DCMS: A data analytics and management system for molecular simulation

Molecular Simulation (MS) is a powerful tool for studying physical/chemical features of large systems and has seen applications in many scientific and engineering domains. During the simulation process, the experiments generate a very large number of atoms and intend to observe their spatial and temporal relationships for scientific analysis. The sheer data volumes and their intensive interactions impose significant challenges for data accessing, managing, and analysis. To date, existing MS software systems fall short on storage and handling of MS data, mainly because of the missing of a platform to support applications that involve intensive data access and analytical process. In this paper, we present the database-centric molecular simulation (DCMS) system our team developed in the past few years. The main idea behind DCMS is to store MS data in a relational database management system (DBMS) to take advantage of the declarative query interface (i.e., SQL), data access methods, query processing, and optimization mechanisms of modern DBMSs. A unique challenge is to handle the analytical queries that are often compute-intensive. For that, we developed novel indexing and query processing strategies (including algorithms running on modern co-processors) as integrated components of the DBMS. As a result, researchers can upload and analyze their data using efficient functions implemented inside the DBMS. Index structures are generated to store analysis results that may be interesting to other users, so that the results are readily available without duplicating the analysis. We have developed a prototype of DCMS based on the PostgreSQL system and experiments using real MS data and workload show that DCMS significantly outperforms existing MS software systems. We also used it as a platform to test other data management issues such as security and compression

Severe Thrombocytosis in Chronic Liver Disease Secondary to Iron Deficiency Anemia: A Case Report

Thrombocytopenia is the commonest haematological abnormality seen in chronic liver disease. Thrombocytosis is of two types: Primary and secondary. In secondary form of thrombocytosis usually there is mild to moderate elevation of platelet count. Here, we present a case of 60 year old patient, a known case of chronic liver disease who presented with severe thrombocytosis secondary to iron deficiencyanaemia. Thrombocytosis normalized with treatment of iron deficiency anaemia with parenteral iron

Hypokalemic Quadriparesis Associated with Dengue: A Case Series

Dengue is an important viral cause of febrile illness in tropical and subtropical regions. Manifestations may range from an asymptomatic infection to life threatening hemorrhagic fever and shock syndrome. Neurological presentations of this disease are rare. Here, we are presenting a case series of three confirmedcases of dengue fever with hypokalemic paralysis presenting as acute pure motor reversible quadriparesis. A clinician should keep dengue virus associated hypokalemic paralysis in mind while dealing with a case of fever with quadriparesis

Perception and preferences of second professional undergraduate medical students for pharmacology teaching: a questionnaire based cross-sectional study

Background: Feedback from students provides an opportunity to assess lacunae in current systems of teaching and forms the basis for framing desired modifications in the teaching methodology to enhance the magnitude of learning. This study was undertaken to know the views of students on current methodology of pharmacology teaching and to delineate the required changes to be made in it.Methods: The questionnaire based cross-sectional study was conducted on 167 students of second professional undergraduate medical students. The questionnaire was divided in 2 different parts. Part A consisted 20 multiple choice questions on perception and preferences of students for pharmacology teaching and opinion on changes to be made was taken in the part B of the questionnaire.Results: Pharmacology was marked as one of the most interesting and useful subjects by 49.1% and 67.06% of students respectively. Central nervous system (19.76%) and endocrinology (17.96%) were two most boring systems. The central (35.92%) and autonomic (31.73%) nervous systems were two most difficult systems to understand. The combination of lecture notes and textbooks was the preferred reading materials of 58.68% of students. The most preferred teaching media was the combination of blackboard and chalk with power point presentation (80.24%). Increased use of figures, flow charts and diagrams, inclusion of more clinical examples and interactive classes were marked as suggested reforms to enhance the outcome of lecture classes.Conclusions: This study revealed that students are in favour of a substantial change in the current teaching methodology of pharmacology in place of outdated and useless methods

Predicting vasospasm risk using first presentation aneurysmal subarachnoid hemorrhage volume: A semi-automated CT image segmentation analysis using ITK-SNAP

PURPOSE: Cerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is a significant complication associated with poor neurological outcomes. We present a novel, semi-automated pipeline, implemented in the open-source medical imaging analysis software ITK-SNAP, to segment subarachnoid blood volume from initial CT head (CTH) scans and use this to predict future radiological vasospasm. METHODS: 42 patients were admitted between February 2020 and December 2021 to our tertiary neurosciences center, and whose initial referral CTH scan was used for this retrospective cohort study. Blood load was segmented using a semi-automated random forest classifier and active contour evolution implemented in ITK-SNAP. Clinical data were extracted from electronic healthcare records in order to fit models aimed at predicting radiological vasospasm risk. RESULTS: Semi-automated segmentations demonstrated excellent agreement with manual, expert-derived volumes (mean Dice coefficient = 0.92). Total normalized blood volume, extracted from CTH images at first presentation, was significantly associated with greater odds of later radiological vasospasm, increasing by approximately 7% for each additional cm3 of blood (OR = 1.069, 95% CI: 1.021–1.120; p < .005). Greater blood volume was also significantly associated with vasospasm of a higher Lindegaard ratio, of longer duration, and a greater number of discrete episodes. Total blood volume predicted radiological vasospasm with a greater accuracy as compared to the modified Fisher scale (AUC = 0.86 vs 0.70), and was of independent predictive value. CONCLUSION: Semi-automated methods provide a plausible pipeline for the segmentation of blood from CT head images in aSAH, and total blood volume is a robust, extendable predictor of radiological vasospasm, outperforming the modified Fisher scale. Greater subarachnoid blood volume significantly increases the odds of subsequent vasospasm, its time course and its severity

