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Traditional blood disease detection based on visual inspection of blood smears using a microscope is time consuming, error-prone, and limited by the physical acuity of the hematologist. To facilitate clinical decision-making, an automated optical image processing system is required. Leukemia is a kind of cancer that is distinguished by the aberrant development of immature, abnormally shaped white blood cells (WBC) known as "blasts." Leukemia is a cancer of the white blood cells (WBCs) that affects the bone marrow and/or blood. A timely, safe, and accurate diagnosis of leukemia at an early stage is critical to treating and preserving patients' lives. The white blood cells in a blood smear are often examined under a microscope to determine the diagnosis.Many machine learning have been created to diagnose various illnesses, such as leukemia, and to deliver a high mis-classification error rate. As a result, we may use a deep learning system to identify microscope pictures for White Blood Cell Count study. The WBC differential counting system was divided into two modules: detection and classification. The detection module first processed the raw bone smear pictures, detecting all WBCs including red blood cells, platelet counts, staining impurities, and so on. The discovered cells were then sent into the categorization module.The categorization module was divided into two phases. In the first step, we separated a large number of cells, such as crushed cells, degenerated cells, and so on, that are not employed in the diagnosis for leukemia. The countable WBCs were then presented for multi-class separation using the Convolutional neural network technique in the second step

Background: Estimation of gestational age was needed for the measurement of intrauterine growth and associated conditions, such as whether infants with a low birth weight are preterm or growth-retarded, and identifying high-risk status for developmental delay. Menstrual history was unreliable in most of the cases and ultrasound was not feasible in low resource area, so in this study we were used anthropometric measurements as a simple tool for assessment of gestational age. Methods: This study was a cross sectional observational study conducted in 380 babies born in January 2020-December 2022 in department of pediatrics, Rajah Muthiah Medical College and Hospital. The data were collected using semi-structured questionnaire and anthropometric assessment. The Epi info 7 software was used to analyse the data. Results: The mean gestational age among neonates studied was 37.06±2.32 weeks, the mean head circumference was 33.48±1.01 cm and majority of them 141 babies had head circumference 33 cm, the mean chest circumference was 30.69±1.08 cm. and majority of the babies, had chest circumference of 30 cm, the mean foot length was 7.64±0.24 cm, the mean length of the babies was 47.9±1.26 cm. Conclusions: Our study conclude that anthropometeric parameters in combination (head, chest circumference, length, foot length, birth weight) and as single parameters (foot length) was a simple and easy tool for assessing gestational age in low resource setting where we can make quick referral.

α,β-Unsaturated compounds containing aldehyde, ketone, nitrile and ester functional groups are versatile precursor for the synthesis of novel organic molecules. All these compounds undergo Michael addition of carbon and other nucleophiles at b-carbon atom of the a,b-unsaturated system. Different carbo- and hetero-cyclic compounds can be synthesized by varying the conditions of cyclisation of the Michael adducts. In this context, it is planned to synthesize pyrazole derivatives and diazepine derivatives by reacting a,b-unsaturated compounds with different Nitrogen nucleophiles such as, o-phenylenediamine etc. The present investigation reports the unexpected hydrogenation of C=C in a,b-unsaturated nitriles and the unexpected synthesis of azine derivatives from a,b-unsaturated nitrileand hydrazinium sulphate.