Forecasting estimated gram crops in rainfed agriculture of Bangladesh

Volume 2, Issue 2, July 200

Measuring patient satisfaction level of out-patient department (OPD): a study on Square Hospital Limited

Volume 3, Issue 2, July 201

Impact of shifting food grain production system on agricultural economy of Bangladesh

Volume 1, Issue 1, July 200

Markov Chain Analysis of Weekly Rainfall Data in Determining Drought-proneness

India. An index based on the parameters of this model has been suggested to indicate the extend of drought-proneness of a region. This study will be useful to agricultural planners and irrigation engineers to identifying the areas where agricultural development should be focused as a long term drought mitigation strategy. Also this study will contribute toward a better understanding of the climatology of drought in a major drought-prone region of the world

Exploring predictors of blood pressures and risk factors associated with hypertension: A comprehensive study on Eastern University in Bangladesh

21-24 December, 2012, ISSN 978984335876

Ovarian carcinoma associated with pregnancy: A clinicopathologic analysis of 23 cases and review of the literature

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to analyze and describe cases of ovarian cancer in pregnant women treated at our center and to review the literature concerned, and to discuss the rationale for therapy.</p> <p>Methods</p> <p>Twenty-Three patients of ovarian malignancies during pregnancy were treated at Vali- Asr Hospital between 1991 and 2002. Data on treatment and follow-up were evaluated.</p> <p>Results</p> <p>The incidence of ovarian carcinoma associated with pregnancy in our series was 0.083/1000 deliveries. Eleven (47.8%) were found with ovarian malignant germ cell tumors, five (21.7%) with low malignant potential tumors, four (17.4%) with invasive epithelial tumors, and three (13%) with sex cord stromal tumors. Seventeen (73.9%) of the patients were diagnosed in stage I and had complete remission. Five of the six in advanced stage died. The mean follow-up was 36.3 months. The prognosis was significantly related with stage and histological type (<it>P </it>< 0.05). Sixteen healthy live babies were recorded in this group, and two premature newborn died of respiratory distress syndrome. Chemotherapy was administered to 44% of the patients, in two cases during pregnancy. Overall survival at 5 years was 61%. In most of case conservative surgical treatment could be performed with adequate staging and debulking.</p> <p>Conclusion</p> <p>Early finding of ascitis by ultrasound and persistent large ovarian mass during pregnancy may be related to malignancy and advanced stage. Pregnant women in advanced stage of ovarian cancer seem to have poor prognosis.</p

Prediction of gestational age using urinary metabolites in term and preterm pregnancies.

Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC-MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value

Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries.

Importance: Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. Objective: To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. Design, Setting, and Participants: This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. Exposures: Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. Main Outcomes and Measures: The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. Results: Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. Conclusions and Relevance: This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB