Background: Hidradenitis suppurativa (HS) is a chronic, inflammatory and debilitating dermatosis characterized by painful nodules, sinus tracts and abscesses in apocrine gland-bearing areas that predominantly affect women worldwide. New therapeutic interventions based on the clinical manifestations of patients have recently been introduced in numerous articles. However, which countries, journals, subject categories, and articles have the ultimate influence remain unknown. This study aimed to display influential entities in 100 top-cited HS-related articles (T100HS) and investigate whether medical subject headings (i.e., MeSH terms) can be used to predict article citations.Methods: T100HS data were extracted from PubMed since 2013. Subject categories were classified by MeSH terms using social network analysis. Sankey diagrams were applied to highlight the top 10 influential entities in T100HS from the three aspects of publication, citations, and the composited score using the hT index. The difference in article citations across subject categories and the predictive power of MeSH terms on article citations in T100HS were examined using one-way analysis of variance and regression analysis.Results: The top three countries (the US, Italy, and Spain) accounts for 54% of the T100HS. The T100HS impact factor (IF) is 12.49 (IF = citations/100). Most articles were published in J Am Acad Dermatol (15%; IF = 18.07). Eight subject categories were used. The "methods" was the most frequent MeSH term, followed by "surgery" and "therapeutic use". Saunte et al, from Roskilde Hospital, Denmark, had 149 citations in PubMed for the most cited articles. Sankey diagrams were used to depict the network characteristics of the T100HS. Article citations did not differ by subject category (F(7, 92) = 1.97, P = .067). MeSH terms were evident in the number of article citations predicted (F(1, 98) = 129.1106; P < .001). Conclusion:We achieved a breakthrough by displaying the characteristics of the T100HS network on the Sankey diagrams. MeSH terms may be used to classify articles into subject categories and predict T100HS citations. Future studies can apply the Sankey diagram to the bibliometrics of the 100 most-cited articles.Abbreviations: DS = descriptive statistics, HS = hidradenitis suppurativa, IBP = impact beam plot, IF = impact factor, MeSH = medical subject headings, NEChiSQ = AAC indicators bases on nodes, edges, and chi-squared statistics, PMC = PubMed Central, RA = research achievements, RD = research domains, SNA = social network analysis, T100HS = 100 top-cited articles on the topic of hidradenitis suppurativa.

Background: Pemphigus vulgaris (PV) is a rare autoimmune blistering disease characterized by intraepithelial and mucocutaneous blister formation and erosion. Numerous articles related to PV have been published. However, which articles have a tremendous influence is still unknown, and factors affecting article citation numbers remain unclear. We aimed to visualize the prominent entities using the top 100 most-cited articles on the topic of PV (T100PV), and investigate whether medical subject headings (i.e., MeSH terms) can be used to predict article citations.Methods: By searching the PubMed Central (PMC) database, the T100PV abstracts since 2011 were downloaded. Citation analysis was performed to compare the dominant entities in article topics, authors, and research institutes using social network analysis (SNA) and Kano diagrams. We examined the MeSH prediction power against article citations using correlation coefficients (CCs).Results: The most cited article (125 times) was authored by Ellebrecht from the University of Pennsylvania in the US. The most productive countries were Germany (28%) and the US (25%). Most articles were published in J Invest Dermatol (16%) and Br J Dermatol (10%). Kasperkiewicz (Germany) and the Normandie University (France) were the most cited authors and research institutes, respectively. The most frequently occurred MeSH terms were administration and dosage, immunology, and metabolism. MeSH terms were evident in the prediction power on the number of article citations (F = 19.77; P < .001). Conclusion:A breakthrough was achieved by developing dashboards to display the T100PV. MeSH terms can be used to predict the T100PV citations. These T100PV visualizations can be applied in future studies.Abbreviations: AWS = author-weighted scheme, CC = correlation coefficient, CD = centrality degree, MeSH = medical subject headings, PV = Pemphigus vulgaris, PMC = PubMed Central, SNA = social network analysis, T100PV = top 100 most-cited articles on the topic of pemphigus vulgaris, VBA = visual basic for application.

Background The COVID-19 pandemic occurred and rapidly spread around the world. Some online dashboards have included essential features on a world map. However, only transforming data into visualizations for countries/regions is insufficient for the public need. This study aims to (1) develop an algorithm for classifying countries/regions into four quadrants inn GSM and (2) design an app for a better understanding of the COVID-19 situation. Methods We downloaded COVID-19 outbreak numbers daily from the Github website, including 189 countries/regions. A four-quadrant diagram was applied to present the classification of each country/region using Google Maps run on dashboards. A novel presentation scheme was used to identify the most struck entities by observing (1) the multiply infection rate (MIR) and (2) the growth trend in the recent 7 days. Four clusters of the COVID-19 outbreak were dynamically classified. An app based on a dashboard aimed at public understanding of the outbreak types and visualizing of the COVID-19 pandemic with Google Maps run on dashboards. The absolute advantage coefficient (AAC) was used to measure the damage hit by COVID-19 referred to the next two countries severely hit by COVID-19. Results We found that the two hypotheses were supported: India (i) is in the increasing status as of April 28, 2021; (ii) has a substantially higher ACC(= 0.81 > 0.70), and (iii) has a substantially higher ACC(= 0.66 < 0.70) as of May 17, 2021. Conclusion Four clusters of the COVID-19 outbreak were dynamically classified online on an app making the public understand the outbreak types of COVID-19 pandemic shown on dashboards. The app with GSM and AAC is recommended for researchers in other disease outbreaks, not just limited to COVID-19.

Periostracum cicadae is widely used for the treatment of skin diseases such as eczema, pruritus, and itching. The current study sought to evaluate the effect of P. cicadae extract on ultraviolet B (UVB) irradiation and identify the mechanisms involved. Photodamage-protective activity of P. cicadae extracts against oxidative challenge was screened using HaCaT keratinocytes. P. cicadae extracts did not affect cell viability but decreased reactive oxygen species (ROS) production. The extract attenuates the expression of interleukin-6 (IL-6), matrix metalloproteinase-2 (MMP-2), and MMP-9 in UVB-treated HaCaT cells. Also, P. cicadae abrogated UVB-induced activation of NF-κB, p53, and activator protein-1 (AP-1). The downmodulation of IL-6 by P. cicadae was inhibited by the p38 inhibitor (SB203580) or JNK inhibitor (SP600125). Moreover, the extract attenuated the expression of NF-κB and induced thrombomodulin in keratinocytes and thereby effectively downregulated inflammatory responses in the skin. The nuclear accumulation and expression of NF-E2-related factor (Nrf2) were increased by P. cicadae treatment. Furthermore, treatment with P. cicadae remarkably ameliorated the skin's structural damage induced by irradiation. This study demonstrates that P. cicadae may protect skin cells against oxidative insult by modulating ROS concentration, IL-6, MMPs generation, antioxidant enzymes activity, and cell signaling pathways.