Culture-Based Identification of Causative Organisms in Ascitic Fluids of Patients with Spontaneous Bacterial Peritonitis Secondary to Decompensated Liver Disease and their Sensitivities to Ceftriaxone as an Empiric Therapy

OBJECTIVES To identify the pathogens in the ascitic fluids of patients with spontaneous bacterial peritonitis and then to determine their sensitivity pattern to ceftriaxone. METHODOLOGY The cross-sectional study was conducted at the Medical Unit-A, Department of Medicine, Hayatabad Medical Complex, Peshawar, from November 2021 to April 2022. Before ceftriaxone treatment was started, a minimum of 10 ml of ascitic fluid was introduced into a blood culture vial. Only patients with a positive culture were registered, and their information was gathered using a proforma. For statistical analysis, SPSS version 23 was used. RESULTSA total of 96 patients were enrolled in our study. There were 62 (59.52%) male and 34 (40.48%) female patients. Based on the isolation and identification of bacteria, the most prevalent bacteria isolated was Escherichia coli in 36 (37.5%) patients, followed by Acinetobacter Spp in 13 (13.54%) patients, Streptococcus spp in 14 (14.58%), Enterococcus spp in 11 (11.45%), Staphylococcus aureus in 9 (9.39%), MRSA in 8(8.33%) and K. Pneumonia in  5(5.21%) patients. The overall sensitivity of ceftriaxone to gram-positive bacteria was observed in 12 (42.85%) isolates, whereas the overall sensitivity of ceftriaxone to gram-negative bacteria was observed in 25 (36.76%) isolates. (p=0.091) (Figure 6). CONCLUSION Our study concludes that gram-negative bacteria were more prevalent than gram-positive bacteria in ascitic fluids of patients with spontaneous bacterial peritonitis. The most common isolated pathogen was E.coli. Gram-negative was more resistant to ceftriaxone as compared to gram-positive bacteria

A Review on Strong Impacts of Thermal Stress on Plants Physiology, Agricultural Yield; and Timely Adaptation in Plants to Heat Stress

In this review, we checked the harsh influence of high temperature or heat stress on plant metabolism and crop yield. Plants can bear a minimum range of temperature; temperature more than this optimum range comes in the term of heat stress. Climate changes increase the number and severity of heat waves that reduced the development of plants and resulted in the death of the entire plant. Heat stress is a major stressful environment that destroys plant growth, biochemical reactions, and the yield of crops across the world. High-temperature influences many physiological and chemical reactions in plants. HS is now a big deal for crop production and the essential goal of agriculture is to maintain a high yield of crops. A plant lives in the conditions of high temperature based on its capacity to receive the HT stimulus, generate and change the signal, and then initiate physiological and biochemical changes. The plants show physiological and biochemical responses to heat the stress, is an active area of research. To deal with HT, different molecular techniques are in progress. After thoroughly reviewed of the different discoveries on plants’ responses, adaptation, and forbearance to HT at the cellular, organelles, and entire plant levels, this article described several approaches that could be taken to increase thermo- forbearance in plants

Explainable Neural Network for Classification of Cotton Leaf Diseases

Every nation’s development depends on agriculture. The term “cash crops” refers to cotton and other important crops. Most pathogens that significantly harm crops also impact cotton. Numerous diseases that influence yield via the leaf, such as powdery mildew, leaf curl, leaf spot, target spot, bacterial blight, and nutrient deficiencies, can affect cotton. Early disease detection protects crops from additional harm. Computerized methods perform a vital role in cotton leaf disease detection at an early stage. The method consists of two core steps such as feature extraction and classification. First, in the proposed method, data augmentation is applied to balance the input data. After that, features are extracted from a pre-trained VGG-16 model and passed to 11 fully convolutional layers, which freeze the majority and randomly initialize convolutional features to subsequently generate a score of the anomaly map, which defines the probability of the lesion region. The proposed model is trained on the selected hyperparameters that produce great classification results. The proposed model performance is evaluated on two publicly available Kaggle datasets, Cotton Leaf and Disease. The proposed method provides 99.99% accuracy, which is competent compared to existing methods

