Adaptive Pressure Control-Continuous Mandatory Ventilation Versus Volume Control-Continuous Mandatory Ventilation: Factors Associated With Initiation, Maintenance, and Adjustment.

BACKGROUND: Adaptive pressure control-continuous mandatory ventilation (APC-CMV) is a frequently utilized ventilator mode in ICU settings. This analysis compared APC-CMV and traditional volume control-continuous mandatory ventilation (VC-CMV) mode, describing factors associated with initiation, maintenance, and changes in settings of each mode. METHODS: We analyzed ventilator data from a retrospective electronic health record data set collected as part of a quality improvement project in a single academic ICU. The majority ventilator mode was defined as the mode comprising the highest proportion of mechanical ventilation time. Multivariable logistic regression was used to identify variables associated with initial and majority APC-CMV or VC-CMV modes. Wilcoxon rank-sum tests were used to compare ventilator setting changes/d and sedation as a function of APC-CMV and VC-CMV majority modes. RESULTS: Among 1,213 subjects initiated on mechanical ventilation from January 2013-March 2017, 68% and 24% were initiated on APC-CMV and VC-CMV, respectively, which composed 62% and 21% of the majority ventilator modes. Age, sex, race, and ethnicity were not associated with the initial or majority APC-CMV or VC-CMV modes. Subjects initiated on APC-CMV spent 88% of the mechanical ventilation time on APC-CMV mode. Compared to VC-CMV, subjects with APC-CMV majority mode experienced more ventilator setting changes/d (1.1 vs 0.8, P < .001). There were no significant differences in sedative medications when comparing subjects receiving APC-CMV versus VC-CMV majority modes. CONCLUSIONS: APC-CMV was highly utilized in the medical ICU. Subjects on APC-CMV had more ventilator setting changes/d than those on VC-CMV. APC-CMV offered no advantage of reduced setting adjustments or less sedation compared to VC-CMV.

Modeling biases from low-pass genome sequencing to enable accurate population genetic inferences.

Low-pass genome sequencing is cost-effective and enables analysis of large cohorts. However, it introduces biases by reducing heterozygous genotypes and low-frequency alleles, impacting subsequent analyses such as demographic history inference. We developed a probabilistic model of low-pass biases from the Genome Analysis Toolkit (GATK) multi-sample calling pipeline, and we implemented it in the population genomic inference software dadi. We evaluated the model using simulated low-pass datasets and found that it alleviated low-pass biases in inferred demographic parameters. We further validated the model by downsampling 1000 Genomes Project data, demonstrating its effectiveness on real data. Our model is widely applicable and substantially improves model-based inferences from low-pass population genomic data.

Computationally Efficient Demographic History Inference from Allele Frequencies with Supervised Machine Learning.

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite-likelihood optimization. However, dadi's optimization procedure can be computationally expensive. Here, we present donni (demography optimization via neural network inference), a new inference method based on dadi that is more efficient while maintaining comparable inference accuracy. For each dadi-supported demographic model, donni simulates the expected AFS for a range of model parameters then trains a set of Mean Variance Estimation neural networks using the simulated AFS. Trained networks can then be used to instantaneously infer the model parameters from future genomic data summarized by an AFS. We demonstrate that for many demographic models, donni can infer some parameters, such as population size changes, very well and other parameters, such as migration rates and times of demographic events, fairly well. Importantly, donni provides both parameter and confidence interval estimates from input AFS with accuracy comparable to parameters inferred by dadi's likelihood optimization while bypassing its long and computationally intensive evaluation process. donni's performance demonstrates that supervised machine learning algorithms may be a promising avenue for developing more sustainable and computationally efficient demographic history inference methods.

Computationally efficient demographic history inference from allele frequencies with supervised machine learning.

