Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms.

Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. We aim to investigate protein biomarkers' role in prognosticating outcomes in ICH patients. We assessed 22 protein biomarkers using targeted proteomics in serum samples obtained from the ICH patient dataset (N = 150). We defined poor outcomes as modified Rankin scale score of 3-6. We incorporated clinical variables and protein biomarkers in regression models and random forest-based machine learning algorithms to predict poor outcomes and mortality. We report Odds Ratio (OR) or Hazard Ratio (HR) with 95% Confidence Interval (CI). We used five-fold cross-validation and bootstrapping for internal validation of prediction models. We included 149 patients for 90-day and 144 patients with ICH for 180-day outcome analyses. In multivariable logistic regression, UCH-L1 (adjusted OR 9.23; 95%CI 2.41-35.33), alpha-2-macroglobulin (aOR 5.57; 95%CI 1.26-24.59), and Serpin-A11 (aOR 9.33; 95%CI 1.09-79.94) were independent predictors of 90-day poor outcome; MMP-2 (aOR 6.32; 95%CI 1.82-21.90) was independent predictor of 180-day poor outcome. In multivariable Cox regression models, IGFBP-3 (aHR 2.08; 95%CI 1.24-3.48) predicted 90-day and MMP-9 (aOR 1.98; 95%CI 1.19-3.32) predicted 180-day mortality. Machine learning identified additional predictors, including haptoglobin for poor outcomes and UCH-L1, APO-C1, and MMP-2 for mortality prediction. Overall, random forest models outperformed regression models for predicting 180-day poor outcomes (AUC 0.89), and 90-day (AUC 0.81) and 180-day mortality (AUC 0.81). Serum biomarkers independently predicted short-term poor outcomes and mortality after ICH. Further research utilizing a multi-omics platform and temporal profiling is needed to explore additional biomarkers and refine predictive models for ICH prognosis.

Gray Matter Atrophy in a 6-OHDA-induced Model of Parkinson's Disease.

INTRODUCTION: Magnetic resonance imaging (MRI) based brain morphometric changes in unilateral 6-hydroxydopamine (6-OHDA) induced Parkinson's disease (PD) model can be elucidated using voxel-based morphometry (VBM), study of alterations in gray matter volume and Machine Learning (ML) based analyses. METHODS: We investigated gray matter atrophy in 6-OHDA induced PD model as compared to sham control using statistical and ML based analysis. VBM and atlas-based volumetric analysis was carried out at regional level. Support vector machine (SVM)-based algorithms wherein features (volume) extracted from (a) each of the 150 brain regions (b) statistically significant features (only) and (c) volumes of each cluster identified after application of VBM (VBM_Vol) were used for training the decision model. The lesion of the 6-OHDA model was validated by estimating the net contralateral rotational behaviour by the injection of apomorphine drug and motor impairment was assessed by rotarod and open field test. RESULTS AND DISCUSSION: In PD, gray matter volume (GMV) atrophy was noted in bilateral cortical and subcortical brain regions, especially in the internal capsule, substantia nigra, midbrain, primary motor cortex and basal ganglia-thalamocortical circuits in comparison with sham control. Behavioural results revealed an impairment in motor performance. SVM analysis showed 100% classification accuracy, sensitivity and specificity at both 3 and 7 weeks using VBM_Vol. CONCLUSION: Unilateral 6-OHDA induced GMV changes in both hemispheres at 7th week may be associated with progression of the disease in the PD model. SVM based approaches provide an increased classification accuracy to elucidate GMV atrophy.

Identifying unstable CNG repeat loci in the human genome: a heuristic approach and implications for neurological disorders.

Tandem nucleotide repeat (TNR) expansions, particularly the CNG nucleotide configuration, are associated with a variety of neurodegenerative disorders. In this study, we aimed to identify novel unstable CNG repeat loci associated with the neurogenetic disorder spinocerebellar ataxia (SCA). Using a computational approach, 15,069 CNG repeat loci in the coding and noncoding regions of the human genome were identified. Based on the feature selection criteria (repeat length >10 and functional location of repeats), we selected 52 repeats for further analysis and evaluated the repeat length variability in 100 control subjects. A subset of 19 CNG loci observed to be highly variable in control subjects was selected for subsequent analysis in 100 individuals with SCA. The genes with these highly variable repeats also exhibited higher gene expression levels in the brain according to the tissue expression dataset (GTEx). No pathogenic expansion events were identified in patient samples, which is a limitation given the size of the patient group examined; however, these loci contain potential risk alleles for expandability. Recent studies have implicated GLS, RAI1, GIPC1, MED15, EP400, MEF2A, and CNKSR2 in neurological diseases, with GLS, GIPC1, MED15, RAI1, and MEF2A sharing the same repeat loci reported in this study. This finding validates the approach of evaluating repeat loci in different populations and their possible implications for human pathologies.

Expanding the phenotypic and genotypic spectrum of DYT-TUBB4A with seven patients from India.

