Recent advances in genome editing strategies for balancing growth and defence in sugarcane (Saccharum officinarum).

Sugarcane (Saccharum officinarum ) has gained more attention worldwide in recent decades because of its importance as a bioenergy resource and in producing table sugar. However, the production capabilities of conventional varieties are being challenged by the changing climates, which struggle to meet the escalating demands of the growing global population. Genome editing has emerged as a pivotal field that offers groundbreaking solutions in agriculture and beyond. It includes inserting, removing or replacing DNA in an organism's genome. Various approaches are employed to enhance crop yields and resilience in harsh climates. These techniques include zinc finger nuclease (ZFN), transcription activator-like effector nuclease (TALEN) and clustered regularly interspaced short palindromic repeats/associated protein (CRISPR/Cas). Among these, CRISPR/Cas is one of the most promising and rapidly advancing fields. With the help of these techniques, several crops like rice (Oryza sativa ), tomato (Solanum lycopersicum ), maize (Zea mays ), barley (Hordeum vulgare ) and sugarcane have been improved to be resistant to viral diseases. This review describes recent advances in genome editing with a particular focus on sugarcane and focuses on the advantages and limitations of these approaches while also considering the regulatory and ethical implications across different countries. It also offers insights into future prospects and the application of these approaches in agriculture.

Vaccination Training for Pharmacy Undergraduates as a Compulsory Part of the Curriculum?-A Multicentric Observation.

In order to increase vaccination rates, the Government of Germany introduced vaccination against influenza and COVID-19 into the regular care administered by pharmacists. However, vaccination training is yet not integrated into the German pharmacy curriculum. Therefore, the Institute for Clinical Pharmacy and Pharmacotherapy in Duesseldorf had developed an innovative vaccination course using high-fidelity simulation for students. To investigate the acceptance further, the course was carried out at three different German universities (Bonn, Duesseldorf, Greifswald). Students were asked to give their self-assessment before and after and satisfaction only after the training course. Responses from 33 participants from the University of Bonn, 42 from the University of Duesseldorf and 49 from the University of Greifswald were analyzed. Every participant at the respective universities showed a significant increase in their self-assessment and indicated a high level of satisfaction with the course. The results also did not differ significantly between the respective universities. Consequently, the results lead to the hypothesis that the satisfaction of pharmacy students with this kind of training using high-fidelity simulation is very high and attractive, and can be recommended for other German universities. The integration of such vaccination training into the German pharmacy curriculum might be a future step.

Method development for simultaneous estimation of Amlodipine Besylate and Perindopril Tertbutyl amine in fixed-dose.

The fixed-dose combination of Amlodipine Besylate (ADB) with Perindopril Tertbutylamine (PTBA) drug is used to treat patients with mild-to-moderate hypertension. In recent times researchers are interested to find the efficient analytical method development and validation for the simultaneous determination of ADB and PTBA in a fixed-dose, film-coated tablet. Therefore, the current study was performed with a reverse-phase liquid chromatography method developed to simultaneously analyze ADB and PTBA in film-coated tablets as fixed-dose combinations. The linearity of the proposed method was calculated by preparing six different mixtures of both ADB and PTBA in the mobile phase. The concentration of both the analytes was analyzed at 56mg/100 mL to 84mg/100 mL and 32mg/100 mL to 48mg/100 mL, respectively. The ratio of acetonitrile and phosphate buffer was 35:65. The flow rate was adjusted to 1.5 ml per minute to reduce the retention time. The validation study was performed for the parameters specificity, linearity, precision, range, limit of detection, limit of quantification, accuracy/biasness, and robustness. The relative percentage standard deviation for Perindopril Tertbutyl amine was 0.148%, and for Amlodipine is 0.312%. These results show that the advanced analysis method for simultaneous analysis of fixed-dose is precise. The theoretical IR spectra were also calculated by Gaussian 9.2 by employing the B3LYP functional at density functional theory (DFT) level study. All these parameters studied in this work authenticate the effectiveness of the developed validation method and ensure its repeatability/reproducibility accordingly. To the best of our knowledge, this is the first time to develop a new fast, and easy method for simultaneous identification and quantification of ADB and PTBA by high-performance liquid chromatography (HPLC) with a time-efficient and cost-effective approach.

Development and Assessment of Innovative High-Fidelity Simulation Vaccination Course Integrating Emergency Cases for Pharmacy Undergraduates-A Randomized Controlled Study.

Recently, pharmacists in Germany were allowed to administer influenza and COVID-19 vaccines for people aged 12 years and older in order to increase vaccination coverage rates. In order to adapt the pharmacy curriculum for clinical practice, an innovative, vaccination training course using a high-fidelity simulator (HFS) was developed, implementing clinical scenarios to manage adverse events. In a randomized controlled trial using a pre and post design with pharmacy undergraduates, the intervention group interacted with an HFS, while the control group was trained with low-fidelity injection pads. Before and after the respective training, each participant went through an objective structured clinical examination (OSCE) and completed a self-assessment questionnaire and knowledge quiz. Both training methods showed a significant increase in skills, but there was also a significant greater increase in the intervention group when compared to the control group, particularly with respect to the vaccination process. Furthermore, every individual in the intervention group improved from the pre- to post-training OSCEs. Therefore, HFS has been proven to be an appropriate tool to train pharmacy students for the purposes of vaccine administration and to prepare for future challenges. Particularly, recognizing and managing adverse reactions can be addressed in a very effective way.

Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans.

Accurate radiogenomic classification of brain tumors is important to improve the standard of diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we propose a novel two-stage MGMT Promoter Methylation Prediction (MGMT-PMP) system that extracts latent features fused with radiomic features predicting the genetic subtype of glioblastoma. A novel fine-tuned deep learning architecture, namely Deep Learning Radiomic Feature Extraction (DLRFE) module, is proposed for latent feature extraction that fuses the quantitative knowledge to the spatial distribution and the size of tumorous structure through radiomic features: (GLCM, HOG, and LBP). The application of the novice rejection algorithm has been found significantly effective in selecting and isolating the negative training instances out of the original dataset. The fused feature vectors are then used for training and testing by k-NN and SVM classifiers. The 2021 RSNA Brain Tumor challenge dataset (BraTS-2021) consists of four structural mpMRIs, viz. fluid-attenuated inversion-recovery, T1-weighted, T1-weighted contrast enhancement, and T2-weighted. We evaluated the classification performance, for the very first time in published form, in terms of measures like accuracy, F1-score, and Matthews correlation coefficient. The Jackknife tenfold cross-validation was used for training and testing BraTS-2021 dataset validation. The highest classification performance is (96.84 ± 0.09)%, (96.08 ± 0.10)%, and (97.44 ± 0.14)% as accuracy, sensitivity, and specificity respectively to detect MGMT methylation status for patients suffering from glioblastoma. Deep learning feature extraction with radiogenomic features, fusing imaging phenotypes and molecular structure, using rejection algorithm has been found to perform outclass capable of detecting MGMT methylation status of glioblastoma patients. The approach relates the genomic variation with radiomic features forming a bridge between two areas of research that may prove useful for clinical treatment planning leading to better outcomes.

Automated facial characterization and image retrieval by convolutional neural networks.

Introduction: Developing efficient methods to infer relations among different faces consisting of numerous expressions or on the same face at different times (e.g., disease progression) is an open issue in imaging related research. In this study, we present a novel method for facial feature extraction, characterization, and identification based on classical computer vision coupled with deep learning and, more specifically, convolutional neural networks. Methods: We describe the hybrid face characterization system named FRetrAIval (FRAI), which is a hybrid of the GoogleNet and the AlexNet Neural Network (NN) models. Images analyzed by the FRAI network are preprocessed by computer vision techniques such as the oriented gradient-based algorithm that can extract only the face region from any kind of picture. The Aligned Face dataset (AFD) was used to train and test the FRAI solution for extracting image features. The Labeled Faces in the Wild (LFW) holdout dataset has been used for external validation. Results and discussion: Overall, in comparison to previous techniques, our methodology has shown much better results on k-Nearest Neighbors (KNN) by yielding the maximum precision, recall, F1, and F2 score values (92.00, 92.66, 92.33, and 92.52%, respectively) for AFD and (95.00% for each variable) for LFW dataset, which were used as training and testing datasets. The FRAI model may be potentially used in healthcare and criminology as well as many other applications where it is important to quickly identify face features such as fingerprint for a specific identification target.

Recent Development of Fluorescent Nanodiamonds for Optical Biosensing and Disease Diagnosis.

The ability to precisely monitor the intracellular temperature directly contributes to the essential understanding of biological metabolism, intracellular signaling, thermogenesis, and respiration. The intracellular heat generation and its measurement can also assist in the prediction of the pathogenesis of chronic diseases. However, intracellular thermometry without altering the biochemical reactions and cellular membrane damage is challenging, requiring appropriately biocompatible, nontoxic, and efficient biosensors. Bright, photostable, and functionalized fluorescent nanodiamonds (FNDs) have emerged as excellent probes for intracellular thermometry and magnetometry with the spatial resolution on a nanometer scale. The temperature and magnetic field-dependent luminescence of naturally occurring defects in diamonds are key to high-sensitivity biosensing applications. Alterations in the surface chemistry of FNDs and conjugation with polymer, metallic, and magnetic nanoparticles have opened vast possibilities for drug delivery, diagnosis, nanomedicine, and magnetic hyperthermia. This study covers some recently reported research focusing on intracellular thermometry, magnetic sensing, and emerging applications of artificial intelligence (AI) in biomedical imaging. We extend the application of FNDs as biosensors toward disease diagnosis by using intracellular, stationary, and time-dependent information. Furthermore, the potential of machine learning (ML) and AI algorithms for developing biosensors can revolutionize any future outbreak.

Percutaneous Repair of Mitral Regurgitation: A Comprehensive Review of Literature.

Mitral regurgitation is the most common valvular disease in the US and the second most common worldwide. Left untreated, it can lead to the development of heart failure, giving rise to increased mortality rates. Mitral valve intervention is usually indicated in severe mitral regurgitation at the onset of symptoms, even if the function of the left ventricle is preserved. A surgical approach is generally favored according to current guidelines, with excellent clinical outcomes. However, the emergence of novel data from contemporary trials indicates that percutaneous, catheter-based approach may have similar improvements in mortality outcomes while maintaining a superior safety profile when compared to the surgical approach. Here, we discuss transcatheter mitral valve repair as a treatment option for mitral regurgitation and summarize the major clinical trials which were recently conducted on transcatheter repair.

Quantitative auto-fluorescence quenching of free and bound NADH in HeLa cell line model with Carbonyl cyanide-p-Trifluoromethoxy phenylhydrazone (FCCP) as quenching agent.

The autofluorescence of endogenous biomolecules (Nicotinamide adenine dinucleotide (NAD, its reduced form NADH and the phosphorylated form NAD(P)H take part in cellular metabolic pathways and has vital importance for in vivo and ex vivo photo diagnostic applications of biological tissues. We present a detailed quenching analysis of Carbonyl cyanide-p-Trifluoromethoxy phenylhydrazone (FCCP) 50-1000 µM and analyzed the fluorescence signal from NADH/ NAD(P)H in vitro (in solution) and in vivo (HeLa cell suspension).The in vitro samples of pure NADH/ NAD(P)H were excited at λ=340±1 nm while the fluorescence signal was collected in the range of 400-550 nm. The quenching process was characterized using excitation emission matrix (EEM) fluorescence spectroscopy and Stern- Volmer plots. The experimental results illustrated maximum fluorescence emission for the control NADH samples (i.e., no FCCP), while the fluorescence signal from the solution progressively decreased with the increasing concentration of the FCCP, until it reaches the base line (i.e., no fluorescence signal) at 1000 µM of FCCP. In vitro study shows that the fluorescence quenching of free NADH was found to be lower than the bound NAD(P)H with similar diminishing trend. The quenching of bound NAD(P)H in cells is attenuated compared to solution quenching possibly due to a contribution from the metabolic/antioxidant response in cells and fluorescence exponential decay curve lies between plated and suspended HeLa cells. A two-fold increase in the fluorescence intensity of NAD(P)H was observed after the bond formation with L-Malate Dehydrogenase (L-MDH, Sigma Aldrich #10127248001) protein This work has applications for sharp tumor demarcation during sensitive surgical procedures as well as to enhance fluorescence based diagnosis of biological tissues.

A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI.

Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.

External Validation of 4C ISARIC Mortality Score in Critically ill COVID-19 Patients from Saudi Arabia.

