A Comparison of DASH Scores Resulting from Different Treatment Options for the Intra-articular Distal Radius Fracture in the Geriatric Population

Wagh, Himanshu; Reddy, Akshay J

doi:10.53043/2320-1991.acb90004

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…The clams that would have a greater feeding rate would be able to filter more food from the environment, leading to an increased rate of survivability in their environment. The mechanism of filter feeding mainly depends on the spacing of the gills in the clams [8,[11][12][13][14][15]. The two main sources of food for C. Fluminea in this experiment were algae and E. coli.…”

Section: Discussionmentioning

confidence: 87%

A Biological Analysis on the Invasiveness of the Corbicula Fluminea

Yeddula¹,

Reddy²,

Liu³

et al. 2021

Appl Cell Biol

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Corbicula fluminea is an invasive species that has been observed to outcompete the native clams at the American River located near Sacramento in the Central Valley in California. We hypothesized that C. Fluminea has advantages exhibited physically including utilization of filter-feeding methods and relative spacing of its cirri as compared to the native American River clams. To investigate what makes the species so successful, we tested C. Fluminea versus the native clams in algal and E.coli environments to predict the relative advantage of a filter feeder. In addition, we used computer programs to digitally analyze the spacing between the actual cirri, which help bivalves capture food particles, of the two species. The findings pointed towards C. Fluminea’s inherent advantage in both physical and genetic traits over the native clams species which allowed it to flourish and successfully invade the American River ecosystem. However, the species’ genetic findings are found through DNA analysis.

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Section: Discussionmentioning

confidence: 87%

A Biological Analysis on the Invasiveness of the Corbicula Fluminea

Yeddula¹,

Reddy²,

Liu³

et al. 2021

Appl Cell Biol

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“…In this investigation, the authors attempt to develop an AI software that can be utilized to diagnose cases where patients have exudative or non-exudative macular degeneration in either their left eye, their right eye, or both eyes. Our investigation builds upon previous studies that have demonstrated how AI software can accurately diagnose macular degeneration within a patient [15][16][17][18][19][20].…”

Section: Discussionmentioning

confidence: 87%

Clinical Wide-Field Retinal Image Deep Learning Classification of Exudative and Non-Exudative Age-Related Macular Degeneration

Tak¹,

Reddy²,

Martel³

et al. 2021

Cureus

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Background: Age-related macular degeneration (AMD) is a disease that currently affects approximately 196 million individuals and is projected to affect 288 million in 2040. As a result, better and earlier detection methods for this disease are needed in an effort to provide a higher quality of care. One way to achieve this is through the utilization of machine learning. A deep neural network, specifically a convoluted neural network (CNN) can be trained to differentiate between different types of AMD images given the proper training data.Methods: In this study, a CNN was trained on 420 Optos wide-field retinal images for 70 epochs in order to classify between exudative and non-exudative AMD. These images were obtained and labeled by ophthalmologists from the Martel Eye Clinic in Rancho Cordova, CA.Results: After completing the study, a model was created with 88% accuracy. Both the training and validation loss started above 1 and ended below 0.2. Despite only analyzing a single image at a time, the model was still able to accurately identify if the individual had AMD in both eyes or one eye only. The model had the most trouble with bilateral non-exudative AMD. Overall the model was fairly accurate in the other categories. It was noted that the neural network was able to further differentiate from a single image if the disease is present in left, right, or both eyes. This is a point of contention for further investigation as it is impossible for the artificial intelligence (AI) to extrapolate the condition of both eyes from only one image.Conclusion: This research fostered the development of a CNN that was able to differentiate between exudative and non-exudative AMD. As well as determine if the disease is present in the right, left, or both eyes with a relatively high degree of accuracy. The model was trained on clinical data and can theoretically be used to classify other clinical images it has never encountered before.

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“…Nevertheless, further research is needed in order to learn more about MMEJ repair. Perhaps trying therapies with different compounds such as EDTA would yield different results, or maybe different AI software could be developed to help process the lab results from recombination at a faster rate [24][25][26][27][28][29][30]. The findings from this paper could help scientists gain a more thorough understanding of diseases such as CJD…”

Section: Discussionmentioning

confidence: 99%

Comparing Effectiveness of Different Microhomology Lengths in Microhomology Mediated End Joining (MMEJ) Repair of DNA Double Stranded Breaks (DSBs)

Senthilraja¹,

Chugh²,

Chugh³

et al. 2021

Appl Cell Biol

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DNA Double-Stranded Breaks (DSBs) are caused by genotoxic agents, such as ionizing radiation and chemical agents, and can cause an affected cell to undergo apoptosis or cell death. The process of microhomology-mediated end joining (MMEJ) shows promising results in the repair of DSBs in DNA. MMEJ is a mutagenic DSB repair mechanism that uses a certain length of homologous nucleotides adjacent to the DSB to align the broken DNA strands for repair. This can result in insertions, deletions, and even translocations of genes at the DSB site. This has led to discussions of debate on whether MMEJ is efficient in repairing DSBs in DNA. Based on the length of microhomology, the effectiveness of the DSB repair can vary. The purpose of this research is to examine MMEJ repair using micro-homologies of different lengths in Saccharomyces cerevisiae cells to test the effectiveness of MMEJ repair. The HIS3 gene located in chromosome 15 in the yeast cell is used to test for MMEJ repair, and the full microhomology length represents 311 base pairs (bp). Various crosses are performed on cells to attain desired genotypes that have the homologous chromosomes in alignment for MMEJ repair. After inducing DSBs, media-based testing is used for testing the efficiency of MMEj repair by checking for the presence of certain genes that may have formed or been deleted during the repair process.

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A Comparison of DASH Scores Resulting from Different Treatment Options for the Intra-articular Distal Radius Fracture in the Geriatric Population

Cited by 6 publications

References 27 publications

A Biological Analysis on the Invasiveness of the Corbicula Fluminea

A Biological Analysis on the Invasiveness of the Corbicula Fluminea

Clinical Wide-Field Retinal Image Deep Learning Classification of Exudative and Non-Exudative Age-Related Macular Degeneration

Comparing Effectiveness of Different Microhomology Lengths in Microhomology Mediated End Joining (MMEJ) Repair of DNA Double Stranded Breaks (DSBs)

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