BACKGROUND: Variants in the TUBB4A gene are associated with dystonia (DYT-TUBB4A), Hypomyelination with Atrophy of the Basal Ganglia and Cerebellum (H-ABC) and spastic paraplegia. Phenotypes intermediate to these three broad phenotypes are also observed. These are rare disorders, and data from diverse populations remains limited. We report seven Indian cases with dystonia phenotype related to TUBB4A mutation. CASES: Among these seven patients, age at onset ranged from 5 to 48 years. Five patients had cranio-cervical onset of dystonia. One patient had prominent parkinsonism with dystonia. Patients responded well to botulinum toxin injected for laryngeal, cervical and jaw dystonia. The patient with parkinsonism responded well to levodopa, albeit with development of dyskinesias. Apart from the common p.Arg2Gly variant in three patients with DYT-TUBB4A, other variants included p.Arg262Pro, p.Arg39Cys and p.Asp245Asn. CONCLUSIONS: We report the first collection of cases with TUBB4A mutation from India. We expand the phenotype to include levodopa-responsive parkinsonism. Indian patients, consistent with global literature, harbor prominent adductor dysphonia, cervical and jaw dystonia, which responds well to botulinum treatment.

Acute bilateral sensorineural hearing loss as presentation of leptomeningeal metastases.

We describe a rare occurrence of bilateral acute severe sensorineural hearing loss in a middle-aged man that heralded the diagnosis of metastatic gastric cancer.

Molecular clues unveiling spinocerebellar ataxia type-12 pathogenesis.

Spinocerebellar Ataxia type-12 (SCA12) is a neurodegenerative disease caused by tandem CAG repeat expansion in the 5'-UTR/non-coding region of PPP2R2B. Molecular pathology of SCA12 has not been studied in the context of CAG repeats, and no appropriate models exist. We found in human SCA12-iPSC-derived neuronal lineage that expanded CAG in PPP2R2B transcript forms nuclear RNA foci and were found to sequester variety of proteins. Further, the ectopic expression of transcript containing varying length of CAG repeats exhibits non-canonical repeat-associated non-AUG (RAN) translation in multiple frames in HEK293T cells, which was further validated in patient-derived neural stem cells using specific antibodies. mRNA sequencing of the SCA12 and control neurons have shown a network of crucial transcription factors affecting neural fate, in addition to alteration of various signaling pathways involved in neurodevelopment. Altogether, this study identifies the molecular signatures of SCA12 disorder using patient-derived neuronal cell lines.

Integration of graph network with kernel SVM and logistic regression for identification of biomarkers in SCA12 and its diagnosis.

Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work proposes a novel four-phase machine learning-based diagnostic framework to find spinocerebellar ataxia type 12 disease-specific atrophic-brain regions and distinguish spinocerebellar ataxia type 12 from healthy using a real structural magnetic resonance imaging dataset. Firstly, each brain region is represented in terms of statistics of coefficients obtained using 3D-discrete wavelet transform. Secondly, a set of relevant regions are selected using a graph network-based method. Thirdly, a kernel support vector machine is used to capture nonlinear relationships among the voxels of a brain region. Finally, the linear relationship among the brain regions is captured to build a decision model to distinguish spinocerebellar ataxia type 12 from healthy by using the regularized logistic regression method. A classification accuracy of 95% and a harmonic mean of precision and recall, i.e. F1-score of 94.92%, is achieved. The proposed framework provides relevant regions responsible for the atrophy. The importance of each region is captured using Shapley Additive exPlanations values. We also performed a statistical analysis to find volumetric changes in spinocerebellar ataxia type 12 group compared to healthy. The promising result of the proposed framework shows that clinicians can use it for early and timely diagnosis of spinocerebellar ataxia type 12.

Generation and characterization of iPSC lines from Friedreich's ataxia patient (FRDA) with GAA.TTC repeat expansion in the Frataxin (FXN) gene's first intron (IGIBi016-A) and a non-FRDA healthy control individual (IGIBi017-A).

Friedreich's ataxia is a spinocerebellar degenerative disease caused by microsatellite (GAA.TTC)n repeat expansion in the first intron of FXN gene. Here, we developed iPSC lines from an FRDA patient (IGIBi016-A) and non-FRDA healthy control (IGIBi017-A). Both iPSC lines displayed typical iPSC morphology, expression of pluripotency markers, regular karyotypes (46, XY; 46, XX), capacity to grow into three germ layers, and FRDA hallmark -GAA repeat expansion and decreased FXN mRNA. Through these iPSC lines, FRDA phenotypes may be replicated in the in vitro assays, by creating neuron subtypes, cardiomyocytes and 3D organoids, for molecular and cellular biomarkers and therapeutic applications.

A Novel Kinematic Gait Parameter-Based Vibrotactile Cue for Freezing of Gait Mitigation among Parkinson's Patients: A Pilot Study.

Auditory and visual cues have been efficacious in laboratory-based freezing of gait (FoG) mitigation in Parkinson's disease (PD). However, real-life applications of these cues are restricted due to inconvenience to the users. Closed-loop vibrotactile cues based on temporal gait events have overcome the shortcomings of auditory and visual cueing. However, kinematic gait parameter-driven vibrotactile cueing has not been explored yet. Kinematic gait parameter-driven cueing is more effective than temporal cueing, according to FoG pathophysiology studies. Therefore, we developed and pilot-tested a novel cueing scheme in which the foot-to-ground angle at heel strike (FGA_HS) is estimated using indigenous instrumented shoes to drive vibrotactile cueing. Ten PD freezers underwent a 6-meter timed walk test in the off-medication state with and without the cue and after medication without the cue. The proposed system potentially mitigated FoG, quantified by a reduction in the ratio of time spent freezing to the total walking time and the number of FoGs. The FoG mitigation potential of the cue was further supported by increased anteroposterior center of pressure progression and FGA_HS. With a future comprehensive validation in a larger number of participants, the novel cue could likely be used in practice and commercialized.