A functional aged human iPSC-cortical neuron model recapitulates Alzheimer's disease, senescence, and the response to therapeutics.

INTRODUCTION: The degeneration of cortical layers is associated with cognitive decline in Alzheimer's disease (AD). Current therapies for AD are not disease-modifying, and, despite substantial efforts, research and development for AD has faced formidable challenges. In addition, cellular senescence has emerged as a significant contributor to therapy resistance. METHODS: Human iPSC-derived cortical neurons were cultured on microelectrode arrays to measure long-term potentiation (LTP) noninvasively. Neurons were treated with pathogenic amyloid-ß (Aß) to analyze senescence and response to therapeutic molecules. RESULTS: Microphysiological recordings revealed Aß dampened cortical LTP activity and accelerated neuronal senescence. Aging neurons secreted inflammatory factors previously detected in brain, plasma, and cerebral spinal fluid of AD patients, in which drugs modulated senescence-related factors. DISCUSSION: This platform measures and records neuronal LTP activity in response to Aß and therapeutic molecules in real-time. Efficacy data from similar platforms have been accepted by the FDA for neurodegenerative diseases, expediting regulatory submissions. HIGHLIGHTS: This work developed a progerontic model of amyloid-ß (Aß)-driven cortical degeneration. This work measured neuronal LTP and correlated function with aging biomarkers. Aß is a driver of neuronal senescence and cortical degeneration. Molecules rescued neuronal function but did not halt Aß-driven senescence. Therapeutic molecules modulated secretion of inflammatory factors by aging neurons.

Inhibition of Metalloproteinases Extends Longevity and Function of In Vitro Human iPSC-Derived Skeletal Muscle.

In vitro culture longevity has long been a concern for disease modeling and drug testing when using contractable cells. The dynamic nature of certain cells, such as skeletal muscle, contributes to cell surface release, which limits the system's ability to conduct long-term studies. This study hypothesized that regulating the extracellular matrix (ECM) dynamics should be able to prolong cell attachment on a culture surface. Human induced pluripotent stem cell (iPSC)-derived skeletal muscle (SKM) culture was utilized to test this hypothesis due to its forceful contractions in mature muscle culture, which can cause cell detachment. By specifically inhibiting matrix metalloproteinases (MMPs) that work to digest components of the ECM, it was shown that the SKM culture remained adhered for longer periods of time, up to 80 days. Functional testing of myofibers indicated that cells treated with the MMP inhibitors, tempol, and doxycycline, displayed a significantly reduced fatigue index, although the fidelity was not affected, while those treated with the MMP inducer, PMA, indicated a premature detachment and increased fatigue index. The MMP-modulating activity by the inhibitors and inducer was further validated by gel zymography analysis, where the MMP inhibitor showed minimally active MMPs, while the inducer-treated cells indicated high MMP activity. These data support the hypotheses that regulating the ECM dynamics can help maximize in vitro myotube longevity. This proof-of-principle strategy would benefit the modeling of diseases that require a long time to develop and the evaluation of chronic effects of potential therapeutics.

A machine learning approach on chest X-rays for pediatric pneumonia detection.

Background: According to the World Health Organization (WHO), pneumonia is the leading infectious cause of death in children below 5 years old. Hence, the early detection of pediatric pneumonia is crucial to reduce its morbidity and mortality rates. Even though chest radiography is the most commonly employed modality for pneumonia detection, recent studies highlight the existence of poor interobserver agreement in the chest X-ray interpretation of healthcare practitioners when it comes to diagnosing pediatric pneumonia. Thus, there is a significant need for automating the detection process to minimize the potential human error. Since Artificial Intelligence tools such as Deep Learning (DL) and Machine Learning (ML) have the potential to automate disease detection, many researchers explored how such tools can be implemented to detect pneumonia in chest X-rays. Notably, the majority of efforts tackled this problem from a DL point of view. However, ML has shown a higher potential for medical interpretability while being less computationally demanding than DL. Objective: The aim of this paper is to automate the early detection process of pediatric pneumonia using ML as it is less computationally demanding than DL. Methods: The proposed approach entails performing data augmentation to balance the classes of the utilized dataset, optimizing the feature extraction scheme, and evaluating the performance of several ML models. Moreover, the performance of this approach is compared to a TL benchmark to evaluate its candidacy. Results: Using the proposed approach, the Quadratic SVM model yielded an accuracy of 97.58%, surpassing the accuracies reported in the current ML literature. In addition, this model classification time was significantly smaller than that of the TL benchmark. Conclusion: The results strongly support the candidacy of the proposed approach in reliably detecting pediatric pneumonia.