Search

The study's objective is to identify the non-linear relationship of differentially expressed genes that vary in terms of the tumour and normal tissue and correct for any variations among the RNA-Seq experiment focused on Oral squamous cell carcinoma samples from patients. A Laplacian Likelihood version of the Generalized Additive Model is proposed and compared with the regular GAM models in terms of the non-linear fitting. The Non-Linear machine learning approach of Laplacian Likelihood-based GAM could complement RNA-Seq Analysis mainly to interpret, validate, and prioritize the patient samples data of differentially expressed genes. The analysis eases the standard parametric presumption and helps discover complexity in the association between the dependent and the independent variable and parameter smoothing that might otherwise be neglected. Concurvity, standard error, deviance, and other statistical verification have been carried out to confirm Laplacian Likelihood-based GAM efficiency.

The novel Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) has emerged to be the reason behind the COVID-19 pandemic. It was discovered in Wuhan, China and then began spreading around the world, impacting the health of millions. Efforts for treatment have been hampered as there are no antiviral drugs that are effective against this virus. In the present study, we have explored the phytochemical constituents of Salvia plebeia R. Br., in terms of its binding affinity by targeting COVID-19 main protease (Mpro) using computational analysis. Molecular docking analysis was performed using PyRx software. The ADMET and drug-likeness properties of the top 10 compounds showing binding affinity greater than or equal to - 8.0 kcal/mol were analysed using pkCSM and DruLiTo, respectively. Based on the docking studies, it was confirmed that Rutin and Plebeiosides B were the most potent inhibitors of the main protease of SARS-CoV-2 with the best binding affinities of - 9.1 kcal/mol and - 8.9 kcal/mol, respectively. Further, the two compounds were analysed by studying their biological activity using the PASS webserver. Molecular dynamics simulation analysis was performed for the selected protein-ligand complexes to confirm their stability at 300 ns. MM-PBSA provided the basis for analyzing the affinity of the phytochemicals towards Mpro by calculating the binding energy, and secondary structure analysis indicated the stability of protease structure when it is bound to Rutin and Plebeiosides B. Altogether, the study identifies Rutin and Plebeiosides B to be potent Mpro inhibitors of SARS-CoV-2. Supplementary Information The online version contains supplementary material available at 10.1007/s42535-021-00304-z.

The gene structure of organisms gets altered when exposed to an abnormal condition which could adversely affect the growth of the target. RNA sequencing can be deployed to identify diseasecausing mutations in the genes of patients for whom genetic analysis failed to return a diagnosis. One such disease is Hypoxia, a condition in which there is a deficiency in the availability of oxygen in the tissues. RNA sequencing helps in analyzing the global expression patterns of hypoxia and in understanding the cellular alterations of those suffering from it. It gives an understanding of the comprehensive regulation of the gene expression by environmental spur or specific factors which can be used to diagnose and treat hypoxia before it gets fatal. Prunus persica is a plant which has a high capacity for anoxic tolerance, and analyzing the gene expression changes which are associated to hypoxia treatments in the root tissues of two genotypes of the peach plant (Flooding tolerant and Flooding sensitive) can prevent physiological disorders. Further, gene ontology is used to cover three domains-cellular component, molecular function and biological processes related to the differentially expressed genes. We use Generalized Linear Models here, to find the differentially expressed genes in Prunus persica when exposed to the conditions of Hypoxia (Absence of Oxygen) and Normoxia (Excess of Oxygen) and find their Ontologies and genomic pathways to understand and diagnose the Processes that are most affected.

Additional file 2. Figure S2: Physician perspective on fatigue.

