Vaginal Microbicides: Detecting Toxicities in Vivo that Paradoxically Increase Pathogen Transmission

BACKGROUND: Microbicides must protect against STD pathogens without causing unacceptable toxic effects. Microbicides based on nonoxynol-9 (N9) and other detergents disrupt sperm, HSV and HIV membranes, and these agents are effective contraceptives. But paradoxically N9 fails to protect women against HIV and other STD pathogens, most likely because it causes toxic effects that increase susceptibility. The mouse HSV-2 vaginal transmission model reported here: (a) Directly tests for toxic effects that increase susceptibility to HSV-2, (b) Determines in vivo whether a microbicide can protect against HSV-2 transmission without causing toxicities that increase susceptibility, and (c) Identifies those toxic effects that best correlate with the increased HSV susceptibility. METHODS: Susceptibility was evaluated in progestin-treated mice by delivering a low-dose viral inoculum (0.1 ID50) at various times after delivering the candidate microbicide to detect whether the candidate increased the fraction of mice infected. Ten agents were tested – five detergents: nonionic (N9), cationic (benzalkonium chloride, BZK), anionic (sodium dodecylsulfate, SDS), the pair of detergents in C31G (C14AO and C16B); one surface active agent (chlorhexidine); two non-detergents (BufferGel®, and sulfonated polystyrene, SPS); and HEC placebo gel (hydroxyethylcellulose). Toxic effects were evaluated by histology, uptake of a 'dead cell' dye, colposcopy, enumeration of vaginal macrophages, and measurement of inflammatory cytokines. RESULTS: A single dose of N9 protected against HSV-2 for a few minutes but then rapidly increased susceptibility, which reached maximum at 12 hours. When applied at the minimal concentration needed for brief partial protection, all five detergents caused a subsequent increase in susceptibility at 12 hours of ~20–30-fold. Surprisingly, colposcopy failed to detect visible sign of the N9 toxic effect that increased susceptibility at 12 hours. Toxic effects that occurred contemporaneously with increased susceptibility were rapid exfoliation and re-growth of epithelial cell layers, entry of macrophages into the vaginal lumen, and release of one or more inflammatory cytokines (Il-1β, KC, MIP 1α, RANTES). The non-detergent microbicides and HEC placebo caused no significant increase in susceptibility or toxic effects. CONCLUSION: This mouse HSV-2 model provides a sensitive method to detect microbicide-induced toxicities that increase susceptibility to infection. In this model, there was no concentration at which detergents provided protection without significantly increasing susceptibility.JHU Woodrow Wilson Fellowship; National Institutes of Health (Program Project A1 45967

Peptides as potent antimicrobials tethered to a solid surface: Implications for medical devices

Medical devices are an integral part of therapeutic management; despite their importance, they carry a significant risk of microbial infection. Bacterial attachment to a medical device is established by a single, multiplying organism, leading to subsequent biofilm formation. To date, no preventative measures have impacted the incidence of device-related infection. We report the bidirectional covalent coupling of an engineered cationic antimicrobial peptide (eCAP), WLBU2, to various biological surfaces is accomplished. These surfaces included (i) a carbohydrate-based gel matrix, (ii) a complex polymeric plastic bead, and (iii) a silica-calcium phosphate nanocomposite associated with bone reconstruction. WLBU2-conjugated surfaces are shown to retain potent antimicrobial activity related to bacterial surface adhesion. This study provides proof of principle that covalently coating laboratory and bone-regenerating materials with eCAPs has the potential for decreasing infection rates of implanted devices. These findings have important consequences to the patient management component of our current health care technology

Development and evaluation of a deep learning model for the detection of multiple fundus diseases based on colour fundus photography.

AIM: To explore and evaluate an appropriate deep learning system (DLS) for the detection of 12 major fundus diseases using colour fundus photography. METHODS: Diagnostic performance of a DLS was tested on the detection of normal fundus and 12 major fundus diseases including referable diabetic retinopathy, pathologic myopic retinal degeneration, retinal vein occlusion, retinitis pigmentosa, retinal detachment, wet and dry age-related macular degeneration, epiretinal membrane, macula hole, possible glaucomatous optic neuropathy, papilledema and optic nerve atrophy. The DLS was developed with 56 738 images and tested with 8176 images from one internal test set and two external test sets. The comparison with human doctors was also conducted. RESULTS: The area under the receiver operating characteristic curves of the DLS on the internal test set and the two external test sets were 0.950 (95% CI 0.942 to 0.957) to 0.996 (95% CI 0.994 to 0.998), 0.931 (95% CI 0.923 to 0.939) to 1.000 (95% CI 0.999 to 1.000) and 0.934 (95% CI 0.929 to 0.938) to 1.000 (95% CI 0.999 to 1.000), with sensitivities of 80.4% (95% CI 79.1% to 81.6%) to 97.3% (95% CI 96.7% to 97.8%), 64.6% (95% CI 63.0% to 66.1%) to 100% (95% CI 100% to 100%) and 68.0% (95% CI 67.1% to 68.9%) to 100% (95% CI 100% to 100%), respectively, and specificities of 89.7% (95% CI 88.8% to 90.7%) to 98.1% (95%CI 97.7% to 98.6%), 78.7% (95% CI 77.4% to 80.0%) to 99.6% (95% CI 99.4% to 99.8%) and 88.1% (95% CI 87.4% to 88.7%) to 98.7% (95% CI 98.5% to 99.0%), respectively. When compared with human doctors, the DLS obtained a higher diagnostic sensitivity but lower specificity. CONCLUSION: The proposed DLS is effective in diagnosing normal fundus and 12 major fundus diseases, and thus has much potential for fundus diseases screening in the real world

Régularisation spatiale de représentations distribuées de mots

Stimulée par l’usage intensif des téléphones mobiles, l’exploitation conjointe des don-nées textuelles et des données spatiales présentes dans les objets spatio-textuels (p. ex. tweets)est devenue la pierre angulaire à de nombreuses applications comme la recherche de lieux d’attraction. Du point de vue scientifique, ces tâches reposent de façon critique sur la représentation d’objets spatiaux et la définition de fonctions d’appariement entre ces objets. Dans cet article,nous nous intéressons au problème de représentation de ces objets. Plus spécifiquement, confortés par le succès des représentations distribuées basées sur les approches neuronales, nous proposons de régulariser les représentations distribuées de mots (c.-à-d. plongements lexicaux ou word embeddings), pouvant être combinées pour construire des représentations d’objets,grâce à leurs répartitions spatiales. L’objectif sous-jacent est de révéler d’éventuelles relations sémantiques locales entre mots ainsi que la multiplicité des sens d’un même mot. Les expérimentations basées sur une tâche de recherche d’information qui consiste à retourner le lieu physique faisant l’objet (sujet) d’un géo-texte montrent que l’intégration notre méthode de régularisation spatiale de représentations distribuées de mots dans un modèle d’appariement de base permet d’obtenir des améliorations significatives par rapport aux modèles de référence