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.

Enhanced Recognition of a Herbal Compound Epiberberine by a DNA Quadruplex-Duplex Structure.

The small molecule epiberberine (EPI) is a natural alkaloid with versatile bioactivities against several diseases including cancer and bacterial infection. EPI can induce the formation of a unique binding pocket at the 5' side of a human telomeric G-quadruplex (HTG) sequence with four telomeric repeats (Q4), resulting in a nanomolar binding affinity (KD approximately 26 nM) with significant fluorescence enhancement upon binding. It is important to understand (1) how EPI binding affects HTG structural stability and (2) how enhanced EPI binding may be achieved through the engineering of the DNA binding pocket. In this work, the EPI-binding-induced HTG structure stabilization effect was probed by a peptide nucleic acid (PNA) invasion assay in combination with a series of biophysical techniques. We show that the PNA invasion-based method may be useful for the characterization of compounds binding to DNA (and RNA) structures under physiological conditions without the need to vary the solution temperature or buffer components, which are typically needed for structural stability characterization. Importantly, the combination of theoretical modeling and experimental quantification allows us to successfully engineer Q4 derivative Q4-ds-A by a simple extension of a duplex structure to Q4 at the 5' end. Q4-ds-A is an excellent EPI binder with a KD of 8 nM, with the binding enhancement achieved through the preformation of a binding pocket and a reduced dissociation rate. The tight binding of Q4 and Q4-ds-A with EPI allows us to develop a novel magnetic bead-based affinity purification system to effectively extract EPI from Rhizoma coptidis (Huang Lian) extracts.

Hydrogen sulfide antagonizes cytokinin to change root system architecture through persulfidation of CKX2 in Arabidopsis.

Hydrogen sulfide (H2S) is an endogenous gaseous signaling molecule, which has been shown to play an important role in plant growth and development by coupling with various phytohormones. However, the relationship between H2S and cytokinin (CTK) and the mechanisms by which H2S and CTK affect root growth remain poorly understood. Endogenous CTK was analyzed by UHPLC-ESI-MS/MS. Persulfidation of cytokinin oxidase/dehydrogenases (CKXs) was analyzed by mass spectrometry (MS). ckx2/CKX2wild-type (WT), OE CKX2 and ckx2/CKX2Cys(C)62alanine(A) transgenic lines were isolated with the ckx2 background. H2S is linked to CTK content by CKX2, which regulates root system architecture (RSA). Persulfidation at cysteine (Cys)62 residue of CKX2 enhances CKX2 activity, resulting in reduced CTK content. We utilized 35S-LCD/oasa1 transgenic lines to investigate the effect of endogenous H2S on RSA, indicating that H2S reduces the gravitropic set-point angle (GSA), shortens root hairs, and increases the number of lateral roots (LRs). The persulfidation of CKX2Cys62 changes the elongation of cells on the upper and lower flanks of LR elongation zone, confirming that Cys62 of CKX2 is the specificity target of H2S to regulate RSA in vivo. In conclusion, this study demonstrated that H2S negatively regulates CTK content and affects RSA by persulfidation of CKX2Cys62 in Arabidopsis thaliana.

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.

NRF2 assessment in discarded liver allografts: A role in allograft function and salvage.

Antioxidant defence mechanisms, such as the nuclear factor-erythroid 2-related-factor-2 (NRF2) axis, are integral to oxidative stress responses and ischemic injury. Hepatic antioxidant capacity is contingent on parenchymal quality, and there is a need to develop new insights into key molecular mechanisms in marginal liver allografts that might provide therapeutic targets. This study examines the clinical relevance of NRF2 in donor livers and its response to normothermic machine perfusion (NMP). Discarded donor livers (n = 40) were stratified into a high NRF2 and low NRF2 group by quantifying NRF2 expression. High NRF2 livers had significantly lower transaminase levels, hepatic vascular inflammation and peri-portal CD3+ T cell infiltration. Human liver allografts (n = 8) were then exposed to 6-h of NMP and high NRF2 livers had significantly reduced liver enzyme alterations and improved lactate clearance. To investigate these findings further, we used a rat fatty-liver model, treating livers with an NRF2 agonist during NMP. Treated livers had increased NRF2 expression and reduced transaminase derangements following NMP compared to vehicle control. These results support the association of elevated NRF2 expression with improved liver function. Targeting this axis could have a rationale in future studies and NRF2 agonists may represent a supplemental treatment strategy for rescuing marginal donor livers.

Gestational Diabetes Mellitus: The Genetic Susceptibility Behind the Disease.

Gestational diabetes mellitus (GDM), a type of pregnancy-specific glucose intolerance or hyperglycemia, is one of the most common metabolic disorders in pregnant women with 16.9% of the global prevalence of gestational hyperglycemia. Not only are women with GDM likely to develop T2DM, but their children are also at risk for birth complications or metabolic disease in adulthood. Therefore, identifying the potential risk factors for GDM is very important in the prevention and treatment of GDM. Previous studies have shown that genetic predisposition is an essential component in the occurrence of GDM. In this narrative review, we describe the role of polymorphisms in different functional genes associated with increased risk for GDM, and available evidence on genetic factors in the risk of GDM is summarized and discussed.

A potential vaccine candidate towards chicken coccidiosis mediated by recombinant Lactobacillus plantarum with surface displayed EtMIC2 protein.

Eimeria tenella (E. tenella) has caused severe economic loss in chicken production, especially after the forbidden use of antibiotics in feed. Considering the drug resistant problem caused by misuse of chemoprophylaxis and live oocyst vaccines can affect the productivity of chickens, also it has the risk to reversion of virulence, the development of efficacious, convenient and safe vaccines is still deeply needed. In this study, the EtMic2 protein of E. tenella was anchored on the surface of Lactobacillus plantarum (L. plantarum) NC8 strain. The newly constructed strain was then used to immunize chickens, followed by E. tenella challenge. The results demonstrated that the recombinant strain could provide efficient protection against E. tenella, shown by increased relative body weight gains, percentages of CD4+ and CD8+ T cells, humoral immune response and inflammatory cytokines. In addition, decreased cecum lesion scores and fecal oocyst shedding were also observed during the experiment. In conclusion, this study proves the possibility to use L. plantarum as a vessel to deliver protective antigen to protect chickens against coccidiosis.

Enhanced immunosuppression improves early allograft function in a porcine kidney transplant model of donation after circulatory death.

It remains controversial whether renal allografts from donation after circulatory death (DCD) have a higher risk of acute rejection (AR). In the porcine large animal kidney transplant model, we investigated the AR and function of DCD renal allografts compared to the non-DCD renal allografts and the effects of increased immunosuppression. We found that the AR was significantly increased along with elevated MHC-I expression in the DCD transplants receiving low-dose immunosuppression; however, AR and renal function were significantly improved when given high-dose immunosuppressive therapy postoperatively. Also, high-dose immunosuppression remarkably decreased the mRNA levels of ifn-g, il-6, tgf-b, il-4, and tnf-a in the allograft at day 5 and decreased serum cytokines levels of IFN-g and IL-17 at day 4 and day 5 after operation. Furthermore, Western blot analysis showed that higher immunosuppression decreased phosphorylation of signal transducer and activator of transcription 3 and nuclear factor kappa-light-chain-enhancer of activated B cells-p65, increased phosphorylation of extracellular-signal-regulated kinase, and reduced the expression of Bcl-2-associated X protein and caspase-3 in the renal allografts. These results suggest that the DCD renal allograft seems to be more vulnerable to AR; enhanced immunosuppression reduces DCD-associated AR and improves early allograft function in a preclinical large animal model.

Graph neural network-based subgraph analysis for predicting adverse drug events.

PURPOSE: Adverse drug events (ADEs) are a significant global public health concern, and they have resulted in high rates of hospital admissions, morbidity, and mortality. Prior to the use of machine learning and deep learning methods, ADEs may not become well recognized until long after a drug has been approved and is widely used, which poses a significant challenge for ensuring patient safety. Consequently, there is a need to develop computational approaches for earlier identification of ADEs not detected during pre-registration clinical trials. METHODS: This paper presents a state-of-the-art network-based approach that models patients as subgraphs composed of nodes of International Classification of Diseases (ICD) codes and directed edges illustrating disease progression. Four Graph Neural Network (GNN) variants were employed to make sub-graph level predictions that answer three Research Questions (RQ): 1) whether ADE(s) would occur given a patient's prior diagnoses history, 2) when an ADE would occur, and 3) which ADE would occur. The first and second RQs were addressed using a binary classification approach. The third RQ was addressed using a multi-label classification model. RESULTS: The proposed network-based approach demonstrated superior performance in predicting ADEs, with the GraphSage model exhibiting the highest accuracy for both RQ 1 (0.8863) and RQ 3 (0.9367), while the Graph Attention Networks (GAT) model was found to perform best for RQ 2 (0.8769). Furthermore, an analysis segmented by ADE classification revealed that while RQs 1 and 3 exhibited minimal variance across different ADE categories, a distinct advantage was observed for categories B, C, and E in the context of RQ 2 when applying this sub-graph method. CONCLUSION: The network-based approach demonstrates the potential of GNNs in supporting the early detection and prevention of ADEs. Accurately predicting ADEs could enable healthcare professionals to make informed clinical decisions, take preventive measures and adjust medication regimens before serious adverse events occur. The proposed prediction method could also lead to optimized usage of healthcare resources by preventing hospital admissions and reducing the overall burden of adverse drug events on the healthcare systems.

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.

Structural identification, rheological properties and immunological receptor of a complex galacturonoglucan from fruits of Schisandra chinensis (Turcz.) Baill.

A complex heteropolysaccharide SCP-2 named schisanan B (Mw = 1.005 × 105 g/mol) was obtained from water extracts of Schisandra chinensis fruits, and its planar structure was finally deduced as a galacturonoglucan by a combination of monosaccharide compositions, methylation analysis, partial acid hydrolysis, enzymatic hydrolysis and 1D/2D-nuclear magnetic resonance spectroscopy. The conformation of SCP-2 exhibited a globular shape with branching in ammonium formate aqueous solutions. The rheological properties of SCP-2 were investigated on concentrations, temperature, pH and salts. The in vitro immunomodulatory activity assay demonstrated that SCP-2 significantly enhanced the production of nitric oxide (NO) and stimulated the secretion of tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) in macrophages. Through a combination of high-resolution live-cell imaging, surface plasmon resonance, and molecular docking techniques, SCP-2 exhibited a strong binding affinity with the Toll-like receptor 4 (TLR4). Moreover, western blot analysis revealed that SCP-2 effectively induced downstream signaling proteins associated with TLR4 activation, thereby promoting macrophage activation. The evidence strongly indicates that TLR4 functions as a membrane protein target in the activation of macrophages and immune regulation induced by SCP-2.

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.

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.

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.

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.

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.

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.

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.