Visual Geometry Group based on U-Shaped Model for Liver/Liver Tumor Segmentation

Liver cancer is the primary reason of death around the globe. Manually detecting the infected tissues is a challenging and time-consuming task. The computerized methods help make accurate decisions and therapy processes. The segmentation accuracy might be increased to reduce the loss rate. Semantic segmentation performs a vital role in infected liver region segmentation. This article proposes a method that consists of two major steps; first, the local Laplacian filter is applied to improve the image quality. The second is the proposed semantic segmentation model in which features are extracted to the pre-trained VGG16 model and passed to the U-shaped network. This model consists of 51 layers: input, 23 convolutional, 4 max pooling, 4 transpose convolutional, 4 concatenated, 8 activation, and 7 batch-normalization. The proposed segmentation framework is trained on the selected hyperparameters that reduce the loss rate and increase the segmentation accuracy. The proposed approach more precisely segments the infected liver region. The proposed approach performance is accessed on two datasets such as 3DIRCADB and LiTS17. The proposed framework provides an average dice score of 0.98, which is far better compared to the existing works

DeepLabv3+-Based Segmentation and Best Features Selection Using Slime Mould Algorithm for Multi-Class Skin Lesion Classification

The development of abnormal cell growth is caused by different pathological alterations and some genetic disorders. This alteration in skin cells is very dangerous and life-threatening, and its timely identification is very essential for better treatment and safe cure. Therefore, in the present article, an approach is proposed for skin lesions’ segmentation and classification. So, in the proposed segmentation framework, pre-trained Mobilenetv2 is utilised in the act of the back pillar of the DeepLabv3+ model and trained on the optimum parameters that provide significant improvement for infected skin lesions’ segmentation. The multi-classification of the skin lesions is carried out through feature extraction from pre-trained DesneNet201 with N × 1000 dimension, out of which informative features are picked from the Slim Mould Algorithm (SMA) and input to SVM and KNN classifiers. The proposed method provided a mean ROC of 0.95 ± 0.03 on MED-Node, 0.97 ± 0.04 on PH2, 0.98 ± 0.02 on HAM-10000, and 0.97 ± 0.00 on ISIC-2019 datasets

Detection of anomaly in surveillance videos using quantum convolutional neural networks

Anomalous behavior identification is the process of detecting behavior that differs from its normal. These incidents will vary from violence to war, road crashes to kidnapping, and so on in a surveillance model. Video anomaly detection from video surveillance is a difficult research activity due to the frequency of anomalous cases. Since certain devices need manual evaluation for the detection of violent or criminal situations at the same time video monitoring of security cameras is also a challenging task and is unreliable. When the data or model dimension is sufficiently large, convolutional neural networks have the limitation of learning inefficiently. Quantum Convolutional Neural Network (QCNN) is the name given to a technology that combines CNN and quantum computing. Quantum computation and CNN are combined to create a more efficient and outperforming solution for solving complicated machine-learning problems. To analyze the anomalies in a sequence of video frames, two models are proposed in this research. In this research 07 layers of Javeria deep convolutional neural network (DCNN) are proposed on the selected hyperparameters named J. DCNN which is also different from the existing models to analyze the abnormal behavior in a video segment. Furthermore, for a comprehensive analysis of the abnormal video frames a model is proposed which is the combination of Javeria quantum and convolutional neural networks (J. QCNN). In this model 04-qubit quantum neural network is used with five layers and an optimal loss rate named J. QCNN. The proposed J. QCNN model is different from the existing deep learning architectures. The proposed models are trained from the scratch for the detection of anomalous from top challenging publicly available video surveillance datasets such as UNI-Crime and UCF Crime. The proposed J. QCNN model classifies the number of violent robberies such as armed thefts containing handguns or knives, and robberies displaying varying levels of viciousness with 0.99 accuracy while J. DCNN model gives 0.97 accuracy. The obtained results are superior in comparison with recent existing cutting-edge published work for real-time anomaly detection in video CCTV

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and features, with variable treatment options. Manual detection of tumors is difficult, time-consuming, and error-prone. Therefore, a significant requirement for computerized diagnostics systems for accurate brain tumor detection is present. In this research, deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningioma, no tumor, and pituitary tumor. The classified tumor images have been passed to the proposed Seg-network where the actual infected region is segmented to analyze the tumor severity level. The outcomes of the reported research have been evaluated on three benchmark datasets such as Kaggle, 2020-BRATS, and local collected images. The model achieved greater than 90% detection scores to prove the proposed model's effectiveness