Targeting the Wnt/ß-catenin cascade in osteosarcoma: The potential of ncRNAs as biomarkers and therapeutics.

Osteosarcoma (OS) is a bone cancer which stems from several sources and presents with diverse clinical features, making evaluation and treatment difficult. Chemotherapy tolerance and restricted treatment regimens hinder progress in survival rates, requiring new and creative therapeutic strategies. The Wnt/ß-catenin system has been recognised as an essential driver of OS development, providing potential avenues for therapy. Non-coding RNAs (ncRNAs), such as circular RNAs (circRNAs), long non-coding RNAs (lncRNAs), and microRNAs (miRNAs), are essential in modulating the Wnt/ß-catenin cascade in OS. MiRNAs control the system by targeting vital elements, while lncRNAs and circRNAs interact with system genes, impacting OS growth and advancement. This paper thoroughly analyses the intricate interplay between ncRNAs and the Wnt/ß-catenin cascade in OS. We examine how uncontrolled levels of miRNAs, lncRNAs, and circRNAs lead to an abnormal Wnt/ß-catenin network, which elevates the development, spread, and susceptibility to the treatment of OS. We emphasise the potential of ncRNAs as diagnostic indicators and avenues for treatment in OS care. The review offers valuable insights for academics and clinicians studying OS aetiology and creating new treatment techniques for the ncRNA-Wnt/ß-catenin cascade. Utilising the oversight roles of ncRNAs in the Wnt/ß-catenin system shows potential for enhancing the outcomes of patients and progressing precision medicine in OS therapy.

ncRNAs and their impact on dopaminergic neurons: Autophagy pathways in Parkinson's disease.

Parkinson's Disease (PD) is a complex neurological illness that causes severe motor and non-motor symptoms due to a gradual loss of dopaminergic neurons in the substantia nigra. The aetiology of PD is influenced by a variety of genetic, environmental, and cellular variables. One important aspect of this pathophysiology is autophagy, a crucial cellular homeostasis process that breaks down and recycles cytoplasmic components. Recent advances in genomic technologies have unravelled a significant impact of ncRNAs on the regulation of autophagy pathways, thereby implicating their roles in PD onset and progression. They are members of a family of RNAs that include miRNAs, circRNA and lncRNAs that have been shown to play novel pleiotropic functions in the pathogenesis of PD by modulating the expression of genes linked to autophagic activities and dopaminergic neuron survival. This review aims to integrate the current genetic paradigms with the therapeutic prospect of autophagy-associated ncRNAs in PD. By synthesizing the findings of recent genetic studies, we underscore the importance of ncRNAs in the regulation of autophagy, how they are dysregulated in PD, and how they represent novel dimensions for therapeutic intervention. The therapeutic promise of targeting ncRNAs in PD is discussed, including the barriers that need to be overcome and future directions that must be embraced to funnel these ncRNA molecules for the treatment and management of PD.

lncRNAs and cyclin-dependent kinases: Unveiling their critical roles in cancer progression.

Long non-coding RNAs (lncRNAs) are a diverse class of RNA molecules that do not code for proteins but play critical roles in gene regulation. One such role involves the modulation of cell cycle progression and proliferation through interactions with cyclin-dependent kinases (CDKs), key regulators of cell division. Dysregulation of CDK activity is a hallmark of cancer, contributing to uncontrolled cell growth and tumor formation. These lncRNA-CDK interactions are part of a complex network of molecular mechanisms underlying cancer pathogenesis, involving various signaling pathways and regulatory circuits. Understanding the interplay between lncRNAs, CDKs, and cancer biology holds promise for developing novel therapeutic strategies targeting these molecular targets for more effective cancer treatment. Furthermore, targeting CDKs, key cell cycle progression and proliferation regulators, offers another avenue for disrupting cancer pathways and overcoming drug resistance. This can open new possibilities for individualized treatment plans and focused therapeutic interventions.

Autophagy-associated non-coding RNAs: Unraveling their impact on Parkinson's disease pathogenesis.

BACKGROUND: Parkinson's disease (PD) is a degenerative neurological condition marked by the gradual loss of dopaminergic neurons in the substantia nigra pars compacta. The precise etiology of PD remains unclear, but emerging evidence suggests a significant role for disrupted autophagy-a crucial cellular process for maintaining protein and organelle integrity. METHODS: This review focuses on the role of non-coding RNAs (ncRNAs) in modulating autophagy in PD. We conducted a comprehensive review of recent studies to explore how ncRNAs influence autophagy and contribute to PD pathophysiology. Special attention was given to the examination of ncRNAs' regulatory impacts in various PD models and patient samples. RESULTS: Findings reveal that ncRNAs are pivotal in regulating key processes associated with PD progression, including autophagy, α-synuclein aggregation, mitochondrial dysfunction, and neuroinflammation. Dysregulation of specific ncRNAs appears to be closely linked to these pathogenic processes. CONCLUSION: ncRNAs hold significant therapeutic potential for addressing autophagy-related mechanisms in PD. The review highlights innovative therapeutic strategies targeting autophagy-related ncRNAs and discusses the challenges and prospective directions for developing ncRNA-based therapies in clinical practice. The insights from this study underline the importance of ncRNAs in the molecular landscape of PD and their potential in novel treatment approaches.

Exploring the viability of Zeatin as a prospective therapeutic candidate for investigating the complex interplay between severe acute respiratory syndrome coronavirus (SARS-CoV) and Alzheimer's disease.

The present research aims to explore the intricate link between SARS-CoV infection and susceptibility to Alzheimer's disease, focusing on the role of APOE4, a genetic factor associated with both conditions. Our research aims to uncover shared molecular pathways, considering APOE4's impact on lipid metabolism, immune responses, and neuroinflammation relevant to COVID-19 and AD. The Chyawanprash phytocompounds were subjected to in-silico ADMET profiling and Zeatin a neuroprotective cytokinin emerged as a promising regulator of the ACE2-SPIKE complex as it exhibits favourable pharmacological attributes, presenting as a non-substrate for Permeability glycoprotein, low Protein Binding Percentage, and distinctive toxicity endpoints. Therapeutic candidate. Zeatin's robust binding disrupts the intricate APOE4-ACE2-SPIKE interplay (AAS), offering a potential therapeutic avenue that is further corroborated by Molecular dynamic simulation as the system remained stable without any major fluctuation throughout the 100ns simulation. The AAS binding free energy, determined as -124.849 +/- 15.513 KJ/mol using MMPBSA assay, reveals significant contributions to complex stability from amino acids including, GLN41: 1.211 kcal/mol, GLU340: 1.188 kcal/mol, ALA344: 1.198 kcal/mol, while ARG38: 2.011 kcal/mol establishes pivotal strong bonds integral to the interaction between AAS and Zeatin. Rigorous cytotoxicity assessments reveal Zeatin's safety profile, highlighting its inhibitory effect on LN18 cell viability that sharply decreases to 32.47% at 200 µg/ml, underscoring its modulatory impact on cellular metabolism. These findings enhance our understanding of the convergent mechanisms linking SARS-CoV and AD, providing valuable insights for potential therapeutic interventions. Further research is warranted to elucidate the specific pathways and molecular mechanisms through which zeatin exerts its protective effects.

Development of yeast and microalgae consortium biofilm growth system for biofuel production.

Background: The current study aimed to develop a laboratory-scale biofilm photobioreactor system for biofuel production. Scope & Approach: During the investigation, Jute was discovered to be the best, cheap, hairy, open-pored supporting material for biofilm formation. Microalgae & yeast consortium was used in this study for biofilm formation. Conclusion: The study identified microalgae and yeast consortium as a promising choice and ideal partners for biofilm formation with the highest biomass yield (47.63 ± 0.93 g/m2), biomass productivity (4.39 ± 0.29 to 7.77 ± 0.05 g/m2/day) and lipid content (36%) over 28 days cultivation period, resulting in a more sustainable and environmentally benign fuel that could become a reality in the near future.

Microbial Diversity and Functional Potential of Keem: A Traditional Starter Culture for Alcoholic Beverage-Application of Next-Generation Amplicon and Shotgun Metagenome Sequences.

"Pakhoi" is an ethnic drink of the Tons valley, Uttarakhand, India produced by fermenting jaggery and barley with the help of a starter culture called "keem". In the present study, we investigated the microbial diversity and associated functional potential of "keem" using shotgun metagenome sequencing and amplicon sequencing. We also compared the taxonomic data obtained using these two sequencing techniques. The results showed that shotgun sequencing revealed a higher resolution of taxonomic profiling as compared to the amplicon sequencing. Furthermore, it was found that the genera detected by shotgun sequencing were valuable for facilitating the fermentation process. Additionally, to understand the functional profiling of the genera, different databases were used for annotation, resulting in a total of 13 metabolic pathways. The five most abundant KEGG functions were genetic information processing, metabolism, translation, cofactor and vitamin metabolism and xenobiotic degradation. In contrast, the top five COG were in order of highest frequency sequences belonging to transcription, followed by general function prediction, carbohydrate transport metabolism, amino acid transport and metabolism and translation and biogenesis. Gene ontology revealed many pathways, biochemical processes and molecular functions associated with the organisms forming the starter culture. Overall, the present study can help to understand the microbial diversity and its role in fermentation of traditional alcoholic beverages using "Keem".

In-silico modeling of the interplay between APOE4, NLRP3, and ACE2-SPIKE complex in neurodegeneration between Alzheimer and SARS-CoV: implications for understanding pathogenesis and developing therapeutic strategies.

The multifaceted interplay between neurodegenerative pathologies, including Alzheimer's disease (AD), and the highly virulent severe acute respiratory syndrome coronavirus (SARS-CoV), is implicated in various conditions. AD and SARS-CoV pathogenesis involve the APOE4 allele, NLRP3 inflammasome, and ACE2-SPIKE complex. APOE4, a genetic polymorphism of the APOE gene, is associated with an increased susceptibility to AD. NLRP3, an inflammatory protein of the innate immune system, plays a pivotal role in immune response cascades. In SARS-CoV, the ACE2 receptor serves as the principal portal for cellular entry, while APOE4 intricately interacts with the ACE2-spike protein complex, enhancing viral internalization process. The interaction of NLRP3 with the ACE2-spike protein complex leads to increased inflammatory signaling. The convergence of APOE4/NLRP3 and ACE2-spike protein complex interactions suggests a possible link between SARS and AD. Therefore, the current research centralizes the association between by utilizing SARS-CoV datasets to explore possible mechanisms that account for the pathogenesis of SARS-CoV and AD. The work is further extended to unveil the molecular interactions of APOE4 and NLRP3 with the ACE2-Spike protein complex at the molecular level by employing molecular dynamics simulation techniques. The therapeutic efficacy of Chyawanprash nutraceuticals is evaluated as their inhibitory potential towards APOE4-ACE2-Spike protein and NLRP3-ACE2-Spike protein complexes. Notably, our simulations unequivocally demonstrate the robust and enduring binding capability of the compound Phyllantidine with the target complexes throughout the simulation period. The findings of the studies further corroborate the primary hypothesis of APOE4 and NLRP3 as driver factors in the pathogenesis of both SARS-CoV and AD. Therefore, this research establishes a paradigm for comprehending the complex interaction between AD and SARS-CoV and lays the groundwork for further study in this domain.Communicated by Ramaswamy H. Sarma.

Deep Neural Networks for Optimal Selection of Features Related to Flu.

In recent times, humans who have been exposed to influenza A viruses (IAV) may not become hostile. Despite the fact that KLRD1 has been discovered as an influenza susceptibility biomarker, it remains to be seen if pre-exposure host gene expression can predict flu symptoms. In this paper, we enable the examination of flu using deep neural networks from input human gene expression datasets with various subtype viruses. This study enables the utilization of these datasets to forecast the spread of flu and can provide the necessary steps to eradicate the flu. The simulation is conducted to test the efficiency of the model in predicting the spread against various input datasets. The results of the simulation show that the proposed method offers a better prediction ability of 2.98% more than other existing methods in finding the spread of flu.

Emerging trends in immunotoxin targeting cancer stem cells.

Cancer stem cells (CSCs) are self-renewing multipotent cells that play a vital role in the development of cancer drug resistance conditions. Various therapies like conventional, targeted, and radiotherapies have been broadly used in targeting and killing these CSCs. Among these, targeted therapy selectively targets CSCs and leads to overcoming disease recurrence conditions in cancer patients. Immunotoxins (ITs) are protein-based therapeutics with selective targeting capabilities. These chimeric molecules are composed of two functional moieties, i.e., a targeting moiety for cell surface binding and a toxin moiety that induces the programmed cell death upon internalization. Several ITs have been constructed recently, and their preclinical and clinical efficacies have been evaluated. In this review, we comprehensively discussed the recent preclinical and clinical advances as well as significant challenges in ITs targeting CSCs, which might reduce the burden of drug resistance conditions in cancer patients from bench to bedside.

Large-Scale Textual Datasets and Deep Learning for the Prediction of Depressed Symptoms.

Millions of people worldwide suffer from depression. Assessing, treating, and preventing recurrence requires early detection of depressive symptoms as depression-related datasets expand and machine learning improves, intelligent approaches to detect depression in written material may emerge. This study provides an effective method for identifying texts describing self-perceived depressive symptoms by using long short-term memory (LSTM) based recurrent neural networks (RNN). On a huge dataset of a suicide and depression detection dataset taken from Kaggle with 233337 datasets, this information channel featured text-based teen questions. Then, using a one-hot technique, medical and psychiatric practitioners extract strong features from probably depressed symptoms. The characteristics outperform the usual techniques, which rely on word frequencies rather than symptoms to explain the underlying events in text messages. Depression symptoms can be distinguished from nondepression signals by using a deep learning system (nondepression posts). Eventually, depression is predicted by the RNN. In the suggested technique, the frequency of depressive symptoms outweighs their specificity. With correct annotations and symptom-based feature extraction, the method may be applied to different depression datasets. Because of this, chatbots and depression prediction can work together.

Molecular mechanisms of developmental pathways in neurological disorders: a pharmacological and therapeutic review.

Developmental signalling pathways such as Wnt/ß-catenin, Notch and Sonic hedgehog play a central role in nearly all the stages of neuronal development. The term 'embryonic' might appear to be a misnomer to several people because these pathways are functional during the early stages of embryonic development and adulthood, albeit to a certain degree. Therefore, any aberration in these pathways or their associated components may contribute towards a detrimental outcome in the form of neurological disorders such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis and stroke. In the last decade, researchers have extensively studied these pathways to decipher disease-related interactions, which can be used as therapeutic targets to improve outcomes in patients with neurological abnormalities. However, a lot remains to be understood in this domain. Nevertheless, there is strong evidence supporting the fact that embryonic signalling is indeed a crucial mechanism as is manifested by its role in driving memory loss, motor impairments and many other processes after brain trauma. In this review, we explore the key roles of three embryonic pathways in modulating a range of homeostatic processes such as maintaining blood-brain barrier integrity, mitochondrial dynamics and neuroinflammation. In addition, we extensively investigated the effect of these pathways in driving the pathophysiology of a range of disorders such as Alzheimer's, Parkinson's and diabetic neuropathy. The concluding section of the review is dedicated to neurotherapeutics, wherein we identify and list a range of biological molecules and compounds that have shown enormous potential in improving prognosis in patients with these disorders.

Multichannel CNN Model for Biomedical Entity Reorganization.

Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowing it to detect more entity connections. The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the development of precision healthcare. In the past, deep learning methods have achieved specific results, but there are the following problems: the model uses static word vectors, which cannot distinguish polysemy; the weight of words is not considered, and the extraction effect of long sentences is poor; the integration of various models can improve the sample imbalance problem, the model is more complex. The purpose of this work is to create a global approach for eliminating different physical entity links, such that the model can effectively extract the interpretation of the expression relationship without having to develop characteristics manually. To this end, a deep multichannel CNN model (MC-CNN) based on the residual structure is proposed, generating dynamic word vectors through BERT (Bidirectional Encoder Representation from Transformers) to improve the accuracy of lexical semantic representation and uses multihead attention to capture the dependencies of long sentences and by designing the Ranking loss function to replace the multimodel ensemble to reduce the impact of sample imbalance. Tested on multiple datasets, the results show that the proposed method has good performance.

Molecular docking and simulation studies of natural compounds of Vitex negundo L. against papain-like protease (PLpro) of SARS CoV-2 (coronavirus) to conquer the pandemic situation in the world.

The severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) is ß-coronavirus that is responsible for the pandemic coronavirus disease 2019 (COVID-19) all over the world. The rapid spread of the novel SARS CoV-2 worldwide is raising a significant global public health issue with nearly 61.86 million people infected and 1.4 million deaths. To date, no specific drugs are available for the treatment of COVID-19. The inhibition of proteases essential for the proteolytic treatment of viral polyproteins is a conventional therapeutic strategy for conquering viral infections. In the study, molecular docking approach was used to screen potential drug compounds among the phytochemicals of Vitex negundo L. against COVID-19 infection. Molecular docking analysis showed that oleanolic acid forms a stable complex and other phyto-compounds ursolic acid, 3ß-acetoxyolean-12-en-27-oic acid and isovitexin of V. negundo natural compounds form a less-stable complex. When compared with the control the synergistic interaction of these compounds shows inhibitory activity against papain-like protease (PLpro) of SARS CoV-2 (COVID-19). The molecular dynamics (MD) simulation (50 ns) were performed on the complexes of PLpro and the phyto-compounds viz. oleanolic acid, ursolic acid, 3ß-acetoxyolean-12-en-27-oic acid and isovitexin followed by the binding free energy calculations using MM-GBSA and these molecules have stable interactions with PLpro protein binding site. The MD simulation study provides more insight into the functional properties of the protein-ligand complex and suggests that these molecules can be considered as a potential drug molecule against COVID-19. In this pandemic situation, these herbal compounds provide a rich resource to produce new antivirals against COVID-19.Communicated by Ramaswamy H. Sarma.

Enabling Artificial Intelligence of Things (AIoT) Healthcare Architectures and Listing Security Issues.

A significant study has been undertaken in the areas of health care and administration of cutting-edge artificial intelligence (AI) technologies throughout the previous decade. Healthcare professionals studied smart gadgets and other medical technologies, along with the AI-based Internet of Things (IoT) (AIoT). Connecting the two regions makes sense in terms of improving care for rural and isolated resident individuals. The healthcare industry has made tremendous strides in efficiency, affordability, and usefulness as a result of new research options and major cost reductions. This includes instructions (AIoT-based) medical advancements can be both beneficial and detrimental. While the IoT concept undoubtedly offers a number of benefits, it also poses fundamental security and privacy concerns regarding medical data. However, resource-constrained AIoT devices are vulnerable to a number of assaults, which can significantly impair their performance. Cryptographic algorithms used in the past are inadequate for safeguarding IoT-enabled networks, presenting substantial security risks. The AIoT is made up of three layers: perception, network, and application, all of which are vulnerable to security threats. These threats can be aggressive or passive in nature, and they can originate both within and outside the network. Numerous IoT security issues, including replay, sniffing, and eavesdropping, have the ability to obstruct network communication. The AIoT-H application is likely to be explored in this research article due to its potential to aid with existing and different technologies, as well as bring useful solutions to healthcare security challenges. Additionally, every day, several potential problems and inconsistencies with the AIoT-H technique have been discovered.

