rehabilitation, nursing, education, social and economic fields. The analysis identifies four promising hotspots in the integration of DHTs in MSK pain management, biomechanics assessment, MSK diagnosis and prediction, and robotics and tele-rehabilitation in arthroplasty care.

Abstract Generalizable and accurate stereo depth estimation is vital for 3D reconstruction, especially in surgery. Supervised learning methods obtain best performance however, limited ground truth data for surgical scenes limits generalizability. Self‐supervised methods don't need ground truth, but suffer from scale ambiguity and incorrect disparity prediction due to inconsistency of photometric loss. This work proposes a two‐phase training procedure that is generalizable and retains the high performance of supervised methods. It entails: (1) performing self‐supervised representation learning of left and right views via masked image modelling (MIM) to learn generalizable semantic stereo features (2) utilizing the MIM pre‐trained model to learn robust depth representation via supervised learning for disparity estimation on synthetic data only. To improve stereo representations learnt via MIM, perceptual loss terms are introduced, which improve the model's stereo representations learnt by explicitly encouraging the learning of higher scene‐level features. Qualitative and quantitative performance evaluation on surgical and natural scenes shows that the approach achieves sub‐millimetre accuracy and lowest errors respectively, setting a new state‐of‐the‐art. Despite not training on surgical nor natural scene data for disparity estimation.

Abstract The applications of self-assembled InAs/GaAs quantum dots (QDs) for lasers and single photon sources strongly rely on their density and quality. Establishing the process parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a multidimensional optimization challenge, usually addressed through time-consuming and iterative trial-and-error. Here, we report a real-time feedback control method to realize the growth of QDs with arbitrary density, which is fully automated and intelligent. We develop a machine learning (ML) model named 3D ResNet 50 trained using reflection high-energy electron diffraction (RHEED) videos as input instead of static images and providing real-time feedback on surface morphologies for process control. As a result, we demonstrate that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5 × 1010 cm−2 down to 3.8 × 108 cm−2 or up to 1.4 × 1011 cm−2. Compared to traditional methods, our approach can dramatically expedite the optimization process and improve the reproducibility of MBE. The concepts and methodologies proved feasible in this work are promising to be applied to a variety of material growth processes, which will revolutionize semiconductor manufacturing for optoelectronic and microelectronic industries.

Abstract Background Periprosthetic joint infection (PJI) is a serious complication after total joint arthroplasty (TJA). Although some risk factors of PJI were well studied, the association between trauma and PJI remains unknown in revision patients. Materials and methods Between 2015 and 2018, a total of 71 patients with trauma history before revisions (trauma cohort) were propensity score matched (PSM) at a ratio of 1 to 5 with a control cohort of revision patients without a history of trauma. Then, the cumulative incidence rate of PJI within 3 years after operation between the two groups was compared. The secondary endpoints were aseptic revisions within 3 postoperative years, complications up to 30 postoperative days, and readmission up to 90 days. During a minimal 3-year follow-up, the survival was comparatively analyzed between the trauma cohort and the control cohort. Results The cumulative incidence of PJI was 40.85% in patients with trauma history against 27.04% in the controls (P = 0.02). Correspondingly, the cumulative incidence of aseptic re-revisions was 12.68% in patients with trauma history compared with 5.07% in the control cohort (P = 0.028). Cox regression revealed that trauma history was a risk factor of PJI (HR, 1.533 [95%CI, (1.019,2.306)]; P = 0.04) and aseptic re-revisions (HR, 3.285 [95%CI, (1.790,6.028)]; P

GeSn materials with Sn contents ranging between 0% and 11% have been successfully grown by using Ge2H6 and SnCl4 in a hot-wall UHV-CVD system at low reaction pressures of 0.2–0.4 Torr. Both fully strained and partially relaxed materials have been produced. It was found that the materials were of high crystallinity as evidenced by few threading dislocations from cross-sectional TEM. Photoluminescence studies have shown that the PL spectra shift to longer wavelengths, and the PL intensity increases exponentially as the Sn contents increase. The photoemission cutoff for a 9.5% Sn sample is beyond 2600 nm, and low temperature PL measurements have confirmed that this sample is already in the direct gap material regime. Prototype photoconductor devices were fabricated with a partially strained 5% Sn sample, showing much smaller dark currents and similar spectral coverage as compared to a fully strained 7.8% Sn device reported in the literature. The reported technical route has provided a new path toward high quality, low cost GeSn epitaxy, and will contribute to the development of group IV optoelectronics.

