Human brain shows recurrent non-canonical microRNA editing events enriched for seed sequence with possible functional consequence

RNA editing is a post-transcriptional modification, which can provide tissue-specific functions not encoded in DNA. Adenosine-to-inosine is the predominant editing event and, along with cytosine-to-uracil changes, constitutes canonical editing. The rest is non-canonical editing. In this study, we have analysed non-canonical editing of microRNAs in the human brain. We have performed massively parallel small RNA sequencing of frontal cortex (FC) and corpus callosum (CC) pairs from nine normal individuals (post-mortem). We found 113 and 90 unique non-canonical editing events in FC and CC samples, respectively. More than 70% of events were in the miRNA seed sequence—implicating an altered set of target mRNAs and possibly resulting in a functional consequence. Up to 15% of these events were recurring and found in at least three samples, also supporting the biological relevance of such variations. Two specific sequence variations, C-to-A and G-to-U, accounted for over 80% of non-canonical miRNA editing events—and revealed preferred sequence motifs. Our study is one of the first reporting non-canonical editing in miRNAs in the human brain. Our results implicate miRNA non-canonical editing as one of the contributing factors towards transcriptomic diversity in the human brain

Comparative study of bioethanol production from sugarcane molasses by using Zymomonas mobilis and Saccharomyces cerevisiae

The study was designed to compare the bioethanol production from Zymomonas mobilis and Saccharomyces cerevisiae using molasses as production medium. The focus was on the retention time at lab scale. Bioethanol and petroleum blend can be used in existing gasoline engines. Present study showed a more cost-effective procedure for production of ethanol from sugar-cane molasses by using bacterial strain "Z. mobilis". Laboratory scale unit was designed to perform the experiments through batch fermentation and to determine the impact of leading parameters, including fermentation temperature, pH, sugar concentration, and nutrients. S. cerevisiae produced 8.3% (v/v) bioethanol provided sugar concentration 14 g /100 ml with the fermentation efficiency of 92.5%. On the contrary, Z.mobilis produced 9.3% (v/v) bioethanol by utilizing 16 g/100 ml sugar with the fermentation efficiency of 90.5%. Effect of nutrients on fermentation was determined using molasses as feedstock. Thin layer chromatography was also performed to assess the possible impurities in molasses as compared to the pure sugar. The pH and fermentation temperature was optimized for the enhanced yield of bioethanol.Key words: Bioethanol, molasses, fermentation, Zymomonas mobilis, Saccharomyces cerevisiae

Diagnostic and prognostic potential of MiR-379/656 MicroRNA cluster in molecular subtypes of breast cancer

Introduction: Breast cancer is the most frequently diagnosed cancer globally and is one of the most important contributors to cancer-related deaths. Earlier diagnosis is known to reduce mortality, and better biomarkers are needed. MiRNA clusters often co-express and target mRNAs in a coordinated fashion, perturbing entire pathways; they thus merit further exploration for diagnostic or prognostic use. MiR-379/656, at chromosome 14q32, is the second largest miRNA cluster in the human genome and implicated in various malignancies including glioblastoma, melanoma, gastrointestinal tumors and ovarian cancer highlighting its potential importance. In this study, we focus on the diagnostic and prognostic potentials of MiR-379/656 in breast cancer and its molecular subtypes. Materials and Methods: We analyzed miRNA and mRNA next generation sequencing data from 903 primary tumors and 90 normal controls (source: The Cancer Genome Atlas). The differential expression profile between tumor and normal was analyzed using DeSEQ2. Penalized logistic regression modelling (lasso regression) was used to assess the predictive potential of MiR-379/656 expression for tumor and normal samples. The association between MiR-379/656 expression and overall patient survival was studied using Cox Proportional-Hazard Model. The target mRNAs (validated) of MiR-379/656 were annotated via pathway enrichment analysis to understand the biological significance of the cluster in breast cancer. Results: The differential expression analysis for 1390 miRNAs (miRnome) revealed 310 upregulated (22.3%) and 176 downregulated (12.66%) miRNAs in breast cancer patients compared with controls. For MiR-379/656, 32 miRNAs (32/42; 76%) were downregulated. The MiR-379/656 cluster was found to be the most differentially expressed cluster in the human genome (p 10−30). The Basal and Luminal B subtypes showed at least 83% (35/42) of the miRNAs to be downregulated. The binomial model prioritized 15 miRNAs, which distinguished breast cancer patients from controls with 99.15 ± 0.58% sensitivity and 77.78 ± 5.24% specificity. Overall, the Basal and Luminal B showed the most effective predictive power with respect to the 15 prioritized miRNAs at MiR-379/656 cluster. The decreased expression of MiR-379/656 was found to be associated with poorer clinical outcome in Basal and Luminal B subtypes, increasing tumor stage and tumor size/extent, and overall patient survival. Pathway enrichment for the validated targets of MiR-379/656 was significant for cancer-related pathways, especially DNA repair, transcriptional regulation by p53 and cell cycle checkpoints (adjusted p-value 0.05). Conclusions: Genome informatics analysis of high throughput data for MiR-379/656 cluster has shown that a subset of 15 miRNAs from MiR-379/656 cluster can be used for the diagnostic and prognostic purpose of breast cancer and its subtypes—especially in Basal and Luminal B

