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Impact of Population Density and Elevation on Tuberculosis Spread and Transmission in Maharashtra, India

Rao et al. | Nov 07, 2021

Impact of Population Density and Elevation on Tuberculosis Spread and Transmission in Maharashtra, India

India accounts for over 2.4 million recorded cases of tuberculosis, about 26% of the world’s cases. This research ascertained the bearing of both the population density and the average elevation above mean sea level (MSL) on the number of cases of TB recorded by the districts in Maharashtra, India. The results found a strong positive correlation between the number of TB cases per thousand people and the population density and a strong negative correlation between the number of TB cases per thousand people and the average elevation above MSL.

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Utilizing 25-Hydroxyvitamin D3 to prevent the appearance of diabetic-like phenotypes in Drosophila melanogaster

Zaverchand et al. | Sep 20, 2021

Utilizing 25-Hydroxyvitamin D3 to prevent the appearance of diabetic-like phenotypes in Drosophila melanogaster

This study aimed to assess the role of 25-hydroxyvitamin D3 solution, at varying concentrations, in protecting vertical transmission of diabetic-like phenotypes. We hypothesized that the highest concentration of vitamin D solution (55 ng/mL) would be most effective in having a protective role. The results indicated that the hypothesis was partially supported; overall, all three concentrations of the vitamin D solution administered to the flies reared on HSDs had a protective effect, to varying extents.

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Zinc-related Treatments Combined with Chloroquine and Gemcitabine for Treating Pancreatic Cancer

Ma et al. | Sep 11, 2021

Zinc-related Treatments Combined with Chloroquine and Gemcitabine for Treating Pancreatic Cancer

Pancreatic cancer is one of the deadliest cancers, with a 10% 5-year survival rate. The authors studied various dosages of TPEN and zinc in combination with Chloroquine and Gemcitabine as treatments to reduce cell proliferation. Results showed that when combined with Chloroquine and Gemcitabine, zinc and TPEN both significantly lowered cell proliferation compared to Gemcitabine, suggesting a synergistic effect that resulted in a more cytotoxic treatment. Further research and clinical trials on this topic are needed to determine whether this could be a viable treatment for pancreatic cancer.

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The relationship between macroinvertebrates, water quality, and the health of Stevens Creek

Li et al. | Aug 18, 2021

The relationship between macroinvertebrates, water quality, and the health of Stevens Creek

Stevens Creek, which flows through Santa Clara County in California, provides a crucial habitat for federally designated threatened steelhead trout, with a portion of the trout’s diet being dependent on the presence and abundance of macroinvertebrates that inhabit the creek. In this article, the authors investigate how the water chemistry within the creek was associated with the abundance and diversity of macroinvertebrates, and subsequently the creek’s health. They conduct qualitative analysis of macroinvertebrates and water quality to obtain a general understanding of the health of Stevens Creek.

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Formation and sticking of air bubbles in water in d-block containers

Gupta et al. | Jun 21, 2021

Formation and sticking of air bubbles in water in d-block containers

Bubbles! In this study, the authors investigate the effects that different materials, temperature, and distance have on the formation of water bubbles on the surface of copper and steel. They calculated mathematical relations based on the outcomes to better understand whether interstitial hydrogen present in the d-block metals form hydrogen bonds with the water bubbles to account for the structural and mechanical stability.

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A comparative analysis of machine learning approaches for prediction of breast cancer

Nag et al. | May 11, 2021

A comparative analysis of machine learning approaches for prediction of breast cancer

Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor.

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