Host proteins interact with viral elements and affect the life cycle of highly pathogenic avian influenza A virus H7N9.

Host-virus interactions can significantly impact the viral life cycle and pathogenesis; however, our understanding of the specific host factors involved in highly pathogenic avian influenza A virus H7N9 (HPAI H7N9) infection is currently restricted. Herein, we designed and synthesized 65 small interfering RNAs targeting host genes potentially associated with various aspects of RNA virus life cycles. Afterward, HPAI H7N9 viruses were isolated and RNA interference was used to screen for host factors likely to be involved in the life cycle of HPAI H7N9. Moreover, the research entailed assessing the associations between host proteins and HPAI H7N9 proteins. Twelve key host proteins were identified: Annexin A (ANXA)2, ANXA5, adaptor related protein complex 2 subunit sigma 1 (AP2S1), adaptor related protein complex 3 subunit sigma 1 (AP3S1), ATP synthase F1 subunit alpha (ATP5A1), COPI coat complex subunit alpha (COP)A, COPG1, heat shock protein family A (Hsp70) member 1A (HSPA)1A, HSPA8, heat shock protein 90 alpha family class A member 1 (HSP90AA1), RAB11B, and RAB18. Co-immunoprecipitation revealed intricate interactions between viral proteins (hemagglutinin, matrix 1 protein, neuraminidase, nucleoprotein, polymerase basic 1, and polymerase basic 2) and these host proteins, presumably playing a crucial role in modulating the life cycle of HPAI H7N9. Notably, ANXA5, AP2S1, AP3S1, ATP5A1, HSP90A1, and RAB18, were identified as novel interactors with HPAI H7N9 proteins rather than other influenza A viruses (IAVs). These findings underscore the significance of host-viral protein interactions in shaping the dynamics of HPAI H7N9 infection, while highlighting subtle variations compared with other IAVs. Deeper understanding of these interactions holds promise to advance disease treatment and prevention strategies.

The 90-Day Survival Threshold: A Pivotal Determinant of Long-Term Prognosis in HBV-ACLF Patients - Insights from a Prospective Longitudinal Cohort Study.

This work aims to explore the long-term prognosis of hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). In this prospective study, eligible inpatients with HBV-ACLF are enrolled and followed up from December 2012 to February 2023, for clinical events, laboratory tests at least every 6 months. Overall, the survival rates at 28 days, 90 days, 1 year, 5 years, and 8 years are 64.7%, 48.8%, 46.1%, 43.8%, and 42.2%, respectively. Among the 8-year mortality and liver transplant cases, ACLF survivors (who survived over 90 days) accounted for 7.8% (9/115). Among 101 patients who survived for more than 90 days, 97.9% of patients achieve virologic response at 1 year. For HBeAg-positive patients, the HBeAg seroconversion are 25.5%, 63.6%, and 76.9% at 1, 5, and 8 years, respectively. Alanine aminotransferase, aspartate aminotransferase, total bilirubin, INR, white blood cell count, and albumin levels gradually improve within the first year. Fibrosis biomarkers APRI, FIB-4 and Chitinase-3-like protein 1 (CHI3L1) levels decreases within the first 5 years. The Cox proportional hazards regression reveal that high total bilirubin (HR = 1.008, p = 0.021) is the independent risk factor for 8-year survival of ALCF survivors. The 90-day period following of HBV-ACLF represented a critical juncture for long-term prognosis, revealing favorable outcomes beyond this timeframe.

Mapping trends and hotspots in research on global influenza vaccine hesitancy: A bibliometric analysis.

Background and Aims: Influenza is one of the most widespread respiratory infections and poses a huge burden on health care worldwide. Vaccination is key to preventing and controlling influenza. Influenza vaccine hesitancy is an important reason for the low vaccination rate. In 2019, Vaccine hesitancy was identified as one of the top 10 threats to global health by the World Health Organization. However, there remains a glaring scarcity of bibliometric research in that regard. This study sought to identify research hotspots and future development trends on influenza vaccine hesitation and provide a new perspective and reference for future research. Methods: We retrieved publications on global influenza vaccine hesitancy from the Web of Science Core Collection database, Scopus, and PubMed databases from inception to 2022. This study used VOSviewer and CiteSpace for visualization analysis. Results: Influenza vaccine hesitancy-related publications increased rapidly from 2012 and peaked in 2022. One hundred and nine countries contributed to influenza vaccine hesitation research, and the United States ranked first with 541 articles and 7161 citations. Vaccines-Basel was the journal with the largest number of published studies on influenza vaccine hesitations. MacDonald was the most frequently cited author. The most popular research topics on influenza vaccine hesitancy were (1) determinants of influenza vaccination in specific populations, such as healthcare workers, children, pregnant women, and so on; (2) influenza and COVID-19 vaccine hesitancy during the COVID-19 pandemic. Conclusions: The trend in the number of annual publications related to influenza vaccine hesitancy indicating the COVID-19 pandemic will prompt researchers to increase their attention to influenza vaccine hesitancy. With healthcare workers as the key, reducing vaccine hesitancy and improving vaccine acceptance in high-risk groups will be the research direction in the next few years.

Neuregulin 1 as a potential biomarker for disease progression in moyamoya disease: A case-control study in Chinese population.

OBJECTIVE: Moyamoya disease (MMD) is a rare and progressive stenosis of cerebral arteries characterized by abnormally proliferative vasculopathy. Current studies have demonstrated that Neuregulin 1 (NRG1) plays a key role in angiogenesis-related disorders. Thus, the aim of our study is to investigate the serum NRG1 levels and their clinical correlations in MMD patients. METHODS: In this study, thirty adult patients with MMD and age-gender matched healthy controls were enrolled from our hospital between July 2020 and April 2022. Peripheral blood samples were collected at baseline, and clinical data were obtained from the electronic medical record system. Serum NRG1 concentrations were measured by enzyme-linked immunosorbent assay. Sanger sequencing was applied to detect the RNF213 p.R4810K mutation. RESULTS: The serum NRG1 levels were significantly higher in MMD patients compared to controls (14.48 ± 10.81 vs.7.54 ± 6.35mmol/L, p < 0.001). No statistical difference in baseline clinical characteristics was found between both groups. Correlation analyses showed that NRG1 levels were positively associated with Suzuki staging (r = 0.4137, p = 0.023) while not related to other clinical features (reduced cerebral blood flow, posterior cerebral artery involvement, bilateral or unilateral steno-occlusive changes). Furthermore, subgroup analysis revealed that MMD patients with the RNF213 p.R4810K mutation presented with significantly higher NRG1 levels than those without the mutation (9.60 ± 0.929 vs. 25.89 ± 4.338 mmol/L, p = 0.001). CONCLUSIONS: Our study suggests that increased serum NRG1 levels may constitute a characteristic feature of MMD, indicating a potential positive correlation with disease progression and the presence of the RNF213 mutation. This positions NRG1 as a potentially crucial target for further studies aimed at comprehending the pathogenesis of MMD.

OHCCPredictor: an online risk stratification model for predicting survival duration of older patients with hepatocellular carcinoma.

