Phase 1 study of safety and preliminary efficacy of intranasal transplantation of human neural stem cells (ANGE-S003) in Parkinson's disease.

BACKGROUND: Intranasal transplantation of ANGE-S003 human neural stem cells showed therapeutic effects and were safe in preclinical models of Parkinson's disease (PD). We investigated the safety and tolerability of this treatment in patients with PD and whether these effects would be apparent in a clinical trial. METHODS: This was a 12-month, single-centre, open-label, dose-escalation phase 1 study of 18 patients with advanced PD assigned to four-time intranasal transplantation of 1 of 3 doses: 1.5 million, 5 million or 15 million of ANGE-S003 human neural stem cells to evaluate their safety and efficacy. RESULTS: 7 patients experienced a total of 14 adverse events in the 12 months of follow-up after treatment. There were no serious adverse events related to ANGE-S003. Safety testing disclosed no safety concerns. Brain MRI revealed no mass formation. In 16 patients who had 12-month Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) data, significant improvement of MDS-UPDRS total score was observed at all time points (p<0.001), starting with month 3 and sustained till month 12. The most substantial improvement was seen at month 6 with a mean reduction of 19.9 points (95% CI, 9.6 to 30.3; p<0.001). There was no association between improvement in clinical outcome measures and cell dose levels. CONCLUSIONS: Treatment with ANGE-S003 is feasible, generally safe and well tolerated, associated with functional improvement in clinical outcomes with peak efficacy achieved at month 6. Intranasal transplantation of neural stem cells represents a new avenue for the treatment of PD, and a larger, longer-term, randomised, controlled phase 2 trial is warranted for further investigation.

Phenotype Transformation of PitNETs.

Phenotype transformation in pituitary neuroendocrine tumors is a little-known and unpredictable clinical phenomenon. Previous studies have not clearly defined and systematically concluded on the causes of this rare phenomenon. Additionally, the mechanisms of phenotype transformation are not well known. We reviewed cases reported in the literature with the aim of defining phenotype transformation in pituitary neuroendocrine tumors. We present an overview of the wide spectrum of phenotype transformation and its clinical features. We also discuss findings on the potential mechanism of this rare transformation, which may be related to PC1/3, the bioactivity of secretory hormones, gene mutations and the plasticity of pituitary neuroendocrine tumors. Clinicians should be aware of this rare phenomenon and more studies on the underlying mechanisms are required.

A long-term prognosis study of human USP8-mutated ACTH-secreting pituitary neuroendocrine tumours.

OBJECTIVE: Somatic variants in the ubiquitin-specific protease 8 (USP8) gene are the most common genetic cause of Cushing disease. We aimed to explore the relationship between clinical outcomes and USP8 status in a single centre. DESIGN, PATIENTS AND MEASUREMENTS: We investigated the USP8 status in 48 patients with pituitary corticotroph tumours. A median of 62 months of follow-up was conducted after surgery from November 2013 to January 2015. The clinical, biochemical and imaging features were collected and analysed. RESULTS: Seven USP8 variants (p.Ser718Pro, p.Ser719del, p.Pro720Arg, p.Pro720Gln, p.Ser718del, p.Ser718Phe, p.Lys713Arg) were identified in 24 patients (50%). USP8 variants showed a female predominance (100% vs. 75% in wild type [WT], p = .022). Patients with p.Ser719del showed an older age at surgery compared to patients with the p.Pro720Arg variant (47- vs. 24-year-olds, p = .033). Patients with p.Pro720Arg showed a higher rate of macroadenoma compared to patients harbouring the p.Ser718Pro variant (60% vs. 0%, p = .037). No significant differences were observed in serum and urinary cortisol and adrenocorticotropin hormone (ACTH) levels. Immediate surgical remission (79% vs. 75%) and long-term hormone remission (79% vs. 67%) were not significantly different between the two groups. The recurrence rate was 21% (4/19) in patients harbouring USP8 variants and 13% (2/16) in WT patients. Recurrence-free survival presented a tendency to be shorter in USP8-mutated individuals (76.7 vs. 109.2 months, p = .068). CONCLUSIONS: Somatic USP8 variants accounted for 50% of the genetic causes in this cohort with a significant female frequency. A long-term follow-up revealed a tendency toward shorter recurrence-free survival in USP8-mutant patients.

Considering Context-Specific microRNAs in Ischemic Stroke with Three "W": Where, When, and What.

MicroRNAs are short non-coding RNA molecules that function as critical regulators of various biological processes through negative regulation of gene expression post-transcriptionally. Recent studies have indicated that microRNAs are potential biomarkers for ischemic stroke. In this review, we first illustrate the pathogenesis of ischemic stroke and demonstrate the biogenesis and transportation of microRNAs from cells. We then discuss several promising microRNA biomarkers in ischemic stroke in a context-specific manner from three dimensions: biofluids selection for microRNA extraction (Where), the timing of sample collection after ischemic stroke onset (When), and the clinical application of the differential-expressed microRNAs during stroke pathophysiology (What). We show that microRNAs have the utilities in ischemic stroke diagnosis, risk stratification, subtype classification, prognosis prediction, and treatment response monitoring. However, there are also obstacles in microRNA biomarker research, and this review will discuss the possible ways to improve microRNA biomarkers. Overall, microRNAs have the potential to assist clinical treatment, and developing microRNA panels for clinical application is worthwhile.

Multi-center application of a convolutional neural network for preoperative detection of cavernous sinus invasion in pituitary adenomas.

OBJECTIVE: Cavernous sinus invasion (CSI) plays a pivotal role in determining management in pituitary adenomas. The study aimed to develop a Convolutional Neural Network (CNN) model to diagnose CSI in multiple centers. METHODS: A total of 729 cases were retrospectively obtained in five medical centers with (n = 543) or without CSI (n = 186) from January 2011 to December 2021. The CNN model was trained using T1-enhanced MRI from two pituitary centers of excellence (n = 647). The other three municipal centers (n = 82) as the external testing set were imported to evaluate the model performance. The area-under-the-receiver-operating-characteristic-curve values (AUC-ROC) analyses were employed to evaluate predicted performance. Gradient-weighted class activation mapping (Grad-CAM) was used to determine models' regions of interest. RESULTS: The CNN model achieved high diagnostic accuracy (0.89) in identifying CSI in the external testing set, with an AUC-ROC value of 0.92 (95% CI, 0.88-0.97), better than CSI clinical predictor of diameter (AUC-ROC: 0.75), length (AUC-ROC: 0.80), and the three kinds of dichotomizations of the Knosp grading system (AUC-ROC: 0.70-0.82). In cases with Knosp grade 3A (n = 24, CSI rate, 0.35), the accuracy the model accounted for 0.78, with sensitivity and specificity values of 0.72 and 0.78, respectively. According to the Grad-CAM results, the views of the model were confirmed around the sellar region with CSI. CONCLUSIONS: The deep learning model is capable of accurately identifying CSI and satisfactorily able to localize CSI in multicenters.

The advantages of multi-level omics research on stem cell-based therapies for ischemic stroke.

Stem cell transplantation is a potential therapeutic strategy for ischemic stroke. However, despite many years of preclinical research, the application of stem cells is still limited to the clinical trial stage. Although stem cell therapy can be highly beneficial in promoting functional recovery, the precise mechanisms of action that are responsible for this effect have yet to be fully elucidated. Omics analysis provides us with a new perspective to investigate the physiological mechanisms and multiple functions of stem cells in ischemic stroke. Transcriptomic, proteomic, and metabolomic analyses have become important tools for discovering biomarkers and analyzing molecular changes under pathological conditions. Omics analysis could help us to identify new pathways mediated by stem cells for the treatment of ischemic stroke via stem cell therapy, thereby facilitating the translation of stem cell therapies into clinical use. In this review, we summarize the pathophysiology of ischemic stroke and discuss recent progress in the development of stem cell therapies for the treatment of ischemic stroke by applying multi-level omics. We also discuss changes in RNAs, proteins, and metabolites in the cerebral tissues and body fluids under stroke conditions and following stem cell treatment, and summarize the regulatory factors that play a key role in stem cell therapy. The exploration of stem cell therapy at the molecular level will facilitate the clinical application of stem cells and provide new treatment possibilities for the complete recovery of neurological function in patients with ischemic stroke.

Integrating network pharmacology and transcriptomic omics reveals that akebia saponin D attenuates neutrophil extracellular traps-induced neuroinflammation via NTSR1/PKAc/PAD4 pathway after intracerebral hemorrhage.

Neutrophils and their production of neutrophil extracellular traps (NETs) significantly contribute to neuroinflammation and brain damage after intracerebral hemorrhage (ICH). Although Akebia saponin D (ASD) demonstrates strong anti-inflammatory activities and blood-brain barrier permeability, its role in regulating NETs formation and neuroinflammation following ICH is uncharted. Our research focused on unraveling the influence of ASD on neuroinflammation mediated by NETs and the mechanisms involved. We found that increased levels of peripheral blood neutrophils post-ICH are correlated with worse prognostic outcomes. Through network pharmacology, we identified ASD as a promising therapeutic target for ICH. ASD administration significantly improved neurobehavioral performance and decreased NETs production in neutrophils. Furthermore, ASD was shown to upregulate the membrane protein NTSR1 and activate the cAMP signaling pathway, confirmed through transcriptome sequencing, western blot, and immunofluorescence. Interestingly, the NTSR1 inhibitor SR48692 significantly nullified ASD's anti-NETs effects and dampened cAMP pathway activation. Mechanistically, suppression of PKAc via H89 negated ASD's anti-NETs effects but did not affect NTSR1. Our study suggests that ASD may reduce NETs formation and neuroinflammation, potentially involving the NTSR1/PKAc/PAD4 pathway post-ICH, underlining the potential of ASD in mitigating neuroinflammation through its anti-NETs properties.

A Bioinspired Flexible Sensor for Electrochemical Probing of Dynamic Redox Disequilibrium in Cancer Cells.

Malignant tumors pose a serious risk to human health. Ascorbic acid (AA) has potential for tumor therapy; however, the mechanism underlying the ability of AA to selectively kill tumor cells remains unclear. AA can cause redox disequilibrium in tumor cells, resulting in the release of abundant reactive oxygen species, represented by hydrogen peroxide (H2 O2 ). Therefore, the detection of H2 O2 changes can provide insight into the selective killing mechanism of AA against tumor cells. In this work, inspired by the ion-exchange mechanism in coral formation, a flexible H2 O2 sensor (PtNFs/CoPi@CC) is constructed to monitor the dynamics of H2 O2 in the cell microenvironment, which exhibits excellent sensitivity and spatiotemporal resolution. Moreover, the findings suggest that dehydroascorbic acid (DHA), the oxidation product of AA, is highly possible the substance that actually acts on tumor cells in AA therapy. Additionally, the intracellular redox disequilibrium and H2 O2 release caused by DHA are positively correlated with the abundance and activity of glucose transporter 1 (GLUT1). In conclusion, this work has revealed the potential mechanism underlying the ability of AA to selectively kill tumor cells through the construction and use of PtNFs/CoPi@CC. The findings provide new insights into the clinical application of AA.

Machine Learning Approaches to Differentiate Sellar-Suprasellar Cystic Lesions on Magnetic Resonance Imaging.

Cystic lesions are common lesions of the sellar region with various pathological types, including pituitary apoplexy, Rathke's cleft cyst, cystic craniopharyngioma, etc. Suggested surgical approaches are not unique when dealing with different cystic lesions. However, cystic lesions with different pathological types were hard to differentiate on MRI with the naked eye by doctors. This study aimed to distinguish different pathological types of cystic lesions in the sellar region using preoperative magnetic resonance imaging (MRI). Radiomics and deep learning approaches were used to extract features from gadolinium-enhanced MRIs of 399 patients enrolled at Peking Union Medical College Hospital over the past 15 years. Paired imaging differentiations were performed on four subtypes, including pituitary apoplexy, cystic pituitary adenoma (cysticA), Rathke's cleft cyst, and cystic craniopharyngioma. Results showed that the model achieved an average AUC value of 0.7685. The model based on a support vector machine could distinguish cystic craniopharyngioma from Rathke's cleft cyst with the highest AUC value of 0.8584. However, distinguishing cystic apoplexy from pituitary apoplexy was difficult and almost unclassifiable with any algorithms on any feature set, with the AUC value being only 0.6641. Finally, the proposed methods achieved an average Accuracy of 0.7532, which outperformed the traditional clinical knowledge-based method by about 8%. Therefore, in this study, we first fill the gap in the existing literature and provide a non-invasive method for accurately differentiating between these lesions, which could improve preoperative diagnosis accuracy and help to make surgery plans in clinical work.

Resting-state functional connectivity in a non-human primate model of cortical ischemic stroke in area F1.

BACKGROUND: The application of functional MRI to non-human primates after stroke has not yet been undertaken. This is the first study to explore the functional connectivity changes in non-human primate models during acute stages after stroke onset. METHODS: Nineteen healthy male cynomolgus monkeys (4-5 years) were used in this study. The photothrombosis model was employed to induce focal ischemic stroke in F1 area in the monkey's left hemisphere. T1-weighted structural images and resting-state functional magnetic resonance imaging (rs-fMRI) of all subjects were obtained using a 3.0 Tesla MRI system on the third day following stroke. Based on the D99 atlas, the structural and functional changes of bilateral F1 areas in monkeys were analyzed using region of interest (ROI)-based functional connectivity (FC). The bilateral F1 areas were selected as the seed regions due to their crucial role in motor control and their potential to unveil the comprehensive functional reorganization of the motor system at a whole-brain level following stroke. RESULTS: Ischemic lesions were observed after the stroke, with larger lesion volumes associated with poorer neurological dysfunction. Compared with baseline condition, left area F1 demonstrated decreased FC with the left cerebellum, left ventral pons and left 5_(PEa). When the ROI was located in the right area F1, ischemic monkeys showed decreased FC in left ventral pons, left cerebellum, left primary visual cortex and left 5_(PEa), accompanied by increased FC in the right orbitofrontal cortex. Importantly, the degree of altered FC between left area F1 and left cerebellum was associated with upper limb tone. CONCLUSIONS: These results provide valuable insights into the early-stage functional connectivity changes in the F1 areas of monkeys under ischemic conditions, highlighting the potential involvement of specific brain regions in the pathophysiology of ischemic injury.

Case report: Identification of potential prognosis-related LAG3 overexpression and DICER1 mutation in pituitary carcinoma: two cases.

Metastatic PitNETs are a rare life-threatening condition with poor prognosis and documentation. Due to the scarce literature and lack of precise treatment, we hope to better characterise PitNET using the next-generation whole exon sequencing (WES) and RNA sequencing. This case study outlines a 54 years-old man and a 52 years-old woman who were both diagnosed with PitNET and analysis of peripheral blood and tumours were performed by WES and RNA sequencing. Analysis showed that DICER1 mutations in precancerous lesions and LAG3 overexpression were significant in aiding the prognosis and diagnosis of PitNETs. The first case with overexpressed LAG3 and DICER1 mutation died 26 months later, and the second case with LAG3 overexpression achieved partial remission. This study revealed that heightened expression of LAG3 offered promising targets for ICI and mutations in DICER1 could provide markers for effective diagnosis and prognosis.

An artificial intelligence-based prognostic prediction model for hemorrhagic stroke.

PURPOSE: The prognosis following a hemorrhagic stroke is usually extremely poor. Rating scales have been developed to predict the outcomes of patients with intracerebral hemorrhage (ICH). To date, however, the prognostic prediction models have not included the full range of relevant imaging features. We constructed a clinic-imaging fusion model based on convolutional neural networks (CNN) to predict the short-term prognosis of ICH patients. MATERIALS AND METHODS: This was a multi-center retrospective study, which included 1990 patients with ICH. Two CNN-based deep learning models were constructed to predict the neurofunctional outcomes at discharge; these were validated using a nested 5-fold cross-validation approach. The models' predictive efficiency was compared with the original ICH scale and the ICH grading scale. Poor neurological outcome was defined as a Glasgow Outcome Scale (GOS) score of 1-3. RESULTS: The training and test sets included 1599 and 391 patients, respectively. For the test set, the clinic-imaging fusion model had the highest area under the curve (AUC = 0.903), followed by the imaging-based model (AUC = 0.886), the ICH scale (AUC = 0.777), and finally the ICH grading scale (AUC = 0.747). CONCLUSION: The CNN prognostic prediction model based on neuroimaging features was more effective than the ICH scales in predicting the neurological outcomes of ICH patients at discharge. The CNN model's predictive efficiency slightly improved when clinical data were included.

Standardized measurement of mid-surface shift of brain based on deep Hough transform.

The measurement of mid-surface shift (MSS), the geometric displacement between the actual mid-surface and the ideal midsagittal plane (iMSP), is of great significance for accurate diagnosis, treatment and prognosis of patients with intracranial hemorrhage (ICH). Most previous studies are subject to inherent inaccuracy on account of calculating midline shift (MLS) based on 2D slices and ignoring pathological conditions. In this study, we propose a novel standardized measurement model to quantify the distance and the overall volume of mid-surface shift (MSS-D, MSS-V). Our work has four highlights. First, we develop an end-to-end network architecture with multiple sub-tasks including the actual mid-surface segmentation, hematoma segmentation and iMSP detection, which significantly improves the efficiency and accuracy of MSS measurement by taking advantage of the common properties among tasks. Second, an efficient iMSP detection scheme is proposed based on the differentiable deep Hough transform (DHT), which converts and simplifies the plane detection problem in the image space into a keypoint detection problem in the Hough space. Third, we devise a sparse DHT strategy and a weighted least square (WLS) method to increase the sparsity of features, improving inference speed and greatly reducing computation cost. Fourth, we design a joint loss function to comprehensively consider the correlation of features between multi-tasks and multi-domains. Extensive validation on our large in-house dataset (519 patients) and the public CQ500 dataset (491 patients), demonstrates the superiority of our method over the state-of-the-art methods.

Photoinduced Nonvolatile Resistive Switching Behavior in Oxygen-Doped MoS2 for a Neuromorphic Vision System.

