Cytogenetic abnormalities in patients with hematological malignancies in Lahore city, Pakistan

Hematological and hematopoietic cells malignancies of the genes and hematopoietic cells are associated with the genetic mutation, often at the chromosomal level. The standard cytogenetic study is widely accepted as one of the main diagnostics and prognostic determinants in patients. Therefore, the current descriptive and cross-sectional study sought to determine the cytogenetic analysis of frequent hematological malignancies in Pakistan. A total of 202 peripheral bone marrow or blood samples from patients with benign and malignant hematological malignancy were taken using a conventional G-banding technique. Among enrolled patients, the mean age was 21.5 years ± 23.4, and gender-wise distribution showed a marked predominance of the male 147 (73%) population compared to the female 55 (27%). Patients in the age group (2-10 years) had the highest frequency, 48 (24%), of hematological neoplasms, followed by age (11-20 years) with 40 (20%). Normal karyotypes (46, XX/46, XY) was found in 51% (n=103) patients. Furthermore, the frequency of complex karyotype was 30 (15%), while normal was seen in 171 (85%) patients. Pre-B Acute Lymphoblastic Leukemia (Pre-B ALL) was the most prevalent malignancy of 66 (33%), followed by Chronic Myelogenous Leukemia (CML) of 41 (20%) and Acute Lymphocytic Leukemia of 29 (14%). Translocation was the most prevalent 50 (25%), followed by hypotriploidy 14 (7%) and monosomy 8 (4%) on chromosome aberration analysis. In addition, t(9:22) translocation was found to be 20 (10%) in CML, with the majority in the age group (31-40 years). This study recommends that karyotyping should be tested frequently in hematological conditions because it may provide insight into the relative chromosomal changes associated with particular malignancies.

Mutations in JAK/STAT and NOTCH1 Genes Are Enriched in Post-Transplant Lymphoproliferative Disorders

Post-transplant lymphoproliferative disorders (PTLD) are diseases occurring in immunocompromised patients after hematopoietic stem cell transplantation (HCT) or solid organ transplantation (SOT). Although PTLD occurs rarely, it may be associated with poor outcomes. In most cases, PTLD is driven by Epstein-Barr virus (EBV) infection. Few studies have investigated the mutational landscape and gene expression profile of PTLD. In our study, we performed targeted deep sequencing and RNA-sequencing (RNA-Seq) on 16 cases of florid follicular hyperplasia (FFH) type PTLD and 15 cases of other PTLD types that include: ten monomorphic (M-PTLD), three polymorphic (P-PTLD), and two classic Hodgkin lymphoma type PTLDs (CHL-PTLD). Our study identified recurrent mutations in JAK3 in five of 15 PTLD cases and one of 16 FFH-PTLD cases, as well as 16 other genes that were mutated in M-PTLD, P-PTLD, CHL-PTLD and FFH-PTLD. Digital image analysis demonstrated significant differences in single cell area, major axis, and diameter when comparing cases of M-PTLD and P-PTLD to FFH-PTLD. No morphometric relationship was identified with regards to a specific genetic mutation. Our findings suggest that immune regulatory pathways play an essential role in PTLD, with the JAK/STAT pathway affected in many PTLDs.

Morphological Evolution: Bioinspired Methods for Analyzing Bioinspired Robots

To fully understand the evolution of complex morphologies, analyses cannot stop at selection: It is essential to investigate the roles and interactions of multiple processes that drive evolutionary outcomes. The challenges of undertaking such analyses have affected both evolutionary biologists and evolutionary roboticists, with their common interests in complex morphologies. In this paper, we present analytical techniques from evolutionary biology, selection gradient analysis and morphospace walks, and we demonstrate their applicability to robot morphologies in analyses of three evolutionary mechanisms: randomness (genetic mutation), development (an explicitly implemented genotype-to-phenotype map), and selection. In particular, we applied these analytical techniques to evolved populations of simulated biorobots—embodied robots designed specifically as models of biological systems, for the testing of biological hypotheses—and we present a variety of results, including analyses that do all of the following: illuminate different evolutionary dynamics for different classes of morphological traits; illustrate how the traits targeted by selection can vary based on the likelihood of random genetic mutation; demonstrate that selection on two selected sets of morphological traits only partially explains the variance in fitness in our biorobots; and suggest that biases in developmental processes could partially explain evolutionary dynamics of morphology. When combined, the complementary analytical approaches discussed in this paper can enable insight into evolutionary processes beyond selection and thereby deepen our understanding of the evolution of robotic morphologies.