BACKGROUND: Although the elderly constitute more than a third of hepatocellular carcinoma (HCC) patients, they have not been adequately represented in treatment and prognosis studies. Thus, there is not enough evidence to guide the treatment of such patients. The objective of this study is to identify the prognostic factors of older patients with HCC and to construct a new prognostic model for predicting their overall survival (OS). METHODS: 2,721 HCC patients aged ≥ 65 were extracted from the public database-Surveillance, Epidemiology, and End Results (SEER) and randomly divided into a training set and an internal validation set with a ratio of 7:3. 101 patients diagnosed from 2008 to 2017 in the First Affiliated Hospital of Zhejiang University School of Medicine were identified as the external validation set. Univariate cox regression analyses and multivariate cox regression analyses were adopted to identify these independent prognostic factors. A predictive nomogram-based risk stratification model was proposed and evaluated using area under the receiver operating characteristic curve (AUC), calibration curves, and a decision curve analysis (DCA). RESULTS: These attributes including age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, alpha-fetoprotein level, fibrosis score, bone metastasis, lung metastasis, and grade were the independent prognostic factors for older patients with HCC while predicting survival duration. We found that the nomogram provided a good assessment of OS at 1, 3, and 5 years in older patients with HCC (1-year OS: (training set: AUC = 0.823 (95%CI 0.803-0.845); internal validation set: AUC = 0.847 (95%CI 0.818-0.876); external validation set: AUC = 0.732 (95%CI 0.521-0.943)); 3-year OS: (training set: AUC = 0.813 (95%CI 0.790-0.837); internal validation set: AUC = 0.844 (95%CI 0.812-0.876); external validation set: AUC = 0.780 (95%CI 0.674-0.887)); 5-year OS: (training set: AUC = 0.839 (95%CI 0.806-0.872); internal validation set: AUC = 0.800 (95%CI 0.751-0.849); external validation set: AUC = 0.821 (95%CI 0.727-0.914)). The calibration curves showed that the nomogram was with strong calibration. The DCA indicated that the nomogram can be used as an effective tool in clinical practice. The risk stratification of all subgroups was statistically significant (p < 0.05). In the stratification analysis of surgery, larger resection (LR) achieved a better survival curve than local destruction (LD), but a worse one than segmental resection (SR) and liver transplantation (LT) (p < 0.0001). With the consideration of the friendship to clinicians, we further developed an online interface (OHCCPredictor) for such a predictive function ( https://juntaotan.shinyapps.io/dynnomapp_hcc/ ). With such an easily obtained online tool, clinicians will be provided helpful assistance in formulating personalized therapy to assess the prognosis of older patients with HCC. CONCLUSIONS: Age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, AFP level, fibrosis score, bone metastasis, lung metastasis, and grade were independent prognostic factors for elderly patients with HCC. The constructed nomogram model based on the above factors could accurately predict the prognosis of such patients. Besides, the developed online web interface of the predictive model provide easily obtained access for clinicians.

Maternal and neonatal outcomes of twin pregnancies complicated by gestational diabetes mellitus.

INTRODUCTION: Gestational diabetes mellitus (GDM) is associated with a higher risk of adverse maternal outcomes, but its effects on maternal and perinatal outcomes of twin pregnancies remain conflicting. METHODS: This retrospective cohort study included all primipara who delivered twin pregnancies at a single tertiary perinatal center between January 1, 2016 and December 31, 2022. Excluded were those who had a single pregnancy, twin pregnancies with pre-existing diabetes, missing information on GDM screening, a delivery before gestational 28 weeks, complications related to monochorionic placentation, multifetal reduction, fetal anomalies, and monochorionic monoamniotic twins. Maternal outcomes included preterm birth, pre-eclampsia, hypothyroidism, preterm premature rupture of membranes (PROM), placental abruption, severe postpartum hemorrhage, and oligohydramnios. Neonatal outcomes included small-for-gestational-age (SGA), large-for-gestational-age (LGA), birthweight, Apgar score, neonatal intensive care unit (NICU) admission, extrauterine growth restriction (EUGR), and neonatal hypoglycemia. RESULTS: A total of 3269 twins were delivered, with 897 women (27.4%) diagnosed with GDM during pregnancies; moreover, 72 (8.0%) of these women received insulin treatment. The GDM group showed a significantly higher maternal age at delivery (≥35 years), as well as incidences of overweight and obesity. These factors also elevated the odds of insulin treatment in GDM women with twin pregnancies (OR = 1.881, 95% CI = 1.073-3.295, P = 0.027; OR = 2.450, 95% CI = 1.422-4.223, P < 0.001; OR = 4.056, 95% CI = 1.728-9.522, P < 0.001, respectively). Chronic hypertension prior to pregnancy was identified as a risk factor for GDM during twin pregnancies (OR = 1.896, 95% CI = 1.290-2.785, P < 0.001), although it did not increase the proportion of women requiring insulin treatment (P = 0.808). Aside from a higher incidence of preterm birth before 37 weeks in insulin-treated GDM twins (OR = 2.096, 95% CI = 1.017-4.321, P = 0.045), there were no significant difference in other maternal outcomes (preterm birth before 34 weeks, pre-eclampsia, hypothyroidism, PROM, placental abruption, placenta previa, severe postpartum hemorrhage, and oligohydramnios) between the GDM group and non-GDM group, and between insulin-treated GDM and non-insulin-treated GDM. The rate of newborns with birthweight <1500 g was significantly lower among twins born to GDM women, but the prevalence of EUGR was notably higher. Additionally, the risk of EUGR was elevated in insulin-treated GDM twins (OR = 3.170, 95% CI = 1.639,6.131, P < 0.001). No significant differences were observed between the GDM group and non-GDM group, or between insulin-treated GDM and non-insulin-treated GDM group in terms of mean birthweight, newborn sex ratio, and incidences of other adverse neonatal outcomes, including gestational age at delivery, LGA, birth weight <2500 g, and 1-min and 5-min Apgar scores. CONCLUSION: Maternal age ≥35 years, overweight or obesity, and chronic hypertension are significant risk factors for GDM during twin pregnancies. Women with GDM during twin pregnancies may achieve similar outcomes compared to those without GDM. However, the women with GDM during twin pregnancies receiving insulin therapy may have a higher risk of preterm birth and EUGR.

