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Despite the introduction of effective combination antiretroviral therapy (cART) AIDS–related Kaposi Sarcoma (AIDS-KS) remains the most common malignancy in HIV positive patients. In advanced stage or progressive forms, chemotherapy (CT) in combination with cART is the treatment of choice. The aim of the study is to evaluate efficacy and tolerability of Pegylated Liposomal Doxorubicin (PLD) as first line CT in AIDS-KS. In this single institution retrospective study PLD (20 mg/m2 IV every 2 weeks for 6 or 12 cycles) in combination with cART was administered in poor risk and some cases of good prognosis or limited cutaneous disease. Response rate and adverse events to treatment was evaluated. We enrolled 33 patients with AIDS-KS: median age 44ys, male 90.9%, Caucasian 72.7%, cART-naïve (simultaneous diagnosis of HIV infection and KS) 84.4%, median lymphocyte CD4+ count 134cells, median HIV viral load 4.9 log10 copies/ml. 32 patients were assigned to a Poor Risk KS stage. Grade 3-4 toxicity was reported in 9 patients. No cardiovascular events or severe sepsis were described. Complete response was reported in 25 of 31 patients evaluable for efficacy. After a median follow-up of 52 months the 3-years PFS was 68.6%. PLD associated with cART is an effective, feasible and well tolerated first-line CT in advanced AIDS-KS.

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Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children’s Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.