Intelligent deep model based on convolutional neural network's and multi-layer perceptron to classify cardiac abnormality in diabetic patients.

The ECG is a crucial tool in the medical field for recording the heartbeat signal over time, aiding in the identification of various cardiac diseases. Commonly, the interpretation of ECGs necessitates specialized knowledge. However, this paper explores the application of machine learning algorithms and deep learning algorithm to autonomously identify cardiac diseases in diabetic patients in the absence of expert intervention. Two models are introduced in this study: The MLP model effectively distinguishes between individuals with heart diseases and those without, achieving a high level of accuracy. Subsequently, the deep CNN model further refines the identification of specific cardiac conditions. The PTB-Diagnostic ECG dataset commonly used in the field of biomedical signal processing and machine learning, particularly for tasks related to electrocardiogram (ECG) analysis. a widely recognized dataset in the field, is employed for training, testing, and validation of both the MLP and CNN models. This dataset comprises a diverse range of ECG recordings, providing a comprehensive representation of cardiac conditions. The proposed models feature two hidden layers with weights and biases in the MLP, and a three-layer CNN, facilitating the mapping of ECG data to different disease classes. The experimental results demonstrate that the MLP and deep CNN based models attain accuracy levels of up to 90.0% and 98.35%, and sensitivity 97.8%, 95.77%, specificity 88.9%, 96.3% F1-Score 93.13%, 95.84% respectively. These outcomes underscore the efficacy of deep learning approaches in automating the diagnosis of cardiac diseases through ECG analysis, showcasing the potential for accurate and efficient healthcare solutions.

Stability and Phase Response Analysis of Optimum Reduced-Order IIR Filter Designs for ECG R-Peak Detection.

Cardiovascular health and training success can be assessed using electrocardiogram (ECG) data. For over a quarter of a century, an individual's resting heart rate is varying more. As a result, it has become the subject of inquiry and reveals the intricate relationship between the human body and its environment. The autonomic nervous system has impact on blood flow system based on the rate of heartbeats. However, heart rate variation (HRV) characteristics analysis throughout the time period has lack of physical activity information. In the presence of patient movement, ECG signal is suffering from hard artefacts. Time-varying HRV parameters can be derived from low-frequency (LF) and high-frequency (HF) domains of the correct frequency. However, sometimes it is critical to ensuring accurate detection of the R-peak position. The proposed ROIIR (reduced-order IIR) offers 8.8% improvement in peak-to-peak swing than earlier IIR filter. We present an advanced filtering algorithm that is used for R-peak detection.

Designing an Optimum and Reduced Order Filter for Efficient ECG QRS Peak Detection and Classification of Arrhythmia Data.

Electrocardiogram (ECG) is commonly used biological signals that show an important role in cardiac analysis. The interpretation and acquisition of QRS complex are significant measures of ECG data dispensation. The R wave has a vital character in the analysis of cardiac rhythm irregularities as well as in the determination of heart rate variability (HRV). This manuscript is proposed to design a new artificial-intelligence-based approach of QRS peak detection and classification of the ECG data. The design of reduced order IIR filter is proposed for the low pass smoothening of the ECG signal data. The min-max optimization is used for optimizing the filter coefficient to design the reduced order filter. In this research paper, elimination of baseline wondering and the power line interferences from the ECG signal is of main attention. The result presented that the accuracy is increased by around 13% over the basic Pan-Tompkins method and around 8% over the existing FIR-filter-based classification rules.

A clinical study of the spectrum of photodermatoses in dark-skinned populations.

BACKGROUND: Photodermatoses are characterized by an abnormal cutaneous response to 'ordinary' light exposure. AIM: To study the spectrum of photodermatoses in populations with dark skin (skin types IV-VI) at a tertiary referral centre. METHODS: Consecutive patients with skin lesions confined to or predominantly located on photoexposed parts of the body and/or with photosensitivity were enrolled in the study, and their clinical details were recorded. Diagnosis was made on clinical grounds, and relevant investigations were carried out if required. Patch and photopatch testing were carried out in patients with chronic actinic dermatitis (CAD). Selected patients with CAD also underwent phototesting with UV (ultraviolet) A and broadband UVB light. RESULTS: We enrolled 362 patients (146 men, 216 women; mean age 35.6 ± 13.6 years), with mean disease duration of 3.4 years. The Fitzpatrick skin types were IV and V (52.8% and 47.2% of patients, respectively). Polymorphic light eruption (PMLE) was the commonest photodermatosis seen, affecting 59.7% of patients, followed by CAD (13.8%), collagen vascular disorders (7.7%), photoaggravated atopic dermatitis (6.1%), actinic lichen planus (ALP; 2.2%) and lichen planus pigmentosus (LPP; 1.6%). The majority (84.5%) of patients were involved in indoor work. Papular PMLE (37%) was the most common variant of PMLE, followed by pinpoint (31%), eczematous (22.2%), lichenoid (5.5%) and plaque-type (4.1%) PMLE. CONCLUSIONS: The spectrum of photodermatoses in Indian patients with dark skin phototypes (IV and V), is similar to that reported from other parts of the world. PMLE was the commonest photodermatosis seen, with the pinpoint and lichenoid variants accounting for over one-third of the PMLE cases. ALP and LPP were also not uncommon in our dark-skinned population.

Attenuation of Clostridium difficile toxin-induced damage to epithelial barrier by ecto-5'-nucleotidase (CD73) and adenosine receptor signaling.

BACKGROUND: Clostridium difficile (Cdf) releases toxins (TcdA and TcdB) that damage the intestinal epithelial barrier. Ecto-5'-nucleotidase (CD73) is expressed on intestinal epithelial cells, and it is hypothesized to protect against toxin-induced epithelial damage through the cleavage of 5'-AMP to adenosine (Ado) and subsequent activation of adenosine receptors (AdoRs). Herein, we sought to assess the potential protective effects of CD73 and AdoR signaling on the injurious effects of Cdf toxins. METHODS: Barrier function was assessed with T84 colonocytes. Transepithelial electrical resistance (TEER), paracellular fluorescein isothiocyanate (FITC)-dextran flux, and tight junction protein (ZO-1) integrity were monitored. Intrarectal installation of Cdf toxin was used to assess epithelial damage in vivo. KEY RESULTS: TcdA/B caused reduced TEER and increased paracellular flux in vitro. Concurrent treatment with 5'-AMP attenuated these responses to Cdf toxin; an effect that was blocked with ZM241385 (AdoRA2 antagonist). APCP, a CD73 inhibitor, also suppressed the protective effects of 5'-AMP on paracellular flux. 5'-AMP reduced toxin-induced disruption of ZO-1, an effect that was abolished by APCP and ZM241385. Inhibition of CD73 with APCP during Cdf toxin exposure led to increased intestinal barrier permeability and epithelial damage in vivo. Intrarectal instillation of 5'-AMP had no effect on toxin-induced intestinal injury. CONCLUSIONS & INFERENCES: Our data suggest that CD73 has a protective role against TcdA/B-induced damage. 5'-AMP treatment attenuated the damaging effects of Cdf toxin in vitro, and inhibitors of CD73 (APCP) and AdoRs (ZM241385) revealed that the cleavage of 5'-AMP to Ado was necessary for the protective effects. Inhibition of CD73 in vivo increases colonic tissue damage and epithelial permeability during Cdf toxin exposure.

Isolated scaphotrapeziotrapezoid osteoarthritis: a possible radiographic marker of chronic scapholunate ligament disruption.

This study was undertaken to describe 'isolated' osteoarthritis of the scaphotrapeziotrapezoid articulation in six patients with chronic scapholunate ligament disruption. Each patient included for study had scapholunate ligament disruption with secondary dorsal intercalated segmental instability. Secondary volar tilt of the scaphoid appeared to be limited in each case by impaction and development of osteoarthritis at the scaphotrapeziotrapezoid articulation. Isolated osteoarthritis at the scaphotrapeziotrapezoid articulation may accompany chronic scapholunate ligament disruption, its identification should trigger a search for associated scapholunate ligament disruption in the absence of a known systemic arthropathy.

MR imaging of dedifferentiated chondrosarcoma.

MR imaging of a dedifferentiated chondrosarcoma of the humerus is presented and correlated with gross and microscopic histology from the surgical specimen. The classification of chondrosarcoma and specific MR signal characteristics that suggest dedifferentiation are discussed.

Cardiac evaluation of the diabetic patient prior to peripheral vascular surgery.

The benefit of preoperative cardiac evaluation in the diabetic patient undergoing peripheral vascular surgery is uncertain. To investigate this issue we performed a retrospective review of 192 procedures performed in diabetic patients for chronic lower extremity arterial occlusive disease. The incidence of adverse postoperative cardiac events was determined, as well as its association with several preoperative factors including symptoms of coronary artery disease (CAD), extent and results of preoperative noninvasive cardiac evaluation, and operative site (aorta vs. lower extremity). The overall death and cardiac complication rates were 10.2% for lower extremity and 25.7% for aortic procedures (p = 0.02). For myocardial infarction and cardiac death alone, the rates were 5.1% and 5.7%, respectively (p > 0.10). Although a history of symptomatic CAD predicted the occurrence of any cardiac complication (28.3% vs. 8.2% [p < 0.01] for the aortic and lower extremity revascularization groups combined), no factor was found to be associated with the occurrence of myocardial infarction and cardiac death alone. In patients with a history of symptomatic CAD, there was no significant difference in the incidence of complications whether or not preoperative noninvasive cardiac testing was performed (28.1% vs. 28.6%, p > 0.10) or, if testing was performed, if the results were abnormal or normal (35.3% vs. 20.0%, p > 0.10). Similar results were obtained in patients with no history of symptomatic CAD. In summary, this retrospective review of our experience with noninvasive evaluation to detect CAD in diabetic patients undergoing peripheral vascular surgery failed to show any benefit in terms of reducing the incidence of postoperative cardiac events.