Cyclic CO2 absorption/desorption property of Li3NaSiO4 under the partial pressure of CO2 for practical applications.

The temperature for realizing the cyclic CO2 absorption/desorption property of Li3NaSiO4 by repetition of switching a CO2/N2 gas mixture and N2 with a partial pressure of CO2, P(CO2), of 0.1 bar was optimized using the pseudo van't Hoff plot of LiNaCO3 + Li2SiO3 ↔ Li3NaSiO4 + CO2 prepared by thermogravimetry at various P(CO2) values.

Convolutional neural network-based skin image segmentation model to improve classification of skin diseases in conventional and non-standardized picture images.

BACKGROUND: For dermatological practices, non-standardized conventional photo images are taken and collected as a mixture of variable fields of the image view, including close-up images focusing on designated lesions and long-shot images including normal skin and background of the body surface. Computer-aided detection/diagnosis (CAD) models trained using non-standardized conventional photo images exhibit lower performance rates than CAD models that detect lesions in a localized small area, such as dermoscopic images. OBJECTIVE: We aimed to develop a convolutional neural network (CNN) model for skin image segmentation to generate a skin disease image dataset suitable for CAD of multiple skin disease classification. METHODS: We trained a DeepLabv3 + -based CNN segmentation model to detect skin and lesion areas and segmented out areas that satisfy the following conditions: more than 80% of the image will be the skin area, and more than 10% of the image will be the lesion area. RESULTS: The generated CNN-segmented image database was examined using CAD of skin disease classification and achieved approximately 90% sensitivity and specificity to differentiate atopic dermatitis from malignant diseases and complications, such as mycosis fungoides, impetigo, and herpesvirus infection. The accuracy of skin disease classification in the CNN-segmented image dataset was almost equal to that of the manually cropped image dataset and higher than that of the original image dataset. CONCLUSION: Our CNN segmentation model, which automatically extracts lesions and segmented images of the skin area regardless of image fields, will reduce the burden of physician annotation and improve CAD performance.

A new method for the preparation of high-purity CO2-absorbing Li3NaSiO4 powder using lithium silicate and sodium carbonate.

The starting materials and temperature for the preparation of Li3NaSiO4 powder, which has attracted attention as a CO2 absorbent, were optimized in this study. Mixtures of Li2CO3, Na2CO3, and SiO2 as well as Li4SiO4, Li2SiO3, and Na2CO3 were subjected to thermogravimetry-differential thermal analysis (TG-DTA) to elucidate their reaction mechanisms. The phase, morphology, specific surface area, and CO2 absorption characteristics of the powder specimens that were obtained by heating the two mixtures were examined by X-ray diffraction (XRD), secondary electron microscopy (SEM), N2 adsorption isotherm and isothermal TG-DTA. Melted LiNaCO3 was generated via the heat treatment of the Li2CO3, Na2CO3, and SiO2 powder mixture, yielding a low-purity bulk specimen with inhomogeneous particle size. However, the use of the Li4SiO4, Li2SiO3, and Na2CO3 mixture as a starting material ensured that no liquid phase was generated during heat treatment and successfully yielded Li3NaSiO4 powder which was purer than the product derived from the Li2CO3/Na2CO3/SiO2 mixture, presumably because of the lower volatility of Li and Na in the solid phase than that in the liquid phase of LiNaCO3. The Li3NaSiO4 powder derived from Li4SiO4, Li2SiO3, and Na2CO3 showed a slightly larger surface area with homogeneous particle size and almost identical CO2 absorption kinetics compared to those of the product obtained from Li2CO3, Na2CO3, and SiO2, in addition to absorbing a higher amount of CO2 owing to its higher purity.

Deep Neural Network for Early Image Diagnosis of Stevens-Johnson Syndrome/Toxic Epidermal Necrolysis.

