The Role of a Disability in Children in Manifesting a Dermatological Disease

2021 ◽  
Vol 10 (4) ◽  
pp. 70-74
Author(s):  
Iordanis Papadopoulos
Keyword(s):  
Dermatological Disease

scholarly journals Use of Artificial Intelligence as a Predictor of the Response to Treatment in Alopecia Areata

Iproceedings ◽  
10.2196/35433 ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. e35433
Author(s):  
Fernando Alarcón-Soldevilla ◽  
Francisco José Hernández-Gómez ◽  
Juan Antonio García-Carmona ◽  
Celia Campoy Carreño ◽  
Ramon Grimalt ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Alopecia Areata ◽  
Predictive Value ◽  
Data Augmentation ◽  
Conflicts Of Interest ◽  
The United States ◽  
Response To Treatment ◽  
Disease Evolution ◽  
Dermatological Disease

Background Artificial intelligence (AI) has emerged in dermatology with some studies focusing on skin disorders such as skin cancer, atopic dermatitis, psoriasis, and onychomycosis. Alopecia areata (AA) is a dermatological disease whose prevalence is 0.7%-3% in the United States, and is characterized by oval areas of nonscarring hair loss of the scalp or body without evident clinical variables to predict its response to the treatment. Nonetheless, some studies suggest a predictive value of trichoscopic features in the evaluation of treatment responses. Assuming that black dots, broken hairs, exclamation marks, and tapered hairs are markers of negative predictive value of the treatment response, while yellow dots are markers of no response to treatment according to recent studies, the absence of these trichoscopic features could indicate favorable disease evolution without treatment or even predict its response. Nonetheless, no studies have reportedly evaluated the role of AI in AA on the basis of trichoscopic features. Objective This study aimed to develop an AI algorithm to predict, using trichoscopic images, those patients diagnosed with AA with a better disease evolution. Methods In total, 80 trichoscopic images were included and classified in those with or without features of negative prognosis. Using a data augmentation technique, they were multiplied to 179 images to train an AI algorithm, as previously carried out with dermoscopic images of skin tumors with a favorable response. Subsequently, 82 new images of AA were presented to the algorithm, and the algorithm classified these patients as responders and non-responders; this process was reviewed by an expert trichologist observer and presented a concordance higher than 90% with the algorithm identifying structures described previously. Evolution of the cases was followed up to truly determine their response to treatment and, therefore, to assess the predictive value of the algorithm. Results In total, 32 of 40 (80%) images of patients predicted as nonresponders scarcely showed response to the treatment, while 34 of 42 (81%) images of those predicted as responders showed a favorable response to the treatment. Conclusions The development of an AI algorithm or tool could be useful to predict AA evolution and its response to treatment. However, further research is needed, including larger sample images or trained algorithms, by using images previously classified in accordance with the disease evolution and not with trichoscopic features. Conflicts of Interest None declared.

Medical conditions and outcomes in 371 hoarded cats from 14 sources: a retrospective study (2011–2014)

2019 ◽  
Vol 22 (6) ◽  
pp. 484-491
Author(s):  
Linda S Jacobson ◽  
Jolene A Giacinti ◽  
Jyothi Robertson
Keyword(s):  
Medical Condition ◽  
Medical Conditions ◽  
Release Rates ◽  
Upper Respiratory Infection ◽  
Random Intercept ◽  
Dermatological Disease ◽  
Common Medical Condition ◽  
Place Of Origin ◽  
Upper Respiratory

Objectives The aims of this study were to: (1) describe the source, route of surrender and signalment of hoarded cats relinquished to the Toronto Humane Society (THS); (2) document the prevalence of medical conditions by group (place of origin); (3) compare medical conditions between institutional hoarding (IH) and non-institutional hoarding (NIH) environments; and (4) report length of stay (LOS) and outcomes in hoarded and non-hoarded cats. Methods A retrospective, descriptive epidemiological study was performed using THS records from between July 2011 and June 2014. The prevalence of medical conditions was calculated for the different groups. Univariable logistic regression with a random intercept to account for autocorrelation among animals from the same group was used to examine the influence of IH and NIH environments on selected medical conditions. LOS and outcomes were calculated for hoarded and non-hoarded cats. Results Three hundred and seventy-one hoarded cats from 14 sources were included. The majority (n = 352/371) were surrendered voluntarily, many with the assistance of a community intermediary. Upper respiratory infection (URI) was the most common medical condition (38% of cats), followed by dermatological disease (30%). The prevalence of medical conditions varied substantially between groups. The odds of URI at intake (odds ratio [OR] 4.35, P = 0.044) and chronic URI (OR 23.70, P <0.0001) were significantly greater for IH compared with NIH. Adoption rates, euthanasia rates and LOS were similar for hoarded and non-hoarded cats. Conclusions and relevance The different prevalence of medical conditions in groups of hoarded cats indicates a continuum of harm and severity in animal hoarding. Hoarded cats can have LOS and live release rates comparable with non-hoarded cats. Cats from IH were significantly more likely to have chronic URI. This study highlights the need for a greater focus on IH, as well as the role of community intermediaries and the potential for a harm reduction approach to animal hoarding.

scholarly journals New horizons for cutaneous microbiology: the role of biofilms in dermatological disease

2011 ◽  
Vol 165 (4) ◽  
pp. 751-759 ◽  
Author(s):  
N. Vlassova ◽  
A. Han ◽  
J.M. Zenilman ◽  
G. James ◽  
G.S. Lazarus
Keyword(s):  
New Horizons ◽  
Dermatological Disease

The role of the complement system in dermatological disease

2012 ◽  
Vol 7 (4) ◽  
pp. 359-366
Author(s):  
Faisal R Ali ◽  
Firas Al-Niaimi
Keyword(s):  
Complement System ◽  
Dermatological Disease ◽  
The Complement System

scholarly journals Use of Artificial Intelligence as a Predictor of the Response to Treatment in Alopecia Areata (Preprint)

2021 ◽  
Author(s):  
Fernando Alarcón-Soldevilla ◽  
Francisco José Hernández-Gómez ◽  
Juan Antonio García-Carmona ◽  
Celia Campoy Carreño ◽  
Ramon Grimalt ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Alopecia Areata ◽  
Predictive Value ◽  
Data Augmentation ◽  
The United States ◽  
Response To Treatment ◽  
Disease Evolution ◽  
Dermatological Disease ◽  
Treatment Responses

BACKGROUND Artificial intelligence (AI) has emerged in dermatology with some studies focusing on skin disorders such as skin cancer, atopic dermatitis, psoriasis, and onychomycosis. Alopecia areata (AA) is a dermatological disease whose prevalence is 0.7%-3% in the United States, and is characterized by oval areas of nonscarring hair loss of the scalp or body without evident clinical variables to predict its response to the treatment. Nonetheless, some studies suggest a predictive value of trichoscopic features in the evaluation of treatment responses. Assuming that black dots, broken hairs, exclamation marks, and tapered hairs are markers of negative predictive value of the treatment response, while yellow dots are markers of no response to treatment according to recent studies, the absence of these trichoscopic features could indicate favorable disease evolution without treatment or even predict its response. Nonetheless, no studies have reportedly evaluated the role of AI in AA on the basis of trichoscopic features. OBJECTIVE This study aimed to develop an AI algorithm to predict, using trichoscopic images, those patients diagnosed with AA with a better disease evolution. METHODS In total, 80 trichoscopic images were included and classified in those with or without features of negative prognosis. Using a data augmentation technique, they were multiplied to 179 images to train an AI algorithm, as previously carried out with dermoscopic images of skin tumors with a favorable response. Subsequently, 82 new images of AA were presented to the algorithm, and the algorithm classified these patients as responders and non-responders; this process was reviewed by an expert trichologist observer and presented a concordance higher than 90% with the algorithm identifying structures described previously. Evolution of the cases was followed up to truly determine their response to treatment and, therefore, to assess the predictive value of the algorithm. RESULTS In total, 32 of 40 (80%) images of patients predicted as nonresponders scarcely showed response to the treatment, while 34 of 42 (81%) images of those predicted as responders showed a favorable response to the treatment. CONCLUSIONS The development of an AI algorithm or tool could be useful to predict AA evolution and its response to treatment. However, further research is needed, including larger sample images or trained algorithms, by using images previously classified in accordance with the disease evolution and not with trichoscopic features.

scholarly journals The role of autophagy-related gene 9B in lichen planus

2021 ◽  
Vol 0 ◽  
pp. 1-3
Author(s):  
Rehab Mohamed Naguib ◽  
Abd-El Aziz El-Rifaie ◽  
Ayat Mohammed Abd El Wahab ◽  
Laila Ahmed Rashed
Keyword(s):  
Lichen Planus ◽  
Normal Control ◽  
Degradation Pathway ◽  
Related Gene ◽  
Dermatological Disease ◽  
Number Of Patients ◽  
Skin Biopsies ◽  
The Stability ◽  
Control Study

Background: Lichen planus (LP) is an idiopathic, chronic, relapsing, inflammatory, autoimmune dermatological disease. The etiopathogenesis of LP is still unclear. Autophagy is a strictly regulated lysosomal degradation pathway that is crucial for maintaining intracellular homeostasis and normal development. The dysregulation of autophagy-associated genes was recognized to increase the susceptibility to multiple diseases, including inflammation, autoimmune disorders and cancer. Aims: Our study aimed to detect the expression of autophagy-related gene 9 b (ATG9B) in LP patients compared to normal control persons to investigate the possible role of autophagy in pathogenesis of this disease. Methods: This case–control study included 30 LP patients and 30 age-, gender-matched healthy controls. Four millimeters punch skin biopsies were obtained from LP lesions and from the controls and they were kept in lysis solution for the stability of the studied parameters and were kept frozen at –80°C till analysis of ATG9B using real-time polymerase chain reaction. Results : The level of ATG9B in lesional skin of LP was significantly decreased compared to normal control persons (P < 0.01); also, there was a non-significant relation between ATG9B level and age, sex, duration and family history among LP patients. Limitations: Limited number of patients included in our study (30 patients). Conclusion: Autophagy may play a role in the pathogenesis of cutaneous LP.

Role of dactinomycin in the improved survival of children with Wilms' tumor

JAMA ◽  
1966 ◽  
Vol 195 (12) ◽  
pp. 1005-1009 ◽  
Author(s):  
D. J. Fernbach
Keyword(s):  
Wilms Tumor

Role of the allergist in diagnosis and management of patients with uveitis

JAMA ◽  
1966 ◽  
Vol 195 (3) ◽  
pp. 167-172 ◽  
Author(s):  
T. E. Van Metre
Keyword(s):  
Diagnosis And Management

The power of norms to sway fused group members

2018 ◽  
Vol 41 ◽  
Author(s):  
Winnifred R. Louis ◽  
Craig McGarty ◽  
Emma F. Thomas ◽  
Catherine E. Amiot ◽  
Fathali M. Moghaddam
Keyword(s):  
Evolutionary Anthropology ◽  
Normative Systems ◽  
Violent Extremism ◽  
Identity Fusion ◽  
Group Members

AbstractWhitehouse adapts insights from evolutionary anthropology to interpret extreme self-sacrifice through the concept of identity fusion. The model neglects the role of normative systems in shaping behaviors, especially in relation to violent extremism. In peaceful groups, increasing fusion will actually decrease extremism. Groups collectively appraise threats and opportunities, actively debate action options, and rarely choose violence toward self or others.

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