A Comparative Study on the Efficacy Between Cystatin C and Creatinine-Based Equations for Early Detection of Renal Damage in Patients of Eastern India.

Chronic kidney disease (CKD) is one of the leading causes of mortality across the globe. Early diagnosis of the disease is important in order to prevent the adverse outcome related to CKD. Many laboratories adopt creatinine-based e-GFR equations which yields imprecise results leading to misdiagnosis of CKD. Emerging studies indicated cystatin C as a better renal marker than creatinine. The aim of the study is to compare the efficacy of CKD epidemiology collaboration (CKD-EPI) creatinine e-GFR equations with (CKD EPI) cystatin-based e-GFR equations alone and in combination with creatinine for early detection of CKD. A cross-sectional study employing 473 patients was conducted. Three estimating GFR equations were calculated based on creatinine and cystatin C. Pearson Correlation study was done to assess the correlation of creatinine and cystatin C with their respective GFRs. A predictive model was developed, and ROC curve was constructed to compare efficacy, sensitivity and specificity of the creatinine and cystatin C based equations. Cystatin C exhibited better negative correlation with GFR than creatinine in correlation study performed with three commonly employed eGFR equations including  CKD EPI Creatine cystatin C combined  equation (2021), cys C alone and CKD EPI  creatinine (2021)  equations respectively[r=(-) 0.801 vs. r=(-)0.786 vs. r=(-)0.773]. Predictive model demonstrated highest efficiency, sensitivity and specificity for creatinine-cystatin C combined equation (88%, 81% and 93%) followed by cystatin C alone equation (73%,63% and 82%) and creatinine-based equation  (61%, 56% and 66% respectively). The study showed better performance of cystatin C based equations for early detection of advance stages in chronic kidney disease as compared to creatinine-based e-GFR equation.

Aluminum toxicity induced Alzheimer's Disease and its potential treatment using antioxidants - a review

Abstract Over the years, a handful of drugs have been approved to be used in the fight against Alzheimer's Disease but unfortunately none of these drugs have proven to be solid-treatments. Alzheimer's Disease is one of the most prominent diseases observed in the elderly population. In this review article, we discuss how aluminum toxicity can lead to neuro degeneration. Aluminum is abundantly present on the earth's crust and hence becomes easily accessible to man. This makes it an obvious choice in the preparation of numerous substances, packaging, etc. Such wide usage of the metal can pave an easy access to the body, leading to toxicities. Aluminum toxicity has been linked to oxidative stress which has an established relation with neurodegeneration and mitochondrial damage. We also discuss how consumption of antioxidants can be useful in combating oxidative stress.

ProMoCell and ProModb: Web services for analyzing interaction-based functionally localized protein modules in a cell.

The modular organization of a cell which can be determined by its interaction network allows us to understand a mesh of cooperation among the functional modules. Therefore, cellular-level identification of functional modules aids in understanding the functional and structural characteristics of the biological network of a cell and also assists in determining or comprehending the evolutionary signal. We develop ProMoCell that performs real-time Web scraping for generating clusters of the cellular level functional units of an organism. ProMoCell constructs the Protein Locality Graphs and clusters the cellular level functional units of an organism by utilizing experimentally verified data from various online sources. Also, we develop ProModb, a database service that houses precomputed whole-cell protein-protein interaction network-based functional modules of an organism using ProMoCell. Our Web service is entirely synchronized with the KEGG pathway database and allows users to generate spatially localized protein modules for any organism belonging to the KEGG genome using its real-time Web scraping characteristics. Hence, the server will host as many organisms as is maintained by the KEGG database. Our Web services provide the users a comprehensive and integrated tool for an efficient browsing and extraction of the spatial locality-based protein locality graph and the functional modules constructed by gathering experimental data from several interaction databases and pathway maps. We believe that our Web services will be beneficial in pharmacological research, where a novel research domain called modular pharmacology has initiated the study on the diagnosis, prevention, and treatment of deadly diseases using functional modules.

