MicroRNA-21 Silencing in Diabetic Nephropathy: Insights on Therapeutic Strategies.

In diabetes, possibly the most significant site of microvascular damage is the kidney. Due to diabetes and/or other co-morbidities, such as hypertension and age-related nephron loss, a significant number of people with diabetes suffer from kidney diseases. Improved diabetic care can reduce the prevalence of diabetic nephropathy (DN); however, innovative treatment approaches are still required. MicroRNA-21 (miR-21) is one of the most studied multipotent microRNAs (miRNAs), and it has been linked to renal fibrosis and exhibits significantly altered expression in DN. Targeting miR-21 offers an advantage in DN. Currently, miR-21 is being pharmacologically silenced through various methods, all of which are in early development. In this review, we summarize the role of miR-21 in the molecular pathogenesis of DN and several therapeutic strategies to use miR-21 as a therapeutic target in DN. The existing experimental interventions offer a way to rectify the lower miRNA levels as well as to reduce the higher levels. Synthetic miRNAs also referred to as miR-mimics, can compensate for abnormally low miRNA levels. Furthermore, strategies like oligonucleotides can be used to alter the miRNA levels. It is reasonable to target miR-21 for improved results because it directly contributes to the pathological processes of kidney diseases, including DN.

Integrating Multi-Omics Data to Construct Reliable Interconnected Models of Signaling, Gene Regulatory, and Metabolic Pathways.

Alteration of the status of the metabolic enzymes could be a probable way to regulate metabolic reprogramming, which is a critical cellular adaptation mechanism especially for cancer cells. Coordination among biological pathways, such as gene-regulatory, signaling, and metabolic pathways is crucial for regulating metabolic adaptation. Also, incorporation of resident microbial metabolic potential in human body can influence the interplay between the microbiome and the systemic or tissue metabolic environments. Systemic framework for model-based integration of multi-omics data can ultimately improve our understanding of metabolic reprogramming at holistic level. However, the interconnectivity and novel meta-pathway regulatory mechanisms are relatively lesser explored and understood. Hence, we propose a computational protocol that utilizes multi-omics data to identify probable cross-pathway regulatory and protein-protein interaction (PPI) links connecting signaling proteins or transcription factors or miRNAs to metabolic enzymes and their metabolites using network analysis and mathematical modeling. These cross-pathway links were shown to play important roles in metabolic reprogramming in cancer scenarios.

Designing neoantigen cancer vaccines, trials, and outcomes.

Neoantigen vaccines are based on epitopes of antigenic parts of mutant proteins expressed in cancer cells. These highly immunogenic antigens may trigger the immune system to combat cancer cells. Improvements in sequencing technology and computational tools have resulted in several clinical trials of neoantigen vaccines on cancer patients. In this review, we have looked into the design of the vaccines which are undergoing several clinical trials. We have discussed the criteria, processes, and challenges associated with the design of neoantigens. We searched different databases to track the ongoing clinical trials and their reported outcomes. We observed, in several trials, the vaccines boost the immune system to combat the cancer cells while maintaining a reasonable margin of safety. Detection of neoantigens has led to the development of several databases. Adjuvants also play a catalytic role in improving the efficacy of the vaccine. Through this review, we can conclude that the efficacy of vaccines can make it a potential treatment across different types of cancers.

Binned Data Provide Better Imputation of Missing Time Series Data from Wearables.

The presence of missing values in a time-series dataset is a very common and well-known problem. Various statistical and machine learning methods have been developed to overcome this problem, with the aim of filling in the missing values in the data. However, the performances of these methods vary widely, showing a high dependence on the type of data and correlations within the data. In our study, we performed some of the well-known imputation methods, such as expectation maximization, k-nearest neighbor, iterative imputer, random forest, and simple imputer, to impute missing data obtained from smart, wearable health trackers. In this manuscript, we proposed the use of data binning for imputation. We showed that the use of data binned around the missing time interval provides a better imputation than the use of a whole dataset. Imputation was performed for 15 min and 1 h of continuous missing data. We used a dataset with different bin sizes, such as 15 min, 30 min, 45 min, and 1 h, and we carried out evaluations using root mean square error (RMSE) values. We observed that the expectation maximization algorithm worked best for the use of binned data. This was followed by the simple imputer, iterative imputer, and k-nearest neighbor, whereas the random forest method had no effect on data binning during imputation. Moreover, the smallest bin sizes of 15 min and 1 h were observed to provide the lowest RMSE values for the majority of the time frames during the imputation of 15 min and 1 h of missing data, respectively. Although applicable to digital health data, we think that this method will also find applicability in other domains.

MTAP loss: a possible therapeutic approach for glioblastoma.

Glioblastoma is the most lethal form of brain tumor with a recurrence rate of almost 90% and a survival time of only 15 months post-diagnosis. It is a highly heterogeneous, aggressive, and extensively studied tumor. Multiple studies have proposed therapeutic approaches to mitigate or improve the survival for patients with glioblastoma. In this article, we review the loss of the 5'-methylthioadenosine phosphorylase (MTAP) gene as a potential therapeutic approach for treating glioblastoma. MTAP encodes a metabolic enzyme required for the metabolism of polyamines and purines leading to DNA synthesis. Multiple studies have explored the loss of this gene and have shown its relevance as a therapeutic approach to glioblastoma tumor mitigation; however, other studies show that the loss of MTAP does not have a major impact on the course of the disease. This article reviews the contrasting findings of MTAP loss with regard to mitigating the effects of glioblastoma, and also focuses on multiple aspects of MTAP loss in glioblastoma by providing insights into the known findings and some of the unexplored areas of this field where new approaches can be imagined for novel glioblastoma therapeutics.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review.

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

Genomic Surveillance and Phylodynamic Analyses Reveal the Emergence of Novel Mutations and Co-mutation Patterns Within SARS-CoV-2 Variants Prevalent in India.

Identification of the genomic diversity and the phylodynamic profiles of prevalent variants is critical to understand the evolution and spread of SARS-CoV-2 variants. We performed whole-genome sequencing of 54 SARS-CoV-2 variants collected from COVID-19 patients in Kolkata, West Bengal during August-October 2020. Phylogeographic and phylodynamic analyses were performed using these 54 and other sequences from India and abroad that are available in the GISAID database. We estimated the clade dynamics of the Indian variants and compared the clade-specific mutations and the co-mutation patterns across states and union territories of India over the time course. Frequent mutations and co-mutations observed within the major clades across time periods do not show much overlap, indicating the emergence of newer mutations in the viral population prevailing in the country. Furthermore, we explored the possible association of specific mutations and co-mutations with the infection outcomes manifested in Indian patients.

Structural and Drug Screening Analysis of the Non-structural Proteins of Severe Acute Respiratory Syndrome Coronavirus 2 Virus Extracted From Indian Coronavirus Disease 2019 Patients.

The novel coronavirus 2 (nCoV2) outbreaks took place in December 2019 in Wuhan City, Hubei Province, China. It continued to spread worldwide in an unprecedented manner, bringing the whole world to a lockdown and causing severe loss of life and economic stability. The coronavirus disease 2019 (COVID-19) pandemic has also affected India, infecting more than 10 million till 31st December 2020 and resulting in more than a hundred thousand deaths. In the absence of an effective vaccine, it is imperative to understand the phenotypic outcome of the genetic variants and subsequently the mode of action of its proteins with respect to human proteins and other bio-molecules. Availability of a large number of genomic and mutational data extracted from the nCoV2 virus infecting Indian patients in a public repository provided an opportunity to understand and analyze the specific variations of the virus in India and their impact in broader perspectives. Non-structural proteins (NSPs) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) virus play a major role in its survival as well as virulence power. Here, we provide a detailed overview of the SARS-CoV2 NSPs including primary and secondary structural information, mutational frequency of the Indian and Wuhan variants, phylogenetic profiles, three-dimensional (3D) structural perspectives using homology modeling and molecular dynamics analyses for wild-type and selected variants, host-interactome analysis and viral-host protein complexes, and in silico drug screening with known antivirals and other drugs against the SARS-CoV2 NSPs isolated from the variants found within Indian patients across various regions of the country. All this information is categorized in the form of a database named, Database of NSPs of India specific Novel Coronavirus (DbNSP InC), which is freely available at http://www.hpppi.iicb.res.in/covid19/index.php.

Analysis of Pan-omics Data in Human Interactome Network (APODHIN).

