Articles

<b>Introduction:</b> Ovarian torsion is a rare gynecological emergency that can result in permanent ovarian loss if not promptly recognized and managed. Cases involving multiple rotations of the ovary are highly unusual and pose a significant risk for ovarian viability. <b>Clinical Description:</b> A 33-year-old P2 woman presented with sudden severe lower abdominal pain. Imaging showed a complex midline adnexal mass, more towards left and mild free fluid with suspected torsion. Emergency laparotomy showed a sixfold torsion of the right ovary with a dermoid cyst. Detorsion and cystectomy were performed with preservation of the ovary. Postoperative recovery was uneventful and histopathology further confirmed a benign dermoid cyst. <b>Discussion:</b> Ovarian torsion with multiple rotations is extremely rare so early recognition and timely surgical intervention enable ovarian salvage even in severe cases. Moreover, Dermoid cysts are the most common predisposing factor. <b>Conclusion:</b> This case highlights the importance of prompt diagnosis and immediate management of ovarian torsion to prevent complications, preserve ovarian integrity and fertility.

The pulmonary characteristics of <i>Staphylococcus </i><i>aure</i><i>u</i><i>s</i> (<i>S</i><i>.</i><i> </i><i>aureus</i>) co-infection with respiratory viruses, such as SARS-CoV-2 and influenza virus, are still unclear.<b> </b><b>Case series</b>: Two patients with methicillin-susceptible <i>S</i><i>.</i><i> </i><i>aureus</i><i> </i>(MSSA) infection in the lungs co-infected with either SARS-CoV-2 or influenza virus are reported. Case 1 was a 66-year-old woman who was admitted with SARS-CoV-2 infection. Her chest X-ray and computed tomography (CT) showed multiple cavity formations with infiltration shadows, and MSSA was detected from her sputum and blood, suggesting COVID-19-related bacterial pneumonia and pulmonary embolism. No catheters had been used, but she had skin eruptions and a history of SARS-CoV-2 vaccination. Ampicillin/sulbactam (ABPC/SBT) was administered, and she finally improved. Case 2 was an 87-year-old man with a history of atopic dermatitis who was admitted with moderate pneumonia, and influenza virus co-infection was found. He showed multiple cavitary shadows, and MSSA was isolated from both his sputum and blood. He was diagnosed with influenza-related bacterial pulmonary embolism. No catheters had been used, but he had a history of influenza vaccination. He was also treated by ABPC/SBT and finally improved. <b>Conclusions:</b> These cases suggest that MSSA showed affinity to the lungs when co-infected with either SARS-CoV-2 or influenza virus, and it presented as septic emboli without catheter use. We should consider MSSA infection when patients have SARS-CoV-2 or influenza virus co-infection, and multiple cavity formation and skin disorders are seen, even though they were vaccinated and no catheters were used.

The effects on the elderly are disproportionately Alzheimer’s disease (AD) is one of the most prevalent and chronic types of dementia. Alzheimer's disease (AD), a fatal illness that can harm brain structures and cells long before symptoms appear, is currently incurable and incurable. Using brain MRI pictures from a publicly accessible Kaggle dataset, this study suggests a prediction model based on Convolutional Neural Networks (CNNs) to help with the early detection of Alzheimer's disease. Four levels of dementia have been applied to the 6,400 photos in the collection: not demented, slightly demented, moderately demented, and considerably mildly demented. Pixel normalization, class balancing utilizing data augmentation techniques, and picture scaling to 128×128 pixels were all part of a thorough workflow for data preparation. To improve the gathering of spatial dependence in volumetric MRI data, a 3D convolutional neural network (CNN) architecture was used. We used important performance measures including F1-score, recall, accuracy, precision, and log loss to gauge the model's effectiveness. A review of the available data indicates that the total F1-score, accuracy, recall, and precision were 99.0%, 99.0%, and 99.38%, respectively. The findings demonstrate the model's potential for practical use in early AD diagnosis and establish its robustness with the help of confusion matrix analysis and performance curves.

Today, the presentation of a three-dimensional model of real-world features is very important and widely used and has attracted the attention of researchers in various fields, including surveying and spatial information systems, and those interested in the three-dimensional reconstruction of buildings. The building is the key part of the information in a three-dimensional city model, so extracting and modeling buildings from remote sensing data is an important step in building a digital model of a city. LiDAR technology due to its ability to map in all three modes of one-dimensional, two-dimensional, and three-dimensional is a suitable solution to provide hyperspectral and comprehensive images of the building in an urban environment. In this review article, a comprehensive review of the methods used in identifying buildings from the past to the present and appropriate solutions for the future is discussed.

Aiming at the time complexity of singular value spectrum weighted Hankel SVD filtering algorithm, a clutter suppression algorithm for ultrasonic color Doppler imaging based on BP neural network model is proposed in this paper. Firstly, using the PRF data collected by portable ultrasound instrument, we verify the singular value weighted Hankel SVD filtering algorithm, and the results show that the algorithm has high accuracy; Then, the BP neural network model is established based on the input and output data of singular value weighted Hankel-SVD filtering algorithm; Finally, the clutter suppression algorithm of ultrasonic color Doppler imaging based on BP neural network model is established. The experimental results show that compared with Hankel SVD filtering algorithm, the clutter suppression algorithm proposed in this paper greatly shortens the operation time without reducing the accuracy, so as to improve the real-time performance of the filtering algorithm.

Although rarely, echinococcosis might present cardiac involvement, with cysts growing inside myocardial structures of arising adjacent to heart. A careful differential diagnosis with other mass formations and rare cardiac tumours is necessary, whenever there is a clinical and radiological suspicion. Imaging studies and serology will establish diagnosis. A multidisciplinary approach is warranted in all cases, with surgical intervention being unavoidable in most settings. Patients generally present with chest pain and dyspnoea. Cases need a close follow up of their postoperative course, while being treated appropriately with albendazole (or mebendazole) for prevention of recurrences.

Cell division that is not controlled leads to cancer, an incurable condition. An early diagnosis has the potential to lower death rates from breast cancer, the most frequent disease in women worldwide. Imaging studies of the breast may help doctors find the disease and diagnose it. This study explores an effectiveness of DL and ML models in a classification of mammography images for breast cancer detection, utilizing the publicly available CBIS-DDSM dataset, which comprises 5,000 images evenly divided between benign and malignant cases. To improve diagnostic accuracy, models such as Gaussian Naïve Bayes (GNB), CNNs, KNN, and MobileNetV2 were assessed employing performance measures including F1-score, recall, accuracy, and precision. The methodology involved data preprocessing techniques, including transfer learning and feature extraction, followed by data splitting for robust model training and evaluation. Findings indicate that MobileNetV2 achieved a highest accuracy99.4%, significantly outperforming GNB (87.2%), CNN (96.7%), and KNN (91.2%). The outstanding capacity of MobileNetV2 to identify between benign and malignant instances was shown by the investigation, which also made use of confusion matrices and ROC curves to evaluate model performance.