Graphic abstract The novel Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) has emerged to be the reason behind the COVID-19 pandemic. It was discovered in Wuhan, China and then began spreading around the world, impacting the health of millions. Efforts for treatment have been hampered as there are no antiviral drugs that are effective against this virus. In the present study, we have explored the phytochemical constituents of Salvia plebeia R. Br., in terms of its binding affinity by targeting COVID-19 main protease (Mpro) using computational analysis. Molecular docking analysis was performed using PyRx software. The ADMET and drug-likeness properties of the top 10 compounds showing binding affinity greater than or equal to − 8.0 kcal/mol were analysed using pkCSM and DruLiTo, respectively. Based on the docking studies, it was confirmed that Rutin and Plebeiosides B were the most potent inhibitors of the main protease of SARS-CoV-2 with the best binding affinities of − 9.1 kcal/mol and − 8.9 kcal/mol, respectively. Further, the two compounds were analysed by studying their biological activity using the PASS webserver. Molecular dynamics simulation analysis was performed for the selected protein–ligand complexes to confirm their stability at 300 ns. MM-PBSA provided the basis for analyzing the affinity of the phytochemicals towards Mpro by calculating the binding energy, and secondary structure analysis indicated the stability of protease structure when it is bound to Rutin and Plebeiosides B. Altogether, the study identifies Rutin and Plebeiosides B to be potent Mpro inhibitors of SARS-CoV-2. Supplementary Information The online version contains supplementary material available at 10.1007/s42535-021-00304-z.

Cytogenetic risk stratification at diagnosis has long been one of the most useful tools to assess prognosis in acute lymphoblastic leukemia (ALL). To examine the prognostic impact of cytogenetic abnormalities on outcomes after allogeneic hematopoietic cell transplantation, we studied 1731 adults with Philadelphia-negative ALL in complete remission who underwent myeloablative or reduced intensity/non-myeloablative conditioning transplant from unrelated or matched sibling donors reported to the Center for International Blood and Marrow Transplant Research. A total of 632 patients had abnormal conventional metaphase cytogenetics. The leukemia-free survival and overall survival rates at 5 years after transplantation in patients with abnormal cytogenetics were 40% and 42%, respectively, which were similar to those in patients with a normal karyotype. Of the previously established cytogenetic risk classifications, modified Medical Research Council-Eastern Cooperative Oncology Group score was the only independent prognosticator of leukemia-free survival (P=0.03). In the multivariable analysis, monosomy 7 predicted post-transplant relapse [hazard ratio (HR)=2.11; 95% confidence interval (95% CI): 1.04-4.27] and treatment failure (HR=1.97; 95% CI: 1.20-3.24). Complex karyotype was prognostic for relapse (HR=1.69; 95% CI: 1.06-2.69), whereas t(8;14) predicted treatment failure (HR=2.85; 95% CI: 1.35-6.02) and overall mortality (HR=3.03; 95% CI: 1.44-6.41). This large study suggested a novel transplant-specific cytogenetic scheme with adverse [monosomy 7, complex karyotype, del(7q), t(8;14), t(11;19), del(11q), tetraploidy/near triploidy], intermediate (normal karyotype and all other abnormalities), and favorable (high hyperdiploidy) risks to prognosticate leukemia-free survival (P=0.02). Although some previously established high-risk Philadelphia-negative cytogenetic abnormalities in ALL can be overcome by transplantation, monosomy 7, complex karyotype, and t(8;14) continue to pose significant risks and yield inferior outcomes.

Differentially Expressed genes related to Glioblastoma Multiforme as an output of RNASeq studies were further studied to conclude new research insights. Glioma is a type of intracranial tumor (within the skull), which can grow rapidly in its malignant stages. Gene expression in Grade II, III and IV Gliomas is analysed using non linear SVM models. The enriched GO terms were identified GOrilla. Pathways related to NOG and ASCL5 gene were studied using Reactome.

Groundwater samples were collected from Peenya Industrial area of Bengaluru, India to check its inherent bacterial population. After the isolates were grown in lead-induced media, the bacteria that could resist lead were further isolated, identified, and grown. The isolated bacterium was identified as Bacillus toyonensis SCE1 species, and the maximum amount of lead taken up by it to sustain itself as a live biomass was 8 ppm. Lead accumulation was studied based on different factors such as pH, concentration, and time. Bacterial characterization through scanning electron microscopy showed there was lead sorption on the bacterial cells. Transmission electron microscopy revealed that accumulated lead ions inside the bacteria were in the range of 180–190 nm, this was found to correlate with the data obtained by the particle size analyzer. The optimum pH for highest lead accumulation was 7.2, for a time period of 160 min. It was observed that the stable production of homogenous nanoparticles using biomass could be achieved by optimizing factors such as incubation period, agitation, pH, temperature, and contact time.

This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.

ChIP-sequencing, otherwise called ChIP-seq, is a technique used to identify protein co-operations with DNA. A crucial advancement to the field of bio-informatics, ChIP sequencing is conducted in research labs around the world to get a better understanding of the way transcription factors and other associated proteins influence the gene in many biological processes and in tackling disease states. ChIP-seq is predominantly a field under the domain of biotechnology, however recent advancements and development of tools to process ChIP data have turned the study into one involving bio-informatics, allowing computer scientists and lab technicians to work on an otherwise scholarly field of biochemistry, molecular biology, microbiology and biomedicine. This report illustrates the predominant work-flow undertaken to sequence chromatin from a cell and to gain insights on the gene/protein of interest. Another aspect added is to use Machine Learning with Bayesian statistical techniques for Peak Calling. The different stages enumerated in this paper have been completed either with the R language or on a Web Server titled Galaxy.org.

The goal of this study was to propose a workflow using machine learning to identify and predict the miRNA targets of Human Immunodeficiency virus 1. miRNAs which is ~21 nt long are attained from larger hairpin RNA precursors and is maintained in the secondary structure of their precursor relatively than in primary chain of successions. The proposition approach for identification and prediction of miRNA targets in hiv1-miR-H1is based on secondary structure and E-value through machine learning. Data Linearity of Length and e-value for sequence match with hiv1-mir-H1 is verified using Kernel PCA. miRNA targets were grouped into clusters thereby indicating similar targets using K-means algorithm. Classification model using Random Forest was implemented regards to each secondary features variable considering feature relevance. A learning methodology is put forward that assimilate and integrate the score returned by various machine learning algorithms to predict cellular hiv1-miR-H1 targets. Gene targets results using TargetScan, miRanda, PITA, DIANA microT and RNAhybrid are also explored for multiple parameters.

Any foreign chemical substance that is unusually present within an organism or is unexpectedly found in the environment at a higher concentration than the permissible limits can be termed a xenobiotic or a pollutant. Such substances include carcinogens, drugs, food additives, hydrocarbons, dioxins, polychlorinated biphenyls, pesticides or even some natural compounds. Pollutants are known for their higher persistence and pervasiveness, and along with their transformed products, they can remain in and interact with the environment for prolonged periods. In this article, the classification of such substances based on their nature, use, physical state, pathophysiological effects, and sources is discussed. The effects of pollutants on the environment, their biotransformation in terms of bioaccumulation, and the different types of remediation such as in situ and ex situ remediation, are also presented.

Microsatellites are known to exhibit ubiquitous presence across all kingdoms of life, including viruses. Members of the family Closteroviridae are the largest RNA viruses and severely affect different agricultural crops worldwide, including1397645907citrus, grapevine, and vegetables. Here we identified and systematically analyzed the nature and distribution of both simple and complex microsatellites present in the complete genome of 36 species belonging to Closteroviridae. Our results showed, in all analyzed genomes, that neither genome size nor GC content had any influence on number, relative abundance, or relative density of microsatellites. For each genome, dinucleotide repeats were found to be highly predominant and AT/TA and AG/GA were the two most abundant dinucleotide repeat motifs. Repeats larger than trinucleotide were relatively rare in these viral genomes. Comparative study of occurrence, abundance, and density of microsatellites among available RNA and DNA viral genomes indicated that simple repeats were less abundant in genomes of Closteroviridae. To our knowledge, this is the first analysis of microsatellites occurring in the largest viral genome that infects plants. Characterization of such variations in repeat sequences would be important in deciphering the origin, mutational processes, and role of repeat sequences in viral genomes.

Hidden Markov Model (HMM) has become one of the interesting methods for the researchers, especially in bioinformatics where different analysis are carried out. These are widely used in science, engineering and many other areas such as bioinformatics, genomic mapping, computer vision, finance and economics, and in social science. HMMs require much smaller training sets, and that the examination of the inner structure of the model provides often a deeper understanding of the phenomenon. In this survey, we first describe the important algorithms for the HMMs, and provide useful comparisons, aiming at their advantages and shortcomings. We then consider the major g applications, such as annotations, gene alignment and profiling of sequences, DNA structure prediction, and pattern recognition. We also list some analysis on how to use HMM for DNA genomes. Finally, we conclude use and perspectives of HMMs in bioinformatics and provide a critical appraisal for the same.

Background: The exact etiology of chronic spontaneous urticaria is unknown in the majority of patients. A subset of chronic spontaneous urticaria can be autoimmune and may be associated with thyroid autoimmunity.Methods: Serum anti thyroid peroxidase (anti-TPO) antibody level and thyroid function tests (TFT) including triiodothyronine (T3), thyroxine (T4) and thyroid stimulating hormone (TSH) levels were estimated in 100patients with chronic spontaneous urticaria and 50 controls. Autologous serum skin test (ASST) was done in 84 chronic urticaria patients and 21 patients with high anti-TPO antibody.Results: High anti-TPO antibodies and abnormal thyroid function were seen in 24 (24%) patients each. High anti-TPO antibody levels were seen in nine (18%) and abnormal TFT in 11 (22%) controls. Abnormal TSH was seen in 22 patients (22%) and 6 (12%) controls. Autologous serum skin test was positive in 53 patients with chronic spontaneous urticaria. It was positive in 16 and negative in five out of 21 patients with high anti-TPO antibody levels. Conclusions: Though there was a slight increase in abnormal anti-TPO antibody level and thyroid dysfunction in patients with chronic spontaneous urticaria compared to controls, it was not statistically significant.

The objective of this paper was to measure the peripheral dose (PD) with diode and thermoluminescence dosimeter (TLD) for intensity modulated radiotherapy (IMRT) with linear accelerator (conventional LINAC), and tomotherapy (novel LINAC). Ten patients each were selected from Trilogy dual-energy and from Hi-Art II tomotherapy. Two diodes were kept at 20 and 25 cm from treatment field edge. TLDs (LiF:MgTi) were also kept at same distance. TLDs were also kept at 5, 10, and 15 cm from field edge. The TLDs were read with REXON reader. The readings at the respective distance were recorded for both diode and TLD. The PD was estimated by taking the ratio of measured dose at the particular distance to the prescription dose. PD was then compared with diode and TLD for LINAC and tomotherapy. Mean PD for LINAC with TLD and diode was 2.52 cGy (SD 0.69), 2.07 cGy (SD 0.88) at 20 cm, respectively, while at 25 cm, it was 1.94 cGy (SD 0.58) and 1.5 cGy (SD 0.75), respectively. Mean PD for tomotherapy with TLD and diode was 1.681 cGy SD 0.53) and 1.58 (SD 0.44) at 20 cm, respectively. The PD was 1.24 cGy (SD 0.42) and 1.088 cGy (SD 0.35) at 25 cm, respectively, for tomotherapy. Overall, PD from tomotherapy was found lower than LINAC by the factor of 1.2-1.5. PD measurement is essential to find out the potential of secondary cancer. PD for both (conventional LINAC) and novel LINACs (tomotherapy) were measured and compared with each other. The comparison of the values for PD presented in this work and those published in the literature is difficult because of the different experimental conditions. The diode and TLD readings were reproducible and both the detector readings were comparable.

