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Cervical cancer is a leading cause of death in underserved areas of Brazil. This prospective randomized trial involved 200 women in southern/central Brazil with abnormal Papanicolaou tests. Participants were randomized by geographic cluster and referred for diagnostic evaluation either at a mobile van upon its scheduled visit to their local community, or at a central hospital. Participants in both arms underwent colposcopy, microscopy, and cervical biopsies. We compared rates of diagnostic follow-up completion between study arms, and also evaluated the diagnostic performance of microscopy compared with colposcopy. There was a 23% absolute and 37% relative increase in diagnostic follow-up completion rates for patients referred to the mobile van (102/117, 87%) compared with the central hospital (53/83, 64%; = 0.0001; risk ratio = 1.37, 95% CI, 1.14-1.63). In 229 cervical sites in 144 patients, colposcopic examination identified sites diagnosed as cervical intraepithelial neoplasia grade 2 or more severe (CIN2+; 85 sites) with a sensitivity of 94% (95% CI, 87%-98%) and specificity of 50% (95% CI, 42%-58%). microscopy with real-time automated image analysis identified CIN2+ with a sensitivity of 92% (95% CI, 84%-97%) and specificity of 48% (95% CI, 40%-56%). Women referred to the mobile van were more likely to complete their diagnostic follow-up compared with those referred to a central hospital, without compromise in clinical care. microscopy in a mobile van provides automated diagnostic imaging with sensitivity and specificity similar to colposcopy..

. Significance: Smartphones come with an enormous array of functionality and are being more widely utilized with specialized attachments in a range of healthcare applications. A review of key developments and uses, with an assessment of strengths/limitations in various clinical workflows, was completed. Aim: Our review studies how smartphone-based imaging (SBI) systems are designed and tested for specialized applications in medicine and healthcare. An evaluation of current research studies is used to provide guidelines for improving the impact of these research advances. Approach: First, the established and emerging smartphone capabilities that can be leveraged for biomedical imaging are detailed. Then, methods and materials for fabrication of optical, mechanical, and electrical interface components are summarized. Recent systems were categorized into four groups based on their intended application and clinical workflow: ex vivo diagnostic, in vivo diagnostic, monitoring, and treatment guidance. Lastly, strengths and limitations of current SBI systems within these various applications are discussed. Results: The native smartphone capabilities for biomedical imaging applications include cameras, touchscreens, networking, computation, 3D sensing, audio, and motion, in addition to commercial wearable peripheral devices. Through user-centered design of custom hardware and software interfaces, these capabilities have the potential to enable portable, easy-to-use, point-of-care biomedical imaging systems. However, due to barriers in programming of custom software and on-board image analysis pipelines, many research prototypes fail to achieve a prospective clinical evaluation as intended. Effective clinical use cases appear to be those in which handheld, noninvasive image guidance is needed and accommodated by the clinical workflow. Handheld systems for in vivo , multispectral, and quantitative fluorescence imaging are a promising development for diagnostic and treatment guidance applications. Conclusions: A holistic assessment of SBI systems must include interpretation of their value for intended clinical settings and how their implementations enable better workflow. A set of six guidelines are proposed to evaluate appropriateness of smartphone utilization in terms of clinical context, completeness, compactness, connectivity, cost, and claims. Ongoing work should prioritize realistic clinical assessments with quantitative and qualitative comparison to non-smartphone systems to clearly demonstrate the value of smartphone-based systems. Improved hardware design to accommodate the rapidly changing smartphone ecosystem, creation of open-source image acquisition and analysis pipelines, and adoption of robust calibration techniques to address phone-to-phone variability are three high priority areas to move SBI research forwar...

Although the effects of ethanol on protein receptors and lipid membranes have been studied extensively, ethanol's effect on vesicles fusing to lipid bilayers is not known. To determine the effect of alcohols on fusion rates, we utilized the nystatin/ergosterol fusion assay to measure fusion of liposomes to a planar lipid bilayer (BLM). The addition of ethanol excited fusion when applied on the cis (vesicle) side, and inhibited fusion on the trans side. Other short-chain alcohols followed a similar pattern. In general, the inhibitory effect of alcohols (trans) occurs at lower doses than the excitatory (cis) effect, with a decrease of 29% in fusion rates at the legal driving limit of 0.08% (w/v) ethanol (IC 50 ¼ 0.2% v/v, 34 mM). Similar inhibitory effects were observed with methanol, propanol, and butanol, with ethanol being the most potent. Significant variability was observed with different alcohols when applied to the cis side. Ethanol and propanol enhanced fusion, butanol also enhanced fusion but was less potent, and low doses of methanol mildly inhibited fusion. The inhibition by trans addition of alcohols implies that they alter the planar membrane structure and thereby increase the activation energy required for fusion, likely through an increase in membrane fluidity. The cis data are likely a combination of the above effect and a proportionally greater lowering of the vesicle lysis tension and hydration repulsive pressure that combine to enhance fusion. Alternate hypotheses are also discussed. The inhibitory effect of ethanol on liposome-membrane fusion is large enough to provide a possible biophysical explanation of compromised neuronal behavior.

This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC