Frontal fibrosing alopecia: demographic and clinical characteristics of 490 cases.

BACKGROUND: Frontal fibrosing alopecia (FFA) is a cicatricial alopecia mostly affecting the frontotemporal hairline. Its aetiology and associated factors remain unclear. OBJECTIVE AND METHODS: An observational, cross-sectional and descriptive study was conducted in France and Germany to identify demographic and health characteristics associated with the severity of FFA. RESULTS: Of 490 included patients, 95% were female, of which 84% were postmenopausal. Age at onset of FFA symptoms ranged between 15 and 89 years, but diagnosis was frequently delayed up to 24 years. Lichen Planopilaris Activity Index scores were low (median 1.8, IQR 1.0 to 3.5). Thyroid function disorders were reported in 13% of men and 35% of women. Abnormal blood lipid levels were found in 42% of tested men and 47% of women. In the bivariate analyses, LPPAI scores were negatively correlated with abnormal testosterone (rs  = -0.775) and oestrogen values (rs  = -0.664), regular use of face cleaning products (rs  = -0.465), hair colourants (rs  = -0.679) and hairspray (rs  = -0.500). CONCLUSIONS: The most common comorbidity was thyroid disease, with proportions higher than in the European population, possibly reflecting a role of thyroid hormones in FFA pathogenesis. The association of abnormal testosterone and oestrogen values with lesser disease activity needs to be explored in further studies. Our correlation analyses do not support a role of leave-on cosmetic products in the pathophysiology of FFA.

A Generative Angular Model of Protein Structure Evolution.

Recently described stochastic models of protein evolution have demonstrated that the inclusion of structural information in addition to amino acid sequences leads to a more reliable estimation of evolutionary parameters. We present a generative, evolutionary model of protein structure and sequence that is valid on a local length scale. The model concerns the local dependencies between sequence and structure evolution in a pair of homologous proteins. The evolutionary trajectory between the two structures in the protein pair is treated as a random walk in dihedral angle space, which is modeled using a novel angular diffusion process on the two-dimensional torus. Coupling sequence and structure evolution in our model allows for modeling both "smooth" conformational changes and "catastrophic" conformational jumps, conditioned on the amino acid changes. The model has interpretable parameters and is comparatively more realistic than previous stochastic models, providing new insights into the relationship between sequence and structure evolution. For example, using the trained model we were able to identify an apparent sequence-structure evolutionary motif present in a large number of homologous protein pairs. The generative nature of our model enables us to evaluate its validity and its ability to simulate aspects of protein evolution conditioned on an amino acid sequence, a related amino acid sequence, a related structure or any combination thereof.

A statistical model for helices with applications.

Motivated by a cutting edge problem related to the shape of α -helices in proteins, we formulate a parametric statistical model, which incorporates the cylindrical nature of the helix. Our focus is to detect a "kink," which is a drastic change in the axial direction of the helix. We propose a statistical model for the straight α -helix and derive the maximum likelihood estimation procedure. The cylinder is an accepted geometric model for α -helices, but our statistical formulation, for the first time, quantifies the uncertainty in atom positions around the cylinder. We propose a change point technique "Kink-Detector" to detect a kink location along the helix. Unlike classical change point problems, the change in direction of a helix depends on a simultaneous shift of multiple data points rather than a single data point, and is less straightforward. Our biological building block is crowdsourced data on straight and kinked helices; which has set a gold standard. We use this data to identify salient features to construct Kink-detector, test its performance and gain some insights. We find the performance of Kink-detector comparable to its computational competitor called "Kink-Finder." We highlight that identification of kinks by visual assessment can have limitations and Kink-detector may help in such cases. Further, an analysis of crowdsourced curved α -helices finds that Kink-detector is also effective in detecting moderate changes in axial directions.

Evidence-based (S3) guideline for the treatment of androgenetic alopecia in women and in men - short version.

Androgenetic alopecia is the most common hair loss disorder, affecting both men and women. Initial signs of androgenetic alopecia usually develop during teenage years leading to progressive hair loss with a pattern distribution. Moreover, its frequency increases with age and affects up to 80% Caucasian men and 42% of women. Patients afflicted with androgenetic alopecia may undergo significant impairment of quality of life. The European Dermatology Forum (EDF) initiated a project to develop evidence-based guidelines for the treatment of androgenetic alopecia. Based on a systematic literature research the efficacy of the currently available therapeutic options was assessed and therapeutic recommendations were passed in a consensus conference. The purpose of the guideline is to provide dermatologists with an evidence-based tool for choosing an efficacious and safe therapy for patients with androgenetic alopecia.

Sensitivity to change of the Dermatology Life Quality Index in adult females with facial acne vulgaris: a validation study.

BACKGROUND: The postadolescent form of acne papulopustulosa, also referred to as 'acne tarda' can have substantial negative impact on Quality of Life, especially in adult female patients. OBJECTIVE: Although the Dermatology Life Quality Index (DLQI) is widely used, empirical evidence about its performance in adult female acne patients is lacking. METHODS: In this prospective cohort study, we have investigated the sensitivity to change of the DLQI in 53 female adult acne patients with mild to moderate facial acne treated with azelaic acid (AzA) 15% gel twice daily over 24 weeks. RESULTS: Mean Investigator Static Global Assessment (ISGA) score was 2.3 (SD 0.5) at baseline and ranged from 0.9 (SD 0.3) to 2.1 (SD 0.4) at the end of the study in the 'Highly Improved' and 'Unchanged' responder groups respectively. The mean baseline DLQI score was 5.1 (SD 4.2). The Effect Size in the responder group 'Highly Improved' was 0.66; in group 'Improved' 0.62 and 0.23 in group 'Unchanged'. At the end of study, the mean DLQI score ranged from 1.1 (SD 1.5) in the 'Highly Improved' group to 3.7 (SD 6.0) in the 'Unchanged' group. CONCLUSION: The results support the sensitivity to change of the DLQI in this population.

Circular piecewise regression with applications to cell-cycle data.

Applications of circular regression models appear in many different fields such as evolutionary psychology, motor behavior, biology, and, in particular, in the analysis of gene expressions in oscillatory systems. Specifically, for the gene expression problem, a researcher may be interested in modeling the relationship among the phases of cell-cycle genes in two species with differing periods. This challenging problem reduces to the problem of constructing a piecewise circular regression model and, with this objective in mind, we propose a flexible circular regression model which allows different parameter values depending on sectors along the circle. We give a detailed interpretation of the parameters in the model and provide maximum likelihood estimators. We also provide a model selection procedure based on the concept of generalized degrees of freedom. The model is then applied to the analysis of two different cell-cycle data sets and through these examples we highlight the power of our new methodology.

Effect of minoxidil topical foam on frontotemporal and vertex androgenetic alopecia in men: a 104-week open-label clinical trial.

BACKGROUND: Topical minoxidil formulations have been shown to be effective in treating androgenetic alopecia (AGA) for 12 months. Efficacy and safety in both frontotemporal and vertex regions over longer application periods have not been studied so far. OBJECTIVES: To evaluate the effect of 5% minoxidil topical foam (5% MTF) in the frontotemporal and vertex areas in patients with moderate AGA over 104 weeks. METHODS: An 80-week, open-label extension phase was performed, following a 24-week randomized, double-blind, placebo-controlled study in men with AGA grade IIIvertex to VI. Group 1 (n = 22) received ongoing 5% MTF for 104 weeks, Group 2 (n = 23) received placebo topical foam (plaTF) until week 24, followed by 5% MTF until week 104 during the extension phase. Frontotemporal and vertex target area non-vellus hair counts (f-TAHC, v-TAHC) and cumulative hair width (f-TAHW, v-TAHW) were assessed at baseline and at weeks 24, 52, 76 and 104. RESULTS: In Group 1, f-TAHW and f-TAHC showed a statistically significant increase from baseline to week 52 and week 76, respectively, returning to values comparable to baseline at week 104. No significant differences were found between baseline and week 104 in v-TAHC in Group 1 as well as f-TAHC, v-TAHC, f-TAHW and v-TAHW values in Group 2. CONCLUSIONS: 5% MTF is effective in stabilizing hair density, hair width and scalp coverage in both frontotemporal and vertex areas over an application period of 104 weeks, while showing a good safety and tolerability profile with a low rate of irritant contact dermatitis.

Evaluation of trichodynia (hair pain) during chemotherapy or tamoxifen treatment in breast cancer patients.