Analysis of Pan-omics Data in Human Interactome Network (APODHIN) is a platform for integrative analysis of transcriptomics, proteomics, genomics, and metabolomics data for identification of key molecular players and their interconnections exemplified in cancer scenario. APODHIN works on a meta-interactome network consisting of human protein-protein interactions (PPIs), miRNA-target gene regulatory interactions, and transcription factor-target gene regulatory relationships. In its first module, APODHIN maps proteins/genes/miRNAs from different omics data in its meta-interactome network and extracts the network of biomolecules that are differentially altered in the given scenario. Using this context specific, filtered interaction network, APODHIN identifies topologically important nodes (TINs) implementing graph theory based network topology analysis and further justifies their role via pathway and disease marker mapping. These TINs could be used as prospective diagnostic and/or prognostic biomarkers and/or potential therapeutic targets. In its second module, APODHIN attempts to identify cross pathway regulatory and PPI links connecting signaling proteins, transcription factors (TFs), and miRNAs to metabolic enzymes via utilization of single-omics and/or pan-omics data and implementation of mathematical modeling. Interconnections between regulatory components such as signaling proteins/TFs/miRNAs and metabolic pathways need to be elucidated more elaborately in order to understand the role of oncogene and tumor suppressors in regulation of metabolic reprogramming during cancer. APODHIN platform contains a web server component where users can upload single/multi omics data to identify TINs and cross-pathway links. Tabular, graphical and 3D network representations of the identified TINs and cross-pathway links are provided for better appreciation. Additionally, this platform also provides few example data analysis of cancer specific, single and/or multi omics dataset for cervical, ovarian, and breast cancers where meta-interactome networks, TINs, and cross-pathway links are provided. APODHIN platform is freely available at http://www.hpppi.iicb.res.in/APODHIN/home.html.

Risk reduction in anesthesia and sedation-An analysis of process improvement towards zero adverse events.

INTRODUCTION: Anesthesia is a complex domain that is highly technical and skill based. Primary Care Physicians often have to do the initial evaluation of surgical patients they encounter during their daily practice before referring them to the surgical team. Thus, the Primary Care Physician's preliminary knowledge in anesthesia processes, risks involved and interventions that can be done to minimize these risks can improve patient-centered care and ultimately patient safety. MATERIALS AND METHODS: The study was conceptualized and conducted in the Department of Anesthesiology from January 2018 to December 2018 in a 600 bed Multispecialty teaching hospital in Bihar, India. The study aimed towards Anesthesia Care related Risk Identification and Reduction and encompassed process improvements. RESULTS: Risk Severity Analysis of the Critical Steps of Anesthesia Care was done. The average Hazard Score reduced from 21.59 during January 2018 to March 2018 to 8.23 during April 2018 to June 2018 subsequently to 3.53 during July 2018 to September 2018 and finally to 2.12 during October 2018 to December 2018. Thus, there was an overall reduction of 90.18% in the Hazard Score from April'18 to June'18 quarter to October 2018 to December 2018 quarter. CONCLUSION: Adverse Anesthesia/Sedation Events reported during the period from January 2019 to December 2019 was "Zero". A systematic approach towards Risk Reduction not only lead to reduction in Hazard Score and Process Improvement but also made the Anesthesia Care Safe which is evident in the consistency of reporting "Zero" Adverse Anesthesia/Sedation Events for the last one year.

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer.

Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

Periodicities in the roughness and biofilm growth on glass substrate with etching time: Hydrofluoric acid etchant.

Adherence of the microorganism to submerged solid surfaces leads to biofilm formation. Biofilm formation modifies the surfaces in favor of bacteria facilitating the survival of the bacteria under different stressed conditions. On the other hand, the formation of biofilm has a direct adverse economic impact in various industries and more importantly in medical practices. This adherence is the reason for the failure of many indwelling medical devices. Surface biofilm adhesion is the key to biofilm growth and stability. Hence this adhesion needs to be substantially lowered to inhibit biofilm stability. Both chemical and physical properties of the surface influence biofilm formation and modulating these properties can control this formation. In this study, we have investigated the effect of Hydrofluoric acid (HF), at a specific concentration as an etchant, on the surface morphology of substrates and the growth of biofilms of Pseudomonas aeruginosa. and Staphylococcus aureus. We find that the bacterial counts on the etched surfaces undergo a periodic increase and decrease. This, on one hand, shows the close correlation between the biofilm growth and the particular roughness scale, and on the other hand, explains the existing contradictory results regarding the effects of etching on substrate roughness and biofilm growth. We propose a simple model of a sequence of hole formation, hole expansion and etching away of the hole walls to form a new, comparatively smooth surface, coupled with the preferential accumulation of bacteria at the hole edges, to explain these periodicities.

Counterion effects on nano-confined metal-drug-DNA complexes.

We have explored morphology of DNA molecules bound with Cu complexes of piroxicam (a non-steroidal anti-inflammatory drug) molecules under one-dimensional confinement of thin films and have studied the effect of counterions present in a buffer. X-ray reflectivity at and away from the Cu K absorption edge and atomic force microscopy studies reveal that confinement segregates the drug molecules preferentially in a top layer of the DNA film, and counterions enhance this segregation.

Interplay of electrostatics and lipid packing determines the binding of charged polymer coated nanoparticles to model membranes.

Understanding of nanoparticle-membrane interactions is useful for various applications of nanoparticles like drug delivery and imaging. Here we report on the studies of interaction between hydrophilic charged polymer coated semiconductor quantum dot nanoparticles with model lipid membranes. Atomic force microscopy and X-ray reflectivity measurements suggest that cationic nanoparticles bind and penetrate bilayers of zwitterionic lipids. Penetration and binding depend on the extent of lipid packing and result in the disruption of the lipid bilayer accompanied by enhanced lipid diffusion. On the other hand, anionic nanoparticles show minimal membrane binding although, curiously, their interaction leads to reduction in lipid diffusivity. It is suggested that the enhanced binding of cationic QDs at higher lipid packing can be understood in terms of the effective surface potential of the bilayers which is tunable through membrane lipid packing. Our results bring forth the subtle interplay of membrane lipid packing and electrostatics which determine nanoparticle binding and penetration of model membranes with further implications for real cell membranes.

Cell-Membrane-Mimicking Lipid-Coated Nanoparticles Confer Raman Enhancement to Membrane Proteins and Reveal Membrane-Attached Amyloid-ß Conformation.

Identifying the structures of membrane bound proteins is critical to understanding their function in healthy and diseased states. We introduce a surface enhanced Raman spectroscopy technique which can determine the conformation of membrane-bound proteins, at low micromolar concentrations, and also in the presence of a substantial membrane-free fraction. Unlike conventional surface enhanced Raman spectroscopy, our approach does not require immobilization of molecules, as it uses spontaneous binding of proteins to lipid bilayer-encapsulated Ag nanoparticles. We apply this technique to probe membrane-attached oligomers of Amyloid-ß40 (Aß40), whose conformation is keenly sought in the context of Alzheimer's disease. Isotope-shifts in the Raman spectra help us obtain secondary structure information at the level of individual residues. Our results show the presence of a ß-turn, flanked by two ß-sheet regions. We use solid-state NMR data to confirm the presence of the ß-sheets in these regions. In the membrane-attached oligomer, we find a strongly contrasting and near-orthogonal orientation of the backbone H-bonds compared to what is found in the mature, less-toxic Aß fibrils. Significantly, this allows a "porin" like ß-barrel structure, providing a structural basis for proposed mechanisms of Aß oligomer toxicity.

Kinetics of dispersion of nanoparticles in thin polymer films at high temperature.

We report the first detailed study of the kinetics of dispersion of nanoparticles in thin polymer films using temperature dependent in situ X-ray scattering measurements. We show a comparably enhanced dispersion at higher temperatures for systems which are otherwise phase segregated at room temperature. Detailed analysis of the time dependent X-ray reflectivity and diffuse scattering data allows us to explore the out-of-plane and in-plane mobility of the nanoparticles in the polymer films. While the out-of-plane motion is diffusive with a diffusion coefficient almost two orders of magnitude lower than that expected in bulk polymer, the in-plane one is found to be super-diffusive resulting in significantly larger in-plane displacement at similar time scales. We discuss the origin of the observed highly anisotropic motion of nanoparticles due to their slaved motion with respect to the anisotropic chain orientation and consequent diffusivity anisotropy of matrix chains. We also suggest strategies to utilize these observations to kinetically improve dispersion in otherwise thermodynamically segregated polymer nanocomposite films.

Atomic force microscopy in biofilm study.

Biofilms have been classically visualized by Scanning Electron Microscopy (SEM). The complex operating procedure of SEM restricts its use in routine practice. There is a need of newer visualizing techniques for examining surfaces of biofilms, in particular under ambient conditions. We have presented the unique advantages of atomic force microscopy (AFM) in studying surfaces of biofilms through analyses of the height images obtained on biofilms of two gram positive and one gram negative bacteria, namely Staphylococcus aureus, Nocardia brasiliensis and Pseudomonas aeruginosa, respectively. Biofilm quality of the three different bacteria, ageing effects on Nocardia spp. biofilm surface and effects of the antibiotic ciprofloxacin at different doses on Staphylococcus and Pseudomonas biofilm surfaces have been investigated under ambient conditions and distinctive features have been observed.

Evolution of nanoparticle-induced distortion on viral polyhedra.

Morphological changes in the polyhedra of the Bombyx mori L. nuclear polyhedrosis virus (BmNPV), a baculovirus causing the deadly grasserie disease in silkworms, brought about by mixing with lipophilically capped amorphous silica nanoparticles (LASN, average size 10 ± 2 nm) were studied with scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy. SEM shows that the regular octagonal polyhedra facets are replaced by a larger number of newly formed irregular ones. The average number of facets reveals a nonlinear growth pattern with nanoparticle (NP) concentration, where an initial linear region ends in a plateau. IR bands corresponding to vibration modes of the capping show (a) a saturation of the area under the band with NP concentration, indicating a correlation with attachment to viral polyhedra and (b) a narrowing of the band per NP from the linear to the plateau portions of the distortion curve, suggesting non-equilibrium and equilibrium situations, respectively.

Nanoparticle surface as activation site.

The immense surface-to-volume (S/V) ratio in nanoparticles leads to large surface energy density. These high densities play the role of sites for activities that are not triggered in bulk materials. Here we present some examples of such distinctive activities taking place at nanoparticle surfaces. Our first example involves the morphological changes in silkworm (Bombyx mori L.) nuclear polyhedrosis virus (BmNPV) brought about by lipophilic amorphous silica nanoparticles (LASN). Microscopy studies show that nanoparticles severely alter the structure of the virus envelope by a 'deflation' of the viral polyhedron and formation of elongated structures. The second example shows the spatial variation in aggregation potential with temperature, for dodecanethiol-capped Au nanoparticles on an amorphous polystyrene film surface. We find that on increasing the temperature from 32 degrees C to 50 degrees C the aggregating potential becomes almost completely confined to the film surface, whereas going over to 100 degrees C the confining potential is overcome and out-of-plane growth takes place. A tentative and qualitative explanation has been attempted.

Nanoparticle-virus complex shows enhanced immunological effect against baculovirus.

Insects protect themselves from majority of infections by a non-specific innate immune system (present in both vertebrates and invertebrates). Bombyx mori nuclear polyhedrosis virus (BmNPV), a baculovirus, causing the deadly grasserie disease is a scourge to silkworm industry and we report here the first success in combating this disease with the help of a nanosilica-virus complex. Hydrophobic aluminium silicate nanoparticles were mixed with live BmNPV in vitro. This mixture was injected into one day old 5th instar silkworm larvae (into the hemocoel at the third abdominal spiracle) before challenging the larvae with live BmNPV via a second injection. This led to substantial enhancement of longevity in the diseased silkworm larvae and 35 +/- 5.3% larvae completed their lifecycle (i.e., formed normal pupae and enclosed as moth). On the other hand, 100% larvae infected with BmNPV alone died within 36 hours. The larvae treated with nanoparticles before infection had a longer lifespan but all of them eventually succumbed, not a single larva metamorphosed to adult stage. Results suggest two pathways of host protective response--one mediated by nanoparticlealone and the second, more important, via non-specific innate immunological mechanism. AFM and confocal studies show that nanoparticles alter 3-D molecular structure of the virus envelope. Possibly this exhibits novel potent epitope(s) which stimulate(s) anti-viral machinery in infected silkworm larvae. SDS-PAGE results suggest that 39 KDa viral protein is the major target of the nanoparticles.