Punicalagin Attenuates Chemotherapy-Induced Hepatotoxicity in Normal Cells

Overview

Current research is focused on identifying drugs that can mitigate chemotherapy-induced side effects to normal 15 tissues, while being cytotoxic to cancer cells. This study was designed to evaluate the potential protective effects of punicalagin against the hepatotoxicity induced by doxorubicin and cyclophosphamide in normal liver cells and its cytotoxic effects in cancer cells. Clone 9 cells were pretreated with punicalagin (50 µM, 24 h) followed by doxorubicin (1 µM, 24 h) and cyclophosphamide (25 µM, 24 h). Cell morphology, intracellular reactive oxygen species (DCDFA Assay), protein levels of SOD2, catalase and GPx (western blotting) and intra cellular glutathione content was studied. Mitochondrial functions were studied by measuring intracellular ATP , MTT assay, TMRM uptake, JC-1 assay, and protein levels of Succinate dehydrogenase, VDAC, DRP1, and PGC-1α (Western Blotting). Cytotoxic effects of punicalagin with and without doxorubicin-cyclophosphamide in MCF7 cells was investigated by measuring cell viability and cell morphology. Punicalagin exhibited robust protection against chemotherapy drug-induced ROS generation by preserving the antioxidant defense system, which was evident in cell morphology. Punicalagin pretreatment also protected mitochondrial functions by significantly preserving ATP content, maintaining mitochondrial membrane potential and levels of critical proteins. Combined treatment of punicalagin and doxorubicin-cyclophosphamide killed 90% of the breast cancer cells, while punicalagin treatment alone had 49% cytotoxicity. In conclusion, punicalagin demonstrated robust protective effect against chemotherapy-induced damage to normal liver cells, while augmenting the cytotoxic effects in combination with chemotherapeutic drugs in cancer cells, suggesting that punicalagin may be considered as a potential adjuvant drug during and after chemotherapy. Improving Early Diagnosis and Treatment Monitoring of Tuberculosis with Novel Machine Learning Cough Analysis Chandra Suda Bentonville High School, Bentonville, AR Tuberculosis (TB), a bacterial disease mainly affecting the lungs, is the leading infectious cause of mortality worldwide before COVID-19. T o prevent TB from spreading within the body, which causes life-threatening complications, timely and effective anti-TB treatment is crucial. Cough, an objective biomarker for TB, is a triage tool that monitors treatment response and regresses with successful therapy. Current gold standards for TB diagnosis are slow or inaccessible, especially in rural areas where TB is most prevalent. In addition, current machine learning (ML) diagnosis research, like utilizing chest radiographs, is ineffective and does not monitor treatment progression. T o enable effective diagnosis, I developed an ensemble model that analyzes, using a novel ML architecture, coughs’ acoustic epidemiology from smartphones’ microphones to diagnose TB. The architecture includes a 2D-CNN and Boost that was trained on 724,964 cough audio samples and demographics from 7 countries. After feature extraction (Mel-spectrograms) and data augmentation (IR-convolution), the model achieved a 94% sensitivity and 87% specificity, surpassing WHO’s requirements for screening tests. The bi-directional LSTM utilizes periodic cough history and the 2D-CNN confidence score to predictively monitor response to TB therapy with the treatment-irregularity algorithm (TIA). The LSTM and TIA effectively (AUC<0.28) monitor the body’s reaction to anti-TB drugs through changes in cough patterns, allowing the ML model to predict a high risk of treatment or dosage irregularity. This early detection of drug irregularity can avert TB relapse, drug-induced liver injury, and drug-resistant strains. This research demonstrates the architecture’s effectiveness in improving TB diagnosis and predictive monitoring.