Gang Zhao

Single-cell sequencing and multi-omics technologies are revolutionizing research on central nervous system (CNS) diseases by enabling high-resolution analysis of cellular heterogeneity and molecular dynamics. Traditional technologies (e.g., bulk sequencing, routine histology) often lack cellular resolution, fail to capture heterogeneity among individual cells, and struggle to reveal subtle molecular changes in early pathogenesis, limiting their ability to clarify complex CNS disease mechanisms and develop precise diagnostic tools. This review comprehensively summarizes the latest advances in single-cell multi-omics methodologies, including genomics, transcriptomics, proteomics, metabolomics, and spatial omics, and their applications in elucidating the pathogenesis, diagnosis, and treatment of common CNS disorders. Representative diseases such as ischemic stroke, Alzheimer’s disease, Parkinson’s disease, viral meningitis, bacterial meningitis, multiple sclerosis, autism spectrum disorder, and depression are used as examples to discuss the current status and future prospects of single-cell multi-omics technologies in CNS disease research. Currently, these technologies have enabled the identification of rare pathogenic cell subsets, the mapping of cell-specific molecular pathways, and the discovery of potential diagnostic biomarkers in several common CNS disorders, though their clinical translation is still hindered by technical costs and standardization issues. In the future, the integration of single-cell multi-omics with spatial transcriptomics, artificial intelligence, and clinical data is expected to further decode the complex pathogenesis of CNS disorders, accelerate the development of targeted therapies, and promote the shift toward personalized medicine in CNS disease management—aligning with translational goals of neuropsychopharmacology.

An ultra-sensitive photoelectrochemical (PEC) biosensor for amyloid-beta 40 (Aβ40), a biomarker for Alzheimer’s disease (AD), was developed using g-C₃N₄ modified with gold nanoparticles (Au NPs) to form Au-C₃N₄. This was further combined with TiO₂ to create a tightly bonded TiO₂/Au-C₃N₄ heterojunction, leading to a highly responsive photocatalytic process. Furthermore, the incorporation of noble metal Au NPs not only enhances photocurrent generation but also securely immobilizes the aptamer through Au–S bonds, providing additional surface binding sites. This significantly increases the sensor's capture efficiency. The sensor exhibited excellent performance, featuring a linear detection range from 10⁻¹⁵ to 10⁻¹¹ g/mL and a remarkably low detection limit (LOD) of 0.33 fg/mL. Moreover, the validation in clinical settings demonstrated the successful detection in real cerebrospinal fluid (CSF) and plasma, from AD patients and non-AD controls. These results strongly suggest that PEC biosensors possess significant potential as cost-effective and highly sensitive tools for detecting ultra-trace substances in human body fluids, which offers promising opportunities for the early screening of high-risk populations for AD. Graphical Abstract

Background: Tuberculous meningitis (TBM) is the most severe form of tuberculosis (TB) and can be difficult to diagnose and treat. We aimed to describe the clinical presentation, diagnosis, disease spectrum, outcome, and prognostic factors of patients treated for TBM in China.Methods: A multicenter retrospective study was conducted from 2009 to 2019 enrolling all presumptive TBM patients referred to Xijing tertiary Hospital from 27 referral centers in and around Shaanxi province, China. Patients with clinical features suggestive of TBM (abnormal CSF parameters) were included in the study if they had adequate baseline information to be classified as “confirmed,” “probable,” or “possible” TBM according to international consensus TBM criteria and remained in follow-up. Patients with a confirmed alternative diagnosis or severe immune compromise were excluded. Clinical presentation, central nervous system imaging, cerebrospinal fluid (CSF) results, TBM score, and outcome—assessed using the modified Barthel disability index—were recorded and compared.Findings: A total of 341 presumptive TBM patients met selection criteria; 63 confirmed TBM (25 culture positive, 42 Xpert-MTB/RIF positive), 66 probable TBM, 163 possible TBM, and 49 “not TBM.” Death was associated with BMRC grade III (OR = 5.172; 95%CI: 2.298–11.641), TBM score ≥ 15 (OR = 3.843; 95%CI: 1.372–10.761), age > 60 years (OR = 3.566; 95%CI: 1.022–12.442), and CSF neutrophil ratio ≥ 25% (OR = 2.298; 95%CI: 1.027–5.139). Among those with confirmed TBM, nearly one-third (17/63, 27.0%) had a TBM score < 12; these patients exhibited less classic meningitis symptoms and signs and had better outcomes compared with those with a TBM score ≥ 12. In this group, signs of disseminated/miliary TB (OR = 12.427; 95%CI: 1.138–135.758) and a higher TBM score (≥15, OR = 8.437; 95%CI: 1.328–53.585) were most strongly associated with death.Conclusion: TBM patients who are older (>60 years) have higher TBM scores or CSF neutrophil ratios, have signs of disseminated/miliary TB, and are at greatest risk of death. In general, more effort needs to be done to improve early diagnosis and treatment outcome in TBM patients.

Background: As one of the leading causes of morbidity and mortality, stroke and its recurrence has attracted more and more attention. Dl-3-n-butylphthalidle(NBP) has been widely used for treating acute ischemic stroke in China and shows a great clinical effect. NBP plays a role in different pathophysiological processes in the treatment of ischemic stroke, including antioxidants, anti-inflammatory, anti-apoptotic, anti-thrombosis, and mitochondrial protection. Many randomized, double-blind, placebo-controlled, multicenter clinical trials suggest that NBP is a safe and effective treatment for ischemic stroke. To sum up, the current research is mainly focused on the short-term treatment of stroke patients with RCT (randomized controlled trial). Therefore, we designed this study to confirm the role of butylphthalide in secondary stroke prevention in the real world.Methods: This study will be a multicenter, prospective real-world trial. We would recruit 8000 patients with ischemic stroke from 78 public hospitals in China. All participants will be allocated to one of two parallel treatment groups according to their own wills: (1) butylphthalide group: 0.2 g of butylphthalide capsules three times daily plus routine treatment (aspirin 50-300 mg/d, clopidogrel 75 mg/d, etc.); (2) control group: routine treatment (aspirin 50-300 mg/d, clopidogrel 75 mg/d, etc.). Treatment duration is 90 consecutive days or more. The primary outcome is recurrence rate of stroke within 1 month, 3 months, 6 months and 1 year in butylphthalide group and control group. The secondary outcomes included NIHSS score, the mRS score, other clinical cardiovascular events within one year (sudden death / myocardial infarction / arrhythmia / heart failure, etc.), and adverse events of patients in groups. NIHSS will be captured in the first month after discharge, and the others will be captured at the same time points as the primary end point.Discussion: This trial will be exploring the efficacy and safety of butylphthalide in secondary prevention of ischemic stroke to expand the scope of application of butylphthalide soft capsules and provide new ideas for enriching the secondary prevention of stroke.Trial registration: Chinese Clinical Trial Registry (ChiCTR). Trial registration number: ChiCTR2000034481. Registered on 6 July 2020,http://www.chictr.org.cn/showproj.aspx?proj=55800

