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Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules.

The authors propose a method to recycle Christmas tree needles into a non-toxic reducing agent for synthesizing copper nanoparticles.

Berberine, a natural product alkaloid, has been shown to exert biological activity via in situ production of singlet oxygen when photo irradiated. Berberine utilizes singlet oxygen in its putative mechanism of action, wherein it forms an activated complex with DNA and photosensitizes triplet oxygen to singlet oxygen to specifically oxidize guanine residues, thereby halting cell replication and leading to cell death. This has potential application in photodynamic therapy, alongside other such compounds which also act as photosensitizers and produce singlet oxygen in situ. The quantification of singlet oxygen in various photosensitizers, including berberine, is essential for determining their photosensitizer efficiencies. We postulated that the singlet oxygen produced by photoirradiation of berberine would be superior in terms of singlet oxygen production to the aforementioned photosensitizers when irradiated with UV light, but inferior under visible light conditions, due to its strong absorbance of UV wavelengths.

A primary cause of diabetes is insulin resistance, which is caused by disruption of insulin signal transduction. The objective of this study was to maximize insulin sensitivity by creating a more effective, early intervention-based treatment to avert severe T2D. This treatment combined metformin, “the insulin sensitizer”, and medicinal plants, curcumin, fenugreek, and nettle.

Medicinal plants could be a good source of medication to combat antibiotic resistance. Dombeya wallichii, which is commonly called Pink Ball Tree in the family Sterculiaceae, has been documented to have medicinal potential. We observed the highest antibacterial activity in the stem extracts, followed by leaf and bark extracts. The extracts were more effective against tested Gram-positive bacteria when compared with Gram-negative strains.

Due to the increase in antimicrobial resistance, alternative medicinal therapies are being explored. Studies have shown that honey and ginger alone have antimicrobial effects on the genera Staphylococcus and Escherichia, including S. epidermidis and E. coli. The authors of this study tested whether a honey-ginger supplement, Jengimiel™, could be used as an antimicrobial agent against S. epidermidis and E. coli K-12.

The authors test the antiproliferative and apoptosis-inducing properties of an extract created from a traditional Indian medicinal plant of the Amaranthus genus.

In this study, the authors investigated the biological mechanism underlying the actions of a traditional medicinal plant, Astragalus membranaceus. Using C. elegans as an experimental model, they tested the effects of AM root on heat stress responses. Their results suggest that AM root extract may enhance the activity of endogenous pathways that mediate cellular responses to heat stress.

Nosocomial infections acquired in hospitals pose a risk to patients, a risk compounded by resistant microorganisms. To combat this problem, researchers have turned to bioactive compounds from medicinal plants such as the widely used neem. In the present study, researchers sought to determine the effectiveness of different neem preparations against several hospital acquired human pathogens. Neem powder in water successfully inhibited microorganism growth making it a potential agent to combat these infections.

Breast cancer is the most common cancer in women, with approximately 300,000 diagnosed with breast cancer in 2023. It ranks second in cancer-related deaths for women, after lung cancer with nearly 50,000 deaths. Scientists have identified important genetic mutations in genes like BRCA1 and BRCA2 that lead to the development of breast cancer, but previous studies were limited as they focused on specific populations. To overcome limitations, diverse populations and powerful statistical methods like genome-wide association studies and whole-genome sequencing are needed. Explainable artificial intelligence (XAI) can be used in oncology and breast cancer research to overcome these limitations of specificity as it can analyze datasets of diagnosed patients by providing interpretable explanations for identified patterns and predictions. This project aims to achieve technological and medicinal goals by using advanced algorithms to identify breast cancer subtypes for faster diagnoses. Multiple methods were utilized to develop an efficient algorithm. We hypothesized that an XAI approach would be best as it can assign scores to genes, specifically with a 90% success rate. To test that, we ran multiple trials utilizing XAI methods through the identification of class-specific and patient-specific key genes. We found that the study demonstrated a pipeline that combines multiple XAI techniques to identify potential biomarker genes for breast cancer with a 95% success rate.