Microneedles: A novel clinical technology for evaluating skin characteristics.

BACKGROUND: Current methods for evaluating efficacy of cosmetics have limitations because they cannot accurately measure changes in the dermis. Skin sampling using microneedles allows identification of skin-type biomarkers, monitoring treatment for skin inflammatory diseases, and evaluating efficacy of anti-aging and anti-pigmentation products. MATERIALS AND METHODS: Two studies were conducted: First, 20 participants received anti-aging treatment; second, 20 participants received anti-pigmentation treatment. Non-invasive devices measured skin aging (using high-resolution 3D-imaging in the anti-aging study) or pigmentation (using spectrophotometry in the anti-pigmentation study) at weeks 0 and 4, and adverse skin reactions were monitored. Skin samples were collected with biocompatible microneedle patches. Changes in expression of biomarkers for skin aging and pigmentation were analyzed using qRT-PCR. RESULTS: No adverse events were reported. In the anti-aging study, after 4 weeks, skin roughness significantly improved in 17 out of 20 participants. qRT-PCR showed significantly increased expression of skin-aging related biomarkers: PINK1 in 16/20 participants, COL1A1 in 17/20 participants, and MSN in 16/20 participants. In the anti-pigmentation study, after 4 weeks, skin lightness significantly improved in 16/20 participants. qRT-PCR showed significantly increased expression of skin-pigmentation-related biomarkers: SOD1 in 15/20 participants and Vitamin D Receptor (VDR) in 15/20 participants. No significant change in TFAP2A was observed. CONCLUSION: Skin sampling and mRNA analysis for biomarkers provides a novel, objective, quantitative method for measuring changes in the dermis and evaluating the efficacy of cosmetics. This approach complements existing evaluation methods and has potential application in assessing the effectiveness of medical devices, medications, cosmeceuticals, healthy foods, and beauty devices.

Automated diagnosis of flatfoot using cascaded convolutional neural network for angle measurements in weight-bearing lateral radiographs.

OBJECTIVES: Diagnosis of flatfoot using a radiograph is subject to intra- and inter-observer variabilities. Here, we developed a cascade convolutional neural network (CNN)-based deep learning model (DLM) for an automated angle measurement for flatfoot diagnosis using landmark detection. METHODS: We used 1200 weight-bearing lateral foot radiographs from young adult Korean males for the model development. An experienced orthopedic surgeon identified 22 radiographic landmarks and measured three angles for flatfoot diagnosis that served as the ground truth (GT). Another orthopedic surgeon (OS) and a general physician (GP) independently identified the landmarks of the test dataset and measured the angles using the same method. External validation was performed using 100 and 17 radiographs acquired from a tertiary referral center and a public database, respectively. RESULTS: The DLM showed smaller absolute average errors from the GT for the three angle measurements for flatfoot diagnosis compared with both human observers. Under the guidance of the DLM, the average errors of observers OS and GP decreased from 2.35° ± 3.01° to 1.55° ± 2.09° and from 1.99° ± 2.76° to 1.56° ± 2.19°, respectively (both p < 0.001). The total measurement time decreased from 195 to 135 min in observer OS and from 205 to 155 min in observer GP. The absolute average errors of the DLM in the external validation sets were similar or superior to those of human observers in the original test dataset. CONCLUSIONS: Our CNN model had significantly better accuracy and reliability than human observers in diagnosing flatfoot, and notably improved the accuracy and reliability of human observers. KEY POINTS: • Development of deep learning model (DLM) that allows automated angle measurements for landmark detection based on 1200 weight-bearing lateral radiographs for diagnosing flatfoot. • Our DLM showed smaller absolute average errors for flatfoot diagnosis compared with two human observers. • Under the guidance of the model, the average errors of two human observers decreased and total measurement time also decreased from 195 to 135 min and from 205 to 155 min.

Automated landmark identification for diagnosis of the deformity using a cascade convolutional neural network (FlatNet) on weight-bearing lateral radiographs of the foot.

Landmark detection in flatfoot radiographs is crucial in analyzing foot deformity. Here, we evaluated the accuracy and efficiency of the automated identification of flatfoot landmarks using a newly developed cascade convolutional neural network (CNN) algorithm, Flatfoot Landmarks AnnoTating Network (FlatNet). A total of 1200 consecutive weight-bearing lateral radiographs of the foot were acquired. The first 1050 radiographs were used as the training and tuning, and the following 150 radiographs were used as the test sets, respectively. An expert orthopedic surgeon (A) manually labeled ground truths for twenty-five anatomical landmarks. Two orthopedic surgeons (A and B, each with eight years of clinical experience) and a general physician (GP) independently identified the landmarks of the test sets using the same method. After two weeks, observers B and GP independently identified the landmarks once again using the developed deep learning CNN model (DLm). The X- and Y-coordinates and the mean absolute distance were evaluated. The average differences (mm) from the ground truth were 0.60 ± 0.57, 1.37 ± 1.28, and 1.05 ± 1.23 for the X-coordinate, and 0.46 ± 0.59, 0.97 ± 0.98, and 0.73 ± 0.90 for the Y-coordinate in DLm, B, and GP, respectively. The average differences (mm) from the ground truth were 0.84 ± 0.73, 1.90 ± 1.34, and 1.42 ± 1.40 for the absolute distance in DLm, B, and GP, respectively. Under the guidance of the DLm, the overall differences (mm) from the ground truth were enhanced to 0.87 ± 1.21, 0.69 ± 0.74, and 1.24 ± 1.31 for the X-coordinate, Y-coordinate, and absolute distance, respectively, for observer B. The differences were also enhanced to 0.74 ± 0.73, 0.57 ± 0.63, and 1.04 ± 0.85 for observer GP. The newly developed FlatNet exhibited better accuracy and reliability than the observers. Furthermore, under the FlatNet guidance, the accuracy and reliability of the human observers generally improved.

Circulating Exosomal miR-1290 for Diagnosis of Epithelial Ovarian Cancer.

The aim of the study was to develop a new diagnostic biomarker for identifying serum exosomal miRNAs specific to epithelial ovarian cancer (EOC) and to find out target gene of the miRNA for exploring the molecular mechanisms in EOC. A total of 84 cases of ovarian masses and sera were enrolled, comprising EOC (n = 71), benign ovarian neoplasms (n = 13). We detected expression of candidate miRNAs in the serum and tissue of both benign ovarian neoplasm group and EOC group using real-time polymerase chain reaction. Immunohistochemistry were constructed using formalin fixed paraffin embedded (FFPE) tissue to detect expression level of suppressor of cytokine signaling 4 (SOCS4). In the EOC group, miRNA-1290 was significantly overexpressed in serum exosomes and tissues as compared to benign ovarian neoplasm group (fold change ≥ 2, p < 0.05). We observed area under the receiver operating characteristic curve (AUC) for miR-1290, using a cut-off of 0.73, the exosomal miR-1290 from serum had AUC, sensitivity, and specificity values of 0.794, 69.2 and 87.3, respectively. In immunohistochemical study, expression of SOCS4 in EOC was lower than that in benign ovarian neoplasm. Serum exosomal miR-1290 could be considered as a biomarker for differential diagnosis of EOC from benign ovarian neoplasm and SOCS4 might be potential target gene of miR-1290 in EOC.

Development of a Nomogram for Carbapenem-Resistant Enterobacteriaceae Acquisition Risk Prediction Among Patients in the Intensive Care Unit of a Secondary Referral Hospital.

PURPOSE: This study aimed to identify the risk factors of carbapenem-resistant Enterobacteriaceae (CRE) acquisition to build a nomogram for CRE acquisition risk prediction and evaluate its performance. METHODS: This unmatched case-control study included 352 adult patients (55 patients and 297 controls) admitted to the intensive care unit (ICU) of a 453-bed secondary referral hospital between January 1, 2018, and September 31, 2019, in Busan, South Korea. The nomogram was built with the identified risk factors using multiple logistic regression analysis. Its performance was analyzed using calibration-in-the-large, the slope of the calibration plot, concordance statistic (c-statistic), and the sensitivity and specificity of the training set, subsets, and a new test set. RESULTS: The risk factors of CRE acquisition among ICU patients at a secondary referral hospital were Acute Physiology and Chronic Health Evaluation II score at the time of admission, use of a central venous catheter and a nasogastric tube, as well as use of cephalosporin antibiotics. At 20.0% of the predicted CRE acquisition risk in the training set, the calibration-in-the-large was 0, slope of the calibration plot was 1, c-statistic was .93, sensitivity was 85.5%, and specificity was 84.8%. The performance was relatively good in the subsets and new test set. CONCLUSION: The nomogram can be used to monitor the CRE acquisition risk for ICU patients who have a similar case mix to patients in the study hospitals. Future studies need to involve more rigorous methodology and larger samples.

[External Validation of Carbapenem-Resistant Enterobacteriaceae Acquisition Risk Prediction Model in a Medium Sized Hospital].

PURPOSE: This study was aimed to evaluate the external validity of a carbapenem-resistant Enterobacteriaceae (CRE) acquisition risk prediction model (the CREP-model) in a medium-sized hospital. METHODS: This retrospective cohort study included 613 patients (CRE group: 69, no-CRE group: 544) admitted to the intensive care units of a 453-beds secondary referral general hospital from March 1, 2017 to September 30, 2019 in South Korea. The performance of the CREP-model was analyzed with calibration, discrimination, and clinical usefulness. RESULTS: The results showed that those higher in age had lower presence of multidrug resistant organisms (MDROs), cephalosporin use ≥ 15 days, Acute Physiology and Chronic Health Evaluation II (APACHE II) score ≥ 21 points, and lower CRE acquisition rates than those of CREP-model development subjects. The calibration-in-the-large was 0.12 (95% CI: - 0.16~0.39), while the calibration slope was 0.87 (95% CI: 0.63~1.12), and the concordance statistic was .71 (95% CI: .63~.78). At the predicted risk of .10, the sensitivity, specificity, and correct classification rates were 43.5%, 84.2%, and 79.6%, respectively. The net true positive according to the CREP-model were 3 per 100 subjects. After adjusting the predictors' cutting points, the concordance statistic increased to .84 (95% CI: .79~.89), and the sensitivity and net true positive was improved to 75.4%. and 6 per 100 subjects, respectively. CONCLUSION: The CREP-model's discrimination and clinical usefulness are low in a medium sized general hospital but are improved after adjusting for the predictors. Therefore, we suggest that institutions should only use the CREP-model after assessing the distribution of the predictors and adjusting their cutting points.