Diffuse Idiopathic Skeletal Hyperostosis: Persistent Sore Throat and Dysphagia in an Elderly Smoker Male

Diffuse idiopathic skeletal hyperostosis (DISH) is rarely symptomatic. However, it can present with dyspnea, hoarseness, dysphagia, and stridor. An 80-year-old chronic smoker male presented with 6-month history of sore throat and progressive dysphagia. Computed tomography of the neck revealed bulky anterior bridging syndesmophytes along the anterior aspect of the cervical spine and facet effusion involving four contiguous vertebrae consistent with DISH. Dysphagia secondary to DISH was diagnosed. Fiberoptic laryngoscopy showed bilateral vocal cord paralysis. Patient’s airway became compromised requiring tracheostomy tube placement. After discussion of therapeutic options, patient agreed on a percutaneous endoscopic gastrostomy tube insertion for nutritional support. Osteophytectomy was left to be discussed further

Modified CHA2DS2-VASc score predicts in-hospital mortality and procedural complications in acute coronary syndrome treated with percutaneous coronary intervention

Background: Current risk prediction models in acute coronary syndrome (ACS) patients undergoing PCI are mathematically complex. This study was undertaken to assess the accuracy of a modified CHA2DS2-VASc score, comprised of easily accessible clinical factors in predicting adverse events. Methods: The National Inpatient Sample (NIS) was queried for ACS patients who underwent PCI between 2010 and 2014. We developed a modified CHA2DS2-VASc score for risk prediction in ACS patients. Multivariate mixed effect logistic regression was utilized to study the adjusted risk for adverse outcomes based on the score. The primary outcome evaluated was in-hospital mortality. Secondary outcomes assessed were stroke, respiratory failure, acute kidney injury, all-cause bleeding, pacemaker insertion, vascular complications, length of stay and cost. Results: There were 252,443 patients admitted with ACS included. Mean age was 62 +/- 12 years. The mean CH3A2DS-VASc score was 1.6 +/- 1.6. The in-hospital mortality rate was 2.5%. CH3A2DS-VASc score was highly correlated with increased rate of mortality and all secondary outcomes. ROC curve analysis for association of CH3A2DS-VASc score with mortality demonstrates that area under the curve (AUC) = 0.83 (95%C: 0.82-0.84). Stepwise increases in CH3A2DS-VASc score correlated with incremental risk, and total score was an independent predictor of mortality (adjusted OR: 1.99 (95%CI: 1.96-2.03) p \u3c 0.001) and all secondary outcomes. Conclusion: This study supports the applicability of the CH3A2DS-VASc score as an accurate risk prediction model for ACS patients undergoing PCI and could supplant more complicated models for quality assurance

Numerical investigation of Prandtl number effect on heat transfer and fluid flow characteristics of a nuclear fuel element

This paper investigates the heat transfer and fluid flow characteristics of liquid metal coolants (such as Sodium, Sodium potassium, Bismuth, Lead, and Lead–bismuth) flowing over a nuclear fuel element having non-uniform internal energy generation numerically using finite difference method. The Full Navier Stokes Equations governing the flow were converted into stream function-Vorticity form and solved simultaneously along with energy equation using central finite difference scheme. For the two dimensional steady state heat conduction and Stream-Function Equation, the discretization was done in the form suitable to solve using ‘Line-by-Line Gauss-Seidel’ solution technique whereas the discretization of Vorticity transport and energy equations were done using Alternating Direction Implicit (ADI) scheme. After discretization the systems of equations were solved using ‘Thomas Algorithm’. The complete task was done by writing a computer code. The results were obtained in the form of variation of Maximum temperature in the fuel element (hot spots) and its location, mean coolant temperature at the exit .The parameters considered for the study were aspect ratio of fuel element, Ar, conduction-convection parameter Ncc, total energy generation parameter Qt, and flow Reynolds number ReH. The results obtained can be used to minimize the Maximum temperature in the fuel element (hot spots)

Inappropriate use of commercial Antinuclear Antibody Testing in a community-based US hospital: a retrospective study

Healthcare providers use antinuclear antibodies (ANAs) to screen and diagnose patients with autoimmune diseases. In the recent years, commercial multiplex ANA kits have emerged as a convenient and fast diagnostic method. Diagnostic testing should follow sequenced algorithms: initial screen followed by specific antibody analysis. Second-level testing as an initial screen for autoimmune disease is inappropriate. We reviewed 68 patients with ANA comprehensive panels over a 6-month period from May 2015 to October 2015. We assessed appropriateness and estimated incurred losses from inappropriate testing. We found 92.6% (63 out of 68) of the ANA comprehensive panel results to be negative. Incurred losses from inappropriate ANA comprehensive panel testing were $66,000. Physicians should become familiar with ANA-sequenced diagnostic algorithms to avoid unnecessary higher level testing

A nurse shadowing program for physicians: Bridging the gap in understanding nursing roles

OBJECTIVE: A physician-nurse shadow program was established to improve interdisciplinary collaboration. BACKGROUND: Ineffective communication between physicians and nurses leads to poor outcomes in patient satisfaction, safety, and associate engagement. Physician unfamiliarity of the nursing process is identified as a root cause. METHODS: First-year resident physicians shadowed nurses for a 4-hour shift. Residents did not function as a physician during the shadowing experience but participated in nursing workflow and tasks. Participants completed a Likert-scale rating and qualitative survey before and after the shift. RESULTS: The survey measured confidence in communication and perception of workflow. Confidence levels increased in all areas by 29% for residents and 34% for nurses. Data demonstrated improved physician understanding of nursing workflow and inspired recommendations to enhance communication. CONCLUSIONS: First-year resident physicians practiced direct communication skills and experienced hands-on nursing care during the shadow program. The initiative provided an environment for mutual learning and interdisciplinary relationship-building

EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network

This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to assess the effectiveness of the proposed DP-CNN architecture across three datasets (NinaPro DB1, DB2, and DB3), encompassing both able-bodied and amputee subjects. Performance metrics, including accuracy, precision, recall, and F1-score, are employed for comprehensive evaluation. The DP-CNN demonstrates notable mean accuracies of 94.93 ± 1.71% and 94.00 ± 3.65% on NinaPro DB1 and DB2 for healthy subjects, respectively. Additionally, it achieves a robust mean classification accuracy of 85.36 ± 0.82% on amputee subjects in DB3, affirming its efficacy. Comparative analysis with previous methodologies on the same datasets reveals substantial improvements of 28.33%, 26.92%, and 39.09% over the baseline for DB1, DB2, and DB3, respectively. The DP-CNN’s superior performance extends to comparisons with transfer learning models for image classification, reaffirming its efficacy. Across diverse datasets involving both able-bodied and amputee subjects, the DP-CNN exhibits enhanced capabilities, holding promise for advancing myoelectric control.</p

Using a Resnet50 with a Kernel Attention Mechanism for Rice Disease Diagnosis

The domestication of animals and the cultivation of crops have been essential to human development throughout history, with the agricultural sector playing a pivotal role. Insufficient nutrition often leads to plant diseases, such as those affecting rice crops, resulting in yield losses of 20–40% of total production. These losses carry significant global economic consequences. Timely disease diagnosis is critical for implementing effective treatments and mitigating financial losses. However, despite technological advancements, rice disease diagnosis primarily depends on manual methods. In this study, we present a novel self-attention network (SANET) based on the ResNet50 architecture, incorporating a kernel attention mechanism for accurate AI-assisted rice disease classification. We employ attention modules to extract contextual dependencies within images, focusing on essential features for disease identification. Using a publicly available rice disease dataset comprising four classes (three disease types and healthy leaves), we conducted cross-validated classification experiments to evaluate our proposed model. The results reveal that the attention-based mechanism effectively guides the convolutional neural network (CNN) in learning valuable features, resulting in accurate image classification and reduced performance variation compared to state-of-the-art methods. Our SANET model achieved a test set accuracy of 98.71%, surpassing that of current leading models. These findings highlight the potential for widespread AI adoption in agricultural disease diagnosis and management, ultimately enhancing efficiency and effectiveness within the sector

RNN and BiLSTM Fusion for Accurate Automatic Epileptic Seizure Diagnosis Using EEG Signals

Epilepsy is a common neurological condition. The effects of epilepsy are not restricted to seizures alone. They comprise a wide spectrum of problems that might impair and reduce quality of life. Even with medication, 30% of epilepsy patients still have recurring seizures. An epileptic seizure is caused by significant neuronal electrical activity, which affects brain activity. EEG shows these changes as high-amplitude spiky and sluggish waves. Recognizing seizures on an electroencephalogram (EEG) manually by a professional neurologist is a time-consuming and labor-intensive process, hence an efficient automated approach is necessary for the identification of epileptic seizure. One technique to increase the speed and accuracy with which a diagnosis of epileptic seizures could be made is by utilizing computer-aided diagnosis systems that are built on deep neural networks, or DNN. This study introduces a fusion of recurrent neural networks (RNNs) and bi-directional long short-term memories (BiLSTMs) for automatic epileptic seizure identification via EEG signal processing in order to tackle the aforementioned informational challenges. An electroencephalogram’s (EEG) raw data were first normalized after undergoing pre-processing. A RNN model was fed the normalized EEG sequence data and trained to accurately extract features from the data. Afterwards, the features were passed to the BiLSTM layers for processing so that further temporal information could be retrieved. In addition, the proposed RNN-BiLSTM model was tested in an experimental setting using the freely accessible UCI epileptic seizure dataset. Experimental findings of the suggested model have achieved avg values of 98.90%, 98.50%, 98. 20%, and 98.60%, respectively, for accuracy, sensitivity, precision, and specificity. To further verify the new model’s efficacy, it is compared to other models, such as the RNN-LSTM and the RNN-GRU learning models, and is shown to have improved the same metrics by 1.8%, 1.69%, 1.95%, and 2.2% on using 5-fold. Additionally, the proposed method was compared to state-of-the-art approaches and proved to be a more accurate categorization of such techniques