Protein Interaction Network-based Deep Learning Framework for Identifying Disease-Associated Human Proteins.

Infectious diseases in humans appear to be one of the most primary public health issues. Identification of novel disease-associated proteins will furnish an efficient recognition of the novel therapeutic targets. Here, we develop a Graph Convolutional Network (GCN)-based model called PINDeL to identify the disease-associated host proteins by integrating the human Protein Locality Graph and its corresponding topological features. Because of the amalgamation of GCN with the protein interaction network, PINDeL achieves the highest accuracy of 83.45% while AUROC and AUPRC values are 0.90 and 0.88, respectively. With high accuracy, recall, F1-score, specificity, AUROC, and AUPRC, PINDeL outperforms other existing machine-learning and deep-learning techniques for disease gene/protein identification in humans. Application of PINDeL on an independent dataset of 24320 proteins, which are not used for training, validation, or testing purposes, predicts 6448 new disease-protein associations of which we verify 3196 disease-proteins through experimental evidence like disease ontology, Gene Ontology, and KEGG pathway enrichment analyses. Our investigation informs that experimentally-verified 748 proteins are indeed responsible for pathogen-host protein interactions of which 22 disease-proteins share their association with multiple diseases such as cancer, aging, chem-dependency, pharmacogenomics, normal variation, infection, and immune-related diseases. This unique Graph Convolution Network-based prediction model is of utmost use in large-scale disease-protein association prediction and hence, will provide crucial insights on disease pathogenesis and will further aid in developing novel therapeutics.

Evaluation of the Role of Routine Laboratory Biomarkers in COVID-19 Patients: Perspective from a Tertiary Care Hospital in India.

To evaluate the role of routine laboratory biomarkers like C Reactive Protein (CRP), Lactate Dehydrogenase (LDH), Interleukin 6 (IL6), Ferritin, Creatinine, Procalcitonin (PCT), Aspartate aminotransferase (AST), Alanine aminotransferase (ALT), Serum Albumin, Total Bilirubin (T Bil), High Sensitive Troponin I (hs troponin I), N Terminal-pro B-type Natriuretic Peptide (NT proBNP), Blood Urea Nitrogen (BUN) and Blood Gases in COVID 19 patients who are admitted with SARS CoV-2 positive test results by real-time reverse transcriptase polymerase chain reaction (rRT PCR) in Kokilaben Dhirubhai Ambani Hospital & Medical Research Institute, Mumbai, India. 100 individuals detected with COVID-19 belonging to the age group 12-83 years (median age 62 years) within the period of 1st March 2020 to 10th July 2020 were studied. The case group consisted of 72 males and 28 females. 40 healthy adults without any history or clinical evidence suggestive of COVID-19 and without any comorbidities, like diabetes, hypertension chronic lung disease, cardiac disease, cancer, and immune-compromised individuals were considered as a control group for the study. Routine laboratory findings of these 100 patients were used to evaluate the abnormalities found in COVID-19 patients. Statistical analysis was carried out on the data after determining whether the data had a normal/log-normal distribution and their significance was determined by calculating the p-value. The percentage of patients showing a decrease or increase from the normal value was calculated. Trend analysis was carried out for the 100 patients considered in the case group. Among them, 6 patients were used as representatives to show the trend in these biomarkers during the course of hospital stay. These 5 severe cases consisted of 2 adult males, 2 adult females, and 1 adolescent girl. This selection is to demonstrate the representation of COVID-19 infection in adult males and females and pediatric multisystem inflammatory syndrome associated with COVID-19 in the younger age group. One mild case (adult male) was also selected in the case study. We found a significant increase in mean values of AST, ALT, Total Billirubin, Creatinine, CRP, PCT, LDH, IL6, Ferritin, Lactate, hsTroponin I, NT Pro BNP and decrease in mean values of Albumin, SO2, and PO2 in COVID 19 cases than control. We applied Receiver Operating Curve (ROC) curve to discriminate case population more precisely than the control population. Therefore, Routine laboratory biomarkers appear to play a significant role in COVID-19 patients.