INTRODUCTION: In women receiving antineoplastic therapy, hair loss is often accompanied by distressing hair or scalp sensations, such as hair pain (trichodynia) and pruritus. A scientific approach to objectively evaluate the course and characteristics of these unpleasant sensations is of great importance for the establishment of treatment strategies. METHODS: An observational cohort study was conducted in 34 female breast cancer patients, postoperatively undergoing chemotherapy (group C, n = 17) or endocrine therapy with tamoxifen (group T, n = 17). For 28 weeks after therapy initiation, patients experiencing hair pain and/or scalp pruritus were required to complete a specially developed diary, based on a modification of pain questionnaires. Sensations were journalized in terms of time of onset, duration, intensity on a numeric rating scale, dependence on touching the scalp or hair and character of the sensation, chosen from given descriptors or using own words. RESULTS: In group C, all patients who completed the questionnaire experienced hair and scalp sensations: 87% both trichodynia and pruritus, 13% trichodynia only. Reported intensities ranged between 1 and 10. In group T, 31% of participants reported hair and scalp sensations: 12% both trichodynia and pruritus, 12% pruritus only, 7% trichodynia only. Intensities were rated between 1 and 5. No sensations were reported after week 11 in either group. CONCLUSIONS: Hair and scalp sensations in group C were significantly more common, lasted longer, and were of greater intensity and more differentiated qualities than in group T. The occurrence of trichodynia in chemotherapy patients corresponded with the onset and duration of hair loss, thus suggesting a possible correlation.

Formulation of probabilistic models of protein structure in atomic detail using the reference ratio method.

We propose a method to formulate probabilistic models of protein structure in atomic detail, for a given amino acid sequence, based on Bayesian principles, while retaining a close link to physics. We start from two previously developed probabilistic models of protein structure on a local length scale, which concern the dihedral angles in main chain and side chains, respectively. Conceptually, this constitutes a probabilistic and continuous alternative to the use of discrete fragment and rotamer libraries. The local model is combined with a nonlocal model that involves a small number of energy terms according to a physical force field, and some information on the overall secondary structure content. In this initial study we focus on the formulation of the joint model and the evaluation of the use of an energy vector as a descriptor of a protein's nonlocal structure; hence, we derive the parameters of the nonlocal model from the native structure without loss of generality. The local and nonlocal models are combined using the reference ratio method, which is a well-justified probabilistic construction. For evaluation, we use the resulting joint models to predict the structure of four proteins. The results indicate that the proposed method and the probabilistic models show considerable promise for probabilistic protein structure prediction and related applications.

Analysis of quantitative changes in hair growth during treatment with chemotherapy or tamoxifen in patients with breast cancer: a cohort study.

BACKGROUND: In women with breast cancer, chemotherapy-induced alopecia is a highly feared but common side-effect of antineoplastic treatment. The onset, pattern and amount of hair loss differ depending on the therapy regimen and have not yet been quantified using standardized techniques. OBJECTIVES: To evaluate objectively and compare the effect of antineoplastic therapy with chemotherapy or tamoxifen on hair loss, quantifying trichological parameters. METHODS: Female patients with breast cancer were included (n = 34), who were receiving chemotherapy (group C, n = 17) or tamoxifen (group T, n = 17) after surgery. Trichological parameters were evaluated once before [week 0 (w0)], twice during (w3, w6) and twice after (w18, w28) the normal 16-week course of chemotherapy, or at corresponding time points during continuous tamoxifen intake. At each visit, anagen and telogen hairs and hair density were quantified by automated phototrichogram in two defined areas: frontal and occipital. RESULTS: Group T generally showed no changes in anagen and telogen hairs or hair density. In group C, anagen hairs and hair density generally followed the same course, decreasing until w6, remaining at a low level during w6-18 and increasing after cessation of chemotherapy, reaching values comparable with or higher than baseline at w28. Telogen hairs increased until w3 then decreased until w6, remaining stable afterwards. CONCLUSIONS: Diffuse hair loss begins shortly after initiation of chemotherapy, mainly as anagen effluvium, with a proportion of anagen to telogen conversion. Hair loss is most prominent after 6 weeks of chemotherapy. Within 3 months after cessation of chemotherapy, hair growth rate returns to baseline values. Tamoxifen did not affect hair growth parameters.

Postnatal maturation of skin barrier function in premature infants.

BACKGROUND: In preterm infants, skin barrier maturation entails regional variability. OBJECTIVES: To characterize postnatal skin barrier development in covered, uncovered and diapered regions in healthy premature infants over a longitudinal observation period. METHODS: Transepidermal water loss (TEWL), stratum corneum hydration (SCH), pH and sebum were measured at postnatal ages of 1-7 days and 2-7 weeks on the forehead, abdomen, thigh and buttock of preterm infants (gestational age 30-37 weeks; n = 48) under monitored ambient conditions. A standard minimal skin care regimen was practised. RESULTS: TEWL increased significantly on the buttock (p = 0.007), while remaining stable on the forehead, abdomen and thigh. SCH and sebum remained stable in all studied body regions with increasing age. On the buttock, pH increased (p = 0.049), while other body regions exhibited a significant decrease (p ≤ 0.019). TEWL (p < 0.001) and SCH (p ≤ 0.002) revealed significantly higher values on the buttock, compared to other body regions. CONCLUSIONS: Stable TEWL, SCH and sebum values may indicate a lack of skin barrier maturation. Postnatal decrease in skin pH suggests an adaptation process with acid mantle formation. Differences in skin barrier development were observed between anatomical regions. SCH, TEWL and pH values demonstrated a distinct course in the diaper area, indicating an impaired skin barrier function in this region.

Log-linear modelling of protein dipeptide structure reveals interesting patterns of side-chain-backbone interactions.

It has long been known that the amino-acid sequence of a protein determines its 3-dimensional structure, but accurate ab initio prediction of structure from sequence remains elusive. We gain insight into local protein structure conformation by studying the relationship of dihedral angles in pairs of residues in protein sequences (dipeptides). We adopt a contingency table approach, exploring a targeted set of hypotheses through log-linear modelling to detect patterns of association of these dihedral angles in all dipeptides considered. Our models indicate a substantial association of the side-chain conformation of the first residue with the backbone conformation of the second residue (side-to-back interaction) as well as a weaker but still significant association of the backbone conformation of the first residue with the side-chain conformation of the second residue (back-to-side interaction). To compare these interactions across different dipeptides, we cluster the parameter estimates for the corresponding association terms. This reveals a striking pattern. For the side-to-back term, all dipeptides which have the same first residue jointly appear in distinct clusters whereas for the back-to-side term, we observe a much weaker pattern. This suggests that the conformation of the first residue affects the conformation of the second.

Hierarchical bayesian modeling of pharmacophores in bioinformatics.

One of the key ingredients in drug discovery is the derivation of conceptual templates called pharmacophores. A pharmacophore model characterizes the physicochemical properties common to all active molecules, called ligands, bound to a particular protein receptor, together with their relative spatial arrangement. Motivated by this important application, we develop a Bayesian hierarchical model for the derivation of pharmacophore templates from multiple configurations of point sets, partially labeled by the atom type of each point. The model is implemented through a multistage template hunting algorithm that produces a series of templates that capture the geometrical relationship of atoms matched across multiple configurations. Chemical information is incorporated by distinguishing between atoms of different elements, whereby different elements are less likely to be matched than atoms of the same element. We illustrate our method through examples of deriving templates from sets of ligands that all bind structurally related protein active sites and show that the model is able to retrieve the key pharmacophore features in two test cases.

A generative, probabilistic model of local protein structure.

Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach.

A probabilistic model of RNA conformational space.

The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling procedure. Both are only partly solved problems. Here, we focus on the problem of conformational sampling. The current state of the art solution is based on fragment assembly methods, which construct plausible conformations by stringing together short fragments obtained from experimental structures. However, the discrete nature of the fragments necessitates the use of carefully tuned, unphysical energy functions, and their non-probabilistic nature impairs unbiased sampling. We offer a solution to the sampling problem that removes these important limitations: a probabilistic model of RNA structure that allows efficient sampling of RNA conformations in continuous space, and with associated probabilities. We show that the model captures several key features of RNA structure, such as its rotameric nature and the distribution of the helix lengths. Furthermore, the model readily generates native-like 3-D conformations for 9 out of 10 test structures, solely using coarse-grained base-pairing information. In conclusion, the method provides a theoretical and practical solution for a major bottleneck on the way to routine prediction and simulation of RNA structure and dynamics in atomic detail.

Effect of Raja yoga meditation on the lipid profile of post-menopausal women.

BACKGROUND: Coronary artery disease is an important cause of death and disability among older women. Modification in lipid profile lowers the risk of coronary artery disease. It is claimed that yoga and transcendental meditation have a cholesterol lowering effect. This study was designed to assess the effect of raja yoga meditation of Brahmakumaris which is very simple to practice, on serum lipids in normal Indian women. METHODS AND RESULTS: 49 normal female volunteers were the subjects. They were divided into pre-menopausal (n=23) and post-menopausal (n=26) groups. They were further divided into non-meditators (who had never done any kind of meditation), short-term meditators (meditating for 6 months to 5 years) and long-term meditators (meditating for more than 5 years). Lipid profile was assessed using their respective reagent sets. Serum cholesterol, triglyceride and low-density lipoprotein-cholesterol in nonmeditators were significantly more in post-menopausal women as compared to pre-menopausal women. Serum cholesterol and low density lipoprotein-cholesterol were significantly lowered in both short and long term meditators as compared to non-meditators in post-menopausal women. No significant difference was observed in lipid profile in pre-menopausal women. CONCLUSION: Raja yoga meditation lowered serum cholesterol and low-density lipoprotein-cholesterol in post-menopausal women thus reducing the risk of coronary artery disease in them.

Bayesian refinement of protein functional site matching.

BACKGROUND: Matching functional sites is a key problem for the understanding of protein function and evolution. The commonly used graph theoretic approach, and other related approaches, require adjustment of a matching distance threshold a priori according to the noise in atomic positions. This is difficult to pre-determine when matching sites related by varying evolutionary distances and crystallographic precision. Furthermore, sometimes the graph method is unable to identify alternative but important solutions in the neighbourhood of the distance based solution because of strict distance constraints. We consider the Bayesian approach to improve graph based solutions. In principle this approach applies to other methods with strict distance matching constraints. The Bayesian method can flexibly incorporate all types of prior information on specific binding sites (e.g. amino acid types) in contrast to combinatorial formulations. RESULTS: We present a new meta-algorithm for matching protein functional sites (active sites and ligand binding sites) based on an initial graph matching followed by refinement using a Markov chain Monte Carlo (MCMC) procedure. This procedure is an innovative extension to our recent work. The method accounts for the 3-dimensional structure of the site as well as the physico-chemical properties of the constituent amino acids. The MCMC procedure can lead to a significant increase in the number of significant matches compared to the graph method as measured independently by rigorously derived p-values. CONCLUSION: MCMC refinement step is able to significantly improve graph based matches. We apply the method to matching NAD(P)(H) binding sites within single Rossmann fold families, between different families in the same superfamily, and in different folds. Within families sites are often well conserved, but there are examples where significant shape based matches do not retain similar amino acid chemistry, indicating that even within families the same ligand may be bound using substantially different physico-chemistry. We also show that the procedure finds significant matches between binding sites for the same co-factor in different families and different folds.

Inference of structure ensembles of flexible biomolecules from sparse, averaged data.

We present the theoretical foundations of a general principle to infer structure ensembles of flexible biomolecules from spatially and temporally averaged data obtained in biophysical experiments. The central idea is to compute the Kullback-Leibler optimal modification of a given prior distribution τ(x) with respect to the experimental data and its uncertainty. This principle generalizes the successful inferential structure determination method and recently proposed maximum entropy methods. Tractability of the protocol is demonstrated through the analysis of simulated nuclear magnetic resonance spectroscopy data of a small peptide.

BAYESIAN ALIGNMENT OF SIMILARITY SHAPES.

We develop a Bayesian model for the alignment of two point configurations under the full similarity transformations of rotation, translation and scaling. Other work in this area has concentrated on rigid body transformations, where scale information is preserved, motivated by problems involving molecular data; this is known as form analysis. We concentrate on a Bayesian formulation for statistical shape analysis. We generalize the model introduced by Green and Mardia for the pairwise alignment of two unlabeled configurations to full similarity transformations by introducing a scaling factor to the model. The generalization is not straight-forward, since the model needs to be reformulated to give good performance when scaling is included. We illustrate our method on the alignment of rat growth profiles and a novel application to the alignment of protein domains. Here, scaling is applied to secondary structure elements when comparing protein folds; additionally, we find that one global scaling factor is not in general sufficient to model these data and, hence, we develop a model in which multiple scale factors can be included to handle different scalings of shape components.