Both Acetabular and Femoral Reconstructions With Impaction Bone Grafting in Revision Total Hip Arthroplasty: Case Series and Literature Review.

Background: Extensive bone loss on femur and acetabulum posed a big challenge to orthopedists in total hip revision surgeries. Impaction bone grafting (IBG) as a valuable bone preservation technique could effectively address this problem. Either IBG revision on the femoral or acetabular side was well studied, while its use on both sides in one operation was not. The aim of this study is to present the outcomes of IBG on both femoral and acetabular sides at first-time hip revision. Methods: We retrospectively reviewed 8 patients (mean follow-up of 5.8 years) undergoing first-time revision with IBG on both acetabular and femoral sides at our institution. The Paprosky classification system was used to classify bone defects. Freeze-dried allografts and cemented prostheses were used in all patients. Postoperative complications and rerevision rates were reported. Results: Five patients presented a Paprosky type IIC acetabular defect, 3 with a type IIIB, IIIA, and IIC defect, respectively. Three patients presented with a type IV femoral defect, 3 with a type IIIB defect, and 2 with a type II defect. Two patients developed complications, while one had an intraoperative femoral fracture and one had delayed wound healing. At the latest follow-up, no patient had rerevisions or operations related to the prosthesis. Conclusions: IBG in combination with cemented prosthesis is a profitable biological reconstruction revision technique that could provide satisfying midterm outcomes. We first propose the use of blood clots mixed with bone grafts for potential bone incorporation enhancement, while its specific effects need to be verified in further studies.

Development and validation of a risk prediction model for osteoporosis in elderly patients with type 2 diabetes mellitus: a retrospective and multicenter study.

BACKGROUND: This study aimed to construct a risk prediction model to estimate the odds of osteoporosis (OP) in elderly patients with type 2 diabetes mellitus (T2DM) and evaluate its prediction efficiency. METHODS: This study included 21,070 elderly patients with T2DM who were hospitalized at six tertiary hospitals in Southwest China between 2012 and 2022. Univariate logistic regression analysis was used to screen for potential influencing factors of OP and least absolute shrinkage. Further, selection operator regression (LASSO) and multivariate logistic regression analyses were performed to select variables for developing a novel predictive model. The area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the performance and clinical utility of the model. RESULTS: The incidence of OP in elderly patients with T2DM was 7.01% (1,476/21,070). Age, sex, hypertension, coronary heart disease, cerebral infarction, hyperlipidemia, and surgical history were the influencing factors. The seven-variable model displayed an AUROC of 0.713 (95% confidence interval [CI]:0.697-0.730) in the training set, 0.716 (95% CI: 0.691-0.740) in the internal validation set, and 0.694 (95% CI: 0.653-0.735) in the external validation set. The optimal decision probability cut-off value was 0.075. The calibration curve (bootstrap = 1,000) showed good calibration. In addition, the DCA and CIC demonstrated good clinical practicality. An operating interface on a webpage ( https://juntaotan.shinyapps.io/osteoporosis/ ) was developed to provide convenient access for users. CONCLUSIONS: This study constructed a highly accurate model to predict OP in elderly patients with T2DM. This model incorporates demographic characteristics and clinical risk factors and may be easily used to facilitate individualized prediction.

Bibliometric analysis of publication trends and topics of influenza-related encephalopathy from 2000 to 2022.

BACKGROUND: Influenza-related encephalopathy is a rapidly progressive encephalopathy that usually presents during the early phase of influenza infection and primarily manifests as central nervous system dysfunction. This study aimed to analyze the current research status and hotspots of influenza-related encephalopathy since 2000 through bibliometrics analysis. METHODS: The Web of Science Core Collection (WOSCC) was used to extract global papers on influenza-related encephalopathy from 2000 to 2022. Meanwhile, the VOSviewer and CiteSpace software were used for data processing and result visualization. RESULTS: A total of 561 published articles were included in the study. Japan was the country that published the most articles, with 205 articles, followed by the United States and China. Okayama University and Tokyo Medical University published the most articles, followed by Nagoya University, Tokyo University, and Juntendo University. Based on the analysis of keywords, four clusters with different research directions were identified: "Prevalence of H1N1 virus and the occurrence of neurological complications in different age groups," "mechanism of brain and central nervous system response after influenza virus infection," "various acute encephalopathy" and "diagnostic indicators of influenza-related encephalopathy." CONCLUSIONS: The research progress, hotspots, and frontiers on influenza-related encephalopathy after 2000 were described through the visualization of bibliometrics. The findings will lay the groundwork for future studies and provide a reference for influenza-related encephalopathy. Research on influenza-related encephalopathy is basically at a stable stage, and the number of research results is related to outbreaks of the influenza virus.

Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012-2021) Multicenter Retrospective Case-control study.

OBJECTIVES: To appraise effective predictors for infection in patients with decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective case-control study. METHODS: Clinical data were retrospectively collected from 6,648 patients with DC admitted to five tertiary hospitals. Indicators with significant differences were determined by univariate analysis and least absolute contraction and selection operator (LASSO) regression. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed infection risk prediction model with simple-tree XGBoost model. Finally, the simple-tree XGBoost model is compared with the traditional logical regression (LR) model. Performances of models were evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. RESULTS: Six features, including total bilirubin, blood sodium, albumin, prothrombin activity, white blood cell count, and neutrophils to lymphocytes ratio were selected as predictors for infection in patients with DC. Simple-tree XGBoost model conducted by these features can predict infection risk accurately with an AUROC of 0.971, sensitivity of 0.915, and specificity of 0.900 in training set. The performance of simple-tree XGBoost model is better than that of traditional LR model in training set, internal verification set, and external feature set (P < 0.001). CONCLUSIONS: The simple-tree XGBoost predictive model developed based on a minimal amount of clinical data available to DC patients with restricted medical resources could help primary healthcare practitioners promptly identify potential infection.

Mesenchymal stem cells in osteoarthritis: The need for translation into clinical therapy.

