Background: Individuals with lichen planopilaris (LPP) typically present with hair loss and an irritated scalp, with signs and symptoms such as itching, erythema, scaling, and burning. Both hair loss and reddened skin may be visible also for other people. Therefore, it is conceivable that individuals with LPP might report a lower mental health status. Accordingly, in the present study, we compared dimensions of mental health between individuals with LPP and healthy controls. Method: A total of 38 individuals with LPP (mean age: 38.24 years, 47.4% females) and 58 gender- and age-matched controls took part in this study. They completed questionnaires covering dimensions of quality of life and self-esteem; experts rated participants’ symptoms of depression and anxiety. Furthermore, dermatologists reported on the illness duration, the LPP activity index, and the severity of alopecia. Results: Compared to healthy controls, individuals with LPP reported lower scores of self-esteem, physical functioning, and mental health and higher scores of role limitations (physical and emotional). Likewise, experts rated higher depression scores in individuals with LPP compared to controls. In individuals with LPP, a higher LPP disease activity and a higher severity of hair loss were associated with higher depression and lower quality-of-life scores. Further, a higher LPP activity index, a lower self-esteem, and higher anxiety scores predicted a poorer quality of life, while illness duration was statistically unrelated. Conclusions: Compared to controls, and irrespectively of the illness duration, individuals with LPP reported a lower quality of life and a lower self-esteem, and experts’ ratings observed higher scores of depression. Experts treating individuals with LPP might pay special attention to the individuals’ mental health status.

BackgroundGranuloma annulare is a benign inflammatory disease of the skin. The etiology and pathogenesis of the disease are not yet determined, but some authors have proposed that it might be associated with a variety of underlying conditions such as thyroid disorders, diabetes mellitus and positive tuberculin skin test.ObjectiveThis study was conducted to find the probable relationship between granuloma annulare and diabetes mellitus, thyroid disorders and positive tuberculin skin test.Patients and methodsA total of 28 patients with granuloma annulare were recruited from our dermatology outpatient clinic, and data on sex, age and distribution of granuloma annulare lesions were collected. Forty-one age- and sex-matched apparently healthy volunteers serving as controls were also included. Thyroid stimulating hormone (TSH), fasting blood sugar (FBS) and tuberculin skin tests were performed for both groups.ResultsMean serum level of FBS in the patient group was significantly higher than the mean serum level of FBS in the control group (110.60±46.31 mg/dL versus 88.39±10.58 mg/dL, respectively, p=0.004). Mean serum level of TSH in the patient group was 3.43±2.73 mIU/L, which was not significantly different from the mean serum level of TSH in the control group (3.26±2.11 mIU/L, p=0.772). Four individuals in the patient group and only 1 in the control group had a positive tuberculin skin test.ConclusionGranuloma annulare patients are better to be assessed for diabetes mellitus, but the relationship between this skin disease and thyroid disorders or tuberculin skin test still needs to be investigated.

The association of androgenetic alopecia (AGA) with cardiovascular risk factors including hypertension, smoking and high body mass index (BMI) has been investigated in several studies. However, the results are discrepant. The aim of the present study was to evaluate the association of AGA with anthropometrics, blood pressure and lifestyle habits. Patients and Methods: In this case-control study, 256 men with AGA and 256 agematched healthy controls were evaluated. Systolic and diastolic blood pressures, as well as anthropometric indices were measured. Data on lifestyle habits including smoking, hookah use and alcohol consumption were also collected. Results: Patients with AGA had significantly lower BMI and lower waist to hip ratio as compared to controls (p = 0.012 and p = 0.002, respectively). Other anthropometrics as well as mean systolic and diastolic blood pressures were not significantly different between the two groups. There was no significant difference between patients and controls in terms of lifestyle habits. Patients with severe AGA (Norwood-Hamilton classification type IV-VII) had significantly higher systolic and diastolic blood pressures as compared to those with mild to moderate AGA (Norwood-Hamilton classification type II, III), but the mean age of the former group was also significantly higher (p < 0.001). Conclusion: In contrast to some previous studies, our findings did not show a significant association between certain cardiovascular risk factors (such as hypertension and smoking) and AGA. Although lower BMI and lower waist to hip ratio were observed in the patient group, these values were within normal limits and therefore not biologically significant.

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Background. Psoriasis is a chronic autoimmune disease impairing significantly the quality of life of the patient. The diagnosis of the disease is done via a visual inspection of the lesional skin by dermatologists. Classification of psoriasis using gene expression is an important issue for the early and effective treatment of the disease. Therefore, gene expression data and selection of suitable gene signatures are effective sources of information. Methods. We aimed to develop a hybrid classifier for the diagnosis of psoriasis based on two machine learning models of the genetic algorithm and support vector machine (SVM). The method also conducts gene signature selection. A publically available gene expression dataset was used to test the model. Results. A number of 181 probe sets were selected among the original 54,675 probes using the hybrid model with a prediction accuracy of 100% over the test set. A number of 10 hub genes were identified using the protein-protein interaction network. Nine out of 10 identified genes were found in significant modules. Conclusions. The results showed that the genetic algorithm improved the SVM classifier performance significantly implying the ability of the proposed model in terms of detecting relevant gene expression signatures as the best features.