Controlling resistance by external fields provides fascinating opportunities for the development of novel devices and circuits, such as temperature-field-induced superconductors, magnetic-field-triggered giant magnetoresistance devices, and electric-field-operated flash memories. In this work, we demonstrate a light-triggered nonvolatile resistive switching behavior in oxygen-doped MoS2. The two-terminal devices exhibit stable light-modulated resistive switching characteristics and optically tunable synaptic properties with an on/off ratio of up to 104. The integrated device with crossbar architecture enables simultaneous image sensing, preprocessing, and storage in a single device, thereby increasing the training efficiency and recognition rate of image recognition tasks. This work presents a novel pathway to develop the next generation of light-controlled memory and artificial vision systems for neuromorphic computing.

OCT3/4 is a potential immunohistochemical biomarker for diagnosis and prognosis of primary intracranial germ cell tumors: a systematic review and meta-analysis.

Introduction: Intracranial germ cell tumors (iGCTs), comprising of germinoma (GE) and non-germinomatous GCT (NGGCT), are a group of heterogenous brain tumors. Immunohistochemical markers, such as placental-like alkaline phosphatase (PLAP), are commonly used in diagnosis but show moderate sensitivity. Organic cation transporter 3/4 (OCT3/4) has been proposed as a novel biomarker for diagnosis and prognosis of iGCTs. This paper aimed to compare OCT3/4 with PLAP as potential immunohistochemical biomarkers in iGCTs diagnosis and clarify the relationship between OCT3/4 and prognosis of patients with iGCTs. Methods: Meta-analyses were performed to estimate pooled percentage point differences in positive rates between OCT3/4 and PLAP, their sensitivities, and correlation between OCT3/4 and prognosis in iGCTs. Results: Nine articles were included representing of 241 patients. A fixed-effects model meta-analysis revealed that OCT3/4s positive rate was 8.6% higher (95% CI, 0.7% lower to 17.9% higher) than that of PLAP. Using fixed-effects models, sensitivities of OCT3/4 as a potential immunohistochemical biomarker in CNS GE and NGGCT were 85% (95% CI, 79% to 89%) and 56% (95% CI, 39% to 71%), respectively. In comparison, PLAP had lower sensitivities in both GE (73%; 95% CI, 64% to 91%) and NGGCT (43%; 95% CI, 27% to 61%). Moreover, OCT3/4 was significantly negatively correlated with 5-year progression free survival in patients with CNS GE (HR = 2.56, 95 % CI 1.47 to 4.44; p = 0.0008). Sensitivity analyses showed similar results. Discussion: This study provides the first comprehensive assessment of the efficacies of OCT3/4 and PLAP in iGCTs detection and prognosis prediction, indicating OCT3/4 seems to be a more sensitive and reliable immunohistochemical marker in iGCT diagnosis.

Corrigendum to "Seminal plasma metabolomics signatures of normosmic congenital hypogonadotropic hypogonadism" [Heliyon 9(4) (March 2023) e14779].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e14779.].

Deep-learning-based automatic segmentation and classification for craniopharyngiomas.

Objective: Neuronavigation and classification of craniopharyngiomas can guide surgical approaches and prognostic information. The QST classification has been developed according to the origin of craniopharyngiomas; however, accurate preoperative automatic segmentation and the QST classification remain challenging. This study aimed to establish a method to automatically segment multiple structures in MRIs, detect craniopharyngiomas, and design a deep learning model and a diagnostic scale for automatic QST preoperative classification. Methods: We trained a deep learning network based on sagittal MRI to automatically segment six tissues, including tumors, pituitary gland, sphenoid sinus, brain, superior saddle cistern, and lateral ventricle. A deep learning model with multiple inputs was designed to perform preoperative QST classification. A scale was constructed by screening the images. Results: The results were calculated based on the fivefold cross-validation method. A total of 133 patients with craniopharyngioma were included, of whom 29 (21.8%) were diagnosed with type Q, 22 (16.5%) with type S and 82 (61.7%) with type T. The automatic segmentation model achieved a tumor segmentation Dice coefficient of 0.951 and a mean tissue segmentation Dice coefficient of 0.8668 for all classes. The automatic classification model and clinical scale achieved accuracies of 0.9098 and 0.8647, respectively, in predicting the QST classification. Conclusions: The automatic segmentation model can perform accurate multi-structure segmentation based on MRI, which is conducive to clearing tumor location and initiating intraoperative neuronavigation. The proposed automatic classification model and clinical scale based on automatic segmentation results achieve high accuracy in the QST classification, which is conducive to developing surgical plans and predicting patient prognosis.