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Many COVID-19 studies are about epidemiological and clinical features but information about secondary bacterial infections is limited. The presentstudy was conducted to determine the prevalence andcharacteristics of bloodstream infections in COVID-19 patients admitted to a tertiary care academic healthcare organization. All blood samples were obtained from patients with COVID-19 admitted were includedin the study. Blood cultures were performed using BD BACTEC ™ FX40, and the diagnosis and bacterialidentification and antimicrobial sensitivity was performed by manual method. 2200 patients with COVID-19 were hospitalized during a 6-month study period in which 315 blood cultures were performed. Of these,24 (7.61%) were positive. The median age of patients with positive blood culture was 38 years and included13 males and 11 females. Seventeen patients (70.83%) needed intensive care in the ICU. Significantcorrelations with blood culture positivity have been noted with parameters such as admission to the ICU,availability of accommodation, basic illness and adverse clinical outcomes. Bloodstream infectionsprevalence in COVID-19 patients is low. However, antibiotic prophylaxis needs to be used with caution, andimmediate discontinuation should be made based on clinical judgment.

Purpose: Leukemia can be detected by an abnormal rise in the number of immature lymphocytes and by a decrease in the number of other blood cells. To diagnose the existence of leukemia, image processing techniques are utilized to examine microscopic peripheral blood smear (PBS) images automatically and swiftly.Methods: To the best of our knowledge, the initial step in subsequent processing is a robust segmentation technique for identifying leukocytes from their surroundings. The paper presents the segmentation of leukocytes in which three color spaces are considered in this study for image enhancement. The proposed algorithm uses marker-based watershed algorithm and peak local maxima.Results: The algorithm was used on three different datasets with various color tones, image resolutions, and magnifications. The average precision for all three-color spaces was same i.e. 94% but Structural Similarity Index Metric (SSIM) and recall of HSV was better than other two.Conclusion: The results of this study will aid experts in narrowing down their options for segmenting leukemia. Based on the comparison, it was concluded that when the colour space correction technique is used, the accuracy of the proposed methodology improves.

We compared the effectiveness of non-invasive ventilation (NIV) provided by helmet mask vs face mask in patients with COVID-19.Between March and May 2021, a single-center, prospective, open-label randomized controlled research was undertaken. Sixty patients were randomly assigned to one of two groups based on the NIV delivery interface. In group I (In both groups, demographics, clinical characteristics, and treatment received were comparable. Around 10% of patients in the helmet mask group were intubated, while 43.3% of patients in the face mask group were intubated (Helmet mask could be a reliable interface for delivery of NIV in COVID-19 and results in a lower rate of endotracheal intubation, better oxygenation with greater patient comfort and shorter ICU stay as compared to face mask used for NIV.Saxena A, Nazir N, Pandey R, Gupta S. Comparison of Effect of Non-invasive Ventilation Delivered by Helmet vs Face Mask in Patients with COVID-19 Infection: A Randomized Control Study. Indian J Crit Care Med 2022;26(3):282-287.

Introduction: Epidural corticosteroid injections are widely used to treat low back pain, but doubts exist about the relative efficacy of particulate versus non-particulate corticosteroids. Epidural corticosteroid injections were performed in 75 patients with chronic radicular pain were evaluated for epidural corticosteroid injections to determine if there was a difference in the efficacy of triamcilone acetate, methylprednisolone acetate, and dexamethasone. Methods: 75 patients presenting with debilitating radicular pain were randomized to receive an injection of triamcilone acetate 40 mg/ml, methylprednisolone acetate 40 mg/ml, and dexamethasone phosphate 7.5 mg/ml at equal doses. Data were collected at 1-month and 3-month follow-up. The primary outcome of the present study was reduction in pain on a visual analog scale (VAS) at 3 months, while the secondary outcome was the type and number of complications in the study group. Results: Regardless of baseline score VAS, pain score decreased in all patients at one and three months. The patients with VAS of very severe also showed a statistically significant success rate at one and three month follow-up [p= 0.043]. No serious complications occurred in all three groups. Conclusion: According to this study, pain relief and functional improvement are similar among all three methylprednisolone acetate, triamcilone acetate and dexamethasone phosphate at 3 months.

In academic achievement, student engagement is considered a prime factor to enhance motivation among students. Student engagement is a constructive approach that refers to the level of concentration, inquisitiveness, and enthusiasm that students demonstrate when they learn. The study aims to elucidate the development of a three-dimensional construct of student engagement based on psycho-education oriented measures such as behavioural, cognitive and emotional engagement. Data from 470 students studying in science, humanities, and engineering streams from different universities of Punjab, was collected via random sampling technique. The factor analysis of the scale reveals that 31 items had three factors, behavioural, cognitive, and affective engagement. Good internal reliability (????=0.889) and adequate convergent and discriminant validities are reported, which establishes good psychometric properties of the scale.

As a part of a growing information society, nowadays the issue of security is more crucial than ever. In order to achieve high level of security, the potential of accurately recognize subjects based on their unique measurable physiological or behavioral characteristics has been receiving an increased concern by the research and development community. As biometrics has advanced, iris has been considered a preferred trait because unique pattern texture, lifetime stability, and regular shape contribute to good segmentation and recognition performance. The incredible uniqueness of iris patterns as well as the ability to capture iris images non-invasively has motivated us to develop automated system for iris recognition based on 2-D iris images. The 2DPCA (two-dimensional Principal Component Analysis) and GA (Genetic Algorithm) have been used as feature extraction and feature selection techniques for reducing the dimensionality of iris features without the loss of relevant Information. The Back Propagation Neural Network (BPNN) is implemented using Levenberg–Marquardt’s learning rule for iris recognition. The experimental results illustrated that the 2DPCA-GA achieved a high classification accuracy of 96.40 %.

Purpose To evaluate the survival rate (fatigue resistance), bonding efficiency and marginal integrity of monolithic zirconia partial and full coverage single restorations adhesively bonded to the tooth structure using air-particle abrasion, a primer with 10-methacryloyloxydecyl dihydrogen phosphate and a composite-resin cement (APC) protocol. Materials and methods Extracted human premolars (N = 32) were randomly divided into four groups of eight specimens each. Premolars were prepared for the following restorations: full crown (group 1, control), mesial-occlusal-distal-facial onlay (MODF, group 2) preserving 2 mm facio-lingual functional cusp width, mesial-occlusal-distal-lingual onlay (MODL, group 3) preserving 2 mm facio-lingual nonfunctional cusp width, mesial-occlusal-distal-buccal-lingual onlay (MODBL, group 4), overlay preparation. All restorations were milled from monolithic 3 mol% yttria (3Y) zirconia blocks (ZirCad, A1 LT, Ivoclar Vivadent) with CAD/CAM software presets at minimum occlusal and axial thicknesses of 1 mm. The intaglio surface of the restorations was air-particle abraded (50 µm Al2 O3 , 2-Bar pressure, 15 s, 10 mm distance) and primed. An adhesive cement system was used to bond the restorations. Each group was subjected to thermomechanical loading for 1.2 million cycles (force = 70 N, 1.4 Hz) with simultaneous thermocycling (5-55°C, 30 s dwell time) using a mastication simulator. All specimens were examined under scanning electron microscope (SEM) analysis (30, 100, and 150×) to evaluate cracks and marginal defects. Fracture of restoration and/or fracture within tooth structure, and debonding were considered modes of failure. Results One specimen from group 2 debonded at 632,000 cycles. None of the specimens failed due to fracture. SEM analysis at 30× indicated marginal integrity issue of the remaining seven intact specimens of group 2 in the area of antagonist contact. No specimens from group 1, 3, and 4 demonstrated marginal integrity issue at 30×. None of the specimens demonstrated any microcrack at 100× and150×. Conclusions Due to its fatigue resistance, 3Y-zirconia is a viable option for partial and full coverage single restorations. Following a strict bonding protocol, zirconia demonstrated durable adhesion to the tooth structure. Occlusal contact on restoration margins should be avoided.

