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Introduction: Polycystic Ovarian Syndrome (PCOS) is a condition that affects a woman’s hormone levels and it is said to be the most common female syndrome. Affects between 4-8% of reproductive-aged women and is associated with reproductive, metabolic and psychological dysfunction. Methods: A Quantitative observational cross-sectional study, we recruited 350 students of Qassim University selected using random sampling according to the inclusive criteria (age between 18-25 years). An online questionnaire designed in Arabic to facilitate the data collection, and university students, participated voluntarily. Statistical data entry and analysis of results was performed using Microsoft Excel. Results: Our sample size was 350 Qassim University students whose ages were between 18-25 years. 71% (n=248) Students heard about the PCOS, 49% of them heard about the symptoms from other people (mother, sister, cousin, etc.). The presence of the symptoms of PCOS among these students was as, the most common symptoms were Hair loss 74%, Mood swing 67%, Acne 61%, and the least common symptom was Voice change 8%. The prevalence of the syndrome among the university students was found to be 12% of these 350 students suffer from PCOS (diagnosed). Conclusion: 71% of studens were aware about the Polycystic Ovary Syndrome. 12% of students suffered from PCOS. Most common symptoms reported was Irregular menstrual cycle, 87%, and the least common symptom was Voice change 13%. 62 % of the students who suffered from PCOS were taking treatments based on hormonal therapy and surgical intervention. Attitude and Practices showed misconceptions about PCOS, and a need for awareness.

Summary Biological processes are characterized by a variety of different genomic feature sets. However, often times when building models, portions of these features are missing for a subset of the dataset. We provide a modeling framework to effectively integrate this type of heterogeneous data to improve prediction accuracy. To test our methodology, we have stacked data from the Cancer Cell Line Encyclopedia to increase the accuracy of drug sensitivity prediction. The package addresses the dynamic regime of information integration involving sequential addition of features and samples. Availability and implementation The framework has been implemented as a R package Sstack, which can be downloaded from https://cran.r-project.org/web/packages/Sstack/index.html, where further explanation of the package is available. Supplementary information Supplementary data are available at Bioinformatics online.

A requirement for precision medicine is the accurate prediction of the sensitivity of a given drug on an individual patient. A very common method for this prediction is the use of Random Forest built on Genomic Features such as gene expression. However, effective drug sensitivity prediction requires the use of multiple heterogeneous dataset but it is rare that all such information is available for all drug and cell line combinations. To effectively incorporate all available data into a drug sensitivity prediction problem requires the use of stacking multiple models, built using a variety of different methods and data. In this article we investigate stacking in the context of drug sensitivity prediction. First, we examine basic models utilized in drug sensitivity prediction including the Random Forest, Neural Network, and K-Nearest Neighbor approach. To stack individual models we utilize a linear stacking method built utilizing a held-out validation set. We then investigate which form of stacking is most effective in improving accuracy. The two forms of stacking investigate are vertical stacking, in which our training samples are split and then stacked, and horizontal stacking, where our features are split before stacking. From a theoretical standpoint we have shown that horizontal stacking outperforms vertical stacking, especially as the sample size becomes large. Our theory is then proven utilizing both synthetically generated data, as well as data extracted from the Cancer Cell Line Encyclopedia. Despite having the best individual predictor, vertical stacking consistently underperformed compared to horizontal stacking. Finally, we build a stacked model utilizing data extracted from a variety of sources. Gene Expression for individual cell lines are taken from the GDSC database, drug target data is data mined from Pubchem and physical features are extracted utilizing PaDEL-Descriptor software. Using this data we aim to predict the Area Under the Curve value for individual drug-cell line combinations. We examine the performance of individual and stacked models. We note that stacking models built on two heterogeneous datasets provide superior performance to stacking different models built on the same dataset. It is also noted that stacking provides a noticeable reduction in the bias of our predictors when the dominant eigenvalue of the principle axis of variation in the residuals is significantly higher than the remaining eigenvalues.

A middle aged woman presenting with fatigue, pruritus, xanthelesma and hepatomegaly remains the typical presentation of primary biliary cirrhosis. Here, we present a case of primary biliary cirrhosis occurring in a male patient, who presented with fatigue and pruritus, had hepato-splenomegaly, raised serum alkaline phosphatase, positive anti-mitrochondrial antibody and negative antinuclear antibody.J MEDICINE July 2015; 16 (2) : 128-131

Background: The spectrum of bacteria causing urinary tract infection (UTI) and their antibiotic sensitivity pattern is not uniform. Escherichia coli and Klebsiella sp. are two common organisms responsible for UTI through-out the world. The incidence of UTI due to infection with extended-spectrum beta-lactamase (ESBL) producing organisms are increasing. This study was aimed to describe the frequency of ESBL positive organisms causing UTI and their antibiotic sensitivity pattern.Methods: This cross-sectional study was done in the Department of Internal Medicine, BIRDEM General Hospital from January to April, 2016.Results: Total number of patients was 137 with females predominance (M:F ratio 1 : 3.7). Mean age was 60.3 ±11.7 years. Most patients (131, 95.6%) of the study population were diabetic and glycaemic control was poor (mean HbA1c 9.3 ±2.3) in these subjects. Common symptoms were fever, vomiting, increased urinary frequency, dysuria, suprapubic pain, and loin pain. Neutrophilic leukocytosis was common (94.9%). E. coli (73.7%) was the commonest aetiological agent followed by Klebsiella (8.8%), Enterococcus (4.4%), Citrobacter (3.6%), Staphylococcus aureus (3.6%), Acinetobacter (2.9%), Enterobacter (1.5%), and Pseudomonas (1.5%). Over half of E. coli and of Klebsiella sp. and 100% of Enterobacter organisms were ESBL positive. Imipenem (100%), amikacin, netilmycin,and nitrofurantoin were among the most sensitive antibiotics.Conclusion: More than half (71, 51.82%) of UTI cases were due to ESBL positive organisms. Imipenem, amikacin, netilmycin, and nitrofurantoin remain the drug of choice.J MEDICINE January 2017; 18 (1) : 3-5

Primary hyperparathyroidism is often asymptomatic, may be detected during routine investigations or may present with features of hypercalcaemia. Vitamin D deficiency causing secondary hyperparathyroidism presents with musculoskeletal symptoms. Studies have shown an association between primary hyperparathyroidism and vitamin D deficiency. It has also been observed that when co-exist, these two conditions contribute to each other’s disease presentation and severity. This case report depicts initial asymptomatic hyperparathyroidism developing symptoms due to co-existing parathyroid adenoma and vitamin D deficiency in a 26-year-old muslim Bangladeshi female who responded well to adequate pre, per and post parathyroidectomy medical managementBirdem Med J 2015; 5(2): 104-106

Introduction: While media use is an integral part of the daily life of teenagers, there are a number of risks associated with social media use, specifically, negative effects on mental health. Teenagers, who are regular media users, have lots of friends, get along well with their parents, and are happy at school, but lack of close friends is the strongest predictors of depression and negative self-views. Our study was based on association between social media users and depression in female teenagers in Buraydah city Qassim Reigon, K.S.A.Methods: It was cross sectional study designed to examine both exposure (social media) and disease outcome (depression) simultaneously for each subject. It was conducted among female teenagers, aged between 13-17 years in Buraydah city, which is located in the central part of Saudi Arabia, during the period of January to July 2014. Study sample; Female teenager (16-19) from high schools in Buraydah. 80 female teenagers were selected randomly. Descriptive statististics; demography, family history, use of social media, and Analytical statististics; Prevalence of depression (outcome); Association between use social media and depression were estimated.Result: The mean age in study group was 16.27 ± 1.46 years. 35% have diagnosis with depression and 48 (60%) addicted users (≥5 h/day) of social media, out of which only 20 (41.67%) had depression.Conclusion: This study showed that people, who spend a lot of time using social media, do not always have depression. Out of 48 participants who used social media more than 5 hours daily it is only 20 (41.67%) who had depression (p

Motivation Random forest (RF) has become a widely popular prediction generating mechanism. Its strength lies in its flexibility, interpretability and ability to handle large number of features, typically larger than the sample size. However, this methodology is of limited use if one wishes to identify statistically significant features. Several ranking schemes are available that provide information on the relative importance of the features, but there is a paucity of general inferential mechanism, particularly in a multi-variate set up. We use the conditional inference tree framework to generate a RF where features are deleted sequentially based on explicit hypothesis testing. The resulting sequential algorithm offers an inferentially justifiable, but model-free, variable selection procedure. Significant features are then used to generate predictive RF. An added advantage of our methodology is that both variable selection and prediction are based on conditional inference framework and hence are coherent. Results We illustrate the performance of our Sequential Multi-Response Feature Selection approach through simulation studies and finally apply this methodology on Genomics of Drug Sensitivity for Cancer dataset to identify genetic characteristics that significantly impact drug sensitivities. Significant set of predictors obtained from our method are further validated from biological perspective. Availability and implementation https://github.com/jomayer/SMuRF Supplementary information Supplementary data are available at Bioinformatics online.

Objectives: To assess the prevalence and distribution of rheumatic diseases at rheumatology clinic in a tertiary level hospital. Methods: This retrospective study was done at rheumatology clinic of BIRDEM from July 2009 to December 2012. Diagnoses were reached by using clinical criteria supplemented by necessary investigations. Results: Total number of patients was 772 with female predominance (F:M ratio of 1.3:1). Mean age was 46.9 (range 13-83) years. Majority (85.9%) were in 3rd to 6th decades. Degenerative diseases (49.9%) were most common, followed by inflammatory conditions (33.5%), soft tissue rheumatism (7%), metabolic bone disease (4.8%) and connective tissue diseases (2.5%). Rheumatoid arthritis (27.7%) was the most prevalent disease, followed by osteoarthritis of knees (26.2%) and lumber spines (23.4). Tendinitis, osteoporosis, fibromyalgia and systemic lupus erythematosus prevailed in 5.6%, 4.8%, 1.4% and 1.3% cases respectively. Common comorbidities were diabetes mellitus (87.9%), hypertension (22.2%), ischaemic heart disease (12.4%), dyslipidaemia (10.1%), fatty liver (7.9%), chronic kidney disease (6.9%) and hypothyroidism (4.5%). Commonly prescribed medications were non-steroidal anti-inflammatory drugs (93%), disease modifying anti-rheumatic drugs (28.9%) and prednisolone (10.6%). Physiotherapy was required in 13.3% cases. In 56.6% cases various combination of treatment was required. In 12% patients receiving methotrexate, adverse effects occurred. Conclusion: Degenerative joint and spine diseases were more common but as an individual disease rheumatoid arthritis was the most prevalent condition. DOI: http://dx.doi.org/10.3329/jom.v15i1.19855 J Medicine 2014; 15: 23-27

Background: Diabetic ketoacidosis (DKA) is one of the most common acute complications of diabetes mellitus (DM). DKA is a recognised presenting feature of type 1 DM, but it commonly complicates previously diagnosed diabetic patients of all types, specially if they get infection or discontinue treatment. Objective: To describe the precipitating causes of DKA. Materials and Methods: This cross-sectional study was done from September to November, 2010 in Bangladesh Institute of Research & Rehabilitation in Diabetes, Endocrine and Metabolic Disorders (BIRDEM). Diagnosed DKA cases were evaluated clinically and by laboratory investigations for identification of precipitating causes. Results: Out of 50 patients, 28 were female. Mean age was 38.3 years. Forty patients (80%) were known diabetics and 10 (20%) were detected diabetic first time during this admission. Severe DKA cases were less common. Infection (20, 40%) was the commonest precipitating cause followed by noncompliance (14, 28%). In 7 (14%) cases no cause could be identified. Other less common causes included acute myocardial infarction, acute pancreatitis, stroke and surgery. Conclusion: Infection and noncompliance were the major precipitants of DKA. So, it is assumed that many DKA cases might be prevented by proper counselling regarding adherence to medication and sick days’ management. DOI: http://dx.doi.org/10.3329/jemc.v4i2.19676 J Enam Med Col 2014; 4(2): 94-97

Bangladesh Journal of Medicine, Vol 24 No 1, 2013, Page 10-13 DOI: http://dx.doi.org/10.3329/bjmed.v24i1.15029

It is a common palace of commercial life today that businesses are conduct not only in the form of a single private or public company, but also in the form of a group of companies consisting of a holding company (which may often be a listed public company) and a number of usually wholly owned subsidiaries and possibly sub-subsidiaries. Nowadays the group from may be employed at quite a small level of private business for tax or other reasons. As with larger groups of companies, different branches of the business or different properties will be located in each subsidiary. It may be convenient for different parts of business to be managed by separate but connected companies. There is in practice a presumption of direction of the wholly-owned subsidiary by its parent company. Because of this direction, the parent company is often fined for the illicit behaviour of the subsidiary. But the presumption of direction can be rebutted and the parent company can deny its liability, proving the independence of the subsidiary. This research work for: To set out some of the arguments and provide links to academic and other material that can shed light on the debate, notably proprietary research that examines the holding companies liability to subsidiary company, from a regulatory and corporate governance perspective under the Companies Act 2006.

The use of drug combinations to increase efficacy and lower resistance to therapy for personalized cancer medicine is being commonly recognized. Approaches have been recently designed to address the selection of drug combinations that can be highly effective across tumor cells but limited research have been conducted on the toxicity of these unique drug combinations. In this article, we approach this problem of combination drug toxicity by analyzing drug synergy over in vitro normal cell lines and generate combination drug concentrations whose combined effect on normal cell lines is less than the maximum monotherapy effect at approved concentrations. We present a mathematical framework for combination response estimation among multiple cell cultures along with stochastic analysis of prediction uncertainty. Results indicate the ability of the proposed framework to generate feasibly combination drug concentrations satisfying monotherapy toxicity constraints.

Objectives: To describe the antibiotic sensitivity pattern ofSalmonella typhi and Salmonella paratyphi from blood culture specimens. Methods: This cross-sectional study was done in the Department of Medicine, BIRDEM from July 2009 to June 2012. Standard laboratory and microbiological procedures were followed for blood culture and antibiotic sensitivity tests. Results: Among the 97 blood culture positive samples, S. typhi was 71 (73.2%) and S. paratyphi was 26 (26.8%). Multi-drug resistant strains of S. typhi and S. paratyphi were 23 (32.4%) and 3 (11.5%) cases respectively. Azithromycin, nalidixic acid, ciprofloxacin, levofloxacin and amoxicillin resistance was also found in a good number of cases (S. typhi and S. paratyphi: 71.8% and 57.7%, 42.3% and 30.8%, 38% and 34.6%, 38% and 26.9% and 38% and 26.9% cases respectively). Nineteen (31.1%) of the 61ciprofloxacin sensitive organisms were resistant to nalidixic acid. Ceftriaxone was sensitive in 100% of S. typhi and S. paratyphi. Cefixim, ciprofloxacin, levofloxacin, imipenem were among the most common sensitive antibiotics (S. typhi and S. paratyphi: 83.1% and 73.1%, 62% and 65.4%, 53.5% and 65.4%, 76.1% and 65.4% cases respectively). Conclusion: Ceftriaxone was the most sensitive antibiotic for treating enteric fever followed by cefixim, imipenem and ciprofloxacin. However, in suspected cases of enteric fever, blood culture should be requested before prescribing antibiotic. DOI: http://dx.doi.org/10.3329/jom.v15i2.20684 J MEDICINE 2014; 15 : 122-124

Precision medicine for cancer involves design of drug sensitivity prediction models that can predict patient response to various drugs. The drug response is usually represented by a single feature such as Area Under the Curve or IC50 derived from the experimental dose response curve. In this article, we consider the idea that predicting the dose response curve and generating the curve features instead of directly predicting the curve characteristics can increase prediction accuracy. Using the cancer cell line encyclopedia database, we illustrate that predicting dose response curve points to calculate AUC instead of directly predicting AUC can reduce prediction mean square error and increase correlation between experimental and predicted values.

Background Melioidosis is endemic in tropical Australia and Southeast Asian countries and its causative organism Burkholderia pseudomallei is a recognized cause of pneumonia in these regions. Recent isolation of the organism in the soil of Kapasia, Gazipur, Bangladesh has proven its exposure among the population residing in endemic areas of our country. Pneumonia is the most common presentation of melioidosis. Acute, subacute and chronic pneumonia due to B. pseudomallei can present as primary or secondary pneumonia. Treatment of such cases are challenging as well. Till date, few cases of acute and chronic pneumonia due to melioidosis occurring in local Bangladeshis as well as in returning travelers to Europe have been reported. To the best of our knowledge, this is the first reported case of primary melioidosis pneumonia declared cured after a 27 weeks of treatment regimen from Bangladesh. Case presentation A 43-year-old Bangladeshi gentleman, known diabetic, hypertensive, smoker, presented with the complaints of recurrent episodes of low to high grade intermittent fever, productive cough with occasional haemoptysis and 10 kg weight loss over one and half months. Poorly responding to conventional antibiotics, he was suspected as a case of pulmonary tuberculosis. Examination and investigations revealed left sided consolidation with cavitary lesion, hepato-splenomegaly and sputum analysis confirmed growth of Burkholderia pseudomallei. The patient was successfully treated as a case of primary melioidosis pneumonia. Conclusion Often misdiagnosed and empirically treated as tuberculosis, untreated melioidosis pneumonia may even lead to death. Therefore, melioidosis should be suspected in appropriate clinical scenario in patients with a history of residing in or traveling to endemic areas. In Bangladesh, time has come to explore whether melioidosis should be considered as an emerging infectious disease.

Diuretic drugs are used almost universally in patients with congestive heart failure, most frequently the potent loop diuretics. Despite their unproven effect on survival, their indisputable efficacy in relieving congestive symptoms makes them first line therapy for most patients. In the treatment of more advanced stages of heart failure diuretics may fail to control salt and water retention despite the use of appropriate doses. Diuretic resistance may be caused by decreased renal function and reduced and delayed peak concentrations of loop diuretics in the tubular fluid, but it can also be observed in the absence of these pharmacokinetic abnormalities. When the effect of a short acting diuretic has worn off, postdiuretic salt retention will occur during the rest of the day. Chronic treatment with a loop diuretic results in compensatory hypertrophy of epithelial cells downstream from the thick ascending limb and consequently its diuretic effect will be blunted. Strategies to overcome diuretic resistance include restriction of sodium intake, changes in dose, changes in timing, and combination diuretic therapy. DOI: http://dx.doi.org/10.3329/jdnmch.v17i1.12193 J. Dhaka National Med. Coll. Hos. 2011; 17 (01): 44-46