Moving towards a unified classification of glioblastomas utilizing artificial intelligence and deep machine learning integration

Glioblastoma a deadly brain cancer that is nearly universally fatal. Accurate prognostication and the successful application of emerging precision medicine in glioblastoma relies upon the resolution and exactitude of classification. We discuss limitations of our current classification systems and their inability to capture the full heterogeneity of the disease. We review the various layers of data that are available to substratify glioblastoma and we discuss how artificial intelligence and machine learning tools provide the opportunity to organize and integrate this data in a nuanced way. In doing so there is the potential to generate clinically relevant disease sub-stratifications, which could help predict neuro-oncological patient outcomes with greater certainty. We discuss limitations of this approach and how these might be overcome. The development of a comprehensive unified classification of glioblastoma would be a major advance in the field. This will require the fusion of advances in understanding glioblastoma biology with technological innovation in data processing and organization

Adoption of Improved Crop Management Practices for Enhancing Productivity of Pigeonpea on Farmers' Fields in Kalaburagi District of India

The present study was carried out at Agricultural Research Station and Krishi Vigyan Kendra, Gulbarga district of India, to know the yield gap between improved package and farmers’ practice under Front Line Demonstration. Pigeonpea [Cajanus cajan (L.) Millsp.]. Being one of the major Kharif pulse crop of Karnataka, it is having lower yield in farmer’s field due to multiple constraints. The major constraints of its lower productivity are non-adoption of improved technologies or Improved Crop Management practices. Front line demonstrations on Improved Crop Management practices were conducted at 99 framer’s fields in five adopted villages of Gulbarga district during Kharif seasons of 2010-11 to 2014-15. The Improved Crop Management practices included use of wilt resistant pigeonpea variety (WRP 1 and TS 3R), Seed treatment with Trichoderma (4 gm kg-1 seeds), use of biofertilizers (Rhizobium and PSB), Integrated nutrient management (25:50:0 NPK kg ha-1 + Zinc Sulphate @ 15 kg ha-1 + Sulphur @ 20 kg ha-1) and Integrated Pest Management. The improved technologies recorded a mean yield of 13.54 q ha-1 which was 18.69 percent higher than the yield obtained with farmers practice (11.10 q ha-1), besides having higher mean net income of Rs.22876 ha-1 with a B:C ratio of 2.68 when compared to farmers practice (Rs. 16177 ha-1 and 2.12)

Analyzing historical and future acute neurosurgical demand using an AI-enabled predictive dashboard

Characterizing acute service demand is critical for neurosurgery and other emergency-dominant specialties in order to dynamically distribute resources and ensure timely access to treatment. This is especially important in the post-Covid 19 pandemic period, when healthcare centers are grappling with a record backlog of pending surgical procedures and rising acute referral numbers. Healthcare dashboards are well-placed to analyze this data, making key information about service and clinical outcomes available to staff in an easy-to-understand format. However, they typically provide insights based on inference rather than prediction, limiting their operational utility. We retrospectively analyzed and prospectively forecasted acute neurosurgical referrals, based on 10,033 referrals made to a large volume tertiary neurosciences center in London, U.K., from the start of the Covid-19 pandemic lockdown period until October 2021 through the use of a novel AI-enabled predictive dashboard. As anticipated, weekly referral volumes significantly increased during this period, largely owing to an increase in spinal referrals (p < 0.05). Applying validated time-series forecasting methods, we found that referrals were projected to increase beyond this time-point, with Prophet demonstrating the best test and computational performance. Using a mixed-methods approach, we determined that a dashboard approach was usable, feasible, and acceptable among key stakeholders

Aids to improve understanding of statistical risk in patients consenting for surgery and interventional procedures: A systematic review

BACKGROUND: Informed consent is an essential process in clinical decision-making, through which healthcare providers educate patients about benefits, risks, and alternatives of a procedure. Statistical risk information is difficult to communicate and the effectiveness of aids aimed at supporting this type of communication is uncertain. This systematic review aims to study the impact of risk communication adjuncts on patients' understanding of statistical risk in surgery and interventional procedures. METHODS: A systematic search was performed across Medline, Embase, PsycINFO, Scopus, and Web of Science until July 2021 with a repeated search in September 2022. RCTs and observational studies examining risk communication tools (e.g., information leaflets and audio-video) in adult (age >16) patients undergoing a surgical or interventional procedure were included. Primary outcomes included the objective assessment of statistical risk recall. Secondary outcomes included patient attitudes with respect to statistical information. Due to the study heterogeneity, a narrative synthesis was performed. RESULTS: A total of 4348 articles were identified, and following abstract and full-text screening 14 articles, including 9 RCTs, were included. The total number of adult patients was 1513. The most common risk communication tool used was written information (n = 7). Most RCTs (7/9, 77.8%) showed statistically significant improvements in patient understanding of statistical risk in the intervention group. Quality assessment found some concerns with all RCTs. CONCLUSION: Risk communication tools appear to improve recall of statistical risk. Additional prospective trials comparing various aids simultaneously are warranted to determine the most effective method of improving understanding