A CNN based m5c RNA methylation predictor.

Post-transcriptional modifications of RNA play a key role in performing a variety of biological processes, such as stability and immune tolerance, RNA splicing, protein translation and RNA degradation. One of these RNA modifications is m5c which participates in various cellular functions like RNA structural stability and translation efficiency, got popularity among biologists. By applying biological experiments to detect RNA m5c methylation sites would require much more efforts, time and money. Most of the researchers are using pre-processed RNA sequences of 41 nucleotides where the methylated cytosine is in the center. Therefore, it is possible that some of the information around these motif may have lost. The conventional methods are unable to process the RNA sequence directly due to high dimensionality and thus need optimized techniques for better features extraction. To handle the above challenges the goal of this study is to employ an end-to-end, 1D CNN based model to classify and interpret m5c methylated data sites. Moreover, our aim is to analyze the sequence in its full length where the methylated cytosine may not be in the center. The evaluation of the proposed architecture showed a promising results by outperforming state-of-the-art techniques in terms of sensitivity and accuracy. Our model achieve 96.70% sensitivity and 96.21% accuracy for 41 nucleotides sequences while 96.10% accuracy for full length sequences.

Regulation of Cell Signaling Pathways by Genistein in Different Cancers: Progress, Prospects and Pitfalls.

On the translational front, integrative genomic approaches have spurred the identification of diverse mechanisms of drug resistance, tumor heterogeneity, metastasis and emerging preclinical targets. Recent breakthroughs in oncogenic cell signaling pathways have forged new links and multi-disciplinary researchers have unraveled different facets of signaling landscapes. Natural product research has witnessed breakneck developments mainly in the context of the ever-expanding list of bioactive components having significantly pharmacological properties. Genistein has gradually gained appreciation because of its multifaceted roles in the prevention and inhibition of carcinogenesis and metastasis. More importantly, the entry of genistein into various phases of clinical trials substantiates the medicinal and pharmacological significance of genistein in cancer chemoprevention. In this review, we have attempted to summarize how genistein regulated different oncogenic pathways in carcinogenesis and metastasis. Furthermore, genistein-mediated regulation of non-coding RNAs is also an interesting feature that has been included in this review to realistically analyze how genistein-mediated control of miRNAs, lncRNAs and circRNAs influence carcinogenesis. In the later sections, we have provided a summary of clinical trials related to genistein for cancer prevention/inhibition. However, apart from the optimistic approaches to further investigate genistein-mediated cancer-inhibitory effects, certain hints have emerged which underscore the pro-metastatic role of genistein. Therefore, the pro-metastatic role of genistein in different cancers should be rationally tested in a broader context because these properties in the future may reduce the enthusiasm in the quest to pursue genistein as a potent cancer chemopreventive agent.

Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning.

DNA copy number variation (CNV) is the type of DNA variation which is associated with various human diseases. CNV ranges in size from 1 kilobase to several megabases on a chromosome. Most of the computational research for cancer classification is traditional machine learning based, which relies on handcrafted extraction and selection of features. To the best of our knowledge, the deep learning-based research also uses the step of feature extraction and selection. To understand the difference between multiple human cancers, we developed three end-to-end deep learning models, i.e., DNN (fully connected), CNN (convolution neural network), and RNN (recurrent neural network), to classify six cancer types using the CNV data of 24,174 genes. The strength of an end-to-end deep learning model lies in representation learning (automatic feature extraction). The purpose of proposing more than one model is to find which architecture among them performs better for CNV data. Our best model achieved 92% accuracy with an ROC of 0.99, and we compared the performances of our proposed models with state-of-the-art techniques. Our models have outperformed the state-of-the-art techniques in terms of accuracy, precision, and ROC. In the future, we aim to work on other types of cancers as well.

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier.

A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and features, with variable treatment options. Manual detection of tumors is difficult, time-consuming, and error-prone. Therefore, a significant requirement for computerized diagnostics systems for accurate brain tumor detection is present. In this research, deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningioma, no tumor, and pituitary tumor. The classified tumor images have been passed to the proposed Seg-network where the actual infected region is segmented to analyze the tumor severity level. The outcomes of the reported research have been evaluated on three benchmark datasets such as Kaggle, 2020-BRATS, and local collected images. The model achieved greater than 90% detection scores to prove the proposed model's effectiveness.

Synthesis, characterization and antibacterial activity of silver nanoparticles using Rhazya stricta.

BACKGROUND: Green synthesis of metallic nanoparticles has gained significant attention in the field of nanomedicine as an environment-friendly and cost-effective alternative in comparison with other physical and chemical methods. Several metals such as silver, gold, iron, titanium, zinc, magnesium and copper have been subjected to nanoformulation for a wide range of useful applications. Silver nanoparticles (AgNPs) are playing a major role in the field of nanomedicine and nanotechnology. They are widely used in diagnostics, therapeutic and pharmaceutical industries. Studies have shown potential inhibitory antimicrobial, anti-inflammatory and antiangiogenesis activities of AgNPs. METHODS: AgNPs have been synthesized using silver nitrate and methanolic root extract of Rhazya stricta that belongs to the Apocynaceae family. Stability and dispersion of nanoparticles were improved by adding xylitol. Synthesized nanoparticles were characterized by UV-Vis spectroscopy, scanning electron microscopy, energy dispersive spectroscopy, X-ray diffractometer and Fourier transforms infrared spectroscopy. Furthermore, the antibacterial effect of the plant extract and the nanoparticles were evaluated against gram-positive (Bacillus subtilis) and gram-negative (Escherichia coli) bacteria. RESULTS: The average size of AgNPs synthesized, was 20 nm with the spherical shape. Rhazya stricta based nanoparticles exhibited improved antibacterial activity against both gram-positive and negative strains.

Quercetin-mediated regulation of signal transduction cascades and microRNAs: Natural weapon against cancer.

Recent technological and analytical breakthroughs in genomics and proteomics have deepened our understanding related to the multifaceted nature of cancer. Because of therapeutically challenging nature of cancer, there has been a renewed interest in phytochemistry, and much attention is currently being given to the identification of signaling pathway inhibitors. Data obtained through high-throughput technologies has provided a broader landscape of wiring maps of complex oncogenic signaling networks, thus revealing novel therapeutic opportunities. Increasingly, it is being realized that although our knowledge related to physiological and pathophysiological roles of signal transduction cascades has evolved rapidly, the clinical development of signaling pathway inhibitors has been challenging. Quercetin has attracted considerable attention because of its amazingly high pharmacological value. Research over decades has sequentially shown that quercetin effectively inhibited cancer development and progression. In this review, we have attempted to set the spotlight on the regulation of different cell signaling pathways by quercetin. We partition this multicomponent review into how quercetin effectively regulates the Wnt/ß-catenin pathway, Janus kinase-signal transducer and activator of transcription pathway, and vascular endothelial growth factor/vascular endothelial growth factor receptor signaling cascade in different types of cancers. We also provide an overview of the regulation of NOTCH and SHH pathways by quercetin. MicroRNAs (miRNAs) have also emerged as versatile regulators of cancer, and contemporary studies have shed light on the ability of quercetin to control different miRNAs in various cancers. We have scattered information related to NOTCH and SHH pathways, and future studies must converge on the investigation of these pathways to see how quercetin modulates the signaling machinery of these pathways.

Regulation of cancer cell signaling pathways by mushrooms and their bioactive molecules: Overview of the journey from benchtop to clinical trials.

Mushrooms represent a tremendous source of biologically useful and pharmacologically active molecules. Recent breakthroughs in cancer genetics, genomics, proteomics and translational research have helped us to develop a better understanding of the underlying mechanisms which are contributory in cancer development and progression. Different signaling pathways particularly, Wnt, SHH, TGF/SMAD and JAK/STAT have been shown to modulate cancer progression and development. Increasingly it is being realized that genetic/epigenetic mutations and loss of apoptosis also mandate a 'multi-molecular' perspective for the development of therapies to treat cancer. In this review we attempted to provide an overview of the regulation of different signaling pathways by mushrooms and their bioactive compounds. Regulation of Wnt and JAK-STAT pathways by mushrooms is deeply studied but we do not have comprehensive information about regulation of TGF/SMAD, Notch and TRAIL induced signaling pathways because of superficially available data. There are outstanding questions related to modulation of oncogenic and tumor suppressor microRNAs by mushrooms in different cancers. Therefore, detailed mechanistic insights related to targeting of multiple pathways by extracts or bioactive compounds from mushrooms will be helpful in bridging our current knowledge gaps and translation of medicinally precious bioactive molecules to clinically effective therapeutics.

Identification of microRNA precursors using reduced and hybrid features.

MicroRNAs (also called miRNAs) are a group of short non-coding RNA molecules. They play a vital role in the gene expression of transcriptional and post-transcriptional processes. However, abnormality of their expression has been observed in cancer, heart diseases and nervous system disorders. Therefore for basic research and microRNA based therapy, it is imperative to separate real pre-miRNAs from false ones (hairpin sequences similar to pre-miRNA stem loops). Different conservation and machine learning methods have been applied for the identification of miRNAs. However, machine learning algorithms have gained more popularity than conservative based algorithms in terms of sensitivity and overall performance. Due to the avalanche of RNA sequences discovered in a post-genomic age, it is necessary to construct a predictor for the identification of pre-microRNAs in humans. We have developed a predictor called MicroR-Pred in which the RNA sequences are formulated by a hybrid feature vector. The novelty of the new predictor is in the use of the partial least squares technique followed by the Random Forest and SVM (Support Vector Machine) algorithms for dimension reduction and classification. The performance of the MicroR-Pred model is quite promising compared to other state-of-the-art miRNA predictors. It has achieved 88.40% and 93.90% accuracies for RF and SVM.

An investigation on the prevalence and efficiency of immunochromatographic testing in suspected malarial patients of Rawalpindi and Islamabad, Pakistan.

BACKGROUND/AIM: The prevalences of Plasmodium falciparum and Plasmodium vivax are increasing rapidly in Pakistan, but recent data on the epidemiology of malaria are not properly reported with scarce diagnostic methods for quick diagnosis. This study was designed to determine the current prevalence and distribution of Plasmodium species in the vicinity of Rawalpindi and Islamabad and report on the validity of the immunochromatographic test (ICT) in diagnosing malarial infections. MATERIALS AND METHODS: A total of 1500 blood samples obtained from a local hospital were screened during the course of this study via microscopic examination and ICT. RESULTS: It was seen that malaria was highly endemic in this region. Both P. vivax and P. falciparum were prevalent in all age groups with high seasonal variations, showing a summer peak for P. vivax and a winter peak for P. falciparum. In a comparative study of the diagnostic methods it was observed that the ICT is 95% sensitive and 100% specific for both P. falciparum and P. vivax, while microscopic study was 100% sensitive and 96.8% specific. CONCLUSION: Epidemiological study of the malarial parasites showed that majority of the patients were from Rawalpindi as compared to Islamabad and that P. vivax was the dominant cause of malarial infection.

Kisspeptin Mediated Signaling in Cancer.

Research over the years has gradually and sequentially highlighted contributory role of hypothalamic- based kisspeptin-signaling axis as a major positive modulator of the neuroendocrinological reproductive axis in mammals. However, a series of landmark studies provided convincing evidence of role of this signaling in regulation of cancer development and progression. It is becoming progressively more understandable that loss or reduction of KISS1 expression in different human cancers correlates inversely with progression of tumor, metastasizing potential and survival. In this review we have attempted to provide an overview highlight of the most recent updates addressing metastasis- suppressing role of KISS1. We also summarize interplay of microRNA and KISS1 in cancer. The miRNA regulation of different genes is a rapidly expanding area of research however, the community lacks a deep understanding of miRNA regulation of KISS1. Recently, emerging laboratory findings have shown that KISS1 is transcriptionally controlled by TCF21 that is in turn regulated by miR-21. Therefore, there is an urgent need for further study of how miRNA directly or indirectly influences KISS1 at the posttranscriptional level. There is also a lack of evidence regarding natural agents that mediate upregulation or downregulation of KISS1. Increasing the knowledge of the KISS1/KISS1R signaling axis will be helpful in achieving personalized medicine.

Is miR-34a a Well-equipped Swordsman to Conquer Temple of Molecular Oncology?

Overwhelmingly increasing advancements in miRNA biology have opened new avenues for pharmaceutical companies to initiate studies on designing effective, safe, and therapeutically active candidates using miRNA mimetics and miRNA inhibitors. In accordance with this approach, development of miravirsen and SPC3649, an LNA-based (locked nucleic acid) antisense molecule against miR-122, to treat hepatitis C has sparked interest in identifying most efficient microRNAs for journey from bench-top toward pharmaceutical industry and breakthroughs in delivery technology will pave the way to 'final frontier'. MRX34, a liposome-formulated mimic of miR-34 for treatment of metastatic cancer with liver involvement and unresectable primary liver cancer, has also entered in clinical trial. There is a successive increase in the research work related to miR-34 biology and miRNA regulation of modulators of intracellular signaling cascades. We partition this review into how miR-34a is regulated by different proteins and how Wnt- and TGF-induced intracellular signaling cascades are modulated by miR-34a. In this review, we bring to limelight how miR-34a regulates its target genes to induce apoptosis and inhibit cell proliferation as evidenced by in vitro and in vivo analysis. We also discuss miR-34 regulation of PDGFR and c-MET and recent advancements in nanotechnologically delivered miR-34a. Spotlight is also set on modulation of chemotherapeutic sensitivity by miR-34a in cancer cells using reconstruction studies. Clinical trial of miR-34 is indicative of its tremendous potential, and continuous cutting research will prove to be effective in efficiently translating laboratory findings into clinically effective therapeutics.

Raman and IR spectroscopic studies of fenamates--conformational differences in polymorphs of flufenamic acid, mefenamic acid and tolfenamic acid.

Solid-state Raman and IR spectra of two polymorphic forms of each of three fenamates (flufenamic acid, mefenamic acid and tolfenamic acid) display subtle but highly reproducible differences. Many of these spectral differences can be ascribed to different conformations of these molecules, involving two of four possible orientations of one substituted benzene ring with respect to the other. Interpretation of the vibrational spectra in terms of conformational differences has been facilitated by DFT calculations at the B3LYP/cc-pVDZ level for each conformer. The calculated spectra are compared with the experimental spectra in order to identify the conformers present in two polymorphic forms in each case, and detailed band assignments are obtained from the DFT calculations.

Surface-enhanced Raman scattering studies of rhodanines: evidence for substrate surface-induced dimerization.

The surface-enhanced Raman scattering (SERS) spectra of rhodanine adsorbed on silver nanoparticles have been examined using 514.5 and 632.8 nm excitation. There is evidence that, under the experimental conditions used, rhodanine undergoes a nanoparticle surface-induced reaction resulting in the formation of a dimeric species via the active methylene group in a process which is analogous to the Knoevenagel reaction. The experimental observations are supported by DFT calculations at the B3-LYP/cc-pVDZ level. Calculated energies for the interaction of the E and Z isomers of the dimers of rhodanine with silver nanoparticles support a model in which the (intra-molecular hydrogen bonded) E isomer dimer is of lower energy than the Z isomer. A strong band, at 1566 cm(-1), in the SERS spectrum of rhodanine is assigned to the nu(C double bond C) mode of the dimer species.

Vibrational spectra of alpha-amino acids in the zwitterionic state in aqueous solution and the solid state: DFT calculations and the influence of hydrogen bonding.

The zwitterionic forms of the two simplest alpha-amino acids, glycine and l-alanine, in aqueous solution and the solid state have been modeled by DFT calculations. Calculations of the structures in the solid state, using PW91 or PBE functionals, are in good agreement with the reported crystal structures, and the vibrational spectra computed at the optimized geometries provide a good fit to the observed IR and Raman spectra in the solid state. DFT calculations of the structures and vibrational spectra of the zwitterions in aqueous solution at the B3-LYP/cc-pVDZ level were found to require both explicit and implicit solvation models. Explicit solvation was modeled by inclusion of five hydrogen-bonded water molecules attached to each of the five possible hydrogen-bonding sites in the zwitterion and the integration equation formalism polarizable continuum model (IEF-PCM) was employed, providing a satisfactory fit to observed IR and Raman spectra. Band assignments are reported in terms of potential-energy distributions, which differ in some respects to those previously reported for glycine and l-alanine.