Atypical Presentation of a Patient With Progressive Multifocal Leukoencephalopathy.

Progressive multifocal leukoencephalopathy (PML) is a rare, demyelinating infectious disease of the central nervous system, primarily affecting immunosuppressed individuals, such as those with acquired immunodeficiency syndrome (AIDS) or undergoing immunosuppressive therapy. The causative agent is the dormant John Cunningham (JC) polyomavirus, which reactivates in immunocompromised patients. PML is diagnosed through clinical observations, imaging, and polymerase chain reaction (PCR) analysis, detecting JC virus deoxyribonucleic acid (DNA) in the cerebrospinal fluid (CSF). Here, we report a case of a 42-year-old male, recently diagnosed with human immunodeficiency virus (HIV), who presented with slurred speech, difficulty articulating, tingling in both feet, difficulty walking, and significant weight loss. Examination revealed absent reflexes, coordination impairment, and diminished vibration sense. Blood tests showed anemia, elevated D-dimer, and HIV-1 positivity with a low CD4 count. CSF analysis indicated a lymphocytic profile with elevated protein and marginally increased adenosine deaminase (ADA). Autoantibody testing was positive for antinuclear antibodies (ANA), but CSF culture and India ink staining were negative. Magnetic resonance imaging (MRI) of the brain revealed hyperintense lesions on T2-weighted and fluid-attenuated inversion recovery (FLAIR) images in the left peritrigonal and parietal white matter, suggesting demyelination. The diagnosis of PML was confirmed by a positive JC virus PCR result from the CSF. The patient was started on combination antiretroviral therapy (cART) and supportive measures to improve immune status. This case underscores the importance of considering PML in patients with new-onset neurological symptoms and immunosuppression.

Leptospirosis and Dengue Coinfection-Associated Multi-Organ Dysfunction Syndrome: A Case Report.

Dengue and leptospirosis are frequently discussed separately, with dengue causing rash and leptospirosis causing jaundice. Currently, there are more and more reports of coinfections. The comparable clinical symptoms of both infections make it challenging to distinguish between leptospirosis and dengue. Differentiating between leptospirosis and dengue is crucial since leptospirosis has a more favorable prognosis with early antibiotic therapy, whereas dengue does not have a specific treatment, although early detection is essential for close monitoring and cautious fluid management. Here, we highlight a case of dengue virus and leptospirosis coinfection in a female who presented with acute febrile illness, dyspnea, and altered sensorium, which progressed to multiorgan dysfunction syndrome, involving the neurological, respiratory, hepatic, and hematological systems.

Diversity and Representation Among United States Participants in Amgen Clinical Trials.

OBJECTIVE: Describe the demographic profile of US participants in Amgen clinical trials over a 10-year period and variations across therapeutic areas, indications, and geographies. METHODS: Cross-sectional retrospective study including participants enrolled (2005-2020) in phase 1-3 trials completed between January 1, 2012 and June 30, 2021. RESULTS: Among 31,619 participants enrolled across 258 trials, one-fifth represented racial minority populations (Asian, 3%; Black or African American, 17%; American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, multiracial, each < 1%); fewer than one-fifth (16%) represented an ethnic minority population (Hispanic or Latino). Compared with census data, representation of racial and ethnic groups varied across US states. Across most therapeutic areas (bone, cardiovascular, hematology/oncology, inflammation, metabolic disorders, neuroscience) except nephrology, participants were predominantly White (72-81%). A similar proportion of males and females were enrolled between 2005 and 2016; male representation was disproportionately higher than female between 2016 and 2020. Across most medical indications, the majority of participants were 18-65 years of age. CONCLUSIONS AND RELEVANCE: While the clinical research community is striving to achieve diversity and proportional representation across clinical trials, certain populations remain underrepresented. Our data provide a baseline assessment of the diversity and representation of US participants in Amgen-sponsored clinical trials and add to a growing body of evidence on the importance of diversity in clinical research. These data provide a foundation for strategies aimed at supporting more equitable and representative research, and a baseline from which to assess the impact of future strategies to advance health equity.

Modality specific U-Net variants for biomedical image segmentation: a survey.

With the advent of advancements in deep learning approaches, such as deep convolution neural network, residual neural network, adversarial network; U-Net architectures are most widely utilized in biomedical image segmentation to address the automation in identification and detection of the target regions or sub-regions. In recent studies, U-Net based approaches have illustrated state-of-the-art performance in different applications for the development of computer-aided diagnosis systems for early diagnosis and treatment of diseases such as brain tumor, lung cancer, alzheimer, breast cancer, etc., using various modalities. This article contributes in presenting the success of these approaches by describing the U-Net framework, followed by the comprehensive analysis of the U-Net variants by performing (1) inter-modality, and (2) intra-modality categorization to establish better insights into the associated challenges and solutions. Besides, this article also highlights the contribution of U-Net based frameworks in the ongoing pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19. Finally, the strengths and similarities of these U-Net variants are analysed along with the challenges involved in biomedical image segmentation to uncover promising future research directions in this area.

CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images.

The pandemic of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19 has been spreading worldwide, causing rampant loss of lives. Medical imaging such as computed tomography (CT), X-ray, etc., plays a significant role in diagnosing the patients by presenting the visual representation of the functioning of the organs. However, for any radiologist analyzing such scans is a tedious and time-consuming task. The emerging deep learning technologies have displayed its strength in analyzing such scans to aid in the faster diagnosis of the diseases and viruses such as COVID-19. In the present article, an automated deep learning based model, COVID-19 hierarchical segmentation network (CHS-Net) is proposed that functions as a semantic hierarchical segmenter to identify the COVID-19 infected regions from lungs contour via CT medical imaging using two cascaded residual attention inception U-Net (RAIU-Net) models. RAIU-Net comprises of a residual inception U-Net model with spectral spatial and depth attention network (SSD) that is developed with the contraction and expansion phases of depthwise separable convolutions and hybrid pooling (max and spectral pooling) to efficiently encode and decode the semantic and varying resolution information. The CHS-Net is trained with the segmentation loss function that is the defined as the average of binary cross entropy loss and dice loss to penalize false negative and false positive predictions. The approach is compared with the recently proposed approaches and evaluated using the standard metrics like accuracy, precision, specificity, recall, dice coefficient and Jaccard similarity along with the visualized interpretation of the model prediction with GradCam++ and uncertainty maps. With extensive trials, it is observed that the proposed approach outperformed the recently proposed approaches and effectively segments the COVID-19 infected regions in the lungs.

A Shrewd Artificial Neural Network-Based Hybrid Model for Pervasive Stress Detection of Students Using Galvanic Skin Response and Electrocardiogram Signals.

Mental illness issues are a very common health issue in youths and adults across the world. The usage of real-time data analytics in health care has a great potential to improve and enhance the quality of health care services, including diagnosis and medical prescription. Stress is one of the major health issues these days, which leads to many acute and sometimes incurable diseases to the students of very young age. Stress affects physiological parameters of the human body; due to these, human emotions may also change. This research paper proposes a hybrid model for pervasive stress detection, which deals with imbalance class problems using real-time data analytics and Internet of Things and it also presents a new stress analysis system to detect stressful conditions of the student, and to diagnose whether they are stressed or relaxed by using a designed set of experimental tasks. Regular monitoring of students'/professionals' health, including measurement of Galvanic Skin Response (GSR) and Electrocardiogram (ECG) data, provides a good understanding of their stress level. Data are acquired by using GSR and ECG sensors for 34 participants while undertaking five different tasks discussed in the proposed experiment. The graphical relationship between heart rate, blood pressure, and skin conductance across various experimental activities highlights the fact as to how physiological parameters of the human body get affected along with the mental status of the students. This article performs accuracy computation by using different machine-learning models such as Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Bagging Classifiers (BAG), Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Network (ANN) followed by tuning with the best set of hyper parameters for each model. The proposed hybrid classification model deals with the class imbalance problem by using the Synthetic Minority Oversampling Technique. The shrewd ANN-based hybrid model achieves 99.4% accuracy on the self-generated dataset for the mental state classification of the students, which is best among other classifiers such as LR, SVM, KNN, BAG, RF, GB, and ANN. The prediction result of all 34 participants of the experiment is also classified into four categories: relaxed, stressed, partially stressed, and happy.

Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks.

The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia. The current diagnostic procedure of COVID-19 follows reverse-transcriptase polymerase chain reaction (RT-PCR) based approach which however is less sensitive to identify the virus at the initial stage. Hence, a more robust and alternate diagnosis technique is desirable. Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment. These datasets have limited samples concerned with the positive COVID-19 cases, which raise the challenge for unbiased learning. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. Accuracy, precision, recall, loss, and area under the curve (AUC) are utilized to evaluate the performance of the models. Considering the experimental results, the performance of each model is scenario dependent; however, NASNetLarge displayed better scores in contrast to other architectures, which is further compared with other recently proposed approaches. This article also added the visual explanation to illustrate the basis of model classification and perception of COVID-19 in CXR images.

Target specific mining of COVID-19 scholarly articles using one-class approach.

The novel coronavirus disease 2019 (COVID-19) began as an outbreak from epicentre Wuhan, People's Republic of China in late December 2019, and till June 27, 2020 it caused 9,904,906 infections and 496,866 deaths worldwide. The world health organization (WHO) already declared this disease a pandemic. Researchers from various domains are putting their efforts to curb the spread of coronavirus via means of medical treatment and data analytics. In recent years, several research articles have been published in the field of coronavirus caused diseases like severe acute respiratory syndrome (SARS), middle east respiratory syndrome (MERS) and COVID-19. In the presence of numerous research articles, extracting best-suited articles is time-consuming and manually impractical. The objective of this paper is to extract the activity and trends of coronavirus related research articles using machine learning approaches to help the research community for future exploration concerning COVID-19 prevention and treatment techniques. The COVID-19 open research dataset (CORD-19) is used for experiments, whereas several target-tasks along with explanations are defined for classification, based on domain knowledge. Clustering techniques are used to create the different clusters of available articles, and later the task assignment is performed using parallel one-class support vector machines (OCSVMs). These defined tasks describes the behavior of clusters to accomplish target-class guided mining. Experiments with original and reduced features validate the performance of the approach. It is evident that the k-means clustering algorithm, followed by parallel OCSVMs, outperforms other methods for both original and reduced feature space.

Non-analgesic effects of opioids: opioids and the endocrine system.

Opioids are among the oldest known and most widely used analgesics. The application of opioids has expanded over the last few decades, especially in the treatment of chronic non-malignant pain. This upsurge in opioid use has been accompanied by the increasingly recognized occurrence of opioid-associated endocrinopathy. This may arise after exposure to enteral, parenteral, or neuraxial opioids. Opioid-associated endocrinopathy consists primarily of hypothalamic-pituitary-gonadal axis or hypothalamic-pituitary-adrenal axis dysfunction and may manifest with symptoms of hypogonadism, adrenal dysfunction, and other hormonal disturbances. Additionally, opioid related endocrine dysfunction may be coupled with such disorders as osteoporosis and mood disturbances including depression. Undesirable changes in pain sensitivity such as opioid-induced hyperalgesia, and reduced potency of opioid analgesia may also be potential consequences of chronic opioid consumption. Few studies to date have been able to establish what degree of opioid exposure, in terms of dose or duration of therapy, may predispose patients to opioid-associated endocrinopathy. This article will review the currently available literature concerning opioid-associated endocrinopathy and will provide recommendations for the evaluation, monitoring, and management of opioid-associated endocrinopathy and its other accompanying undesired effects.