Background Epstein-Barr virus (EBV) is a double-stranded DNA oncogenic virus. Several types of solid tumors, such as nasopharyngeal carcinoma, EBV-associated gastric carcinoma, and lymphoepithelioma-like carcinoma of the lung, have been linked to EBV infection. Currently, several TCR-T-cell therapies for EBV-associated tumors are in clinical trials, but due to the suppressive immune microenvironment of solid tumors, the clinical application of TCR-T-cell therapy for EBV-associated solid tumors is limited. Figuring out the mechanism by which EBV participates in the formation of the tumor immunosuppressive microenvironment will help T cells or TCR-T cells break through the limitation and exert stronger antitumor potential.Methods Flow cytometry was used for analyzing macrophage differentiation phenotypes induced by EBV-infected and EBV-uninfected tumors, as well as the function of T cells co-cultured with these macrophages. Xenograft model in mice was used to explore the effects of M2 macrophages, TCR-T cells, and matrix metalloprotein 9 (MMP9) inhibitors on the growth of EBV-infected tumors.Results EBV-positive tumors exhibited an exhaustion profile of T cells, despite the presence of a large T-cell infiltration. EBV-infected tumors recruited a large number of mononuclear macrophages with CCL5 and induced CD163+M2 macrophages polarization through the secretion of CSF1 and the promotion of autocrine IL10 production by mononuclear macrophages. Massive secretion of MMP9 by this group of CD163+M2 macrophages induced by EBV infection was an important factor contributing to T-cell exhaustion and TCR-T-cell therapy resistance in EBV-positive tumors, and the use of MMP9 inhibitors improved the function of T cells cocultured with M2 macrophages. Finally, the combination of an MMP9 inhibitor with TCR-T cells targeting EBV-positive tumors significantly inhibited the growth of xenografts in mice.Conclusions MMP9 inhibitors improve TCR-T cell function suppressed by EBV-induced M2 macrophages. TCR-T-cell therapy combined with MMP9 inhibitors was an effective therapeutic strategy for EBV-positive solid tumors.

Background S100a8/9 (S100 calcium binding protein a8/9) belongs to the S100 family and has gained a lot of interest as a critical regulator of inflammatory response. Our previous study found that S100a8/9 homolog promoted aortic valve sclerosis in mice with chronic kidney disease. However, the role of S100a8/9 in pressure overload‐induced cardiac hypertrophy remains unclear. The present study was to explore the role of S100a8/9 in cardiac hypertrophy. Methods and Results Cardiomyocyte‐specific S100a9 loss or gain of function was achieved using an adeno‐associated virus system, and the model of cardiac hypertrophy was established by aortic banding‐induced pressure overload. The results indicate that S100a8/9 expression was increased in response to pressure overload. S100a9 deficiency alleviated pressure overload‐induced hypertrophic response, whereas S100a9 overexpression accelerated cardiac hypertrophy. S100a9‐overexpressed mice showed increased FGF23 (fibroblast growth factor 23) expression in the hearts after exposure to pressure overload, which activated calcineurin/NFAT (nuclear factor of activated T cells) signaling in cardiac myocytes and thus promoted hypertrophic response. A specific antibody that blocks FGFR4 (FGF receptor 4) largely abolished the prohypertrophic response of S100a9 in mice. Conclusions In conclusion, S100a8/9 promoted the development of cardiac hypertrophy in mice. Targeting S100a8/9 may be a promising therapeutic approach to treat cardiac hypertrophy.

