Reproductive Deficits Induced by Prenatal Antimüllerian Hormone Exposure Require Androgen Receptor in Kisspeptin Cells

Polycystic ovary syndrome (PCOS) is a common reproductive disorder characterized by elevated androgens and antimüllerian hormone (AMH). These hormones remain elevated throughout pregnancy, and potential effects of hormone exposure on offspring from women with PCOS remain largely unexplored. Expanding on recent reports of prenatal AMH exposure in mice, we have fully characterized the reproductive consequences of prenatal AMH (pAMH) exposure throughout the lifespan of first- and second-generation offspring of both sexes. We also sought to elucidate mechanisms underlying pAMH-induced reproductive effects. There is a known reciprocal relationship between AMH and androgens, and in PCOS and PCOS-like animal models, androgen feedback is dysregulated at the level of the hypothalamus. Kisspeptin neurons express androgen receptors and play a critical role in sexual development and function. We therefore hypothesized that pAMH-induced reproductive phenotypes would be mediated by androgen signaling at the level of kisspeptin cells. We tested the pAMH model in kisspeptin-specific androgen receptor knockout (KARKO) mice and found that virtually all pAMH-induced phenotypes assayed are eliminated in KARKO offspring compared to littermate controls. By demonstrating the necessity of androgen receptor in kisspeptin cells to induce pAMH phenotypes, we have advanced understanding of the interactions between AMH and androgens in the context of prenatal exposure, which could have significant implications for children of women with PCOS.

A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest algorithms were trained with these features. Area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity and F1-scores were used to evaluate model performance. The XGBoost model outperformed all other models with a test AUC of 0.79, accuracy of 74%, specificity of 73%, and a sensitivity of 77%. The Random Forest model performed comparably to XGBoost across multiple metrics, but it had a test sensitivity of 31%. The SVM model did not perform >70% in any of the test metrics. The XGBoost model had the highest sensitivity and accuracy for the detection of epilepsy. Development of machine learning algorithms trained with rfMRI latency data could provide an adjunctive method for the diagnosis and evaluation of epilepsy with the goal of enabling timely and appropriate care for patients.

Cap-Based Transcranial Optical Tomography in an Awake Infant

Although Blood Oxygenation Level Dependent (BOLD) functional MRI (fMRI) is widely used to examine brain function in adults, the need for general anesthesia limits its practical utility in infants and small children. Functional Near-Infrared Spectroscopy - Diffuse Optical Tomography (fNIRS-DOT) imaging promises to be an alternative brain network imaging technique. Yet current versions of continuous-wave fNIRS-DOT systems are restricted to the cortical surface measurements and do not probe deep structures that are frequently injured especially in premature infants. Herein we report a transcranial near infrared optical imaging system, called Cap-based Transcranial Optical Tomography (CTOT) able to image whole brain hemodynamic activity with 3 seconds of data acquisition time. We show the system is capable of whole brain oxygenation mapping in an awake child, and that tomographically reconstructed static CTOT-derived oxy- and deoxygenated blood volumes are spatially correlated with the time-averaged BOLD fMRI volumes. By removing time bottlenecks in the current system, dynamic CTOT mapping should be possible, which would then enable evaluation of functional connectivity in awake infants.

KTP laser and neutral red phototherapy of human squamous cell carcinoma

Neutral red (NR) is a cationic, nontoxic vital dye employed as a histologic stain for proliferating cells; it has been used clinically for photodynamic treatment of herpes simplex virus lesions. NR is selectively taken up and concentrated by mitotic cells, an important characteristic for more effective antineoplastic agents. In the present study, UCLA-SO-P3 human squamous carcinoma cells displayed minimal toxicity when incubated with up to 50 microg/ml NR in the absence of light. However, cells incubated with greater than 0.5 microg/ml NR followed by exposure to KTP laser light at 532 nm exhibited nearly 100% tumor cell death. The degree of cell toxicity was proportional to NR dose and laser light fluence. This study demonstrates that NR is an excellent cancer cell photosensitizer in vitro, and, after adding additional in vivo preclinical testing, may prove to be a useful agent in photodynamic destruction of head and neck tumors.