Wright State University

Prurigo nodularis (PN) is a chronic, pruritic skin disorder characterized by significant morbidity and historically limited treatment options. The recent approval of biologic therapies has rapidly changed the treatment landscape, with dupilumab and nemolizumab now Food and Drug Administration (FDA)-approved for PN. This article provides a clinically relevant overview of completed randomized trials, highlighting differences in efficacy, timing of response, and symptom targets. Data were extracted from six compound randomized clinical trials (two dupilumab trials and four nemolizumab trials) identified on ClinicalTrials.gov through January 2025. Across phase 3 studies, dupilumab demonstrated sustained reductions in itch severity and lesional disease, with approximately 58-60% of patients achieving a ≥ 4-point improvement in Worst Itch Numeric Rating Scale (WI-NRS) scores and 45-48% achieving Investigator Global Assessment for Prurigo Nodularis-Stage (IGA PN-S) success by week 24. Nemolizumab showed a more rapid onset of antipruritic effect, with 41-56% of patients achieving a ≥ 4-point improvement in Peak Pruritus Numeric Rating Scale (PP-NRS) scores as early as week 4, along with clinically meaningful improvements in sleep disturbance by week 16. Both therapies demonstrated manageable safety profiles, with predominantly mild to moderate adverse events and low rates of serious adverse events, although nemolizumab was associated with higher overall rates of mild to moderate adverse events. These findings suggest that dupilumab may offer greater lesional clearance at later time points, whereas nemolizumab may provide faster itch relief and less sleep disturbance. These findings support a phenotype-guided approach to biologic selection while highlighting the need for further comparative studies.

ABSTRACT The rapid advancement of artificial intelligence (AI) is transforming the landscape of behavioral health, inviting systemic therapists to reconsider their professional identities, competencies, and ethical responsibilities. This paper presents a reflective dialogue between Dr. Afarin Rajaei and Dr. Katherine Hertlein, exploring how AI intersects with relational practice, professional training, and clinical ethics. Drawing upon Hertlein’s emerging conceptual framework for AI competence in systemic therapy, the authors discuss the knowledge, ethical awareness, and relational sensibilities necessary for responsible integration of AI in clinical and educational contexts. Their exchange illuminates both the opportunities and cautions of AI adoption, emphasizing that systemic therapists must remain reflexive human agents guiding technology toward compassionate and equitable care.

The Nyquist Folding Receiver is an architecture that uses Compressed Sensing to convert analog radio frequency signals into digital signals. Analog-to-Digital Converter architectures that implement Compressed Sensing are collectively known as Analog-to-Information. Sparse bandlimited analog signals with frequency bands above the Nyquist frequency of a traditional Analog-to-Digital Converter can be recovered by Analog-to-Information receivers. Recovery of these signals is affected by the selection of a Compressed Sensing recovery algorithm. Typical recovery algorithms selected for recovery of Nyquist Folding Receiver-compressed outputs use iterative methods to find the solution. This work presents a machine learning approach to signal reconstruction. The proposed method uses a neural network to learn the mapping from compressed samples to the original signal. The neural network is trained on a set of synthetic signals generated by a new open-source Analog-to-Information simulator called SpectraMelt. The results show that the neural network can effectively reconstruct the original signal from the compressed samples, achieving better performance than traditional iterative methods.

