Database Files Research Articles

Impact of cannabis on outcome in patients following traumatic injury

IntroductionOutcomes of trauma patients who tested positive for cannabis at the time of admission showed variable results. Sample size and research methodology that was used in prior studies may have resulted in the conflict. The purpose of the study was to evaluate the impact of cannabis use on outcomes in trauma patients using national data. Our hypothesis was that the use of cannabis will impact outcomes. MethodsThe trauma quality improvement program (TQIP) Participant Use File (PUF) database of the calendar years 2017 and 2018 were accessed for the study. All trauma patients aged 12 years old and above who were tested for cannabis at the time of initial evaluation were included in the study. Variables included in the study were: race, sex, injury severity score (ISS), Glasgow Coma Scale (GCS) Score, Abbreviated Injury Scale (AIS) score of different body regions and comorbidities. Excluded from the study were all patients who were not tested for cannabis or tested for cannabis but were also tested positive for alcohol and other drugs and those suffering from mental conditions. Propensity matched analysis was performed. The outcome of interest was overall in-hospital mortality and complications. ResultsPropensity matched analysis created 28,028 pairs. The analysis showed no significant difference in-hospital mortality between cannabis positive and cannabis negative groups (3.2% vs. 3.2%). The median length of hospital stay in both groups was not significantly different (4 [IQR: 3–8] vs. 4 [IQR: 2–8] days). No significant difference was found between the two groups regarding hospital complications except in pulmonary embolism (PE) with 0.1% less incidence of PE in the cannabis positive group compared to the cannabis negative group (0.4 vs. 0.5%). The incidence of DVT was identical in both groups (0.9% vs. 0.9%). ConclusionCannabis was not associated with overall in-hospital mortality or morbidity. There was a slight decrease in the incidence of PE in the cannabis positive group.

Imputation accuracy for genomic selection using embryo biopsy samples in Gir

The aim of this study was to establish a platform for genomic selection of in vitro-fertilized (IVF) Gir embryos. Multiple displacement amplification (MDA)-based embryo biopsy samples were genotyped, and genomic estimated breeding values (GEBV) for milk yield (305MY) were calculated. The concordance of GEBV and accuracy between embryo biopsies and the respective liveborn were assessed. Imputation was performed using two panels (Z-Chip and Bovine HD, Illumina) based on a database of 73,110 lactating cow’s database and pedigree files from 147,131 animals. Biopsied embryos had similar pregnancy rates (39% vs 40%), pregnancy loss rates (18% vs 20%), and pregnancy length compared to Control embryos. After genotyping, low call rate means were detected for biopsy samples compared to the respective calf samples (0.80 vs 0.98). Imputation presented 0.83 (Z-Chip) and 0.96 (HD) accuracy (CORRanim). Embryo GEBV accuracy levels were higher in BovineHD imputation (0.82) than Z-Chip imputation (0.55) or no imputation (0.62), and the correlation between embryo/calf pairs’ accuracy was 0.85 for BovineHD imputation, 0.11 for Z-Chip imputation, and 0.02 for no imputation. GEVB estimates correlation between embryo/calf pairs was 0.87 for BovineHD imputation, 0.80 for Z-Chip imputation, and 0.41 before imputation. The call rate of embryo samples did not affect the correlation between embryo/calf pairs for accuracy and GEBV before and after BovineHD imputation. Embryos obtained on the same farm presented GEBV 305MY differences of up to 800 kg, emphasizing the expected impact of embryo genomic selection for the Gir breed.

Prevalence of Stone Disease and Procedure Trends in the United States

To evaluate the prevalence of stone disease and procedure trends among US commercial and Medicare populations. Retrospective analyses for a US commercial population and Medicare population were conducted using the Merative MarketScan Commercial Database and Medicare Standard Analytic File (5% sample), respectively. Patients aged 18+ were included if they had an encounter in any setting with a primary stone diagnosis or stone procedure (ureteroscopy [URS], percutaneous nephrolithotomy (PCNL), or shockwave lithotripsy [SWL]) between 2011 and 2019. Analyses were conducted at the patient level. Over the study time horizon, the prevalence of stone disease showed a small yet statistically significant decrease in the commercial population (1.04%-1.01%; P<0.0001) and a statistically significant increase in the Medicare population (1.84%-2.34%; P<0.0001). URSprocedure volumes increased by 22.6% (P<0.0001) in the commercial population and by 56.6% (P<0.0001) in the Medicare population over the study period. Similarly, PCNLprocedure volumes increased by 17.4% (P<0.0001) in the commercial population and 27.5% (P<0.0001) in the Medicare population. Procedure volumes for SWL decreased by 26.9% in the commercial population (P<0.0001) and by 3.8% in the Medicare population although the results were not statistically significant. The prevalence of stone disease showed a small decrease in the US commercial population and an increase in the Medicare population over the study time horizon. In both populations, increasing procedure volumes were observed for URS and PCNL while decreasing volumes were observed for SWL.

The Materials Provenance Store

We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages the metadata and experimental provenance from acquisition of raw materials, through synthesis, to a broad range of materials characterization techniques. Given the primary research goal of materials discovery of solar fuels materials, many of the characterization experiments involve electrochemistry, along with optical, structural, and compositional characterizations. The MPS is populated with all information required for executing common data queries, which typically do not involve direct query of raw data. The result is a database file that can be distributed to users so that they can independently execute queries and subsequently download the data of interest. We propose this strategy as an approach to manage the highly heterogeneous and distributed data that arises from materials science experiments, as demonstrated by the management of over 30 million experiments run on over 12 million samples in the present MPS release.

MP37-15 PERCUTANEOUS TIBIAL NEUROMODULATION INITIAL THERAPY COMPLIANCE AND SUBSEQUENT THIRD-LINE TREATMENT PATTERNS

You have accessJournal of UrologyCME1 Apr 2023MP37-15 PERCUTANEOUS TIBIAL NEUROMODULATION INITIAL THERAPY COMPLIANCE AND SUBSEQUENT THIRD-LINE TREATMENT PATTERNS Priya Padmanabhan, Bernadette Zwaans, Ryan Boldt, and Charlotte Wu Priya PadmanabhanPriya Padmanabhan More articles by this author , Bernadette ZwaansBernadette Zwaans More articles by this author , Ryan BoldtRyan Boldt More articles by this author , and Charlotte WuCharlotte Wu More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003275.15AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Percutaneous Tibial Neuromodulation (PTNM) is a therapy for Overactive Bladder (OAB) delivered over 12 consecutive weekly sessions. However, little is known about the application and adherence of this treatment in a real-world setting. This analysis aims to summarize patients’ time to complete treatment course and examine trends of third-line therapy use--Sacral Neuromodulation (SNM) and Botox®--during and after the treatment course. METHODS: Optum’s Clinformatics® Data Mart (CDM) Database and CMS Research Identifiable File (RIF) healthcare claims were queried for adults diagnosed with urgency, frequency and urge incontinence and underwent PTNM treatment during 2019—allowing for a 1-year follow-up period. Patients were required to have continuous enrollment and pharmacy coverage. PTNM treatment within 30 days of a previous treatment was defined as part of the initial therapy. Compliance was defined as 12 PTNM-related visits. Primary outcomes were percentage of compliant PTNM-related visits within 1 year and total number of patients with SNM and Botox® within the same year, segmented by 12-, 15-, 22-, and 52-week periods. RESULTS: 3,087 patients were included from CDM and 9,727 patients from RIF data. Completion of initial treatment (12 treatments) increased over time; 16% at week 12 to 42% by week 52 and 24% to 38% for CDM and RIF data, respectively (Figure 1; both p<0.001). Similarly, utilization of third-line therapy increased over time and was higher for non-compliant patients: 0.2% of compliant and 0.8% of non-compliant CDM patients used Botox within 12 weeks. However, 4.9% of compliant and 6.4% of non-compliant CDM patients used Botox within 52 weeks; p=0.035. Similarly, SNM implant within 12 weeks was <1% for CDM patients, and within 52 weeks were 1.2% of compliant and 2.6% of non-compliant patients; p=0.002. Patients in the RIF data followed similar trends. CONCLUSIONS: Most patients are not compliant with the recommended treatment regimen of 12 PTNM treatments in 12 weeks. Non-compliant patients were more likely to pursue third-line treatment options. Signaling the effectiveness of PTNM therapy for patients with OAB symptoms may be severely underestimated. This is valuable in consideration of implantable tibial technology. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e520 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Priya Padmanabhan More articles by this author Bernadette Zwaans More articles by this author Ryan Boldt More articles by this author Charlotte Wu More articles by this author Expand All Advertisement PDF downloadLoading ...

Respiratory Syncytial Virus Outbreak in Infants and Young Children during COVID-19 Pandemic in Taiwan

Respiratory syncytial virus (RSV) is a major burden of disease in babies and young children, including hospitalizations and deaths. RSV is a seasonal disease that peaks when temperatures decrease in temperate zones and humidity increases in tropical regions. Existing research reveals that RSV hospitalization activity is year-round in Taiwan, which is a subtropical region with small peaks in spring and fall. The monthly distribution and COVID-19 pandemic impact were unclear. The aim of this study was to investigate Taiwan's RSV hospitalization seasonality and the COVID-19 pandemic effects. The National Health Insurance Database and Death Registration Files from the Center for Health and Welfare Data Science Center were connected to birth data for this study. RSV hospitalization (RSVH) in infants aged 0-1 years ranged from 0.9518% (2009) to 1.7113% (2020), substantially higher than in children aged 1-5. Most years had 2 or 3 RSV epidemic seasons in 0-5-year-olds over the 13-year follow-up. RSVH incidence was low until the autumn of 2020, when a major rise occurred after September and lasted until December 2020. We detected RSVH peaks in February-May and July-August. The 2020 RSV outbreak was found at the end of 2020.

Computational Design for Identification of Human Anti-MUC1 Heteroclitic Peptides in the Treatment of HER2-Positive Breast Cancer through Neural Network Training and Monomeric based Design.

Generation of the human anti-MUC1 peptide through neural network training and monomeric design method. Analyzing 9-mer peptide potential computationally for treatment of HER2-positive breast cancer. With the advancements of cancer genome atlas project (TCGA), cancer dependancy project (DepMap) and human protein atlas (HPA), large-scale datasets are generated for oncology studies. However, after development of redefined breast cancer drug targets, there are key issues in successful breast cancer treatments that needed to be pursued which paved the pathway for new approaches or strategies. In that respect, our research data aimed to represent a new aspect of breast cancer drug development studies. Extract human MUC1 sequences from various databases. Perform neural networking method for novel peptides sequences. Analyze the potentiality of generated heteroclitic peptide sequences for suitable vaccine candidate for breast cancer treatment. Input scaffolds of protein database (PDB) files for human MUC1 were retrieved and loaded into Evo design server with monomeric based design option. Further, neural network training approaches were followed and other computational tools were used for alignment-independent prediction of protective antigens and subunit vaccines potency of designed heteroclitic peptides. Study findings revealed two human anti-MUC1 heteroclitic peptides of 9mers (WAVWTYVSV, FMSFYIMNL), which showed the lowest energy cluster and sequence identity, normalized relative error rate of secondary structure, solvent accessibility, backbone torsion angles for neural networking and RMSD values in evolutionary profiling, and online MHCPred IC50 interaction values. VaxiGen v2.0 server revealed subunit vaccine potency values of in-silico designed two heteroclitic peptides were 0.1551 (WAVWTYVSV) and 0.3508 (FMSFYIMNL) with a threshold value of 0.5 followed by AllerTOP v2.0 for their allergenicity nature in immunogenic reactions. Computationally designed heteroclitic peptide WAVWTYVSV indicated promising values which can be utilised as drug delivery or tumour marker candidate in the treatment of human breast cancer by eliciting lyse of tumor cells.

Network based Intrusion Detection System using the SPSS Method

An invasion detection system (NIDS) that makes use of networks can spot malicious activities on a network. For NIDS to inspect each and every packet of communication, including unicast traffic, invalid network access is often required. Along with being small structures that don't block traffic, NIDS are able to keep an eye on usernames and passwords, check for any signs of odd activity in the file integrity logs, port logs, mysql database file storage, etc. and alert the part of implementing as necessary. Since NIDS keeps an eye on live data, it can spot problems as they happen. On the other hand, HIDS uses historical data to identify skilled hackers who employ cutting-edge methods that are difficult to detect in real-time. A monitoring system called an intrusion notification system (IDS) looks for abnormal activity and sends out alarms when it does. A Counter Terrorism Center (SOC) analyst senior incident handler can look into the issue and take the necessary steps to resolve the threat based on these notifications. Every incoming packets are read by an NIDS, which scans them for any anomalous activity. Depending on the gravity of the danger, steps can be taken, such as alerting system administrators or preventing the initial IP address from connecting to the network. In the past decades, due to developments Increased use of networking techniques and internet, digital. Contacts entered into everything that happens on the world market. The hacker penetration attempts are taking place in parallel with these advances. Networks are expanding as well. Without permission, they attempted to modify some network data or increase network traffic in order to launch a denial-of-service attack. Intrusion prevention systems (IDS) are also favoured, especially for detecting malicious within a network system, even though a firewall may appear like an useful solution to avoid this type of attack. Machine learning algorithms have helped IDS become more successful in recent years, depending on the consequences of the training/learning process. Knowing what is important is very difficult and the learning algorithm is fast according to the problem Type. The complexity of the task, the size of the data sets, the number of nodes, the network design, the intended error rate, etc. all affect the choice of algorithm. Examine several network training functions in artificial neural networks with numerous layers created to provide effective intrusion detection systems. test outcomes The study provides evidence of the usefulness of the approach. Considering their speed and true-positive detection rates Death Penalty. SPSS statistics is multivariate analytics, business intelligence, and criminal investigation data management, advanced analytics, developed by IBM for a statistical software package. A long time, spa inc. Was created by, IBM purchased it in 2009. The brand name for the most recent versions is IBM SPSS statistics. Network Security, Intrusion Detection System, Neural Networks and Training Functions. The Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .860which indicates 86% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis. Emotional Intelligence the Cronbach's Alpha Reliability result. The overall Cronbach's Alpha value for the model is .860which indicates 86% reliability. From the literature review, the above 50% Cronbach's Alpha value model can be considered for analysis.

Using cloud infrastructure to facilitate data collection and conversion of HLA diagnostic data for the 18th International HLA and Immunogenetics Workshop.

The International HLA and Immunogenetics Workshop (IHIW) is a recurring gathering of researchers, technologists and clinicians where participants contribute to collaborative projects with a variety of goals, and come to consensus on definitions and standards for representing HLA and immunogenic determinants. The collaborative and international nature of these workshops, combined with the multifaceted goals of several specific workshop components, necessitates the collection and curation of a wide assortment of data, as well as an adaptable platform for export and analysis. With the aim of ensuring data quality and creation of reusable datasets, specific standards and nomenclature conventions are continuously being developed, and are an integral part of IHIW. Here we present the 18th IHIW Database, a purpose-built and extensible cloud-based file repository and web application for collecting and analyzing project-specific data. This platform is based on open-source software and uses established HLA data standards and web technologies to facilitate de-centralized data repository ownership, reduce duplicated efforts, and promote continuity for future IHIWs.

Emergency department burden of hyperemesis gravidarum in the United States from 2006 to 2014

Hyperemesis gravidarum is the most severe form of nausea and vomiting of pregnancy, potentially affecting both maternal and pregnancy health. Hyperemesis gravidarum often results in emergency department visits, although the exact frequency and costs associated with these visits have not been well studied. This study aimed to analyze the trends in hyperemesis gravidarum emergency department visits, inpatient admissions, and the associated costs between 2006 and 2014. Patients were identified from the 2006 and 2014 Nationwide Emergency Department Sample database files using International Classification of Diseases, Ninth Revision diagnosis codes. Patients with a primary diagnosis of hyperemesis gravidarum, nausea and vomiting of pregnancy, and all nondelivery pregnancy-related diagnoses (all antepartum visits) were identified. All groups were analyzed; trends in demographics, number of emergency department visits, and visit costs were compared. Costs were inflation-adjusted to 2021 US dollars. Emergency department visits for hyperemesis gravidarum increased by 28% from 2006 to 2014; however, the proportion of those who were subsequently admitted to the hospital decreased. The average cost of an emergency department visit for hyperemesis gravidarum increased by 65% ($2156 to $3549), as opposed to an increase of 60% for all antepartum visits ($2218 to $3543). The aggregate cost for all hyperemesis gravidarum visits increased by 110% ($383,681,346 to $806,696,513) from 2006 to 2014, which was similar to the increase observed for all antepartum emergency department visits. From 2006 to 2014, emergency department visits for hyperemesis gravidarum increased by 28%, with associated costs increasing by 110%, whereas the number of admissions from the emergency department for hyperemesis gravidarum decreased by 42%.

Embedded Devices Security: Design and Implementation of a Light RDBMS Encryption Utilizing Multi-Core Processors

The pervasive proliferation of embedded, mobile, and IoT devices continue to change our lifestyle dramatically. However, the huge increase in these devices has come with critical breaches to data resting inside them. Many types of such data are considered to be sensitive and confidential. Because the most sensitive data of such devices are resting in databases, focusing on encrypting SQLite databases will be more efficient than full disk encryption (FDE). While SQLite is a very popular, lightweight, and easy-to-use relational database suitable for embedded and mobile devices, its stored data suffers serious security risks. If an attacker can gain access to higher system privileges or find a way to access the database plain file, he can tamper with the database files and user-sensitive data, which breaches the security CIA triad of SQLite. To ensure data confidentiality in SQLite databases of embedded devices, we present a design and implementation of a parallel database encryption system, called SQLite-XTS. The developed system encrypts the database pages on-the-fly in a transparent manner without user intervention. Because performance is a critical issue, SQLite-XTS utilizes multi-core processors coming with most current mobile and embedded devices. The developed parallel SQLite-XTS was successfully implemented and integrated into a testbed device. To assess the performance and feasibility of this system, it was compared to three other SQLite implementations: plain SQLite, serial XTS SQLite, and SQLCipher-CBC. The results show that SQLite-XTS reduces the overhead of database encryption from 30.8% with serial implementation to 17.8% when SQLite-XTS is used. This provides the developed system with an efficiency of 73% compared with its serial counterpart. The results clarify that SQLite-XTS introduces significant performance improvements compared to other implementations. Experiments also show that the system has a very low impact on the memory of these resource-limited devices.

Електроакустичний тракт детектора для виявлення малих безпілотних літальних апаратів

Small unmanned aerial vehicles have become increasingly popular in recent years for both commercial and recreational use. However, with the onset of war, the threat of using such drones for illegal purposes such as reconnaissance operations and terrorist attacks on the most vulnerable and important targets — military equipment, government buildings, airports or places with a large concentration of people — has rapidly increased. As a result, many methods have been developed for detecting drones, but the most promising of these is considered to be the acoustic method. This article discusses the implementation of a simple and inexpensive acoustic detector with a single microphone, enabling the detection of the sound emitted by the drone engines and propellers, which can then be analyzed to determine the location and trajectory of the unmanned aircraft. To register sound waves, it was proposed to use the electret microphone CMA-4544PF-W with a foam windscreen, and to ensure a wide dynamic range and protect the detector from overload, an amplifier with an automatic gain control system with feedback regulation based on the integrated circuit MAX9814. For further digital signal processing on a PC, an analog-to-digital converter with a sampling frequency of 48 kHz and a bit depth of 16 bits will be used, as well as an external sound card CM6206 with a line input. As a result of test flights, a database of audio files of the noise of the DJI Mavic 2 Pro quadcopter was created, which is now actively used by the military for reconnaissance, the accuracy of object detection by the acoustic detector was studied. The distance from the drone to the microphone affected the accuracy of detection, and the maximum value of reliable detection was 40 meters. The device successfully performs its functions, given its low cost and ease of use, but requires improvement to enhance its characteristics. The spectra of the obtained audio recordings of quadcopter emissions make it possible to determine the main frequencies of tones, the number of which coincides with the number of electric motors and are important features for further identification of the drone.

Sintesis dan Karakterisasi Fe₂TiO₅ dari Besi Oksida dan Titanium Dioksida dengan Metode Planetary Ball-Mill

The method of solid hydrogen storage through integration in metal hydride compounds is a new, promising method. This research has succeeded in synthesizing and characterizing the metal hydride alloy Fe-Ti system through a planetary ball-mill process. Based on the XRF analysis, it was found that the composition of Fe oxide content reached 56.22% while Ti oxide reached 43.78%. The FTIR study showed absorption at wavenumbers of 900 cm-1 and 500 cm-1. Both are typical absorptions for Ti-O and Fe-O groups, suggesting that the synthesized sample was successful. It can be seen from the XRD data that some of the diffractogram peaks of the resulting Fe-Ti compound match the peaks of the Fe2TiO5 and TiO2 compounds obtained from the Powder Diffraction File (PDF) database number 00-003-0374. After the refinement was performed, information was obtained that the synthesis of Fe-Ti alloy material using the planetary ball mill technique produced Fe2TiO5.

