Large standard deviations and logarithmic-normality: the truth about hemocyte counts in Drosophila

A Novel TCGA-Validated, MiRNA-Based Signature for Prediction of Breast Cancer Prognosis and Survival

Breast cancer (BC) is the most common cancer affecting women and the leading cause of cancer-related deaths worldwide. Compelling evidence indicates that microRNAs (miRNAs) are inextricably involved in the development of cancer. Here, we constructed a novel model, based on miRNA-seq and clinical data downloaded from The Cancer Genome Atlas (TCGA). Data from a total of 962 patients were included in this study, and the relationships among their clinicopathological features, survival, and miRNA-seq expression levels were analyzed. Hsa-miR-186 and hsa-miR-361 were identified as internal reference miRNAs and used to normalize miRNA expression data. A five-miRNA signature, constructed using univariate and multivariate Cox regression, was significantly associated with disease-specific survival (DSS) of patients with BC. Kaplan–Meier (KM) and receiver operating characteristic (ROC) analyses were conducted to confirm the clinical significance of the five-miRNA signature. Finally, a nomogram was constructed based on the five-miRNA signature to evaluate its clinical value. Cox regression analysis revealed that a five-miRNA signature was significantly associated with DSS of patients with BC. KM analysis demonstrated that the signature could efficiently distinguish high- and low-risk patients. Moreover, ROC analysis showed that the five-miRNA signature exhibited high sensitivity and specificity in predicting the prognosis of patients with BC. Patients in the high-risk subgroup who received adjuvant chemotherapy had a significantly lower incidence of mortality than those who did not. A nomogram constructed based on the five-miRNA signature was effective in predicting 5-year DSS. This study presents a novel five-miRNA signature as a reliable prognostic tool to predict DSS and provide theoretical reference significance for individualized clinical decisions for patients with BC.

Tissue-autonomous immune response regulates stress signaling during hypertrophy

Postmitotic tissues are incapable of replacing damaged cells through proliferation, but need to rely on buffering mechanisms to prevent tissue disintegration. By constitutively activating the Ras/MAPK-pathway via Ras^V12-overexpression in the postmitotic salivary glands (SGs) of Drosophila larvae, we overrode the glands adaptability to growth signals and induced hypertrophy. The accompanied loss of tissue integrity, recognition by cellular immunity, and cell death are all buffered by blocking stress signaling through a genuine tissue-autonomous immune response. This novel, spatio-temporally tightly regulated mechanism relies on the inhibition of a feedback-loop in the JNK-pathway by the immune effector and antimicrobial peptide Drosomycin. While this interaction might allow growing SGs to cope with temporary stress, continuous Drosomycin expression in Ras^V12-glands favors unrestricted hypertrophy. These findings indicate the necessity to refine therapeutic approaches that stimulate immune responses by acknowledging their possible, detrimental effects in damaged or stressed tissues.

Tissues and organs work hard to maintain balance in everything from taking up nutrients to controlling their growth. Ageing, wounding, sickness, and changes in the genetic code can all alter this balance, and cause the tissue or organ to lose some of its cells. Many tissues restore this loss by dividing their remaining cells to fill in the gaps. But some – like the salivary glands of fruit fly larvae – have lost this ability.

Tissues like these rely on being able to sense and counteract problems as they arise so as to not lose their balance in the first place. The immune system and stress responses are crucial for this process. They trigger steps to correct the problem and interact with each other to find a common decision about the fate of the affected tissue. To better understand how the immune system and stress response work together, Krautz, Khalili and Theopold genetically manipulated cells in the salivary gland of fruit fly larvae. These modifications switched on signals that stimulate cells to keep growing, causing the salivary gland’s tissue to slowly lose its balance and trigger the stress and immune response.

The experiments showed that while the stress response instructed the cells in the gland to die, a peptide released by the immune system called Drosomycin blocked this response and prevented the tissue from collapsing. The cells in the part of the gland not producing this immune peptide were consequently killed by the stress response. When all the cells in the salivary gland were forced to produce Drosomycin, none of the cells died and the whole tissue survived. But it also allowed the cells in the gland to grow uncontrollably, like a tumor, threatening the health of the entire organism.

Mapping the interactions between immune and stress pathways could help to fine-tune treatments that can prevent tissue damage. Fruit flies share many genetic features and molecular pathways with humans. So, the next step towards these kinds of treatments would be to screen for similar mechanisms that block stress activation in damaged human tissues. But this research carries a warning: careless activation of the immune system to protect stressed tissues could lead to uncontrolled tissue growth, and might cause more harm than good.

Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

BACKGROUND

Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset.

METHODS

Retrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset.

RESULTS

270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001; DOR 4.358), SOFA (0.716; P < .001; DOR 3.720), and SIRS (0.655; P < .001; DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset.

CONCLUSIONS

The MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.

A system for the high-throughput measurement of the shear modulus distribution of human red blood cells

Reduced deformability of red blood cells (RBCs) can affect the hemodynamics of the microcirculation and reduce oxygen transport efficiency. It is also well known that reduced RBC deformability is a signature of various physical disorders, including sepsis, and that the primary determinant of RBC deformability is the membrane shear modulus. To measure the distribution of an individual's RBC shear modulus with high throughput, we a) developed a high-fidelity computational model of RBCs in confined microchannels to inform design decisions; b) created a novel experimental system combining microfluidic flow, imaging, and image analysis; and c) performed automated comparisons between measured quantities and numerical predictions to extract quantitative measures of the RBC shear modulus for each of thousands of cells. We applied our computational simulation platform to construct the appropriate deformability figure(s) of merit to quantify RBC stiffness based on an experimentally measured, steady-state cell shape in flow through a microchannel. In particular, we determined a shape parameter based on the second moment of the cell shape that is sensitive to the changes in the membrane stiffness and cell size. We then conducted microfluidic experiments and developed custom automated image processing codes to identify and track the position and shape of individual RBCs within micro-constrictions. The fabricated microchannels include a square cross-section imaging region (7 by 7 μm) and we applied order 10 kPa pressure differences to induce order 10 mm s^-1 cell velocities. The combination of modeling, microfluidics, and imaging enables, for the first time, quantitative measurement of the shear moduli of thousands of RBCs in human blood samples. We demonstrate the high-throughput features by sensitive quantification of the changes in the distribution of RBC stiffness with aging. This combined measurement and computational platform is ultimately intended to diagnose blood cell disorders in patients.

Breath ammonia and ethanol increase in response to a high protein challenge

Quantifying changes in ammonia and ethanol in blood and body fluid assays in response to food is cumbersome. We used breath analysis of ammonia, ethanol, hydrogen (an accepted standard of gut transit) and acetone to investigate gastrointestinal physiology. In 30 healthy participants, we measured each metabolite serially over 6 h in control and high protein trials. Two-way repeated measures ANOVA compared treatment (control versus intervention), change from baseline to maximum and interaction of treatment and time change. Interaction was significant for ammonia (p < 0.0001) and hydrogen (p < 0.0001). We describe the dynamic measurement of multiple metabolites in response to an oral challenge.

A Drosophila immune response against Ras-induced overgrowth

Our goal is to characterize the innate immune response against the early stage of tumor development. For this, animal models where genetic changes in specific cells and tissues can be performed in a controlled way have become increasingly important, including the fruitfly Drosophila melanogaster. Many tumor mutants in Drosophila affect the germline and, as a consequence, also the immune system itself, making it difficult to ascribe their phenotype to a specific tissue. Only during the past decade, mutations have been induced systematically in somatic cells to study the control of tumorous growth by neighboring cells and by immune cells. Here we show that upon ectopic expression of a dominant-active form of the Ras oncogene (Ras^V12), both imaginal discs and salivary glands are affected. Particularly, the glands increase in size, express metalloproteinases and display apoptotic markers. This leads to a strong cellular response, which has many hallmarks of the granuloma-like encapsulation reaction, usually mounted by the insect against larger foreign objects. RNA sequencing of the fat body reveals a characteristic humoral immune response. In addition we also identify genes that are specifically induced upon expression of Ras^V12. As a proof-of-principle, we show that one of the induced genes (santa-maria), which encodes a scavenger receptor, modulates damage to the salivary glands. The list of genes we have identified provides a rich source for further functional characterization. Our hope is that this will lead to a better understanding of the earliest stage of innate immune responses against tumors with implications for mammalian immunity.

Loss of Hemolectin reduces the survival of Drosophila larvae after wounding

Coagulation involving both hemocytes and humoral factors is important for insect survival and immune defense. Hemolectin is a major larval clotting factor in Drosophila, and hemolymph from hml mutants does not clot ex vivo. Yet surprisingly third instar hml larvae survived injury as well as controls. The number of hemocytes in circulation changes during larval development. Reasoning that this could affect coagulation, we studied larval survival after injury at different stages. We found that hml larvae survived less than controls when injured during the feeding stage with fewer hemocytes. This important in vivo result reinforces the role of Hemolectin in larval hemostasis. A subtle effect of hml on immunity was found in adults. Similar experiments on hml mutant larvae gave different results, but feeding stage hml larvae were differentially sensitive to infections with different strains of Serratia marcescens.

Problems with using the normal distribution--and ways to improve quality and efficiency of data analysis

Background

The Gaussian or normal distribution is the most established model to characterize quantitative variation of original data. Accordingly, data are summarized using the arithmetic mean and the standard deviation, by ± SD, or with the standard error of the mean, ± SEM. This, together with corresponding bars in graphical displays has become the standard to characterize variation.

Methodology/Principal Findings

Here we question the adequacy of this characterization, and of the model. The published literature provides numerous examples for which such descriptions appear inappropriate because, based on the “95% range check”, their distributions are obviously skewed. In these cases, the symmetric characterization is a poor description and may trigger wrong conclusions. To solve the problem, it is enlightening to regard causes of variation. Multiplicative causes are by far more important than additive ones, in general, and benefit from a multiplicative (or log-) normal approach. Fortunately, quite similar to the normal, the log-normal distribution can now be handled easily and characterized at the level of the original data with the help of both, a new sign, ^x/, times-divide, and notation. Analogous to ± SD, it connects the multiplicative (or geometric) mean * and the multiplicative standard deviation s* in the form * ^x/s*, that is advantageous and recommended.

Conclusions/Significance

The corresponding shift from the symmetric to the asymmetric view will substantially increase both, recognition of data distributions, and interpretation quality. It will allow for savings in sample size that can be considerable. Moreover, this is in line with ethical responsibility. Adequate models will improve concepts and theories, and provide deeper insight into science and life.