Linear programming based computational technique for leukemia classification using gene expression profile.

Cancer is a serious public health concern worldwide and is the leading cause of death. Blood cancer is one of the most dangerous types of cancer. Leukemia is a type of cancer that affects the blood cell and bone marrow. Acute leukemia is a chronic condition that is fatal if left untreated. A timely, reliable, and accurate diagnosis of leukemia at an early stage is critical to treating and preserving patients' lives. There are four types of leukemia, namely acute lymphocytic leukemia, acute myelogenous leukemia, chronic lymphocytic in extracting, and chronic myelogenous leukemia. Recognizing these cancerous development cells is often done via manual analysis of microscopic images. This requires an extraordinarily skilled pathologist. Leukemia symptoms might include lethargy, a lack of energy, a pale complexion, recurrent infections, and easy bleeding or bruising. One of the challenges in this area is identifying subtypes of leukemia for specialized treatment. This Study is carried out to increase the precision of diagnosis to assist in the development of personalized plans for treatment, and improve general leukemia-related healthcare practises. In this research, we used leukemia gene expression data from Curated Microarray Database (CuMiDa). Microarrays are ideal for studying cancer, however, categorizing the expression pattern of microarray information can be challenging. This proposed study uses feature selection methods and machine learning techniques to predict and classify subtypes of leukemia in gene expression data CuMiDa (GSE9476). This research work utilized linear programming (LP) as a machine-learning technique for classification. Linear programming model classifies and predicts the subtypes of leukemia Bone_Marrow_CD34, Bone Marrow, AML, PB, and PBSC CD34. Before using the LP model, we selected 25 features from the given dataset of 22283 features. These 25 significant features were the most distinguishing for classification. The classification accuracy of this work is 98.44%.

Use of smokeless tobacco among groups of Pakistani medical students - a cross sectional study.

BACKGROUND: Use of smokeless tobacco is common in South Asia. Tobacco is a major preventable cause of morbidity and mortality. Doctors make one of the best avenues to influence patients' tobacco use. However, medical students addicted to tobacco are likely to retain this habit as physicians and are unlikely to counsel patients against using tobacco. With this background, this study was conducted with the objective of determining the prevalence of smokeless tobacco among Pakistani medical students. METHODS: A cross sectional study was carried out in three medical colleges of Pakistan - one from the north and two from the southern region. 1025 students selected by convenient sampling completed a peer reviewed, pre-tested, self-administered questionnaire. Questions were asked regarding lifetime use (at least once or twice in their life), current use (at least once is the last 30 days), and established use (more than 100 times in their life) of smokeless tobacco. Chi square and logistic regression analyses were used. RESULTS: Two hundred and twenty (21.5%) students had used tobacco in some form (smoked or smokeless) in their lifetime. Sixty six (6.4%) students were lifetime users of smokeless tobacco. Thirteen (1.3%) were daily users while 18 (1.8%) fulfilled the criterion for established users. Niswar was the most commonly used form of smokeless tobacco followed by paan and nass. Most naswar users belonged to NWFP while most paan users studied in Karachi. On univariate analysis, lifetime use of smokeless tobacco showed significant associations with the use of cigarettes, student gender (M > F), student residence (boarders > day scholars) and location of the College (NWFP > Karachi). Multivariate analysis showed independent association of lifetime use of smokeless tobacco with concomitant cigarette smoking, student gender and location of the medical college. CONCLUSION: The use of smokeless tobacco among medical students cannot be ignored. The governments should add the goal of eliminating smokeless tobacco to existing drives against cigarette smoking. Drives in Karachi should focus more on eliminating paan usage while those in NWFP should focus more on the use of naswar. Medical colleges should provide greater education about the myths and hazards of smokeless tobacco.

Liaison psychiatry and depression in medical inpatients.

OBJECTIVE: To assess the frequency of depression among hospitalized patients, the socio-demographic variables associated with depression and the number of cases referred by physicians to Psychiatry. METHODS: A cross-sectional study was carried out at the Aga Khan University Hospital Karachi. An anonymous Urdu version of the WHO-developed self-reporting questionnaire (SRQ) was administered to inpatients meeting the inclusion criteria. Data was analyzed by SPSS version 13.0. RESULT: Of the 225 patients approached, 178 completed the questionnaire (men= 45.2%, women = 54.8%). The mean age of the sample was 45.2 years. Out of the total 30.5% of patients were identified as having probable depression, among which housewives were more likely to be depressed compared to others (p=0.031). Among variable comparison, there with secondary school education or below and those with psychiatric co-morbidities, showed significantly greater prevalence of depression (p=0.003) and (p=0.005) respectively. Attending physicians correctly diagnosed 7 (13%) patients and referred only 3 patients to Psychiatry over the previous month. CONCLUSION: The prevalence of depression among inpatients is comparable to that in the general population. Being a housewife, level at or below secondary school education and having a past psychiatric history are significant factors associated with depression in medical inpatients. A very small number of depressed cases were referred to a psychiatrist.