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Agenesis of the Corpus Callosum (ACC) is a birth defect where an infant’s corpus callosum, the structure linking the brain’s two hemispheres to allow interhemispheric communication, fails to develop in a typical manner during pregnancy. Existing research on the connection between ACC and epilepsy leaves significant gaps, due to the lack of focused investigation. One important gap is the degree to which ACC may impact the course of epilepsy treatment and outcomes. The present study was conducted to test the hypotheses that epilepsy is highly prevalent among individuals with ACC, and that those with both ACC and epilepsy have a lower response rate to anticonvulsant drugs than other patients treated with anticonvulsant drugs. A weighted average of epilepsy rates was calculated from a review of existing literature, which supported the hypothesis that epilepsy was more common among individuals with ACC (25.11%) than in the general population (1.2%). An empirical survey administered to 57 subjects or parents of subjects showed that rate of intractable epilepsy among study subjects with both ACC and epilepsy was substantially higher than the rate found in the general population, indicating that individuals with both conditions had a lower response rate to the anticonvulsant drugs. This study contributes novel results regarding the potential for concurrence of ACC and epilepsy to interfere with anticonvulsant drug treatment. We also discuss implications for how medical professionals may use the findings of this study to add depth to their treatment decisions.

Hydrogels are commonly used in medicine, pharmaceuticals, and agriculture. Hydrogels absorb water by swelling and re-release this water by diffusion. This study sought to synthesize a biodegradable, cellulose-based hydrogel that is more effective at absorbing and re-releasing water than those produced by current methods. We tested the compressive strength of both the dry and swollen gels and the tensile strength of the swollen gels to elucidate the gel structure.

Here, based on the identification of androgapholide as a potential therapeutic treatment against cancer, Alzheimer's disease, diabetes, and multiple sclerosis, due to its ability to inhibit a signaling pathway in immune system function, the authors sought ways to optimize the natural product human systems by manipulating its chemical structure. Through the semisynthesis of a natural product along with computational studies, the authors developed an understanding of the kinetic mechanisms of andrographolide and semisynthetic analogs in the context of Michael additions.

With the COVID-19 pandemic necessitating the transition to remote learning, disruption to daily school routine has impacted educational experiences on a global scale. As a result, it has potentially worsened reading achievement gaps typically exacerbated by long summer months. To address literacy skill retention and pandemic-induced social isolation, the non-profit organization ByKids4Kids has created a reading program, “Kindles4Covid Virtual Reading Buddies Program,” to instill a structure for youth to read together and connect with the convenience of Amazon Kindle devices. In this article, the authors determine the efficacy of their invaluable program by assessing changes in reading frequency and self-reported connectedness among program participants.

Recurrent neural networks (RNNs) are useful for text generation since they can generate outputs in the context of previous ones. Baroque music and language are similar, as every word or note exists in context with others, and they both follow strict rules. The authors hypothesized that if we represent music in a text format, an RNN designed to generate language could train on it and create music structurally similar to Bach’s. They found that the music generated by our RNN shared a similar structure with Bach’s music in the input dataset, while Bachbot’s outputs are significantly different from this experiment’s outputs and thus are less similar to Bach’s repertoire compared to our algorithm.

In this work, the authors investigate the accuracy with which two different population growth models can predict population growth over time. They apply the Malthusian law or Logistic law to US population from 1951 until 2019. To assess how closely the growth model fits actual population data, a least-squared curve fit was applied and revealed that the Logistic law of population growth resulted in smaller sum of squared residuals. These findings are important for ensuring optimal population growth models are implemented to data as population forecasting affects a country's economic and social structure.

This study examined the impact of erosion on the performance of a triangular aerofoil at a low Reynolds number (Re = 10,000), relevant for harsh conditions like those on Mars.

The authors test different machine learning algorithms to remove background noise from audio to help people with hearing loss differentiate between important sounds and distracting noise.

People with Type One diabetes often rely on Continuous Blood Glucose Monitors (CGMs) to track their blood glucose and manage their condition. Researchers are now working to help people with Type One diabetes more easily monitor their health by developing models that will future blood glucose levels based on CGM readings. Jalla and Ghanta tackle this issue by exploring the use of AI models to forecast blood glucose levels with CGM data.

Microscopic beauty is hiding in common kitchen ingredients - even vanillin flavoring can be turned into mesmerizing artwork by crystallizing the vanillin and examining it under a polarizing microscope. Wang and Pang explore this hidden beauty by determining the optimal conditions to grow crystalline vanillin films and by creating computer simulations of chemical interactions between vanillin molecules.