Autism spectrum disorder (ASD) is hard to correctly diagnose due to the very subjective nature of diagnosing it: behavior analysis. Due to this issue, we sought to find a machine learning-based method that diagnoses ASD without behavior analysis or helps reduce misdiagnosis.

We are looking into natural ways to help hair grow better and stronger by studying keratin synthesis in human hair follicles. The reason for conducting this research was to have the ability to control hair growth through future innovations. We wanted to answer the question: How can we find natural ways to enhance hair growth by understanding the connection with natural resources, particularly keratin dynamics? The main focus of this experiment is understanding the promotion of keratin synthesis within human hair follicles, which is important for hair development and health. While keratin is essential for the growth and strength of body tissues, including skin and hair, our research hints at its specific synthesis within hair follicles. In our research utilizing castor oil, coconut oil, a turmeric and baking soda mixture, and a sugar, honey, and lemon mixture, we hypothesize that oils, specifically coconut oil and castor oil, will enhance keratin synthesis, whereas mixtures, such as the turmeric and baking soda mixture and the sugar, honey, and lemon mixture, will result in a decrease keratin synthesis. The methods used show how different natural substances influence keratin formation within the hair follicles. The experiment involved applying natural resources to hair strands and follicles, measuring their length under the microscope daily, and assessing their health and characteristics over seven days. In summary, our research helps us understand how hair grows better. We found that using natural items like essential oils effectively alters keratin growth within the hair follicles and hair strands.

The authors use molecular modeling to test analogs of the stearoyl-coenzyme A desaturase 1 (SCD1) inhibitor MF-438 with implications for future development of Parkinson's disease therapeutics.

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.

In this study, the authors assess the antioxidant properties of vitamins A, C and E given to mustard plants after oxidative damage. This research shows an interesting comparison of the vitamins' effect on plant recovery and health.

In this study, the authors analyze gene expression datasets to determine if there is a core set of genes dysregulated during nonalcoholic steatohepatitis.

In this study, we performed orthotopic auto-transplantation of fresh ovarian tissues by transplanting unilateral half ovarian tissue to the contralateral ovary in the ICR (Institute of Cancer Research) strain of outbred, heterogeneous mice to determine if the transplanted tissue could be functional. We found that the freshly transplanted mouse ovarian tissue survived and functional, as histochemical and immunofluorescence assays have shown that not only both follicles at different developing stages and corpus luteum are available, but the morphology of them are properly maintained within the transplanted tissue.

Here, through protein-ligand docking, the authors investigated the effect of the interaction of emodin with serine/threonine kinases, a subclass of kinases that is overexpressed in many cancers, which is implicated in phosphorylation cascades. Through molecular dynamics theyfound that emodin forms favorable interactions with chitosan and chitosan PEG (polyethylene glycol) copolymers, which could aid in loading drugs into nanoparticles (NPs) for targeted delivery to cancerous tissue. Both polymers demonstrated reasonable entrapment efficiencies, which encourages experimental exploration of emodin through targeted drug delivery vehicles and their anticancer activity.

Intelligent vehicles utilize a combination of video-enabled object detection and radar data to traverse safely through surrounding environments. However, since the most momentary missteps in these systems can cause devastating collisions, the margin of error in the software for these systems is small. In this paper, we hypothesized that a novel object detection system that improves detection accuracy and speed of detection during adverse weather conditions would outperform industry alternatives in an average comparison.

Seeking to develop a better understanding of the chemical and physical properties of amino acids that compose proteins, here the authors investigated the unusual relative insolubility of racemic mixtures of D- and L-serine compared to the solubility of pure D- or L-serine. The authors used a combination of microscopy and temperature measurements alongside previous X-ray diffraction studies to conclude that racemic DL-serine crystals consist of comparatively stronger hydrogen bond interactions compared to crystals of pure enantiomers. These stronger interactions were found to result in the unique release of heat during the crystallization of racemic mixtures.