Seedling growth and fall armyworm feeding preference influenced by dhurrin production in sorghum

Cyanogenic glucosides (CGs) play a key role in host-plant defense to insect feeding; however, the metabolic tradeoffs between synthesis of CGs and plant growth are not well understood. In this study, genetic mutants coupled with nondestructive phenotyping techniques were used to study the impact of the CG dhurrin on fall armyworm [Spodoptera frugiperda (J.E. Smith)] (FAW) feeding and plant growth in sorghum [Sorghum bicolor (L.) Moench]. A genetic mutation in CYP79A1 gene that disrupts dhurrin biosynthesis was used to develop sets of near-isogenic lines (NILs) with contrasting dhurrin contents in the Tx623 bmr6 genetic background. The NILs were evaluated for differences in plant growth and FAW feeding damage in replicated greenhouse and field trials. Greenhouse studies showed that dhurrin-free Tx623 bmr6 cyp79a1 plants grew more quickly than wild-type plants but were more susceptible to insect feeding based on changes in green plant area (GPA), total leaf area, and total dry weight over time. The NILs exhibited similar patterns of growth in field trials with significant differences in leaf area and dry weight of dhurrin-free plants between the infested and non-infested treatments. Taken together, these studies reveal a significant metabolic tradeoff between CG biosynthesis and plant growth in sorghum seedlings. Disruption of dhurrin biosynthesis produces plants with higher growth rates than wild-type plants but these plants have greater susceptibility to FAW feeding.

Cystic Fibrosis Treatment Options Discrepancy Globally

Cystic fibrosis, or rather known as CF, is a common monogenic disease caused by genetic mutation on CFTR on chromosome 7. Progressive obstructive pulmonary disease, sinusitis, exocrine pancreatic insufficiency leading to malabsorption and malnutrition, liver and pancreatic dysfunction, and male infertility are all characteristics of the disease. Persistent pulmonary infections are caused by a lack of CFTR or its decreased function, leading to bronchiectasis and progressive lung destruction. Despite the fact that CF patients' lives are shortening, early diagnosis has helped improve patients' life span to a median age of around 50 years, including newborn screening, mild form identification, and a proactive therapy approach. Pancreatic enzyme replacement, respiratory physiotherapy, mucolytics, and strong antibiotic therapy are among treatments for CF. For the majority of people with severe symptoms, a lung or liver transplant is necessary. The CFTR protein is affected by a large number of mutations, each of which have diverse effects. Despite advances in our understanding of CFTR function and contemporary therapy, most of our knowledge of cystic fibrosis remains unclear. With the recent addition of mutation-specific treatments, future advances in health and quality of life for people with CF are likely to improve. The focus of research is on novel medications that restore CFTR function, some of which are now accessible and have a positive therapeutic impact, while others are showing promising preliminary results.

A Case of Carbamazepine-Induced Aggravation of Self-Limited Epilepsy with Centrotemporal Spikes Epilepsy and Valproate-Induced Hyperammonemic Encephalopathy in a Child with Heterozygous Gene Variant of Carbomoyl Phosphatase Synthetase Deficiency

Antiepileptics drugs are the mainstay of the management of epilepsy in children. Sodium valproate (VPA) and carbamazepine (CBZ) are widely used medications in childhood epilepsy. Hyperammonemia has been described as a known side effect of valproate therapy. It is known that VPA-associated HA is common among patients who hold genetic mutations of the carbomoyl phosphatase synthase 1 gene (CPS1). Aggravation of self-limited epilepsy with centrotemporal spikes (SLECTS) is a rare side effect of CBZ. Here, we present a child who had CBZ-induced aggravation of rolandic epilepsy and VPA-induced HA encephalopathy in the background of an unrecognised heterozygous gene variant of CPS1. An 8-year-old boy with SLECTS presented with a history of abnormal behaviours and drowsiness. He was apparently well until six years when he developed seizures in favour of rolandic epilepsy. His electroencephalogram (EEG) showed bilateral predominantly on the right-sided central-temporal spikes and waves. The diagnosis of SLECTS was made, and he was commenced on CBZ. Though he showed some improvement at the beginning, his seizure frequency increased when the dose of CBZ was increased. His repeat EEG showed electrical status in slow-wave sleep, and CBZ was stopped. Subsequently, he was started on VPA, and with that, he developed features of encephalopathy. He had elevated serum ammonia with normal liver functions. VPA was stopped with the suspicion of VPA-induced hyperammonemia. Tandem mass spectrometry did not show significant abnormality in the amino acid profile. Specific genetic analysis revealed a c.2756 C > T.p (Ser919Leu) heterozygote genetic mutation of the CSP 1 gene. This is a classic example where side effects of treatment determine the choice of antiepileptics drugs (AEDs) in childhood epilepsy. It is essential to keep in mind that SLECTS can be aggravated with certain AEDs, and VPA-induced HA in the absence of live failure could be due to underlying inherited metabolic disorders.

Leptin: Mechanisms Involved In Signaling and Resistance

Leptin is secreted mainly by white adipocyte tissue, and it circulates at levels positively correlated with fat mass, thus reflecting primarily the amount of energy stored in adipose tissue. Leptin levels also change with acute changes in energy intake and thus, secondarily reflect acute energy availability. Several potential mechanisms behind leptin resistance have been identified including: Inflammatory signaling, elevated free fatty acids, high leptin and genetic mutation in OB and DBU genes. This review summaries all the physiological, biological aspects of leptin hormone including increases energy expenditure, thermogenesis, heart rate, blood pressure but decreases glycaemia. This review summarizes the pharmacological and nonpharmacological treatment of leptin hormone imbalances.

A Novel Ensemble Stacking Classification of Genetic Variations Using Machine Learning Algorithms

Genetics is the clinical review of congenital mutation, where the principal advantage of analyzing genetic mutation of humans is the exploration, analysis, interpretation and description of the genetic transmitted and inherited effect of several diseases such as cancer, diabetes and heart diseases. Cancer is the most troublesome and disordered affliction as the proportion of cancer sufferers is growing massively. Identification and discrimination of the mutations that impart to the enlargement of tumor from the unbiased mutations is difficult, as majority tumors of cancer are able to exercise genetic mutations. The genetic mutations are systematized and categorized to sort the cancer by way of medical observations and considering clinical studies. At the present time, genetic mutations are being annotated and these interpretations are being accomplished either manually or using the existing primary algorithms. Evaluation and classification of each and every individual genetic mutation was basically predicated on evidence from documented content built on medical literature. Consequently, as a means to build genetic mutations, basically, depending on the clinical evidences persists a challenging task. There exist various algorithms such as one hot encoding technique is used to derive features from genes and their variations, TF-IDF is used to extract features from the clinical text data. In order to increase the accuracy of the classification, machine learning algorithms such as support vector machine, logistic regression, Naive Bayes, etc., are experimented. A stacking model classifier has been developed to increase the accuracy. The proposed stacking model classifier has obtained the log loss 0.8436 and 0.8572 for cross-validation data set and test data set, respectively. By the experimentation, it has been proved that the proposed stacking model classifier outperforms the existing algorithms in terms of log loss. Basically, minimum log loss refers to the efficient model. Here the log loss has been reduced to less than 1 by using the proposed stacking model classifier. The performance of these algorithms can be gauged on the basis of the various measures like multi-class log loss.