Industrial control systems are facing ever-increasing security challenges due to the large-scale access of heterogeneous devices in the open Internet environment. Existing anomaly detection methods are mainly based on the priori knowledge of industrial control protocols (ICPs) whose protocol specifications, communication mechanism, and data format are already known. However, when these knowledge are blank, namely, unknown ICPs, existing methods become powerless to detect the anomaly data. To tackle this challenge, we propose a multigranularity feature automatic marking-based deep learning method to classify unknown ICPs for anomaly detection. First, to obtain the feature sequences without priori knowledge assisting, we propose a multigranularity feature extraction algorithm to extract both byte and half-byte information by fully utilizing the intensive key information in the header field of the application layer. Then, to label the feature sequences for deep learning, we propose a feature automatic marking algorithm that utilizes the inconsistency feature sequences to dynamically update the feature sequence set. With the labeled feature sequences, we employ deep learning with 1-D convolutional neural network and gated recurrent unit to classify the unknown ICPs and realize anomaly detection. Extensive experiments on two public datasets show that both the accuracy and precision of the proposed method reach above 98.4%, which is better than the three benchmark methods.

With the wide applications of industrial wireless network technologies, the industrial control system (ICS) is evolving from wired and centralized to wireless and distributed, during which eavesdropping and attacking become serious problems. To guarantee the security of wireless and distributed ICS, this article establishes an end-edge collaborative lightweight secure federated learning (LSFL) architecture and proposes an LSFL anomaly detection strategy. Specifically, we first design a residual multihead self-attention convolutional neural network for local feature learning, where the variability and dependence of spatial-temporal features can be sufficiently evaluated. Then, to reduce the wireless communication cost for parameter exchange and edge federal learning, we propose a dynamic parameter pruning algorithm by evaluating the contribution of each parameter based on the information entropy gain. Furthermore, to ensure the parameter security during wireless transmission in the open radio environment, we propose an adaptive key generation algorithm for parameter encryption. Finally, the proposed strategy is experimentally validated on representative datasets, including Smart Meter, NSL-KDD, and UNSW-NB15. Experimental results demonstrate that the proposed strategy achieves 99% accuracy on different datasets, where at least 89.6% wireless communication cost is reduced and tampering/injecting attacks are defended.

Dietary lipids provide energy for animals and can also be converted into other nutrients (such as non-essential amino acids), which play a role in saving protein. The Chinese stripe-necked turtle is a protected and endangered species that has been bred in captivity; however, basic data on lipid requirements remain unavailable. In this study, 360 Mauremys sinensis (body weight of 65.32 ± 0.15 g) were randomly divided into six groups with three replicates per group; the turtles were fed experimental diets supplemented with various levels of fish oil (i.e., 1% (control group, CG), 3.5% (HF-1), 6% (HF-2), 8.5% (HF-3), 11% (HF-4), and 13.5% (HF-5)) for 10 weeks. The results showed that compared with CG, increasing the fish oil level promoted the growth performance of turtles, and the HF-3 group achieved the best effect. The HF-4 group showed the highest increases in the hepatosomatic index and viscerosomatic index. In addition, increased lipid levels also increased the crude lipid content and reduced the crude protein content in muscle tissue. Oil red O staining showed that the liver lipid content increased with the level of supplemented fish oil, which is consistent with the results of the hepatosomatic index. Compared with CG, triglyceride, total cholesterol, and low-density lipoprotein cholesterol increased significantly in both the liver and serum when fish oil levels exceeded 8.5% (p < 0.05), while high-density lipoprotein cholesterol decreased significantly. Aspartate transaminase and cerealthirdtransaminase levels in serum increased significantly when fish oil levels exceeded 8.5% (p < 0.05). Moreover, the activities of antioxidant enzymes (GSH-Px, SOD, T-AOC, and CAT) and MDA showed similar results, indicating that high fish oil levels (8.5–13.5%) caused liver tissue damage in M. sinensis. Increased fish oil levels significantly upregulated the expression levels of cytokines (IFN-γ, TNF-α, TGF-β1, IL-10, and IL-12) (p < 0.05), downregulated the expression levels of antioxidant enzyme-related genes (cat, mn-sod, and gsh-px), and increased apoptosis of liver cells. Supplementation of the diet with 3.5–6% fish oil improved the growth performance of M. sinensis, and the turtles maintained a beneficial immune status. The results provide a scientific basis for optimizing the commercial feed formula of M. sinensis.