Romatizmal hastalığı olan bireylerde COVID-19 klinik özelliklerinin ve sonuçlarının değerlendirilmesi

features and course of coronavirus disease 2019 (COVID-19) in individuals with rheumatic disease.&#x0D; Materials and Methods: This retrospective study was carried out at the Erciyes University rheumatology outpatient clinic from July 1 to August 1, 2021. The demographic and clinical data and summarized COVID-19 history, clinical course of COVID-19, fatigue, and pain levels of patients with rheumatic disease were obtained from our institutional electronic registration database and patient files. &#x0D; Results: Recruited participants were 106 individuals (83% female, %17 male) with rheumatic disease who had been confirmed by laboratory tests to have COVID-19 and recovered from the COVID-19 infection. Their mean age and body mass index (BMI) were 48.69±11.5 years and 29.89±6.76 kg/m2, respectively. Additionally, 21 (19.8%) had been hospitalized, and five (4.7%) had been admitted to the intensive care unit. The most common rheumatic diseases were axial spondyloarthritis (40; 37.7%) and rheumatoid arthritis (26 cases; 24.5%). Patients who received conventional synthetic disease-modifying drugs (csDMARDs) reportedly experienced more pain, fatigue, and headaches than those in the biologic agent and non-steroidal anti-inflammatory drug (NSAID) groups. &#x0D; Conclusion: Our study results reveal similar symptoms and hospitalization rates among patients with rheumatic disease who recovered from COVID-19 and received either csDMARDs, biologic agents, or NSAIDs. However, patients in the csDMARD group reported more pain, fatigue, and headache compared to the other groups.

Profiling the BLAST bioinformatics application for load balancing on high-performance computing clusters

BackgroundThe Basic Local Alignment Search Tool (BLAST) is a suite of commonly used algorithms for identifying matches between biological sequences. The user supplies a database file and query file of sequences for BLAST to find identical sequences between the two. The typical millions of database and query sequences make BLAST computationally challenging but also well suited for parallelization on high-performance computing clusters. The efficacy of parallelization depends on the data partitioning, where the optimal data partitioning relies on an accurate performance model. In previous studies, a BLAST job was sped up by 27 times by partitioning the database and query among thousands of processor nodes. However, the optimality of the partitioning method was not studied. Unlike BLAST performance models proposed in the literature that usually have problem size and hardware configuration as the only variables, the execution time of a BLAST job is a function of database size, query size, and hardware capability. In this work, the nucleotide BLAST application BLASTN was profiled using three methods: shell-level profiling with the Unix “time” command, code-level profiling with the built-in “profiler” module, and system-level profiling with the Unix “gprof” program. The runtimes were measured for six node types, using six different database files and 15 query files, on a heterogeneous HPC cluster with 500+ nodes. The empirical measurement data were fitted with quadratic functions to develop performance models that were used to guide the data parallelization for BLASTN jobs.ResultsProfiling results showed that BLASTN contains more than 34,500 different functions, but a single function, RunMTBySplitDB, takes 99.12% of the total runtime. Among its 53 child functions, five core functions were identified to make up 92.12% of the overall BLASTN runtime. Based on the performance models, static load balancing algorithms can be applied to the BLASTN input data to minimize the runtime of the longest job on an HPC cluster. Four test cases being run on homogeneous and heterogeneous clusters were tested. Experiment results showed that the runtime can be reduced by 81% on a homogeneous cluster and by 20% on a heterogeneous cluster by re-distributing the workload.DiscussionOptimal data partitioning can improve BLASTN’s overall runtime 5.4-fold in comparison with dividing the database and query into the same number of fragments. The proposed methodology can be used in the other applications in the BLAST+ suite or any other application as long as source code is available.

660: RECURRENT GUILLAIN-BARRE SYNDROME: CLINICAL PROFILE AND OUTCOME OF CHILDREN FROM A NORTH INDIAN PICU

Introduction: Guillian-Barre Syndrome (GBS) is an acute flaccid ascending paralysis, usually considered a monophasic illness. Recurrences do occur in an undefined group of patients. However, there is a dearth of literature on recurrent GBS, especially in the pediatric age group. We studied the subgroup of recurrent GBS in children and compared the clinical, neurological, treatment and outcomes of these children with non-recurrent GBS. We also compared different episodes of recurrent GBS. Methods: Patients’ case records were retrieved retrospectively from the electronic database and case record files. All children diagnosed with GBS admitted to PICU from 2010 to March 2022 were screened. Clinical profile, nerve conduction study, treatment and outcome details were recorded. Recurrent GBS cases, as defined by the National Institute of Neurological and Communicative Diseases and Strokes (NINCDS) criteria, were analyzed separately. The comparison was made between the first episode of recurrent GBS with non-recurrent GBS, and predictors of recurrence were identified. A comparison of course and outcome was also done between different episodes of GBS in recurrent cases. Results: 11(4.7%) out of 234 cases admitted to PICU between 2010 and March 2022 had recurrent GBS. Respiratory (p-0.015) and gastrointestinal illness (0.007) as preceding illnesses were predictors of recurrence. No difference was noted between the first episode of recurrent GBS and non-recurrent GBS. The first and second episodes of GBS in 11 recurrent cases were similar in course and outcome. Conclusions: Recurrent GBS is underreported but a known entity. Respiratory and gastrointestinal illness in first-episode predicted recurrence. We did not observe any difference between the 2 episodes of recurrent GBS. However, more data is required to find the features of recurrent GBS so that limited PICU resources can be used judiciously and intervention planned accordingly.

Utilization of Chest and Abdominopelvic CT for Traumatic Injury From 2011 to 2018: Evaluation Using a National Commercial Database.

BACKGROUND. Increases in the use of CT to evaluate patients presenting with trauma have raised concern about inappropriate imaging. The evolving utilization of CT for trauma evaluation may be impacted by injury severity. OBJECTIVE. The purpose of this study was to explore patterns in utilization of chest and abdominopelvic CT among trauma-related emergency department (ED) visits across the United States. METHODS. This retrospective study was conducted with national commercial claims information extracted from the MarketScan Commercial Database. Trauma-related ED encounters were identified from the 2011-2018 MarketScan database files and classified by injury severity score (minor, intermediate, and major injuries) on the basis of International Classification of Diseases codes. ED encounters were also assessed for chest CT, abdominopelvic CT, and single-encounter chest and abdominopelvic CT examinations. Utilization per 1000 trauma-related ED encounters was determined. Multivariable Poisson regression models were used to determine incidence rate ratios (IRRs) as a measure of temporal changes in utilization. RESULTS. From 2011 to 2018, 8,369,092 trauma-related ED encounters were identified (5,685,295 for minor, 2,624,944 for intermediate, and 58,853 for major injuries). Utilization of chest CT per 1000 trauma-related ED encounters increased from 4.9 to 13.5 examinations (adjusted IRR, 1.15 per year; minor injuries, from 2.2 to 7.7 [adjusted IRR, 1.17]; intermediate injuries, from 8.5 to 21.5 [adjusted IRR, 1.16]; major injuries, from 117.8 to 200.1 [adjusted IRR, 1.08]). Utilization of abdominopelvic CT per 1000 trauma-related ED encounters increased from 7.5 to 16.4 (adjusted IRR, 1.12; minor injuries, 4.8 to 12.2 [adjusted IRR, 1.13]; intermediate injuries, 10.6 to 21.7 [adjusted IRR, 1.13]; major injuries, 134.8 to 192.6 [adjusted IRR, 1.07]). Utilization of single-encounter chest and abdominopelvic CT per 1000 trauma-related ED encounters increased from 3.4 to 8.9 [adjusted IRR, 1.16; minor injuries, 1.1 to 4.6 [adjusted IRR, 1.18]; intermediate injuries, 6.4 to 16.4 [adjusted IRR, 1.16]; major injuries, 99.6 to 179.9 [adjusted IRR, 1.08]). CONCLUSION. National utilization of chest and abdominopelvic CT for trauma-related ED encounters increased among commercially insured patients from 2011 to 2018, particularly for single-encounter chest and abdominopelvic CT examinations and for minor injuries. CLINICAL IMPACT. Given concerns about increased cost and detection of incidental findings, further investigation is warranted to explore the potential benefit of single-encounter chest and abdominopelvic CT examinations of patients with minor injuries and to develop strategies for optimizing appropriateness of imaging orders.