Background: The psychosocial impact of obesity is of concern in the present world but remains less studied compared to its physical consequences. There is a lack of sufficient data from Asian and Indian population in particular. Objectives: To study the prevalence of psychiatric morbidity among the obese. To demonstrate the association of psychiatric morbidity with obesity and lipid profile. Settings and Design: A cross-sectional study at a tertiary care center, Kerala, India. Methodology: One hundred and twelve subjects, both male and female, who attended the obesity clinic from May to December 2013 participated in the study. Sociodemographic data and clinical history were collected from each individual. Psychiatric diagnosis was made using MINI-Plus. Hospital Anxiety and Depression Scale (HADS) was rated in all subjects. Statistical Analysis: Done using SPSS version 18 (Chicago IL, USA). Results: Thirty-seven (33%) subjects were assessed to have a psychiatric disorder. Depressive disorder (17.9%) was the most common diagnosis. The mean body mass index (BMI) of the study group was 31.4 ΁ 4.1. No significant association was found for psychiatric disorders with BMI and lipid profile. HADS depression score was significantly associated with triglycerides (TG), low density lipoprotein (LDL) and very low density lipoprotein (VLDL). HADS anxiety score was also found to have significant but weak relationship with VLDL (P = 0.054). Conclusion: Obesity is associated with high risk for psychiatric disorders. Management of obesity requires a multidisciplinary approach. Further explorations are needed to ascertain the cause or effect nature of obesity with psychiatric disorders.

Killer immunoglobulin like receptor (KIR) ligand mismatches cause natural killer (NK) cell alloreactivity in haploidentical stem cell transplantation (SCT) affecting engraftment, graft versus host disease (GVHD) and graft versus leukemia effects. The role of patient/donor KIR genotypes in HLA identical bone marrow transplant (BMT) for patients with non-malignant disorders is less clear. We have analyzed the KIR genotypes in patients with β thalassaemia major who underwent HLA-A, B, DR identical related-donor SCT between February, 1996 to June, 2003. Patients who expired before day 30 (n=13) and for whom DNA samples were unavailable (n=25) were excluded from the study. Retrospective analysis was carried out in 59 patients to evaluate the effect of KIR genotype on the outcome of BMT. All patients received unmanipulated bone marrow and standard cyclosporine /short course methotrexate regimen as prophylaxis against GVHD. Genomic DNA was prospectively extracted from recipient and donor peripheral blood mononuclear cells. Genotyping of 14 KIR genes (8 inhibitory -2DL1, 2DL2, 2DL3, 2DL4, 2DL5, 3DL1, 3DL2 and 3DL3; and 6 activating - 2DS1, 2DS2, 2DS3, 2DS4, 2DS5 and 3DS1) was done by PCR-SSP. The effect of other potential risk factors for GVHD and rejection like recipient’s age, donor’s age, sex mismatch, female donor to male recipient, conditioning regimen, cell dose, and Lucarelli class at BMT was also analyzed. Among 59 patients, 7 (11.8%) rejected the graft. None of the patients who could not be evaluated had rejection of the graft. Two patients had primary graft failure and were not evaluable for the analysis of acute GVHD. Of the remaining 57 patients, 32 (56 %) patients showed evidence of acute GVHD, 10 (31.2%) being grade III-IV. Nine patients expired before day 100 and were not evaluable for chronic GVHD. Of the 50 evaluable patients, 6 (12%) patients developed chronic GVHD. No significant effect of donor/recipient KIR genotype was found on the incidence or severity of acute or chronic GVHD. Among 7 patients who rejected the graft, 6 had identical KIR genotype to their donors. It was disparate in one, with the donor KIR genotype having additional KIR genes (2DL2 and 2DS2) that were absent in the recipient. Further analysis revealed that KIR genotype identical pairs (n=30) had a statistically significant increased risk of graft rejection (Log rank test) in comparison to KIR genotype disparate pairs (n=29) (both graft versus host and host versus graft direction) (p=0.0463). On multivariate analysis including KIR genotype and the other factors listed above, the KIR genotype identical pairs had an 8-fold higher risk of rejection (RR=8.73; 95%CI- 0.83-92.33; p=0.072). Other risk factors assessed in this analysis showed the following relative risks: patient’s age (RR=1.031, p=0.792), sex mismatched transplants (RR=1.455, p=0.693), cell dose (RR=1.013, p=0.961), conditioning regimen (RR=1.308, p= 0.790) and status at BMT (Class I/II versus Class III) (RR=2.750, p=0.384). Our data suggests that identical recipient /donor KIR genotype is a risk factor for graft rejection in patients with β thalassaemia major undergoing related HLA identical SCT. The biological basis for this observation needs evaluation.