Docking Peptides on Proteins: How to Open a Lock, in the Dark, with a Flexible Key

In this issue of Structure, Schindler et al. (2015b) present us with pepATTRACT, a protocol embedded in the ATTRACT docking engine for fully blind flexible peptide docking on proteins that yields high quality models of complexes

Spotlight On State of Food Hardship in New York City: Lessons learned during the pandemic and where we go from here

COVID-19 pandemic, with more than one in three New Yorkers sometimes or often running out of food or worrying that food would run out before they had money to buy more. The pandemic brought new and devastating challenges in quick succession, with half of New Yorkers losing work-related income at the peak of the pandemic, not knowing how they would make rent or keep food on the table, or when things would get back to "normal."In the face of uncertainty, actions were taken at federal, state, and local levels to stabilize income and provide a buffer against new experiences of material hardship. These included the substantial expansion of the unemployment insurance program, stimulus payments, the increase in Supplemental Nutritional Assistance Program (SNAP) payments, and eviction moratoria. 2020 also saw community-based organizations across the city quickly adapt to meet needs and deliver services while maintaining public health guidelines. This included the substantial expansion of emergency food assistance programs, with food pantries changing their hours, protocols, and delivery mechanisms. Data from the Poverty Tracker show that these supply-side changes aligned with an increased demand for food – between 2019 and 2020, the number of families in the Poverty Tracker sample receiving food from a food pantry more than doubled. And among foreign-born New Yorkers, who were less likely to benefit from the federal policy expansions, the number of people using food pantries nearly tripled. This sharp increase suggests that the role of pantries in the lives of New Yorkers changed over the course of the pandemic, and many of these changes may continue to play a role in fighting food hardship through the pandemic recovery

A machine learning study analyzing the association between patient-, tooth- and treatment-level factors on the outcome of root canal therapy

Zielsetzung: Das Erkennen und Bewerten von Risikofaktoren einer Wurzelkanalbehandlung (WKB) stellt einen entscheidenden Schritt bei der Therapieplanung zahnärztlicher Behandler*innen dar. Das maschinelle Lernen (ML) fand innerhalb der letzten Dekade vermehrt auch Anwendung in der Zahnmedizin. Das Ziel unserer Studie war es zum einen, Zusammenhänge zwischen zahn-, patienten- und behandlungsspezifischen Faktoren und der Prognose einer WKB zu detektieren und folgend ein Faktorgewichtungsranking zu erstellen; zum anderen sollte analysiert werden, inwiefern durch die Anwendung komplexer ML Modelle Vorhersagen über den Erfolg oder Misserfolg einer WKB möglich werden. Methodik: Es wurden Patientenfälle untersucht, die im Zeitraum von 2016 bis 2020 eine WKB am CharitéCentrum 03 für Zahn-, Mund- und Kieferheilkunde erhalten hatten und mindestens sechs Monate nachuntersucht worden sind. Bei der Analyse wurden sowohl Patientendaten als auch Röntgenbilder bewertet und nach zuvor festgelegten Kriterien ausgewertet. Ein Misserfolg der Behandlung war definiert durch das Bestehen klinischer Symptome und/oder radiologischer Auffälligkeiten. Mithilfe einer logistischen Regression (logR) wurden Zusammenhänge zwischen den einzelnen Faktoren detektiert. Neben der logR sollten komplexere ML Modelle (Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB)) eingesetzt werden, um Vorhersagen über den Erfolg oder Misserfolg einer WKB in einem Testdatensatz zu treffen. Zur Bewertung der Vorhersagegüte wurden ROC-Kurven (Receiver Operating Characteristic) und AUC-Werte (Area under the curve) eingesetzt. Anschließend wurde auf Basis der relativen Faktorgewichtung der einzelnen Modelle eine modellübergreifende Rangfolge der Faktoren erstellt, um deren Wichtigkeit für die Vorhersage zu beschreiben. Ergebnisse: Insgesamt wurden 591 Zähne von 458 Patient*innen (weiblich n = 216 (47,2%), männlich n = 242 (52,8%)) analysiert. Die Gesamterfolgsrate aller Behandlungen betrug 79,5%. LogR zeigte, dass vor allem zahnbezogene Faktoren einen signifikanten Einfluss auf den Ausgang einer WKB hatten. Die wichtigsten Variablen waren hierbei ein schwerer alveolärer Knochenabbau von 66-100% (OR 6,48; 95% CI [2,86; 14,89], p<0,001) und ein erhöhter Periapikaler Index von größer oder gleich 4 (OR 4,59 [2,44; 8,79], p<0,001). Misserfolge waren auch für Revisionstherapien signifikant häufiger (OR 1,77 [1,01; 2,86], p<0,01). Bei den patientenbezogenen Faktoren war lediglich das Rauchen mit einem Misserfolg einer WKB assoziiert (OR 2,05 [1,18; 3,53], p<0,05). Die Vorhersagegüte der verschiedenen ML Modelle blieb insgesamt stark begrenzt (ROCAUC: logR 0,63 [0,53; 0,73]; GBM 0,59 [0,50; 0,68]; RF 0,59 [0,50; 0,68]; XGB 0,60 [0,50; 0,70]). Schlussfolgerungen: Misserfolge einer WKB waren primär mit zahnbezogenen Faktoren assoziiert. Vorhersagen über den Ausgang einer Behandlung waren auch mit komplexeren ML Modellen nur eingeschränkt möglich.Objective: Identifying potential risk factors of a root canal treatment (RCT) is a crucial step in endodontic treatment planning. Machine learning (ML) was found beneficial for health care applications in recent years; it has also been applied in dentistry. We intended to detect tooth-, patient- and treatment-level covariates associated with the outcome of endodontic therapy, and rank them according to their importance. Additionally, we aimed to apply ML for predicting the outcome of RCT. Methods: We analyzed patients who received one or more RCT with at least six months follow-up at the Charité Dental Clinic between 2016 and 2020. To derive covariates, patient data including medical history and treatment protocols as well as periapical radiographs were employed. Failure was defined as persistent clinical symptoms and/or radiographical signs of persisting or progressing apical periodontitis. By using logistic regression (logR) on the full data set we analyzed associations between covariates and outcomes. LogR and more complex ML models (Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB)) were then trained and their performance for predicting success or failure of root canal therapy assessed on a separate test data. ROC (Receiver Operating Characteristic) curves and AUC (Area under the curve) values were employed to evaluate the predictive performance. Mean rank values were calculated to construct a ranking showing the relative importance of each factor. Results: A total of 591 teeth from 458 patients (female n = 216 (47.2%), male n = 242 (52.8%)) were examined. The overall success rate of root canal treatments was 79.5%. LogR showed that tooth-related covariates were significantly associated with the outcome of root canal therapy, with severe alveolar bone loss (ABL 66-100%) (OR 6.48, 95% CI [2.86, 14.89], p<0.001) and a PAI-Score higher or equal to 4 (OR 4.59, 95% CI [2.44, 8.79], p<0.001) increasing the risk of failure. Retreatments showed similarly increased risks (OR 1.77, 95% CI [1.01, 2.86], p<0.01) and smoking was significantly associated with failure on patient-level (OR 2.05, 95% CI [1.18, 3.53], p<0.05). The predictive performance of all ML models was limited (ROCAUC: logR 0.63 [0.53, 0.73]; GBM 0.59 [0.50, 0.68]; RF 0.59 [0.50, 0.68]; XGB 0.60 [0.50, 0.70]). Conclusions: Failure of root canal therapy was primarily associated with tooth-related factors. In general, predicting the outcome of RCT with ML models was only limitedly possible

Regulation of UGT1A1 and HNF1 transcription factor gene expression by DNA methylation in colon cancer cells

<p>Abstract</p> <p>Background</p> <p>UDP-glucuronosyltransferase 1A1 (UGT1A1) is a pivotal enzyme involved in metabolism of SN-38, the active metabolite of irinotecan commonly used to treat metastatic colorectal cancer. We previously demonstrated aberrant methylation of specific CpG dinucleotides in UGT1A1-negative cells, and revealed that methylation state of the <it>UGT1A1 </it>5'-flanking sequence is negatively correlated with gene transcription. Interestingly, one of these CpG dinucleotides (CpG -4) is found close to a HNF1 response element (HRE), known to be involved in activation of <it>UGT1A1 </it>gene expression, and within an upstream stimulating factor (USF) binding site.</p> <p>Results</p> <p>Gel retardation assays revealed that methylation of CpG-4 directly affect the interaction of USF1/2 with its cognate sequence without altering the binding for HNF1-alpha. Luciferase assays sustained a role for USF1/2 and HNF1-alpha in <it>UGT1A1 </it>regulation in colon cancer cells. Based on the differential expression profiles of <it>HNF1A </it>gene in colon cell lines, we also assessed whether methylation affects its expression. In agreement with the presence of CpG islands in the <it>HNF1A </it>promoter, treatments of UGT1A1-negative HCT116 colon cancer cells with a DNA methyltransferase inhibitor restore <it>HNF1A </it>gene expression, as observed for <it>UGT1A1</it>.</p> <p>Conclusions</p> <p>This study reveals that basal <it>UGT1A1 </it>expression in colon cells is positively regulated by HNF1-alpha and USF, and negatively regulated by DNA methylation. Besides, DNA methylation of <it>HNF1A </it>could also play an important role in regulating additional cellular drug metabolism and transporter pathways. This process may contribute to determine local inactivation of drugs such as the anticancer agent SN-38 by glucuronidation and define tumoral response.</p

Les fondements d'une intervention précoce

ETAP

Reconnaissance des parcours individuels dans une méthodologie de groupe

Les apprenants adultes qui font la démarche de s'inscrire à un cours de FLE en France apportent avec eux leurs projets, leurs habitudes de travail, leurs goûts, qui sont autant de facteurs de diversité dans un groupe-classe. La méthodologie communicative, avec la notion d'analyse des besoins, et plus encore celle de centration sur l'apprenant, se trouve en théorie en adéquation avec cette réalité. Mais elle entre aussi en tension avec les conditions d'un enseignement collectif en milieu institutionnel. C'est à cette interface entre groupe-classe et apprenant singulier, entre classe et milieu extérieur, que se situe la recherche-action dont nous rendons compte ici, et qui se donne pour objectif d'intégrer à une méthodologie de groupe la prise en compte des profils individuels des apprenants.Adult learners who decide to enroll in a French as a Foreign Language Course in France bring with them their projects, their work habits and their tastes, factors which contribute to the diversity of the class. The communicative methodology, with its notion of needs analysis, and particularly that of learner-centeredness, corresponds in theory to this reality. But it also enters into conflict with the conditions of teaching to groups in an institutional setting. The field study presented in this article focuses on the interface between a class grouping and a particular pupil, between the classroom setting and the surrounding environment. Its objective is to integrate into group methodology a consideration of the individual profiles of the learners