Evaluation of Genetic Variations in Maize Seedlings Exposed to Electric Field Based on Protein and DNA Markers

The current study analyzed proteins and nuclear DNA of electric fields (ELF) exposed and nonexposed maize seedlings for different exposure periods using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), isozymes, random amplified polymorphic DNA (RAPD), and comet assay, respectively. SDS-PAGE analysis revealed total of 46 polypeptides bands with different molecular weights ranging from 186.20 to 36.00 KDa. It generated distinctive polymorphism value of 84.62%. Leucine-aminopeptidase, peroxidase, and catalase isozymes showed the highest values of polymorphism (100%) based on zymograms number, relative front (Rf), and optical intensity while esterase isozyme generated polymorphism value of 83.33%. Amino acids were analyzed using high-performance liquid chromatography, which revealed the presence of 17 amino acids of variable contents ranging from 22.65% to 28.09%. RAPD revealed that 78 amplified DNA products had highly polymorphism value (95.08%) based on band numbers, with variable sizes ranging from 120 to 992 base pairs and band intensity. Comet assay recorded the highest extent of nuclear DNA damage as percentage of tailed DNA (2.38%) and tail moment unit (5.36) at ELF exposure of maize nuclei for 5 days. The current study concluded that the longer ELF exposing periods had genotoxic stress on macromolecules of maize cells and biomarkers used should be augmented for reliable estimates of genotoxicity after exposure of economic plants to ELF stressors

Efficient Hybrid Algorithm for Human Action Recognition

Recently, researchers have sought to find the ideal way to recognize human actions through video using artificial intelligence due to the multiplicity of applications that rely on it in many fields. In general, the methods have been divided into traditional methods and deep learning methods, which have provided a qualitative leap in the field of computer vision. Convolutional neural network CNN and recurrent neural network RNN are the most popular algorithms used with images and video. The researchers combined the two algorithms to search for the best results in a lot of research. In an attempt to obtain improved results in motion recognition through video, we present in this paper a combined algorithm, which is divided into two main parts, CNN and RNN. In the first part there is a preprocessing stage to make the video frame suitable for the input of both CNN networks which consist of a fusion of Inception-ResNet-V2 and GoogleNet to obtain activations, with the previously trained wights in Inception-ResNet-V2 and GoogleNet and then passed to a deep Gated Recurrent Units (GRU) connected to a fully connected SoftMax layer to recognize and distinguish the human action in the video. The results show that the proposed algorithm gives better accuracy of 97.97% with the UCF101 dataset and 73.12% in the hdmb51 data set compared to those present in the related literature.</jats:p

Biotyping, virulotyping and biofilm formation ability of ESBL-Klebsiella pneumoniae isolates from nosocomial infections

Abstract The aim of this study was to investigate the frequency, molecular characterization, virulence genes, resistance genes and antimicrobial profile of nosocomial extended spectrum beta lactamase producing Klebsiella species. A total of 22 (12.2%) K. pneumoniae strains were isolated from 180 clinical samples collected from hospitalized patients in Egypt. K. pneumoniae biotypes were B1 (72.8%), B3 (13.6%) and B4 (13.6%). The isolates were classified for the capsular serotypes, 86.4% (20/22) were of K1 serotype, while only two isolates (13.64%) were of K2 serotype. Hypermucoviscous K. pneumoniae isolates accounted for 68.2%. Biofilm formation ability of K. pneumoniae was determined by microtitre plate method. The majority of the isolates (40.9%) were moderate biofilm producers, while 27.3% were strong biofilm producers. All K. pneumoniae strains were positive for fimH and traT genes, while magA was identified in only 63.6% of the isolates. The antibiotic susceptibility profile of the isolates (n = 22) was determined by the disc diffusion technique using 23 different antibiotics. Streptomycin and imipenem are the most effective antibiotics against 22 tested K. pneumoniae isolates with sensitivity rates of 63.64% and 54.54% respectively. All tested K. pneumoniae isolates showed high resistance to amoxicillin∕clavulanate (100%), cefuroxime (100%) and ceftazidime (95.45%). Extended spectrum beta lactamases (ESBL) production and the presence of ESBL-related genes were tested in the isolates. All the isolates tested positive for blaVIM, NDM1 and blaTEM, while only 81.8 %tested positive for the blaSHV gene. Increasing antimicrobial resistance in K. pneumoniae causing nosocomial infections limits the use of antimicrobial agents for treatment. Furthermore, the spread of biofilm, multiple drug resistant and ESBL-producing K. pneumoniae isolates is a public threat for hospitalized patients.</jats:p

Application of Rough Sets to Predict the Breast Cancer Risk Association with Routine Blood Analyses

For women around the globe, breast cancer has been a significant cause of mortality. Around the same time, early diagnosis and high cancer prediction precision are critical to improving the quality of care and the recovery rate of the patient. Expert systems and machine learning techniques are gaining prominence in this area as a result of efficient classification and high diagnostic ability. This paper introduces a model of hybrid prediction (RS QA) based on a rough set theoryand a quasi-optimal (AQ) rule induction algorithm. To find a minimal set of attributes that completely define the results, a rough set tool is used. The selected characteristics were collected, ensuring the high standard of the classification. Then to produce the decision rules, we use the quasi-optimal (AQ) rule induction algorithm. These hybrid prediction models allow expert systems to be built based on the conceptual rules of the IF THEN sort. The suggested experiment is performed using the Coimbra Breast Cancer Dataset (BCCD) based on sets of measures that can be obtained in routine blood tests. Using classification precision, sensitivity, specificity, and receiver operating characteristics (ROC) curves, the efficiency of our suggested approach was assessed. Experimental results indicate the highest classification accuracy (91.7 percent), sensitivity (83.3 percent), and precision (94.3) obtained by the proposed (RS_QA) model.</jats:p

