Low platelet count at diagnosis of anti-neutrophil cytoplasmic antibody-associated vasculitis is correlated with the severity of disease and renal prognosis.

Antineutrophil cytoplasmic antibody-associated vasculitis (AAV) is an autoimmune disease that involves inflammation of blood vessels. There is increasing evidence that platelets play a crucial role not only in hemostasis but also in inflammation and innate immunity. In this study, we explored the relationship between platelet count, clinical characteristics, and the prognosis of patients with AAV. We divided 187 patients into two groups based on their platelet count. Clinicopathological data and prognostic information were retrospectively gathered from medical records. Univariate and multivariate regression analyses were used to identify risk factors for prognosis, including end-stage renal disease (ESRD) and mortality. The cutoff point for platelet count was set at 264.5 × 109/L, as determined by the receiver operating characteristic (ROC) curve for predicting progression to ESRD in patients with AAV. We observed patients with low platelet count (platelets < 264.5 × 109/L) had lower leukocytes, hemoglobin, complement, acute reactants, and worse renal function (P for eGFR < 0.001). They were also more likely to progress to ESRD or death compared to the high platelet count group (platelets > 264.5 × 109/L) (P < 0.0001, P = 0.0338, respectively). Low platelet count was potentially an independent predictor of poor renal prognosis in the multivariate regression analysis [HR 1.670 (95% CI 1.019-2.515), P = 0.014]. Lower platelet count at diagnosis is associated with more severe clinical characteristics and impaired renal function. Therefore, platelet count may be an accessible prognostic indicator for renal outcomes in patients with AAV.

Distinction and quantification of Panax polysaccharide extracts via attenuated total reflectance-Fourier transform infrared spectroscopy with first-order derivative processing.

Derivative spectroscopy is used to separate the small absorption peaks superimposed on the main absorption band, which is widely adopted in modern spectral analysis to increase both the valid spectral information and the identification accuracy. In this study, a method based on attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) with first-order derivative (FD) processing combined with chemometrics is proposed for rapid qualitative and quantitative analysis of Panax ginseng polysaccharides (PGP), Panax notoginseng polysaccharides (PNP), and Panax quinquefolius polysaccharides (PQP). First, ATR-FTIR with FD processing was used to establish the discriminant model combined with principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA). After that, two-dimensional ATR-FTIR based on single-characteristic temperature as external interference (2D-sATR-FTIR) was established using ATR-FTIR with FD processing. Then, ATR-FTIR with FD processing was combined with PLS to establish and optimize the quantitative regression model. Finally, the established discriminant model and 2D-sATR-FTIR successfully distinguished PGP, PNP and PQP, and the optimal PLS regression model had a good prediction ability for the Panax polysaccharide extracts content. This strategy provides an efficient, economical and nondestructive method for the distinction and quantification of PGP, PNP and PQP in a short detection time.

Road networks and socio-demographic factors to explore COVID-19 infection during its different waves.

The COVID-19 pandemic triggered an unprecedented level of restrictive measures globally. Most countries resorted to lockdowns at some point to buy the much-needed time for flattening the curve and scaling up vaccination and treatment capacity. Although lockdowns, social distancing and business closures generally slowed the case growth, there is a growing concern about these restrictions' social, economic and psychological impact, especially on the disadvantaged and poorer segments of society. While we are all in this together, these segments often take the heavier toll of the pandemic and face harsher restrictions or get blamed for community transmission. This study proposes a road-network-based networked approach to model mobility patterns between localities during lockdown stages. It utilises a panel regression method to analyse the effects of mobility in transmitting COVID-19 in an Australian context, together with a close look at a suburban population's characteristics like their age, income and education. Firstly, we attempt to model how the local road networks between the neighbouring suburbs (i.e., neighbourhood measure) and current infection count affect the case growth and how they differ between delta and omicron variants. We use a geographic information system, population and infection data to measure road connections, mobility and transmission probability across the suburbs. We then looked at three socio-demographic variables: age, education and income and explored how they moderate independent and dependent variables (infection rates and neighbourhood measures). The result shows strong model performance to predict infection rate based on neighbourhood road connection. However, apart from age in the delta variant context, the other variables (income and education level) do not seem to moderate the relationship between infection rate and neighbourhood measure. The results indicate that suburbs with a more socio-economically disadvantaged population do not necessarily contribute to more community transmission. The study findings could be potentially helpful for stakeholders in tailoring any health decision for future pandemics.

C5a enhances Vδ1 T cells recruitment via the CCL2-CCR2 axis in IgA nephropathy.

BACKGROUND: Mucosal immune-associated γδ T cells have been implicated in IgA nephropathy (IgAN). However, the involvement of Vδ1 T cells, the major γδ T cells subtype, in renal damage and the mechanism underlying their migration from peripheral blood to kidney in IgAN remain unclear. METHODS: Clinical data from IgAN patients and healthy controls (HC) were analyzed. Phenotypes and chemokine receptors of γδ T cell were compared between IgAN patients and HC. Immunohistochemistry and immunofluorescence were performed to assess the infiltration of γδ T cell subsets and the expression of chemokine in renal tissues. In vitro, C5a was used to stimulate the human glomerular mesangial cells (HMCs) and chemotaxis experiment was used to examine Vδ1 T cells migration. Correlation between Vδ1 T cells and related clinical indicators were analyzed. RESULTS: IgAN patients exhibited decreased Vδ1 T cell in blood but increased levels in kidneys compared to HC. Increased CCR2-expressing Vδ1 T cells and serum level of CCL2 were observed in IgAN patients. CCL2 co-localized with CCR2 in HMCs of IgAN. In vitro, C5a enhanced Vδ1 T cells recruitment by HMCs through CCL2-CCR2 axis. Importantly, circulating Vδ1 T cell levels showed a negatively correlated with both the urinary protein creatinine ratio (UACR) and 24-hour urine protein (UP). Moreover, kidney infiltration of Vδ1 cells positively correlated with UACR, UP, mesangial hyperplasia and renal tubule atrophy/interstitial fibrosis in IgAN. CONCLUSIONS: C5a-induced production of CCL2 by HMCs facilitates Vδ1 T cells recruitment via the CCL2-CCR2 axis, contributing to renal damage in IgAN.

Pathogenic bacteria exploit transferrin receptor transcytosis to penetrate the blood-brain barrier.

The human blood-brain barrier (BBB) comprises a single layer of brain microvascular endothelial cells (HBMECs) protecting the brain from bloodborne pathogens. Meningitis is among the most serious diseases, but the mechanisms by which major meningitis-causing bacterial pathogens cross the BBB to reach the brain remain poorly understood. We found that Streptococcus pneumoniae, group B Streptococcus, and neonatal meningitis Escherichia coli commonly exploit a unique vesicle fusion mechanism to hitchhike on transferrin receptor (TfR) transcytosis to cross the BBB and illustrated the details of this process in human BBB model in vitro and mouse model. Toll-like receptor signals emanating from bacteria-containing vesicles (BCVs) trigger K33-linked polyubiquitination at Lys168 and Lys181 of the innate immune regulator TRAF3 and then activate the formation of a protein complex containing the guanine nucleotide exchange factor RCC2, the small GTPase RalA and exocyst subcomplex I (SC I) on BCVs. The distinct function of SEC6 in SC I, interacting directly with RalA on BCVs and the SNARE protein SNAP23 on TfR vesicles, tethers these two vesicles and initiates the fusion. Our results reveal that innate immunity triggers a unique modification of TRAF3 and the formation of the HBMEC-specific protein complex on BCVs to authenticate the precise recognition and selection of TfR vesicles to fuse with and facilitate bacterial penetration of the BBB.

Urinary isomorphic red blood cells for the prediction of disease severity and renal outcomes in MPO-ANCA-associated vasculitis: a retrospective cohort study.