High-Performance Whole-Cell Simulation Exploiting Modular Cell Biology Principles.

One of the grand challenges of this century is modeling and simulating a whole cell. Extreme regulation of an extensive quantity of model and simulation data during whole-cell modeling and simulation renders it a computationally expensive research problem in systems biology. In this article, we present a high-performance whole-cell simulation exploiting modular cell biology principles. We prepare the simulation by dividing the unicellular bacterium, Escherichia coli (E. coli), into subcells utilizing the spatially localized densely connected protein clusters/modules. We set up a Brownian dynamics-based parallel whole-cell simulation framework by utilizing the Hamiltonian mechanics-based equations of motion. Though the velocity Verlet integration algorithm possesses the capability of solving the equations of motion, it lacks the ability to capture and deal with particle-collision scenarios. Hence, we propose an algorithm for detecting and resolving both elastic and inelastic collisions and subsequently modify the velocity Verlet integrator by incorporating our algorithm into it. Also, we address the boundary conditions to arrest the molecules' motion outside the subcell. For efficiency, we define one hashing-based data structure called the cellular dictionary to store all of the subcell-related information. A benchmark analysis of our CUDA C/C++ simulation code when tested on E. coli using the CPU-GPU cluster indicates that the computational time requirement decreases with the increase in the number of computing cores and becomes stable at around 128 cores. Additional testing on higher organisms such as rats and humans informs us that our proposed work can be extended to any organism and is scalable for high-end CPU-GPU clusters.

An unusual neurological presentation: systemic lupus erythematosus mimicking central nervous system infection.

Systemic lupus erythematosus (SLE) is an autoimmune multisystem disease. Childhood-onset SLE is extremely rare and comprises only 10% to 20% of all cases. In this case report, we present a 9-year-old boy from northeastern India who presented with fever, cough, vague abdominal pain, lethargy and swelling of face and legs. Initial impression was one of sepsis with central nervous system (CNS) involvement and was treated accordingly. Detailed clinical examination with subsequent laboratory and imaging studies clinched the diagnosis of SLE. The patient showed rapid resolution of symptoms with immunoglobulins, cyclophosphamide and steroid therapy. A brief discussion on childhood neuropsychiatric lupus syndrome and SLE with CNS infections is included here.

A network-based zoning for parallel whole-cell simulation.

Motivation: In Computational Cell Biology, whole-cell modeling and simulation is an absolute requirement to analyze and explore the cell of an organism. Despite few individual efforts on modeling, the prime obstacle hindering its development and progress is its compute-intensive nature. Towards this end, little knowledge is available on how to reduce the enormous computational overhead and which computational systems will be of use. Results: In this article, we present a network-based zoning approach that could potentially be utilized in the parallelization of whole-cell simulations. Firstly, we construct the protein-protein interaction graph of the whole-cell of an organism using experimental data from various sources. Based on protein interaction information, we predict protein locality and allocate confidence score to the interactions accordingly. We then identify the modules of strictly localized interacting proteins by performing interaction graph clustering based on the confidence score of the interactions. By applying this method to Escherichia coli K12, we identified 188 spatially localized clusters. After a thorough Gene Ontology-based analysis, we proved that the clusters are also in functional proximity. We then conducted Principal Coordinates Analysis to predict the spatial distribution of the clusters in the simulation space. Our automated computational techniques can partition the entire simulation space (cell) into simulation sub-cells. Each of these sub-cells can be simulated on separate computing units of the High-Performance Computing (HPC) systems. We benchmarked our method using proteins. However, our method can be extended easily to add other cellular components like DNA, RNA and metabolites. Availability and implementation: . Supplementary information: Supplementary data are available at Bioinformatics online.