Revealing Novel Genes Related to Parkinson's Disease Pathogenesis and Establishing an associated Model.

Parkinson's disease (PD) represents a multifaceted neurological disorder whose genetic underpinnings warrant comprehensive investigation. This study focuses on identifying genes integral to PD pathogenesis and evaluating their diagnostic potential. Initially, we screened for differentially expressed genes (DEGs) between PD and control brain tissues within a dataset comprising larger number of specimens. Subsequently, these DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to discern relevant gene modules. Notably, the yellow module exhibited a significant correlation with PD pathogenesis. Hence, we conducted a detailed examination of the yellow module genes using a cytoscope-based approach to construct a protein-protein interaction (PPI) network, which facilitated the identification of central hub genes implicated in PD pathogenesis. Employing two machine learning techniques, including XGBoost and LASSO algorithms, along with logistic regression analysis, we refined our search to three pertinent hub genes: FOXO3, HIST2H2BE, and HDAC1, all of which demonstrated a substantial association with PD pathogenesis. To corroborate our findings, we analyzed two PD blood datasets and clinical plasma samples, confirming the elevated expression levels of these genes in PD patients. The association of the genes with PD, as reflected by the area under the curve (AUC) values for FOXO3, HIST2H2BE, and HDAC1, were moderate for each gene. Collectively, this research substantiates the heightened expression of FOXO3, HIST2H2BE, and HDAC1 in both PD brain and blood samples, underscoring their pivotal contribution to the pathogenesis of PD.

ADORA3: A Key Player in the Pathogenesis of Intracranial Aneurysms and a Potential Diagnostic Biomarker.

BACKGROUND: The effects of genes on the development of intracranial aneurysms (IAs) remain to be elucidated, and reliable blood biomarkers for diagnosing IAs are yet to be established. This study aimed to identify genes associated with IAs pathogenesis and explore their diagnostic value by analyzing IAs datasets, conducting vascular smooth muscle cells (VSMC) experiments, and performing blood detection. METHODS: IAs datasets were collected and the differentially expressed genes were analyzed. The selected genes were validated in external datasets. Autophagy was induced in VSMC and the effect of selected genes was determined. The diagnostic value of selected gene on the IAs were explored using area under curve (AUC) analysis using IAs plasma samples. RESULTS: Analysis of 61 samples (32 controls and 29 IAs tissues) revealed a significant increase in expression of ADORA3 compared with normal tissues using empirical Bayes methods of "limma" package; this was further validated by two external datasets. Additionally, induction of autophagy in VSMC lead to upregulation of ADORA3. Conversely, silencing ADORA3 suppressed VSMC proliferation and autophagy. Furthermore, analysis of an IAs blood sample dataset and clinical plasma samples demonstrated increased ADORA3 expression in patients with IA compared with normal subjects. The diagnostic value of blood ADORA3 expression in IAs was moderate when analyzing clinical samples (AUC: 0.756). Combining ADORA3 with IL2RB or CCR7 further enhanced the diagnostic ability for IAs, with the AUC value over 0.83. CONCLUSIONS: High expression of ADORA3 is associated with IAs pathogenesis, likely through its promotion of VSMC autophagy. Furthermore, blood ADORA3 levels have the potential to serve as an auxiliary diagnostic biomarker for IAs.

The impact of COVID-19 on a Southern Chinese cohort with neuromyelitis optica spectrum disorders.

BACKGROUND: There are few studies on risk factors for coronavirus disease 2019 (COVID-19) infection in patients with Neuromyelitis Optica Spectrum Disorders (NMOSD). The relationship between NMOSD relapse and COVID-19 needs to be evaluated. The objective of our study is to identify the risk factors of COVID-19 infection and NMOSD relapse among NMOSD patients with COVID-19. METHOD: A total of 379 NMOSD patients registered in a NMOSD database were included in this case-control study after the end of the COVID-19 quarantine and restriction policies on December 6, 2022 in China. Data were obtained from the database. Additional information was obtained by questionnaires and the Neurology out-patient clinic. The clinical characteristics of NMOSD patients with COVID-19 were described. Risk factors associated with COVID-19 infection and outcome among patients with NMOSD were analyzed. Risk factors associated with relapse in NMOSD patients with COVID-19 were also identified. RESULTS: 239 (63.1%) NMOSD patients were infected with COVID-19. Patients with NMOSD who were infected with COVID-19, in comparison to those without COVID-19, were younger at the time of interview (median [IQR] age: 43.00 [32.00-55.00] vs 49.50 [35.25-56.00] years, P = 0.029), younger at NMOSD onset (median [IQR] age: 38.00 [27.00-51.00] vs 45.00 [32.00-52.75] years, P = 0.013), had abnormal visual evoked potentials before infection (73.4% vs 54.3% P = 0.029), had lower baseline Activities of Daily Living Scale (ADL) scores (median [IQR] ADL: 14.00 [14.00-16.00] vs 14.00 [14.00-19.00], P = 0.014) or lower baseline modified Rankin Scale (mRS) scores (1.12±0.749 vs 1.33±0.991, P = 0.037), and were less frequently treated with more than 10 mg prednisone or 8 mg methylprednisolone (25.0% vs 36.0%,p = 0.026). All 9 NMOSD patients who had symptomatic cerebral syndrome developed moderate/severe COVID-19. A higher percentage of patients with moderate/severe COVID-19 experienced more than one core clinical NMOSD symptoms (61.5% vs 55.1%, p = 0.044), compared to patients with mild COVID-19. Higher risk of NMOSD relapse among NMOSD patients with COVID-19 was associated with higher Expanded Disability Status Scale (EDSS) scores (median[IQR] EDSS: 2.00 [1.00-3.00] vs 1.50 [1.00-2.25], P = 0.037) and drug treatments disruption (21.6% vs 5.0% P<0.001). CONCLUSIONS: NMOSD patients with younger age, lower baseline ADL or mRS had higher incidence of being diagnosed with COVID-19 during pandemic. Glucocorticoid use may decrease the risk of COVID-19. NMOSD patients with symptomatic cerebral syndrome before the COVID-19 pandemic are associated with worse COVID-19 outcomes. Drug treatment disruption may result in relapse among NMOSD patients with COVID-19.

Clinical Characteristics of Myasthenia Gravis Patients with COVID-19 in Guangxi, China: A Case-Control Study.

Purpose: With the adjustment of prevention strategies in December 2022, coronavirus disease 2019 (COVID-19) became widely prevalent in China. This study is aimed to describe the clinical characteristics of myasthenia gravis (MG) patients with COVID-19 and identify risk factors of exacerbation in MG patients with COVID-19 in Guangxi. Patients and Methods: A total of 489 MG patients and 587 control subjects in Guangxi during the COVID-19 pandemic were enrolled in this case-control study. After contacting the participants, the clinical data of MG patients and the control group were analyzed. The clinical characteristics of MG patients with COVID-19 were described. Multivariable logistic regression analysis was used for discovering independent risk factors of MG exacerbation in the patients with MG and COVID-19. Results: A total of 311 (75.30%) MG patients and 428 (72.91%) control subjects were infected with COVID-19, and 64.31% of MG patients with COVID-19 were women. The median age at the time of interview was 41 (IQR: 28, 54) years old, and median onset age was 36 (IQR: 24, 51), both of which were lower than those in MG patients without COVID-19. MG duration was 24 (IQR: 9, 72) months. About 44.69% of patients were generalized MG (GMG). About 11.90% of MG patients with COVID-19 showed severe COVID-19 symptoms and the duration of symptomatic COVID-19 was 9.57 ± 6.79 days, higher than those in the control group. About 35.69% MG patients with immunosuppressive drugs were infected with COVID-19, which is higher than those in the non-infected MG patients (21.57%). A total of 120 (38.59%) MG patients with COVID-19 had comorbidities. About 21 (20.19%) of the 104 MG patients without vaccination showed severe COVID-19 symptoms. Multivariable logistic regression analysis showed that baseline MG activities of daily living profile (MG-ADL, OR 1.280, 95% CI: 1.010-1.621, p = 0.041), duration of COVID-19 (OR 1.158, 95% CI: 1.100-1.220, p < 0.001), GMG (OR 2.331, 95% CI: 1.228, 4.426, p = 0.010), and lack of COVID vaccination (OR 2.075, 95% CI: 1.152, 3.738, p = 0.015) were independent factors of exacerbation in MG patients with COVID-19. Conclusion: MG patients with immunosuppressive drugs, younger onset, longer MG duration, or comorbidities are more susceptible to COVID-19. The baseline MG-ADL, duration of symptomatic COVID-19, GMG, and lack of COVID-19 vaccination are independent risk factors of exacerbation in MG patients with COVID-19.

Co-expression Network Analysis Reveals Novel Genes Underlying Alzheimer's Disease Pathogenesis.

Background: The pathogenesis of Alzheimer's disease (AD) remains to be elucidated. This study aimed to identify the hub genes in AD pathogenesis and determine their functions and pathways. Methods: A co-expression network for an AD gene dataset with 401 samples was constructed, and the AD status-related genes were screened. The hub genes of the network were identified and validated by an independent cohort. The functional pathways of hub genes were analyzed. Results: The co-expression network revealed a module that related to the AD status, and 101 status-related genes were screened from the trait-related module. Gene enrichment analysis indicated that these status-related genes are involved in synaptic processes and pathways. Four hub genes (ENO2, ELAVL4, SNAP91, and NEFM) were identified from the module, and these hub genes all participated in AD-related pathways, but the associations of each gene with clinical features were variable. An independent dataset confirmed the different expression of hub genes between AD and controls. Conclusions: Four novel genes associated with AD pathogenesis were identified and validated, which provided novel therapeutic targets for AD.

[Preparation of hydrophilic polymer brushes magnetic microspheres by surface-initiated for magnetic dispersive solid phase extraction of tetracycline antibiotic residues from honey].

Activators regenerated by electron transfer-atom transfer radical polymerization (ARGET-ATRP) was applied to the continuous grafting of polybasic polymers and poly(ethylene glycol) (PEG) brushes on the surface of the magnetic microspheres (MMs). At first, the MMs were coated with silica gel, modified by amino group, and then 2-bromoisobutyryl bromide was grafted on the surface of MMs. After that, the hydrophilic polymer brushes magnetic microspheres (HMMs) were prepared by polymerization on the surface of the MMs. HMMs were characterized by transmission electron microscope (TEM) and Fourier transform-infrared (FT-IR) spectrometer. The adsorption performance to protein was studied. The results demonstrated that HMMs have the relatively uniform particle size, good dispersity and excellent protein resistance properties. Tetracycline antibiotics (TCs, tetracycline hydrochloride, chlortetracycline hydrochloride and doxycycline hydrochloride) in honey samples were determined by magnetic dispersion solid phase extraction (MDSPE) using the prepared HMMs and high performance liquid chromatography (HPLC). The average recoveries were obtained in the range of 85.8%-94.5%. The limits of detection (LODs) and limits of quantification (LOQs) of the proposed method were in the ranges of 1.92-2.56 µg/kg and 6.40-8.53 µg/kg, respectively. The proposed method was successfully applied to fast clean-up and enrichment of the TCs residues in honey.

VEGF levels in CSF and serum in mild ALS patients.

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disorder involving both upper and lower motor neurons in the cerebral cortex, brainstem and spinal cord. Vascular endothelial growth factor (VEGF) was originally described as a factor with a regulatory role in vascular growth and development, and now it also functions as a neurotrophic factor protecting motoneurons from insults such as oxidative stress, hypoxia and glutamate-excitotoxicity, but the role of VEGF in ALS is still unclear. The aim of this study is to measure cerebrospinal fluid (CSF) and serum VEGF levels in patients with ALS, and to investigate whether there are correlations between CSF and serum VEGF levels and clinical parameters of the disease and whether VEGF has a prognostic and evaluating potential for ALS. Results showed that VEGF levels were found to increase significantly in CSF and serum in ALS patients studied; they were positively and significantly correlated with the disease duration in ALS patients and inversely and significantly correlated with disease progression rate (DPR) of ALS patients. Moreover, CSF and serum from ALS patients with long duration and slow disease progression rate revealed higher VEGF levels as compared to ALS patients with short duration and rapid disease progression rate. In conclusion, VEGF upregulation may indicate an activation of compensatory responses in ALS which may reflect or in fact account for increased duration and slow disease progression rate. We propose that VEGF may be a useful biomarker having the prognostic and evaluating potential for ALS.

Two-level time-domain decomposition based distributed method for numerical solutions of pharmacokinetic models.

In order to predict variations of drug concentration during a given period of time, numerical solutions of pharmacokinetic models need to be obtained efficiently. Analytical solutions of linear pharmacokinetic models are usually obtained using the Laplace transform and inverse Laplace tables. The derivations of solutions to complex nonlinear models are tedious, and such solution process may be difficult to implement as a robust software code. For nonlinear models, the fourth-order Runge-Kutta (RK4) is the most classical numerical method in obtaining approximate numerical solutions, which is impossible to be implemented in distributed computing environments without much modification. The reason is that numerical solutions obtained by using RK4 can only be computed in sequential time steps. In this paper, time-domain decomposition methods are adapted for nonlinear pharmacokinetic models. The numerical Inverse Laplace method for PharmacoKinetic models (ILPK) is implemented to solve pharmacokinetic models with iterative inverse Laplace transform in each time interval. The distributed ILPK algorithm, which is based on a two-level time-domain decomposition concept, is proposed to improve its efficiency. Solutions on the coarser temporal mesh at the top level are obtained one by one, and then those on the finer temporal mesh at the bottom level are calculated concurrently by using those initial solutions that have been obtained at the top level decomposition. Accuracy and efficiency of the proposed algorithm and its distributed equivalent are investigated by using several test models. Results indicate that the ILPK algorithm and its distributed equivalent are good candidates for both linear and nonlinear pharmacokinetic models.