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Differential Gene Expression in Healing and Non-Healing Diabetic Foot Ulcers and Discovery of Novel Ratiometric Biomarker to Predict Healing Outcome
No full textDiabetic foot ulcers (DFUs) are common and difficult to treat because the mechanisms behind unsuccessful responses to treatment are poorly understood. The goals of this study were to identify differences in healing and non-healing human DFUs using debrided tissue samples and to identify possible biomarkers of non-healing. First, DFU tissue samples collected over 12 weeks of treatment from 27 subjects (n = 12 healing and n = 15 non-healing) were analysed using a focused panel of 227 inflammation and wound healing-related human genes and 16S ribosomal RNA amplicon sequence to identify microbial species. Gene expression and correlation with microbial species differed between healing and non-healing DFUs. While no individual genes analysed at the initial time point could accurately predict healing outcome 12 weeks later, several 2-gene ratios were highly accurate. The ratio of C3AR1/CCL22 predicted healing outcome in the discovery cohort with an area under the receiver operator characteristic (ROC) curve (AUC) of 0.96. The AUC was 0.80 when tested on 74 unique samples collected at later time points from the discovery cohort, and the AUC was 0.69 when validated in a completely independent cohort of n = 51 subjects and using quantitative reverse transcription polymerase chain reaction (qRTPCR) as a more translational method of detection. The AUC increased to 0.75 when initial wound area was included. Overall, the results suggest that differences in inflammation contribute to differential healing outcomes in human chronic DFUs, and associated biomarkers may be used to predict healing outcome to guide treatment decisions
Taking an Intersectional Approach to Diversity, Equity, and Inclusion Interventions and Policies
No full textIntersectionality, or the interconnected nature of social identities that create overlapping systems of advantage and disadvantage, is a rapidly expanding field of interest in the social sciences. However, there has been limited application of intersectionality to practical solutions regarding diversity, equity, and inclusion (DEI) in the workplace. In this article, we outline some common DEI challenges that organizations often try to address (e.g., harmful negative stereotypes of minoritized groups), discussing how intersectionality makes these challenges more nuanced. We then consider proven DEI initiatives and policies (e.g., data gathering and DEI goal transparency) and articulate how an intersectional lens can increase their effectiveness. We include broad recommendations for integrating intersectionality into DEI interventions
Second Life: Having a Child in the Digital Age [book review]
No full textSecond Life: Having a Child in the Digital AgeAmanda Hess Doubleday, 2025, 272 page
Flipping the script: Predicting chemical composition in metal-halide perovskites from optical spectroscopy
No full textUsing Artificial Neural Networks to Simulate Social Category Learning: A Tutorial
No full textThis tutorial introduces the use of connectionist (or artificial neural net-work) computational modeling to understand mechanisms for social psychological phenomena. We explain how modeling requires researchers to make explicit their assumptions and make concrete their operationalization for hypothesized mechanisms. The tutorial walks readers through the conceptual steps of how to design an artificial neural network based on Lei et al. (2020) and how to implement key theoretical assumptions.The tutorial is also accompanied by a detailed and commented technical guide available on the Open Science Framework. The model replicated key findings from past behavioral data and provides support for hypothesized theoretical assumptions. Implications for how and why researchers might incorporate modeling into their own research and theorizing are discusse
The Soviet Union and the Construction of the Global Market: Energy and the Ascent of Finance in Cold War Europe, 1964–1971 [book review]
No full text
Charge regulation in peptide self-assembly and hydrogelation
No full textThe peptide Ac-KGSFSIQYTYHVD-CONH₂ (KD), derived from residues 37–49 of human semenogelin I, forms a pH-responsive hydrogel in an aqueous environment with tunable mechanical properties that evolve over time. We hypothesize that KD self-assembles into a hydrogel through a pH-dependent mechanism involving predominantly a change in histidine protonation state, leading to structural transformations that modulate its mechanical properties. Time-resolved nuclear magnetic resonance (NMR) spectroscopy and cryo-transmission electron microscopy (cryo-TEM) were employed to elucidate the gelation process and structural evolution of KD. pH measurements were conducted to monitor changes in peptide interactions during self-assembly. Rheological studies, including oscillatory and stationary rheology, were performed to assess the mechanical properties of the hydrogel under varying pH conditions. A gradual pH drift was observed, associated with a modulation of the ionizable histidine side chain pKa as KD assembled into β-sheet fibrils, integrating into the hydrogel network. Cryo-TEM analysis revealed two distinct nanostructural morphologies: fibrils and twisted curly nanostructures with uniform dimensions, demonstrating micro- and nanoscale transformations over time. Rheological measurements indicated a substantial increase in the elastic modulus as the pH shifted, confirming the dynamic tunability of the hydrogel. Under buffered conditions, KD rapidly formed hydrogels within the experimental dead time, indicating its quick responsiveness to environmental changes. These results provide mechanistic insights into the time-dependent self-assembly of KD and highlight its potential as a pH-tunable hydrogel for therapeutic applications, paving the way for the rational design of next-generation peptide-based biomaterials
