Geographical Variations in Early Onset Colorectal Cancer in the United States between 2001 and 2020.

BACKGROUND: Colorectal cancer remains the second leading cause of cancer-related death in the US. As early-onset colorectal cancer (EO-CRC) becomes more prevalent in the US, research attention has shifted towards identifying at-risk populations. Previous studies have highlighted the rising rate of early-onset adenocarcinoma (ADC) and neuroendocrine tumors (NET) in the US. However, data on geographical variations of EO-CRC are scarce. Hence, our study aims to analyze time trends in EO-CRC incidence rates across various US regions and to assess these trends by sex and histopathological subtypes (ADC and NET). METHODS: We analyze data spanning from 2001 to 2020 from the United States Cancer Statistics (USCS) database, covering nearly 98% of the US population. Using SEER*Stat software version (8.4.2, NCI), we calculated EO-CRC incidence rates among adults aged 20-54 years, adjusting for the age standard 2000 US population. The rates were categorized by sex and US geographical regions into west, midwest, northeast, and south. Time trends, reported as annual percentage change (APC) and average APC (AAPC), were generated via Joinpoint Regression software (v.5.0.2, NCI) utilizing the weighted Bayesian Information Criteria "BIC" method to generate the best-fit trends with a two-sided p-value cutoff at 0.05. The rates were also stratified by histopathology into ADC and NET. RESULTS: Between 2001 and 2020, a total of 514,875 individuals were diagnosed with early-onset CRC in the US, with 54.78% being men. Incidence rates and trends varied across geographical regions. In the western region (comprising 106,685 patients, 54.85% men), incidence rates significantly increased in both women (AAPC = 1.37, p < 0.001) and men (AAPC = 1.34, p < 0.001). Similarly, in the midwestern region (with 110,380 patients, 55.46% men), there were significant increases in incidence rates among women (AAPC = 1.06, p < 0.001) and men (AAPC = 1.35, p < 0.001). The northeastern region (with 94,758 patients, 54.53% men) also witnessed significant increases in incidence rates for both women (AAPC = 0.71, p < 0.001) and men (AAPC = 0.84, p < 0.001). In contrast, the southern region (with 203,052 patients, 54.48% men) experienced slower increases in incidence rates among both women and men (AAPC = 0.25, p < 0.05 in women; AAPC = 0.66, p < 0.05 in men). When stratified by histopathology, incidence rates for adenocarcinomas (ADC) increased in all regions, most notably in the west (AAPC = 1.45, p < 0.05), and least in the south (AAPC = 0.46, p < 0.05). Conversely, for neuroendocrine tumors (NET), while incidence rates increased similarly across all regions, the pace was notably faster compared to ADC, particularly in the west (AAPC = 3.26, p < 0.05) and slower in the south (AAPC = 2.24, p < 0.05) Discussion: Our analysis of nationwide US data spanning two decades and encompassing over half a million early-onset CRC patients, representing nearly 98% of the US population, highlights significant temporal variation in incidence rates across various geographical regions. The most substantial increases in incidence rates were observed in the west, while the least pronounced changes were noted in the south, affecting both men and women. These trends persisted across the main CRC histopathological subtypes, with NET exhibiting a notably swifter pace of increase compared with ADC. These findings hold important implications for public health strategies and underscore the need for targeted interventions to address the rising burden of early-onset CRC across different regions in the US.

Early-Onset Colorectal Cancer: Are Neuroendocrine Tumors or Adenocarcinomas the Culprit? Analysis of the Largest U.S. Cancer Incidence Database, 2001-2020.

(1) Background: While prior data showed an increasing incidence of colorectal cancer (CRC) in young adults, the contribution of adenocarcinoma (ADC) and neuroendocrine tumors (NETs) to this trend is not well studied. Therefore, we conducted a comparative analysis of the incidence rates and time trends of colorectal ADC and NETs in young adults (aged 24-54) using the United States Cancer Statistics (USCS) database. (2) Methods: Age-adjusted CRC incidence rates between 2001 and 2020 were calculated and categorized by sex, histopathology, and stage at diagnosis. Annual percentage change (APC) and average APC (AAPC) were computed via joinpoint regression utilizing weighted Bayesian information criteria to generate the simplest trend. Pairwise comparative analysis of ADC and NETs was conducted using tests of identicalness and parallelism. (3) Results: In this study, 514,875 patients were diagnosed with early-onset-CRC between 2001 and 2020 (54.8% men). While CRC incidence was significantly increased, including both ADC (448,670 patients) and NETs (36,205 patients), a significantly greater increase was seen for NETs (AAPC = 2.65) compared to ADC (AAPC = 0.91), with AAPC difference = 1.73 (p = 0.01) and non-identical non-parallel trends (p-values < 0.001). This was most notable in males (AAPC difference = 1.81, p = 0.03) and for early-stage tumors (AAPC difference = 3.56, p < 0.001). (4) Conclusions: Our study, covering ~98% of the U.S. population provides the first comparative analysis of early-onset CRC histopathological subtypes, showing that the rate of increase of NETs in young adults is much greater than that of ADC. Given that patients with NETs with malignant behavior can experience significant mortality, our findings are importance, highlighting the rapidly increasing NET incidence in young adults and encouraging early screening that can improve outcomes.

EfficientRMT-Net-An Efficient ResNet-50 and Vision Transformers Approach for Classifying Potato Plant Leaf Diseases.

The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early and late blight caused by Alternaria solani and Phytophthora infestans, significantly impact the quantity and quality of global potato production. We hypothesize that the integration of Vision Transformer (ViT) and ResNet-50 architectures in a new model, named EfficientRMT-Net, can effectively and accurately identify various potato leaf diseases. This approach aims to overcome the limitations of traditional methods, which are often labor-intensive, time-consuming, and prone to inaccuracies due to the unpredictability of disease presentation. EfficientRMT-Net leverages the CNN model for distinct feature extraction and employs depth-wise convolution (DWC) to reduce computational demands. A stage block structure is also incorporated to improve scalability and sensitive area detection, enhancing transferability across different datasets. The classification tasks are performed using a global average pooling layer and a fully connected layer. The model was trained, validated, and tested on custom datasets specifically curated for potato leaf disease detection. EfficientRMT-Net's performance was compared with other deep learning and transfer learning techniques to establish its efficacy. Preliminary results show that EfficientRMT-Net achieves an accuracy of 97.65% on a general image dataset and 99.12% on a specialized Potato leaf image dataset, outperforming existing methods. The model demonstrates a high level of proficiency in correctly classifying and identifying potato leaf diseases, even in cases of distorted samples. The EfficientRMT-Net model provides an efficient and accurate solution for classifying potato plant leaf diseases, potentially enabling farmers to enhance crop yield while optimizing resource utilization. This study confirms our hypothesis, showcasing the effectiveness of combining ViT and ResNet-50 architectures in addressing complex agricultural challenges.

MixNet-LD: An Automated Classification System for Multiple Lung Diseases Using Modified MixNet Model.

The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control its severity, which is usually irrevocable. The fundamental objective of this endeavor is to build a consistent and automated approach for establishing the intensity of lung illness. This paper describes MixNet-LD, a unique automated approach aimed at identifying and categorizing the severity of lung illnesses using an upgraded pre-trained MixNet model. One of the first steps in developing the MixNet-LD system was to build a pre-processing strategy that uses Grad-Cam to decrease noise, highlight irregularities, and eventually improve the classification performance of lung illnesses. Data augmentation strategies were used to rectify the dataset's unbalanced distribution of classes and prevent overfitting. Furthermore, dense blocks were used to improve classification outcomes across the four severity categories of lung disorders. In practice, the MixNet-LD model achieves cutting-edge performance while maintaining model size and manageable complexity. The proposed approach was tested using a variety of datasets gathered from credible internet sources as well as a novel private dataset known as Pak-Lungs. A pre-trained model was used on the dataset to obtain important characteristics from lung disease images. The pictures were then categorized into categories such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer using a linear layer of the SVM classifier with a linear activation function. The MixNet-LD system underwent testing in four distinct tests and achieved a remarkable accuracy of 98.5% on the difficult lung disease dataset. The acquired findings and comparisons demonstrate the MixNet-LD system's improved performance and learning capabilities. These findings show that the proposed approach may effectively increase the accuracy of classification models in medicinal image investigations. This research helps to develop new strategies for effective medical image processing in clinical settings.

RDS-DR: An Improved Deep Learning Model for Classifying Severity Levels of Diabetic Retinopathy.

A well-known eye disorder called diabetic retinopathy (DR) is linked to elevated blood glucose levels. Cotton wool spots, confined veins in the cranial nerve, AV nicking, and hemorrhages in the optic disc are some of its symptoms, which often appear later. Serious side effects of DR might include vision loss, damage to the visual nerves, and obstruction of the retinal arteries. Researchers have devised an automated method utilizing AI and deep learning models to enable the early diagnosis of this illness. This research gathered digital fundus images from renowned Pakistani eye hospitals to generate a new "DR-Insight" dataset and known online sources. A novel methodology named the residual-dense system (RDS-DR) was then devised to assess diabetic retinopathy. To develop this model, we have integrated residual and dense blocks, along with a transition layer, into a deep neural network. The RDS-DR system is trained on the collected dataset of 9860 fundus images. The RDS-DR categorization method demonstrated an impressive accuracy of 97.5% on this dataset. These findings show that the model produces beneficial outcomes and may be used by healthcare practitioners as a diagnostic tool. It is important to emphasize that the system's goal is to augment optometrists' expertise rather than replace it. In terms of accuracy, the RDS-DR technique fared better than the cutting-edge models VGG19, VGG16, Inception V-3, and Xception. This emphasizes how successful the suggested method is for classifying diabetic retinopathy (DR).

Dermo-Seg: ResNet-UNet Architecture and Hybrid Loss Function for Detection of Differential Patterns to Diagnose Pigmented Skin Lesions.

Convolutional neural network (CNN) models have been extensively applied to skin lesions segmentation due to their information discrimination capabilities. However, CNNs' struggle to capture the connection between long-range contexts when extracting deep semantic features from lesion images, resulting in a semantic gap that causes segmentation distortion in skin lesions. Therefore, detecting the presence of differential structures such as pigment networks, globules, streaks, negative networks, and milia-like cysts becomes difficult. To resolve these issues, we have proposed an approach based on semantic-based segmentation (Dermo-Seg) to detect differential structures of lesions using a UNet model with a transfer-learning-based ResNet-50 architecture and a hybrid loss function. The Dermo-Seg model uses ResNet-50 backbone architecture as an encoder in the UNet model. We have applied a combination of focal Tversky loss and IOU loss functions to handle the dataset's highly imbalanced class ratio. The obtained results prove that the intended model performs well compared to the existing models. The dataset was acquired from various sources, such as ISIC18, ISBI17, and HAM10000, to evaluate the Dermo-Seg model. We have dealt with the data imbalance present within each class at the pixel level using our hybrid loss function. The proposed model achieves a mean IOU score of 0.53 for streaks, 0.67 for pigment networks, 0.66 for globules, 0.58 for negative networks, and 0.53 for milia-like-cysts. Overall, the Dermo-Seg model is efficient in detecting different skin lesion structures and achieved 96.4% on the IOU index. Our Dermo-Seg system improves the IOU index compared to the most recent network.

A Residual-Dense-Based Convolutional Neural Network Architecture for Recognition of Cardiac Health Based on ECG Signals.

Cardiovascular disorders are often diagnosed using an electrocardiogram (ECG). It is a painless method that mimics the cyclical contraction and relaxation of the heart's muscles. By monitoring the heart's electrical activity, an ECG can be used to identify irregular heartbeats, heart attacks, cardiac illnesses, or enlarged hearts. Numerous studies and analyses of ECG signals to identify cardiac problems have been conducted during the past few years. Although ECG heartbeat classification methods have been presented in the literature, especially for unbalanced datasets, they have not proven to be successful in recognizing some heartbeat categories with high performance. This study uses a convolutional neural network (CNN) model to combine the benefits of dense and residual blocks. The objective is to leverage the benefits of residual and dense connections to enhance information flow, gradient propagation, and feature reuse, ultimately improving the model's performance. This proposed model consists of a series of residual-dense blocks interleaved with optional pooling layers for downsampling. A linear support vector machine (LSVM) classified heartbeats into five classes. This makes it easier to learn and represent features from ECG signals. We first denoised the gathered ECG data to correct issues such as baseline drift, power line interference, and motion noise. The impacts of the class imbalance are then offset by resampling techniques that denoise ECG signals. An RD-CNN algorithm is then used to categorize the ECG data for the various cardiac illnesses using the retrieved characteristics. On two benchmarked datasets, we conducted extensive simulations and assessed several performance measures. On average, we have achieved an accuracy of 98.5%, a sensitivity of 97.6%, a specificity of 96.8%, and an area under the receiver operating curve (AUC) of 0.99. The effectiveness of our suggested method for detecting heart disease from ECG data was compared with several recently presented algorithms. The results demonstrate that our method is lightweight and practical, qualifying it for continuous monitoring applications in clinical settings for automated ECG interpretation to support cardiologists.

Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images.

Computed tomography (CT) scans, or radiographic images, were used to aid in the early diagnosis of patients and detect normal and abnormal lung function in the human chest. However, the diagnosis of lungs infected with coronavirus disease 2019 (COVID-19) was made more accurately from CT scan data than from a swab test. This study uses human chest radiography pictures to identify and categorize normal lungs, lung opacities, COVID-19-infected lungs, and viral pneumonia (often called pneumonia). In the past, several CAD systems using image processing, ML/DL, and other forms of machine learning have been developed. However, those CAD systems did not provide a general solution, required huge hyper-parameters, and were computationally inefficient to process huge datasets. Moreover, the DL models required high computational complexity, which requires a huge memory cost, and the complexity of the experimental materials' backgrounds, which makes it difficult to train an efficient model. To address these issues, we developed the Inception module, which was improved to recognize and detect four classes of Chest X-ray in this research by substituting the original convolutions with an architecture based on modified-Xception (m-Xception). In addition, the model incorporates depth-separable convolution layers within the convolution layer, interlinked by linear residuals. The model's training utilized a two-stage transfer learning process to produce an effective model. Finally, we used the XgBoost classifier to recognize multiple classes of chest X-rays. To evaluate the m-Xception model, the 1095 dataset was converted using a data augmentation technique into 48,000 X-ray images, including 12,000 normal, 12,000 pneumonia, 12,000 COVID-19 images, and 12,000 lung opacity images. To balance these classes, we used a data augmentation technique. Using public datasets with three distinct train-test divisions (80-20%, 70-30%, and 60-40%) to evaluate our work, we attained an average of 96.5% accuracy, 96% F1 score, 96% recall, and 96% precision. A comparative analysis demonstrates that the m-Xception method outperforms comparable existing methods. The results of the experiments indicate that the proposed approach is intended to assist radiologists in better diagnosing different lung diseases.

DR-NASNet: Automated System to Detect and Classify Diabetic Retinopathy Severity Using Improved Pretrained NASNet Model.

Diabetes is a widely spread disease that significantly affects people's lives. The leading cause is uncontrolled levels of blood glucose, which develop eye defects over time, including Diabetic Retinopathy (DR), which results in severe visual loss. The primary factor causing blindness is considered to be DR in diabetic patients. DR treatment tries to control the disease's severity, as it is irreversible. The primary goal of this effort is to create a reliable method for automatically detecting the severity of DR. This paper proposes a new automated system (DR-NASNet) to detect and classify DR severity using an improved pretrained NASNet Model. To develop the DR-NASNet system, we first utilized a preprocessing technique that takes advantage of Ben Graham and CLAHE to lessen noise, emphasize lesions, and ultimately improve DR classification performance. Taking into account the imbalance between classes in the dataset, data augmentation procedures were conducted to control overfitting. Next, we have integrated dense blocks into the NASNet architecture to improve the effectiveness of classification results for five severity levels of DR. In practice, the DR-NASNet model achieves state-of-the-art results with a smaller model size and lower complexity. To test the performance of the DR-NASNet system, a combination of various datasets is used in this paper. To learn effective features from DR images, we used a pretrained model on the dataset. The last step is to put the image into one of five categories: No DR, Mild, Moderate, Proliferate, or Severe. To carry this out, the classifier layer of a linear SVM with a linear activation function must be added. The DR-NASNet system was tested using six different experiments. The system achieves 96.05% accuracy with the challenging DR dataset. The results and comparisons demonstrate that the DR-NASNet system improves a model's performance and learning ability. As a result, the DR-NASNet system provides assistance to ophthalmologists by describing an effective system for classifying early-stage levels of DR.

Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture.

Hypertensive retinopathy (HR) is a serious eye disease that causes the retinal arteries to change. This change is mainly due to the fact of high blood pressure. Cotton wool patches, bleeding in the retina, and retinal artery constriction are affected lesions of HR symptoms. An ophthalmologist often makes the diagnosis of eye-related diseases by analyzing fundus images to identify the stages and symptoms of HR. The likelihood of vision loss can significantly decrease the initial detection of HR. In the past, a few computer-aided diagnostics (CADx) systems were developed to automatically detect HR eye-related diseases using machine learning (ML) and deep learning (DL) techniques. Compared to ML methods, the CADx systems use DL techniques that require the setting of hyperparameters, domain expert knowledge, a huge training dataset, and a high learning rate. Those CADx systems have shown to be good for automating the extraction of complex features, but they cause problems with class imbalance and overfitting. By ignoring the issues of a small dataset of HR, a high level of computational complexity, and the lack of lightweight feature descriptors, state-of-the-art efforts depend on performance enhancement. In this study, a pretrained transfer learning (TL)-based MobileNet architecture is developed by integrating dense blocks to optimize the network for the diagnosis of HR eye-related disease. We developed a lightweight HR-related eye disease diagnosis system, known as Mobile-HR, by integrating a pretrained model and dense blocks. To increase the size of the training and test datasets, we applied a data augmentation technique. The outcomes of the experiments show that the suggested approach was outperformed in many cases. This Mobile-HR system achieved an accuracy of 99% and an F1 score of 0.99 on different datasets. The results were verified by an expert ophthalmologist. These results indicate that the Mobile-HR CADx model produces positive outcomes and outperforms state-of-the-art HR systems in terms of accuracy.

Sequelae of Infective Endocarditis: Ruptured Aortic Root Abscess in a 38-Year-Old Female With Complicated Infective Endocarditis.

A 38-year-old female with no known comorbidities or previous history of heart disease presented to the hospital with a three-day history of drowsiness and shortness of breath. Transthoracic echocardiography was performed, which showed large vegetations on aortic and tricuspid valves. In addition, there was severe aortic regurgitation with a possible abscess on the non-coronary cusp of the aortic valve. The patient was admitted, and a provisional diagnosis of disseminated tuberculosis, Infective endocarditis (IE), and sepsis was made. Surgical intervention was planned. Intraoperative findings revealed that a fistula had formed connecting the aorta and right atrium, which was closed with an autologous graft derived from the patient's pericardial tissue. Vegetations were removed, and the aortic valve was replaced with a metallic valve. This case report presents a patient with complicated IE with a ruptured aortic root abscess. Mechanical complications associated with IE, such as in our case, are rare among patients with IE. However, surgical intervention should be considered as an option in complicated cases of IE when standard therapy fails.

Etiology, clinical characteristics, and outcome of infective endocarditis: 10-year experience from a tertiary care center in Pakistan.

This study was conducted to assess the clinical characteristics, causative agents, complications, and outcomes of infective endocarditis (IE) among patients presenting to our tertiary care center over the last decade. This retrospective cohort study included all adult patients admitted to the Aga Khan University Hospital with the diagnosis of IE over a ten-year period from 2010 to 2020.  Outcomes variables included complications during hospitalization, surgical intervention, mortality, and length of stay. We identified a total of 305 cases out of which 176 (58%) were males and 129 (42%) were females. The mean age of the patients was 46.9±18.8 years. 95 (31%) had prosthetic valves in place. Staphylococcus aureus was isolated in 54 (39%) patients followed by coagulase-negative Staphylococcus in 23 (17%). Echocardiography revealed vegetations and abscesses in 236 (77%) and 4 (1%) patients, respectively. The most common valvular complication was mitral valve regurgitation found in 26 (9%) patients, followed by tricuspid valve regurgitation in 13 (4%) patients and aortic valve regurgitation in 11 (3%) patients. Furthermore, 81 (27%) patients suffered from heart failure and 66 (22%) from a stroke during hospitalization. The mean hospital length of stay was 10.4 ± 10.6 days. 64 (21%) patients required surgical repair and the overall mortality rate was 25%. Prosthetic valve endocarditis (OR = 3.74, 95% CI = 2.15-6.50, p<0.001), chronic kidney disease (OR = 2.51, 95% CI = 1.15-5.47, p=0.036), previous stroke (OR = 2.42, 95% CI = 1.18-4.96, p=0.026), and ischemic heart disease (OR = 3.04, 95% CI = 1.50-6.16, p=0.003) were significantly associated with an increased risk of mortality. In conclusion, our study provided valuable data on the clinical characteristics and outcomes of patients with IE in a developing country. S. aureus was the most common causative agent. Heart failure and stroke were the most common complications. The presence of prosthetic valves, history of chronic kidney disease, ischemic heart disease and previous stroke were associated with a significantly increased risk of mortality. Surgical management was not associated with improved outcomes.

An Automatic Detection and Classification System of Five Stages for Hypertensive Retinopathy Using Semantic and Instance Segmentation in DenseNet Architecture.

The stage and duration of hypertension are connected to the occurrence of Hypertensive Retinopathy (HR) of eye disease. Currently, a few computerized systems have been developed to recognize HR by using only two stages. It is difficult to define specialized features to recognize five grades of HR. In addition, deep features have been used in the past, but the classification accuracy is not up-to-the-mark. In this research, a new hypertensive retinopathy (HYPER-RETINO) framework is developed to grade the HR based on five grades. The HYPER-RETINO system is implemented based on pre-trained HR-related lesions. To develop this HYPER-RETINO system, several steps are implemented such as a preprocessing, the detection of HR-related lesions by semantic and instance-based segmentation and a DenseNet architecture to classify the stages of HR. Overall, the HYPER-RETINO system determined the local regions within input retinal fundus images to recognize five grades of HR. On average, a 10-fold cross-validation test obtained sensitivity (SE) of 90.5%, specificity (SP) of 91.5%, accuracy (ACC) of 92.6%, precision (PR) of 91.7%, Matthews correlation coefficient (MCC) of 61%, F1-score of 92% and area-under-the-curve (AUC) of 0.915 on 1400 HR images. Thus, the applicability of the HYPER-RETINO method to reliably diagnose stages of HR is verified by experimental findings.

Management of Gunshot Injuries of Mandible with Open Reduction and Internal Fixation versus Closed Reduction and Maxillo-mandibular Fixation.

Background/objectives Gunshot injuries are known to cause severe morbidity and mortality when facial regions are involved. Management of the gunshot wounds of the face comprises of securing an airway, controlling hemorrhage, identifying other injuries and definite repair of the traumatic facial deformities. The objective of the present study was to compare the clinical outcome (infection and nonunion) of open reduction and internal fixation versus closed reduction and maxillo-mandibular fixation (CR-MMF) in the treatment of gunshot injuries of the mandible. Materials & methods This study was conducted at Oral and Maxillofacial Surgery Department of Shaheed Zulfiqar Ali Bhutto Medical University/Pakistan Institute of Medical Sciences Islamabad, Pakistan. Ninety gunshot mandibular fractures were randomly allocated in two equal groups. In group-A, 45 patients were treated by open reduction and internal fixation while in group-B, 45 patients were also managed by closed reduction and maxillo-mandibular fixation. Post-operative complications (infection, non-union) were evaluated clinically and radiographically in both groups. Results Patients treated by open reduction and internal fixation were having more complications in terms of infection (17.8%) as compared to closed reduction (4.4%) with a p-value 0.044. Whereas non-union was more in closed reduction (15.6%) as compared to open reduction and internal fixation group (2.2%) with a significant p-value 0.026. Conclusion Both the treatment modalities can be used in the management of gunshot injuries of mandible and there is need for further studies to have clear guideline in this regard in best interest of patients, community and health care providers.

Auricular neural stimulation as a new non-invasive treatment for opioid detoxification.

The recent opioid crisis is one of the rising challenges in the history of modern health care. New and effective treatment modalities with less adverse effects to alleviate and manage this modern epidemic are critically needed. The FDA has recently approved two non-invasive electrical nerve stimulators for the adjunct treatment of symptoms of acute opioid withdrawal. These devices, placed behind the ear, stimulate certain cranial nerves with auricular projections. This neural stimulation reportedly generates a prompt effect in terms of alleviation of withdrawal symptoms resulting from acute discontinuation of opioid use. Current experimental evidence indicates that this type of non-invasive neural stimulation has excellent potential to supplement medication assisted treatment in opioid detoxification with lower side effects and increased adherence to treatment. Here, we review current findings supporting the use of non-invasive neural stimulation in detoxification from opioid use. We briefly outline the neurophysiology underlying this approach of auricular electrical neural stimulation and its role in enhancing medication assisted treatment in treating symptoms of opioid withdrawal. Considering the growing deleterious impact of addictive disorders on our society, further studies on this emerging treatment modality are warranted.

Development and validation of a novel rheumatoid arthritis knowledge assessment scale in Pakistani patients with rheumatoid arthritis.

