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Background The thrombolytic effect for ischemic stroke (IS) is affected by complex factors, such as acute onset of stroke, short therapeutic time window, various individual patient factors, treatment model, types and doses of medicines as well as mode of administration. To identify the influencing factors of thrombolytic effect, most existing studies adopt statistical methods, while rare studies use artificial intelligence (AI) -based algorithms.Objective To establish models using AI-based algorithms for IS patients based on the real-world data including general patient characteristics, medication model and recovery effects, to achieve precise individualized thrombolytic treatment and provide data support for clinical prescription decisions.Methods A retrospective design was used. The clinical information of IS patients (n=55 621) was extracted from the Yidu Cloud scientific research big data server system of the Second Affiliated Hospital of Dalian Medical University from January 1, 2001 to December 31, 2021, among whom 1 855 with complete information were enrolled according to the inclusion criteria. Thrombolysis effect was evaluated by comparing the National Institutes of Health Stroke Scale (NIHSS) score measured at admission and discharge, and those with an improvement in the NIHSS score by ≥4 points and

Background: Parkinson's Disease (PD) is a neurodegenerative disorder, and eye movement abnormalities are a significant symptom of its diagnosis. In this paper, we developed a multi-task driven by eye movement in a virtual reality (VR) environment to elicit PD-specific eye movement abnormalities. The abnormal features were subsequently modeled by using the proposed deep learning algorithm to achieve an auxiliary diagnosis of PD. Methods: We recruited 114 PD patients and 125 healthy controls and collected their eye-tracking data in a VR environment. Participants completed a series of specific VR tasks, including gaze stability, pro-saccades, anti-saccades, and smooth pursuit. After the tasks, eye movement features were extracted from the behaviors of fixations, saccades, and smooth pursuit to establish a PD diagnostic model. Results: The performance of the models was evaluated through cross-validation, revealing a recall of 97.65%, an accuracy of 92.73%, and a receiver operator characteristic area under the curve (ROC-AUC) of 97.08% for the proposed model. Conclusion: We extracted PD-specific eye movement features from the behaviors of fixations, saccades, and smooth pursuit in a VR environment to create a model with high accuracy and recall for PD diagnosis. Our method provides physicians with a new auxiliary tool to improve the prognosis and quality of life of PD patients. [ABSTRACT FROM AUTHOR]

Background: Continuous theta burst stimulation (cTBS) is a much more rapid protocol than low-frequency repetitive transcranial magnetic stimulation (rTMS), but no clinical trial has yet investigated the efficacy and mechanisms of cTBS for the treatment of generalized anxiety disorder. The purpose of this study was to compare the clinical effects and a oscillations induced by cTBS versus 1 Hz rTMS as predictors of response, and to assess the underlying mechanisms of the therapeutic effects of cTBS in patients with generalized anxiety disorder. Methods: We randomly allocated 120 patients with generalized anxiety disorder to receive cTBS (n = 41), 1 Hz rTMS (n = 40) or sham cTBS (n = 39) over the right dorsolateral prefrontal cortex; we also included healthy controls (n = 30) to compare neurophysiological data. We analyzed changes in Hamilton Anxiety Rating Scale scores and [alpha] oscillations (frequency and power) at baseline, posttreatment and 1-month follow-up. Results: After 20 sessions of treatment, patients' anxiety had improved and [alpha] power had increased in the cTBS and 1 Hz rTMS groups. However, at 1-month follow-up the cTBS group had significantly more responders and remitters, and higher [alpha] oscillations than the 1 Hz rTMS group (post hoc analysis: cTBS > rTMS > sham). At baseline, [alpha] frequency was inversely correlated with psychological symptom scores on the Hamilton Anxiety Rating Scale (r = -0.613, p < 0.001); post-treatment, this correlation was present only in the cTBS group (r = -0.685, p < 0.001). Limitations: Electroencephalography data were limited to the [alpha] band. Conclusion: Our findings provide evidence for the clinical use of cTBS, a novel brain stimulation protocol. Its therapeutic effects may be the result of increasing [alpha] frequency, thereby improving the psychological symptoms of generalized anxiety disorder., Introduction Generalized anxiety disorder (GAD) is one of the most prevalent neuropsychiatric disorders, and 50 % of patients have persistent anxiety despite treatment, (1) raising their risk for other systemic [...]

Moreover, the results of serum BDG and the CSF routine test results gradually improved. Cerebrospinal fluid (CSF) metagenomic next-generation sequencing (mNGS) and CSF serum (1,3)- -D-glucan (BDG) were used to aid in the diagnosis, and serum BDG was used to track the efficacy of antifungal therapy. This implied that serum BDG levels were useful in therapeutically monitoring CNS I Candida i infections, which is consistent with the results of Salvatore et al.[10] In conclusion, we report the first case of encephalomyelitis caused by I Candida parapsilosis i . Pathogens that cause fungal infections in the central nervous system (CNS) include the I Cryptococcus i , I Aspergillus i , I Histoplasma capsulatum i , and I Candida i species, among which the I Candida i species are uncommon. [Extracted from the article]

Abstract Background Cathepsin C (Cat C) functions as a central coordinator for activation of many serine proteases in inflammatory cells. It has been recognized that Cat C is responsible for neutrophil recruitment and production of chemokines and cytokines in many inflammatory diseases. However, Cat C expression and its functional role in the brain under normal conditions or in neuroinflammatory processes remain unclear. Our previous study showed that Cat C promoted the progress of brain demyelination in cuprizone-treated mice. The present study further investigated the Cat C expression and activity in lipopolysaccharide (LPS)-induced neuroinflammation in vivo and in vitro. Methods C57BL/6 J mice were intraperitoneally injected with either 0.9% saline or lipopolysaccharide (LPS, 5 mg/kg). Immunohistochemistry (IHC) and in situ hybridization (ISH) were used to analyze microglial activation, TNF-α, IL-1β, IL-6, iNOS mRNAs expressions and cellular localization of Cat C in the brain. Nitrite assay was used to examine microglial activation in vitro; RT-PCR and ELISA were used to determine the expression and release of Cat C. Cat C activity was analyzed by cellular Cat C assay kit. Data were evaluated for statistical significance with paired t test. Results Cat C was predominantly expressed in hippocampal CA2 neurons in C57BL/6 J mice under normal conditions. Six hours after LPS injection, Cat C expression was detected in cerebral cortical neurons; whereas, twenty-four hours later, Cat C expression was captured in activated microglial cells throughout the entire brain. The duration of induced Cat C expression in neurons and in microglial cells was ten days and three days, respectively. In vitro, LPS, IL-1β and IL-6 treatments increased microglial Cat C expression in a dose-dependent manner and upregulated Cat C secretion and its activity. Conclusions Taken together, these data indicate that LPS and proinflammatory cytokines IL-1β, IL-6 induce the expression, release and upregulate enzymatic activity of Cat C in microglial cells. Further investigation is required to determine the functional role of Cat C in the progression of neuroinflammation, which may have implications for therapeutics for the prevention of neuroinflammation-involved neurological disorders in the future.

This study aimed to assess changes in white matter microstructure among patients undergoing obstructive sleep apnea hypopnea syndrome (OSAHS) complicated by cognitive impairment through neurite orientation dispersion and density imaging (NODDI), and evaluate the relationship to cognitive impairment as well as the diagnostic performance in early intervention. Totally 23 OSAHS patients, 43 OSAHS patients complicated by cognitive impairment, and 15 healthy controls were enrolled in OSA, OSACI and HC groups of this work. NODDI toolbox and FMRIB's Software Library (FSL) were used to calculate neurite density index (NDI), Fractional anisotropy (FA), volume fraction of isotropic water molecules (Viso), and orientation dispersion index (ODI). Tract-based spatial statistics (TBSS) were carried out to examine the above metrics with one-way ANOVA. This study explored the correlations of the above metrics with mini-mental state examination (MMSE), and montreal cognitive assessment (MoCA) scores. Furthermore, receiver operating characteristic (ROC) curves were plotted. Meanwhile, area under curve (AUC) values were calculated to evaluate the diagnostic performance of the above metrics. NDI, ODI, Viso, and FA were significantly different among different brain white matter regions, among which, difference in NDI showed the greatest statistical significance. In comparison with HC group, OSA group had reduced NDI and ODI, whereas elevated Viso levels. Conversely, compared to the OSA group, the OSACI group displayed a slight increase in NDI and ODI values, which remained lower than HC group, viso values continued to rise. Post-hoc analysis highlighted significant differences in these metrics, except for FA, which showed no notable changes or correlations with neuropsychological tests. ROC analysis confirmed the diagnostic efficacy of NDI, ODI, and Viso with AUCs of 0.6908, 0.6626, and 0.6363, respectively, whereas FA's AUC of 0.5042, indicating insufficient diagnostic efficacy. This study confirmed that NODDI effectively reveals microstructural changes in white matter of OSAHS patients with cognitive impairment, providing neuroimaging evidence for early clinical diagnosis and intervention. [ABSTRACT FROM AUTHOR]

Mechanical thrombectomy (MT) has become an effective re-airway method for cerebral ischemia-reperfusion injury (CI/RI). However, at present, there are few studies on the impact of MT therapy on the prognosis of CI/RI patients at home and abroad. Therefore, this paper aims to analyze the relevant factors affecting the prognosis of CI/RI patients after MT therapy. The main regulatory miRNAs during CI/RI in patients with MT were screened and studied. Serums were obtained from 80 patients (moderate to severe stroke) who underwent MT. Clinical information was recorded using a unified standard questionnaire. According to the modified Rankin Scale, the patients were divided into a good prognosis group and a poor prognosis group. The clinical data were compared respectively, and univariate and multivariate Logistic regression analysis was performed. ROC curves were drawn, and Kaplan-Merier method determined whether different NIHSS scores at admission had any difference in the in-hospital survival rate of CI/RI patients treated with MT. miRNAs in serum were detected and screened out. Cell and animal models were established, in which miRNAs and apoptotic molecules were detected. miRNA target genes were predicted, and the mechanism of miRNA regulation of apoptosis was verified. Gender, smoking, drinking, diabetes, hypertension, hyperlipidemia, age, and alcohol consumption suggested no difference in the two groups. The rates of smoking history, diabetes, hypertension, and hyperlipidemia in the poor prognosis group were higher than those in the good prognosis group. Smoking and diabetes were independent risk factors for poor prognosis. miR-127-5p expression in CI/RI patients with poor prognosis was higher than that in those with good prognosis. miR-127-5p expression was also elevated in both cell and animal models. Cell apoptosis was weakened after miR-127-5p knockdown, and tissue infarction in animal models was also reduced. FAIM2 was a target gene of miR-127-5p. silencing FAIM2 enhanced apoptosis after miR-127-5p knockdown. miR-127-5p/FAIM2 axis can be a new strategy to treat and prevent brain injury in CI/RI patients treated with MT.

For the near-infrared (NIR) spectral analysis of the concentration of blood glucose, the calibration accuracy can be affected because of the existing of outlier samples. In this research, a Monte-Carlo cross validation (MCCV) method is constructed for eliminating outlier samples. The human blood plasma experiment in vitro and the human body experiment in vivo were introduced to evaluate the MCCV method for its application effect in NIR spectral analysis of blood glucose. And the uninformative sample elimination method based on modified uninformative variable elimination (MUVE-USE) was employed in this study for the comparison with MCCV. The results indicated that, like the MUVE-USE method, the outlier samples elimination method based on MCCV could be used to eliminate the outlier samples which came from gross errors (such as bad sample) or system errors (such as baseline drift). In addition, the outlier samples from the random errors of uncertain causes which affect model accuracy can be eliminated simultaneously by MCCV. The elimination of multiple outlier samples is beneficial to the improvement of prediction accuracy of calibration model.