TEVAR Versus Optimal Medical Therapy for Uncomplicated Type B Aortic Dissection: A Systematic Review & Meta-analysis

Background: This meta-analysis reviewed the outcomes of patients with uncomplicated Type B aortic dissection (unTBAD) treated with either thoracic endovascular aortic repair (TEVAR) plus optimal medical therapy (OMT) or OMT alone. The study evaluated both short-term and long-term outcomes to assess whether TEVAR improved overall mortality and reduced complications such as retrograde type A dissection, stroke, paraplegia, and aortic remodeling. Methods: We conducted comprehensive searches on PubMed, Ovid, Scopus, and Excerpta Medica Database (EMBASE) to identify studies comparing long-term outcomes of TEVAR and OMT for unTBAD. Using the Population, Intervention, Comparator, and Outcome (PICO) framework and adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we formulated the search query. The search was limited to titles and abstracts and included thorough citation reviews. Each study was evaluated against strict inclusion and exclusion criteria for eligibility. Statistical analysis was performed using Jamovi (Version 2.5) from the Jamovi project (2024). Results: The total number of patients with unTBAD in our analysis was 25,650, with 21,041 receiving optimal medical therapy (OMT) and 4,609 treated with TEVAR. The OMT group had an average age of 61.7 years, with 69.7% treated in the acute phase (&lt;15 days). Their mean maximum aortic diameter was 38.5 mm, and the false lumen (FL) diameter averaged 25.4 mm. In the TEVAR group, the average age was 59.7 years, with 76.2% treated acutely. The average maximum aortic diameter was 40.2 mm, and the FL diameter was 26.4 mm. A pooled analysis showed no significant difference in 30-day and inhospital mortality rates (log odds ratio 0.1058, P = 0.4971). However, a random-effects model indicated a log odds ratio of -1.3696, resulting in an odds ratio of 0.2542 (P &lt; 0.0001). Survival rates were 95.4% for the OMT group and 98% for the TEVAR group. At 1 year, survival was 90.2% for the OMT group (95% confidence interval [CI]: 85.7-90.7) and 94% for the TEVAR group (95% CI: 90.6-97.5). At 2 years, OMT survival was 71.8% (95% CI: 63.4-80.1) compared to 83.7% for TEVAR (95% CI: 75.6-91.8). At 3 years, survival was 82.2% for OMT (95% CI: 77.4-87) and 89.9% for TEVAR (95% CI: 86.1-93.7). Conclusion: The role of TEVAR in managing unTBAD remains debated. While studies show improved aortic remodeling post-TEVAR, there is no definitive evidence for increased survival rates. It is essential to conduct randomized trials and develop guidelines that include high-risk features for assessing this complex patient group

Outcomes of Vascular and Endovascular Interventions Performed During the Coronavirus Disease 2019 (COVID-19) Pandemic: The Vascular and Endovascular Research Network (VERN) Covid-19 Vascular Service (COVER) Tier 2 Study

The aim of the COVER Study is to identify global outcomes and decision making for vascular procedures during the pandemic

Outcomes following vascular and endovascular procedures performed during the first COVID-19 pandemic wave

Objective: The first COVID-19 pandemic wave was a period of reduced surgical activity and redistribution of resources to only those with late stage or critical presentations. This Vascular and Endovascular Research Network COVID-19 Vascular Service (COVER) study aimed to describe the six-month outcomes of patients who underwent open surgery and or endovascular interventions for major vascular conditions during this period. Methods: In this international, multicentre, prospective, observational study, centres recruited consecutive patients undergoing vascular procedures over a 12-week period. The study opened in March 2020 and closed to recruitment in August 2020. Patient demographics, procedure details, and post-operative outcomes were collected on a secure online database. The reported outcomes at 30 days and six months were post-operative complications, re-interventions, and all cause in-hospital mortality rate. Multivariable logistic regression was used to assess factors associated with six-month mortality rate. Results: Data were collected on 3 150 vascular procedures, including 1 380 lower limb revascularisations, 609 amputations, 403 aortic, 289 carotid, and 469 other vascular interventions. The median age was 68 years (interquartile range 59, 76), 73.5% were men, and 1.7% had confirmed COVID-19 disease. The cumulative all cause in-hospital, 30-day, and six-month mortality rates were 9.1%, 10.4%, and 12.8%, respectively. The six-month mortality rate was 32.1% (95% CI 24.2e40.8%) in patients with confirmed COVID-19 compared with 12.0% (95% CI 10.8e13.2%) in those without. After adjustment, confirmed COVID-19 was associated with a three times higher odds of six-month death (adjusted OR 3.25, 95% CI 2.18e4.83). Increasing ASA grade (3e5 vs. 1e2), frailty scores 4e9, diabetes mellitus, and urgent and or immediate procedures were also independently associated with increased odds of death by six months, while statin use had a protective effect. Conclusion: During the first wave of the pandemic, the six-month mortality rate after vascular and endovascular procedures was higher compared with historic pre-pandemic studies and associated with COVID-19 disease