Low expression of interleukin-1 receptor antagonist correlates with poor prognosis via promoting proliferation and migration and inhibiting apoptosis in oral squamous cell carcinoma.

BACKGROUND: Biomarkers are biochemical indicators that can identify changes in the structure or function of systems, organs, or cells and can be used to monitor a wide range of biological processes, including cancer. Interleukin-1 receptor antagonist (IL1RA) is an important inflammatory suppressor gene and tumor biomarker. The goal of this study was to investigate the expression of IL1RA, its probable carcinogenic activity, and its diagnostic targets in oral squamous cell carcinoma (OSCC). RESULTS: We discovered that IL1RA was expressed at a low level in OSCC tumor tissues compared to normal epithelial tissues and that the expression declined gradually from epithelial hyperplasia through dysplasia to carcinoma in situ and invasive OSCC. Low IL1RA expression was associated not only with poor survival but also with various clinicopathological markers such as increased infiltration, recurrence, and fatalities. Following cellular phenotyping investigations in OSCC cells overexpressing IL1RA, we discovered that recovering IL1RA expression decreased OSCC cell proliferation, migration, and increased apoptosis. CONCLUSIONS: In summary, our investigation highlighted the possible involvement of low-expression IL1RA in OSCC cells in promoting invasive as well as metastatic and inhibiting apoptosis, as well as the efficacy of IL1RA-focused monitoring in the early detection and treatment of OSCC.

Development of a TaqMan Real-Time PCR for Early and Accurate Detection of Anthracnose Pathogen Colletotrichum siamense in Pachira glabra.

Anthracnose, caused by Colletotrichum siamense, is a destructive disease of Pachira glabra in southern China. Early and proper monitoring and quantification of C. siamense is of importance for disease control. A calmodulin (CAL) gene-based TaqMan real-time PCR assay was developed for efficient detection and quantification of C. siamense, which reliably detected as low as 5 pg of genomic DNA and 12.8 fg (5800 copies) of target DNA. This method could specifically recognize all tested C. siamense isolates, while no amplification was observed in other closely related Colletotrichum species. The assay could still detect C. siamense in plant mixes, of which only 0.01% of the tissue was infected. A dynamic change in the amount of C. siamense population was observed during infection, suggesting that this real-time PCR assay can be used to monitor the fungal growth progression in the whole disease process. Moreover, the method enabled the detection of C. siamense in naturally infected and symptomless leaves of P. glabra trees in fields. Taken together, this specific TaqMan real-time PCR provides a rapid and accurate method for detection and quantification of C. siamense colonization in P. glabra, and will be useful for prediction of the disease to reduce the epidemic risk.

Beyond guilty by association at scale: searching for causal variants on the basis of genome-wide summary statistics.

Understanding the causal genetic architecture of complex phenotypes is essential for future research into disease mechanisms and potential therapies. Here, we present a novel framework for genome-wide detection of sets of variants that carry non-redundant information on the phenotypes and are therefore more likely to be causal in a biological sense. Crucially, our framework requires only summary statistics obtained from standard genome-wide marginal association testing. The described approach, implemented in open-source software, is also computationally efficient, requiring less than 15 minutes on a single CPU to perform genome-wide analysis. Through extensive genome-wide simulation studies, we show that the method can substantially outperform usual two-stage marginal association testing and fine-mapping procedures in precision and recall. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD), we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline. The identified putative causal variants achieve state-of-the-art agreement with massively parallel reporter assays and CRISPR-Cas9 experiments. Additionally, we applied the method to a retrospective analysis of 67 large-scale GWAS summary statistics since 2013 for a variety of phenotypes. Results reveal the method's capacity to robustly discover additional loci for polygenic traits and pinpoint potential causal variants underpinning each locus beyond conventional GWAS pipeline, contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses.

Second-order group knockoffs with applications to GWAS.

Conditional testing via the knockoff framework allows one to identify -- among large number of possible explanatory variables -- those that carry unique information about an outcome of interest, and also provides a false discovery rate guarantee on the selection. This approach is particularly well suited to the analysis of genome wide association studies (GWAS), which have the goal of identifying genetic variants which influence traits of medical relevance. While conditional testing can be both more powerful and precise than traditional GWAS analysis methods, its vanilla implementation encounters a difficulty common to all multivariate analysis methods: it is challenging to distinguish among multiple, highly correlated regressors. This impasse can be overcome by shifting the object of inference from single variables to groups of correlated variables. To achieve this, it is necessary to construct "group knockoffs." While successful examples are already documented in the literature, this paper substantially expands the set of algorithms and software for group knockoffs. We focus in particular on second-order knockoffs, for which we describe correlation matrix approximations that are appropriate for GWAS data and that result in considerable computational savings. We illustrate the effectiveness of the proposed methods with simulations and with the analysis of albuminuria data from the UK Biobank. The described algorithms are implemented in an open-source Julia package Knockoffs.jl, for which both R and Python wrappers are available.

Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression.

Identifying which variables do influence a response while controlling false positives pervades statistics and data science. In this paper, we consider a scenario in which we only have access to summary statistics, such as the values of marginal empirical correlations between each dependent variable of potential interest and the response. This situation may arise due to privacy concerns, e.g., to avoid the release of sensitive genetic information. We extend GhostKnockoffs He et al. [2022] and introduce variable selection methods based on penalized regression achieving false discovery rate (FDR) control. We report empirical results in extensive simulation studies, demonstrating enhanced performance over previous work. We also apply our methods to genome-wide association studies of Alzheimer's disease, and evidence a significant improvement in power.

First Report of Leaf Brown Spot Caused by Diaporthe phoenicicola on Pachira glabra in China.

Pachira glabra is an increasingly important ornamental landscape tree in southern China. In August 2022, brown spots were observed on P. glabra leaves in Xiangtan City, Hunan Province, China (27.932°N, 113.020°E), affecting up to 40% of the 792 trees surveyed. On each diseased tree, nearly 30% leaves had symptoms, with an average severity of 21.2 ± 5.8% (n=100). The disease initially started as small yellow lesions along leaf margins, which later progressed to pale brown to brown with dark brown borders, eventually coalescing into large necrotic areas. Thirty symptomatic leaf samples (2 × 2 mm) were surfaced-sterilized in 75% ethanol for 10 s, 2% NaOCl for 30 s, rinsed in sterile water three times, placed on potato dextrose agar (PDA), and incubated at 25°C for 5 to 7 days in dark. Eight morphologically similar isolates were obtained from diseased leaf samples through single-spore isolation. On PDA, colonies initially appeared white, turning gray, while the reverse developed a pale yellowish hue. Aerial mycelia were white, cottony, and developed visible black pycnidia with oil droplets at maturity. The α-conidia were unicellular, hyaline, aseptate, oval or fusiform, usually with 1 or 2 guttule(s) and rounded at each end. These conidia were 5.3-8.6 × 1.7-2.5 µm (avg. 6.7 × 2.2 µm, n = 100) and present more frequently than ß-conidia.The ß-conidia were unicellular, hyaline, aseptate, filiform, straight or hamate, eguttulate, 14.6-23.3 × 0.4-1.3 µm (avg. 18.4 × 0.9 µm, n = 30). Morphologically, the fungi were identified as Diaporthe sp. (Udayanga et al. 2014). For molecular identification, the internal transcribed spacer region (ITS), translation elongation factor 1α (EF1-α), calmodulin (CAL), tubulin 2 (TUB2), and histone H3 (HIS3) sequences of all isolates were amplified from genomic DNA, using primers ITS4/ITS5 (White et al. 1990), TEF-2/728F and CALD-38F/CALD-752R (Carbone and Kohn 1999), Bt2a/Bt2b and H3-1a/H3-1b (Glass and Donaldson 1995; Crous et al. 2004), respectively. The GenBank accession numbers for a representative isolate gpg2023-1 were OR533573 (ITS), OR570887 (EF1-α), OR570888 (TUB2), OR570890 (CAL), and OR570889 (HIS3). BLAST results showed that the ITS, EF1-α, TUB2, HIS, and CAL sequences were 99%, 99%, 99%, 99%, and 98% identity, respectively, with those of Diaporthe phoenicicola (GenBank: KC343032.1, KC343758.1, KC344000.1, KC343516.1, and KC343274.1). To confirm the pathogen's identity, phylogenetic analysis using MEGA7.0 based on Maximum Likelihood was constructed. Isolate gpg2023-1 clustered with D. phoenicicola. Based on morphological and molecular data, the fungus was identified as D. phoenicicola. Next, pathogenicity tests were performed three times on one-year-old potted P. glabra plants. For each isolate, twelve healthy leaves on each of three plants were either wounded by a sterile needle or left unwounded, and then sprayed with a conidial suspension (1×106 conidia/ml) for each isolate. Control plants received with sterile water only. Plants were kept in a greenhouse at 25°C, 80% relative humidity, with a 12-h photoperiod. All wounded, inoculated leaves developed brown spot symptoms similar to those observed in the field with six days, while unwounded leaves and control plants remained symptom-free. The fungus was reisolated from all diseased leaves, fulfilling Koch's postulates and proving D. phoenicicola as the causative agent of this brown spot disease on P. glabra. While D. pachirae has been reported to cause leaf spot on P. glabra in Brazil (Milagres et al. 2018), this study marks the first report of D. phoenicicola causing leaf brown spot on P. glabra in China. This finding can help develop control strategies for this disease.

Reconstruction Filters Improving the Spatial Resolution and Signal-to-Noise Ratio of Surface Plasmon Resonance Microscopy.

Benefitting from high sensitivity, real-time, and label-free imaging, surface plasmon resonance microscopy (SPRM) has become a powerful tool for dynamic detection of nanoparticles. However, the evanescent propagation of surface plasmon polaritons (SPPs) induces interference between scattered and launched SPPs, which deteriorates the spatial resolution and signal-to-noise ratio (SNR). Due to the simplicity and fast processing, image reconstruction based on deconvolution has shown the feasibility of improving the spatial resolution of SPRM imaging. Retrieving the particle scattering from SPRM interference imaging by filters is crucial for reconstruction. In this work, we illustrate the effect of filters extracting SPP scattering of nanoparticles with different sizes and shapes for reconstruction. The results indicate that the optimum filters are determined by the material of nanoparticles instead of particle sizes. The reconstruction of single Au and PS nanospheres as well as Ag nanowires with optimum filters is achieved. The reconstructed spatial resolution is improved to 254 nm, and the SNR is increased by 8.1 times. Our research improves the quality of SPRM imaging and provides a reliable method for fast detection of particles with diverse sizes and shapes.

Omnibus test for restricted mean survival time based on influence function.

The restricted mean survival time (RMST), which evaluates the expected survival time up to a pre-specified time point τ, has been widely used to summarize the survival distribution due to its robustness and straightforward interpretation. In comparative studies with time-to-event data, the RMST-based test has been utilized as an alternative to the classic log-rank test because the power of the log-rank test deteriorates when the proportional hazards assumption is violated. To overcome the challenge of selecting an appropriate time point τ, we develop an RMST-based omnibus Wald test to detect the survival difference between two groups throughout the study follow-up period. Treating a vector of RMSTs at multiple quantile-based time points as a statistical functional, we construct a Wald χ2 test statistic and derive its asymptotic distribution using the influence function. We further propose a new procedure based on the influence function to estimate the asymptotic covariance matrix in contrast to the usual bootstrap method. Simulations under different scenarios validate the size of our RMST-based omnibus test and demonstrate its advantage over the existing tests in power, especially when the true survival functions cross within the study follow-up period. For illustration, the proposed test is applied to two real datasets, which demonstrate its power and applicability in various situations.

