Background: Seborrheic dermatitis is an inflammatory skin disease that affects 1–3% of the general population. The Malassezia species has been implicated as the main causative agent; however, the bacterial flora of the skin may also play role in the etiopathogenesis. Therefore, we investigated the most common bacterial agent of the skin flora of patients with seborrheic dermatitis. Materials and Methods: Fifty-one patients with seborrheic dermatitis and 50 healthy individuals are included in this study. Sterile cotton swabs were rubbed on the scalp of the participants for bacterial culture. Colonial morphology was identified with gram stain and catalase test. Results: Staphylococcus aureus was isolated from 25 (49%) patients with seborrheic dermatitis and 10 (20%) healthy individuals within the control group. Coagulase-negative staphylococci were isolated from 24 (47.1%) patients with seborrheic dermatitis and 17 (34%) healthy individuals within the control group. Diphtheroids were present in 2 (3.9%) patients and 1 (2%) subject within the control group. Gram-negative bacilli were present only in 1 (2%) patient. Hemolytic streptococci and bacilli were identified in 1 (2%) subject from each group. Colonization of coagulase-negative staphylococci, diphtheroids, gram-negative bacilli, hemolytic streptococci, and bacillus did not differ between patients and healthy controls. However, S. aureus colonization was significantly more common in patients with seborrheic dermatitis than in healthy controls. Conclusion: Within this study we revealed that S. aureus colonization was significantly higher among the patients. Therefore, we propose that, in addition to the Malassezia species, S. aureus may play a role in the etiopathogenesis of seborrheic dermatitis.

Introduction Various factors like physiological and emotional stress, drugs and nutritional deficiencies can result in hair loss. Results of laboratory tests examining the underlying aetiology of hair loss vary in patients. Aim We aimed to compare the serum levels of ferritin, folate,...

Introduction: Isotretinoin is a commonly used systemic retinoid for treating acne. However, isotretinoin may lead to elevated serum levels of triglycerides and cholesterol, and it may affect liver function tests. Moreover, the effect of isotretinoin on hematological parameters remains controversial. This study examines changes in the blood chemistry panel, hematological parameters, and inflammation biomarkers of patients diagnosed with acne and treated with isotretinoin. Methods: The study included 70 patients (59 females and 11 males, between ages 18 and 37) with moderate to severe acne vulgaris treated with isotretinoin. The medical records and laboratory findings of the participants were reviewed retrospectively between March 2017 and September 2018. All the patients whose necessary laboratory test results could be obtained from the collected data were included in the study. Results: Serum total cholesterol, HDL, LDL, triglyceride, mean corpuscular hemoglobin levels, and platelet/lymphocyte ratio increased, whereas white blood cell count and mean platelet volume (p = 0.036) decreased after isotretinoin treatment. Conclusions: The results of this study revealed that mean platelet volume decreased significantly 3 months after the initiation of isotretinoin treatment. Therefore, we suggest considering mean platelet volume as an inflammatory marker in patients with acne treated with isotretinoin. However, this research should be replicated under more randomized conditions in a prospective study to reach a definitive conclusion.

To compare the serum levels of 25-hydroxyvitamin D, ferritin, folate, vitamin B12, zinc, and thyroid stimulating hormone between patients with warts and healthy individuals.This retrospective study enrolled 40 patients with warts and 40 healthy individuals ...

Assigning medical codes for patients is essential for healthcare organizations, not only for billing purposes but also for maintaining accurate records of patients' medical histories and analyzing the outputs of certain procedures. Due to the abundance of disease codes, it can be laborious and time-consuming for medical specialists to manually assign these codes to each procedure. To address this problem, we discuss the automatic prediction of ICD-9 codes, the most popular and widely accepted system of medical coding. We introduce a two-stream deep learning framework specifically designed to analyze multi-modal data. This framework is applied to the extensive and publicly available MIMIC-III dataset, enabling us to leverage both numerical and text-based data for improved ICD-9 code prediction. Our system uses text representation models to understand the text-based medical records; the Gated Recurrent Unit (GRU) to model the numerical health records; and fuses these two streams to automatically predict the ICD-9 codes used in the intensive care unit. We discuss the preprocessing and classification methods and demonstrate that our proposed two-stream model outperforms other state-of-the-art studies in the literature.