Proximal Femur Fractures in the Elderly—A Novel Modality to Predict Mortality: The Neutrophil-to-Lymphocyte Ratio

Background: The assessment and identification of elderly patients with proximal femur fractures (PFF) who are at high risk of postoperative mortality may influence the treatment decision-making process. The purpose of this study was to determine whether the neutrophil-to-lymphocyte ratio (NLR) could be used to predict postoperative mortality in the elderly population. Methods: A four-year retrospective cohort study of electronic medical records was conducted at a single tertiary care hospital between 2015 and 2018. Data from 1551 patients aged 65 years and older who underwent surgical treatment for PFF were collected and analyzed. The data included complete blood counts at admission, demographic information, underlying illnesses, type of surgery, and postoperative mortality and complications during the first year of follow-up. A survival analysis model was utilized. Results: The mean age was 90.76 ± 1.88 years, 1066 (68.7%) women. Forty-four (2.8%) patients experienced postoperative infection. A higher NLR0 was independently associated with higher all-cause mortality rates in patients who underwent surgical treatment for PFF (p = 0.041). Moreover, the mean NLR0 value was higher when the death occurred earlier after surgery (p 0 levels may serve as a potentially valuable, inexpensive, and reliable prognostic biomarker to improve risk stratification for elderly patients who are candidates for PFF surgery. Furthermore, with additional research, we could potentially develop a treatment algorithm to identify patients at high risk of postoperative mortality

Diagnostic value of T-wave morphology changes during "QT stretching" in patients with long QT syndrome

Specific T-wave patterns on the resting electrocardiogram (ECG) aid in diagnosing long QT syndrome (LQTS) and identifying the specific genotype. However, provocation tests often are required to establish a diagnosis when the QT interval is borderline at rest. The purpose of this study was to determine whether T-wave morphology changes provoked by standing aid in the diagnosis of LQTS and determination of the genotype. The quick-standing test was performed by 100 LQTS patients (40 type 1 [LQT1], 42 type 2 [LQT2], 18 type 3 [LQT3]) and 100 controls. Logistic regression was used to determine whether T-wave morphology changes provoked by standing added to the already established diagnostic value of QTc stretching in identifying LQTS. During maximal QT stretching, the T-wave morphologies that best discriminated LQTS from controls included "notched," "late-onset," and "biphasic" T waves. These 3 categories were grouped into a category named "abnormal T-wave response to standing." During quick standing, a QTc stretched ≥490 ms increased the odds of correctly identifying LQTS. T-wave morphology changes provoked by standing were most helpful for identifying LQT2, less helpful for LQT1, and least helpful for LQT3. The sudden heart rate acceleration produced by abrupt standing not only increases the QTc but also exposes abnormal T waves that are valuable for diagnosing LQT

Biologically-Inspired Visual Simulation of Insect Swarms

Representing the majority of living animals, insects are the most ubiquitous biological organisms on Earth. Being able to simulate insect swarms could enhance visual realism of various graphical applications. However, the very complex nature of insect behaviors makes its simulation a challenging computational problem. To address this, we present a general biologically-inspired framework for visual simulation of insect swarms. Our approach is inspired by the observation that insects exhibit emergent behaviors at various scales in nature. At the low level, our framework automatically selects and configures the most suitable steering algorithm for the local collision avoidance task. At the intermediate level, it processes insect trajectories into piecewise-linear segments and constructs probability distribution functions for sampling waypoints. These waypoints are then evaluated by the Metropolis-Hastings algorithm to preserve global structures of insect swarms at the high level. With this biologically inspired, data-driven approach, we are able to simulate insect behaviors at different scales and we evaluate our simulation using both qualitative and quantitative metrics. Furthermore, as insect data could be difficult to acquire, our framework can be adopted as a computer-assisted animation tool to interpret sketch-like input as user control and generate simulations of complex insect swarming phenomena