Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care.

The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient "data fingerprint" of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians' standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5-7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5-10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5-7 cm H2O and 53.6% more frequently PEEP levels of 7-9 cmH2O. VentAI avoided high (>55%) FiO2 values (59.8% decrease), while preferring the range of 50-55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients

Heparan Sulfate Induces Necroptosis in Murine Cardiomyocytes: A Medical-in-Silico Approach Combining In Vitro Experiments and Machine Learning (vol 9, 393, 2018)

A Corrigendum on Heparan Sulfate Induces Necroptosis in Murine Cardiomyocytes: A Medical-In silico Approach Combining In vitro Experiments and Machine Learning by Zechendorf E, Vaßen P, Zhang J, Hallawa A, Martincuks A, Krenkel O, Müller-Newen G, Schuerholz T, Simon T-P, Marx G, Ascheid G, Schmeink A, Dartmann G, Thiemermann C and Martin L (2018). Front. Immunol. 9:393. doi: 10.3389/fimmu.2018.00393 In the original article, there was an error in the Author Contributions section. The wording used to declare the contribution of Elisabeth Zechendorf was not clear. The new Author Contributions section appears below. Conception and design: EZ, LM, GD, AS, and CT. In vitro experiments and data analyses: EZ, LM, TS, T-PS, AM, GM-N, OK, GM, and PV. Medical in silico experiments and data analyses: EZ, PV, JZ, GD, AS, LM, AH, and GA. EZ wrote the manuscript. Correction of the manuscript: EZ, PV, LM, CT, GM, GD, T-PS, and AS. All the authors reviewed and finally approved the manuscript. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated

Simultaneous in situ genotyping and phenotyping of human papillomavirus cervical lesions:comparative sensitivity and specificity

The sensitivity and specificity of immunocytochemistry were compared with those of non-isotopic in situ hybridisation (NISH) for the direct detection of human papillomaviruses in biopsy specimens. Four monoclonal antibodies raised to the capsid protein of HPV16 were less specific than NISH: all four reacted with lesions containing HPV33, and HPV18. Absolute discrimination of HPV types, therefore, was not possible with the monoclonal antibodies used in this study. The relative sensitivities of these antibodies were also lower than NISH. Sequential immunocytochemistry and NISH on the same section showed that 2.9-13.0 times as many cells were positive by NISH than by immunocytochemistry using the most sensitive monoclonal antibody. These data indicate that NISH has higher diagnostic specificity and sensitivity than immunocytochemistry using monoclonal antibodies to the HPV16 capsid protein

Variations in the association of papillomavirus E2 proteins with mitotic chromosomes

The E2 protein segregates episomal bovine papillomavirus (BPV) genomes to daughter cells by tethering them to mitotic chromosomes, thus ensuring equal distribution and retention of viral DNA. To date, only the BPV1 E2 protein has been shown to bind to mitotic chromosomes. We assessed the localization of 13 different animal and human E2 proteins from seven papillomavirus genera, and we show that most of them are stably bound to chromosomes throughout mitosis. Furthermore, in contrast to the random association of BPV1 E2 with mitotic chromosomes, several of these proteins appear to bind to more specific regions of mitotic chromosomes. Using human papillomavirus (HPV) type 8 E2, we show that this region is adjacent to centromeres/kinetochores. Therefore, E2 proteins from both HPV and animal papillomavirus bind to mitotic chromosomes, and there are variations in the specificity of this binding. Only the α-papillomavirus E2 proteins do not stably associate with mitotic chromatin throughout mitosis. These proteins closely associate with prophase chromosomes and bind to chromosomes in telophase but not in metaphase. However, extraction of mitotic cells before fixation results in α-E2 proteins binding to the pericentromeric region of metaphase chromosomes, as observed for HPV8 E2. We postulate that this is the authentic target of these E2 proteins but that additional factors or a specialized cellular environment is required to stabilize this association. Thus, E2-mediated tethering of viral genomes to mitotic chromosomes is a common strategy of papillomaviruses, but different viruses have evolved different variations of this theme