The efficient management of resource sharing plays a crucial role in the cloud execution environment. The constraints such as heterogeneity and dynamic nature of resources need to be addressed towards managing the cloud resources efficiently. The provisioning and scheduling of resources with respect to the tasks depends primarily on the quality of service (QoS) requirements of cloud applications and is a challenging task. For the complete satisfaction of the client, execution of tasks should be as per the QoS parameters; hence a QoS aware cloud framework is required for the purpose mapping of resources efficiently. To handle the complex issue of the resource placement problem, a cloud architectural framework named cloud orchestrated framework for efficient resource placement presents efficient and effective management and placement of resources in the cloud. In this paper, a novel QoS aware resource placement algorithm is proposed based on the social spider mating strategy that manages and places tasks for the computation of resources automatically by optimizing the QoS metrics as a significant feature. The performance of proposed algorithm is evaluated in the cloud and results show that the proposed framework performs better in terms of execution cost, execution time, throughput, and availability, reliability, waiting time, turnaround time, utilization and convergence of cloud resources and utilizes these resources optimally.

Inter-ply fiber hybridization seems to be a very propitious technique to improve the mechanical properties of FRP composites. However, the mechanical response of these hybrid composites under cryogenic conditions has not yet fully explored experimentally. In this research paper, a systematic examination of the flexural behavior of hybrid samples conditioned at ambient temperature, zero, as well as cryogenic conditions has been carried out. In this study, hybrid polymer composites have been pondered by carbon and glass fiber as reinforcement. Fractography of the fractured samples was done by using scanning electron microscopy for featuring failure mechanisms and to validate most of the reasons mentioned in the literature regarding alteration in the properties at each tested condition. These composite materials performance might be governed by polymer relaxation at lower temperatures, matrix cracking, cryogenic hardening, and compressive residual stresses due to the difference in the coefficient of thermal expansion of the fibers and matrix. Fiber hybridization method significantly alleviated the limitations of carbon fiber such as low stain to failure, catastrophic failure, and its cost for the application to cryogenic fuel tanks for space vehicles, support structures of superconducting magnets, etc.

Segmentation of blood cells is a prerequisite step in automated morphological analysis of blood smear images, cell count determination and diagnosis of various diseases such as leukemia. It is extremely challenging due to different sizes, shapes, morphological characteristics and overlapping of blood cells. Due to its complicated nature, it is generally performed as a sequence of steps. However, sequential segmentation results in restricted accuracy due to cascading of errors that creep during each stage. On the contrary, pixel-wise segmentation of blood cells is a single step task and gives promising results. In this paper, we propose LeukoSegmenter, a double encoder-decoder for precise pixel-wise segmentation of leukocytes from blood smear images. It uses pre-trained ResNet18 based encoders and U-Net based decoders. Feature maps obtained from the first network are utilised as attention maps. These are used as input in conjunction with the original 3-channel image to obtain final mask from the second network. This mechanism allows the latter encoder-decoder pair to focus explicitly on leukocytes and ignore other blood cells and debris, thus improving the segmentation accuracy. Experiments on ALL-IDB1 dataset show that the proposed LeukoSegmenter achieves intersection-over-union score of 94.6827% and Dice score of 97.1987% which is superior than that of state-of-the-art methods.

In the past decade, there has been a remarkable evolution of convolutional neural networks (CNN) for biomedical image processing. These improvements are inculcated in the basic deep learning-based models for computer-aided detection and prognosis of various ailments. But implementation of these CNN based networks is highly dependent on large data in case of supervised learning processes. This is needed to tackle overfitting issues which is a major concern in supervised techniques. Overfitting refers to the phenomenon when a network starts learning specific patterns of the input such that it fits well on the training data but leads to poor generalization abilities on unseen data. The accessibility of enormous quantity of data limits the field of medical domain research. This paper focuses on utility of data augmentation (DA) techniques, which is a well-recognized solution to the problem of limited data. The experiments were performed on the Brain Tumor Segmentation (BraTS) dataset which is available online. The results signify that different DA approaches have upgraded the accuracies for segmenting brain tumor boundaries using CNN based model.