Morrisernstsen1124

vity of CT for identifying malignant pathology.

Several neonatal simulation-training programs have been deployed during the last decade, and in a growing number of studies, researchers have investigated the effects of simulation-based team training. This body of evidence remains to be compiled.

We performed a systematic review of the effects of simulation-based team training on clinical performance and patient outcome.

Medline, Embase, Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library.

Two authors included studies of team training in critical neonatal situations with reported outcomes on clinical performance and patient outcome.

Two authors extracted data using a predefined template and assessed risk of bias using the Cochrane risk-of-bias tool 2.0 and the Newcastle-Ottawa quality assessment scale.

We screened 1434 titles and abstracts, evaluated 173 full texts for eligibility, and included 24 studies. We identified only 2 studies with neonatal mortality outcomes, and no conclusion could be reached regarding the simulation-based evaluations 3 to 6 months later. The current evidence was insufficient to conclude on neonatal mortality after simulation-based team training because no studies were available from developed countries. In future work, researchers should include patient outcomes or clinical proxies of treatment quality whenever possible.Understanding the mechanics of blood flow is necessary for developing insights into mechanisms of physiology and vascular diseases in microcirculation. Given the limitations of technologies available for assessing in vivo flow fields, in vitro methods based on traditional microfluidic platforms have been developed to mimic physiological conditions. However, existing methods lack the capability to provide accurate assessment of these flow fields, particularly in vessels with complex geometries. Conventional approaches to quantify flow fields rely either on analyzing only visual images or on enforcing underlying physics without considering visualization data, which could compromise accuracy of predictions. Here, we present artificial-intelligence velocimetry (AIV) to quantify velocity and stress fields of blood flow by integrating the imaging data with underlying physics using physics-informed neural networks. We demonstrate the capability of AIV by quantifying hemodynamics in microchannels designed to mimic saccular-shaped microaneurysms (microaneurysm-on-a-chip, or MAOAC), which signify common manifestations of diabetic retinopathy, a leading cause of vision loss from blood-vessel damage in the retina in diabetic patients. We show that AIV can, without any a priori knowledge of the inlet and outlet boundary conditions, infer the two-dimensional (2D) flow fields from a sequence of 2D images of blood flow in MAOAC, but also can infer three-dimensional (3D) flow fields using only 2D images, thanks to the encoded physics laws. AIV provides a unique paradigm that seamlessly integrates images, experimental data, and underlying physics using neural networks to automatically analyze experimental data and infer key hemodynamic indicators that assess vascular injury.High levels of the intermediate filament protein keratin 17 (K17) are associated with poor prognoses for several human carcinomas. Studies in mouse models have shown that K17 expression is positively associated with growth, survival, and inflammation in skin and that lack of K17 delays onset of tumorigenesis. K17 occurs in the nucleus of human and mouse tumor keratinocytes where it impacts chromatin architecture, gene expression, and cell proliferation. We report here that K17 is induced following DNA damage and promotes keratinocyte survival. The presence of nuclear K17 is required at an early stage of the double-stranded break (DSB) arm of the DNA damage and repair (DDR) cascade, consistent with its ability to associate with key DDR effectors, including γ-H2A.X, 53BP1, and DNA-PKcs. Mice lacking K17 or with attenuated K17 nuclear import showed curtailed initiation in a two-step skin carcinogenesis paradigm. The impact of nuclear-localized K17 on DDR and cell survival provides a basis for the link between K17 induction and poor clinical outcomes for several human carcinomas.DNA-methyltransferase inhibitors (DNMTis), such as azacitidine and decitabine, are used clinically to treat myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). K-Ras(G12C) inhibitor 9 Decitabine activates the transcription of endogenous retroviruses (ERVs), which can induce immune response by acting as cellular double-stranded RNAs (dsRNAs). Yet, the posttranscriptional regulation of ERV dsRNAs remains uninvestigated. Here, we find that the viral mimicry and subsequent cell death in response to decitabine require the dsRNA-binding protein Staufen1 (Stau1). We show that Stau1 directly binds to ERV RNAs and stabilizes them in a genome-wide manner. Furthermore, Stau1-mediated stabilization requires a long noncoding RNA TINCR, which enhances the interaction between Stau1 and ERV RNAs. Analysis of a clinical patient cohort reveals that MDS and AML patients with lower Stau1 and TINCR expressions exhibit inferior treatment outcomes to DNMTi therapy. Overall, our study reveals the posttranscriptional regulatory mechanism of ERVs and identifies the Stau1-TINCR complex as a potential target for predicting the efficacy of DNMTis and other drugs that rely on dsRNAs.MYCN-amplified neuroblastoma is a lethal subset of pediatric cancer. MYCN drives numerous effects in the cell, including metabolic changes that are critical for oncogenesis. The understanding that both compensatory pathways and intrinsic redundancy in cell systems exists implies that the use of combination therapies for effective and durable responses is necessary. Additionally, the most effective targeted therapies exploit an "Achilles' heel" and are tailored to the genetics of the cancer under study. We performed an unbiased screen on select metabolic targeted therapy combinations and correlated sensitivity with over 20 subsets of cancer. We found that MYCN-amplified neuroblastoma is hypersensitive to the combination of an inhibitor of the lactate transporter MCT1, AZD3965, and complex I of the mitochondrion, phenformin. Our data demonstrate that MCT4 is highly correlated with resistance to the combination in the screen and lowly expressed in MYCN-amplified neuroblastoma. Low MCT4 combines with high expression of the MCT2 and MCT1 chaperone CD147 in MYCN-amplified neuroblastoma, altogether conferring sensitivity to the AZD3965 and phenformin combination.