Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

Thoracic paravertebral block for analgesia after modified radical mastectomy

Background: Surgical intervention is associated with postoperative pain, nausea and vomiting. Paravertebral blockade (PVB) has been advocated as a useful technique for analgesia after breast surgery. Aims and Objectives: The aim is to study the efficacy of PVB and associated complications against intramuscular diclofenac sodium 0.75mg. Materials and Methods: Fifty patients of ASA grade I and II were randomized to receive either PVB (group A) or intramuscular diclofenac sodium (group B); there were 25 patients in each group. Group A patients received PVB with catheter at T3 and T6 levels with 0.3ml/kg 0.25% bupivacaine, whereas group B patients received intramuscular diclofenac sodium preoperatively. All patients were observed for quality and duration of analgesia, incidence of nausea and vomiting, hemodynamic stability, and complication. Results: The patients given PVB experienced lower visual analog score (VAS) at rest (P < 0.001) and longer duration of analgesia (P < 0.001) on movement (P < 0.0001) for 1 to 12 h in postoperative period as compared to group B. In group A, fewer patients required rescue analgesia and experienced less postoperative nausea and vomiting as compared to group B. Conclusion: PVB provides better pain control and decreased nausea and vomiting after modified radical mastectomy

The whole genome sequences and experimentally phased haplotypes of over 100 personal genomes

Background: Since the completion of the Human Genome Project in 2003, it is estimated that more than 200,000 individual whole human genomes have been sequenced. A stunning accomplishment in such a short period of time. However, most of these were sequenced without experimental haplotype data and are therefore missing an important aspect of genome biology. In addition, much of the genomic data is not available to the public and lacks phenotypic information. Findings: As part of the Personal Genome Project, blood samples from 184 participants were collected and processed using Complete Genomics’ Long Fragment Read technology. Here, we present the experimental whole genome haplotyping and sequencing of these samples to an average read coverage depth of 100X. This is approximately three-fold higher than the read coverage applied to most whole human genome assemblies and ensures the highest quality results. Currently, 114 genomes from this dataset are freely available in the GigaDB repository and are associated with rich phenotypic data; the remaining 70 should be added in the near future as they are approved through the PGP data release process. For reproducibility analyses, 20 genomes were sequenced at least twice using independent LFR barcoded libraries. Seven genomes were also sequenced using Complete Genomics’ standard non-barcoded library process. In addition, we report 2.6 million high-quality, rare variants not previously identified in the Single Nucleotide Polymorphisms database or the 1000 Genomes Project Phase 3 data. Conclusions: These genomes represent a unique source of haplotype and phenotype data for the scientific community and should help to expand our understanding of human genome evolution and function. Electronic supplementary material The online version of this article (doi:10.1186/s13742-016-0148-z) contains supplementary material, which is available to authorized users

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic “cognitive” functions that we associate with our mind, such as “learning” and “solving problem”. New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors