Power-Efficient and Highly Scalable Parallel Graph Sampling using FPGAs

Energy efficiency is a crucial problem in data centers where big data is generally represented by directed or undirected graphs. Analysis of this big data graph is challenging due to volume and velocity of the data as well as irregular memory access patterns. Graph sampling is one of the most effective ways to reduce the size of graph while maintaining crucial characteristics. In this paper we present design and implementation of an FPGA based graph sampling method which is both time- and energy-efficient. This is in contrast to existing parallel approaches which include memory-distributed clusters, multicore and GPUs. Our strategy utilizes a novel graph data structure, that we call COPRA that allows time- and memory-efficient representation of graphs suitable for reconfigurable hardware such as FPGAs. Our experiments show that our proposed techniques are 2x faster and 3x more energy efficient as compared to serial CPU version of the algorithm. We further show that our proposed techniques give comparable speedups to GPU and multi-threaded CPU architecture while energy consumption is 10x less than GPU and 2x less than CPU

Temporal Analysis of Antimicrobial Susceptibility in Salmonella: A Three-Year Surveillance Study

AbstractObjective: The study aimed to assess the antimicrobial susceptibility patterns of Salmonella sp. strainsover a three-year period in a tertiary care hospital.Methodology: It was a retrospective observational study. Electronic medical records were utilized fordata retrieval from 2017 to 2019. The study parameters included individuals of all age groups, andgenders diagnosed with typhoid based on positive blood cultures. Identification, speciation, andantimicrobial susceptibility testing adhered to standardized protocols, with statistical analysis conductedusing SPSS 25.0.Results: Among 769 Salmonella isolates, 709 cases were reported in 2019, making it the highestincidence over the study period. Extensively drug-resistant (XDR) strains peaked in 2019, comprisingapproximately 50% of cases, while multiple drug-resistant strains accounted for 25%. Notably, resistanceto ampicillin, ciprofloxacin, co-trimoxazole, and ceftriaxone exhibited a consistent upward trend over thethree-year span. Ciprofloxacin demonstrated the highest resistance, with only 4% sensitivity amongSalmonella isolates.Conclusion: The findings have highlighted a concerning escalation in antimicrobial resistance amongSalmonella Typhi strains in Punjab, Pakistan, particularly evident in the prevalence of extensively drugresistantstrains. Multi-drug and extensively drug-resistant strains of Salmonella are difficult to treat andmay give rise to even more drug resistance if not treated appropriately, leading to a vicious cycle of resistance.Keywords: Typhoid fever, antimicrobial resistance, Drug-resistant strains, public health, Pakista

Cytotoxic Evaluation and Molecular Docking Studies of Aminopyridine Derivatives as Potential Anticancer Agents

Background: The development of resistance to available anticancer drugs is increasingly becoming a major challenge and new chemical entities could be unveiled to compensate for this therapeutic failure. Objectives: The current study demonstrated whether N-protected and deprotected amino acid derivatives of 2aminopyridine could attenuate tumor development using colorectal cancer cell lines. Methods: Biological assays were performed to investigate the anticancer potential of synthesized compounds. The in silico ADME profiling and docking studies were also performed by docking the designed compounds against the active binding site of beta-catenin (CTNNB1) to analyze the binding mode of these compounds. Four derivatives 4a, 4b, 4c, and 4d were selected for investigation of in vitro anticancer potential using colorectal cancer cell line HCT 116. The anti-tumor activities of synthesized compounds were further validated by evaluating the inhibitory effects of these compounds on the target protein beta-catenin through in vitro enzyme inhibitory assay. Results: The docking analysis revealed favorable binding energies and interactions with the target proteins. The in vitro MTT assay on colorectal cancer cell line HCT 116 and HT29 revealed potential anti-tumor activities with an IC50 range of 3.7-8.1µM and 3.27-7.7 µM, respectively. The inhibitory properties of these compounds on the concentration of beta-catenin by ELISA revealed significant percent inhibition of target protein at 100 µg/ml. Conclusion: In conclusion, the synthesized compounds showed significant anti-tumor activities both in silico and in vitro, having potential for further investigating its role in colorectal cancer

Association of ABO blood group with delayed cerebral ischemia and clinical outcomes following aneurysmal subarachnoid hemorrhage in Pakistan

Background: The ABO blood type, due to its various hemostaseologic properties, has been associated with several vascular diseases, including aneurysmal subarachnoid hemorrhage (aSAH). However, the role of ABO blood type in delayed cerebral ischemia (DCI) onset and other clinical outcomes after aSAH is largely unexplored. This study aimed to investigate the association between ABO blood type and outcomes after aSAH, primarily DCI. Methods: A retrospective analysis was made on the data collected from 175 aSAH patients at a tertiary supraregional neurosurgery department over 5 years. Socio-demographic factors, clinical variables (DCI, mFG, WFNS grade, and Glasgow Outcome Scale at discharge), EVD placement, and aneurysm size were analyzed for their association with ABO blood type. Results: DCI was reported in 25% of patients with ‘O’ blood type and 9.6% with ‘non-O’ blood type. A stepwise logistic regression model showed that after adjusting for BMI, mFG, WFNS grade, and EVD placement, ‘O’ type blood group was an independent risk factor for DCI, greatly increasing the risk of DCI as compared to ‘non-O’ type groups (OR = 3.27, 95% CI: 1.21–8.82). Conclusion: This study provides evidence that individuals with ‘O’ blood type may have a higher risk of DCI onset after aSAH. However, further studies are essential to address the limitations of our work and confirm our findings

Global, regional, and national burden of other musculoskeletal disorders, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

Background Musculoskeletal disorders include more than 150 different conditions affecting joints, muscles, bones, ligaments, tendons, and the spine. To capture all health loss from death and disability due to musculoskeletal disorders, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) includes a residual musculoskeletal category for conditions other than osteoarthritis, rheumatoid arthritis, gout, low back pain, and neck pain. This category is called other musculoskeletal disorders and includes, for example, systemic lupus erythematosus and spondylopathies. We provide updated estimates of the prevalence, mortality, and disability attributable to other musculoskeletal disorders and forecasted prevalence to 2050. Methods Prevalence of other musculoskeletal disorders was estimated in 204 countries and territories from 1990 to 2020 using data from 68 sources across 23 countries from which subtraction of cases of rheumatoid arthritis, osteoarthritis, low back pain, neck pain, and gout from the total number of cases of musculoskeletal disorders was possible. Data were analysed with Bayesian meta-regression models to estimate prevalence by year, age, sex, and location. Years lived with disability (YLDs) were estimated from prevalence and disability weights. Mortality attributed to other musculoskeletal disorders was estimated using vital registration data. Prevalence was forecast to 2050 by regressing prevalence estimates from 1990 to 2020 with Socio-demographic Index as a predictor, then multiplying by population forecasts. Findings Globally, 494 million (95% uncertainty interval 431–564) people had other musculoskeletal disorders in 2020, an increase of 123·4% (116·9–129·3) in total cases from 221 million (192–253) in 1990. Cases of other musculoskeletal disorders are projected to increase by 115% (107–124) from 2020 to 2050, to an estimated 1060 million (95% UI 964–1170) prevalent cases in 2050; most regions were projected to have at least a 50% increase in cases between 2020 and 2050. The global age-standardised prevalence of other musculoskeletal disorders was 47·4% (44·9–49·4) higher in females than in males and increased with age to a peak at 65–69 years in male and female sexes. In 2020, other musculoskeletal disorders was the sixth ranked cause of YLDs globally (42·7 million [29·4–60·0]) and was associated with 83 100 deaths (73 600–91 600). Interpretation Other musculoskeletal disorders were responsible for a large number of global YLDs in 2020. Until individual conditions and risk factors are more explicitly quantified, policy responses to this burden remain a challenge. Temporal trends and geographical differences in estimates of non-fatal disease burden should not be overinterpreted as they are based on sparse, low-quality data.publishedVersio

COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries

Widespread acceptance of COVID-19 vaccines is crucial for achieving sufficient immunization coverage to end the global pandemic, yet few studies have investigated COVID-19 vaccination attitudes in lower-income countries, where large-scale vaccination is just beginning. We analyze COVID-19 vaccine acceptance across 15 survey samples covering 10 low- and middle-income countries (LMICs) in Asia, Africa and South America, Russia (an upper-middle-income country) and the United States, including a total of 44,260 individuals. We find considerably higher willingness to take a COVID-19 vaccine in our LMIC samples (mean 80.3%; median 78%; range 30.1 percentage points) compared with the United States (mean 64.6%) and Russia (mean 30.4%). Vaccine acceptance in LMICs is primarily explained by an interest in personal protection against COVID-19, while concern about side effects is the most common reason for hesitancy. Health workers are the most trusted sources of guidance about COVID-19 vaccines. Evidence from this sample of LMICs suggests that prioritizing vaccine distribution to the Global South should yield high returns in advancing global immunization coverage. Vaccination campaigns should focus on translating the high levels of stated acceptance into actual uptake. Messages highlighting vaccine efficacy and safety, delivered by healthcare workers, could be effective for addressing any remaining hesitancy in the analyzed LMICs.Publisher PDFPeer reviewe

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease