Biomass-derived carbon dots as fluorescent quantum probes to visualize and modulate inflammation.

Quantum dots, which won the Nobel Prize in Chemistry, have recently gained significant attention in precision medicine due to their unique properties, such as size-tunable emission, high photostability, efficient light absorption, and vibrant luminescence. Consequently, there is a growing demand to identify new types of quantum dots from various sources and explore their potential applications as stimuli-responsive biosensors, biomolecular imaging probes, and targeted drug delivery agents. Biomass-waste-derived carbon quantum dots (CQDs) are an attractive alternative to conventional QDs, which often require expensive and toxic precursors, as they offer several merits in eco-friendly synthesis, preparation from renewable sources, and cost-effective production. In this study, we evaluated three CQDs derived from biomass waste for their potential application as non-toxic bioimaging agents in various cell lines, including human dermal fibroblasts, HeLa, cardiomyocytes, induced pluripotent stem cells, and an in-vivo medaka fish (Oryzias latipes) model. Confocal microscopic studies revealed that CQDs could assist in visualizing inflammatory processes in the cells, as they were taken up more by cells treated with tumor necrosis factor-α than untreated cells. In addition, our quantitative real-time PCR gene expression analysis has revealed that citric acid-based CQDs can potentially reduce inflammatory markers such as Interleukin-6. Our studies suggest that CQDs have potential as theragnostic agents, which can simultaneously identify and modulate inflammatory markers and may lead to targeted therapy for immune system-associated diseases.

MOFs for next-generation cancer therapeutics through a biophysical approach-a review.

Metal-organic frameworks (MOFs) have emerged as promising nanocarriers for cancer treatment due to their unique properties. Featuring high porosity, extensive surface area, chemical stability, and good biocompatibility, MOFs are ideal for efficient drug delivery, targeted therapy, and controlled release. They can be designed to target specific cellular organelles to disrupt metabolic processes in cancer cells. Additionally, functionalization with enzymes mimics their catalytic activity, enhancing photodynamic therapy and overcoming apoptosis resistance in cancer cells. The controllable and regular structure of MOFs, along with their tumor microenvironment responsiveness, make them promising nanocarriers for anticancer drugs. These carriers can effectively deliver a wide range of drugs with improved bioavailability, controlled release rate, and targeted delivery efficiency compared to alternatives. In this article, we review both experimental and computational studies focusing on the interaction between MOFs and drug, explicating the release mechanisms and stability in physiological conditions. Notably, we explore the relationship between MOF structure and its ability to damage cancer cells, elucidating why MOFs are excellent candidates for bio-applicability. By understanding the problem and exploring potential solutions, this review provides insights into the future directions for harnessing the full potential of MOFs, ultimately leading to improved therapeutic outcomes in cancer treatment.

Surface charge-dependent cytokine production using near-infrared emitting silicon quantum dots.

Toll-like receptor 9 (TLR-9) is a protein that helps our immune system identify specific DNA types. Upon detection, CpG oligodeoxynucleotides signal the immune system to generate cytokines, essential proteins that contribute to the body's defence against infectious diseases. Native phosphodiester type B CpG ODNs induce only Interleukin-6 with no effect on interferon-α. We prepared silicon quantum dots containing different surface charges, such as positive, negative, and neutral, using amine, acrylate-modified Plouronic F-127, and Plouronic F-127. Then, class B CpG ODNs are loaded on the surface of the prepared SiQDs. The uptake of ODNs varies based on the surface charge; positively charged SiQDs demonstrate higher adsorption compared to SiQDs with negative and neutral surface charges. The level of cytokine production in peripheral blood mononuclear cells was found to be associated with the surface charge of SiQDs prior to the binding of the CpG ODNs. Significantly higher levels of IL-6 and IFN-α induction were observed compared to neutral and negatively charged SiQDs loaded with CpG ODNs. This observation strongly supports the notion that the surface charge of SiQDs effectively regulates cytokine induction.

Chalcone derivatives' interaction with human serum albumin and cyclooxygenase-2.

Chalcone derivatives are an extremely valuable class of compounds, primarily due to the keto-ethylenic group, CO-CH[double bond, length as m-dash]CH-, they contain. Moreover, the presence of a reactive α,ß-unsaturated carbonyl group confers upon them a broad range of pharmacological properties. Recent developments in heterocyclic chemistry have led to the synthesis of chalcone derivatives, which have been biologically investigated for their activity against certain diseases. In this study, we investigated the binding of new chalcone derivatives with COX-2 (cyclooxygenase-2) and HSA (Human Serum Albumin) using spectroscopic and molecular modeling studies. COX-2 is commonly found in cancer and plays a role in the production of prostaglandin E (2), which can help tumors grow by binding to receptors. HSA is the most abundant protein in blood plasma, and it transports various compounds, including hormones and fatty acids. The conformation of chalcone derivatives in the HSA complex system was established through fluorescence steady and excited state spectroscopy techniques and FTIR analyses. To gain a more comprehensive understanding, molecular docking, and dynamics were conducted on the target protein (COX-2) and transport protein (HSA). In addition, we conducted density-functional theory (DFT) and single-point DFT to understand intermolecular interaction in protein active sites.

A biophysical approach of tyrphostin AG879 binding information in: bovine serum albumin, human ErbB2, c-RAF1 kinase, SARS-CoV-2 main protease and angiotensin-converting enzyme 2.

Viral infections cause significant health problems all over the world, and it is critical to develop treatments for these problems. Antivirals that target viral genome-encoded proteins frequently cause the virus to become more resistant to treatment. Because viruses rely on several cellular proteins and phosphorylation processes that are essential to their life cycle, drugs targeting host-based targets could be a viable treatment option. To reduce costs and improve efficiency, existing kinase inhibitors could be repurposed as antiviral medications; however, this method rarely works, and specific biophysical approaches are required in the field. Because of the widespread use of FDA-approved kinase inhibitors, it is now possible to better understand how host kinases contribute to viral infection. The purpose of this article is to investigate the tyrphostin AG879 (Tyrosine kinase inhibitor) binding information in Bovine Serum Albumin (BSA), human ErbB2 (HER2), C-RAF1 Kinase (c-RAF), SARS-CoV-2 main protease (COVID 19), and Angiotensin-converting enzyme 2 (ACE-2).Communicated by Ramaswamy H. Sarma.

Nucleic acid-based small molecules as targeted transcription therapeutics for immunoregulation.

Transcription therapy is an emerging approach that centers on identifying the factors associated with the malfunctioning gene transcription machinery that causes diseases and controlling them with designer agents. Until now, the primary research focus in therapeutic gene modulation has been on small-molecule drugs that target epigenetic enzymes and critical signaling pathways. However, nucleic acid-based small molecules have gained popularity in recent years for their amenability to be pre-designed and realize operative control over the dynamic transcription machinery that governs how the immune system responds to diseases. Pyrrole-imidazole polyamides (PIPs) are well-established DNA-based small-molecule gene regulators that overcome the limitations of their conventional counterparts owing to their sequence-targeted specificity, versatile regulatory efficiency, and biocompatibility. Here, we emphasize the rational design of PIPs, their functional mechanisms, and their potential as targeted transcription therapeutics for disease treatment by regulating the immune response. Furthermore, we also discuss the challenges and foresight of this approach in personalized immunotherapy in precision medicine.

Bat-associated microbes: Opportunities and perils, an overview.

The potential biotechnological uses of bat-associated bacteria are discussed briefly, indicating avenues for biotechnological applications of bat-associated microbes. The uniqueness of bats in terms of their lifestyle, genomes and molecular immunology may predispose bats to act as disease reservoirs. Molecular phylogenetic analysis has shown several instances of bats harbouring the ancestral lineages of bacterial (Bartonella), protozoal (Plasmodium, Trypanosoma cruzi) and viral (SARS-CoV2) pathogens infecting humans. Along with the transmission of viruses from bats, we also discuss the potential roles of bat-associated bacteria, fungi, and protozoan parasites in emerging diseases. Current evidence suggests that environmental changes and interactions between wildlife, livestock, and humans contribute to the spill-over of infectious agents from bats to other hosts. Domestic animals including livestock may act as intermediate amplifying hosts for bat-origin pathogens to transmit to humans. An increasing number of studies investigating bat pathogen diversity and infection dynamics have been published. However, whether or how these infectious agents are transmitted both within bat populations and to other hosts, including humans, often remains unknown. Metagenomic approaches are uncovering the dynamics and distribution of potential pathogens in bat microbiomes, which might improve the understanding of disease emergence and transmission. Here, we summarize the current knowledge on bat zoonoses of public health concern and flag the gaps in the knowledge to enable further research and allocation of resources for tackling future outbreaks.

Development of biomass waste-based carbon quantum dots and their potential application as non-toxic bioimaging agents.

Over recent years, carbon quantum dots (CQDs) have advanced significantly and gained substantial attention for their numerous benefits. These benefits include their simple preparation, cost-effectiveness, small size, biocompatibility, bright luminescence, and low cytotoxicity. As a result, they hold great potential for various fields, including bioimaging. A fascinating aspect of synthesizing CQDs is that it can be accomplished by using biomass waste as the precursor. Furthermore, the synthesis approach allows for control over the physicochemical characteristics. This paper unequivocally examines the production of CQDs from biomass waste and their indispensable application in bioimaging. The synthesis process involves a simple one-pot hydrothermal method that utilizes biomass waste as a carbon source, eliminating the need for expensive and toxic reagents. The resulting CQDs exhibit tunable fluorescence and excellent biocompatibility, making them suitable for bioimaging applications. The successful application of biomass-derived CQDs has been demonstrated through biological evaluation studies in various cell lines, including HeLa, Cardiomyocyte, and iPS, as well as in medaka fish eggs and larvae. Using biomass waste as a precursor for CQDs synthesis provides an environmentally friendly and sustainable alternative to traditional methods. The resulting CQDs have potential applications in various fields, including bioimaging.

Food Intake and Colorectal Cancer.

Colorectal cancer (CRC) accounts for considerable mortalities worldwide. Several modifiable risk factors, including a high intake of certain foods and beverages can cause CRC. This review summarized the latest findings on the intake of various foods, nutrients, ingredients, and beverages on CRC development, with the objective of classifying them as a risk or protective factor. High-risk food items include red meat, processed meat, eggs, high alcohol consumption, sugar-sweetened beverages, and chocolate candy. Food items that are protective include milk, cheese and other dairy products, fruits, vegetables (particularly cruciferous), whole grains, legumes (particularly soy beans), fish, tea (particularly green tea), coffee (particularly among Asians), chocolate, and moderate alcohol consumption (particularly wine). High-risk nutrients/ingredients include dietary fat from animal sources and industrial trans-fatty acids (semisolid/solid hydrogenated oils), synthetic food coloring, monosodium glutamate, titanium dioxide, and high-fructose corn sirup. Nutrients/ingredients that are protective include dietary fiber (particularly from cereals), fatty acids (medium-chain and odd-chain saturated fatty acids and highly unsaturated fatty acids, including omega-3 polyunsaturated fatty acids), calcium, polyphenols, curcumin, selenium, zinc, magnesium, and vitamins A, C, D, E, and B (particularly B6, B9, and B2). A combination of micronutrients and multi-vitamins also appears to be beneficial in reducing recurrent adenoma incidence.

A review on medicinally important heterocyclic compounds and importance of biophysical approach of underlying the insight mechanism in biological environment.

Heterocyclic derivatives have more interesting biological properties which hold a remarkable place in pharmaceutical industries due to their unique physiochemical properties and ease of adaption in various biological environments. Of many, the above-said derivatives have been recently examined for their promising action against a few malignancies. Specifically, anti-cancer research has benefited from these derivatives' natural flexibility and dynamic core scaffold. In any case, concerning some other promising anti-cancer drugs, heterocyclic derivative doesn't come without deficiencies. To be a successful drug candidate it should poses Absorption, Distribution, Metabolism and Eliminations (ADME) parameter, and must also have good binding interaction towards carrier protein as well as DNA and less in toxic nature, economically feasible. In this review, we described the overview of biologically important heterocyclic derivatives and their main application in medicine. Further, we focus types of biophysical techniques to understand the binding interaction mechanism.Communicated by Ramaswamy H. Sarma.

Nano-bio interaction between human immunoglobulin G and nontoxic, near-infrared emitting water-borne silicon quantum dot micelles.

In recent years, the field of nanomaterials has exponentially expanded with versatile biological applications. However, one of the roadblocks to their clinical translation is the critical knowledge gap about how the nanomaterials interact with the biological microenvironment (nano-bio interactions). When nanomaterials are used as drug carriers or contrast agents for biological imaging, the nano-bio interaction-mediated protein conformational changes and misfolding could lead to disease-related molecular alterations and/or cell death. Here, we studied the conformation changes of human immunoglobulin G (IgG) upon interaction with silicon quantum dots functionalized with 1-decene, Pluronic-F127 (SiQD-De/F127 micelles) using UV-visible, fluorescence steady state and excited state kinetics, circular dichroism, and molecular modeling. Decene monolayer terminated SiQDs are accumulated inside the Pluronic F127 shells to form SiQD-De/F127 micelles and were shown to bind strongly with IgG. In addition, biological evaluation studies in cell lines (HeLa, Fibroblast) and medaka fish (eggs and larvae) showed enhanced uptake and minimal cytotoxicity. Our results substantiate that engineered QDs obviating the protein conformational changes could have adept bioefficacy.

Nanoparticle-Based Artificial Mitochondrial DNA Transcription Regulator: MitoScript.

The growing knowledge of the links between aberrant mitochondrial gene transcription and human diseases necessitates both an effective and dynamic approach to control mitochondrial DNA (mtDNA) transcription. To address this challenge, we developed a nanoparticle-based synthetic mitochondrial transcription regulator (MitoScript). MitoScript provides great colloidal stability, excellent biocompatibility, efficient cell uptake, and selective mitochondria targeting and can be monitored in live cells using near-infrared fluorescence. Notably, MitoScript controlled mtDNA transcription in a human cell line in an effective and selective manner. MitoScript targeting the light strand promoter region of mtDNA resulted in the downregulation of ND6 gene silencing, which eventually affected cell redox status, with considerably increased reactive oxygen species (ROS) generation. In summary, we developed MitoScript for the efficient, nonviral modification of mitochondrial DNA transcription. Our platform technology can potentially contribute to understanding the fundamental mechanisms of mitochondrial disorders and developing effective treatments for mitochondrial diseases.

Mitochondria and G-quadruplex evolution: an intertwined relationship.

G-quadruplexes (G4s) are non-canonical structures formed in guanine (G)-rich sequences through stacked G tetrads by Hoogsteen hydrogen bonding. Several studies have demonstrated the existence of G4s in the genome of various organisms, including humans, and have proposed that G4s have a regulatory role in various cellular functions. However, little is known regarding the dissemination of G4s in mitochondria. In this review, we report the observation that the number of potential G4-forming sequences in the mitochondrial genome increases with the evolutionary complexity of different species, suggesting that G4s have a beneficial role in higher-order organisms. We also discuss the possible function of G4s in mitochondrial (mt)DNA and long noncoding (lnc)RNA and their role in various biological processes.

Chemical Probe-Based Nanopore Sequencing to Selectively Assess the RNA Modifications.

Nanopore direct RNA sequencing (dRNA-Seq) reads reveal RNA modifications through consistent error profiles specific to a modified nucleobase. However, a null data set is required to identify actual RNA modification-associated errors for distinguishing it from confounding highly intrinsic sequencing errors. Here, we reveal that inosine creates a signature mismatch error in dRNA-Seq reads and obviates the need for a null data set by harnessing the selective reactivity of acrylonitrile for validating the presence of actual inosine modifications. Selective reactivity of acrylonitrile toward inosine altered multiple dRNA-Seq parameters like signal intensity and trace value. We also deduced the stoichiometry of inosine modification through deviation in signal intensity and trace value using this chemical biology approach. Furthermore, we devised Nano ICE-Seq, a protocol to overcome the low coverage issue associated with direct RNA sequencing. Taken together, our chemical probe-based approach may facilitate the knockout-free detection of disease-associated RNA modifications in clinical scenarios.

Artificial intelligence in microbial natural product drug discovery: current and emerging role.

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

Structural colour enhanced microfluidics.

Advances in microfluidic technology towards flexibility, transparency, functionality, wearability, scale reduction or complexity enhancement are currently limited by choices in materials and assembly methods. Organized microfibrillation is a method for optically printing well-defined porosity into thin polymer films with ultrahigh resolution. Here we demonstrate this method to create self-enclosed microfluidic devices with a few simple steps, in a number of flexible and transparent formats. Structural colour, a property of organized microfibrillation, becomes an intrinsic feature of these microfluidic devices, enabling in-situ sensing capability. Since the system fluid dynamics are dependent on the internal pore size, capillary flow is shown to become characterized by structural colour, while independent of channel dimension, irrespective of whether devices are printed at the centimetre or micrometre scale. Moreover, the capability of generating and combining different internal porosities enables the OM microfluidics to be used for pore-size based applications, as demonstrated by separation of biomolecular mixtures.

An informatics approach to distinguish RNA modifications in nanopore direct RNA sequencing.

Modifications in RNA can influence their structure, function, and stability and play essential roles in gene expression and regulation. Methods to detect RNA modifications rely on biophysical techniques such as chromatography or mass spectrometry, which are low throughput, or on high throughput short-read sequencing techniques based on selectively reactive chemical probes. Recent studies have utilized nanopore-based fourth-generation sequencing methods to detect modifications by directly sequencing RNA in its native state. However, these approaches are based on modification-associated mismatch errors that are liable to be confounded by SNPs. Also, there is a need to generate matched knockout controls for reference, which is laborious. In this work, we introduce an internal comparison strategy termed "IndoC," where features such as 'trace' and 'current signal intensity' of potentially modified sites are compared to similar sequence contexts on the same RNA molecule within the sample, alleviating the need for matched knockout controls. We first show that in an IVT model, 'trace' is able to distinguish between artificially generated SNPs and true pseudouridine (Ψ) modifications, both of which display highly similar mismatch profiles. We then apply IndoC on yeast and human ribosomal RNA to demonstrate that previously reported Ψ sites show marked changes in their trace and signal intensity profiles compared with their unmodified counterparts in the same dataset. Finally, we perform direct RNA sequencing of RNA containing Ψ intact with a chemical probe adduct (N-cyclohexyl-N'-ß-(4-methylmorpholinium) ethylcarbodiimide [CMC]) and show that CMC reactivity also induces changes in trace and signal intensity distributions in a Ψ specific manner, allowing their separation from high mismatch sites that display SNP-like behavior.

Classification of COVID-19 from chest x-ray images using deep features and correlation coefficient.

COVID-19 is a viral disease that in the form of a pandemic has spread in the entire world, causing a severe impact on people's well being. In fighting against this deadly disease, a pivotal step can prove to be an effective screening and diagnosing step to treat infected patients. This can be made possible through the use of chest X-ray images. Early detection using the chest X-ray images can prove to be a key solution in fighting COVID-19. Many computer-aided diagnostic (CAD) techniques have sprung up to aid radiologists and provide them a secondary suggestion for the same. In this study, we have proposed the notion of Pearson Correlation Coefficient (PCC) along with variance thresholding to optimally reduce the feature space of extracted features from the conventional deep learning architectures, ResNet152 and GoogLeNet. Further, these features are classified using machine learning (ML) predictive classifiers for multi-class classification among COVID-19, Pneumonia and Normal. The proposed model is validated and tested on publicly available COVID-19 and Pneumonia and Normal dataset containing an extensive set of 768 images of COVID-19 with 5216 training images of Pneumonia and Normal patients. Experimental results reveal that the proposed model outperforms other previous related works. While the achieved results are encouraging, further analysis on the COVID-19 images can prove to be more reliable for effective classification.

Targeted elimination of mutated mitochondrial DNA by a multi-functional conjugate capable of sequence-specific adenine alkylation.

Mutations in mitochondrial DNA (mtDNA) cause mitochondrial diseases, characterized by abnormal mitochondrial function. Although eliminating mutated mtDNA has potential to cure mitochondrial diseases, no chemical-based drugs in clinical trials are capable of selective modulation of mtDNA mutations. Here, we construct a class of compounds encompassing pyrrole-imidazole polyamides (PIPs), mitochondria-penetrating peptide, and chlorambucil, an adenine-specific DNA-alkylating reagent. The sequence-selective DNA binding of PIPs allows chlorambucil to alkylate mutant adenine more efficiently than other sites in mtDNA. In vitro DNA alkylation assay shows that our compound 8950A-Chb(Cl/OH) targeting a nonpathogenic point mutation in HeLa S3 cells (m.8950G>A) can specifically alkylate the mutant adenine. Furthermore, the compound reduces the mtDNA possessing the target mutation in cultured HeLa S3 cells. The programmability of PIPs to target different sequences could allow this class of compounds to be developed as designer drugs targeting pathogenic mutations associated with mitochondrial diseases in future studies.

AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs.

According to the World Health Organization (WHO), novel coronavirus (COVID-19) is an infectious disease and has a significant social and economic impact. The main challenge in fighting against this disease is its scale. Due to the outbreak, medical facilities are under pressure due to case numbers. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep-representations over a Gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides a fast diagnosis of COVID-19 and can scale seamlessly. The work presents a comprehensive evaluation of previously proposed approaches for X-ray based disease diagnosis. The approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets, and then linearly regressing the predictions from an ensemble of classifiers which take the latent vector as input. We experimented with publicly available datasets having three classes: COVID-19, normal and pneumonia yielding an overall accuracy and AUC of 0.91 and 0.97, respectively. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset to classify among Atelectasis, Effusion, Infiltration, Nodule, and Pneumonia classes. The results demonstrate that the proposed model has better understanding of the X-ray images which make the network more generic to be later used with other domains of medical image analysis.