Challenges of AI driven diagnosis of chest X-rays transmitted through smart phones: a case study in COVID-19.

Healthcare delivery during the initial days of outbreak of COVID-19 pandemic was badly impacted due to large number of severely infected patients posing an unprecedented global challenge. Although the importance of Chest X-rays (CXRs) in meeting this challenge has now been widely recognized, speedy diagnosis of CXRs remains an outstanding challenge because of fewer Radiologists. The exponential increase in Smart Phone ownership globally, including LMICs, provides an opportunity for exploring AI-driven diagnostic tools when provided with large volumes of CXRs transmitted through Smart Phones. However, the challenges associated with such systems have not been studied to the best of our knowledge. In this paper, we show that the predictions of AI-driven models on CXR images transmitted through Smart Phones via applications, such as WhatsApp, suffer both in terms of Predictability and Explainability, two key aspects of any automated Medical Diagnosis system. We find that several existing Deep learning based models exhibit prediction instability-disagreement between the prediction outcome of the original image and the transmitted image. Concomitantly we find that the explainability of the models deteriorate substantially, prediction on the transmitted CXR is often driven by features present outside the lung region, clearly a manifestation of Spurious Correlations. Our study reveals that there is significant compression of high-resolution CXR images, sometimes as high as 95%, and this could be the reason behind these two problems. Apart from demonstrating these problems, our main contribution is to show that Multi-Task learning (MTL) can serve as an effective bulwark against the aforementioned problems. We show that MTL models exhibit substantially more robustness, 40% over existing baselines. Explainability of such models, when measured by a saliency score dependent on out-of-lung features, also show a 35% improvement. The study is conducted on WaCXR dataset, a curated dataset of 6562 image pairs corresponding to original uncompressed and WhatsApp compressed CXR images. Keeping in mind that there are no previous datasets to study such problems, we open-source this data along with all implementations.

Quantum dots: The cutting-edge nanotheranostics in brain cancer management.

Quantum dots (QDs) are semiconductor nanocrystals possessing unique optoelectrical properties in that they can emit light energy of specific tunable wavelengths when excited by photons. They are gaining attention nowadays owing to their all-around ability to allow high-quality bio-imaging along with targeted drug delivery. The most lethal central nervous system (CNS) disorders are brain cancers or malignant brain tumors. CNS is guarded by the blood-brain barrier which poses a selective blockade toward drug delivery into the brain. QDs have displayed strong potential to deliver therapeutic agents into the brain successfully. Their bio-imaging capability due to photoluminescence and specific targeting ability through the attachment of ligand biomolecules make them preferable clinical tools for coming times. Biocompatible QDs are emerging as nanotheranostic tools to identify/diagnose and selectively kill cancer cells. The current review focuses on QDs and associated nanoformulations as potential futuristic clinical aids in the continuous battle against brain cancer.

Carnosic acid attenuates doxorubicin-induced cardiotoxicity by decreasing oxidative stress and its concomitant pathological consequences.

This work aimed to reveal the protective mechanism of CA against Dox (doxorubicin)-induced cardiotoxicity. In isolated murine cardiomyocytes, CA showed a concentration-dependent cytoprotective effect against Dox. Dox treatment significantly (p < 0.01) increased the formation of reactive oxygen species (ROS), increased NO levels, activated NADPH oxidase, and inactivated the cellular redox defense mechanism in cardiac cells, resulting in augmented oxidative stress in cardiomyocytes and rat hearts. Dox-induced oxidative stress significantly (p < 0.01) upregulated several pathogenic signal transductions, which induced apoptosis, inflammation, and fibrosis in cardiomyocytes and murine hearts. In contrast, CA significantly (p < 0.05-0.01) reciprocated Dox-induced cardiac apoptosis, inflammation, and fibrosis by suppressing oxidative stress and interfering with pathological signaling events in both isolated murine cardiomyocytes and rat hearts. CA treatment significantly (p < 0.05-0.01) countered Dox-mediated pathological changes in blood parameters in rats. Histological examinations backed up the pharmacological findings. In silico chemometric investigations predicted potential interactions between CA and studied signal proteins, as well as the drug-like features of CA. Thus, it would be concluded that CA has the potential to be regarded as an effective agent to alleviate Dox-mediated cardiotoxicity in the future.

