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Peripartum cardiomyopathy (PPCM) is an idiopathic form of pregnancy-induced heart failure associated with preeclampsia. Circulating factors in late pregnancy are thought to contribute to both diseases, suggesting a common underlying pathophysiological process. However, what drives this process remains unclear. Using serum proteomics, we identified the senescence-associated secretory phenotype (SASP), a marker of cellular senescence associated with biological aging, as the most highly up-regulated pathway in young women with PPCM or preeclampsia. Placentas from women with preeclampsia displayed multiple markers of amplified senescence and tissue aging, as well as overall increased gene expression of 28 circulating proteins that contributed to SASP pathway enrichment in serum samples from patients with preeclampsia or PPCM. The most highly expressed placental SASP factor, activin A, was associated with cardiac dysfunction or heart failure severity in women with preeclampsia or PPCM. In a murine model of PPCM induced by cardiomyocyte-specific deletion of the gene encoding peroxisome proliferator–activated receptor γ coactivator-1α, inhibiting activin A signaling in the early postpartum period with a monoclonal antibody to the activin type II receptor improved heart function. In addition, attenuating placental senescence with the senolytic compound fisetin in late pregnancy improved cardiac function in these animals. These findings link senescence biology to cardiac dysfunction in pregnancy and help to elucidate the pathogenesis underlying cardiovascular diseases of pregnancy. Editor's summary: Peripartum cardiomyopathy is a form of heart failure linked to preeclampsia. Roh et al. found that secreted proteins associated with senescence, a process linked to aging and cellular stress, were increased in sera from patients with peripartum cardiomyopathy or preeclampsia. Placentas from patients with preeclampsia also had elevated gene expression and tissue signatures of senescence. The most up-regulated senescence-associated secretory protein, activin A, was associated with worse heart function in human pregnancy cohorts that included patients with peripartum cardiomyopathy or preeclampsia. In a mouse model of peripartum cardiomyopathy with the peroxisome proliferator–activated receptor γ coactivator-1α gene deleted in cardiomyocytes, placentas also showed an amplified senescence phenotype. Treating these mice with the senolytic compound fisetin during mid to late pregnancy reduced placental senescence and improved heart function. Treatment with a monoclonal antibody directed against the activin type II receptor similarly improved heart function. These findings suggest that cardiovascular diseases of pregnancy could be targeted through reducing pathological cellular senescence in the placenta. —Brandon Berry [ABSTRACT FROM AUTHOR]

Background: Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models. This systematic review explores ML's potential to assist and improve neurosurgical care. Method: A systematic literature search was performed in the PubMed and Embase databases to identify all potentially relevant studies up to January 1, 2017. All studies were included that evaluated ML models assisting neurosurgical treatment. Results: Of the 6,402 citations identified, 221 studies were selected after subsequent title/abstract and full-text screening. In these studies, ML was used to assist surgical treatment of patients with epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson's disease, traumatic brain injury, and hydrocephalus. Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction. Conclusions: ML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques. [ABSTRACT FROM AUTHOR]