Background: The immune response associated with periprosthetic joint infection (PJI) is an emerging but relatively unexplored topic. The aim of this study was to investigate immune cell infiltration in periprosthetic tissues and identify potential immune-related biomarkers. Methods: The GSE7103 dataset from the GEO database was selected as the data source. Differentially expressed genes (DEGs) and significant modular genes in weighted correlation network analysis (WGCNA) were identified. Functional enrichment analysis and transcription factor prediction were performed on the overlapping genes. Next, immune-related genes from the ImmPort database were matched. The protein-protein interaction (PPI) analysis was performed to identify hub genes. CIBERSORTx was used to evaluate the immune cell infiltration pattern. Spearman correlation analysis was used to evaluate the relationship between hub genes and immune cells. Results: A total of 667 DEGs were identified between PJI and control samples, and 1847 PJI-related module genes were obtained in WGCNA. Enrichment analysis revealed that the common genes were mainly enriched in immune and host defense-related terms. TFEC, SPI1, and TWIST2 were the top three transcription factors. Three hub genes, SDC1, MMP9, and IGF1, were identified in the immune-related PPI network. Higher levels of plasma cells, CD4+ memory resting T cells, follicular helper T cells, resting mast cells, and neutrophils were found in the PJI group, while levels of M0 macrophages were lower. Notably, the expression of all three hub genes correlated with the infiltration levels of seven types of immune cells. Conclusion: The present study revealed immune infiltration signatures in the periprosthetic tissues of PJI patients. SDC1, MMP9, and IGF1 were potential immune-related biomarkers for PJI.

Abstract Objectives The proper procedure for inferior pulmonary ligament (IPL) during upper lobectomy remains a topic of debate. To address this matter, we carried out a trial comparing the clinical outcomes of IPL preservation versus IPL dissection during thoracoscopic upper lobectomy (TUL). Methods Patients undergoing thoracoscopic left/right upper lobectomy (TLUL/TRUL) were assigned to either the dissection group (Group D) or the preservation group (Group P). Our primary objective was to quantify and compare the alterations in postoperative residual bronchial angle and lung volume changes between the two groups. Our secondary objective encompassed the assessment of various other intraoperative and postoperative outcomes. Results Following adherence to the inclusion and exclusion criteria, we enrolled 100 patients (41 left and 59 right) in Group P and 108 patients (41 left and 67 right) in Group D for the study. Our findings revealed that in TLUL, Group P was able to reduce the degree of postoperative residual bronchial angle change (P 0.05) with regard to alterations in lung volume or the occurrence of postoperative complications—except for the duration of postoperative hospital stay (P

Abstract Requirements management (RM) plays an important role in requirements engineering. The development of machine learning (ML) is in full swing, and many ML software management techniques had been used to improve the performance of RM methods. However, as no research study is known that exists systematically to summarise the ML methods used in RM. To fill this gap, this paper adopts the systematic mapping study to survey the state‐of‐the‐art ML methods for RM primary studies and were finally selected in this mapping, which was published on 36 conferences and journals. The 24 factors affecting the ML method of RM are determined, of which 9, 11 and 4 are the three parts of RM, namely requirements baseline maintenance, requirements traceability and requirements change management separately. The 18 objectives of the ML method for RM are summarised, of which 6, 7 and 5 are the three parts of RM. The eight ML methods used in RM and their time sequence are summarised. The 18 evaluation indexes for RM in the ML method are determined, and the performance of these methods on these parameters is analysed. The research direction of this paper is of great significance to the research of researchers in demand management.

The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact of enhanced vegetation greening and global climate change on the regional hydrological cycle remains inadequately understood. This study employed the distributed hydrological model ESSI-3, reanalysis datasets, and multi-source satellite remote sensing data to quantitatively evaluate the influences of climate change and vegetation dynamics on regional hydrological processes. The study period spans from 2000 to 2020, during which there were significant increases in regional precipitation and leaf area index (p < 0.05). The hydrological simulation results exhibited strong agreement with observed river discharge, evapotranspiration, and terrestrial water storage anomalies, thereby affirming the ESSI-3 model’s reliability in hydrological change assessment. By employing both a constant scenario that solely considered climate change and a dynamic scenario that integrated vegetation dynamics, the findings reveal that: (1) Regionally, climate change driven by increased precipitation significantly augmented runoff fluxes (0.4 mm/year) and water storage components (2.57 mm/year), while evapotranspiration trends downward, attributed primarily to reductions in solar radiation and wind speed; (2) Vegetation greening reversed the decreasing trend in evapotranspiration to an increasing trend, thus exerting a negative impact on runoff and water storage. However, long-term simulations demonstrated that regional runoff fluxes (0.38 mm/year) and water storage components (2.21 mm/year) continue to increase, mainly due to precipitation increments surpassing those of evapotranspiration; (3) Spatially, vegetation greening altered the surface soil moisture content trend in the eastern forested areas from an increase to a decrease. These findings suggested that sub-regional ecological restoration initiatives, such as afforestation, significantly influence the hydrological cycle, especially in areas with higher vegetation greening. Nevertheless, persistent increases in precipitation could effectively mitigate the moisture deficits induced by vegetation greening. The study’s outcomes provide a basis for alleviating concerns regarding potential water consumption risks associated with future ecological restoration and extensive vegetation greening projects, thereby offering scientific guidance for sustainable water resource management.

Abstract Background B7-H3 is an important immune checkpoint molecule that plays a negative role in immune regulation. This study was aimed to explore B7-H3 expression in HIV-infected patients and its clinical significance. Methods To explore the expression and clinical significance of B7-H3 in HIV-infected patients, we investigated the B7-H3 expression pattern and the correlation of B7-H3 expression with clinical parameters of HIV-infected patients with different levels of CD4+ T cells. To assess the role of B7-H3 in regulating the function of T cells in HIV infection, we performed a proliferation assay and T cell function test in vitro. Results B7-H3 expression in HIV-infected patients was significantly higher than that in healthy controls. mB7-H3 expression on CD4+CD25high T cells and CD14+ monocytes increased with disease progression. mB7-H3 expression on CD4+CD25high T cells and monocytes was negatively correlated with lymphocyte count, CD4+T cell count, and positively correlated with HIV viral load in HIV-infected patients. when the number of CD4+ T cells in HIV-infected patients was ≥ 200/µL, sB7-H3 and mB7-H3 expression levels on CD4+CD25high T cells and monocytes were negatively correlated with lymphocyte count, CD4+T cell count. sB7-H3 and mB7-H3 expression on monocytes were positively correlated with HIV viral load. B7-H3 inhibited the proliferation of lymphocytes and the secretion of IFN-γ in vitro, especially the ability of CD8+ T cells to secrete IFN-γ. Conclusions B7-H3 played an important negative regulatory role in anti-HIV infection immunity. It could be used as a potential biomarker for the progression of HIV infection and a novel target for the treatment of HIV infection.

Abstract Aims Periprosthetic joint infection (PJI) is one of the most serious complications after total joint arthroplasty (TJA) but the characterization of the periprosthetic environment microbiome after TJA remains unknown. Here, we performed a prospective study based on metagenomic next-generation sequencing to explore the periprosthetic microbiota in patients with suspected PJI. Methods We recruited 28 patients with culture-positive PJI, 14 patients with culture-negative PJI, and 35 patients without PJI, which was followed by joint aspiration, untargeted metagenomic next-generation sequencing (mNGS), and bioinformatics analysis. Our results showed that the periprosthetic environment microbiome was significantly different between the PJI group and the non-PJI group. Then, we built a “typing system” for the periprosthetic microbiota based on the RandomForest Model. After that, the ‘typing system’ was verified externally. Results We found the periprosthetic microbiota can be classified into four types generally: “Staphylococcus type,” “Pseudomonas type,” “Escherichia type,” and “Cutibacterium type.” Importantly, these four types of microbiotas had different clinical signatures, and the patients with the former two microbiota types showed obvious inflammatory responses compared to the latter ones. Based on the 2014 Musculoskeletal Infection Society (MSIS) criteria, clinical PJI was more likely to be confirmed when the former two types were encountered. In addition, the Staphylococcus spp. with compositional changes were correlated with C-reactive protein levels, the erythrocyte sedimentation rate, and the synovial fluid white blood cell count and granulocyte percentage. Conclusions Our study shed light on the characterization of the periprosthetic environment microbiome in patients after TJA. Based on the RandomForest model, we established a basic “typing system” for the microbiota in the periprosthetic environment. This work can provide a reference for future studies about the characterization of periprosthetic microbiota in periprosthetic joint infection patients.