If not angioedema, what is it? Diagnostic approach to facial edema.

Facial edema is a relatively frequent clinical presentation encountered in patients seen in allergology and dermatology clinics. The differential diagnosis is broad, and sometimes the definitive diagnosis can be a challenge for the clinician. Facial angioedema itself encompasses different etiopathologies (histaminergic, bradykinergic, etc.) that must be distinguished from other causes of facial edema, such as allergic contact dermatitis, granulomatous conditions, inflammatory causes, infections, neoplasms or paraneoplastic syndromes, autoimmune diseases, among other entities hereby referred as miscellanea. A proper diagnostic approach is essential to order the appropriate tests, as well as to prescribe a targeted treatment. This review focuses on entities that present with facial edema and summarize their characteristic clinical features.

Multiple minute digitate hyperkeratosis: 10 years from Caccetta's algorithm. A case report and narrative review.

To date, the clinical appearance and histological features of multiple minute digitate hyperkeratosis have been well characterized. However, there is no consensus on its treatment. After a comprehensive search of the databases MEDLINE, EMBASE, Web of Science, and the Cochrane Library and Database of Systematic Reviews, we have summarized the available clinical evidence regarding the therapeutic approaches already reported for this entity since its first description in 1967. Additional publications were identified within the references of retrieved papers. Sixty-five articles have been revised, resulting in a total of 73 compatible cases. The histopathological features and different classifications used through history have also been considered, updating and completing the available knowledge. Ultimately, we propose topical treatment with 5 % 5-fluorouracil formulated with 10 % salicylic acid as a potential treatment that has been used successfully in a 51-year-old woman at our facility. Further research in form of prospective or comparative studies is encouraged for a better conceptualization of the therapeutics of this disease.

Night work, chronotype and risk of endometrial cancer in the Screenwide case-control study.

BACKGROUND: Circadian disruption caused by night work has been associated with hormonal-related cancers such as breast and prostate cancer. Data on the role of circadian factors in the aetiology of endometrial cancer, an oestrogen-associated cancer, are scarce. METHODS: We examined the association between endometrial cancer and night shift work, chronotype (a characteristic correlating with preference for morning or evening activity) and sleep duration, in 180 incident cases and 218 hospital controls. Participants were interviewed face-to-face by trained interviewers to collect information on sociodemographic factors, familial, medical, occupational history (including work shifts), sleep duration and chronotype, and other lifestyle factors. We used logistic regression models adjusted for potential confounders to estimate ORs and 95% CIs. RESULTS: After adjustment by potential confounders, we found an inverse not statistically significant association between ever worked in night shifts and endometrial cancer (OR=0.64; 95% CI=0.35 to 1.16). Associations were irrespective of shift type (permanent or rotating nights) or duration of night work. We did not observe any statistically significant association between endometrial cancer and sleep duration, while inconsistent patterns were observed for chronotype and endometrial cancer risk. CONCLUSIONS: These data do not support a role for circadian disruption in the carcinogenesis of endometrial cancer.

Prediction of unconventional protein secretion by exosomes.

MOTIVATION: In eukaryotes, proteins targeted for secretion contain a signal peptide, which allows them to proceed through the conventional ER/Golgi-dependent pathway. However, an important number of proteins lacking a signal peptide can be secreted through unconventional routes, including that mediated by exosomes. Currently, no method is available to predict protein secretion via exosomes. RESULTS: Here, we first assembled a dataset including the sequences of 2992 proteins secreted by exosomes and 2961 proteins that are not secreted by exosomes. Subsequently, we trained different random forests models on feature vectors derived from the sequences in this dataset. In tenfold cross-validation, the best model was trained on dipeptide composition, reaching an accuracy of 69.88% ± 2.08 and an area under the curve (AUC) of 0.76 ± 0.03. In an independent dataset, this model reached an accuracy of 75.73% and an AUC of 0.840. After these results, we developed ExoPred, a web-based tool that uses random forests to predict protein secretion by exosomes. CONCLUSION: ExoPred is available for free public use at http://imath.med.ucm.es/exopred/ . Datasets are available at http://imath.med.ucm.es/exopred/datasets/ .

Identification of CD8+ T cell epitopes through proteasome cleavage site predictions.

BACKGROUND: We previously introduced PCPS (Proteasome Cleavage Prediction Server), a web-based tool to predict proteasome cleavage sites using n-grams. Here, we evaluated the ability of PCPS immunoproteasome cleavage model to discriminate CD8+ T cell epitopes. RESULTS: We first assembled an epitope dataset consisting of 844 unique virus-specific CD8+ T cell epitopes and their source proteins. We then analyzed cleavage predictions by PCPS immunoproteasome cleavage model on this dataset and compared them with those provided by a related method implemented by NetChop web server. PCPS was clearly superior to NetChop in term of sensitivity (0.89 vs. 0.79) but somewhat inferior with regard to specificity (0.55 vs. 0.60). Judging by the Mathew's Correlation Coefficient, PCPS predictions were overall superior to those provided by NetChop (0.46 vs. 0.39). We next analyzed the power of C-terminal cleavage predictions provided by the same PCPS model to discriminate CD8+ T cell epitopes, finding that they could be discriminated from random peptides with an accuracy of 0.74. Following these results, we tuned the PCPS web server to predict CD8+ T cell epitopes and predicted the entire SARS-CoV-2 epitope space. CONCLUSIONS: We report an improved version of PCPS named iPCPS for predicting proteasome cleavage sites and peptides with CD8+ T cell epitope features. iPCPS is available for free public use at https://imed.med.ucm.es/Tools/pcps/ .