Staphylococcus aureus is a major pathogen in both hospitals and the community and can cause systemic infections such as pneumonia. Multi-drug resistant strains, such as Methicillin-resistant S. aureus (MRSA) are particularly worrisome. In order to reduce the development of bacterial resistance, we hypothesized that two selected traditional Chinese medicines, Shuang-Huang-Lian (SHL) and Lan-Qin, would be effective against S. aureus. The results showed that SHL had a synergistic effect with gentamicin as well as additive effects with penicillin and cefazolin against S. aureus compared with using antibiotics alone.

Levy & Levy tackle the optimization of the coronary artery bypass graft, a life-saving surgical technique that treats artery blockage due to coronary heart disease. The authors develop a novel auto-suturing method that saves time, allows for an increased number of sutures, and improves graft quality over hand suturing. The authors also show that increasing the number of sutures from four to five with their new method significantly improves graft quality. These promising findings may help improve outcomes for patients undergoing surgery to treat coronary heart disease.

Irrespective of the final application of a molecule, synthetic accessibility is the rate-determining step in discovering and developing novel entities. However, synthetic complexity is challenging to quantify as a single metric, since it is a composite of several measurable metrics, some of which include cost, safety, and availability. Moreover, defining a single synthetic accessibility metric for both natural products and non-natural products poses yet another challenge given the structural distinctions between these two classes of compounds. Here, we propose a model for synthetic accessibility of all chemical compounds, inspired by the Central Limit Theorem, and devise a novel synthetic accessibility metric assessing the overall feasibility of making chemical compounds that has been fitted to a Gaussian distribution.

Emissions from oil and natural gas (O&G) wells such as nitrogen dioxide (NO₂), volatile organic compounds (VOCs), and ozone (O₃) can severely impact the health of communities located near wells. In this study, we used O&G activity and wind-carried emissions to quantify the extent to which O&G wells affect the air quality of nearby communities, revealing that NO₂, NO_x, and NO are correlated to O&G activity. We then developed a novel land use regression (LUR) model using machine learning based on O&G prevalence to predict emissions.

This study hypothesized that a machine learning model could accurately predict the severity of California wildfires and determine the most influential meteorological factors. It utilized a custom dataset with information from the World Weather Online API and a Kaggle dataset of wildfires in California from 2013-2020. The developed algorithms classified fires into seven categories with promising accuracy (around 55 percent). They found that higher temperatures, lower humidity, lower dew point, higher wind gusts, and higher wind speeds are the most significant contributors to the spread of a wildfire. This tool could vastly improve the efficiency and preparedness of firefighters as they deal with wildfires.

Antibiotics are one of the most common treatments for bacterial infections, but the emergence of antibiotic resistance is a major threat to the control of infectious diseases. Many factors contribute to the development of antibiotic resistance. One is bacterial conjugation from Gram-positive to Gram-negative bacteria where there is a transfer of resistance genes from Gram-positive to Gram-negative bacteria that could increase antibiotic resistance in the latter. In light of these observations, we decided to test whether Gram-negative bacteria that came into contact with Gram-positive bacteria had a higher resistance to the antimicrobial properties of spices than Gram-negative bacteria that did not come into contact with Gram-positive bacteria.

In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.

Neural networks are used throughout modern society to solve many problems commonly thought of as impossible for computers. Fountain and Rasmus designed a convolutional neural network and ran it with varying levels of training to see if consistent, accurate, and precise changes or patterns could be observed. They found that training introduced and strengthened patterns in the weights and visualizations, the patterns observed may not be consistent between all neural networks.

Acquired drug resistance is an increasing challenge in treating cancer with chemotherapy. One mechanism
behind this resistance is the increased inflammation that supports the progression and development of
cancer that arises because of the drug’s presence. Integrative oncology is the field that focuses on including natural products alongside traditional therapy to create a treatment that focuses on holistic patient well-being.
In this study, the authors demonstrate that the use of an herbal formulation, consisting of turmeric and green tea, alongside a traditional chemotherapeutic drug, 5-fluorouracil (FU) significantly decreases the level of cytokines produced in breast cancer cells when compared to the levels produced when exposed solely to the chemo drug. The authors conclude that this combination of treatment, based on the principle of integrative oncology, shows potential for reducing the resistance against treatment conferred through increased inflammation. Consequently, this suggests a prospective way forward in improving the efficacy of cancer treatment.

Each year, over 100,000 patients die from Sudden Unexpected Death in Epilepsy (SUDEP). A reliable seizure warning system can help patients stay safe. This work presents a comprehensive, comparative analysis of three different signal processing algorithms for automated seizure/ictal detection. The experimental results show that the proposed methods can be effective for accurate automated seizure detection and monitoring in clinical care.