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Inefficient penetration of cancer drugs into the interior of the three-dimensional (3D) tumor tissue limits drugs' delivery. The authors hypothesized that the addition of phospholipase A2 (PLA2) would increase the permeability of the drug doxorubicin for efficient drug penetration. They found that 1 mM PLA2 had the highest permeability. Increased efficiency in drug delivery would allow lower concentrations of drugs to be used, minimizing damage to normal cells.

A significant percentage of cancer survivors develop a second primary cancer. Using data of deceased patients provided by the Peninsula Regional Medical Center, Li and Holdai conducted a retrospective statistical analysis to investigate whether the type of the first cancer affects the occurrence time and type of the second primary cancer.

This study hypothesizes that nanoparticles derived from corn (cNPs)may have anti-proliferative effects on bone cancer and metastasized bone cancer. It finds that human osteosarcoma and human lung carcinoma metastasized to bone marrow cell viability decreased to 0% when treated with cNPs. Overall, these results indicate that cNPs have anti-proliferative effects on bone cancer cells and cancer cells that metastasize to the bone.

The authors looked at whether traditional Chinese medicine remedies that target the lungs and liver would reduce inflammation in a planaria model. They found that the two active compounds they tested were able to decrease induced inflammation by 97-98%.

In this report, Bhardwaj and Sharma tested whether placing specific plants indoors can reduce levels of indoor air pollution that can lead to lung-related illnesses. Using machine learning, they show that plants improved overall indoor air quality and reduced levels of particulate matter. They suggest that plant-based interventions coupled with sensors may be a useful long-term solution to reducing and maintaining indoor air pollution.

In this article the authors look at the lung microbiome in patients with cystic fibrosis to determine what the major bacterial species present are.

During transfer of organs from a donor to a patient, the organs deteriorate in part due to damage by free radicals. Application of antioxidant solutions could extend organ preservation times. The authors found that vitamin E and butylated hydroxytoluene seemed to be most effective in arresting cell damage of a bovine lung.

Many common respiratory illnesses like bronchitis, asthma, and chronic obstructive pulmonary disease (COPD) lead to bronchial inflammation and, subsequently, a blockage. However, there are many difficulties in measuring the severity of the blockage. A numeric metric to determine the degree of the blockage severity is necessary. To tackle this demand, we aimed to develop a novel human respiratory model and design a deep-learning program that can constantly monitor and report bronchial blockage by recording breath sounds in a non-intrusive way.

With healthy lung performance being critical to daily function and maintenance of physical health, the authors of this study explored the impact of airflow training from playing a wind instrument on respiratory system function. With careful quantification of peak expiratory flow of individuals who played the trumpet, the authors found no expiratory capacity difference between students who played the trumpet and students who did not play a wind instrument.

Given an association between nicotine addiction and gene expression, we hypothesized that expression of genes commonly associated with smoking status would have variable expression between smokers and non-smokers. To test whether gene expression varies between smokers and non-smokers, we analyzed two publicly-available datasets that profiled RNA gene expression from brain (nucleus accumbens) and lung tissue taken from patients identified as smokers or non-smokers. We discovered statistically significant differences in expression of dozens of genes between smokers and non-smokers. To test whether gene expression can be used to predict whether a patient is a smoker or non-smoker, we used gene expression as the training data for a logistic regression or random forest classification model. The random forest classifier trained on lung tissue data showed the most robust results, with area under curve (AUC) values consistently between 0.82 and 0.93. Both models trained on nucleus accumbens data had poorer performance, with AUC values consistently between 0.65 and 0.7 when using random forest. These results suggest gene expression can be used to predict smoking status using traditional machine learning models. Additionally, based on our random forest model, we proposed KCNJ3 and TXLNGY as two candidate markers of smoking status. These findings, coupled with other genes identified in this study, present promising avenues for advancing applications related to the genetic foundation of smoking-related characteristics.