Persistent parental RNAi in the beetle Tribolium castaneum involves maternal transmission of long double-stranded RNA

Parental RNA interference (pRNAi) is a powerful and widely used method for gene-specific knockdown. Yet in insects its efficacy varies between species, and how the systemic RNAi response is transmitted from mother to offspring remains elusive. Using the flour beetle Tribolium castaneum, we report an RT-qPCR strategy to unmask the presence of double-stranded RNA (dsRNA) distinct from endogenous mRNA. We find that the injected dsRNA is directly transmitted into the egg and persists throughout embryogenesis. Despite this depletion of dsRNA from the mother, we show that strong pRNAi can persist for months before waning at strain-specific rates. In seeking the receptor proteins for cellular uptake of long dsRNA into the egg, we lastly present a phylogenomics profiling approach to ascertain macroevolutionary distributions of candidate proteins. We demonstrate a visualization strategy based on taxonomically hierarchical assessment of orthology clustering data to rapidly assess gene age and copy number changes, refined by several lines of sequence-based evidence. We use this approach to document repeated losses of SID-1-like channel proteins in the arthropods, including wholesale loss in the Heteroptera (true bugs), which are nonetheless highly sensitive to pRNAi. Overall, we elucidate practical considerations for insect pRNAi against a backdrop of outstanding questions on the molecular mechanism of dsRNA transmission to achieve long-term, systemic knockdown.

CXCL10 levels at hospital admission predict COVID-19 outcome: hierarchical assessment of 53 putative inflammatory biomarkers in an observational study

Background Host inflammation contributes to determine whether SARS-CoV-2 infection causes mild or life-threatening disease. Tools are needed for early risk assessment. Methods We studied in 111 COVID-19 patients prospectively followed at a single reference Hospital fifty-three potential biomarkers including alarmins, cytokines, adipocytokines and growth factors, humoral innate immune and neuroendocrine molecules and regulators of iron metabolism. Biomarkers at hospital admission together with age, degree of hypoxia, neutrophil to lymphocyte ratio (NLR), lactate dehydrogenase (LDH), C-reactive protein (CRP) and creatinine were analysed within a data-driven approach to classify patients with respect to survival and ICU outcomes. Classification and regression tree (CART) models were used to identify prognostic biomarkers. Results Among the fifty-three potential biomarkers, the classification tree analysis selected CXCL10 at hospital admission, in combination with NLR and time from onset, as the best predictor of ICU transfer (AUC [95% CI] = 0.8374 [0.6233–0.8435]), while it was selected alone to predict death (AUC [95% CI] = 0.7334 [0.7547–0.9201]). CXCL10 concentration abated in COVID-19 survivors after healing and discharge from the hospital. Conclusions CXCL10 results from a data-driven analysis, that accounts for presence of confounding factors, as the most robust predictive biomarker of patient outcome in COVID-19. Graphic abstract

Power System Transient Stability Assessment Based on Snapshot Ensemble LSTM Network

Data-driven methods using synchrophasor measurements have a broad application prospect in Transient Stability Assessment (TSA). Most previous studies only focused on predicting whether the power system is stable or not after disturbance, which lacked a quantitative analysis of the risk of transient stability. Therefore, this paper proposes a two-stage power system TSA method based on snapshot ensemble long short-term memory (LSTM) network. This method can efficiently build an ensemble model through a single training process, and employ the disturbed trajectory measurements as the inputs, which can realize rapid end-to-end TSA. In the first stage, dynamic hierarchical assessment is carried out through the classifier, so as to screen out credible samples step by step. In the second stage, the regressor is used to predict the transient stability margin of the credible stable samples and the undetermined samples, and combined with the built risk function to realize the risk quantification of transient angle stability. Furthermore, by modifying the loss function of the model, it effectively overcomes sample imbalance and overlapping. The simulation results show that the proposed method can not only accurately predict binary information representing transient stability status of samples, but also reasonably reflect the transient safety risk level of power systems, providing reliable reference for the subsequent control.

A Multi-Level System of Performance Evaluation Using Diagnosis-Related Groups for Cost Containment

This study aims to develop a performance evaluation system that can facilitate performance evaluation at region, hospital, and department levels to enable better cost management for sustainable development. A multi-level system of performance evaluation informs a hierarchical assessment of cost management from regions to hospitals to departments using diagnosis-related group (DRGs). Various metrics are developed employing the variances between targets and actuals where targets are determined from two perspectives: benchmarking using external regional prices and change management using internal data. Targets for the latter are statistically based and specifically incorporate variability. The model is applied to two hospitals, twenty departments, nine DRGs and 1071 inpatients. The analyses indicate that the approach can provide a practical evaluation tool that allows for particular characteristics at multiple levels. The system provides macro-micro and external-internal perspectives in performance, enabling high-level variances to be decomposed thereby identifying sources of performance variability and financial impact.

Delirium and delirium severity predict the trajectory of the Hierarchical Assessment of Balance and Mobility (HABAM) in hospitalised older people: findings from the DECIDE Study

Background Delirium is common, distressing and associated with poor outcomes. Despite this, delirium remains poorly recognised, resulting in worse outcomes. There is an urgent need for methods to objectively assess for delirium. Physical function has been proposed as a potential surrogate marker, but few studies have monitored physical function in the context of delirium. We examined if trajectories of physical function are affected by the presence and severity of delirium in a representative sample of hospitalised participants over 65 years. Methods During hospital admissions in 2016, we assessed participants from the DECIDE study daily for delirium and physical function, using the Hierarchical Assessment of Balance and Mobility (HABAM). We used linear mixed models to assess the effect of delirium and delirium severity during admission on HABAM trajectory. Results Of 178 participants, 58 experienced delirium during admission. Median HABAM scores in those with delirium were significantly higher (indicating worse mobility) than those without delirium. Modelling HABAM trajectories, HABAM scores at first assessment were worse in those with delirium than those without, by 0.76 (95% CI: 0.49-1.04) points. Participants with severe delirium experienced a much greater perturbance in their physical function, with an even lower value at first assessment and slower subsequent improvement. Conclusions Physical function was worse in those with delirium compared to without. This supports the assertion that motor disturbances are a core feature of delirium and monitoring physical function, using a tool such as the HABAM, may have clinical utility as a surrogate marker for delirium and its resolution.

The Hierarchical Assessment of Balance and Mobility (HABAM): a useful tool to estimate length of hospital stay following hip and knee replacement

Background/Aims The Hierarchical Assessment of Balance and Mobility is a measure of balance and mobility that can detect recovery of physical function, and can be used to identify patients at risk of delayed discharge. The aim of this study was to investigate the use of Hierarchical Assessment of Balance and Mobility scores as a predictor of length of hospital stay in patients following hip and knee replacement. Methods Hierarchical Assessment of Balance and Mobility scores were collected on 191 patients following primary total joint replacement. Regression and receiver operating characteristic curve analyses were conducted to assess the relationship between Hierarchical Assessment of Balance and Mobility and length of stay. Results Hierarchical Assessment of Balance and Mobility scores of <31 on the first post-operative day predicted longer than mean length of stay (4 days) with sensitivity and specificity of 79% and 83%. Receiver operating characteristic curve analyses showed that a Hierarchical Assessment of Balance and Mobility score of 50 was the optimal cut-off point for discharge. Conclusions Hierarchical Assessment of Balance and Mobility offers a practical way to quantify and objectively track patients' physical function, and can help identify patients at risk of an increased length of stay on post-operative day one.