Design of High-Payload Ascorbyl Palmitate Nanosuspensions for Enhanced Skin Delivery.

A high-payload ascorbyl palmitate (AP) nanosuspension (NS) was designed to improve skin delivery following topical application. The AP-loaded NS systems were prepared using the bead-milling technique, and softly thickened into NS-loaded gel (NS-G) using hydrophilic polymers. The optimized NS-G system consisted of up to 75 mg/mL of AP, 0.5% w/v of polyoxyl-40 hydrogenated castor oil (Kolliphor® RH40) as the suspending agent, and 1.0% w/v of sodium carboxymethyl cellulose (Na.CMC 700 K) as the thickening agent, in citrate buffer (pH 4.5). The NS-G system was embodied as follows: long and flaky nanocrystals, 493.2 nm in size, -48.7 mV in zeta potential, and 2.3 cP of viscosity with a shear rate of 100 s-1. Both NS and NS-G provided rapid dissolution of the poorly water-soluble antioxidant, which was comparable to that of the microemulsion gel (ME-G) containing AP in solubilized form. In an ex vivo skin absorption study using the Franz diffusion cell mounted on porcine skin, NS-G exhibited faster absorption in skin, providing approximately 4, 3, and 1.4 times larger accumulation than that of ME-G at 3, 6, and 12 h, respectively. Therefore, the high-payload NS makes it a promising platform for skin delivery of the lipid derivative of ascorbic acid.

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" - there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification.

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge.

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" - there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification.

Reformulation of Trivers-Willard hypothesis for parental investment.

The Trivers-Willard hypothesis (TWH) plays a central role in understanding the optimal investment strategies to male and female offspring. Empirical studies of TWH, however, yielded conflicting results. Here, we present models to predict optimal comprehensive multi-element parental strategies composed of primary sex ratio, brood size, resource allocation among offspring, and the resultant secondary sex ratio. Our results reveal that the optimal strategy depends on sex differences in the shape of offspring fitness function rather than in fitness variance. Also, the slope of the tangent line (through the origin) to the offspring fitness function can be used to predict the preferred offspring sex. We also briefly discuss links between the model and the empirical research. This comprehensive reformulation of TWH will offer a thorough understanding of multi-element parental investment strategies beyond the classical TWH.

Ultra-Wideband Multi-Dye-Sensitized Upconverting Nanoparticles for Information Security Application.

Multi-dye-sensitized upconverting nanoparticles (UCNPs), which harvest photons of wide wavelength range (450-975 nm) are designed and synthesized. The UCNPs embedded in a photo-acid generating layer are integrated on destructible nonvolatile resistive memory device. Upon illumination of light, the system permanently erases stored data, achieving enhanced information security.

High-throughput in vivo genotoxicity testing: an automated readout system for the somatic mutation and recombination test (SMART).

Genotoxicity testing is an important component of toxicity assessment. As illustrated by the European registration, evaluation, authorization, and restriction of chemicals (REACH) directive, it concerns all the chemicals used in industry. The commonly used in vivo mammalian tests appear to be ill adapted to tackle the large compound sets involved, due to throughput, cost, and ethical issues. The somatic mutation and recombination test (SMART) represents a more scalable alternative, since it uses Drosophila, which develops faster and requires less infrastructure. Despite these advantages, the manual scoring of the hairs on Drosophila wings required for the SMART limits its usage. To overcome this limitation, we have developed an automated SMART readout. It consists of automated imaging, followed by an image analysis pipeline that measures individual wing genotoxicity scores. Finally, we have developed a wing score-based dose-dependency approach that can provide genotoxicity profiles. We have validated our method using 6 compounds, obtaining profiles almost identical to those obtained from manual measures, even for low-genotoxicity compounds such as urethane. The automated SMART, with its faster and more reliable readout, fulfills the need for a high-throughput in vivo test. The flexible imaging strategy we describe and the analysis tools we provide should facilitate the optimization and dissemination of our methods.

An image-based algorithm for precise and accurate high throughput assessment of drug activity against the human parasite Trypanosoma cruzi.

We present a customized high content (image-based) and high throughput screening algorithm for the quantification of Trypanosoma cruzi infection in host cells. Based solely on DNA staining and single-channel images, the algorithm precisely segments and identifies the nuclei and cytoplasm of mammalian host cells as well as the intracellular parasites infecting the cells. The algorithm outputs statistical parameters including the total number of cells, number of infected cells and the total number of parasites per image, the average number of parasites per infected cell, and the infection ratio (defined as the number of infected cells divided by the total number of cells). Accurate and precise estimation of these parameters allow for both quantification of compound activity against parasites, as well as the compound cytotoxicity, thus eliminating the need for an additional toxicity-assay, hereby reducing screening costs significantly. We validate the performance of the algorithm using two known drugs against T.cruzi: Benznidazole and Nifurtimox. Also, we have checked the performance of the cell detection with manual inspection of the images. Finally, from the titration of the two compounds, we confirm that the algorithm provides the expected half maximal effective concentration (EC50) of the anti-T. cruzi activity.

Robust dose-response curve estimation applied to high content screening data analysis.

BACKGROUND AND METHOD: Successfully automated sigmoidal curve fitting is highly challenging when applied to large data sets. In this paper, we describe a robust algorithm for fitting sigmoid dose-response curves by estimating four parameters (floor, window, shift, and slope), together with the detection of outliers. We propose two improvements over current methods for curve fitting. The first one is the detection of outliers which is performed during the initialization step with correspondent adjustments of the derivative and error estimation functions. The second aspect is the enhancement of the weighting quality of data points using mean calculation in Tukey's biweight function. RESULTS AND CONCLUSION: Automatic curve fitting of 19,236 dose-response experiments shows that our proposed method outperforms the current fitting methods provided by MATLAB®;'s nlinfit function and GraphPad's Prism software.

An image analysis algorithm for malaria parasite stage classification and viability quantification.

With more than 40% of the world's population at risk, 200-300 million infections each year, and an estimated 1.2 million deaths annually, malaria remains one of the most important public health problems of mankind today. With the propensity of malaria parasites to rapidly develop resistance to newly developed therapies, and the recent failures of artemisinin-based drugs in Southeast Asia, there is an urgent need for new antimalarial compounds with novel mechanisms of action to be developed against multidrug resistant malaria. We present here a novel image analysis algorithm for the quantitative detection and classification of Plasmodium lifecycle stages in culture as well as discriminating between viable and dead parasites in drug-treated samples. This new algorithm reliably estimates the number of red blood cells (isolated or clustered) per fluorescence image field, and accurately identifies parasitized erythrocytes on the basis of high intensity DAPI-stained parasite nuclei spots and Mitotracker-stained mitochondrial in viable parasites. We validated the performance of the algorithm by manual counting of the infected and non-infected red blood cells in multiple image fields, and the quantitative analyses of the different parasite stages (early rings, rings, trophozoites, schizonts) at various time-point post-merozoite invasion, in tightly synchronized cultures. Additionally, the developed algorithm provided parasitological effective concentration 50 (EC50) values for both chloroquine and artemisinin, that were similar to known growth inhibitory EC50 values for these compounds as determined using conventional SYBR Green I and lactate dehydrogenase-based assays.

Multiple-region segmentation without supervision by adaptive global maximum clustering.

In this paper, we propose a new method of segmenting an image into several sets of pixels with similar intensity values called regions. A multiple-region segmentation problem is unstable because the result considerably depends on the number of regions given a priori. Therefore, one of the most important tasks in solving the problem is automatically finding the number of regions. The method we propose is able to find the reasonable number of distinct regions not only for clean images but also for noisy ones. Our method is made up of two procedures. First, we develop the adaptive global maximum clustering. In this procedure, we deal with an image histogram and automatically obtain the number of significant local maxima of the histogram. This number indicates the number of different regions in the image. Second, we derive a simple and fast calculation to segment an image composed of distinct multiple regions. Then, we split an image into multiple regions according to the previous procedure. Finally, we show the efficiency of our method by comparing it with other previous methods.

Three-dimensional evaluation of the lamina cribrosa using spectral-domain optical coherence tomography in glaucoma.

PURPOSE: To introduce a novel, digital, three-dimensional (3D) reconstruction of the optic nerve head (ONH) and to use this method to evaluate the 3D configuration of the lamina cribrosa (LC) in patients with primary open-angle glaucoma. METHODS: Optic discs of 137 eyes of 137 patients with open-angle glaucoma were scanned with enhanced depth-imaging spectral domain-optical coherence tomography (SD-OCT). 3D images of the ONH were then reconstructed from B-scan images using maximum intensity projection (MIP) and texture-based volume rendering (VRT). The performance of the threshold segmentation by MIP and VRT was assessed by comparing the distance of the anterior LC surface from the reference line set at the Bruch's membrane opening level (LC depth) measured within both of the 3D images and the B-scan images. RESULTS: The LC configuration could be evaluated three dimensionally in ∼95% of patients scanned with enhanced depth-imaging SD-OCT. The mean LC depth was 559.50 ± 151.98, 558.97 ± 152.39, and 560.22 ± 152.26 µm in B-scan, MIP, and VRT images, respectively. There were excellent agreements between the values (intraclass correlation coefficient = 1.000 between MIP and B-scan, and 0.999 between VRT and B-scan). The configuration of the LC varied considerably among individual glaucoma patients. CONCLUSIONS: This method provides measurable 3D images of the LC that enable comprehensive evaluation of the LC configuration. This technique should facilitate the investigation of the LC in glaucomatous eyes.