Widely used for cell-based therapy in various medical fields, mesenchymal stem cells (MSCs) show capacity for anti-inflammatory effects, anti-apoptotic activity, immunomodulation, and tissue repair and regeneration. As such, they can potentially be used to treat osteoarthritis (OA). However, MSCs from different sources have distinct advantages and disadvantages, and various animal models and clinical trials using different sources of MSCs are being conducted in OA regenerative medicine. It is now widely believed that the primary tissue regeneration impact of MSCs is via paracrine effects, rather than direct differentiation and replacement. Cytokines and molecules produced by MSCs, including extracellular vesicles with mRNAs, microRNAs, and bioactive substances, play a significant role in OA repair. This chapter outlines the properties of MSCs and recent animal models and clinical trials involving MSCs-based OA therapy, as well as how the paracrine effect of MSCs acts in OA cartilage repair. Additionally, it discusses challenges and controversies in MSCs-based OA therapy. Despite its limits and unanticipated hazards, MSCs have the potential to be translated into therapeutic therapy for future OA treatment.

RNF213 loss-of-function promotes pathological angiogenesis in moyamoya disease via the Hippo pathway.

Moyamoya disease is an uncommon cerebrovascular disorder characterized by steno-occlusive changes in the circle of Willis and abnormal vascular network development. Ring finger protein 213 (RNF213) has been identified as an important susceptibility gene for Asian patients, but researchers have not completely elucidated whether RNF213 mutations affect the pathogenesis of moyamoya disease. Using donor superficial temporal artery samples, whole-genome sequencing was performed to identify RNF213 mutation types in patients with moyamoya disease, and histopathology was performed to compare morphological differences between patients with moyamoya disease and intracranial aneurysm. The vascular phenotype of RNF213-deficient mice and zebrafish was explored in vivo, and RNF213 knockdown in human brain microvascular endothelial cells was employed to analyse cell proliferation, migration and tube formation abilities in vitro. After bioinformatics analysis of both cell and bulk RNA-seq data, potential signalling pathways were measured in RNF213-knockdown or RNF213-knockout endothelial cells. We found that patients with moyamoya disease carried pathogenic mutations of RNF213 that were positively associated with moyamoya disease histopathology. RNF213 deletion exacerbated pathological angiogenesis in the cortex and retina. Reduced RNF213 expression led to increased endothelial cell proliferation, migration and tube formation. Endothelial knockdown of RNF213 activated the Hippo pathway effector Yes-associated protein (YAP)/tafazzin (TAZ) and promoted the overexpression of the downstream effector VEGFR2. Additionally, inhibition of YAP/TAZ resulted in altered cellular VEGFR2 distribution due to defects in trafficking from the Golgi apparatus to the plasma membrane and reversed RNF213 knockdown-induced angiogenesis. All these key molecules were validated in ECs isolated from RNF213-deficient animals. Our findings may suggest that loss-of-function of RNF213 mediates the pathogenesis of moyamoya disease via the Hippo pathway.

Rnf-213 Knockout Induces Pericyte Reduction and Blood-Brain Barrier Impairment in Mouse.

Moyamoya disease (MMD) is a rare cerebrovascular disorder characterized by progressive occlusion of the internal carotid artery and the formation of an abnormal compensatory capillary network at the base of the brain. Genomics studies identified Ring finger protein 213 (RNF213) as a common genetic factor that increases the susceptibility to MMD in East Asian people. However, the function of RNF213 and its roles in pathogenesis of MMD is unclear. Here, we showed that genetic knockout of Rnf213 in mice causes significant pericyte reduction and blood-brain barrier impairment in the cortex. These phenotypes are accompanied with microglia activation and elevated level of proinflammatory cytokines. Additionally, Rnf213-deficient mice showed reduced expression of tight junction proteins, including Occludin, Claudin-5, and ZO-1. Together, these data suggested that RNF213 might contribute to the pathogenesis of MMD through disruption of pericyte homeostasis and blood-brain barrier integrity by dysregulation of inflammatory responses and tight junction formation.

How are Aging and Osteoarthritis Related?

Osteoarthritis is the most prevalent degenerative joint disease and one of the leading causes of physical impairment in the world's aging population. The human lifespan has significantly increased as a result of scientific and technological advancements. According to estimates, the world's elderly population will increase by 20% by 2050. Aging and age-related changes are discussed in this review in relation to the development of OA. We specifically discussed the cellular and molecular changes that occur in the chondrocytes during aging and how these changes may make synovial joints more susceptible to OA development. These changes include chondrocyte senescence, mitochondrial dysfunction, epigenetic modifications, and decreased growth factor response. The age-associated changes occur not only in the chondrocytes but also in the matrix, subchondral bone, and synovium. This review aims to provide an overview of the interplay between chondrocytes and matrix and how age-related changes affect the normal function of cartilage and contribute to OA development. Understanding the alterations that affect the function of chondrocytes will emerge new possibilities for prospective therapeutic options for the treatment of OA.

Influence of Pore Size and Surface Functionalization of Mesoporous Silica Nanoparticles on the Solubility and Antioxidant Activity of Confined Coenzyme Q10.

Coenzyme Q10 is a potent antioxidant that plays an important role in the maintenance of various biochemical pathways of the body and has a wide range of therapeutic applications. However, it has low aqueous solubility and oral bioavailability. Mesoporous silica nanoparticles (MCM-41 and SBA-15 types) exhibiting varying pore sizes and modified with phosphonate and amino groups were used to study the influence of pore structure and surface chemistry on the solubility, in vitro release profile, and intracellular ROS inhibition activity of coenzyme Q10. The particles were thoroughly characterized to confirm the morphology, size, pore profile, functionalization, and drug loading. Surface modification with phosphonate functional groups was found to have the strongest impact on the solubility enhancement of coenzyme Q10 when compared to that of pristine and amino-modified particles. Phosphonate-modified MCM-41 nanoparticles (i.e., MCM-41-PO3) induced significantly higher coenzyme Q10 solubility than the other particles studied. Furthermore, MCM-41-PO3 led to a twofold decrease in ROS generation in human chondrocyte cells (C28/I2), compared to the free drug in a DMSO/DMEM mixture. The results confirmed the significant contribution of small pore size and negative surface charge of MSNs that enable coenzyme Q10 confinement to allow enhanced drug solubility and antioxidant activity.

CMM: A CNN-MLP Model for COVID-19 Lesion Segmentation and Severity Grading.

In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervised UNet (MDS-UNet), finally implementing the severity grading by a multi-layer preceptor (MLP). In MDS-UNet, shape prior information is fused with the input CT image to reduce the searching space of the potential segmentation outputs. The multi-scale input compensates for the loss of edge contour information in convolution operations. In order to enhance the learning of multiscale features, the multi-scale deep supervision extracts supervision signals from different upsampling points on the network. In addition, it is empirical that the lesion which has a whiter and denser appearance tends to be more severe in the COVID-19 CT image. So, the weighted mean gray-scale value (WMG) is proposed to depict this appearance, and together with the lung and lesion area to serve as input features for the severity grading in MLP. To improve the precision of lesion segmentation, a label refinement method based on the Frangi vessel filter is also proposed. Comparative experiments on COVID-19 public datasets show that our proposed CMM achieves high accuracy on COVID-19 lesion segmentation and severity grading. Source codes and datasets are available at our GitHub repository (https://github.com/RobotvisionLab/COVID-19-severity-grading.git).

Inhibition of Leukotriene A4 Hydrolase Suppressed Cartilage Degradation and Synovial Inflammation in a Mouse Model of Experimental Osteoarthritis.

OBJECTIVE: Chronic inflammation plays an important role in the osteoarthritis (OA) pathology but how this influence OA disease progression is unclear. Leukotriene B4 (LTB4) is a potent proinflammatory lipid mediator generated from arachidonic acid through the sequential activities of 5-lipoxygenase, 5-lipoxygenase-activating protein, Leukotriene A4 hydrolase (LTA4H) and its downstream product LTB4. The aim of this study is to investigate the involvement and the potential therapeutic target of the LTB4 pathway in OA disease progression. DESIGN: Both clinical human cartilage samples (n = 7) and mice experimental OA models (n = 6) were used. The levels of LTA4H and leukotriene B4 receptor 1 were first examined using immunostaining in human OA/non-OA cartilage and mice experimental OA models. We also determined whether the LTA4H pathway was associated with cartilage degeneration and synovitis inflammation in OA mice models and human articular chondrocytes. RESULTS: We found that both LTA4H and LTB4 receptor (BLT1) were highly expressed in human and mice OA cartilage. Inhibition of LTA4H suppressed cartilage degeneration and synovitis in OA mice model. Furthermore, inhibition of LTA4H promoted cartilage regeneration by upregulating chondrogenic genes expression such as aggrecan (ACAN), collagen 2A1 (COL2A1), and SRY-Box transcription factor 9 (SOX9). CONCLUSIONS: Our results indicate that the LTA4H pathway is a crucial regulator of OA pathogenesis and suggest that LTA4H could be a therapeutic target in combat OA.