No abstract available

Ultrasound elastography is a medical imaging technique to visualize the tissue stiffness using a conventional ultrasound machine. To compute the elastogram, the radiofrequency echo signals are acquired before and after a small applied deformation. Then the local tissue displacement field is estimated by searching the peak of the cross-correlation function between small segments of pre- and post- deformation signals. Finally, the corresponding strain distribution is imaged by differentiating the displacement field. However, the pre- and post- signals are not exactly the shifted versions of one another and hence the displacement estimation is inaccurate and the resulting strain image is highly noisy. In this paper, we propose a novel technique to estimate the strain image from the ratios of the peak frequencies of the pre- and post- deformation signals. Unlike other time/frequency domain reported schemes, our proposed technique does not use cross-correlation or any other similarity measures and directly estimate the strain in the power spectral density domain. Simulation experiments are conducted at various strain conditions and the results show that the proposed method can be an effective and computationally efficient algorithm for ultrasound elastography.

Supplementary information to application of transfer learning for cancer drug sensitivity prediction. Figure S1. Illustration of kNN image regression prediction for unknown GDSC AUC dataset using the available CCLE data. Figure S2. Illustration of change in performance for a single validation set with change in the number of nearest neighbors. Figure S3. Illustration of prediction for a single iteration for the Updated kNN image regression prediction. Figure S4. Illustration of shift between GDSC and CCLE AUC distributions. Table S1. Comparison of MSE for reconstruction and corresponding cost function value for both optimized latent vector and mean latent vector. Table S2. Comparison of Pearson correlation and NRMSE among kNN Image Regression Prediction, Latent Regression Prediction and Direct Prediction of GDSC sensitivity using CCLE data. Table S3. Comparison of Pearson correlation and NRMSE among kNN Image Regression Prediction, Latent Regression Prediction and Direct Prediction of CCLE sensitivity using GDSC data. Table S4. Comparison of Pearson correlation and NRMSE among combined Latent Regression & updated kNN Image Regression Prediction, kNN Image regression Prediction, Latent Regression Prediction and Direct Prediction of GDSC drug sensitivity using CCLE data. Table S5. Comparison of Pearson correlation and NRMSE among combined Latent Regression & updated kNN Image Regression Prediction, kNN Image regression Prediction, Latent Regression Prediction and Direct Prediction of CCLE drug sensitivity using GDSC data. Table S6. Comparison of K-fold cross-validation results for 4 GDSC drug sensitivity prediction approaches using CCLE â Mapped Prediction, CCLE model Prediction, Combined Model Prediction and Direct Prediction. (PDF 648 kb)

Supplementary information to application of transfer learning for cancer drug sensitivity prediction. Figure S1. Illustration of kNN image regression prediction for unknown GDSC AUC dataset using the available CCLE data. Figure S2. Illustration of change in performance for a single validation set with change in the number of nearest neighbors. Figure S3. Illustration of prediction for a single iteration for the Updated kNN image regression prediction. Figure S4. Illustration of shift between GDSC and CCLE AUC distributions. Table S1. Comparison of MSE for reconstruction and corresponding cost function value for both optimized latent vector and mean latent vector. Table S2. Comparison of Pearson correlation and NRMSE among kNN Image Regression Prediction, Latent Regression Prediction and Direct Prediction of GDSC sensitivity using CCLE data. Table S3. Comparison of Pearson correlation and NRMSE among kNN Image Regression Prediction, Latent Regression Prediction and Direct Prediction of CCLE sensitivity using GDSC data. Table S4. Comparison of Pearson correlation and NRMSE among combined Latent Regression & updated kNN Image Regression Prediction, kNN Image regression Prediction, Latent Regression Prediction and Direct Prediction of GDSC drug sensitivity using CCLE data. Table S5. Comparison of Pearson correlation and NRMSE among combined Latent Regression & updated kNN Image Regression Prediction, kNN Image regression Prediction, Latent Regression Prediction and Direct Prediction of CCLE drug sensitivity using GDSC data. Table S6. Comparison of K-fold cross-validation results for 4 GDSC drug sensitivity prediction approaches using CCLE â Mapped Prediction, CCLE model Prediction, Combined Model Prediction and Direct Prediction. (PDF 648 kb)