Background: ISARIC mortality score is a risk stratification tool that helps predict the in-hospital mortality of COVID-19 patients. However, this tool was developed and validated in a British population, and thus, the external validation of this tool in local populations is important. Objectives: External validation of the ISARIC mortality score in COVID-19 patients from a large Saudi Arabian intensive care unit (ICU). Methods: This is a retrospective study that included all adult patients with COVID-19 admitted to the ICU of King Saud Medical City, Riyadh, Saudi Arabia, from March 2020 to June 2021. Patients who were pregnant or had pulmonary tuberculosis/human immunodeficiency virus were excluded along with patients with missing variables. Data were collected to calculate the ISARIC mortality score and then fitting receiver operator characteristic curve against patients' outcome. Results: A total of 1493 critically ill COVID-19 patients were included. The mortality was 38%, the area under the curve of the score was 0.81 (95% confidence interval [CI]: 0.79-0.83, P < 0.001) and the cutoff value correctly classified 72.7% of the cohort. The cutoff value of >9 had sensitivity of 70.5% (95% CI: 66.6-74.3); specificity, 73.97% (95% CI: 71-76.8); positive predictive value, 62.4% (95% CI: 59.5-65.2) and negative predictive value, 80.2% (95% CI: 78.2-82.4). Conclusion: The ISARIC score was found to have excellent predictive ability for mortality in critically ill COVID-19 patients in our Saudi Arabian cohort. A cutoff score of >9 was the optimal criterion.

Mineral biofortification of vegetables through soil-applied poultry mortality compost.

Intensive agricultural practices lower soil fertility, particularly micronutrients which are rarely applied to soils as chemical fertilizers. Micronutrient deficiency in soils results in inferior product quality and micronutrient malnutrition in humans. Application of compost to soil may improve crop yields and quality by enhancing macro- and micronutrients availability, enhancing soil microbial population, and improving soil physicochemical properties. Poultry mortality compost (PMC) was prepared by decomposing dead poultry birds with poultry litter in an aerated bin through indigenous microbial populations. The prepared PMC was used as an amendment in three field experiments during 2017-18 and 2018-19 to investigate the effect on yield and nutritional quality of potato, carrot, and radish. In these field trials, two compost levels, i.e., 1250 kg ha-1 (PMC1) and 1850 kg ha-1 (PMC2) were compared with the control (no compost application). The results revealed a 10-25% increase in root or tuber yield at PMC2 compared to that in the control. A substantial increase in Zn, Fe, and Mn concentrations in vegetable root/tubers was also observed. Organic matter content and microbial biomass were improved in the soil with PMC application leading to better soil health and better nutrient availability. These studies led us to conclude that the application of PMC not only enhances the vegetable yield but also biofortifies vegetables with micronutrients such as Zn, Fe, and Mn extending agricultural sustainability and eliminating micronutrient malnutrition in humans.

Concentration and Localization of Fe and Zn in Wheat Grain as Affected by Its Application to Soil and Foliage.

Nutritional status of people can be improved by enhancing zinc (Zn) and iron (Fe) content in cereals used as staple mainly in poor resource countries. Zinc and Fe were applied through soil and foliage in a study to biofortify wheat grains. Foliar application of both micronutrients increased the growth and grain vigor as compared to soil application and control. Also, foliar application significantly enhanced Zn and Fe concentration in grain pre-dominantly localized in aleurone layer. Exogeneous application of Fe and Zn was found beneficial for plant growth and enhanced Fe and Zn concentrations in grain, however aleurone layer and embryonic region of the grain showed higher accumulations than that in endosperm. Therefore, understanding of physiological and molecular pathways for uptake and localization of Fe and Zn in wheat grains need to be critically examined to improve their concentration in grain to achieve the biofortification targets.

Simulated Annealing-Based Image Reconstruction for Patients With COVID-19 as a Model for Ultralow-Dose Computed Tomography.

