Purpose: This study synthesized the literature on eating difficulties among older adults with dementia in long-term care facilities. Methods: A scoping review, using the framework proposed by Arksey & O'Malley (2005) and supplemented by Levac et al. (2010), was conducted. Literature was searched from RISS, KISS, DBpia, PubMed, and CINAHL. Two reviewers independently selected studies and extracted their characteristics, using pre-tested forms to determine final inclusion. In total, 1283 articles were identified, and 13 articles were used for the final analysis. Results: The Eating Behavior Scale and the Edinburgh Feeding Evaluation in Dementia scale were the most widely utilized measurement tools. The most common factors related to eating behavior in older adults with dementia were cognitive and physical functions in the individual domain, the caregiver's attitude toward eating in the inter-individual domain, and meal type in the environmental domain. Conclusion Measurement tools reflecting the eating behavior of older adults with dementia must be developed to obtain a comprehensive understanding of this issue and create effective interventions for the specific context of long-term care facilities in Korea. The results of this study are intended to serve as a basis to develop meal support programs for older adults with dementia.

BACKGROUND: This study aimed to properly manage diseases such as blood sugar control so that patients with diabetes can benefit from both medication and health activities. Also, these health practices are greatly influenced by self-rated health, a subjective assessment of health status. Because self-rated health does not necessarily match the objective health status, it is important to identify which factors affect self-rated health. METHODS: For the study, the data was gathered from the 4th–6th National Health Nutrition Survey (2007–2015). Out of the total 73,353 participants in the survey, 2,303 patients with uncontrolled blood sugar with an HbA1c level of more than 7% were selected for the final study. Dependent variables fell into two categories depending on how the participant reported whether he or she was in good health or not. Independent variables included socio-demographics, health behavioral, and health status factors. This study performed logistic regression analysis. RESULTS: Out of 2,303 participants, 18.1% reported that their heath was ‘good,’ despite the fact that their blood sugar level was not controlled. After running a logistic regression model, the odds ratio of groups that perceive subjective health awareness as good was higher in the groups of people as below: in the people over 60 years old; in the people who graduated from a junior college or higher than those who had a level of education of primary school completion or less; in the people living in Chungnam than those living in Seoul; and in the group with hypertriglyceridemia. CONCLUSION The study identified factors associated with those failed to perceive the blood sugar level as a severe health problem despite of the fact that blood sugar was not controlled. To improve public health, diabetes management policies need to be addressed to population groups with these problems above.

Methyl anthranilate (MA) is a botanical fragrance used in food flavoring with unexplored potential in anti-pigment cosmetics. MA dose-dependently reduced melanin content without affecting cell viability, inhibited dendrite elongation and melanosome transfer in the co-culture system of human melanoma cells (MNT-1) and human keratinocyte cell line (HaCaT), and downregulated melanogenic genes, including tyrosinase, tyrosinase-related protein 1 and 2 (TRP-1, TRP-2). Additionally, MA decreased cyclic adenosine monophosphate (cAMP) production and exhibited a significant anti-pigmentary effect in Melanoderm™. These results suggest that MA is a promising anti-pigmentary agent for replacing or complementing existing anti-pigmentary cosmetics.

PURPOSE: An animal periodontitis model is essential for research on the pathogenesis and treatment of periodontal disease. In this study, we have introduced a lipopolysaccharide (LPS) of a periodontal pathogen to the alveolar bone defect of experimental animals and investigated its suitability as a periodontitis model. METHODS: Alveolar bone defects were made in both sides of the mandibular third premolar region of nine beagle dogs. Then, the animals were divided into the following groups: silk ligature tied on the cervical region of tooth group, Porphyromonas gingivalis LPS (P.g. LPS)-saturated collagen with silk ligature group, and no ligature or P.g. LPS application group as the control. The plaque index and gingival index were measured at 0 and 4 weeks postoperatively. The animals were then euthanized and prepared for histologic evaluation. RESULTS: The silk ligature group and P.g. LPS with silk ligature group showed a significantly higher plaque index at 4 weeks compared to the control (P<0.05). No significant difference was found in the plaque index between the silk ligature group and P.g. LPS with silk ligature group. The P.g. LPS with silk ligature group showed a significantly higher gingival index compared to the silk ligature group or the control at 4 weeks (P<0.05). Histologic examination presented increased inflammatory cell infiltration in the gingival tissue and alveolar bone of the P.g. LPS with silk ligature group. CONCLUSIONS: An additional P.g. LPS-saturated collagen with silk ligature ensured periodontal inflammation at 4 weeks. Therefore, P.g. LPS with silk ligature application to surgically created alveolar bone defects may be a candidate model for experimental periodontitis.
Animal Experimentation ; Animals ; Bicuspid ; Collagen ; Dogs ; Inflammation ; Ligation ; Lipopolysaccharides ; Models, Animal ; Periodontal Diseases ; Periodontal Index ; Periodontitis ; Porphyromonas gingivalis ; Silk ; Tooth

Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet.