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Objective: The research aimed to examine whether the carotid condition is linked to coronary artery disease in sufferers sent to exercise echocardiography and when it raises the exercise echocardiography capability to forecast coronary artery disease. Place and Duration: During the last five years, continuous subjects over eighteen years with diagnosed coronary artery disease underwent exercise echocardiography and carotid ultrasound at Indus Hospital Karachi. Study Design: Cross-sectional Methodology: We studied 312 participants without past heart disease who experienced exercise echocardiography, coronary angiography, and carotid ultrasonography over five years. Specific variables had been described as proportions, and evaluation among groups was according to Fisher’s exact and chi-square tests. Constant variables had been described as mean or median whenever their distribution deceased from average, and variations had been evaluated through the un-paired t-test. Continuous and binary quantitative variables have been likened to utilizing binary logistic regression. IBM SPSS, Version 20 was utilized. Results: 178 (57.1 percent) participants had substantial coronary artery disease. Components linked to CAD in multi-variate evaluation were FPG (OR 2.04, p=0.062), pre-test possibility of CAD greater than 65 percent (odds ratio 7.42, p less than0.004), positive exercise echocardiography (odds ratio 42.04, p less than 0.004) and carotid plaque presence (odds ratio 5.8, p= 0.052). There wasn't a statistical factor in the area below the curve after the inclusion of carotid plaque to exercise echocardiography results (3.08 vs. 3.24, p=1.05) nor the level of sensitivity, uniqueness, predictive efficiency, or values. Conclusion: Carotid plaque is linked to CAD in sufferers going through exercise echocardiography (EE), although its inclusion to EE doesn't improve coronary artery disease (CAD) prediction, most likely because of inadequate mathematical power. Carotid plaque reclassified 1 / 3 of sufferers to high risk class in spite of negative exercise echocardiography (EE) or absence of coronary artery disease; these subjects take advantage of aggressive primary prevention treatments. Keywords: Carotid Ultrasonography, Coronary Artery Disease, echocardiography

Background: Tuberculous meningitis is a well known consequence of tuberculosis, related with top mortality and morbidity in emergent nations. Prompt recognition can play a noteworthy part in rewarding and treating patients of tuberculous meningitis. Aim: To Ascertain the accuracy of contrast FLAIR MRI for diagnosis of tuberculous meningitis in children considering CSF culture as benchmark. Setting: Radiology Department of AKUH. Study Design: Cross-sectional study. Place and Duration: This study was conducted at Department of Radiology, Aga Khan University Hospital, Karachi From January 2015 to December 2016 Sample Selection: A total of 148 paediatric patients with clinical impression of tuberculous meningitis for 6 months were incorporated in study. FLAIR images were carried out from skull base to vertex in coronal plane from nasion to occipital protuberance with interval of 1.5 mm and 5 mm sections were taken. Data Analysis: Percentage were calculated for presentation of variables including FLAIR imaging findings, CSF Culture results and gender. Sensitivity, specificity, accuracy, positive and negative predictive values of FLAIR calculated and presented in percentages. Chi square test was applied taking P

UNet and its variations achieve state-of-the-art performances in medical image segmentation. In end-to-end learning, the training with high-resolution medical images achieves higher accuracy for medical image segmentation. However, the network depth, a massive number of parameters, and low receptive fields are issues in developing deep architecture. Moreover, the lack of multi-scale contextual information degrades the segmentation performance due to the different sizes and shapes of regions of interest. The extraction and aggregation of multi-scale features play an important role in improving medical image segmentation performance. This paper introduces the MH UNet, a multi-scale hierarchical-based architecture for medical image segmentation that addresses the challenges of heterogeneous organ segmentation. To reduce the training parameters and increase efficient gradient flow, we implement densely connected blocks. Residual-Inception blocks are used to obtain full contextual information. A hierarchical block is introduced between the encoder-decoder for acquiring and merging features to extract multi-scale information in the proposed architecture. We implement and validate our proposed architecture on four challenging MICCAI datasets. Our proposed approach achieves state-of-the-art performance on the BraTS 2018, 2019, and 2020 Magnetic Resonance Imaging (MRI) validation datasets. Our approach is 14.05 times lighter than the best method of BraTS 2018. In the meantime, our proposed approach has 2.2 times fewer training parameters than the top 3D approach on the ISLES 2018 Computed Tomographic Perfusion (CTP) testing dataset. MH UNet is available at https://github.com/parvezamu/MHUnet.

Purpose In patients with massive or recurrent gastrointestinal bleeding (GIB) which is not amenable to endoscopic therapy, angiographic interventions are often employed. We report our ten-year experience of empiric transcatheter arterial embolization (TAE) for patients with massive or recurrent GIB. Methods All patients who had undergone empiric TAE at our hospital between March 2004 and June 2015 were identified using the institutional radiology information system. A retrospective chart review was performed using a structured pro forma. Technical success rate, 30-day clinical success rate, 30-day mortality rate, and rate of procedural complications were computed. Statistical analysis was performed using Statistical Package for Social Sciences (SPSS) version 20. Results A total of 32 patients had undergone empiric TAE for GIB during the study period. The median age of subjects was 56 years and two-thirds of them were male (68.7%). Gastroduodenal (n=24), ileocolic (n=3), left gastric (n=2), right gastroepiploic (n=1), and branches of superior and middle rectal arteries (n=1) were embolized using microcoils (n=25), polyvinyl alcohol particles (n=25), and gelatin sponge (n=3)--either alone or in combination. Technical and 30-day clinical success rates were 96.9% (31/32) and 71.9% (23/32), respectively. The 30-day mortality rate for our cohort was 21.9% (7/32). One patient developed re-bleeding at two days after the initial procedure and required repeat embolization. Coil migration (n=3) and access site hematoma (n=1) were the observed procedural complications. Conclusion Empiric TAE can be a useful treatment option for selected patients with massive or recurrent GIB that is not amenable to endoscopic therapy.

The exact position of dead tissue in ischemic stroke lesions plays an important role to cure the life-threatening condition. However, this issue stays difficult caused by variation of ischemic strokes such as shape and location. Fully convolutional neural networks (FCN) have great potential for semantic image segmentation. Recently, UNet based CNN architectures have secured outstanding performance in the application of medical imaging. The primary problem is data imbalanced in the application of such networks which are very common in medical data application such as lesion applications where non-lesion voxels are higher than lesion voxels. Data imbalance generates low recall and high precision on the prediction of trained networks. Biases towards the particular class are not suitable for many medical works where false positives are less than false negatives. A lot of approaches are developed to tackle this issue including balanced sampling, sample re-weighting, and recently focal loss and similarity loss functions. In this paper, we proposed hyper densely connected network along with hybrid loss functions for ischemic stroke lesions segmentation. The densely connected network exploits the contextual information of multi-modalities, and hybrid loss function uses to overcome the data imbalance. We evaluate our performance on ISLES dataset. Our approach performs well as compared to other existing methods.

The non-engineered construction of masonry structures has been proved to be the most vulnerable during earthquake and have cited maximum damage during past earthquakes in India [4]. India is a country which is known for its culture and historical importance, therefore the need for an extensive monitoring and stability assessment of historical monuments is required. The historical monuments have been built without taking into consideration the effect of seismic parameters mentioned in latest Indian Standard of Earthquake Resistant Design of Structures. The present study is an attempt to assess the vulnerability & the stability of the historical masonry structures against the horizontal forces produced by major earthquakes using finite element (FE) simulation technique [1, 2]. The Wazirpur Tomb - an example of historical construction has been considered for the present study. The construction of box-like structure of the tomb has been done using locally available sandstone & lime mortar. The Response Spectrum Analysis [3] has been carried out & the response in terms of engineering parameters, such as natural frequency of structure, displacements, stresses etc. have been noted & discussed. The maximum stresses obtained from FE simulation technique have been compared with the permissible compressive & flexural stresses.

