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Introduction: Hashimoto’s thyroiditis is a chronic autoimmune lymphocytic thyroiditis characterised by thyroid autoantibodies. Early detection and treatment of this condition help in reducing the morbidity and mortality associated with it. The aim of the study was to find out the prevalence of Hashimoto’s thyroiditis among patients with thyroid disorders visiting a tertiary care centre. Methods: A descriptive cross-sectional study was conducted among patients visiting the outpatient department of a tertiary care centre. Data from 14 April 2017 to 13 April 2019 was collected between 30 June 2022 to 15 September 2022 from medical records. Ethical approval was obtained from the Nepal Health Research Council. Hashimoto’s thyroiditis was diagnosed based on clinical presentation and positive antibodies to thyroid antigens. Convenience sampling method was used. The point estimate was calculated at a 95% Confidence Interval. Results: Among 813 patients with thyroid disorders, 393 (48.33%) (44.89-51.77, 95% Confidence Interval) had Hashimoto’s thyroiditis. The manifestation of the spectrum of Hashimoto’s thyroiditis were euthyroid in 215 (54.70%), subclinical hypothyroidism in 102 (25.95%), subclinical hyperthyroidism in 23 (5.85%), overt hyperthyroidism in 9 (2.30%) and overt hypothyroidism in 4 (1.02%). Conclusions: The prevalence of Hashimoto’s thyroiditis among patients with thyroid disorders was higher than in other studies done in similar settings.

Abstract Cloud technology is not immune to bugs and issue tracking. A dedicated system is required that will extremely error prone and less cumbersome and must command a high degree of collaboration, flexibility of operations and smart decision making. One of the primary goals of software engineering is to provide high-quality software within a specified budget and period for cloud-based technology. However, defects found in Cloud-Based Bug Tracking software’s can result in quality reduction as well as delay in the delivery process. Therefore, software testing plays a vital role in ensuring the quality of software in the cloud, but software testing requires higher time and cost with the increase of complexity of user requirements. This issue is even cumbersome in the embedded software design. Early detection of defect-prone components in general and embedded software helps to recognize which components require higher attention during testing and thereby allocate the available resources effectively and efficiently. This research was motivated by the demand of minimizing the time and cost required for Cloud-Based Bug Tracking Software testing for both embedded and general-purpose software while ensuring the delivery of high-quality software products without any delays emanating from the cloud. Not withstanding that several machine learning techniques have been widely applied for building software defect prediction models in general, achieving higher prediction accuracy is still a challenging task. Thus, the primary aim of this research is to investigate how deep learning methods can be used for Cloud-Based Bug Tracking Software defect detection with a higher accuracy. The research conducted an experiment with four different configurations of Multi-Layer Perceptron neural network using five publicly available software defect datasets. Results of the experiments show that the best possible network configuration for software defect detection model using Multi-Layer Perceptron can be the prediction model with two hidden layers having 25 neurons in the first hidden layer and 5 neurons in the second hidden layer.

Abstract Blockchain is the latest boon in the world which handles mainly banking and finance. The blockchain is also used in the healthcare management system for effective maintenance of electronic health and medical records. The technology ensures security, privacy, and immutability. Federated Learning is a revolutionary learning technique in deep learning, which supports learning from the distributed environment. This work proposes a framework by integrating the blockchain and Federated Deep Learning in order to provide a tailored recommendation system. The work focuses on two modules of blockchain-based storage for electronic health records, where the blockchain uses a Hyperledger fabric and is capable of continuously monitoring and tracking the updates in the Electronic Health Records in the cloud server. In the second module, LightGBM and N-Gram models are used in the collaborative learning module to recommend a tailored treatment for the patient’s cloud-based database after analyzing the EHR. The work shows good accuracy. Several metrics like precision, recall, and F1 scores are measured showing its effective utilization in the cloud database security.

Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%)

Detection of malignant lung nodules from Computed Tomography (CT) images is a significant task for radiologists. But, it is time-consuming in nature. Despite numerous breakthroughs in studies on the application of deep learning models for the identification of lung cancer, researchers and doctors still face challenges when trying to deploy the model in clinical settings to achieve improved accuracy and sensitivity on huge datasets. In most situations, deep convolutional neural networks are used for detecting the region of the main nodule of the lung exclusive of considering the neighboring tissues of the nodule. Although the accuracy achieved through CNN is good enough but this models performance degrades when there are variations in image characteristics like: rotation, tiling, and other abnormal image orientations. CNN does not store relative spatial relationships among features in scanned images. As CT scans have high spatial resolution and are sensitive to misalignments during the scanning process, there is a requirement of a technique which helps in considering spatial information of image features also. In this paper, a hybrid model named VCNet is proposed by combining the features of VGG-16 and capsule network (CapsNet). VGG-16 model is used for object recognition and classification. CapsNet is used to address the shortcomings of convolutional neural networks for image rotation, tiling, and other abnormal image orientations. The performance of VCNeT is verified on the Lung Image Database Consortium (LIDC) image collection dataset. It achieves higher testing accuracy of 99.49% which is significantly better than MobileNet, Xception, and VGG-16 that has achieved an accuracy of 98, 97.97, and 96.95%, respectively. Therefore, the proposed hybrid VCNet framework can be used for the clinical purpose for nodule detection in lung carcinoma detection.