Predicting the DNA Conductance Using a Deep Feedforward Neural Network Model.

Double-stranded DNA (dsDNA) has been established as an efficient medium for charge migration, bringing it to the forefront of the field of molecular electronics and biological research. The charge migration rate is controlled by the electronic couplings between the two nucleobases of DNA/RNA. These electronic couplings strongly depend on the intermolecular geometry and orientation. Estimating these electronic couplings for all the possible relative geometries of molecules using the computationally demanding first-principles calculations requires a lot of time and computational resources. In this article, we present a machine learning (ML)-based model to calculate the electronic coupling between any two bases of dsDNA/dsRNA and bypass the computationally expensive first-principles calculations. Using the Coulomb matrix representation which encodes the atomic identities and coordinates of the DNA base pairs to prepare the input dataset, we train a feedforward neural network model. Our neural network (NN) model can predict the electronic couplings between dsDNA base pairs with any structural orientation with a mean absolute error (MAE) of less than 0.014 eV. We further use the NN-predicted electronic coupling values to compute the dsDNA/dsRNA conductance.

Suppress, and Not Just Flatten: Strategies for Rapid Suppression of COVID19 Transmission in Small World Communities.

Many countries have introduced Lockdowns to contain the COVID19 epidemic. Lockdowns, though an effective policy for containment, imposes a heavy cost on the economy as it enforces extreme social distancing measures on the whole population. The objective of this note is to study alternatives to Lockdown which are either more targeted or allows partial opening of the economy. Cities are often spatially organized into wards. We introduce Multi-lattice small world (MLSW)  network as a model of a city where each ward is represented by a 2D lattice and each vertex in the latex represents an agent endowed with SEIR dynamics. Through simulation studies on MLSW, we examine a variety of candidate suppression policies and find that restricting Lockdowns to infected wards can indeed out-perform global Lockdowns in both reducing the attack rate and also shortening the duration of the epidemic. Even policies such as partial opening of the economy, such as Two-Day Work Week, can be competitive if augmented with extensive Contact Tracing.

Airway devices in paediatric anaesthesia.

Airway devices were first used in children since 1940 and thereafter an increasingly large number of paediatric airway devices have come into our armamentarium. To control and protect the airway in children during anaesthesia, in intensive care unit or in emergency department either tracheal intubation is performed under direct or indirect visualization of vocal cords with the help of laryngoscopes or video-laryngoscopes respectively or it can be done blindly or by using special instruments such as fiberoptic laryngoscope, lighted stylet or Bullard laryngoscope to name a few. Airway also can be maintained with the help of Laryngeal mask airways, oropharyngeal and nasopharyngeal airways. Updating our information and knowledge regarding these developments is pivotal to our practice of paediatric anaesthesia. With a thorough search of books, MEDLINE, MEDNET, clinical trials.gov.in, this article aims at focusing and understanding a brief basis of paediatric devices and their use.

Comparative evaluation of analgesic sparing efficacy between dexmedetomidine and clonidine used as adjuvant to ropivacaine in thoracic paravertebral block for patients undergoing breast cancer surgery: A prospective, randomized, double-blind study.

INTRODUCTION: Thoracic paravertebral block (TPVB) is an effective method for intra- and post-operative pain management in thoracic surgeries. For a long time, various adjuvants have been tried for prolonging the duration of TPVB. OBJECTIVE: In this prospective study, we have compared the analgesic sparing efficacy of dexmedetomidine and clonidine, two α2 adrenergic agonists, administered along with ropivacaine for TPVB for breast cancer surgery patients. MATERIALS AND METHODS: Forty-four breast cancer surgery patients undergoing general anesthesia (GA) were randomly divided into Group C and Group D (n = 44 each) receiving preoperative TPVB at T3-5 level with 0.5% ropivacaine solution admixture with clonidine and dexmedetomidine, respectively. Cancer surgery was performed under GA. Intraoperative fentanyl and propofol requirement was compared. Visual analogue scale was used for pain assessment. Total dose and mean time to administration of first rescue analgesic diclofenac sodium was noted. Side effects and hemodynamic parameters were also noted. RESULTS: Intraoperative fentanyl and propofol requirement was significantly less in dexmedetomidine group than clonidine. The requirement of diclofenac sodium was also significantly less and later in Group D than Group C. Hemodynamics, and side effects were comparable among two groups. CONCLUSION: Dexmedetomidine provided better intraoperative as well as postoperative analgesia than clonidine when administered with ropivacaine in TPVB before breast cancer surgery patients without producing remarkable side effects.

Bayesian Modeling of Temporal Coherence in Videos for Entity Discovery and Summarization.

A video is understood by users in terms of entities present in it. Entity Discovery is the task of building appearance model for each entity (e.g., a person), and finding all its occurrences in the video. We represent a video as a sequence of tracklets, each spanning 10-20 frames, and associated with one entity. We pose Entity Discovery as tracklet clustering, and approach it by leveraging Temporal Coherence (TC): the property that temporally neighboring tracklets are likely to be associated with the same entity. Our major contributions are the first Bayesian nonparametric models for TC at tracklet-level. We extend Chinese Restaurant Process (CRP) to TC-CRP, and further to Temporally Coherent Chinese Restaurant Franchise (TC-CRF) to jointly model entities and temporal segments using mixture components and sparse distributions. For discovering persons in TV serial videos without meta-data like scripts, these methods show considerable improvement over state-of-the-art approaches to tracklet clustering in terms of clustering accuracy, cluster purity and entity coverage. The proposed methods can perform online tracklet clustering on streaming videos unlike existing approaches, and can automatically reject false tracklets. Finally we discuss entity-driven video summarization- where temporal segments of the video are selected based on the discovered entities, to create a semantically meaningful summary.

HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials.

MOTIVATION: T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics and are critically dependent on experimentally obtained training data. Analysis of a training set from Immune Epitope Database (IEDB) for several alleles indicates that the sampling of the peptide space is extremely sparse covering a tiny fraction of the possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction. RESULTS: We present a new epitope prediction method that has four distinct computational modules: (i) structural modelling, estimating statistical pair-potentials and constraint derivation, (ii) implicit modelling and interaction profiling, (iii) feature representation and binding affinity prediction and (iv) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles. CONCLUSIONS: HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any Class-1 HLA allele, by estimating the binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It relies on the strength of the mechanistic understanding of peptide-HLA recognition and provides an estimate of the total ligand space for each allele. The performance of HLaffy is seen to be superior to the currently available methods. AVAILABILITY AND IMPLEMENTATION: The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy CONTACT: : nchandra@biochem.iisc.ernet.in SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pediatric patient with Bombay blood group: A rare case report.

Bombay blood group is a rare blood group in which there is the absence of H antigen and presence of anti-H antibodies. At the time of blood grouping, this blood group mimics O blood group due to the absence of H antigen, but it shows incompatibility with O group blood during cross matching. Serum grouping or reverse grouping are essential for confirmation of the diagnosis. Patients carrying this blood group can receive blood only from a person with this blood group. Reported cases of anesthesia in the pediatric patient with Bombay blood group are relatively rare. Here, we present successful anesthetic management along with intraoperative blood transfusion in a pediatric patient with Bombay blood group posted for ovarian cystectomy.

NETGEM: Network Embedded Temporal GEnerative Model for gene expression data.

BACKGROUND: Temporal analysis of gene expression data has been limited to identifying genes whose expression varies with time and/or correlation between genes that have similar temporal profiles. Often, the methods do not consider the underlying network constraints that connect the genes. It is becoming increasingly evident that interactions change substantially with time. Thus far, there is no systematic method to relate the temporal changes in gene expression to the dynamics of interactions between them. Information on interaction dynamics would open up possibilities for discovering new mechanisms of regulation by providing valuable insight into identifying time-sensitive interactions as well as permit studies on the effect of a genetic perturbation. RESULTS: We present NETGEM, a tractable model rooted in Markov dynamics, for analyzing the dynamics of the interactions between proteins based on the dynamics of the expression changes of the genes that encode them. The model treats the interaction strengths as random variables which are modulated by suitable priors. This approach is necessitated by the extremely small sample size of the datasets, relative to the number of interactions. The model is amenable to a linear time algorithm for efficient inference. Using temporal gene expression data, NETGEM was successful in identifying (i) temporal interactions and determining their strength, (ii) functional categories of the actively interacting partners and (iii) dynamics of interactions in perturbed networks. CONCLUSIONS: NETGEM represents an optimal trade-off between model complexity and data requirement. It was able to deduce actively interacting genes and functional categories from temporal gene expression data. It permits inference by incorporating the information available in perturbed networks. Given that the inputs to NETGEM are only the network and the temporal variation of the nodes, this algorithm promises to have widespread applications, beyond biological systems.The source code for NETGEM is available from https://github.com/vjethava/NETGEM.

Prediction of candidate primary immunodeficiency disease genes using a support vector machine learning approach.

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data.

BACKGROUND: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. RESULTS: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. CONCLUSION: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational - experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

RAPID: Resource of Asian Primary Immunodeficiency Diseases.

Availability of a freely accessible, dynamic and integrated database for primary immunodeficiency diseases (PID) is important both for researchers as well as clinicians. To build a PID informational platform and also as a part of action to initiate a network of PID research in Asia, we have constructed a web-based compendium of molecular alterations in PID, named Resource of Asian Primary Immunodeficiency Diseases (RAPID), which is available as a worldwide web resource at http://rapid.rcai.riken.jp/. It hosts information on sequence variations and expression at the mRNA and protein levels of all genes reported to be involved in PID patients. The main objective of this database is to provide detailed information pertaining to genes and proteins involved in primary immunodeficiency diseases along with other relevant information about protein-protein interactions, mouse studies and microarray gene-expression profiles in various organs and cells of the immune system. RAPID also hosts a tool, mutation viewer, to predict deleterious and novel mutations and also to obtain mutation-based 3D structures for PID genes. Thus, information contained in this database should help physicians and other biomedical investigators to further investigate the role of these molecules in PID.

Comparison of protein structures by growing neighborhood alignments.

BACKGROUND: Design of protein structure comparison algorithm is an important research issue, having far reaching implications. In this article, we describe a protein structure comparison scheme, which is capable of detecting correct alignments even in difficult cases, e.g. non-topological similarities. The proposed method computes protein structure alignments by comparing, small substructures, called neighborhoods. Two different types of neighborhoods, sequence and structure, are defined, and two algorithms arising out of the scheme are detailed. A new method for computing equivalences having non-topological similarities from pairwise similarity score is described. A novel and fast technique for comparing sequence neighborhoods is also developed. RESULTS: The experimental results show that the current programs show better performance on Fischer and Novotny's benchmark datasets, than state of the art programs, e.g. DALI, CE and SSM. Our programs were also found to calculate correct alignments for proteins with huge amount of indels and internal repeats. Finally, the sequence neighborhood based program was used in extensive fold and non-topological similarity detection experiments. The accuracy of the fold detection experiments with the new measure of similarity was found to be similar or better than that of the standard algorithm CE. CONCLUSION: A new scheme, resulting in two algorithms, have been developed, implemented and tested. The programs developed are accessible at http://mllab.csa.iisc.ernet.in/mp2/runprog.html.

Projections for fast protein structure retrieval.

BACKGROUND: In recent times, there has been an exponential rise in the number of protein structures in databases e.g. PDB. So, design of fast algorithms capable of querying such databases is becoming an increasingly important research issue. This paper reports an algorithm, motivated from spectral graph matching techniques, for retrieving protein structures similar to a query structure from a large protein structure database. Each protein structure is specified by the 3D coordinates of residues of the protein. The algorithm is based on a novel characterization of the residues, called projections, leading to a similarity measure between the residues of the two proteins. This measure is exploited to efficiently compute the optimal equivalences. RESULTS: Experimental results show that, the current algorithm outperforms the state of the art on benchmark datasets in terms of speed without losing accuracy. Search results on SCOP 95% nonredundant database, for fold similarity with 5 proteins from different SCOP classes show that the current method performs competitively with the standard algorithm CE. The algorithm is also capable of detecting non-topological similarities between two proteins which is not possible with most of the state of the art tools like Dali.