AIM: Rheumatoid arthritis (RA) is a chronic progressive disabling disease that mainly affects joints. Studies documenting Pakistani patients' knowledge regarding RA disease are lacking and there is a need for such endeavor. The purpose of this study was to develop and validate a novel research tool to document patient knowledge about RA disease. METHODS: A novel research instrument known as the rheumatoid arthritis knowledge assessment scale (RAKAS) which consisted of 13 items, was formulated by a rheumatology panel and used for this study. This study was conducted in rheumatology clinics of three tertiary care hospitals in Karachi, Pakistan. The study was conducted in March-April 2018. Patients were recruited using a randomized computer-generated list of appointments. Sample size was calculated based on item-to-respondent ratio of 1:15. The validities, factor structure, sensitivity, reliability and internal consistency of RAKAS were assessed. The study was approved by the institutional Ethics Committee. RESULTS: A total of 263 patients responded to the study. Content validity was 0.93 and response rate was 89.6%. Factor analysis revealed a 3-factor structure. Fit indices, namely normed fit index (NFI), Tucker Lewis index (TLI), comparative fit index (CFI) and root mean square of error approximation (RMSEA) were calculated with satisfactory results, that is, NFI, TLI and CFI > 0.9, and RMSEA < 0.06. Internal consistency (α) was 0.62, that is, acceptable. All items had a high discrimination index, that is, >19 and difficulty index <0.95. Sensitivity and specificity of RAKAS were above 90%. The tool established construct and known group validities. CONCLUSION: A novel tool to document disease knowledge in patients with RA was formulated and validated.

Things We Do For No Reason™: Use of Antipsychotic Medications in Patients with Delirium.

Inspired by the ABIM Foundation's Choosing Wisely® campaign, the "Things We Do for No Reason™" (TWDFNR™) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR™ series do not represent "black and white" conclusions or clinical practice standards but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion.

Clozapine-induced myocarditis in an adolescent male with DiGeorge Syndrome.

DiGeorge Syndrome (22q11.2 deletion syndrome) is a chromosomal disorder associated with both congenital heart malformations and schizophrenia, which is often treatment-resistant and may warrant treatment with clozapine. Clozapine-induced myocarditis (CIM) is a rare complication of clozapine therapy, with a reported incidence ranging from 0.015% to 3%. Fulminant CIM has a nonspecific presentation in both adult and pediatric populations and a mortality rate approaching 50%. Few cases of pediatric CIM have been documented in the literature. This report highlights a case of CIM in an adolescent male with DiGeorge Syndrome whose clinical course was characterized by a subtle, nonspecific presentation and resolution with supportive care.

A rare cause of postpartum headache.

Postpartum women can develop headache, and their assessment requires a thorough and multidisciplinary approach. If the headache is unresponsive to treatment and accompanied by neurological deficit, neuroimaging needs to be undertaken to rule out other life-threatening causes. 1 We present a case of 35-year-old woman with pre-eclampsia and diet-controlled gestational diabetes mellitus, who had normal vaginal delivery at 40 weeks. She had an epidural analgesia for pain relief during labour, but had inadvertent dural puncture during the procedure and developed headache 24 hours after delivery. The headache was managed conservatively and she was discharged home, but was readmitted 8 days later with worsening headache. The headache was postural on admission but became continuous, developed neurological symptoms in the form of ataxic hemiparesis and convulsions. After neuroimaging, she was found to have cerebral venous sinus thrombosis. She was commenced on anticoagulants and anticonvulsants and made a complete recovery.

Computational fluid dynamic simulations for dispersion of nanoparticles in a magnetohydrodynamic liquid: a Galerkin finite element method.

This investigation studies the effects of the thermo-physical properties of four types of nano-metallic particles on the thermo-physical properties of radiative fluid in the presence of buoyant forces and Joule heating (ohmic dissipation). The Galerkin finite element algorithm is used to perform computations and simulated results are displayed in order to analyze the behavior of velocity and temperature of copper, silver, titanium dioxide and aluminum oxide-nanofluids. All the simulations are performed with η max = 6 computational tolerance 10-6 for 200 elemental discretizations. Due to the dispersion of nano-sized particles in the base fluid, an increase in the thermal conduction is noticed. This study also predicts future improvements in the thermal systems. Due to magnetic field and fluid flow interaction, the electrical energy converts into heat. This is undesirable in many thermal systems. Therefore, control of Joule heating in the design of thermos systems is necessary. However, this dissipation of heat may be desirable in some biological fluid flows. An increase in energy losses is noted as magnetic intensity is increased.