In this paper we propose to use fuzzy logic in detecting emotional content from text. Fuzzy logic was developed to deal with concepts that do not have well-defined, sharp boundaries, which theoretically is ideal for emotion as no well-defined boundaries are defined for emotion categories (e.g., happiness, sadness, surprise, fear, disgust, and anger).The transition from one physiological state of emotion to another is also gradual and could easily be modeled by fuzzy logic approach.

Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare.

Esophageal perforation has a high mortality rate. Fluoroscopic esophagography (FE) is the procedure of choice for diagnosing esophageal perforation. However, FE can be difficult to perform in seriously ill patients. We retrospectively reviewed charts and scans of all patients who had undergone thoracic CT (TCT) without oral contrast and FE for suspicion of esophageal perforation at our hospital between October, 2010 and December, 2015. Scans were interpreted by a single consultant radiologist having > 5 years of relevant experience. Statistical analysis was performed using SPSS version 20. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of TCT were computed using FE as reference standard. Of 122 subjects, 106 (83%) were male and their median age was 42 [inter-quartile range (IQR) 29–53] years. Esophageal perforation was evident on FE in 15 (8%) cases. Sensitivity, specificity, PPV and NPV of TCT for detecting esophageal perforation were 100, 54.6, 23.4 and 100%, respectively. When TCT was negative (n = 107), an alternative diagnosis was evident in 65 cases. Thoracic computed tomography (TCT) had 100% sensitivity and negative predictive value for excluding esophageal perforation. FE may be omitted in patients who have no evidence of mediastinal collection, pneumomediastinum or esophageal wall defect on TCT. However, in the presence of any of these features, FE is still necessary to confirm or exclude the presence of an esophageal perforation.

To determine the diagnostic validity of thyroid ultrasound in differentiating between benign and malignant thyroid nodules.The cross-sectional study was conducted at Aga Khan University Hospital, Karachi, from August 2011 to January 2013, and comprised all patients of either gender with thyroid nodules referred for ultrasound thyroid and fine needle aspiration cytology. Ultrasonography was performed by radiologists and ultrasound parameters were assessed and compared with cytology results in all nodules. Diagnostic validity of each ultrasound feature was calculated.Of the 101 patients, 81(80%) were female. The overall mean age was 43±13 years (range: 15-73 years). On histocytopathology, 96(95%) nodules were benign and 5(4.9%) were malignant. The sensitivity and specificity of ultrasound features in predicting malignancy were calcification 80% and 68%; hypoechogenecity 80% and 52%; ill-defined lobulated margin 40% and 96%; solid 80% and 40%; taller than wider 50% and 63%. Each ultrasound feature had negative predictive value ranging from 95% to 98% in malignant nodules.Identification of calcification, hypoechogenecity and solid with ill-defined margins in a thyroid nodule on ultrasound was helpful in suspecting thyroid malignancy and warranted urgent diagnostic fine needle aspiration cytology.

Objective of this article is to investigate the success (by determining citizen’s intention to use and satisfaction) of Land Record Information Systems (LRMIS) from perspective of Pakistan. The success of this egovernment information system is investigated using an incorporated IS success model. Model formulated here features constructs such as System Quality, Service Quality, and Information Quality, Perceived Risk, Perceived Ease of Use, Perceived Satisfaction, Perceived Usefulness, Awareness, Trust, Information Security and Behavioral Intention. The suggested incorporated research model of IS was validated using feedbacks taken from 250 citizens across different cities of Pakistan. Verifiable findings showed the positive and substantial linkage between all 17 hypothesized associations of 10 different constructs. Validated data-based outcomes and argumentations provided in this research can help government to enrich upon and to completely avail the potentiality of LRMIS as serviceable and an effective computer system toward a digital, secure, lucid and uncorrupted society.