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Persistent pathogen infection is a known cause of malignancy, although with sparse systematic evaluation across tumor types. We present a comprehensive landscape of 1060 infectious pathogens across 239 whole exomes and 1168 transcriptomes of breast, lung, gallbladder, cervical, colorectal, and head and neck tumors. We identify known cancer-associated pathogens consistent with the literature. In addition, we identify a significant prevalence of Fusobacterium in head and neck tumors, comparable to colorectal tumors. The Fusobacterium-high subgroup of head and neck tumors occurs mutually exclusive to human papillomavirus, and is characterized by overexpression of miRNAs associated with inflammation, elevated innate immune cell fraction and nodal metastases. We validate the association of Fusobacterium with the inflammatory markers IL1B, IL6 and IL8, miRNAs hsa-mir-451a, hsa-mir-675 and hsa-mir-486-1, and MMP10 in the tongue tumor samples. A higher burden of Fusobacterium is also associated with poor survival, nodal metastases and extracapsular spread in tongue tumors defining a distinct subgroup of head and neck cancer.

Nervous system, being the most critical part of the human body has attracted many neuro-surgeons to diagnose the neurological diseases which are of primary concern. It’s been a challenge since many years. The recent report of the World Health Organization’s declares that neurological syndrome, such as, Alzheimer’s disease, affects around one billion human beings. As a consequence of neurological disorder there have been around 6.8 million deaths globally. Along with being an irremediable Disease it is at the same time a progressive brain disease which gradually diminishes the cognitive ability and affects memory which in turn affects routine life. It is prevalent cause of dementia among the elderly. This paper presents the work which assesses the efficacy of classification using unsupervised learning along with the image processing employed on the images of Magnetic Resonance Imaging scans to calculate the probability of early detection of Alzheimer’s disease. The whole brain atrophy is considered as strong diagnostic test for Alzheimer’s disease. The paper expresses the image processing methods such as pixel thresholding and unsupervised learning methods like k-means clustering, and a tailored algorithm incorporated for this specific case. The algorithm has been implemented using platforms, OpenCV and R libraries (for k means clustering), which expedites the effectiveness of the developed prototype which can be used in the hospitals/clinics, reducing the need for any proprietary software. The final output of the prototype can assist the doctors to diagnose Alzheimer’s disease at an early stage. These results can be co-related with psychiatric results for better understanding and treatment required for Alzheimer’s disease.

Background Rapid analysis of SARS-CoV-2 genomic data plays a crucial role in surveillance and adoption of measures in controlling spread of Covid-19. Fast, inclusive and adaptive methods are required for the heterogenous SARS-CoV-2 sequence data generated at an unprecedented rate. Results We present an updated version of the SARS-CoV-2 analysis module of our automated computational pipeline, Infectious Pathogen Detector (IPD) 2.0, to perform genomic analysis to understand the variability and dynamics of the virus. It adopts the recent clade nomenclature and demonstrates the clade prediction accuracy of 92.8%. IPD 2.0 also contains a SARS-CoV-2 updater module, allowing automatic upgrading of the variant database using genome sequences from GISAID. As a proof of principle, analyzing 208,911 SARS-CoV-2 genome sequences, we generate an extensive database of 2.58 million sample-wise variants. A comparative account of lineage-specific mutations in the newer SARS-CoV-2 strains emerging in the UK, South Africa and Brazil and data reported from India identify overlapping and lineages specific acquired mutations suggesting a repetitive convergent and adaptive evolution. Conclusions A novel and dynamic feature of the SARS-CoV-2 module of IPD 2.0 makes it a contemporary tool to analyze the diverse and growing genomic strains of the virus and serve as a vital tool to help facilitate rapid genomic surveillance in a population to identify variants involved in breakthrough infections. IPD 2.0 is freely available from http://www.actrec.gov.in/pi-webpages/AmitDutt/IPD/IPD.html and the web-application is available at http://ipd.actrec.gov.in/ipdweb/.

We present an updated version of our automated computational pipeline, Infection Pathogen Detector IPD 2.0 with a SARS-CoV-2 module, to perform genomic analysis to understand the pathogenesis and virulence of the virus. Analysing the currently available 208911 SARS-CoV2 genome sequences (as accessed on 28 Dec 2020), we generate an extensive database of sample- wise variants and clade annotation, which forms the core of the SARS-CoV-2 analysis module of the analysis pipeline. A comparative account of lineage-specific mutations in the newer SARS-CoV-2 strains emerging in the UK, South Africa and Brazil along with data reported from India identify overlapping and lineages specific acquired mutations suggesting a repetitive convergent and adaptive evolution. Thus, the persistence of pandemic may lead to the emergence of newer regional strains with improved fitness. IPD 2.0 also adopts the recent dynamic clade nomenclature and shows improvement in accuracy of clade assignment, processing time and portability, to its predecessor and thus could be a vital tool to help facilitate genomic surveillance in a population to identify variants involved in breakthrough infections.