Introduction Early diagnosis and etiological treatment can effectively improve the prognosis of patients with autoimmune encephalitis (AE). However, anti-neuronal antibody tests which provide the definitive diagnosis require time and are not always abnormal. By using natural language processing (NLP) technology, our study proposes an assisted diagnostic method for early clinical diagnosis of AE and compares its sensitivity with that of previously established criteria. Methods Our model is based on the text classification model trained by the history of present illness (HPI) in electronic medical records (EMRs) that present a definite pathological diagnosis of AE or infectious encephalitis (IE). The definitive diagnosis of IE was based on the results of traditional etiological examinations. The definitive diagnosis of AE was based on the results of neuronal antibodies, and the diagnostic criteria of definite autoimmune limbic encephalitis proposed by Graus et al. used as the reference standard for antibody-negative AE. First, we automatically recognized and extracted symptoms for all HPI texts in EMRs by training a dataset of 552 cases. Second, four text classification models trained by a dataset of 199 cases were established for differential diagnosis of AE and IE based on a post-structuring text dataset of every HPI, which was completed using symptoms in English language after the process of normalization of synonyms. The optimal model was identified by evaluating and comparing the performance of the four models. Finally, combined with three typical symptoms and the results of standard paraclinical tests such as cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), or electroencephalogram (EEG) proposed from Graus criteria, an assisted early diagnostic model for AE was established on the basis of the text classification model with the best performance. Results The comparison results for the four models applied to the independent testing dataset showed the naïve Bayesian classifier with bag of words achieved the best performance, with an area under the receiver operating characteristic curve of 0.85, accuracy of 84.5% (95% confidence interval [CI] 74.0–92.0%), sensitivity of 86.7% (95% CI 69.3–96.2%), and specificity of 82.9% (95% CI 67.9–92.8%), respectively. Compared with the diagnostic criteria proposed previously, the early diagnostic sensitivity for possible AE using the assisted diagnostic model based on the independent testing dataset was improved from 73.3% (95% CI 54.1–87.7%) to 86.7% (95% CI 69.3–96.2%). Conclusions The assisted diagnostic model could effectively increase the early diagnostic sensitivity for AE compared to previous diagnostic criteria, assist physicians in establishing the diagnosis of AE automatically after inputting the HPI and the results of standard paraclinical tests according to their narrative habits for describing symptoms, avoiding misdiagnosis and allowing for prompt initiation of specific treatment.

Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope. Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage. Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly. Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists. Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.

Fast and accurate cerebrospinal fluid cytology is the key to the diagnosis of many central nervous system diseases. However, in actual clinical work, cytological counting and classification of cerebrospinal fluid are often time-consuming and prone to human error. In this report, we have developed a deep neural network (DNN) for cell counting and classification of cerebrospinal fluid cytology. The May-Grünwald-Giemsa (MGG) stained image is annotated and input into the DNN network. The main cell types include lymphocytes, monocytes, neutrophils, and red blood cells. In clinical practice, the use of DNN is compared with the results of expert examinations in the professional cerebrospinal fluid room of a First-line 3A Hospital. The results show that the report produced by the DNN network is more accurate, with an accuracy of 95% and a reduction in turnaround time by 86%. This study shows the feasibility of applying DNN to clinical cerebrospinal fluid cytology.

The microbiome is prevalent throughout human bodies, with profound health implications. However, it remains unclear whether it is present and active in human CSF, which has been long considered aseptic due to the blood-brain barrier.

Objective: Tuberculosis infection of the Central Nervous System can cause severe inflammation in microglia, and NLRP3 inflammasome is also an important source of inflammation in microglia. Therefore, in this study, we used a co-culture model of rat microglia and tuberculosis H37Ra strain to explore the influence of tuberculosis infection on the NLRP3 inflammasome in microglia and its regulation mechanism.Methods: We cultured primary microglia from SD rats and co-cultured with tuberculosis H37Ra strain for 4 hours to establish a co-culture model. At the same time, MCC950, Z-YVAD-FMK, BAY-11-7082, Dexamethasone, RU486, BzATP, BBG and extracellular high potassium environment were used to intervene the co-cultivation process. Subsequently, western blot, real-time PCR, ELISA and other methods were used to detect the changes of NLRP3 inflammasome-related molecules in microglia.Results: After co-cultivation, the NLRP3 inflammasomes in microglia were activated and released a large amount of IL-18 and IL-1β. By regulating NLRP3 inflammasome complex, caspase-1, NF-κB and P2X7R during the co-culture process, it could effectively reduce the release of IL-18 and IL-1β, and the mortality of microglia.Conclusion: Our results indicate that the NLRP3 inflammasome pathway is an important part of the inflammatory response of microglia caused by tuberculosis infection. By intervening the NLRP3 inflammasome pathway, it can significantly reduce the inflammatory response and mortality of microglia during the tuberculosis H37Ra strain infection. This research can help us further understand the inflammatory response mechanism of the central nervous system during tuberculosis infection and improve its treatment.