A Customized Efficient Deep Learning Model for the Diagnosis of Acute Leukemia Cells Based on Lymphocyte and Monocyte Images

Data Availability Statement: The data related to this article are publicly available on the GitHub platform under the title Ansari acute leukemia images.Copyright © 2023 by the authors. The production of blood cells is affected by leukemia, a type of bone marrow cancer or blood cancer. Deoxyribonucleic acid (DNA) is related to immature cells, particularly white cells, and is damaged in various ways in this disease. When a radiologist is involved in diagnosing acute leukemia cells, the diagnosis is time consuming and needs to provide better accuracy. For this purpose, many types of research have been conducted for the automatic diagnosis of acute leukemia. However, these studies have low detection speed and accuracy. Machine learning and artificial intelligence techniques are now playing an essential role in medical sciences, particularly in detecting and classifying leukemic cells. These methods assist doctors in detecting diseases earlier, reducing their workload and the possibility of errors. This research aims to design a deep learning model with a customized architecture for detecting acute leukemia using images of lymphocytes and monocytes. This study presents a novel dataset containing images of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). The new dataset has been created with the assistance of various experts to help the scientific community in its efforts to incorporate machine learning techniques into medical research. Increasing the scale of the dataset is achieved with a Generative Adversarial Network (GAN). The proposed CNN model based on the Tversky loss function includes six convolution layers, four dense layers, and a Softmax activation function for the classification of acute leukemia images. The proposed model achieved a 99% accuracy rate in diagnosing acute leukemia types, including ALL and AML. Compared to previous research, the proposed network provides a promising performance in terms of speed and accuracy; and based on the results, the proposed model can be used to assist doctors and specialists in practical applications.This research received no external funding

TAT-peptide conjugated repurposing drug against SARS-CoV-2 main protease (3CLpro): potential therapeutic intervention to combat COVID-19

The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that originated in Chinese city of Wuhan has caused around 906,092 deaths and 28,040,853 confirmed cases worldwide (WHO, 11 September, 2020). In a life-threatening situation, where there is no specific and licensed anti-COVID-19 vaccine or medicine available; the repurposed drug might act as a silver bullet. Currently, more than 211 vaccines, 80 antibodies, 31 antiviral drugs, 35 cell-based, 6 RNA-based and 131 other drugs are in clinical trials. It is therefore utter need of the hour to develop an effective drug that can be used for the treatment of COVID-19 before a vaccine can be developed. One of the best-characterized and attractive drug targets among coronaviruses is the main protease (3CL^{pro}). Therefore, the current study focuses on the molecular docking analysis of TAT-peptide^{47–57} (GRKKRRQRRRP)-conjugated repurposed drugs (i.e., lopinavir, ritonavir, favipiravir, and hydroxychloroquine) with SARS-CoV-2 main protease (3CL^{pro} to discover potential efficacy of TAT-peptide (TP) - conjugated repurposing drugs against SARS-CoV-2. The molecular docking results validated that TP-conjugated ritonavir, lopinavir, favipiravir, and hydroxychloroquine have superior and significantly enhanced interactions with the target SARS-CoV-2 main protease. In-silico approach employed in this study suggests that the combination of the drug with TP is an excelling alternative to develop a novel drug for the treatment of SARS-CoV-2 infected patients. The development of TP based delivery of repurposing drugs might be an excellent approach to enhance the efficacy of the existing drugs for the treatment of COVID-19. The predictions from the results obtained provide invaluable information that can be utilized for the choice of candidate drugs for in vitro, in vivo and clinical trials. The outcome from this work prove crucial for exploring and developing novel cost-effective and biocompatible TP conjugated anti-SARS-CoV-2 therapeutic agents in immediate future

Electrochemical defluorination of water: an experimental and morphological study

This experimental study concerns the elimination of fluoride from water using an electrocoagulation reactor having a variable flow direction in favour of increasing the electrolysing time, saving the reactor area, and water mixing. The detention time of the space-saver EC reactor (S-SECR) was measured and compared to the traditional reactors using an inert dye (red drain dye). Then, the influence of electrical current (1.5 ≤ δ ≤ 3.5 mA cm2), pH of water (4 ≤ pH ≤ 10), and distance between electrodes (5 ≤ ϕ ≤ 15) on the defluoridation of water was analysed. The effect of the electrolysing activity on the electrodes’ morphology was studied using scanning electron microscopy (SEM). Additionally, the operational cost was calculated. The results confirmed the removal of fluoride using S-SECR met the guideline of the World Health Organization (WHO) for fluoride levels in drinking water of ≤1.5 mg/L. S-SECR abated fluoride concentration from 20 mg/L to the WHO’s guideline at δ, ϕ, pH, operational cost, and power consumption of 2.5 mA cm2, 5 mm, 7, 0.346 USD m3, and 5.03 kWh m3, respectively. It was also found the S-SECR enhanced the detention time by 190% compared to the traditional reactors. The appearance of dents and irregularities on the surface of anodes in the SEM images proves the electrolysing process

