Tear Secretion Induced by Selective Stimulation of Corneal and Conjunctival Sensory Nerve Fibers

Purpose. To measure the increase in tear secretion evoked by selective stimulation of the different populations of sensory receptors of the cornea and conjunctiva by using moderate and intense mechanical, chemical, and cold stimuli. Methods. Six healthy subjects participated in the study. Tear secretion was measured in both eyes by the Schirmer’s test conducted under control conditions and after stimulation of the center of the cornea and the temporal conjunctiva with a gas esthesiometer. Mechanical stimulation consisted in three pulses of 3 seconds’ duration of warmed air (at 34°C on the eye surface) applied at moderate (170 mL/min) and high (260 mL/min) flow rates. Cold thermal stimulation was made with cooled air that produced a corneal temperature drop of −1°C or −4.5°C. Chemical (acidic) stimulation was performed with a jet of gas containing a mixture of 80% CO2 in air. Results. The basal volume of tear secretion increased significantly (P < 0.05, paired t-test) after stimulation of the cornea with high-flow mechanical stimuli (260 mL/min), intense cooling pulses (−4.5°C), and chemical stimulation (80% CO2). The same stimuli were ineffective when applied to the conjunctiva. Moderate mechanical (170 mL/min) and cold (−1°C) stimulation of the cornea or the conjunctiva did not change significantly the volume of tear secretion. Conclusions. Reflex tear secretion caused by corneal stimulation seems to be chiefly due to activation of corneal polymodal nociceptors, whereas selective excitation of corneal mechanonociceptors or cold receptors appears to be less effective in evoking an augmented lacrimal secretion. Conjunctival receptors stimulated at equivalent levels do not evoke an increased tear secretion.

Metagenomic detection of viral pathogens in Spanish honeybees: co-infection by Aphid Lethal Paralysis, Israel Acute Paralysis and Lake Sinai Viruses

The situation in Europe concerning honeybees has in recent years become increasingly aggravated with steady decline in populations and/or catastrophic winter losses. This has largely been attributed to the occurrence of a variety of known and "unknown", emerging novel diseases. Previous studies have demonstrated that colonies often can harbour more than one pathogen, making identification of etiological agents with classical methods difficult. By employing an unbiased metagenomic approach, which allows the detection of both unexpected and previously unknown infectious agents, the detection of three viruses, Aphid Lethal Paralysis Virus (ALPV), Israel Acute Paralysis Virus (IAPV), and Lake Sinai Virus (LSV), in honeybees from Spain is reported in this article. The existence of a subgroup of ALPV with the ability to infect bees was only recently reported and this is the first identification of such a strain in Europe. Similarly, LSV appear to be a still unclassified group of viruses with unclear impact on colony health and these viruses have not previously been identified outside of the United States. Furthermore, our study also reveals that these bees carried a plant virus, Turnip Ringspot Virus (TuRSV), potentially serving as important vector organisms. Taken together, these results demonstrate the new possibilities opened up by high-throughput sequencing and metagenomic analysis to study emerging new diseases in domestic and wild animal populations, including honeybees.

A Diagnostic Calculator for Detecting Glaucoma on the Basis of Retinal Nerve Fiber Layer, Optic Disc, and Retinal Ganglion Cell Analysis by Optical Coherence Tomography

Purpose: The purpose of this study was to develop and validate a multivariate predictive model to detect glaucoma by using a combination of retinal nerve fiber layer (RNFL), retinal ganglion cell-inner plexiform (GCIPL), and optic disc parameters measured using spectral-domain optical coherence tomography (OCT). Methods: Five hundred eyes from 500 participants and 187 eyes of another 187 participants were included in the study and validation groups, respectively. Patients with glaucoma were classified in five groups based on visual field damage. Sensitivity and specificity of all glaucoma OCT parameters were analyzed. Receiver operating characteristic curves (ROC) and areas under the ROC (AUC) were compared. Three predictive multivariate models (quantitative, qualitative, and combined) that used a combination of the best OCT parameters were constructed. A diagnostic calculator was created using the combined multivariate model. Results: The best AUC parameters were: inferior RNFL, average RNFL, vertical cup/disc ratio, minimal GCIPL, and inferior-temporal GCIPL. Comparisons among the parameters did not show that the GCIPL parameters were better than those of the RNFL in early and advanced glaucoma. The highest AUC was in the combined predictive model (0.937; 95% confidence interval, 0.911–0.957) and was significantly (P = 0.0001) higher than the other isolated parameters considered in early and advanced glaucoma. The validation group displayed similar results to those of the study group. Conclusions: Best GCIPL, RNFL, and optic disc parameters showed a similar ability to detect glaucoma. The combined predictive formula improved the glaucoma detection compared to the best isolated parameters evaluated. The diagnostic calculator obtained good classification from participants in both the study and validation groups.