Roles and mechanisms of exosomal microRNAs in viral infections.

Exosomes are small extracellular vesicles with a diameter of 30-150 nm that originate from endosomes and fuse with the plasma membrane. They are secreted by almost all kinds of cells and can stably transfer different kinds of cargo from donor to recipient cells, thereby altering cellular functions for assisting cell-to-cell communication. Exosomes derived from virus-infected cells during viral infections are likely to contain different microRNAs (miRNAs) that can be transferred to recipient cells. Exosomes can either promote or suppress viral infections and therefore play a dual role in viral infection. In this review, we summarize the current knowledge about the role of exosomal miRNAs during infection by six important viruses (hepatitis C virus, enterovirus A71, Epstein-Barr virus, human immunodeficiency virus, severe acute respiratory syndrome coronavirus 2, and Zika virus), each of which causes a significant global public health problem. We describe how these exosomal miRNAs, including both donor-cell-derived and virus-encoded miRNAs, modulate the functions of the recipient cell. Lastly, we briefly discuss their potential value for the diagnosis and treatment of viral infections.

A preoperative predictive model for stage IV endometriosis.

This was a retrospective study that evaluated a total of 280 patients who underwent surgery for complete removal of endometriosis to develop and validate the predictive model for stage IV endometriosis. The differences between stage I-III and stage IV endometriosis were performed by logistic regression. A model for the prediction of stage IV endometriosis was constructed, which was subsequently validated. The independent variables were visual analogue scale (VAS)≥4 [3.855, 95% confidence interval (CI): 1.675-8.871, p = 0.002], painful nodularity on uterosacral ligaments (13.954, 95% CI: 1.658-117.423, p = 0.015), and bilateral endometriosis (5.933, 95% CI: 1.931-18.225, p = 0.002). The AUC of the model was 0.777, with a sensitivity of 71.9% and specificity of 76.3% for stage IV endometriosis. Therefore, a complete collection of patient information prior to surgery, asking about pain and VAS scores, careful completion of pelvic examinations, and application of imaging techniques are conducive to better diagnosis and prediction of advanced endometriosis.IMPACT STATEMENTWhat is already known on this subject? Endometriosis, a chronic disease causing pain and infertility, is characterised by endometrial-like tissue outside the uterine cavity, which is often treated via surgery at present. Considering the risks of surgery, it is necessary to identify patients with stage IV endometriosis through non-invasive predictive models for adequate preparation for surgery. However, there is no reliable non-invasive predictive model now, despite utilisation of patient medical history, symptoms especially pain-related ones, pelvic examinations, laboratory examinations, and images in the preoperative diagnosis of endometriosis in the clinic.What do the results of this study add? A model developed based on three simple, accessible and non-invasive indicators displays good performance in predicting stage IV endometriosis.What are the implications of these findings for clinical practice and/or further research? It is conducive to diagnosing and predicting advanced endometriosis before surgery, so as to reduce the difficulty and improve the safety of surgery.

Enterovirus A71 Promotes Exosome Secretion by the Nonstructural Protein 3A Interacting with Rab27a.

Exosomes are small membrane-bound vesicles which are intraluminal vesicles (ILVs) secreted to the extracellular space after multivesicular bodies (MVBs) fuse with the plasma membrane. Although it is known that exosomes play a multitude of roles during viral infection, the mechanism that regulates their secretion during viral infection is unknown. Here, we found that enterovirus A71 (EV-A71) infection increased exosome secretion both in vivo and in vitro. Importantly, the expression of nonstructural protein 3A was sufficient to promote exosome secretion, while a mutation affecting the amino acid 18 position abrogated this effect, without changing the size of exosomes in vivo or in vitro. Transmission electron microscopy (TEM) analysis revealed that 3A decreases the number of MVBs and ILVs in vivo and in vitro, which suggested 3A may boost the fusion between MVBs and the plasma membrane. Furthermore, we demonstrated that an interaction between 3A and the small GTPase protein, Rab27a, protected Rab27a from ubiquitination, resulted in increasing exosome release. Data indicated a novel mechanism by which EV-A71 3A modifies exosome secretion during viral infection. IMPORTANCE Research has shown that viral infection impacts exosome secretion, but its regulation mechanisms remain poorly understood. Nonstructural protein 3A of EV-A71 interacts with many host factors and is involved in the remodeling of cellular membranes. In this investigation, we applied exogenous expression of 3A protein for exploring its regulation on exosome secretion and utilized immunoprecipitation combined with proteomics approaches to identify 3A-interacting factors. Our results demonstrate that 3A protein upregulates the release of the exosomes and that the 3A mutant strain of EV-A71 induce less exosome release compared with the EV-A71 wild type. Viral 3A protein interacts with the host factor Rab27a to prevent it from being ubiquitinated, which in turn improves exosome secretion both in vitro and in vivo. EV-A71 3A protein is a novel viral factor in the control of exosome production.

Structure and Function of Sodium Channel Nav1.3 in Neurological Disorders.

Nav1.3, encoded by the SCN3A gene, is a voltage-gated sodium channel on the cell membrane. It is expressed abundantly in the fetal brain but little in the normal adult brain. It is involved in the generation and conduction of action potentials in excitable cells. Nav1.3 plays an important role in many neurological diseases. The aim of this review is to summarize new findings about Nav1.3 in the field of neurology. Many mutations of SCN3A can lead to neuronal hyperexcitability and then cause epilepsy. The rapid recovery from inactivation and slow closed-state inactivation kinetics of Nav1.3 leads to a reduced activation threshold of the channel and a high frequency of firing of neurons. Hyperactivity of Nav1.3 also induces increased excitability of sensory neurons, a lower nociceptive threshold, and neuropathic pain. This review summarizes the structure and the function of Nav1.3 and focuses on its relationship with epilepsy and neuropathic pain.

Enterovirus-71 utilizes small extracellular vesicles to cross the blood-brain barrier for infecting the central nervous system via transcytosis.

BACKGROUND: Central nervous system (CNS) infections caused by Enterovirus 71 (EV71) pose a serious threat to children, causing severe neurogenic complications and even fatality in some patients. However, the pathogenesis of EV71 infections in the CNS remains unclear. METHODS: An in vitro blood-brain barrier (BBB) model was constructed by coculturing brain microvascular endothelial cells (BMECs) and astrocytes in transwell inserts for simulating CNS infections. EV71 virions and small extracellular vesicles (sEVs) derived from EV71-infected cells (EV71-sEVs) were isolated from the cell culture supernatant by density gradient centrifugation. The BBB model was separately infected with EV71 virions and EV71-sEVs. The mechanism of crossing the BBB was determined by inhibiting the different endocytic modes. A murine model of EV71 infection was constructed for confirming the results of in vitro experiments. RESULTS: The EV71-sEVs containing viral components were endocytosed by BMECs and released on the abluminal side of the BBB model, where they infected the astrocytes without disrupting the BBB in the early stages of infection. The integrity of the tight junctions (TJs) between BMECs was breached via downregulation of PI3K/Akt signaling in the late stages of infection. CONCLUSIONS: EV71 utilized the circulating sEVs for infecting the CNS by crossing the BBB.

Bayesian SIR model with change points with application to the Omicron wave in Singapore.

The Omicron variant has led to a new wave of the COVID-19 pandemic worldwide, with unprecedented numbers of daily confirmed new cases in many countries and areas. To analyze the impact of society or policy changes on the development of the Omicron wave, the stochastic susceptible-infected-removed (SIR) model with change points is proposed to accommodate the situations where the transmission rate and the removal rate may vary significantly at change points. Bayesian inference based on a Markov chain Monte Carlo algorithm is developed to estimate both the locations of change points as well as the transmission rate and removal rate within each stage. Experiments on simulated data reveal the effectiveness of the proposed method, and several stages are detected in analyzing the Omicron wave data in Singapore.