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite likelihood optimization. However, dadi's optimization procedure can be computationally expensive. Here, we developed donni (demography optimization via neural network inference), a new inference method based on dadi that is more efficient while maintaining comparable inference accuracy. For each dadi-supported demographic model, donni simulates the expected AFS for a range of model parameters then trains a set of Mean Variance Estimation neural networks using the simulated AFS. Trained networks can then be used to instantaneously infer the model parameters from future input data AFS. We demonstrated that for many demographic models, donni can infer some parameters, such as population size changes, very well and other parameters, such as migration rates and times of demographic events, fairly well. Importantly, donni provides both parameter and confidence interval estimates from input AFS with accuracy comparable to parameters inferred by dadi's likelihood optimization while bypassing its long and computationally intensive evaluation process. donni's performance demonstrates that supervised machine learning algorithms may be a promising avenue for developing more sustainable and computationally efficient demographic history inference methods.

The Role of the Human Cytomegalovirus UL133-UL138 Gene Locus in Latency and Reactivation.

Human cytomegalovirus (HCMV) latency, the means by which the virus persists indefinitely in an infected individual, is a major frontier of current research efforts in the field. Towards developing a comprehensive understanding of HCMV latency and its reactivation from latency, viral determinants of latency and reactivation and their host interactions that govern the latent state and reactivation from latency have been identified. The polycistronic UL133-UL138 locus encodes determinants of both latency and reactivation. In this review, we survey the model systems used to investigate latency and new findings from these systems. Particular focus is given to the roles of the UL133, UL135, UL136 and UL138 proteins in regulating viral latency and how their known host interactions contribute to regulating host signaling pathways towards the establishment of or exit from latency. Understanding the mechanisms underlying viral latency and reactivation is important in developing strategies to block reactivation and prevent CMV disease in immunocompromised individuals, such as transplant patients.

The association between post-cardiac arrest cerebral oxygenation and survival with favorable neurological outcomes: A multicenter study.

OBJECTIVE: Cerebral oximetry is a non-invasive system that uses near infrared spectroscopy to measure regional cerebral oxygenation (rSO2) in the frontal lobe of the brain. Post-cardiac arrest rSO2 may be associated with survival and neurological outcomes in out-of-hospital cardiac arrest patients; however, no studies have examined relationships between rSO2 and neurological outcomes following in-hospital cardiac arrest (IHCA). We tested the hypothesis that rSO2 following IHCA is associated with survival and favorable neurological outcomes. DESIGN: Prospective study from nine acute care hospital in the United States and United Kingdom. PATIENTS: Convenience sample of IHCA patients admitted to the intensive care unit with post-cardiac arrest syndrome. INTERVENTIONS: Cerebral oximetry monitoring (Equanox 7600, Nonin Medical, MN, USA) during the first 48 h after IHCA. MEASUREMENTS AND MAIN RESULTS: Subject's rSO2 was calculated as the mean of collected data at different time intervals: hourly between 1-6 h, 6-12 h, 12-18 h, 18-24 h and 24-48 h. Demographic data pertaining to possible confounding variables for rSO2 and primary outcome were collected. The primary outcome was survival with favorable neurological outcomes (cerebral performance scale [CPC] 1-2) vs severe neurological injury or death (CPC 3-5) at hospital discharge. Univariate and multivariate statistical analyses were performed to correlate cerebral oximetry values and other variables with the primary outcome. Among 87 studied patients, 26 (29.9%) achieved CPC 1-2. A significant difference in mean rSO2 was observed during hours 1-2 after IHCA in CPC 1-2 vs CPC 3-5 (73.08 vs. 66.59, p = 0.031) but not at other time intervals. There were no differences in age, Charlson comorbidity index, APACHE II scores, CPR duration, mean arterial pressure, PaO2, PaCO2, and hemoglobin levels between two groups. CONCLUSIONS: There may be a significant physiological difference in rSO2 in the first two hours after ROSC in IHCA patients who achieve favorable neurological outcomes, however, this difference may not be clinically significant.

The Combination of Metformin and Valproic Acid Has a Greater Anti-tumoral Effect on Prostate Cancer Growth In Vivo than Either Drug Alone.

BACKGROUND/AIM: The hypoglycemic drug metformin (MET) and the anti-epileptic drug valproic acid (VPA) have individually shown anti-tumor effects in prostate cancer in vitro. The present study intended to investigate the efficacy of the combination of MET and VPA in prostate cancer treatment in a pre-clinical xenograft model. MATERIALS AND METHODS: Prostate cancer cell lines (LNCaP and PC-3) were inoculated under the skin of BALB/c nude mice. The mice were treated with 200 µl/ml MET and/or 0.4% (w/v) VPA diluted in drinking water, or with vehicle control, and were monitored until the tumor volume reached 2,000 mm3 Evaluation of toxicity of the drug combination was determined in liver and kidney by histology. RESULTS: In both LNCaP and PC-3 xenografts, MET combined with VPA significantly reduced tumor growth during the first 4 weeks following treatment, and delayed the time-to-tumor volume of 2,000 mm3 by 90 days, as compared to MET or to VPA alone, and to vehicle control. There was no significant difference in total mouse weight, liver or kidney morphology in response to combination treatment (MET+VPA) compared to MET or VPA alone and vehicle control. CONCLUSION: The combination treatment of MET with VPA is more effective at slowing prostate tumor growth in vivo compared to either drug alone, in mouse xenografts. These pre-clinical results support previous in vitro data and also demonstrate the low toxicity of the combination of these drugs, suggesting that this may be a potential new therapy to be investigated in clinical trials for prostate cancer.

Combination Therapies Using Metformin and/or Valproic Acid in Prostate Cancer: Possible Mechanistic Interactions.

Prostate cancer (PCa) is the most frequent cancer in men. The evolution from local PCa to castration-resistant PCa, an end-stage of disease, is often associated with changes in genes such as p53, androgen receptor, PTEN, and ETS gene fusion products. Evidence is accumulating that repurposing of metformin (MET) and valproic acid (VPA) either when used alone, or in combination, with another therapy, could potentially play a role in slowing down PCa progression. This review provides an overview of the application of MET and VPA, both alone and in combination with other drugs for PCa treatment, correlates the responses to these drugs with common molecular changes in PCa, and then describes the potential for combined MET and VPA as a systemic therapy for prostate cancer, based on potential interacting mechanisms.

Chronic low dose ethanol induces an aggressive metastatic phenotype in TRAMP mice, which is counteracted by parthenolide.

Despite advances in prostate cancer therapy, dissemination and growth of metastases results in shortened survival. Here we examined the potential anti-cancer effect of the NF-κB inhibitor parthenolide (PTL) and its water soluble analogue dimethylaminoparthenolide (DMAPT) on tumour progression and metastasis in the TRansgenic Adenocarcinoma of the Mouse Prostate (TRAMP) model of prostate cancer. Six-week-old male TRAMP mice received PTL (40 mg/kg in 10% ethanol/saline), DMAPT (100 mg/kg in sterile water), or vehicle controls by oral gavage thrice weekly until palpable tumour formation. DMAPT treatment slowed normal tumour development in TRAMP mice, extending the time-to-palpable prostate tumour by 20%. PTL did not slow overall tumour development, while the ethanol/saline vehicle used to administer PTL unexpectedly induced an aggressive metastatic tumour phenotype. Chronic ethanol/saline vehicle upregulated expression of NF-κB, MMP2, integrin ß1, collagen IV, and laminin, and induced vascular basement membrane degradation in primary prostate tumours, as well as increased metastatic spread to the lung and liver. All of these changes were largely prevented by co-administration with PTL. DMAPT (in water) reduced metastasis to below that of water-control. These data suggest that DMAPT has the potential to be used as a cancer preventive and anti-metastatic therapy for prostate cancer. Although low levels of ethanol consumption have not been shown to strongly correlate with prostate cancer epidemiology, these results would support a potential effect of chronic low dose ethanol on metastasis and the TRAMP model provides a useful system in which to further explore the mechanisms involved.

The Combination of Metformin and Valproic Acid Induces Synergistic Apoptosis in the Presence of p53 and Androgen Signaling in Prostate Cancer.

We investigated the potential of combining the hypoglycemic drug metformin (MET) and the antiepileptic drug valproic acid (VPA), which act via different biochemical pathways, to provide enhanced antitumor responses in prostate cancer. Prostate cancer cell lines (LNCaP and PC-3), normal prostate epithelial cells (PrEC), and patient-derived prostate tumor explants were treated with MET and/or VPA. Proliferation and apoptosis were assessed. The role of p53 in response to MET + VPA was assessed in cell lines using RNAi in LNCaP (p53+) and ectopic expression of p53 in PC-3 (p53-). The role of the androgen receptor (AR) was investigated using the AR antagonist enzalutamide. The combination of MET and VPA synergistically inhibited proliferation in LNCaP and PC-3, with no significant effect in PrEC. LNCaP, but not PC-3, demonstrated synergistic intrinsic apoptosis in response to MET + VPA. Knockdown of p53 in LNCaP (p53+, AR+) reduced the synergistic apoptotic response as did inhibition of AR. Ectopic expression of p53 in PC-3 (p53-, AR-) increased apoptosis in response to MET + VPA. In patient-derived prostate tumor explants, MET + VPA also induced a significant decrease in proliferation and an increase in apoptosis in tumor cells. In conclusion, we demonstrate that MET + VPA can synergistically kill more prostate cancer cells than either drug alone. The response is dependent on the presence of p53 and AR signaling, which have critical roles in prostate carcinogenesis. Further in vivo/ex vivo preclinical studies are required to determine the relative efficacy of MET + VPA as a potential treatment for prostate cancer. Mol Cancer Ther; 16(12); 2689-700. ©2017 AACR.

Validation study of a computer-based open surgical trainer: SimPraxis(®) simulation platform.

BACKGROUND: Technological advances have dramatically changed medical education, particularly in the era of work-hour restrictions, which increasingly highlights a need for novel methods to teach surgical skills. The purpose of this study was to evaluate the validity of a novel, computer-based, interactive, cognitive simulator for training surgeons to perform pelvic lymph node dissection (PLND). METHODS: Eight prostate cancer experts evaluated the content of the simulator. Contextual aspects of the simulator were rated on a five-point Likert scale. The experts and nine first-year residents completed a simulated PLND. Time and deviations were logged, and the results were compared between experts and novices using the Mann-Whitney test. RESULTS: Before training, 88% of the experts felt that a validated simulator would be useful for PLND training. After testing, 100% of the experts felt that it would be more useful than standard video training. Eighty-eight percent stated that they would like to see the simulator in the curriculum of residency programs and 56% thought it would be useful for accreditation purposes. The experts felt that the simulator aided in overall understanding, training indications, concepts and steps of the procedure, training how to use an assistant, and enhanced the knowledge of anatomy. Median performance times taken by experts and interns to complete a PLND procedure on the simulator were 12.62 and 23.97 minutes, respectively. Median deviation from the incorporated procedure pathway for experts was 24.5 and was 89 for novices. CONCLUSION: We describe an interactive, computer-based simulator designed to assist in mastery of the cognitive steps of an open surgical procedure. This platform is intuitive and flexible, and could be applied to any stepwise medical procedure. Overall, experts outperformed novices in their performance on the trainer. Experts agreed that the content was acceptable, accurate, and representative.

Quantitative analysis of neural stem cell migration and tracer clearance in the rat brain by MRI.

PURPOSE: This study describes a quantitative method to estimate the migratory capacity of neural stem cells (NSCs) using magnetic resonance imaging. PROCEDURES: NSCs were labeled with ferumoxides and injected stereotaxically into the corpus callosum of the normal rat brain. Control subjects received either free ferumoxides or nonviable labeled cells. Subjects were scanned after initial injection and at 1 week. Image sets were coregistered, compared morphologically, and analyzed parametrically to determine migration speed. RESULTS: Subjects receiving injections of viable cells had a significantly greater spread of the tracer after 1 week than either control group (p< 0.05). The speed of migration for viable NSCs was significantly higher than that of controls along the corpus callosum (p < 0.05). Migratory speeds estimated from histology and imaging were significantly correlated. CONCLUSIONS: This study provides a quantitative assessment of posttransplantation neural stem cell migration that is clearly distinguishable from tracer clearance.