BACKGROUND: Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN) is a life-threatening cutaneous adverse drug reaction (cADR). Distinguishing SJS/TEN from nonsevere cADRs is difficult, especially in the early stages of the disease. OBJECTIVE: To overcome this limitation, we developed a computer-aided diagnosis system for the early diagnosis of SJS/TEN, powered by a deep convolutional neural network (DCNN). METHODS: We trained a DCNN using a dataset of 26,661 individual lesion images obtained from 123 patients with a diagnosis of SJS/TEN or nonsevere cADRs. The DCNN's accuracy of classification was compared with that of 10 board-certified dermatologists and 24 trainee dermatologists. RESULTS: The DCNN achieved 84.6% sensitivity (95% confidence interval [CI], 80.6-88.6), whereas the sensitivities of the board-certified dermatologists and trainee dermatologists were 31.3 % (95% CI, 20.9-41.8; P < .0001) and 27.8% (95% CI, 22.6-32.5; P < .0001), respectively. The negative predictive value was 94.6% (95% CI, 93.2-96.0) for the DCNN, 68.1% (95% CI, 66.1-70.0; P < .0001) for the board-certified dermatologists, and 67.4% (95% CI, 66.1-68.7; P < .0001) for the trainee dermatologists. The area under the receiver operating characteristic curve of the DCNN for a SJS/TEN diagnosis was 0.873, which was significantly higher than that for all board-certified dermatologists and trainee dermatologists. CONCLUSIONS: We developed a DCNN to classify SJS/TEN and nonsevere cADRs based on individual lesion images of erythema. The DCNN performed significantly better than did dermatologists in classifying SJS/TEN from skin images.

Ehlers-Danlos syndrome type IV with a novel COL3A1 exon 14 skipping variation confirmed by Tohoku Medical Megabank Organization genomic database.

A novel COL3A1 variant was identified in a Japanese case of Ehlers-Danlos syndrome type IV (EDS-IV) with a characteristic "Madonna" face, fragile uterus, and easy bruising in addition to a history of cavernous sinus fistula. We confirmed variable diameters of collagen fibrils in the dermis and decrease in type 3 collagen production from cultured fibroblasts. Genomic DNA sequencing of the COL3A1 region and COL3A1 cDNA sequence expressing in cultured fibroblasts identified that a nucleotide variation at c.951+2T>G on intron 14 leads to skipping of exon 14 in COL3A1 cDNA. The novel variation in the splice site of COL3A1 region g.IVS14+2T>G was not listed in the EDS-IV pathogenic genetic databases including Human Gene Mutation Database, ClinVar, and Leiden Open Variation Database. Using the whole genome sequence database of 8380 Japanese individuals reported by the Tohoku Medical Megabank Organization (ToMMo) cohort study, we also confirmed that COL3A1 g.IVS14+2T>G was not a common single nucleotide variation in the Japanese population, although 13 EDS-related COL3A1 variants were identified in the ToMMo database of 8380 Japanese individuals. These results demonstrated that our case of EDS-IV was a result of the novel variation of COL3A1 g.IVS14+2T>G. These statistical genetics approaches with the combination of the ToMMo database of 8380 Japanese individuals and pathogenic genetic databases are a useful method to confirm the uniqueness of novel variation in Japanese.

Classification of large-scale image database of various skin diseases using deep learning.

PURPOSE: The purpose of this study was to develop a deep learning-based computer-aided diagnosis system for skin disease classification using photographic images of patients. The targets are 59 skin diseases, including localized and diffuse diseases captured by photographic cameras, resulting in highly diverse images in terms of the appearance of the diseases or photographic conditions. METHODS: ResNet-18 is used as a baseline model for classification and is reinforced by metric learning to boost generalization in classification by avoiding the overfitting of the training data and increasing the reliability of CADx for dermatologists. Patient-wise classification is performed by aggregating the inference vectors of all the input patient images. RESULTS: The experiment using 70,196 images of 13,038 patients demonstrated that classification accuracy was significantly improved by both metric learning and aggregation, resulting in patient accuracies of 0.579 for Top-1, 0.793 for Top-3, and 0.863 for Top-5. The McNemar test showed that the improvements achieved by the proposed method were statistically significant. CONCLUSION: This study presents a deep learning-based classification of 59 skin diseases using multiple photographic images of a patient. The experimental results demonstrated that the proposed classification reinforced by metric learning and aggregation of multiple input images was effective in the classification of patients with diverse skin diseases and imaging conditions.

Facial UV photo imaging for skin pigmentation assessment using conditional generative adversarial networks.

Skin pigmentation is associated with skin damages and skin cancers, and ultraviolet (UV) photography is used as a minimally invasive mean for the assessment of pigmentation. Since UV photography equipment is not usually available in general practice, technologies emphasizing pigmentation in color photo images are desired for daily care. We propose a new method using conditional generative adversarial networks, named UV-photo Net, to generate synthetic UV images from color photo images. Evaluations using color and UV photo image pairs taken by a UV photography system demonstrated that pigment spots were well reproduced in synthetic UV images by UV-photo Net, and some of the reproduced pigment spots were difficult to be recognized in color photo images. In the pigment spot detection analysis, the rate of pigment spot areas in cheek regions for synthetic UV images was highly correlated with the rate for UV photo images (Pearson's correlation coefficient 0.92). We also demonstrated that UV-photo Net was effective for floating up pigment spots for photo images taken by a smartphone camera. UV-photo Net enables an easy assessment of pigmentation from color photo images and will promote self-care of skin damages and early signs of skin cancers for preventive medicine.

Edible oil methods to remove asphalt on burns.

Asphalt, also known as bitumen, is a viscous liquid or a semi-solid form of petroleum. In cases of hot liquid asphalt splash, asphalt broadly adheres to the skin surface and is hard to remove from skin. Because accidental burns from hot liquid asphalt splash rarely occur, there is no consensus about initial approaches to remove adherent asphalt from skin. We reviewed articles relating to asphalt burns and summarized methods to remove adherent asphalt from skin, including our present case in which we successfully removed adherent asphalt by edible butter and vegetable oil. We summarized information of 127 cases and classified agents used to remove asphalt in four categories: (i) medicines; (ii) health-care products; (iii) foods; and (iv) solvents. Before the 1990s, antimicrobial topical medicines were mainly reported to treat asphalt burns but it took half a day or more to remove asphalt. Mineral oils and edible oils such as butter and vegetable oil are easily available in grocery stores and could emulsify to remove asphalt in a few hours. From the review of articles and our experience, edible oils are useful agents for the first approach to remove asphalt from the point of view of efficacy, safety, availability and expense.

Plexiform Fibrohistiocytic Tumor on the Ear: Case Report and Immunohistochemical Investigation of Stromal Factor.

Plexiform fibrohistiocytic tumor (PFT) is a rare mesenchymal neoplasm of intermediate malignant potential with a high local recurrence rate. In this report, we describe a case of PFT on the ear, which showed a dense deposition of periostin (POSTN) in the stromal areas of the tumor. In addition, dense infiltration of CD163+CD206- tumor-associated macrophages (TAMs) was detected in the same areas as POSTN. Since POSTN was previously reported to possess immunomodulatory effects on TAMs, our present report suggested the significance of the POSTN/TAMs axis in the progression of PFT.

Somatic BRAF c.1799T>A p.V600E Mosaicism syndrome characterized by a linear syringocystadenoma papilliferum, anaplastic astrocytoma, and ocular abnormalities.

Genetic mosaicism for somatic mutations of oncogenes is common in genodermatoses, which can be complicated with extra-cutaneous abnormalities. Here we describe an infant with a congenital anaplastic astrocytoma, a linear syringocystadenoma papilliferum, and ocular abnormalities. The BRAF c.1799T>A p.V600E mutation was detected in both the brain and skin tumor cells but not in the blood or normal skin cells, suggesting somatic mosaicsism for the mutation. Clinically, the brain tumor gradually became life threatening without any response to conventional chemotherapies including carboplatin, etoposide, and temozolomide. Vemurafenib, a BRAF p.V600E inhibitor, was administered daily after the detection of the BRAF mutation. This single-agent therapy was dramatically effective against the anaplastic astrocytoma; the tumor regressed, the cerebrospinal fluid cell count and protein levels decreased to normal levels, and hydrocephalus resolved. Moreover, other lesions including a corneal cyst also responded to vemurafenib. The brain tumor continued shrinking after 6 months of treatment. We present a genodermatosis syndrome associated with BRAF c.1799T>A p.V600E mosaicism. This syndrome may represent a new entity in the mosaic RASopathies, partly overlapping with Schimmelpenning-Feuerstein-Mims syndrome, which is driven by mosaicism of HRAS and/or KRAS activating mutations. Screening for BRAF c.1799T>A p.V600E is especially useful for those with malignant tumors, because it is one of the most-druggable targets.