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Abstract Hair loss is one of the most common skin problems experienced by more than half of the world's population. In East Asia, medicinal herbs have been used widely in clinical practice to treat hair loss. Recent studies, including systematic literature reviews, indicate that medicinal herbs may demonstrate potential effects for hair loss treatment. In a previous study, we identified medical herbs used frequently for alopecia treatment. Herein, we explored the potential novel therapeutic mechanisms of 20 vital medicinal herbs for alopecia treatment that could distinguish them from known mechanisms of conventional drugs using network pharmacology analysis methods. We determined the herb-ingredient–target protein networks and ingredient-associated protein (gene)-associated pathway networks and calculated the weighted degree centrality to define the strength of the connections. Data showed that 20 vital medicinal herbs could exert therapeutic effects on alopecia mainly mediated via regulation of various target genes and proteins, including acetylcholinesterase (AChE), phospholipase A2 (PLA2) subtypes, ecto-5-nucleotidase (NTE5), folate receptor (FR), nicotinamide N-methyltransferase (NNMT), and quinolinate phosphoribosyltransferase (QPRT). Findings regarding target genes/proteins and pathways of medicinal herbs associated with alopecia treatment offer insights for further research to better understand the pathogenesis and therapeutic mechanism of medicinal herbs for alopecia treatment with traditional herbal medicine.

Abstract Faces can be categorized in various ways, for example as male or female or as belonging to a specific biogeographic ancestry (race). Here we tested the importance of the main facial features for race perception. We exchanged inner facial features (eyes, mouth or nose), face contour (everything but those) or texture (surface information) between Asian and Caucasian faces. Features were exchanged one at a time, creating for each Asian/Caucasian face pair ten facial variations of the original face pair. German and Korean participants performed a race classification task on all faces presented in random order. The results show that eyes and texture are major determinants of perceived biogeographic ancestry for both groups of participants and for both face types. Inserting these features in a face of another race changed its perceived biogeographic ancestry. Contour, nose and mouth, in that order, had decreasing and much weaker influence on race perception for both participant groups. Exchanging those features did not induce a change of perceived biogeographic ancestry. In our study, all manipulated features were imbedded in natural looking faces, which were shown in an off-frontal view. Our findings confirm and extend previous studies investigating the importance of various facial features for race perception.

Abstract The classification of Alzheimer’s disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various populations. In this study, we developed a convolutional neural network (CNN)-based AD classification algorithm using magnetic resonance imaging (MRI) scans from AD patients and age/gender-matched cognitively normal controls from two populations that differ in ethnicity and education level. These populations come from the Seoul National University Bundang Hospital (SNUBH) and Alzheimer’s Disease Neuroimaging Initiative (ADNI). For each population, we trained CNNs on five subsets using coronal slices of T1-weighted images that cover the medial temporal lobe. We evaluated the models on validation subsets from both the same population (within-dataset validation) and other population (between-dataset validation). Our models achieved average areas under the curves of 0.91–0.94 for within-dataset validation and 0.88–0.89 for between-dataset validation. The mean processing time per person was 23–24 s. The within-dataset and between-dataset performances were comparable between the ADNI-derived and SNUBH-derived models. These results demonstrate the generalizability of our models to different patients with different ethnicities and education levels, as well as their potential for deployment as fast and accurate diagnostic support tools for AD.

Abstract Background Although alopecia affects the quality of life, its pathogenesis is unknown, because cellular interactions in the hair follicle are complex. Several authors have suggested using herbal medicine to treat alopecia, and bioinformatics and network pharmacology may constitute a new research strategy in this regard because herbal medicines contain various chemical components. This study used association rule mining (ARM) and network analysis to analyze the combinations of medicinal herbs used to treat alopecia. Methods We searched Chinese, Korean, and English databases for literature about alopecia treatment, extracting the names of each herbal prescription and herb. The meridian tropism and classification category of each herb were also investigated. Using ARM, we identified frequently combined two-herb and three-herb sets. Using network analysis, we divided the herbs into several modules according to prescription pattern. Results Fifty-six articles and 489 herbal medicines were included—312 internal and 177 external medicines. Among the 312 medicinal herbs used in internal medicine group, the most frequently combined two-herb set was Polygonum multiflorum Thunb. (何首烏) and Angelica sinensis (Oliv.) Dlels (當歸). The most frequently used three-herb combination was Polygonum multiflorum Thunb., Angelica sinensis (Oliv.) Dlels, and Ligusticum chuanxiong Hort. (川芎). In network analysis, three modules were identified. The herbs of Module 1 were related to the liver and kidney meridians, and those of Module 3 were related to the Stomach meridian. Conclusions We identified the frequency, characteristics, and functional modules of herb combinations frequently used in alopecia treatment. We confirmed the value of classical medicinal herb theory. This finding will prompt further bioinformatics and network pharmacology research on alopecia.

Magnetic resonance (MR) imaging plays a highly important role in radiotherapy treatment planning for the segmentation of tumor volumes and organs. However, the use of MR is limited, owing to its high cost and the increased use of metal implants for patients. This study is aimed towards patients who are contraindicated owing to claustrophobia and cardiac pacemakers, and many scenarios in which only computed tomography (CT) images are available, such as emergencies, situations lacking an MR scanner, and situations in which the cost of obtaining an MR scan is prohibitive. From medical practice, our approach can be adopted as a screening method by radiologists to observe abnormal anatomical lesions in certain diseases that are difficult to diagnose by CT. The proposed approach can estimate an MR image based on a CT image using paired and unpaired training data. In contrast to existing synthetic methods for medical imaging, which depend on sparse pairwise-aligned data or plentiful unpaired data, the proposed approach alleviates the rigid registration of paired training, and overcomes the context-misalignment problem of unpaired training. A generative adversarial network was trained to transform two-dimensional (2D) brain CT image slices into 2D brain MR image slices, combining the adversarial, dual cycle-consistent, and voxel-wise losses. Qualitative and quantitative comparisons against independent paired and unpaired training methods demonstrated the superiority of our approach.

By biological criteria, human “races” do not exist (e.g., Cosmides et al., 2003). Nevertheless, every-day life and research from various fields show that we robustly and reliably perceive humans as belonging to different race groups. Here, we investigate the bases for our quick and easy judgments, by measuring the influence of manipulated facial features on race classification. Asian and Caucasian faces of our 3-dimensional face database ( http://faces.kyb.tuebingen.mpg.de ) were paired according to sex, age, and overall appearance. With these Asian-Caucasian face pairs we created a variety of mixed-race faces, by exchanging facial features between both faces of a pair: eyes, nose, mouth, “outer” features, shape, or texture. Original and modified faces were shown in a simple race classification task. We tested 24 Westerners (Germany) and 24 Easterners (South Korea). In both groups, eyes and texture were major determinants for race classification, followed by face shape, and then outer features, mouth, nose, which only had a weak influence on perceived face. Eastern participants classified Caucasian original faces better than Asian original faces, while Western participants were similarly good at classifying both races. Western participants—but not their Eastern counterparts—were less susceptible to eye, shape, and texture manipulations in other-race faces than in their own-race faces. A closer look at the data suggests that this effect mainly originates from differences in processing male and female faces in Western participants only. Our results provide more evidence of differences between observers from different cultural and ethnic backgrounds in face perception and processing.