Abstract Activating mutations in KRAS occur at high frequency in colorectal, lung, and pancreatic cancers, which together account for a substantial proportion of global cancer mortality. Mutant KRAS is constitutively biased toward the GTP-bound state, driving persistent proliferative signaling, but simultaneously imposes oncogenic stresses that threaten cellular homeostasis. To sustain transformation, KRAS-mutant cells engage adaptive stress-response mechanisms, many of which converge on translational control mediated by the eIF2-eIF2B axis. While eIF2B is classically known as a guanine nucleotide exchange factor (GEF) for eIF2 during translation initiation, its potential role in directly regulating oncogenic signaling pathways has remained unexplored. Here, we identify a non-canonical function of eIF2B as a direct activator of mutant KRAS signaling. We demonstrate that eIF2B forms a tripartite complex with SOS and mutant KRAS at the plasma membrane (PM), thereby enhancing KRAS activation and tumorigenic signaling. Biochemical assays and structural modeling support an interaction between the catalytic ε subunit of eIF2B and the allosteric Ras-binding site of SOS, stabilizing SOS in an active conformation. This interaction potentiates SOS-mediated GDP/GTP exchange on mutant KRAS and promotes KRAS nanoclustering at the PM. Importantly, eIF2B exhibits marked specificity for mutant KRAS4B, but not KRAS4A, HRAS, or NRAS. This selectivity arises from KRAS4B’s unique polybasic membrane-anchoring domain and from eIF2B-dependent remodeling of plasma membrane lipid composition. eIF2B enhances glycosphingolipid (GSL) biosynthesis, particularly GM3 and SM4, through translational upregulation of B4GALT5, generating a membrane environment that preferentially supports mutant KRAS4B anchoring and signaling. Disruption of GSL synthesis impairs formation of the eIF2B:SOS:KRAS complex and selectively reduces mutant KRAS activation. Notably, eIF2B’s stimulation of mutant KRAS signaling occurs independently of eIF2α phosphorylation, separating its translational stress-response function from its oncogenic signaling role. Functionally, eIF2B promotes tumor growth specifically in KRAS-mutant cancer models, including human xenografts and an autochthonous KRAS G12C-driven lung adenocarcinoma model. Clinically, high expression of eIF2Bε correlates with poorer outcomes in patients with KRAS-mutant tumors. Our findings identify eIF2B as a previously unrecognized regulator of mutant KRAS-driven tumorigenesis that links translational control, membrane lipid remodeling, and oncogenic signaling. By coordinating SOS activation, KRAS membrane nanoclustering, and selective translation, eIF2B emerges as a central modulator of KRAS oncogenic output and a potential therapeutic and prognostic target. In vivo targeting of eIF2B supports its use as a combinatorial strategy with KRAS inhibition to broaden the therapeutic window in KRAS-mutant cancers. This abstract was edited and refined with the assistance of generative artificial intelligence to improve clarity and conciseness. Citation Format: Hyungdong Kim, Shiqi Diao, Kwang-Jin Cho, Hyun-Ro Lee, Junchen Liu, Pascal Egea, Tatu Pantsar, Milla Kurki, Nour Ghaddar, Shuo Wang, Jia Yi Zou, Mehdi Amiri, Ritchel Gannaban, John F. Hancock, Kylie M. Rice, Atsuo Sasaki, John Asara, Brajendra Tripathi, Douglas Lowy, Rosalie Lawrence, Maria Hatzoglou, Carlos R. Azpilcueta-Nicolas, Jean-Philip Lumb, John Columbus, Thomas J. Turbyville, Christopher B. Marshall, Mitsuhiko Ikura, Jay T. Groves, Nahum Sonenberg, Peter Walter, Antonis E. Koromilas. eIF2B Selectively Anchors and Activates Mutant KRAS4B [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RAS Oncogenesis and Therapeutics; 2026 Mar 5-8; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(5_Suppl_1):Abstract nr B001.

Few approved treatments exist for children with Crohn's disease (CD). The REALITI study retrospectively assessed the effectiveness and safety of ustekinumab in real-world clinical settings for children with CD. Data were collected from the prospective ImproveCareNow (ICN) registry for pediatric patients (≥ 2 to <18 years old) and young adult patients (≥ 18 to <26 years old), regardless of baseline CD severity. Additional analyses were conducted for a subset of patients who had moderately-to-severely active CD (short pediatric CD activity index [sPCDAI] ≥30). Key Week-52 endpoints included clinical remission (sPCDAI ≤10) and corticosteroid-free (CF) clinical remission. Safety events of interest were assessed at Week 52. Overall, 479 patients with CD were treated with ustekinumab, 348 pediatric patients and 131 young adults; most were biologic-exposed (pediatric, 98.9%; young adult, 95.4%). At Week 52 (observed case; excluding patients without Week 52 data), clinical remission was achieved by 47.3% (125/264) of pediatric patients and 44.8% (39/87) of young adults, and CF clinical remission by 41.3% (109/264) and 39.1% (34/87), respectively. At Week 52 (observed case), among patients with moderately-to-severely active CD, clinical remission was achieved by 36.9% (41/111) of pediatric patients and 34.3% (12/35) of young adults, and CF clinical remission by 31.5% (35/111) and 28.6% (10/35), respectively. Ustekinumab was well tolerated, with no new safety signals identified. In the REALITI study of real-world data from the ICN registry, the effectiveness and safety of ustekinumab treatment through 52 weeks were similar in pediatric and young adult patients with CD. ClinicalTrials.gov identifier: NCT05242458; https://clinicaltrials.gov/study/NCT05242458.

This commentary highlights the transformative journey of implementing a flipped, problem-based learning curriculum within an internal medicine residency program. Initially met with resistance from both faculty and residents accustomed to traditional lecture-based learning, the experience underscored the critical role of leadership in driving culture change. Despite the challenge, a comprehensive redesign, including 75 new PBL sessions and faculty development, yielded significant improvements: higher in-training exam scores and increased first-time board pass rates. The key takeaway emphasizes that successful curricular reform in graduate medical education requires a commitment to active learning, investment in faculty facilitation skills, and a data-driven approach to demonstrate positive outcomes. Shifting from lecture-based to active learning environments empowers learners and prepares them for self-directed learning and independent practice.

Developing effective therapies for COVID-19-triggered acute respiratory distress syndrome (ARDS) remains a major challenge. Inflammatory signaling molecules have emerged as promising druggable targets. This study employed an integrated network pharmacology and molecular modeling strategy to investigate the anti-inflammatory phytoconstituents (PCs) against COVID-19-triggered ARDS. Putative targets of PCs and disease genes were retrieved from multiple databases, followed by PPI and enrichment analyses, along with network visualization. Network pharmacology identified key phytoconstituents: 1-dehydro-6-gingerol, apigenin-7-glucoside, and quercetin, and crucial targets including EGFR, JAK2, RELA, HSP90AA1, and PIK3CA from the F-B-PCs-T-P network. Enriched inflammatory pathways included Toll-like receptor, PI3K-Akt, NOD-like receptor, and HIF-1 signaling. Molecular docking and dynamics simulations further confirmed stable, high-affinity interactions between these targets and selected PCs. Specifically, apigenin-7-glucoside showed strong binding with JAK2 and RELA, while quercetin favoured JAK2 and EGFR. Overall, the findings underscore the therapeutic potential of these PCs and validate integrated in silico approaches (in) drug discovery. A strategy to identify potential phytoconstituents and targets for treating COVID-19-triggered ARDS. ● Network pharmacology identified EGFR, JAK2, RELA, HSP90AA1, and PIK3CA as crucial targets in COVID-19 triggered ARDS. ● Apigenin-7-glucoside showed strong binding affinity with JAK2 (–10.0 kcal/mol) and RELA (–8.0 kcal/mol). ● Quercetin exhibited favourable interactions with JAK2 (–9.3 kcal/mol) and EGFR (–8.5 kcal/mol). ● Repurposing apigenin-7-glucoside and quercetin as potential therapeutic candidates for COVID-19 triggered ARDS.

ABSTRACT Linear models in recession analysis have been widely used, particularly at larger scales and longer times, to make accurate predictions of streamflow recession. However, their derivation has been somewhat impenetrable until now, since water flowing out of the soil of a catchment and into streams flows out of an area, but water stored in the soil is stored in a volume. This mismatch in units, as given in Euclidean geometry, requires a relationship that has a length scale included and which makes the flow a non‐linear function of the volume. However, in percolation theory, the surface area has two terms: one that is proportional to the volume V as well as the customary Euclidean term proportional to V 1−1/ d . Here, d = 2 or 3 is used as a generalisation of 3, in case the vertical dimension of the flow in the catchment can be considered negligibly thin in comparison with the scale of the catchment heterogeneity. Then, the Euclidean term is proportional to V 1−1/2 = V 1/2 . The term linear in V must dominate at large volumes or areas in either case, since its dependence on length is to a larger power than the Euclidean surface area. Thus, identification of the principal flow paths as near the percolation threshold automatically concentrates flow across a boundary as having the same units as the interior, making Q proportional to S and d Q /d t proportional to d S /d t . The mass conservation constraint then makes d S /d t proportional to − Q in low flow regimes without P or ET, generating the ‘linear’ model, d Q /d t proportional to − Q . Results are tested against known recession constants for Iowa streams.

Previous research has shown that tendon graft soaking in vancomycin or tobramycin solution has no negative effects on graft mechanical properties, but there are no studies that have investigated graft mechanical properties after soaking grafts in gentamicin. Additionally, nearly all published biomechanical studies are based on data collected from a mechanical load frame or strain gauge, which does not provide insight on local graft strains compared with 3-dimensional digital image correlation (3D-DIC). The purpose of this study was to use 3D-DIC to investigate the effects of vancomycin, tobramycin, and gentamicin soaking on tendon graft mechanical properties. It was hypothesized that (1) no significant difference in mechanical properties exists between the saline control, vancomycin, tobramycin, and gentamicin groups and (2) local graft strain at the graft failure location will be greater than global strain spanning the entire graft length. Controlled laboratory study. Human tibialis anterior, peroneus longus, and tibialis posterior tendon grafts were prepared and evenly separated into 4 groups: control, vancomycin (5.0 mg/mL), tobramycin (1.0 mg/mL), and gentamicin (0.8 mg/mL). Grafts were soaked in antibiotic solution for 10 minutes, then removed and painted via airbrush with water-based black paint. Uniaxial tension testing was then completed at a strain rate of 10 mm/min. Data collected were used to calculate Young modulus (YM), elasticity limit (EL), ultimate tensile strength (UTS), and failure strain (FS). There were no significant differences in YM (P = .49), EL (P = .62), UTS (P = .98), and FS (P = .14) between control, vancomycin, tobramycin, and gentamicin, respectively. Additionally, local strain at graft failure location was larger than global strain across the length of the graft. Soaking tendon grafts in vancomycin, tobramycin, or gentamicin does not alter the mechanical properties of grafts under uniaxial loading. If vancomycin use is not possible or is contraindicated for certain patients, surgeons can soak grafts in tobramycin or gentamicin to achieve similarly effective infection mitigation without weakening the graft.

The growing dependence on third-party foundries for integrated circuit (IC) fabrication has created major security concerns because of hardware Trojan (HT) insertion risks. Traditional detection methods, including side-channel analysis and golden reference models, face limitations such as sensitivity to noise, high cost, and impracticality for large-scale deployment. This work introduces a machine learning framework for HT detection that eliminates the need for golden references. The framework automatically extracts statistical features from chip data, groups chips into clusters, and uses an internal filtering process to identify the most reliable patterns. These patterns are then used to guide a learning model that can accurately separate Trojan-infected chips from clean ones. Experimental evaluation demonstrates that the proposed method achieves high detection accuracy with zero false negatives, while remaining resilient against adversarial perturbations. These findings indicate that cluster-filtered pseudo-labeling provides a practical and scalable solution for enhancing hardware security in modern IC supply chains.