Personal Authentication Based on Curvelet Transform of Palm Print Moments

Image transformation provide deep meaning about images feature, so many type of image transformation are appear in the last decade years, one of them is curvelet transformation which improve the image processing techniques specially in field of feature extraction. Personal authentication adopt biometric information to be one of the major coefficients in this field.&#x0D; Palm print one of the main approaches for personal identification. So studying the moments extracted from coefficients of curvelet transform of palm print image adopted in order to get high efficient system for personalization systems. Two major phases are constructed in this research to adopt the moments of low frequency coefficient of the curvelet for personal identification. In the first phase a database was built for 50 persons by acquisition nine images for both hands (9 for left hand and 9for right hand). images are acquired and then processed to extract ROI (region of interest) by looking for the palm centroid then a square shape will be fixed based on that centroid. This preprocess play an important step for stable features. Histogram is applied to the images and then apply SOBLE operator and morphological operation to highlight features of palm print, then apply decomposition on each image based on curvelet transformation. Select low frequency coefficient (which hold the details). Evaluation of seven moments for each image (18images) then store there in the database file (so each person will have 126 values), this phase called personal database preparation. While the second phase is the detection phase, which apply the same steps to evaluate the moments as done in first phase then go through the database looking for the closest person to the tested one.&#x0D; System evaluation measured by statistical metrics which show good result goes up to 96% when applied on 50 person with different acquisition conditions. Also the effect of ROI dimension with individual hands and integrated both of them studied, which yield to recommended dimension of 192*192.

Survival rates and outcomes of pregnancies with prenatal diagnosis of trisomy 18: A 16-year experience from a public hospital in South Africa.

Many studies, largely from high-income countries (HIC), have reported outcomes in babies with trisomy 18 (T18), with a paucity of data from Africa. Knowledge of outcomes is important in counselling women prenatally diagnosed with T18. We aimed to review all prenatally diagnosed cases of T18 between January 2006 and December 2021. Demographic data, diagnosis, gestation and outcome data were obtained from the Astraia® database and patient files. We included 88 pregnant women of whom 46 terminated their pregnancies (30 beyond 24weeks' gestation). Three underwent foeticides, one had a caesarean section for maternal obstetric reasons and 26 underwent inductions of labour without foetal monitoring. Four neonates were live born but none lived >8h. In those who continued their pregnancies, the mean gestation at delivery was 34.8weeks, 14 (33%) were live births and only 5 survived for >24h with none surviving to 1year of life. In our cohort, infants with T18 had lower live birth rates and shorter survival than in the current literature from HIC. This may be due to the implementation of non-aggressive intrapartum care and comfort care for the neonates. This has implications for counselling in our setting.

Evaluating the performance of machine-learning regression models for pharmacokinetic drug-drug interactions.

Combination therapy or concomitant drug administration can be associated with pharmacokinetic drug-drug interactions, increasing the risk of adverse drug events and reduced drug efficacy. Thus far, machine-learning models have been developed that can classify drug-drug interactions. However, to enable quantification of the pharmacokinetic effects of a drug-drug interaction, regression-based machine learning should be explored. Therefore, this study investigated the use of regression-based machine learning to predict changes in drug exposure caused by pharmacokinetic drug-drug interactions. Fold changes in exposure relative to substrate drug monotherapy were collected from 120 clinical drug-drug interaction studies extracted from the Washington Drug Interaction Database and SimCYP compound library files. Drug characteristics (features) were collected such as structure, physicochemical properties, in vitro pharmacokinetic properties, cytochrome P450 metabolic activity, and population characteristics. Three different regression-based supervised machine-learning models were then applied to the prediction task: random forest, elastic net, and support vector regressor. Model performance was evaluated using fivefold cross-validation. Strongest performance was observed with support vector regression, with 78% of predictions within twofold of the observed exposure changes. The results show that changes in drug exposure can be predicted with reasonable accuracy using regression-based machine-learning models trained on data available early in drug discovery. This has potential applications in enabling earlier drug-drug interaction risk assessment for new drug candidates.