Background Melasma continues to be a disease that is difficult to treat with no fully satisfactory results. The role of a fractional CO2 laser in its treatment is controversial. The addition of tranexamic acid (TXA) might be helpful. Objectives To assess the efficacy of a low‐power fractional CO2 laser alone versus its combination with tranexamic acid used either topically or intradermally for melasma treatment. Methods A randomized comparative split‐face study included a total of thirty female patients with bilateral, symmetrical melasma. The whole face was subjected to treatment via a low‐power (12 Watts) fractional ablative CO2 laser. One side was randomly assigned to topical application of tranexamic acid solution after the session immediately or intradermal microinjection of tranexamic acid prior to the laser session. Sessions were conducted every 4–6 weeks for five consecutive sessions. Assessments were done using the melasma area severity index MASI score, melanin index (MI), and erythema index (EI) before sessions and 2 weeks after the final session. Results After treatment, there was significant reduction in the MASI score on both sides of the face; the side treated with the fractional CO2 laser alone and the side treated with fractional CO2 laser combined with TXA (topically or intradermal injection) (P‐values 0.007, <0.001, and 0.016, respectively). MI was significantly lower on the side receiving fractional CO2 laser alone and the side receiving fractional CO2 laser combined with intradermal injection of TXA (P‐values <0.001 and 0.003, respectively), while the EI showed significant improvement only on the side receiving fractional CO2 laser alone (P‐value = 0.023). Although patients reported no differences in improvement on either treated side, the degree of improvement regarding the MASI score was better on the side receiving fractional CO2 laser alone. Regarding MI, the degree of improvement was higher on the side receiving fractional CO2 laser combined with intradermal injection of TXA than on the side receiving fractional CO2 laser alone; however, this improvement did not reach statistical significance. Minimal complications occurred in the form of mild pain. Conclusion A low‐power fractional CO2 laser is an effective, safe treatment for melasma. However, the addition of tranexamic acid (either topically or intradermally) to a fractional CO2 laser should be further studied. Lasers Surg. Med. 51:27–33, 2019. © 2018 Wiley Periodicals, Inc.

IgG3 is the IgG subclass with the strongest effector functions among all four IgG subclasses and the highest degree of allelic variability among all constant immunoglobulin genes. Due to its genetic position, IgG3 is often the first isotype an antibody switches to before IgG1 or IgG4. Compared with the other IgG subclasses, it has a reduced half-life which is probably connected to a decreased affinity to the neonatal Fc receptor (FcRn). However, a few allelic variants harbor an amino acid replacement of His435 to Arg that reverts the half-life of the resulting IgG3 to the same level as the other IgG subclasses. Because of its functional impact, we hypothesized that the p.Arg435His variation could be associated with susceptibility to autoantibody-mediated diseases like pemphigus vulgaris (PV) and bullous pemphigoid (BP). Using a set of samples from German, Turkish, Egyptian, and Iranian patients and controls, we were able to demonstrate a genetic association of the p.Arg435His variation with PV risk, but not with BP risk. Our results suggest a hitherto unknown role for the function of IgG3 in the pathogenesis of PV.

Replicating the association of single-nucleotide polymorphisms in the TNFAIP3, IL12B and IL23R genes with psoriasis vulgaris, in subjects from different ethnic backgrounds, underlines their importance in the pathogenesis of the disease. In contrast, the lack of any association between rs20541 (IL13) and psoriasis in our Egyptian cohort suggests the existence of important inter-ethnic genetic differences in psoriasis susceptibility.

Psoriasis is a chronic inflammatory disorder of the skin, with genetic factors reportedly involved in the disease pathogenesis. Numerous studies reported psoriasis candidate genes. However, these tend to involve mostly in the European and Asian populations. Here, we report the first genome‐wide association study (GWAS) in an Egyptian population, identifying susceptibility variants for psoriasis using a two‐stage case‐control design. In the first discovery stage, we carried out a genome‐wide association analysis using the Infinium® Global Screening Array‐24 v1.0, on 253 cases and 449 control samples of Egyptian descent. In the second replication stage, 26 single‐nucleotide polymorphisms (SNPs) were selected for replication in additional 321 cases and 253 controls. In concordance with the findings from previous studies on other populations, we found a genome‐wide significant association between the MHC locus and the disease at rs12199223 (Pcomb = 6.57 × 10−18) and rs1265181 (Pcomb = 1.03 × 10−10). Additionally, we identified a novel significant association with the disease at locus, 4q32.1 (rs12650590, Pcomb = 4.49 × 10−08) in the vicinity of gene GUCY1A3, and multiple suggestive associations, for example rs10832027 (Pcomb = 7.28 × 10−06) and rs3770019 (Pcomb = 1.02 × 10−05). This proposes the existence of important interethnic genetic differences in psoriasis susceptibility. Further studies are necessary to elucidate the downstream pathways of the new candidate loci.

Data annotation is a critical step to train a text model but it is tedious, expensive and time-consuming. We present a language independent method to train a sentiment polarity model with limited amount of manuallylabeled data. Word embeddings such as Word2Vec are efficient at incorporating semantic and syntactic properties of words, yielding good results for document classification. However, these embeddings might map words with opposite polarities, to vectors close to each other. We train Sentiment Specific Word Embeddings (SSWE) on top of an unsupervised Word2Vec model, using either Recurrent Neural Networks (RNN) or Convolutional Neural Networks (CNN) on data auto-labeled as "Positive" or "Negative". For this task, we rely on the universality of emojis and emoticons to auto-label a large number of French tweets using a small set of positive and negative emojis and emoticons. Finally, we apply a transfer learning approach to refine the network weights with a small-size manuallylabeled training data set. Experiments are conducted to evaluate the performance of this approach on French sentiment classification using benchmark data sets from SemEval 2016 competition. We were able to achieve a performance improvement by using SSWE over Word2Vec. We also used a graph-based approach for label propagation to auto-generate a sentiment lexicon.