Optimization scheme of machine learning model for genetic division between northern Han, southern Han, Korean and Japanese.

Han Chinese, Korean and Japanese are the main populations of East Asia, and Han Chinese presents a gradient admixture from north to south. There are differences among the East Asian populations in genetic structure. To achieve fine-scale genetic classification of southern (S-) and northern (N-) Han Chinese, Korean and Japanese individuals in this study, we collected and analyzed 1185 ancestry informative SNPs (AISNPs) from previous literature reports and our laboratory findings. First, two machine learning algorithms, softmax and randomForest, were used to build genetic classification models. Then, phylogenetic tree, STRUCTURE and principal component analysis were used to evaluate the performance of classification for different AISNP panels. The 234-AISNP panel achieved a fine-scale differentiation among the target populations in four classification schemes. The accuracy of the softmax model was 92%, which realized the accurate classification of the S-Han, N-Han, Korean and Japanese individuals. The two machine learning models tested in this study provided important references for the high-resolution discrimination of close-range populations and will be useful tools to optimize marker panels for developing forensic DNA ancestry inference systems.

Can Even a Small Amount of Greenery Be Helpful in Reducing Stress? A Systematic Review.

A positive experience of nature triggers beneficial mental and physical responses. Today, we live in a rapidly urbanizing world where access to nature is often limited. Against this backdrop, this systematic review investigated studies on the effectiveness of small-scale greenery for stress reduction. We searched EMBASE, Cochrane, Web of Science, Scopus, PubMed, and Science Direct, searching databases from inception to April 2022. Studies were screened against predetermined criteria, and the risk of bias was assessed using the Cochrane Handbook for Systematic Reviews of Interventions for RCTs and The Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool. Of the 2500 records identified, we screened 1817 citations for eligibility, which included 13 RCT studies and 6 non-RCT studies. The studies were conducted in eight different countries. The study populations included office workers, students, senior citizens, and patients with specific diseases. Research has mainly focused on indoor greening, with relatively little research on small-scale outdoor greening. All included studies assessed the impact of the intervention on various stress reduction-related outcomes, with the most common stress measures being blood pressure and the State Trait Anxiety Inventory (STAI). Various beneficial effects of the interventions on human health were reported in all 19 studies, 15 of which reported positive effects on stress reduction. All included studies were at high risk of bias. It is recommended that future studies in this area take appropriate measures to reduce bias and improve quality in order to build a strong evidence-based medical foundation. According to our findings, even very small-scale greening, including indoor green walls and potted plants, may provide effective help for stress relief. Understanding the physiological and psychological benefits of small-scale greenery can help better provide more opportunities for urban residents to engage with nature in the context of dense urban trends, as well as provide some reference for urban design planning.

Novel Au-Se Nanoprobes for Specific Thrombin Detection in Diagnosis of Lung Cancer.

Thrombin is associated with malignant tumors and promotes tumor development, metastasis, and angiogenesis, therefore its identification especially in lung cancer cells is crucial. Because the interference of in vivo biothiols caused false positive findings with prior gold fluorescent nanoprobes, in this manuscript, an Au-selenol(Se) nanoprobe (5-FAM-peptide-Se-AuNPs) that could specifically detect thrombin was designed and compared to traditional Au-S nanoprobes. For reaching this goal, fluorophore-bearing thrombin-specific peptide containing selenol at the end was synthesized. The nanoprobe may be broken by thrombin to regain its fluorescence in lung cancer cells, allowing for high-sensitivity thrombin detection. Since the Au-Se bond is more stable than the Au-S bond, the accuracy of the detection results can be guaranteed. The probe synthesis method is simple and cost-effective, as well as having high biocompatibility. Low concentrations of thrombin can be detected and imaged in lung cancer cells. The synthetic method of this probe opens up new avenues for the application of Au-Se bonds.

Characterization of the tenofovir resistance-associated mutations in the hepatitis B virus isolates across genotypes A to D.

Tenofovir is wildly used to treat chronic hepatitis B (CHB) infection due to good potency and high genetic barrier to drug resistance. To date, it remains controversial whether hepatitis B virus (HBV) could be resistant to tenofovir. This study used multiple HBV isolates across genotypes A to D to characterize the reported tenofovir resistance-associated mutations identified from a few clinical case reports. Our data demonstrated that all the rtS78T, rtA194T, and CYEI (S106C, H126Y, D134E, and L269I) mutants exhibited no resistance to tenofovir in vitro. In contrast, the quadruple mutation MLVV (rtL180M, rtT184L, rtA200V, and rtM204V) decreased tenofovir susceptibility, with increased half maximal efficient concentration ranging from 3.28-fold to 5.34-fold, but it severely impaired viral fitness by reducing both replication capacity and infectivity of the mutants. Interestingly, basal core promoter (BCP) mutations A1762T/G1764A and precore (PC) mutation G1896A restored the replication capacity of the MLVV mutant to a level even higher than the wild-type virus without BCP and PC mutations. In conclusion, our data support the role of tenofovir as a first-line agent to treat CHB infection but also indicate tenofovir-resistant mutants could emerge due to multiple mutations accumulated during long-term therapy, particularly in hepatitis B e antigen-negative patients.

Circulating Abnormal Extracellular Vesicles: Their Mechanism for Crossing Blood-Brain Barrier, Effects on Central Nervous System and Detection Methods.

Central nervous system (CNS) diseases are difficult to treat and harmful. Many CNS diseases are secondary to peripheral diseases, such as tumor brain metastases (BMS), viral infections and inflammation of the brain, and their pathogenic factors travel through the circulatory system to the brain, eventually leading to lesions. Extracellular vesicles (EVs) play an important role in this process. Recent studies have shown that, extracellular EVs can effectively cross the blood- brain barrier (BBB) through endocytosis and they transmit molecular signals in cell-to-cell communication. Abnormal EVs produced in the lesion portion transport pathogenic factors, including miRNAs, proteins, and virions into the CNS. These pathogenic factors participate in cellular pathways to interfere with homeostasis or are themselves pathogens that directly damage CNS. In addition, different or specific pathological molecules in EVs are potential disease markers. We herein reviewed pathways through which the abnormal EVs cross BBB and adverse effects of abnormal exosomes. We also and summarized their existing detection techniques, so as to provide basis for prevention and early diagnosis of secondary diseases.

A high-performance SNP panel developed by machine-learning approaches for characterizing genetic differences of Southern and Northern Han Chinese, Korean, and Japanese individuals.

Population stratification analyses targeting genetically closely related East Asians have revealed that distinguishable differentiation exists between Han Chinese, Korean, and Japanese individuals, as well as between southern (S-) and northern (N-) Han Chinese. Previous studies offer a number of choices for ancestry informative single nucleotide polymorphisms (AISNPs) to discriminate East-Asian populations. In this study, we collected and examined the efficiency of 1185 AISNPs using frequency and genotype data from various publicly available databases. With the aim to perform fine-scale classification of S-Han, N-Han, Korean, and Japanese subjects, machine-learning methods (Softmax and Random Forest) were used to screen a panel of highly informative AISNPs and to develop a superior classification model. Stepwise classification was implemented to increase and balance the discrimination in the process of AISNP selection, first discriminating Han, Korean, and Japanese individuals, and then characterizing stratification between S-Han and N-Han. The final 272-AISNP panel is an alternative optimization of various previous works, which promises reliable and >90% accuracy in classification of the four East-Asian groups. This AISNP panel and the machine-learning model could be a useful and superior choice in medical genome-wide association studies and in forensic investigations for unknown suspect identity.