BACKGROUND: Hematuria is common in myeloperoxidase anti-neutrophil cytoplasmic antibody associated vasculitis (ANCA-MPO). Previous studies have mainly focused on urinary dysmorphic red blood cells and few have reported the clinical significance of isomorphic urinary red blood cells. Therefore, the main aim of this study was to assess the predictive yield  of urinary isomorphic red blood cells for disease severity and renal outcomes in patients with ANCA-MPO associated vasculitis. METHODS: A total of 191 patients with ANCA-MPO associated vasculitis with hematuria were retrospectively selected and were divided into two groups (with isomorphic red blood cells versus dysmorphic red blood cells) according to the percentage of isomorphic red blood cells on urinary sediment analysis. Clinical, biological and pathological data at diagnosis were compared. Patients were followed up for a median of 25 months and progression to end-stage kidney disease and death were regarded as main outcome events. Additionally, univariate and multivariate Cox regression models were used to estimate the risk factors for end-stage kidney disease. RESULTS: Out of 191 patients, 115 (60%) had ≥ 70% and 76 (40%) had < 30% urine isomorphic red blood cells. Compared with patients in the dysmorphic red blood cell group, patients in the isomorphic red blood cell group had a significantly lower estimated glomerular filtration rate (eGFR) [10.41 mL/min (IQR 5.84-17.06) versus 12.53 (6.81-29.26); P = 0.026], higher Birmingham Vasculitis Activity Score [16 (IQR 12-18) versus 14 (10-18); P = 0.005] and more often received plasma exchange [40.0% versus 23.7% (P = 0.019)] at diagnosis. Kidney biopsies revealed a higher proportion of patients with glomerular basement membrane fracture in the isomorphic red blood cell group [46.3% versus 22.9% (P = 0.033)]. Furthermore, patients with predominant urinary isomorphic red blood cells were more likely to progress to end-stage kidney disease [63.5% versus 47.4% (P = 0.028)] and had a higher risk of death [31.3% versus 19.7% (P = 0.077)]. The end-stage kidney disease-free survival was lower in patients in the isomorphic red blood cell group (P = 0.024). However, urine isomorphic red blood cells ≥ 70% could not predict the presence of end-stage kidney disease in multivariate Cox analysis. CONCLUSION: Myeloperoxidase-anti-neutrophil cytoplasmic antibody associated vasculitis patients with predominant urinary isomorphic red blood cells at diagnosis had more severe clinical manifestations and a higher risk of poor renal outcomes. In this respect, urinary isomorphic red blood cells could be viewed as a promising biomarker of ANCA_MPO vasculitis severity and progression.

Circulating exosomes from brain death and cardiac death donors have distinct molecular and immunologic properties: A pilot study.

BACKGROUND AND AIMS: Comparison of donation after brain death (DBD) and donation after cardiac death (DCD) lung tissue before transplantation have demonstrated activation of pro-inflammatory cytokine pathway in DBD donors. The molecular and immunological properties of circulating exosomes from DBD and DCD donors were not previously described. METHODS: We collected plasma from 18 deceased donors (12 DBD and six DCD). Cytokines were analyzed by 30-Plex luminex Panels. Exosomes were analyzed for liver self-antigen (SAg), Transcription Factors and HLA class II (HLA-DR/DQ) using western blot. C57BL/6 animals were immunized with isolated exosomes to determine strength and magnitude of immune responses. Interferon (IFN)-γ and tumor necrosis factor-α producing cells were quantified by ELISPOT, specific antibodies to HLA class II antigens were measured by ELISA RESULTS: We demonstrate increased plasma levels of IFNγ, EGF, EOTAXIN, IP-10, MCP-1, RANTES, MIP-ß, VEGF, and interleukins - 6/8 in DBD plasma versus DCD. MiRNA isolated from exosome of DBD donors demonstrated significant increase in miR-421, which has been reported to correlate with higher level of Interleukin-6. Higher levels of liver SAg Collagen III (p = .008), pro-inflammatory transcription factors (NF-κB, p < .05; HIF1α, p = .021), CIITA (p = .011), and HLA class II (HLA-DR, p = .0003 and HLA-DQ, p = .013) were detected in exosomes from DBD versus DCD plasma. The circulating exosomes isolated from DBD donors were immunogenic in mice and led to the development of Abs to HLA-DR/DQ. CONCLUSIONS: This study provides potential new mechanisms by which DBD organs release exosomes that can activate immune pathways leading to cytokine release and allo-immune response.

Interpretable Drug-to-Drug Network Features for Predicting Adverse Drug Reactions.

Recent years have witnessed booming data on drugs and their associated adverse drug reactions (ADRs). It was reported that these ADRs have resulted in a high hospitalisation rate worldwide. Therefore, a tremendous amount of research has been carried out to predict ADRs in the early phases of drug development, with the goal of reducing possible future risks. The pre-clinical and clinical phases of drug research can be time-consuming and cost-ineffective, so academics are looking forward to more extensive data mining and machine learning methods to be applied in this field of study. In this paper, we try to construct a drug-to-drug network based on non-clinical data sources. The network presents underlying relationships between drug pairs according to their common ADRs. Then, multiple node-level and graph-level network features are extracted from this network, e.g., weighted degree centrality, weighted PageRanks, etc. After concatenating the network features to the original drug features, they were fed into seven machine learning models, e.g., logistic regression, random forest, support vector machine, etc., and were compared to the baseline, where there were no network-based features considered. These experiments indicate that all the tested machine-learning methods would benefit from adding these network features. Among all these models, logistic regression (LR) had the highest mean AUROC score (82.1%) across all ADRs tested. Weighted degree centrality and weighted PageRanks were identified to be the most critical network features in the LR classifier. These pieces of evidence strongly indicate that the network approach can be vital in future ADR prediction, and this network-based approach could also be applied to other health informatics datasets.

Normothermic Machine Perfusion for Declined Livers: A Strategy to Rescue Marginal Livers for Transplantation.

BACKGROUND: Organ waste is a major cause of the donor liver shortage. Roughly 67% of recovered organ donors have liver utilization annually. A new technology called normothermic machine perfusion (NMP) offers a way to recover marginal and declined livers for transplant. We report interim results of the RESTORE trial (FDA investigational drug exemption trial NCT04483102) that aims to transplant NMP-treated livers that would otherwise be discarded. STUDY DESIGN: Declined livers were screened for NMP eligibility (eg donation after circulatory death [DCD] grafts with warm ischemic time <40 minutes, donation after brain death [DBD] grafts with cold ischemic time <8 hours). Livers meeting pre-NMP eligibility criteria received NMP using the OrganOx metra device for a minimum of 4 hours. All NMP-treated livers meeting the viability criteria were transplanted to consented recipients. RESULTS: Over 22 months, 60 declined livers from three organ procurement organizations (OPOs; 40 DCD and 20 DBD donor livers) were offered, and 22 livers (10 DCD and 12 DBD livers) met the pre-NMP eligibility. After NMP, 16 of 22 livers passed viability testing and were transplanted into needy recipients (median Model for End-Stage Liver Disease [MELD] score of 8, range 6 to 24), resulting in a 72.7% rescue rate (50% DCD, 91.7% DBD). The rate of early allograft dysfunction was 31.3%, but there were no graft-related deaths, primary nonfunction, or instances of nonanastomotic biliary strictures. CONCLUSIONS: Interim results of the RESTORE trial suggest that a sizable number of declined livers can be reclaimed. They are safe for transplantation and can enable lower MELD patients at high risk of morbidity and mortality to receive lifesaving grafts while offering OPOs a way to allocate more livers and reduce organ waste.

Clinical characteristics and outcomes of MPO-ANCA-associated glomerulonephritis with bronchiectasis: A retrospective case-control study.

BACKGROUND: The association of bronchiectasis with myeloperoxidase (MPO) antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (MPO-AAV) has been widely described in recent studies. However, the clinical features and outcomes of MPO-ANCA-associated glomerulonephritis (MPO-ANCA GN) patients with bronchiectasis remain enigmatic. METHODS: MPO-ANCA GN patients with bronchiectasis were compared to MPO-ANCA GN patients alone. Clinical imaging, pathological tests, and follow-up examination data of patients were collected retrospectively. Progression to end-stage renal disease (ESRD) and death was treated as endpoint events. RESULTS: 153 cases (52 patients with bronchiectasis) were included in this study. Compared to MPO-ANCA GN patients alone, MPO-ANCA GN patients with bronchiectasis exhibited a lower level of proteinuria (p = 0.019) and relatively higher eGFR level. MPO-ANCA GN patients with bronchiectasis had less frequent incidences of interstitial lung disease (ILD) and emphysema (p<0.001, p = 0.016, respectively) but with higher rates of pulmonary infection (p<0.001). Bronchiectasis severity (the modified Reiff score) was positively correlated with MPO antibody titers (ρ=0.480, p<0.001), but not with shorter renal survival. A relatively higher remission rate was been seen in MPO-ANCA GN patients with bronchiectasis, who showed reduced susceptibility in progressing to ESRD in multivariate analysis (p = 0.043, HR=0.542, 95% CI 0.299-0.982). One-and three-year overall survival rates were 88.2% and 77.3% for MPO-ANCA GN with bronchiectasis cases versus 83.7% and 67.2% for MPO-ANCA GN patients alone (p = 0.431, p = 0.241, respectively). CONCLUSION: The severity of bronchiectasis was correlated with anti-MPO antibody titers in MPO-ANCA GN patients. For MPO-ANCA GN patients, bronchiectasis associated with good renal prognosis, but it did not improve overall survival.

Neutrophil-mediated delivery of the combination of colistin and azithromycin for the treatment of bacterial infection.

Novel treatment strategies are in urgent need to deal with the rapid development of antibiotic-resistant superbugs. Combination therapies and targeted drug delivery have been exploited to promote treatment efficacies. In this study, we loaded neutrophils with azithromycin and colistin to combine the advantages of antibiotic combinations, targeted delivery, and immunomodulatory effect of azithromycin to treat infections caused by Gram-negative pathogens. Delivery of colistin into neutrophils was mediated by fusogenic liposome, while azithromycin was directly taken up by neutrophils. Neutrophils loaded with the drugs maintained the abilitity to generate reactive oxygen species and migrate. In vitro assays demonstrated enhanced bactericidal activity against multidrug-resistant pathogens and reduced inflammatory cytokine production by the drug-loaded neutrophils. A single intravenous administration of the drug-loaded neutrophils effectively protected mice from Pseudomonas aeruginosa infection in an acute pneumonia model. This study provides a potential effective therapeutic approach for the treatment of bacterial infections.

Simple Detection of DNA Methyltransferase with an Integrated Padlock Probe.

DNA methyltransferases (MTases) can be regarded as biomarkers, as demonstrated by many studies on genetic diseases. Many researchers have developed biosensors to detect the activity of DNA MTases, and nucleic acid amplification, which need other probe assistance, is often used to improve the sensitivity of DNA MTases. However, there is no integrated probe that incorporates substrates and template and primer for detecting DNA MTases activity. Herein, we first designed a padlock probe (PP) to detect DNA MTases, which combines target detection with rolling circle amplification (RCA) without purification or other probe assistance. As the substrate of MTase, the PP was methylated and defended against HpaII, lambda exonuclease, and ExoI cleavage, as well as digestion, by adding MTase and the undestroyed PP started RCA. Thus, the fluorescent signal was capable of being rapidly detected after adding SYBRTM Gold to the RCA products. This method has a detection limit of approximately 0.0404 U/mL, and the linear range was 0.5-110 U/mL for M.SssI. Moreover, complex biological environment assays present prospects for possible application in intricacy environments. In addition, the designed detection system can also screen drugs or inhibitors for MTases.

A nondestructive solution to quantify monosaccharides by ATR-FTIR and multivariate regressions: A case study of Atractylodes polysaccharides.

The quality evaluation of nature polysaccharides is a tough nut to crack because of its high Mw distributions and larger polarity property. It is well-known that infrared spectroscopy and multiple regression modeling have been used for quantitative examinations in multiple fields, but it has not been applied to the compositional analysis of polysaccharides. In this study, attenuated total reflectance-fourier transform infrared spectroscopy is used to simultaneously quantify aldoses, ketose and uronic acids in Atractylodes polysaccharides by a combination of multivariate regressions. After experience of different data processing pretreatments, the resulting spectrum contains maximum amount of information of monosaccharide contents in Atractylodes polysaccharides. In this case, different smoothing points, derivatives, SNV and MSC are used in the pre-modeling spectrum processing and VIP screening is used to reduce the number of variables to simplify the calculation of the model. All the most optimal prediction models have both good prediction ability (R2 ≥ 0.9 and RPD > 3) and no over fitting (RMSEP/RMSEC < 3). This strategy has opened a new possibility for the nondestructive determination of complex monosaccharide compositions of natural polysaccharides in a short detection time, low equipment requirement and high experimental safety.

Comparing the Impact of Road Networks on COVID-19 Severity between Delta and Omicron Variants: A Study Based on Greater Sydney (Australia) Suburbs.

The Omicron and Delta variants of COVID-19 have recently become the most dominant virus strains worldwide. A recent study on the Delta variant found that a suburban road network provides a reliable proxy for human mobility to explore COVID-19 severity. This study first examines the impact of road networks on COVID-19 severity for the Omicron variant using the infection and road connections data from Greater Sydney, Australia. We then compare the findings of this study with a recent study that used the infection data of the Delta variant for the same region. In analysing the road network, we used four centrality measures (degree, closeness, betweenness and eigenvector) and the coreness measure. We developed two multiple linear regression models for Delta and Omicron variants using the same set of independent and dependent variables. Only eigenvector is a statistically significant predictor for COVID-19 severity for the Omicron variant. On the other hand, both degree and eigenvector are statistically significant predictors for the Delta variant, as found in a recent study considered for comparison. We further found a statistical difference (p < 0.05) between the R-squared values for these two multiple linear regression models. Our findings point to an important difference in the transmission nature of Delta and Omicron variants, which could provide practical insights into understanding their infectious nature and developing appropriate control strategies accordingly.

A User-Centered Evaluation of a COVID-19 Intelligent Query System (COVID-IQS).

As the fight against COVID-19 continues, it is critical to discover and accumulate knowledge in scientific literature to combat the pandemic. In this work, we shared the experience in developing an intelligent query system on COVID-19 literature. We conducted a user-centered evaluation with 12 researchers in our institution and identified usability issues in four categories: distinct user needs, functionality errors, suboptimal information display, and implementation errors. Furthermore, we shared two lessons for building such a COVID-19 literature search engine. We will deploy the system and continue refining it through multiple phases of evaluation to aid in redesigning the system to accommodate different user roles as well as enhancing repository features to support collaborative information seeking. The successful implementation of the COVID-IQS can support knowledge discovery and hypothesis generation in our institution and can be shared with other institutions to make a broader impact.

Ficus hirta Vahl. Ameliorates Nonalcoholic Fatty Liver Disease through Regulating Lipid Metabolism and Gut Microbiota.

Nonalcoholic fatty liver disease (NAFLD) has gradually become one of the most serious liver diseases threatening human health in the world. Currently, Chinese herbal medicine is a potentially important treatment option for NAFLD, and the development of effective Chinese herbal medicine has a good prospect. Previous studies have suggested that Ficus hirta Vahl. (FV) has various protective effects on the liver. In this study, we investigated the therapeutic outcomes of FV treatment for the liver disease and its underlying mechanism using HepG2 cell lines induced by palmitate (PA) and mouse model fed with high-fat diet (HFD). FV mainly exerts pharmacological effects by mediating lipid metabolism and inflammation. During the lipid metabolism regulation process, CD36, SREBP-1, SCD1, PPAR γ, ACOX1, and CPT1α are the key factors related to the healing effects of FV on NAFLD. During the inflammation process, the downregulation of IL-6, IL-1ß, and TNF-α is involved in alleviation of NAFLD. Furthermore, CD36 overexpression promotes lipid abnormal metabolism and inflammation in PA-induced HepG2 cells, while CD36 knockdown and FV supplementation reverse these responses. In addition, FV also modulates gut microbiota composition, such as Allobaculum, Faecalibaculum, and Butyricicoccus in HFD-fed mice. In summary, our findings demonstrated that FV exerted a beneficial preventive and therapeutic effect on NAFLD by improving lipid metabolism and inflammation as well as regulating the structure of gut microbiota, and therefore, FV may be a candidate for the treatment of NAFLD.

Metabolomic Mechanisms of Radix Fici Hirtae against Carbon Tetrachloride-Induced Acute Liver Damage in Mice.