Primary and malignant cholangiocytes undergo CD40 mediated Fas dependent Apoptosis, but are insensitive to direct activation with exogenous fas ligand

Introduction Cholangiocarcinoma is a rare malignancy of the biliary tract, the incidence of which is rising, but the pathogenesis of which remains uncertain. No common genetic defects have been described but it is accepted that chronic inflammation is an important contributing factor. We have shown that primary human cholangiocyte and hepatocyte survival is tightly regulated via co-operative interactions between two tumour necrosis family (TNF) receptor family members; CD40 and Fas (CD95). Functional deficiency of CD154, the ligand for CD40, leads to a failure of clearance of biliary tract infections and a predisposition to cholangiocarcinoma implying a direct link between TNF receptor-mediated apoptosis and the development of cholangiocarcinoma. Aims To determine whether malignant cholangiocytes display defects in CD40 mediated apoptosis. By comparing CD40 and Fas-mediated apoptosis and intracellular signalling in primary human cholangiocytes and three cholangiocyte cell lines. Results Primary cholangiocytes and cholangiocyte cell lines were relatively insensitive to direct Fas-mediated killing with exogenous FasL when compared with Jurkat cells, which readily underwent Fas-mediated apoptosis, but were extremely sensitive to CD154 stimulation. The sensitivity of cells to CD40 activation was similar in magnitude in both primary and malignant cells and was STAT-3 and AP-1 dependent in both. Conclusions 1) Both primary and malignant cholangiocytes are relatively resistant to Fas–mediated killing but show exquisite sensitivity to CD154, suggesting that the CD40 pathway is intact and fully functional in both primary and malignant cholangiocytes 2) The relative insensitivity of cholangiocytes to Fas activation demonstrates the importance of CD40 augmentation of Fas dependent death in these cells. Agonistic therapies which target CD40 and associated intracellular signalling pathways may be effective in promoting apoptosis of malignant cholangiocytes

Adsorption of fluoride on a green adsorbent derived from wastepaper: Kinetic, isotherm and characterisation study

The excessive concentration of fluoride (F−) in water represents a grave problem for several countries, especially those that depend on groundwater as a main source of drinking water. Therefore, many treatment methods, such as chemical precipitation and membrane, were practised to remove F− from water. However, the traditional methods suffer from many limitations, such as the high cost and the slowness. Hence, many studies have been directed towards developing novel and effective water defluoridation methods. In this context, the current study investigates the development of an eco-friendly adsorbent by extracting Ca, Al, and Fe from industrial by-products, precipitating them on sand particles, and using this new adsorbent to remove F− from water. The removal experiments were commenced under different pH levels (3-10), contact times (0–240 minutes) and concentrations of F− (7.5–37.5 mg/L). X-ray fluorescence (XRF), X-ray diffraction Investigator (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX) methods were used to characterise the green adsorbent. Adsorption isotherm and kinetic studies were also conducted to define the adsorption type. The results confirmed that the new adsorbent could remove as high as 86% of F− at pH, contact time, agitation speed and adsorbent dose of 10, 180 minutes, 200 rpm and 15 mg/L, respectively. The characterisation studies prove the occurrence of the sorption process and the suitability of the morphology of the adsorbent for F− removal. Adsorption kinetics follow better with a pseudo-first-order model that indicates the predominance of physisorption, which agrees with the FTIR results. The isotherm study indicated that Langmuir isotherm is more suitable for representing data with an R2 value of 0.992, which means the adsorption of F− occurs as monolayer adsorption on homogeneous sites on the surface of the new adsorbent. In summary, it can be concluded that the developed adsorbent in this study could be a promising alternative to the traditional F− removal methods

A high-order FEM formulation for free and forced vibration analysis of a nonlocal nonlinear graded Timoshenko nanobeam based on the weak form quadrature element method

The purpose of this paper is to provide a high-order finite element method (FEM) formulation of nonlocal nonlinear nonlocal graded Timoshenko based on the weak form quadrature element method (WQEM). This formulation offers the advantages and flexibility of the FEM without its limiting low-order accuracy. The nanobeam theory accounts for the von Kármán geometric nonlinearity in addition to Eringen’s nonlocal constitutive models. For the sake of generality, a nonlinear foundation is included in the formulation. The proposed formulation generates high-order derivative terms that cannot be accounted for using regular first- or second-order interpolation functions. Hamilton’s principle is used to derive the variational statement which is discretized using WQEM. The results of a WQEM free vibration study are assessed using data obtained from a similar problem solved by the differential quadrature method (DQM). The study shows that WQEM can offer the same accuracy as DQM with a reduced computational cost. Currently the literature describes a small number of high-order numerical forced vibration problems, the majority of which are limited to DQM. To obtain forced vibration solutions using WQEM, the authors propose two different methods to obtain frequency response curves. The obtained results indicate that the frequency response curves generated by either method closely match their DQM counterparts obtained from the literature, and this is despite the low mesh density used for the WQEM systems