The proposed algorithm of inverse problem of computed tomography (CT), using limited views, is based on stochastic techniques, namely simulated annealing (SA). The selection of an optimal cost function for SA-based image reconstruction is of prime importance. It can reduce annealing time, and also X-ray dose rate accompanying better image quality. In this paper, effectiveness of various cost functions, namely universal image quality index (UIQI), root-mean-squared error (RMSE), structural similarity index measure (SSIM), mean absolute error (MAE), relative squared error (RSE), relative absolute error (RAE), and root-mean-squared logarithmic error (RMSLE), has been critically analyzed and evaluated for ultralow-dose X-ray CT of patients with COVID-19. For sensitivity analysis of this ill-posed problem, the stochastically estimated images of lung phantom have been reconstructed. The cost function analysis in terms of computational and spatial complexity has been performed using image quality measures, namely peak signal-to-noise ratio (PSNR), Euclidean error (EuE), and weighted peak signal-to-noise ratio (WPSNR). It has been generalized for cost functions that RMSLE exhibits WPSNR of 64.33 ± 3.98 dB and 63.41 ± 2.88 dB for 8 × 8 and 16 × 16 lung phantoms, respectively, and it has been applied for actual CT-based image reconstruction of patients with COVID-19. We successfully reconstructed chest CT images of patients with COVID-19 using RMSLE with eighteen projections, a 10-fold reduction in radiation dose exposure. This approach will be suitable for accurate diagnosis of patients with COVID-19 having less immunity and sensitive to radiation dose.

Continuous renal replacement therapy with the addition of CytoSorb cartridge in critically ill patients with COVID-19 plus acute kidney injury: A case-series.

Our aim was to investigate continuous renal replacement therapy (CRRT) with CytoSorb cartridge for patients with life-threatening COVID-19 plus acute kidney injury (AKI), sepsis, acute respiratory distress syndrome (ARDS), and cytokine release syndrome (CRS). Of 492 COVID-19 patients admitted to our intensive care unit (ICU), 50 had AKI necessitating CRRT (10.16%) and were enrolled in the study. Upon ICU admission, all had AKI, ARDS, septic shock, and CRS. In addition to CRRT with CytoSorb, all received ARDS-net ventilation, prone positioning, plus empiric ribavirin, interferon beta-1b, antibiotics, hydrocortisone, and prophylactic anticoagulation. We retrospectively analyzed inflammatory biomarkers, oxygenation, organ function, duration of mechanical ventilation, ICU length-of-stay, and mortality on day-28 post-ICU admission. Patients were 49.64 ± 8.90 years old (78% male) with body mass index of 26.70 ± 2.76 kg/m2 . On ICU admission, mean Acute Physiology and Chronic Health Evaluation (APACHE) II was 22.52 ± 1.1. Sequential Organ Function Assessment (SOFA) score was 9.36 ± 2.068 and the ratio of partial arterial pressure of oxygen to fractional inspired concentration of oxygen (PaO2 /FiO2 ) was 117.46 ± 36.92. Duration of mechanical ventilation was 17.38 ± 7.39 days, ICU length-of-stay was 20.70 ± 8.83 days, and mortality 28 days post-ICU admission was 30%. Nonsurvivors had higher levels of inflammatory biomarkers, and more unresolved shock, ARDS, AKI, and pulmonary emboli (8% vs. 4%, P < .05) compared to survivors. After 2 ± 1 CRRT sessions with CytoSorb, survivors had decreased SOFA scores, lactate dehydrogenase, ferritin, D-dimers, C-reactive protein, and interleukin-6; and increased PaO2 /FiO2 ratios, and lymphocyte counts (all P < .05). Receiver-operator-curve analysis showed that posttherapy values of interleukin-6 (cutoff point >620 pg/mL) predicted in-hospital mortality for critically ill COVID-19 patients (area-under-the-curve: 0.87, 95% CI: 0.81-0.93; P = .001). No side effects of therapy were recorded. In this retrospective case-series, CRRT with the CytoSorb cartridge provided a safe rescue therapy in life-threatening COVID-19 with associated AKI, ARDS, sepsis, and hyperinflammation.

Vertical vs horizontal bullying: A need of socio-interactive ethical trainings at workplace.

