Objective To evaluate the relation of human immunodeficiency virus (HIV)-1 ribonucleic acid (RNA) loads in cerebrospinal fluid with central neurological diseases. Methods The inpatients with HIV-1 infection diagnosed by Public Health Clinical Center of Chengdu between January 1st, 2015 and March 1st, 2018 were retrospectively included. The included patients were divided into central neurological disease group and non-central neurological disease group, and high viral load group and low viral load group. The demographic data, CD4+ T lymphocyte count, routine detection of cerebrospinal fluid, HIV RNA load in cerebrospinal fluid and plasma of patients with and without central neurological diseases were observed and compared.Multiple logistic regression analysis was used to identify risk factors for central neurological diseases. Results A total of 367 patients were included. In the central neurological disease group, 210 cases (57.22%) were complicated with central neurological diseases, and cryptococcus infection was the most. Compared with the non-central neurological disease group, the increase rate of cerebrospinal fluid cell counts, cerebrospinal fluid cell counts, cerebrospinal fluid HIV RNA positivity and cerebrospinal fluid HIV RNA load were higher in the central neurological disease group (P<0.05). Logistic regression analysis showed that HIV RNA load in cerebrospinal fluid≥100 000 copies/mL and CD4+ T lymphocyte count<200 cells/mm3 were risk factors for central neurological diseases. Conclusion Cerebrospinal fluid HIV RNA load≥100 000 copies/mL is an independent risk factor for HIV/AIDS patients with central neurological diseases and clinical treatment should take this factor into consideration to reasonably optimize the selection of antiretroviral therapy.
Age is the main cause of neurodegenerative changes in the central nervous system (CNS), and the loss of neurons would increase with the migration of the disease. The current treatment is also mainly used to relieve symptoms, while the function of CNS is very difficult to recover. The emergence of endogenous stem cells has brought new hope for the treatment of CNS diseases. However, this nerve regeneration is only in some specific areas, and the recovery of neural function remains unknown. More and more experts in the field of neuroscience have carried out various in vivo or in vitro experiments, in order to increase nerve regeneration and nerve function recovery through mechanism research, in the expectation that the results would be applied to the treatment of CNS diseases. This article reviews the recent progress of endogenous neural stem cells in degenerative diseases of CNS.
Objective To develop a novel prediction model based on cerebrospinal fluid (CSF) lactate for early identification of high-risk central nervous system (CNS) infection patients in the emergency setting. Methods Patients diagnosed with CNS infections admitted to the Department of Emergency Medicine of West China Hospital, Sichuan University between January 1, 2020 and December 31, 2023 were retrospectively selected. Patients were classified into a survival group and a death group according to their 28-day survival status, and clinical characteristics were compared between groups. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of 28-day mortality, which were subsequently used to construct a nomogram. Results A total of 173 patients were included, comprising 135 in the survival group and 38 in the death group. Multivariate analysis identified the Acute Physiology and Chronic Health Evaluation Ⅳ (APACHE Ⅳ) score [odds ratio (OR)=1.027, 95% confidence interval (CI) (1.002, 1.055), P=0.034], CSF lactate [OR=1.147, 95%CI (1.025, 1.286), P=0.018], and interleukin-6 [OR=1.002, 95%CI (1.001, 1.004), P=0.002] as independent predictors of 28-day mortality. The integrated model combining APACHE Ⅳ score, CSF lactate, and interleukin-6, demonstrated superior predictive performance compared with the APACHE Ⅳ score alone (P=0.020), and showed good calibration (Hosmer-Lemeshow P=0.50). Conclusions This tool may provide a useful instrument for emergency physicians to assess the 28-day mortality risk in patients with CNS infections, potentially facilitating early and targeted interventions for high-risk individuals. However, as the findings of this study are derived from a single-center retrospective dataset, the clinical applicability of this model requires further external validation through large-scale, prospective, multicenter studies to evaluate its generalizability.
Idiopathic intracranial hypertension (IIH) is a neurological disease, characterized by increased intracranial pressure and papilledema, and often associated with headache, transient loss of vision and pulsatile tinnitus. IIH typically occurs in women of childbearing age. Over 90.0% of patients are with obesity or over weighted. Loss of sensory visual function is the major morbidity associated with IIH and some patients even develop into blindness. Most patients will have varied degrees of visual impairment, or even a few become blind. Frisén grading system, visual field examination and spectral-domain optical coherence tomography can be used to evaluate and monitor the IIH papilledema functionally and morphologically. In recent years, IIH treatment trials in other countries confirmed that, weight loss and low-salt diet combined with acetazolamide treatment has a clear improvement for IIH patients with mild visual impairment. In-depth understanding of the etiology, clinical manifestations, diagnostic criteria and the main treatment has important clinical significance for IIH patients
ObjectiveTo analyze the effect of carotid artery stenosis degree and intervention for carotid artery stenosis on the incidence of central nervous system complications after off-pump coronary artery bypass grafting (OPCABG) and explore the influencing factors. MethodsA total of 1 150 patients undergoing OPCABG in our hospital from June 2018 to June 2021 were selected and divided into two groups according to whether there were central nervous system complications, including a central nervous system complication group [n=61, 43 males and 18 females with a median age of 68.0 (63.0, 74.0) years] and a non-central nervous system complication group [n=1 089, 796 males and 293 females with a median age of 65.5 (59.0, 70.0) years]. The risk factors for central nervous system complications after OPCABG were analyzed. ResultsUnivariate analysis showed that age, smoking, hyperlipidemia, preoperative left ventricular ejection fraction, intra-aortic ballon pump (IABP), postoperative arrhythmia, postoperative thoracotomy and blood transfusion volume were associated with central nervous system complications. The incidence of central nervous system complications in patients with severe carotid artery stenosis or occlusion (11.63%) was higher than that in the non-stenosis and mild stenosis patients (4.80%) and moderate stenosis patients (4.76%) with a statistical difference (P=0.038). The intervention for carotid artery stenosis before or during the operation did not reduce the incidence of central nervous system complications after the operation (42.11% vs. 2.99%, P<0.001). Age, postoperative arrhythmia, severe unilateral or bilateral carotid artery stenosis and occlusion were independent risk factors for postoperative central nervous system complications (P<0.05). Conclusion The age, smoking, hyperlipidemia, preoperative left ventricular ejection fraction, intraoperative use of IABP, postoperative arrhythmia, secondary thoracotomy after surgery, blood transfusion volume and OPCABG are associated with the incidence of postoperative central nervous system complications in patients. Age, postoperative arrhythmia, severe unilateral or bilateral carotid artery stenosis and occlusion are independent risk factors for postoperative central nervous system complications. In patients with severe carotid artery stenosis, preoperative treatment of the carotid artery will not reduce the incidence of central nervous system complications.
ObjectiveTo systematically evaluate the clinical value of machine learning (ML) for predicting the neurological outcome of out-of-hospital cardiac arrest (OHCA), and to develop a prediction model. MethodsWe searched the PubMed, Web of Science, EMbase, CNKI, Wanfang database from January 1, 2011 to November 24, 2021. Studies on ML for predicting neurological outcomes in OHCA pateints were collected. Two researchers independently screened the literature, extracted the data and evaluated the bias of the included literature, evaluated the accuracy of different models and compared the area under the receiver operating characteristic curve (AUC). ResultsA total of 20 studies were included. Eleven of the studies were from open source databases and nine were from retrospective studies. Sixteen studies directly predicted OHCA neurological outcomes, and four predicted OHCA neurological outcomes after target temperature management. A total of seven ML algorithms were used, among which neural network was the ML algorithm with the highest frequency (n=5), followed by support vector machine and random forest (n=4). Three papers used multiple algorithms. The most frequently used input characteristic was age (n=19), followed by heart rate (n=17) and gender (n=13). A total of 4 studies compared the predictive value of ML with other classical statistical models, and the AUC value of ML model was higher than that of classical statistical models. ConclusionExisting evidence suggests that ML can more accurately predict OHCA nervous system outcomes, and the predictive performance of ML is superior to traditional statistical models in certain situations.