Publications by authors named "Taimoor Sheikh"

Article Synopsis
  • The study addresses the difficulty of distinguishing between different nuclear types in histopathology, which is crucial for accurate diagnosis.
  • A new framework was introduced that enhances the segmentation of nuclei by utilizing radiomic features to analyze their characteristics and train classifiers.
  • Testing on the MoNuSAC2020 dataset showed that this framework achieved top-tier performance in segmenting various nuclear types, demonstrating its effectiveness across different organ images and types of radiomic features.
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Tremendous strides have been made in the molecular and cytogenetic classification of acute lymphoblastic leukemia based on gene expression profiling data, leading to an expansion of entities in the recent International Consensus Classification (ICC) of myeloid neoplasms and acute leukemias and 2022 WHO Classification of Tumours: Haematolymphoid Tumors, 5th edition. This increased diagnostic and therapeutic complexity can be overwhelming, and this review compares nomenclature differences between the ICC and WHO 5th edition publications, compiles key features of each entity, and provides a diagnostic algorithmic approach. In covering B-lymphoblastic leukemia (B-ALL), we divided the entities into established (those present in the revised 4th edition WHO) and novel (those added to either the ICC or WHO 5th edition) groups.

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Article Synopsis
  • Rett syndrome (RTT) is a serious neurodevelopmental disorder mainly linked to mutations in the MECP2 gene, with one specific mutation (L124W) showing preserved speech in an atypical RTT patient.
  • The study found that the L124W mutation impacts neuron features, like increased input resistance and reduced dendritic complexity, but is less severe compared to MECP2 null neurons, which display significant morphological and electrical abnormalities.
  • Analysis of neural network activity revealed that L124W neurons have enhanced synchronous burst frequency, while MECP2 null neurons showed reduced frequency and altered burst characteristics, highlighting how different MECP2 mutations lead to varying RTT phenotypes.
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An automatic pathological diagnosis is a challenging task because histopathological images with different cellular heterogeneity representations are sometimes limited. To overcome this, we investigated how the holistic and local appearance features with limited information can be fused to enhance the analysis performance. We propose an unsupervised deep learning model for whole-slide image diagnosis, which uses stacked autoencoders simultaneously feeding multiple-image descriptors such as the histogram of oriented gradients and local binary patterns along with the original image to fuse the heterogeneous features.

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Nucleolin (NCL/C23; OMIM: 164035) is a major nucleolar protein that plays a critical role in multiple processes, including ribosome assembly and maturation, chromatin decondensation, and pre-rRNA transcription. Due to its diverse functions, nucleolin has frequently been implicated in pathological processes, including cancer and viral infection. We recently identified a frameshifting indel mutation of , p.

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PIDD1 encodes p53-Induced Death Domain protein 1, which acts as a sensor surveilling centrosome numbers and p53 activity in mammalian cells. Early results also suggest a role in DNA damage response where PIDD1 may act as a cell-fate switch, through interaction with RIP1 and NEMO/IKKg, activating NF-κB signaling for survival, or as an apoptosis-inducing protein by activating caspase-2. Biallelic truncating mutations in CRADD-the protein bridging PIDD1 and caspase-2-have been reported in intellectual disability (ID), and in a form of lissencephaly.

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Article Synopsis
  • Anomaly detection faces challenges due to the rarity of anomalies, which leads to unbalanced data issues; synthetic anomalies are proposed as a potential solution for this problem.
  • The article introduces a two-level hierarchical latent space representation using autoencoders to create robust feature representations for generating synthetic anomalies without prior examples.
  • The proposed method successfully generates pseudo outlier samples, enabling the training of effective binary classifiers for real anomaly detection, and shows strong performance across multiple benchmarking tests.
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Diagnosis of pathologies using histopathological images can be time-consuming when many images with different magnification levels need to be analyzed. State-of-the-art computer vision and machine learning methods can help automate the diagnostic pathology workflow and thus reduce the analysis time. Automated systems can also be more efficient and accurate, and can increase the objectivity of diagnosis by reducing operator variability.

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Background: MeCP2-a chromatin-binding protein associated with Rett syndrome-has two main isoforms, MeCP2-E1 and MeCP2-E2, differing in a few N-terminal amino acid residues. Previous studies have shown brain region-specific expression of these isoforms which, in addition to their different cellular localization and differential expression during brain development, suggest that they may also have non-overlapping molecular mechanisms. However, differential functions of MeCP2-E1 and E2 remain largely unexplored.

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Exploring genes and pathways underlying intellectual disability (ID) provides insight into brain development and function, clarifying the complex puzzle of how cognition develops. As part of ongoing systematic studies to identify candidate ID genes, linkage analysis and next-generation sequencing revealed Zinc Finger and BTB Domain Containing 11 (ZBTB11) as a novel candidate ID gene. ZBTB11 encodes a little-studied transcription regulator, and the two identified missense variants in this study are predicted to disrupt canonical Zn2+-binding residues of its C2H2 zinc finger domain, leading to possible altered DNA binding.

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Mutations in the methyl-CpG-binding protein-2 gene (MECP2) are commonly associated with Rett syndrome. However, it has long been appreciated that there exists a spectrum of neuropsychiatric phenotypes associated with MECP2 variants. The most frequent Rett missense mutations are located in either the methyl-CpG-binding domain (MBD) or transcription repression domain (TRD).

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Methyl CpG-binding protein 2 (MeCP2), the mutated protein in Rett syndrome (RTT), is a crucial chromatin-modifying and gene-regulatory protein that has two main isoforms (MeCP2_E1 and MeCP2_ E2) due to the alternative splicing and switching between translation start codons in exons one and two. Functionally, these two isoforms appear to be virtually identical; however, evidence suggests that only MeCP2_E1 is relevant to RTT, including a single RTT missense mutation in exon 1, Ala2Val. Here, we show that N-terminal co- and post-translational modifications differ for MeCP2_E1 and MeCP2_E1-Ala2Val, which result in different protein degradation rates in vitro.

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Mutations in the MECP2 gene cause Rett syndrome (RTT). MeCP2 binds to chromocentric DNA through its methyl CpG-binding domain (MBD) to regulate gene expression. In heterozygous females the variable phenotypic severity is modulated by non-random X-inactivation, thus making genotype-phenotype comparisons unreliable.

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Objective: To identify the differential expression of candidate gluconeogenic genes which may initiate hepatitis C virus (HCV) related metabolic disorder during early stages of disease.

Methods: Patients of diverse age and sex, with positive HCV genotype 3 (HCV-3) RNA in serum and with no history of other related infections, co-infections, alcoholism, diabetes or chemotherapeutic treatments were considered for this study. Semi-quantitative reverse transcriptase PCR analysis and quantitative fold change analysis of the fresh liver biopsies of eight chronically infected HCV-3 patients and six healthy individuals were evaluated for three potential biomarkers involved in glucose homeostasis induction, namely mitochondrial phosphoenolpyruvate carboxykinase 2 (PCK2), glucose-6-phosphatase catalytic subunit (G6PC) and associated forkhead box protein 01 (FOXO1).

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There are two known mRNA degradation pathways, 3' to 5' and 5' to 3'. We identified likely pathogenic variants in two genes involved in these two pathways in individuals with intellectual disability. In a large family with multiple branches, we identified biallelic variants in DCPS in three affected individuals; a splice site variant (c.

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Mirror movements (MRMV) are involuntary movements on one side of the body that mirror voluntary movements on the opposite side. Congenital mirror movement disorder is a rare, typically autosomal-dominant disorder, although it has been suspected that some sporadic cases may be due to recessive inheritance. Using a linkage analysis and a candidate gene approach, two genes have been implicated in congenital MRMV disorder to date: DCC on 18q21.

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Background: Mutations in MECP2 are the main cause of Rett Syndrome. To date, no pathogenic synonymous MECP2 mutation has yet been identified. Here, we investigated a de novo synonymous variant c.

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Background: Epstein Barr Virus (EBV) plays a significant role as a cofactor in the process of tumorigenesis and has consistently been associated with a variety of malignancies. EBV encoded RNAs (EBER1 and EBER2) are the most abundant viral transcripts in latently EBV-infected cells and their role in viral infection is still unclear. Formalin fixed paraffin embedded (FFPE) tissues of surgically removed carcinoma biopsies are widely available form but have never been exploited for expressional studies previously in Pakistan.

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