Bullying at workplaces is a growing problem associated with several antecedents and outcomes of psychological disorders. Extant literature shows the relationship of victimization with the owning-up curriculum to confront bullying, but so far there is little research to cure the issue of bullying through ethics and there is no study to check the impact of ethical lessons and exercises in training materials to curb bullying at workplace. The current paper is based on a literature review of aggression and bullying, and chiefly addresses the HR practices in rescuing the people from peer victimization. Conceptually segregating supervisor-subordinate (vertical) and peer-peer (horizontal) bullying psychology, this paper attempts to see what possible corrective steps can be taken by senior managers, supervisors and psychologists to resolve this critical problem through interior capacities. In this paper, we have developed three propositions for conducting a future empirical investigation in connection to a cohort study. By addressing the counterproductive/deviant behavior, the present report is likely to be beneficial for psychology scientists and HR practitioners in noticing and resolving the bullying issues at the workplace for the well-being of employees.

A review of the medical hyperspectral imaging systems and unmixing algorithms' in biological tissues.

Hyperspectral fluorescence imaging (HFI) is a well-known technique in the medical research field and is considered a non-invasive tool for tissue diagnosis. This review article gives a brief introduction to acquisition methods, including the image preprocessing methods, feature selection and extraction methods, data classification techniques and medical image analysis along with recent relevant references. The process of fusion of unsupervised unmixing techniques with other classification methods, like the combination of support vector machine with an artificial neural network, the latest snapshot Hyperspectral imaging (HSI) and vortex analysis techniques are also outlined. Finally, the recent applications of hyperspectral images in cellular differentiation of various types of cancer are discussed.

Acuities into tolerance mechanisms via different bioassay during Brassicaceae-Alternaria brassicicola interaction and its impact on yield.

Heavy losses by dark leaf spot disease in oilseed Brassica have incited research towards identifying sources of genetic tolerance against causal pathogen, Alternaria brassicicola. Several morpho-molecular parameters were evaluated to test the performance of field mustard and rapeseed genotypes under artificial inoculation with this pathogen. During Brassica-Alternaria interaction, physio-biochemical defense response was witnessed in tolerant genotypes. Two tolerant genotypes (one for field mustard and one for rapeseed), i.e., EC250407 and EC1494 were identified. However, necrotic lesions were more prominent in susceptible genotypes with minimum chlorophyll (chlorophyll a, chlorophyll b and total chlorophyll) and carotenoids contents. Contrary to photosynthetic pigments, increase in total soluble protein (TSP) contents was observed with disease progression in susceptible genotypes. Tolerant genotypes of field mustard and rapeseed displayed remarkable increase in the activities of redox enzyme in infected leaves with least yield loss (6.47% and 5.74%) and disease severity index (DSI) of 2.9 and 2.1, respectively. However, yield/plant showed close association with other morpho-yield parameters, photosynthetic pigments and redox enzymes (superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD)) activities except silique length and TSP. Based on the results of morpho-biochemical analyses, redox enzymes and morphological parameters; their interplay is proposed to determine the tolerance outcome of the Brassica-A. brassicicola interaction.

A comparative study between a single-dose and 24-hour multiple-dose antibiotic prophylaxis for elective hysterectomy.

INTRODUCTION: Surgical site infections (SSIs) are a major health issue in surgical specialties in terms of health care costs and patients' clinical outcomes. At the level of the patient, prolonged hospital stays or readmissions for SSIs, can affect the patient's quality of life. At the level of the health care system, it exhausts the hospital's resources and increases the burden on the medical staff due to the need for continuous wound care, microbiological cultures, laboratory tests and medications. In this study, we assessed the effectiveness of two antibiotic prophylaxis regimens for the prevention of SSIs in patients undergoing elective hysterectomy surgeries. METHODOLOGY: A retrospective cohort, analyzing 141 patients, was conducted between November 2016 and January 2019 at a university hospital. We compared the efficacy of a single dose vs. 24-hour multiple doses of Cefazolin in patients who underwent elective hysterectomy for benign or malignant indications. The secondary objective was to identify potential risk factors associated with SSIs. RESULTS: There was no statistically significant difference between both groups (p = 0.872). Obesity and a laparotomy surgical approach are risk factors to the development of SSIs (p = 0.001 and 0.014, respectively). Other potential risk factors include the duration of hospital stay, the duration of the surgery and the amount of blood loss. CONCLUSIONS: Although the rate of SSIs is not significantly different between both groups, risk stratification can be done after screening patients and the prophylactic regimen must be tailored for each patient in a cost-effective manner and using a multidisciplinary approach.