Role of cytohormonal study in normal pregnancy and in threatened abortion

Background: It is certain from clinical experience of many that one or more hemorrhages in early pregnancy can still end up in good fetal outcome. So, our study deals with comparison of cytohormonal study in pregnancy and threatened abortion. The study was conducted with the aim of utilizing colpocytogram as a tool in assessing and treating cases of threatened abortion and comparing them with normal pregnant women.Methods: Patients attending antenatal care unit on outdoor basis and labelled as normal pregnancy cases were considered as control group. The patients of threatened abortion were studied when they were admitted in Gynecology department for indoor treatment. Patients were studied taking into consideration their age, parity, number of abortions, complaints (P/V bleeding, pain in abdomen), gestational age, per abdomen and per vaginal findings and also USG findings and vaginal smear pattern.Results: Maximum number of patients was present in the age group of 21-25 years in both the groups. While only 13.33% had normal smear pattern in threatened abortion group. 86.67% patients in threatened abortion group showed abnormal smear pattern. There is statistically significant difference was found (p<0.05). There is statistically significant was found (P<0.01) and indicates good effects of the drug on the vaginal epithelium.Conclusions: The cytohormonal study acts as a simple, reliable, good, noninvasive method for evaluation of hormonal pattern in normal pregnancy and threatened abortion.

Deep Learning for Fruit Grading: A State-of-the-Art Review

In the food industry, grading fruit quality is a critical responsibility. Throughout this process, fruits are sorted and categorized in by their quality. Fruit grading can be done using both machine learning and visual assessment. Visual inspection is subjective and can be influenced by human prejudice. Machine learning can produce more accurate and unbiased results. Deep learning-based methods can be used to evaluate the fruit quality by teaching a neural network to recognize various quality parameters like size, color, and defects. Deep learning methodologies for evaluating fruit quality offer further benefits. They are neutral and accurate, and they can manage enormous amounts of data. They can also save labor expenses and improve the efficiency of the grading process. Deep learning methods are useful for evaluating fruit quality, but they have several drawbacks. These include an intricate neural network, overfitting, and a lack of high-quality training data. Addressing these issues is crucial for the success of deep learning in fruit quality evaluation. In this paper, various significant deep-learning methods for evaluating fruit quality are described. The methods' advantages and disadvantages are also discussed. The study gives the researcher pointers on how to improve current strategies or create fresh ones to improve performance in terms of training effectiveness, accuracy, etc

Hendersonula toruloidea Nattrass. fungus on new host from Nandurbar district (M.S.)

The present investigation report deals with the fungus collected from Nandurbar district, it is being new addition to the North Maharashtra region. Ipomea fistulosa Mort ex. Choisy is being reported as a new host substrate for Hendersonula toruloidea Nattrass

Research Notes: G. B. Pant University of Agriculture and Technology

Singh et al. (1974) reported the inheritance as well as the pollen behaviour of 3 male-sterile lines of soybean , viz: \u27Semmes M.S.1\u27 , \u27Semmes M.S.2\u27 and \u27N 69-2774\u27. They observed monogenic inheritance with sterility being the recessive trait in all these lines. Semmes M.S.1 had nonfunctional pollen but of the same size as that of normal pollen grains; Semmes M.S.2 had no pollen at all, whereas N 69-2774 had nonfunctional pollen but these were much bigger as compared to the normal pollen grains

FORMULATION AND EVALUATION OF BESIFLOXACIN NON-ERODIBLE OCULAR INSERTS

Objective: Ocular inserts offer many advantages over conventional dosage forms, like increased ocular residence, the possibility of releasing a drug at a slow and constant rate, accurate dosing, exclusion of preservatives, and increased shelf life. Besifloxacin is a very important drug for the treatment of infectious conjunctivitis. The present study was aimed to formulate and evaluate Besifloxacin Non-Erodible Ocular Insert using Pullulan and polyvinyl pyrrolidone as a drug reservoir, PEG 400 as a plasticizer, and Eudragit RS-100 as a rate-controlling membrane. Methods: Central composite design was employed to study the effect of independent variables, i.e., effects of Pullulan amount (X1) and PVP (X2) on the dependent variables, i.e., % moisture absorption and In vitro diffusion rate. After evaluation of all thirteen batches of ocular insert reservoir formulation, BSF2 and BSF4 were selected as a satisfactory formulation and was sandwiched between rate-controlling membrane, which was made up of Eudragit RS-100 (3 and 5%). Results: The drug content of all formulations was found to be in the range of 95.33 to 99.89 %. In vitro diffusion of Besifloxacin from reservoir formulations (BSF1 to BSF13) was found to be 62.44 to 70.62 %. In vitro diffusion rate of an ocular insert of Besifloxacin can offer benefits such as increasing residence time, prolonging drug release in the eye for 24 h. Eudragit RS-100, as a sustained drug release polymer, showed promising sustained released action. Conclusion: The study concluded that Besifloxacin non-erodible ocular inserts can be successfully developed using Pullulan and polyvinyl pyrrolidone, which will sustain the release of the drug also reduce the frequency of administration, and thereby may help to improve patient compliance

Radiation Heat Transfer In A Particulate Medium Using A Ray Tracing Method

In the present work, a complete 3D simulation of ray tracing model is developed for studying the radiation heat transfer, associated with laser based additive manufacturing, in both thick and thin particulate beds by using the Monte Carlo method. Additional program is developed for creating different types of packing structures such as simple cubic, rhombohydral and random packing. The scattering mechanisms in the particulate beds for large opaque spheres are evaluated using the specular and diffuse reflection methods. Further, a novel approach has been added to the model to include isotropic, forward and backward scattering mechanisms for a medium which consists of particles with very small size parameters. Henyey Greenstein phase function is used to evaluate the scattering for extremely small, particulate porous beds. For thick layers, a thorough study has been carried out on the effect of porosity, bed thickness, power inputs and different bed configurations. Whereas for thin layers, the substrate conditions are studied in detail. Then they are analyzed for variation in energy absorbed. The effects of reflective and absorbing boundary conditions are also studied. For the incoming beam both uniform and Gaussian distributions with different angles of incidence has been simulated. The effect of various size parameters on the radiative transport has also been compared for both thick and thin layers. Finally, for thin layers, the model is compared with the two flux method and the unit cell Monte-Carlo method

Prognostic and Prediction Modelling with Radiomics for Non-Small Cell Lung Cancer

With advancements in Artificial Intelligence (AI) improvements in cancer care can be achieved. In this work, AI models for lung cancer were built to enhance the accuracy and automation of end-to-end clinical decision support systems. The lung auto-segmentation and deep learning tumour detection model can be used by clinicians to rapidly improve disease diagnosis and treatment in cancer care. The newly developed radiomic models such as survival models, automatic classification of tumour histopathology and fractal analysis for non-small lung cancer, are currently being verified and validated. A cloud-based platform for image analytics can help connect experienced radiologists practicing in the large cities to physicians in remote villages and towns. Furthermore, cloud-based clinical decision support systems can empower physicians and healthcare workers in primary care to improve their diagnosis, treatment strategies and throughput