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.

The objective of this experiment was to determine what nitrate concentration and pH would provide the most optimal growth for algae. In order to determine this, algae from the American River was taken and grown in solutions with different concentrations of nitrate, and H+ ions. It was hypothesized that the algae would grow best in solutions with neutral pH levels or solutions with high nitrate concentrations. Different amounts of sodium nitrate were added to beakers with similar amounts of water and algae in order to create environments with varying concentrations of nitrate. Different amounts of NaOH and acetic acid were added to beakers with similar amounts of water and algae in order to simulate environments with varying pH levels. This experiment was conducted in order to determine how fertilizer runoffs affect algal growth. Fertilizer run offs carry nitrate ions into rivers and lakes, which cause algal blooms to form. It was hypothesized that as the concentration of nitrate increases, the growth rate of the algae would also increase. This is due to the fact that the literature supports the idea that as nitrate is added to rivers and lakes the number of algae present increases. The results from the experiment demonstrated that the most optimal concentration of nitrate in the water for algal growth was in between 1-2 Molar and that the most optimal pH for algal growth was in between 7-8.

We present a severe case of orbital necrotizing fasciitis that was treated utilizing negative pressure wound therapy (NPWT).The conditions caused by the disease and the utility of the treatment were discussed. Additionally, the functionality and the process of the treatment were thoroughly analyzed. Potential complications from utilizing NPWT were also identified. When the patient was tested, it was found that he had intra op cultures with group B Streptococcus pyogenes (Strep pyogenes). CT scans were also conducted to analyze his right lateral periorbital tissue. Subsequently, the patient was admitted to the ICU, where a wound vacuum-assisted closure (VAC) was placed on his right eye. Once the NPWT was complete, the patient was prescribed antibiotics and was able to improve the health within his right eye.

We report a rare case of orbital cellulitis and endogenous endophthalmitis, sepsis, meningitis with a brain abscess and a septic knee secondary to Streptococcus pneumonia.The problems of diagnosis, utility of CT and MRI scanning in the intensive care setting is discussed. The patient was admitted in an obtundated state to the ICU, was noted to have sepsis with blood culture positivity for S pneumoniae. She was noted to have meningitis, a septic knee, a brain abscess and conjunctival injection. CT and MRI scanning did not reveal any ocular or orbital abnormalities. Patient began with a sore throat and knee pain. Despite antibiotic treatment, she became septic with blood culture positivity for S. pneumoniae. She was noted to have knee cellulitis and a brain abscess.

Background Degenerative spinal conditions (DSCs) involve a diverse set of pathologies that significantly impact health and quality of life, affecting many individuals at least once during their lifetime. Treatment approaches are varied and complex, reflecting the intricacy of spinal anatomy and kinetics. Diagnosis and management pose challenges, with the accurate detection of lesions further complicated by age-related degeneration and surgical implants. Technological advancements, particularly in artificial intelligence (AI) and deep learning, have demonstrated the potential to enhance detection of spinal lesions. Despite challenges in dataset creation and integration into clinical settings, further research holds promise for improved patient outcomes. Methods This study aimed to develop a DSC detection and classification model using a Kaggle dataset of 967 spinal X-ray images at the Department of Neurosurgery of Arrowhead Regional Medical Center, Colton, California, USA. Our entire workflow, including data preprocessing, training, validation, and testing, was performed by utilizing an online-cloud based AI platform. The model's performance was evaluated based on its ability to accurately classify certain DSCs (osteophytes, spinal implants, and foraminal stenosis) and distinguish these from normal X-rays. Evaluation metrics, including accuracy, precision, recall, and confusion matrix, were calculated. Results The model achieved an average precision of 0.88, with precision and recall values of 87% and 83.3%, respectively, indicating its high accuracy in classifying DSCs and distinguishing these from normal cases. Sensitivity and specificity values were calculated as 94.12% and 96.68%, respectively. The overall accuracy of the model was calculated to be 89%. Conclusion These findings indicate the utility of deep learning algorithms in enhancing early DSC detection and screening. Our platform is a cost-effective tool that demonstrates robust performance given a heterogeneous dataset. However, additional validation studies are required to evaluate the model's generalizability across different populations and optimize its seamless integration into various types of clinical practice.