Severe fever with thrombocytopenia syndrome virus trends and hotspots in clinical research: A bibliometric analysis of global research.

Background: Since severe fever with thrombocytopenia syndrome virus (SFTSV) was first reported in 2009, a large number of relevant studies have been published. However, no bibliometrics analysis has been conducted on the literature focusing on SFTSV. This study aims to evaluate the research hotspots and future development trends of SFTSV research through bibliometric analysis, and to provide a new perspective and reference for future SFTSV research and the prevention of SFTSV. Methods: We retrieved global publications on SFTSV from the Web of Science Core Collection (WoSCC) and Scopus databases from inception of the database until 2022 using VOSviewer software and CiteSpace was used for bibliometric analysis. Results: The number of SFTSV-related publications has increased rapidly since 2011, peaking in 2021. A total of 45 countries/regions have published relevant publications, with China topping the list with 359. The Viruses-Basel has published the most papers on SFTSV. In addition, Yu et al. have made the greatest contribution to SFTSV research, with their published paper being the most frequently cited. The most popular SFTSV study topics included: (1) pathogenesis and symptoms, (2) characteristics of the virus and infected patients, and (3) transmission mechanism and risk factors for SFTSV. Conclusions: In this study, we provide a detailed description of the research developments in SFTSV since its discovery and summarize the SFTSV research trends. SFTSV research is in a phase of explosive development, and a large number of publications have been published in the past decade. There is a lack of collaboration between countries and institutions, and international collaboration and exchanges should be strengthened in the future. The current research hotpots of SFTSV is antiviral therapy, immunotherapy, virus transmission mechanism and immune response.

A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010-2022) multicenter retrospective case-control study.

BACKGROUND: Length of stay (LOS) is an important metric for evaluating the management of inpatients. This study aimed to explore the factors impacting the LOS of inpatients with type-2 diabetes mellitus (T2DM) and develop a predictive model for the early identification of inpatients with prolonged LOS. METHODS: A 13-year multicenter retrospective study was conducted on 83,776 patients with T2DM to develop and validate a clinical predictive tool for prolonged LOS. Least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis were adopted to build the risk model for prolonged LOS, and a nomogram was taken to visualize the model. Furthermore, receiver operating characteristic curves, calibration curves, and decision curve analysis and clinical impact curves were used to respectively validate the discrimination, calibration, and clinical applicability of the model. RESULTS: The result showed that age, cerebral infarction, antihypertensive drug use, antiplatelet and anticoagulant use, past surgical history, past medical history, smoking, drinking, and neutrophil percentage-to-albumin ratio were closely related to the prolonged LOS. Area under the curve values of the nomogram in the training, internal validation, external validation set 1, and external validation set 2 were 0.803 (95% CI [confidence interval] 0.799-0.808), 0.794 (95% CI 0.788-0.800), 0.754 (95% CI 0.739-0.770), and 0.743 (95% CI 0.722-0.763), respectively. The calibration curves indicated that the nomogram had a strong calibration. Besides, decision curve analysis, and clinical impact curves exhibited that the nomogram had favorable clinical practical value. Besides, an online interface ( https://cytjt007.shinyapps.io/prolonged_los/ ) was developed to provide convenient access for users. CONCLUSION: In sum, the proposed model could predict the possible prolonged LOS of inpatients with T2DM and help the clinicians to improve efficiency in bed management.