Potential inhibitors of SARS-CoV-2 (COVID 19) proteases PLpro and Mpro/ 3CLpro: molecular docking and simulation studies of three pertinent medicinal plant natural components.

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) - coronavirus disease 2019 (COVID-19) has raised a severe global public health issue and creates a pandemic situation. The present work aims to study the molecular -docking and dynamic of three pertinent medicinal plants i.e. Eurycoma harmandiana, Sophora flavescens and Andrographis paniculata phyto-compounds against SARS-COV-2 papain-like protease (PLpro) and main protease (Mpro)/3-chymotrypsin-like protease (3CLpro). The interaction of protein targets and ligands was performed through AutoDock-Vina visualized using PyMOL and BIOVIA-Discovery Studio 2020. Molecular docking with canthin-6-one 9-O-beta-glucopyranoside showed highest binding affinity and less binding energy with both PLpro and Mpro/3CLpro proteases and was subjected to molecular dynamic (MD) simulations for a period of 100ns. Stability of the protein-ligand complexes was evaluated by different analyses. The binding free energy calculated using MM-PBSA and the results showed that the molecule must have stable interactions with the protein binding site. ADMET analysis of the compounds suggested that it is having drug-like properties like high gastrointestinal (GI) absorption, no blood-brain barrier permeability and high lipophilicity. The outcome revealed that canthin-6-one 9-O-beta-glucopyranoside can be used as a potential natural drug against COVID-19 protease.

Modeling novel Anti-Viral peptides (AVPs) with in-silico docking simulations against corona virus.

The havoc created by Corona virus has been dealt with using various integrative approaches adopted by laboratories through-out the world. Use of anti-viral peptides (AVPs) although new but has shown tremendous potential against many pathogens. Previously AVPs have been designed against spike protein of corona virus which is the major entry mediating molecule. Using various in-silico strategies, in this research work AVPs have been modeled against lesser studied viral proteins namely ORF7a protein, Envelope protein (E), Nucleoprotein (N), and Non-Structural protein (Nsp1 and Nsp2). The predicted AVPs have been docked against various host as well as viral proteins. The interaction of small AVPs seems capable of interfering with binding between viral protein and its host counterpart. Therefore, these AVPs can act as a deterrent against novel corona virus, which requires further validation through laboratory techniques.

Mining genomic patterns in Mycobacterium tuberculosis H37Rv using a web server Tuber-Gene.

Mycobacterium tuberculosis (MTB), causative agent of tuberculosis, is one of the most dreaded diseases of the century. It has long been studied by researchers throughout the world using various wet-lab and dry-lab techniques. In this study, we focus on mining useful patterns at genomic level that can be applied for in silico functional characterization of genes from the MTB complex. The model developed on the basis of the patterns found in this study can correctly identify 99.77% of the input genes from the genome of MTB strain H37Rv. The model was tested against four other MTB strains and the homologue M. bovis to further evaluate its generalization capability. The mean prediction accuracy was 85.76%. It was also observed that the GC content remained fairly constant throughout the genome, implicating the absence of any pathogenicity island transferred from other organisms. This study reveals that dinucleotide composition is an efficient functional class discriminator for MTB complex. To facilitate the application of this model, a web server Tuber-Gene has been developed, which can be freely accessed at http://www.bifmanit.org/tb2/.

ProCoS: Protein composition server.

ProCoS is a free online tool for computing different combinations of peptide compositions. It is developed as an applet and a server with a capability to handle multiple FASTA sequences. The generalized algorithm for computing poly-amino acid composition forms the core of ProCoS. It produces output in different formats for easy visualization of results. It also allows composition analysis of sequences in full or in specific parts. Thus, ProCoS is user-friendly, flexible and unique.