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Background: A stroke can either be modifiable or non-modifiable triggered by various risk factors. Globally, modifiable stroke could ignite in the form of diabetes, smoking, hypertension, hyperlipidemia, and various heart diseases Aim: The present study intended to evaluate the prevalence of modifiable risk factors in stroke patients Methods: This cross-sectional study was conducted on 182 stroke patients admitted to the Medicine Department of TCH name from January 2021 to October 2021. Clinically diagnosed stroke patients of age 20 years or above confirmed by magnetic resonance imaging (MRI) or computed tomography (CT) were enrolled. Demographic details, previous medical history, and concerns investigation were gathered in pre-designed proforma. For data analysis, SPSS version 20 was used Results: The overall mean age of the 182stroke patients was 58.43±9.45 years with an age range from 20 years to 85years. Of the total 182 patients, 104 (57.1%) were male and 78 (42.9%) were female. The male to female ratio was 1.33:1. The prevalence of stroke types such as subarachnoid hemorrhage, cerebral infraction, and intracerebral hemorrhages was 4.5%, 58.4%, and 37.1% respectively. The incidence of risk factors such as ischemic heart disease, previous stroke, high cholesterol levels, smoking history, diabetes, and hypertension was 11 (6.04%), 18 (9.9%), 24 (13.2%), 33 (18.1%),38 (20.9%), and 113 (62.1%) respectively Conclusion: Our study found that hypertension is the major modifiable risk factor for stroke development followed by other risk factors such as diabetes, higher cholesterol levels, smoking history, and ischemic heart diseases. Also, it has been observed that stroke prevention is better than cure by identifying various risk factors and initiation of stroke prevention education. Controlling modifiable risk factors mitigate the mortality rate among stroke patients. Keywords: Ischemic Stroke; Modifiable Risk factors; Hemorrhagic Stroke

Background and Objective: The diabetic Mellitus common and spiking complication is Diabetic peripheral neuropathy (DPN). It is frequently associated with thyroid dysfunction. The subclinical hypothyroidism prevalence and clinical outcomes have been investigated by various studies. The present study aims to determine the prevalence of subclinical hypothyroidism in diabetic peripheral neuropathy patients. Methods: This cross-sectional study was carried out on 164 diabetic neuropathy patients attending the medicine department of Qazi Hussain Ahmad Medical Complex, Nowshera KPK during the study period from 2020 to 2021. Patients’ demographic details, clinical history, and neurological examination were recorded. Normal free thyroxin value was set as a referential standard for subclinical hypothyroidism diagnosis among all the patients. Diabetic neuropathy patients’ clinical manifestations were recorded as per neurological screening instrument. The clinical scoring system was utilized for DPN severity categorization into mild (6-8), moderate (9-11), and severe (>12). SPSS version 20 and a logistic regression model were used for data analysis. Results: This study enrolled 164 diabetic peripheral neuropathy patients. Of the total 164, 69 (42.1%) were male and 95 (57.9%) were female. The overall mean age was 49.61±13.72 years. The prevalence of subclinical hypothyroidism was 32 [19.5%; 95% CI; 14.3%-23.7%]. The diabetic peripheral neuropathy patients with subclinical hypothyroidism had a higher prevalence of severity 86 [52.4%; 95%CI] compared to DNP patients without SCH 46 [28.04%; 95% CI] with a 3% level of significance. A higher HbA1c and HOMA-IR was found in patients with their respective values were (8.3±1.1 against 7.2±1.3) where p-value

A complex domain-based approach of active contour model has been proposed for image segmentation which deforms iteratively to partition an image into various useful regions. A new region-based complex pressure force function has been designed which gives shape to the dynamic active contour using image forces, and efficiently control the propagation of moving interface. This model makes the level set function binary, uses Gaussian smoothing kernel to regulate and avoid re-initialisation procedure. The working scheme of the model is as: the real image data is converted into complex data by iota (i) times and the average iota (i) times of horizontal and vertical components of gradient is inserted in the model to develop complete complex gradient. The proposed model can be implemented by simple finite difference scheme. The efficiency and robustness of the authors model have been verified.

A novel region based Active Contour Model (ACM) for image segmentation is presented using image local information for intensity inhomogeneity images. A transcendental (trigonometric) energy functional based on Local Fitted Image (LFI) energy is suggested to extract the image local information. The difference between the original and fitting image is introduced as an angular constraint of the trigonometric function. This new approach has the ability to handle images with severe intensity inhomogeneity, multiple intensity classes and regions with non-homogeneous pixel intensities. To regularize and maintain the level set function, Gaussian filtering procedure is adopted that not only maintains the level set consistency but also eliminates re-initialization procedure to avoid computational complexity. Furthermore, the proposed model holds boundary regularization property and achieves sub-pixel accuracy. Experimental results on several real and synthetic images demonstrate that the proposed model has higher performance and better accuracy in comparison to the state-of-the-art models.