Background: Radix Fici Hirtae (RFH), known as Cantonese ginseng, is an alternative folk medicine that is widely used to treat various diseases in southern China. The aim of this study was to investigate the effect and metabolic mechanisms of pretreatment with RFH on the serum metabolic profiles of carbon tetrachloride (CCl4) induced acute liver injury in mice. Methods: Mice fed with the water extract of RFH at a dose of 1.5 g/kg and 0.75 g/kg for consecutive 7 days, and then serum samples were taken for the metabolomic analysis. Furthermore, the bioinformatics and pathways analysis were measured. Results: The UHPLC-Orbitrap/MS based-metabolomic analysis identified 20 differential metabolic markers in serum of CCl4-induced liver injury mice compared to that of the normal controls, which were mainly related to the metabolism of amino acids and fatty acids. Furthermore, most of these biomarkers contributing to CCl4 induction were ameliorated by RFH, and the bioinformatics and pathways analysis revealed that therapeutic actions of RFH were mainly involved in the regulation of the oxidative stress responses and energy homeostasis. Conclusion: These findings provide potential metabolic mechanism for future study and allow for hypothesis generation about the hepatoprotective effects of Radix Fici Hirtae.

Spatio-Temporal Characteristics of PM2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016-2021.

Fine particulate matter (PM2.5) has a continuing impact on the environment, climate change and human health. In order to improve the accuracy of PM2.5 estimation and obtain a continuous spatial distribution of PM2.5 concentration, this paper proposes a LUR-GBM model based on land-use regression (LUR), the Kriging method and LightGBM (light gradient boosting machine). Firstly, this study modelled the spatial distribution of PM2.5 in the Chinese region by obtaining PM2.5 concentration data from monitoring stations in the Chinese study region and established a PM2.5 mass concentration estimation method based on the LUR-GBM model by combining data on land use type, meteorology, topography, vegetation index, population density, traffic and pollution sources. Secondly, the performance of the LUR-GBM model was evaluated by a ten-fold cross-validation method based on samples, stations and time. Finally, the results of the model proposed in this paper are compared with those of the back propagation neural network (BPNN), deep neural network (DNN), random forest (RF), XGBoost and LightGBM models. The results show that the prediction accuracy of the LUR-GBM model is better than other models, with the R2 of the model reaching 0.964 (spring), 0.91 (summer), 0.967 (autumn), 0.98 (winter) and 0.976 (average for 2016-2021) for each season and annual average, respectively. It can be seen that the LUR-GBM model has good applicability in simulating the spatial distribution of PM2.5 concentrations in China. The spatial distribution of PM2.5 concentrations in the Chinese region shows a clear characteristic of high in the east and low in the west, and the spatial distribution is strongly influenced by topographical factors. The seasonal variation in mean concentration values is marked by low summer and high winter values. The results of this study can provide a scientific basis for the prevention and control of regional PM2.5 pollution in China and can also provide new ideas for the acquisition of data on the spatial distribution of PM2.5 concentrations within cities.

Suburban Road Networks to Explore COVID-19 Vulnerability and Severity.

The Delta variant of COVID-19 has been found to be extremely difficult to contain worldwide. The complex dynamics of human mobility and the variable intensity of local outbreaks make measuring the factors of COVID-19 transmission a challenge. The inter-suburb road connection details provide a reliable proxy of the moving options for people between suburbs for a given region. By using such data from Greater Sydney, Australia, this study explored the impact of suburban road networks on two COVID-19-related outcomes measures. The first measure is COVID-19 vulnerability, which gives a low score to a more vulnerable suburb. A suburb is more vulnerable if it has the first COVID-19 case earlier and vice versa. The second measure is COVID-19 severity, which is proportionate to the number of COVID-19-positive cases for a suburb. To analyze the suburban road network, we considered four centrality measures (degree, closeness, betweenness and eigenvector) and core-periphery structure. We found that the degree centrality measure of the suburban road network was a strong and statistically significant predictor for both COVID-19 vulnerability and severity. Closeness centrality and eigenvector centrality were also statistically significant predictors for COVID-19 vulnerability and severity, respectively. The findings of this study could provide practical insights to stakeholders and policymakers to develop timely strategies and policies to prevent and contain any highly infectious pandemics, including the Delta variant of COVID-19.