Background and aims Angiographic guidance for percutaneous coronary intervention (PCI) has significant limitations in interpretation. The superior spatial resolution of optical coherence tomography (OCT) can provide meaningful clinical benefits, although limited data is available on Asian populations. This study aimed to determine whether OCT can provide additional advantages and useful clinical information beyond that obtained by angiography alone in decision making for PCI. Methods This was an observational study based on a single tertiary cardiac center in Pakistan, which includes 67 patients who underwent coronary angiogram and stenting. Their pre and post stenting OCT findings were recorded. Any additional intervention was also recorded. The data were analysed using IBM SPSS software version 26.0. Results The mean age was 55.00 ± 9.00 years. Majority of the patients were males (65.7%). On angiography, there was an equal number of stable and ruptured plaques (38.8%). Post stenting results showed 29.9% under deployed stents and 34.3% were either undersized or mal-apposed. Out of 67 patients, 50 (74.6%) needed re-intervention after PCI. Among different procedures, post-dilatation was most common. Conclusion The main OCT benefit is in borderline lesions on CA, in whom OCT identifies significant coronary stenosis and leads to PCI indication in patients. In the post-PCI context, OCT leads to an indication of PCI optimisation in half of the coronary lesions.
Warfarin is a readily available anticoagulant used worldwide in a variety of clinical scenarios. Patients who need more than 15 mg/day are considered to be warfarin resistant. Numerous genes have been implicated in warfarin pharmacogenetics, with genes encoding CYP2C9 and VKORC1 shown to be the most important determinants of drug dosage requirements. A 27-year-old woman was admitted as she had a sub-therapeutic international normalized ratio (INR) after prosthetic mitral valve replacement. Even after a warfarin dose of 50 mg/day, her INR was not in the therapeutic range, so the heart team decided to replace her metallic valve with a bioprosthetic valve, thus alleviating the need for anticoagulation.
Precipitation prediction (PP) have become one of the significant research areas of deep learning (DL) and machine vision (MV) techniques are frequently used to predict the weather variables (WV). Since the climate change has left significant impact upon weather variables (WV) and continuously changes are observed in temperature, humidity, cloud patterns and other factors. Although cloud images contain sufficient information to predict the precipitation pattern but due to changes in climate, the complex cloud patterns and rapid shape changing behavior of clouds are difficult to consider for rainfall prediction. Prediction of rainfall would provide more meticulous assistance to the farmers to know about the weather conditions and to care their cash crops. This research proposes a framework to classify the dark cloud patterns (DCP) for prediction of precipitation. The framework consists upon three steps to classify the cloud images, first step tackles noise reduction operations, feature selection and preparation of datasets. Second step construct the decision model by using convolutional neural network (CNN) and third step presents the performance visualization by using confusion matrix, precision, recall and accuracy measures. This research contributes (1) real-world clouds datasets (2) method to prepare datasets (3) highest classification accuracy to predict estimated as 96.90%.
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