Quantification of injury burden using multiple data sources: a longitudinal study

Quantification of injury burden is vital for injury prevention, as it provides a guide for setting policies and priorities. This study generated a set of Hong Kong specific disability weights (DWs) derived from patient experiences and hospital records. Patients were recruited from the Accident and Emergency Department (AED) of three major trauma centers in Hong Kong between September 2014 and December 2015 and subsequently interviewed with a focus on health-related quality of life at most three times over a 12-month period. These patient-reported data were then used for estimation of DWs. The burden of injury was determined using the mortality and inpatient data from 2001 to 2012 and then compared with those reported in the UK Burden of Injury (UKBOI) and global burden of diseases (GBD) studies. There were 22,856 mortality cases and 817,953 morbidity cases caused by injuries, in total contributing to 1,027,641 disability-adjusted life years (DALYs) in the 12-year study timeframe. Estimates for DALYs per 100,000 in Hong Kong amounted to 1192, compared with 2924 in UKBOI and 3459 in GBD. Our findings support the use of multiple data sources including patient-reported data and hospital records for estimation of injury burden

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Variant palmaris profundus enclosed by an unusual loop of the median nerve

According to the usual description in most anatomy texts, the median nerve in the forearm passes between the 2 heads of pronator teres. It continues distally between flexor digitorum superficialis and profundus almost to the retinaculum. Muscular branches leave the nerve near the elbow and supply all superficial muscles of the anterior part of the forearm except flexor carpi ulnaris. Many variations of the median nerve in the forearm have been reported (Urban & Krosman, 1992). The palmaris profundus is also a rare anomaly of the forearm (Dyreby & Engber, 1982). It originates from the radial side of the common flexor tendon in the proximal forearm and inserts into the undersurface of the palmar aponeurosis. The origin of palmaris profundus may be close to the median nerve and its branches, and may be involved in compressive neuropathy of the anterior interosseous nerve. Its tendon crossing through the carpal canal has been implicated in the carpal tunnel syndrome (reviewed by Lahey & Aulicino, 1986). In some cases, palmaris profundus was found enclosed in a common fascial sheath with the median nerve (Stark, 1992; Sahinoglu et al. 1994). To indicate its close association with the median nerve, the palmaris profundus was also named ‘musculus comitans nervi mediani’ (Sahinoglu et al. 1994). This article reports an unusual loop of the median nerve encircling an anomalous palmaris profundus in the forearm, which, to the best of our knowledge, has not been previously described

Glossogyne tenuifolia Extract Increases Nitric Oxide Production in Human Umbilical Vein Endothelial Cells

The vascular nitric oxide (NO) system has a protective effect in atherosclerosis. NO is generated from the conversion of L-arginine to L-citrulline by the enzymatic action of endothelial NO synthase (eNOS). Compounds with the effect of enhancing eNOS expression are considered to be candidates for the prevention of atherosclerosis. In this study, extracts from the aerial, root, and whole plant of Glossogyne tenuifolia (GT) were obtained with ethanol, n-hexane, ethyl acetate (EA), and methanol extraction, respectively. The effects of these GT extracts on the synthesis of NO and the expression of eNOS in human umbilical vein endothelial cells (HUVECs) were investigated. NO production was determined as nitrite by colorimetry, following the Griess reaction. The treatment of HUVECs with EA extract from the root of GT and n-hexane, methanol, and ethanol extract from the aerial, root, and whole plant of GT increased NO production in a dose-dependent manner. When at a dose of 160 μg/mL, NO production increased from 0.9 to 18.4-fold. Among these extracts, the methanol extract from the root of GT (R/M GTE) exhibited the most potent effect on NO production (increased by 18.4-fold). Furthermore, using Western blot and RT–PCR analysis, treatment of HUVECs with the R/M GTE increased both eNOS protein and mRNA expression. In addition, Western blot analysis revealed that the R/M GTE increased eNOS phosphorylation at serine1177 as early as 15 min after treatment. The chemical composition for the main ingredients was also performed by HPLC analysis. In conclusion, the present study demonstrated that GT extracts increased NO production in HUVECs and that the R/M GTE increased NO production via increasing eNOS expression and activation by phosphorylation of eNOS at serine1177

Perfluorooctane sulfonate induces autophagy-associated apoptosis through oxidative stress and the activation of extracellular signal-regulated kinases in renal tubular cells.

Perfluorooctane sulfonate (PFOS) is among the most abundant organic pollutants and is widely distributed in the environment, wildlife, and humans. Its toxic effects and biological hazards are associated with its long elimination half-life in humans. However, how it affects renal tubular cells (RTCs) remains unclear. In this study, PFOS was observed to mediate the increase in reactive oxygen species (ROS) generation, followed by the activation of the extracellular-signal-regulated kinase 1/2 (ERK1/2) pathway, which induced autophagy in RTCs. Although PFOS treatment induced autophagy after 6 h, prolonged treatment (24 h) reduced the autophagic flux by increasing lysosomal membrane permeability (LMP), leading to increased p62 protein accumulation and subsequent apoptosis. The increase in LMP was visualized through increased green fluorescence with acridine orange staining, and this was attenuated by 3-methyladenine, an autophagy inhibitor. N-acetyl cysteine and an inhibitor of the mitogen-activated protein kinase kinases (U0126) attenuated autophagy and apoptosis. Taken together, these results indicate that ROS activation and ROS-mediated phosphorylated ERK1/2 activation are essential to activate autophagy, resulting in the apoptosis of PFOS-treated RTCs. Our findings provide insight into the mechanism of PFOS-mediated renal toxicity

Widen the Applicability of a Convolutional Neural-Network-Assisted Glaucoma Detection Algorithm of Limited Training Images across Different Datasets

Automated glaucoma detection using deep learning may increase the diagnostic rate of glaucoma to prevent blindness, but generalizable models are currently unavailable despite the use of huge training datasets. This study aims to evaluate the performance of a convolutional neural network (CNN) classifier trained with a limited number of high-quality fundus images in detecting glaucoma and methods to improve its performance across different datasets. A CNN classifier was constructed using EfficientNet B3 and 944 images collected from one medical center (core model) and externally validated using three datasets. The performance of the core model was compared with (1) the integrated model constructed by using all training images from the four datasets and (2) the dataset-specific model built by fine-tuning the core model with training images from the external datasets. The diagnostic accuracy of the core model was 95.62% but dropped to ranges of 52.5–80.0% on the external datasets. Dataset-specific models exhibited superior diagnostic performance on the external datasets compared to other models, with a diagnostic accuracy of 87.50–92.5%. The findings suggest that dataset-specific tuning of the core CNN classifier effectively improves its applicability across different datasets when increasing training images fails to achieve generalization