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Breast carcinoma is the commonest cancer among females in a majority of the Indian cities. The advances in the research and management have improved the breast cancer survival significantly in the past three decades globally. Adenocarcinoma is the commonest histological variant which arises from the ductal epithelia. The myoepithelial cells (ME) are the normal components of the breast parenchyma, which separate the ductal epithelia from the basement membrane and the stroma. The pure ME cell carcinoma is extremely rare and only 38 cases have been reported so far in the indexed literature. This may owe to the difficulties in the identification, and the non availability of established diagnostic criteria. We herein describe the clinical, radiological and the pathological characteristics of a case of myoepithelial carcinoma, to supplement the available literature. The possible impact of these cells in clinical practice was also reviewed. Identification and the further research on the genesis of these tumours, and the pathways by which the ME cells regulate the milieu interior of the breast epithelia, may unravel new molecular targets to prevent or treat both epithelial and myoepithelial cancers at early stages.

Animal related injuries are frequently reported in India and other countries, where bulls are used for sporting events as well as in places where farming and livestock rearing is practised. The presentation is, many times, atypical and misleading as well. They have unique mechanics of injury. The patterns of the injury are reviewed. An intra-peritoneal urinary bladder injury which is caused by a perineal bull gore with a pneumoperitoneum is unusual and it has not been reported in the literature which was reviewed. We are reporting a successfully treated 25 years old male patient from the slopes of the southern district of Manipur, India, who had presented 40 hours after he was injured. The identification and prompt exploration, keeping in mind the mechanics of bull goring, helps the surgeons to adequately deal such atypical injuries, for optimal outcomes.

Urolithiasis leading to renal failure is a very common occurrence. But if the patient is co-infected with genitourinary tuberculosis, then it becomes all the more unusual and challenging, especially if the patient is immunocompetent. This patient, who presented to us with bilateral urolithiasis and features of renal failure, underwent left nephrectomy after thorough investigations. The biopsy revealed features of renal tuberculosis. The patient was put on anti-tubercular therapy (ATT) and later, he underwent right ureteroscopic lithotripsy. He completed his course of ATT and is on regular follow-up. His serum creatinine also stabilised with regular hemodialysis. The main aim of this case report is to bring to light this unusual and interesting presentation of bilateral urolithiasis with genito-urinary TB presenting as renal failure, which was successfully managed.

Mesenteric Fibromatosis (MF) is a proliferative fibroblastic lesion of small intestinal mesentery. It constitutes 8% of all desmoid tumours, which represent 0.03% of all neoplasm. It is histologically benign but may invade locally and recur after excision. It occurs sporadically or in association with Familial adenomatous polyposis (FAP) mutation as a component of Gardner’s syndrome. The presenting features of MF are asymptomatic abdominal mass, abdominal discomfort or pain, bowel or ureteral obstruction, intestinal perforation, fistula, functional impairment of ileoanal anastomosis following colectomy in FAP cases. A 29-year-old male presented with a swelling on the right side of the umbilicus for six months and dull aching pain for two months. Fine needle aspiration cytology, ultrasonography, contrast enhanced computerized tomography findings were inconclusive. After Exploratory laparotomy, a mass approx 6x5x4 cm in ileal mesentery was identified and excised along with 20cm of ileum. End to end anastomosis was done and specimen was sent for histopathology which confirmed the diagnosis of MF. Considering the rarity of this tumour and difficulties in diagnostic and therapeutic ambit, we believe it is justified to describe this case which came to our observation.

In VANETs, user equipment (UE) schedules tasks by prioritizing them based on urgency and resource availability to ensure timely and efficient communication and processing. Effective task scheduling and resource allocation in VANET are crucial for maintaining low latency, high reliability, and optimal resource utilization for real-time vehicular communications. However, existing works often face limitations such as inadequate handling of dynamic network conditions, leading to increased latency and suboptimal resource usage. In this paper, we introduced a precise model by proposing Optimizing Task Offloading in Vehicular Network named as OTO framework. Initially, UEs are clustered using an Improved Fuzzy Algorithm (IFA) to reduce latency and energy consumption, with optimal clusters determined by a cluster validity index. Clustering considers distance, location, RSSI, link stability, and trust values, and cluster heads (CH) chosen based on distance, trust, and link stability. Following this, tasks from UE are classified using a Hybrid Deep Learning (HDL) algorithm, with LiteCNN for classification into emergency and non-emergency tasks and LiteLSTM for scheduling to reduce the weight matrix and overfitting. Dual scheduling based on task length, delay sensitivity, QoS, priority, resource consumption, and queue length reduces execution time and latency. Finally, the scheduled tasks are allocated to the optimal edge server based on task load, resource availability, waiting time, and distance using the RL-based Multi-agent Deep Reinforcement Learning (MA-DRL) algorithm, where edge servers act as sellers and users as buyers, reducing latency due to high convergence. In order to, evaluate and prove the efficacy of proposed OTO framework, we performed comparative analysis in terms of several performance metrics where our proposed OTO model outperforms other existing approaches. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) with Cloud Computing (CC) offers seamless connectivity in the healthcare environment which provide remote monitoring and diagnosis to the patients based on their health status. However, remote healthcare environment faced with security, privacy, bandwidth, and latency constraints which can be addressed by adopting blockchain, CC, and Edge Computing (EC) with medical IoT applications. In this research, HEART SAVIOUR model is developed which ensures real time remote heart disease analysis using Deep Learning (DL) and Transformer based method. The propounded research was tested and trained on the Hungarian and Cleveland dataset from the UCI repository. Initially, the patient data are passed to the edge gateway which are preprocessed in three folds which includes missing value replacement, noise reduction, and data normalization respectively. Within the edge gateway, the pre-processed data are subjected to encryption for guaranteeing secure communication using Binary Search Encryption Algorithm (BSEA). The encrypted sensitive data is then passed to the cloud server for automated remote heart disease analysis using Dense Nested Four Way Transformer Network (DNFW-Net). The analyzed results are securely stored in the block chain and based on the request raised by the healthcare specialists the automated and reliable reports are generated and securely provided to the remote patients. We have validated the proposed research on five performance metrics with 10% to 100% data distribution in which the proposed work achieves achievable performance than the existing works. The inclusion of edge computing, encryption, and block chain technologies with advanced AI algorithms, we ensure superior remote heart disease detection performance than the prior works. [ABSTRACT FROM AUTHOR]

The ability to facilitate high-performance task offloading while maintaining participant confidence is crucial, but not essential, to Cloud-Edge-Network (CEN) computing due to the geographic distribution and operation by various parties. Additionally, conflicts of interest may arise among the highly dynamic and diverse CEN members who provide resources. This study proposes a collaborative task offloading framework for CEN computing, called Trustable Block Chain and Bandwidth Sensible-based Task Offloading (TBBS-TO) and resource allocation empowered CEN. The E-PEFT consensus algorithm for block chain in task offloading optimizes resource allocation and task execution by dynamically adjusting consensus parameters based on environmental factors and performance feedback. Moreover, in our work for alleviating heterogeneous issues IoT users are mobility aware clustering is performed using Bi-directional Clustering Algorithm based on Local Density (BCALoD). In this work, block chain is essential to BC-CED's core functions, such as task delegation, resource utilization brokerage, and bandwidth sensible resource allocation. By modifying the block chain consensus procedure, TBBS-TO distinguish itself from other solutions by enabling participants to reach a consensus on task offloading. To achieve this, we formulate the offloading problem by considering both network performance and the computational capabilities of potential nodes. Using Multi-agent Double Deep Q-Network (MA-DDQN) based technique, TBBS-TO allow participants to compete for the right to produce a block by evaluating offloading policies and selecting the most effective one for the next period. Additionally, dynamically bandwidth sensible resource allocation is performed by considering significant parameters. Comprehensive testing on a commercial block chain platform has shown that TBBS-TO outperforms existing solutions in task offloading and blockchain maintenance. [ABSTRACT FROM AUTHOR]

Forecasting energy demand is essential for efficient grid management as it promotes steady operations, efficient markets, and sustainable energy practices. In this study, previously observed, evenly spaced energy consumption data are analysed using recurrent neural networks based on Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures to extract important insights, features, and remarkable patterns. First, the study examines the influence of meteorological features on energy consumption. The most significant meteorological features are determined by computing the MIC and Pearson's correlation coefficient. The selected features are then combined with historical energy consumption data to feed the neural network. Second, to improve and optimise the performance of the proposed models, two technical indicators - the daily energy usage average and the simple moving average - are considered. The following are some instances of comparisons in terms of prediction accuracy: (1) The MAPE of the proposed model is 2.47, whereas that of the current model is 4.03. (2) The MAPE of the existing model is 25.83, whereas the proposed solution is 18.68. (3) The MAPE of the suggested model is 24.8, while the MAPE of the current model is 26.6. (4) The MAPE of the present model is 4.77, whereas the suggested approach's is 4.42. [ABSTRACT FROM AUTHOR]

Multi Organ and tumor segmentation is the challenging task in medical imaging and surgical planning scenarios due to its diverse applications includes lesions and organs measurements and disease diagnosis respectively. Although collecting and examining labels for all classes pose severe challenges. Furthermore, Graphical Processing Unit (GPU) optimization emerge as another critical factor for multi organ and tumor segmentation. To address the mentioned conventional challenge, we designed a deep learning-based model named "Intelligent Segmentor" which performs automated segmentation in end-to-end fashion with novel semi supervised training approach. Initially, the obtained multi organ CT images is then subjected to pre-processing in terms of geometric standardization, noise removal, and intensity normalization respectively. The pre-processed image is then further provided to dual view training for effective Pseudolabel generation. The labelled data along with generated pseudolabels are provided to train the model for amplifying the model performance. After that, there are two inputs are provided to the designed segmentation model which includes dual encoders such as GoogleNet and VGG-16 for contextual and spatial information extraction in five stages, Tweaked Feature Pyramidal Network (TFPN) for dimensionality reduction and side features extraction, and Gated Fusion Module (GFM) for fusing the side features to form unified feature map. Finally, the unified feature map is the examined through convolution layers for multi organ and tumor output. We adopted FLARE 2023 dataset for validating the proposed work with existing works on 13 various organs and tumor segmentation tasks. From the results, the proposed research achieves better Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD) through online validation and final testing than the existing works [ABSTRACT FROM AUTHOR]