In this study, the load frequency control of a two-area thermal generation system based on renewable energy sources is considered. When solar generation is used in one of the control areas, the system becomes nonlinear and complicated. Zero deviations in the frequencies and the flow of power through the tie lines are achieved by considering load disturbances. A novel grey wolf optimizer, which is a metaheuristic algorithm motivated by grey wolves is utilized for tuning the controller gains. The proportional, integral, and derivative gains values are optimized for the two-area Solar integrated Thermal Plant (STP). As the load connected to the system varies continuously with time, random load variation is also applied to observe the effectiveness of the proposed optimization method. Sensitivity analyses have also been adopted with the deviation in the time constants of different systems. Inertia constant variations of both areas are considered from −25% to +25%, with or without STP. The proposed algorithm shows good dynamic performance as shown from the simulation results in terms of settling time, overshoot values, and undershoot values. The power in the tie line achieves zero deviation quite rapidly in solar-based cases compared to those without STP.

Multilocular Cystic Renal Neoplasm of Low Malignant Potential (MCRNLMP) represents a rare variant of clear cell (conventional) renal carcinoma. They constitute between 3-6% of clear cell Renal Cell Carcinoma (RCC). The RCC constitutes less than 1% of all renal tumours. The MCRNLMP has an excellent prognosis with no reports of recurrence or metastasis. Authors reported a case of 67-year-old male patient on account of its rarity, co-existing pyelonephritis and incidental detection of MCRNLMP. Differentiation between MCRNLMP, RCC variants and other cystic lesions with clear cells is important as prognosis, treatment differ markedly. Radiological Bosnaik classification of complex cysts in category IIF and III is challenging and requires microscopic examination for correct diagnosis. Histopathology shows cysts separated and fibrous septae having groups of clear cells with low grade nuclei. Immunohistochemistry with Carbonic Anhydrase-IX (CA-IX), Epithelial Membrane Antigen (EMA), Cytokeratin (CK7) and CD10 is confirmatory. Diagnosis requires detailed imaging studies, meticulous grossing of nephrectomy specimens, extensive sampling of cystic tissue to find clear cells and immunohistochemistry (IHC).

In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy, birth defects and congenital abnormalities are related terms. Fetal abnormalities have been commonly observed in industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%.

Background The symptoms, radiography, biochemistry and healthcare utilisation of patients with COVID-19 following discharge from hospital have not been well described.Methods Retrospective analysis of 401 adult patients attending a clinic following an index hospital admission or emergency department attendance with COVID-19. Regression models were used to assess the association between characteristics and persistent abnormal chest radiographs or breathlessness.Results 75.1% of patients were symptomatic at a median of 53 days post discharge and 72 days after symptom onset and chest radiographs were abnormal in 47.4%. Symptoms and radiographic abnormalities were similar in PCR-positive and PCR-negative patients. Severity of COVID-19 was significantly associated with persistent radiographic abnormalities and breathlessness. 18.5% of patients had unscheduled healthcare visits in the 30 days post discharge.Conclusions Patients with COVID-19 experience persistent symptoms and abnormal blood biomarkers with a gradual resolution of radiological abnormalities over time. These findings can inform patients and clinicians about expected recovery times and plan services for follow-up of patients with COVID-19.

Abstract A low cost, non-explosive process for the synthesis of graphene oxide (GO) is demonstrated. Using suitable choice of reaction parameters including temperature and time, this recipe does not require expensive membranes for filtration of carbonaceous and metallic residues. A pre-cooling protocol is introduced to control the explosive nature of the highly exothermic reactions during the oxidation process. This alleviates the requirement for expensive membranes and completely eliminates the explosive nature of intermediate reaction steps when compared to existing methods. High quality of the synthesized GO is corroborated using a host of characterization techniques including X-ray diffraction, optical spectroscopy, X-ray photoemission spectroscopy and current-voltage characteristics. Simple reduction protocol using ultra-violet light is demonstrated for potential application in the area of photovoltaics. Using different reduction protocols together with the proposed inexpensive method, reduced GO samples with tunable conductance over a wide range of values is demonstrated. Density functional theory is employed to understand the structure of GO. We anticipate that this scalable approach will catalyze large scale applications of GO.

Insulation break in a permanent pacemaker lead is a rare long-term complication. We describe an elderly male with a VVIR pacemaker, who presented with an episode of presyncope more than 3 years after the initial implantation procedure, attributed to insulation break possibly caused by lead entrapment in components of the medial subclavicular musculotendinous complex (MSMC) and repeated compressive damage over time during ipsilateral arm movement requiring lead replacement. The differential diagnosis of a clinical presentation when pacing stimuli are present with failure to capture and the role of the MSMC in causing lead damage late after implantation are discussed.