Line-field confocal optical coherence tomography in lichen planopilaris and frontal fibrosing alopecia: A pilot study.

Background And Objectives: Lichen planopilaris (LPP) and frontal fibrosing alopecia (FFA) are common causes of cicatricial alopecia. While several studies have demonstrated the usefulness of non-invasive imaging methods such as reflectance confocal microscopy (RCM) and optical coherence tomography (OCT) for the diagnosis of scarring alopecia, this study aimed to identify characteristic features of cicatricial alopecia in LPP/FFA using line-field confocal OCT (LC-OCT).

Patients And Methods: Fifty-one patients (26 LPP, 24 FFA, 1 LPP and FFA) were prospectively analyzed with LC-OCT at three defined locations on the scalp: (1) scarring area = lesion, (2) scar-hair boundary = transition zone and (3) healthy area for the presence of the following pre-defined criteria: no hair follicles left, destructed hair follicles, dermal sclerosis, no rimming of the dermal papillae, epidermal and dermal inflammatory infiltrate, infundibular hyperkeratosis, dilated blood vessels, hypervascularization, melanophages, epidermal pigment incontinence.

Improved survival of advanced melanoma patients receiving immunotherapy with concomitant antithrombotic therapy - A multicenter study on 2419 patients from the prospective skin cancer registry ADOReg.

Background: Cancer immunotherapy has revolutionized melanoma treatment, but the high number of non-responders still emphasizes the need for improvement of therapy. One potential avenue for enhancing anti-tumor treatment is through the modulation of coagulation and platelet activity. Both have been found to play an important role in the tumor microenvironment, tumor growth and metastasis.

Fatigue failure testing of human hair: Weibull-analysis for constant strain experiments.

Fatigue failure testing of materials is an important aspect of assessing their strength and resilience under long-term, oscillatory stresses and/or strains. This also applies to human hair. For this investigation, we decided to complement existing experience on cyclic tests at various levels of constant stress with those at various constant strains (4-30%).

Predicting executive functioning from walking features in Parkinson's disease using machine learning.

Parkinson's disease is characterized by motor and cognitive deficits. While previous work suggests a relationship between both, direct empirical evidence is scarce or inconclusive. Therefore, we examined the relationship between walking features and executive functioning in patients with Parkinson's disease using state-of-the-art machine learning approaches.