Citations

ABSTRACT Motor neuron plasticity and survival depend on several trophic factors, with signaling via the tropomyosin receptor kinase B (TrkB) receptor being particularly important beyond development and across the lifespan. We hypothesized that differences in motor neuron properties may reflect differential trophic influences across motor units within a single motor unit pool. Based on extensive previous characterization of rat phrenic motor neurons (PhMN), somal surface area was used as an indicator of motor unit type (smaller neurons innervating slow‐twitch, fatigue‐resistant units versus larger neurons innervating fast‐twitch, fatigable units). To examine whether TrkB expression reflects these size‐related properties, TrkB mRNA transcripts were studied in individual PhMNs labeled retrogradely with Alexa Fluor 488‐conjugated cholera toxin subunit β from five adult male Sprague–Dawley rats. Longitudinal sections of the cervical C3–C5 spinal cord (10 μm thick) were processed using fluorescence in situ hybridization (RNAscope) and imaged using confocal microscopy to measure individual PhMN size and full‐length TrkB mRNA transcripts in nuclear and cytoplasmic compartments. Across 287 PhMNs, significant size variation existed both within and between animals. Across all animals, somal surface area averaged 4385 ± 1627 μm 2 with mean values per animal ranging from 3544 to 5461 μm 2 . PhMNs were classified into tertiles within each animal based on somal surface area. Individual PhMNs averaged 1070 ± 685 TrkB mRNA transcripts per motor neuron. Larger PhMNs in the upper tertile by size expressed a greater total number of TrkB mRNA transcripts per motor neuron, with values on average threefold higher compared to smaller motor neurons in the lower tertile ( p < 0.001). Cytoplasmic and nuclear TrkB mRNA density (transcripts/volume) displayed no significant effect of PhMN somal surface area tertile. Overall, the greater TrkB mRNA levels in larger motor units responsible for higher force, expulsive motor behaviors may underlie their functional adaptations and resilience to injury or disease. image

Directed differentiation protocols have recently been developed to produce stem cell-derived α (SC-α) cells as a potential component of a complete cell-based therapy for T1D, to complement the more widely studied stem cell-derived β (SC-β) cells. Differentiation protocols for SC-β cells produce off-target cell populations implicated in the development of outgrowths in SC-β cell grafts, but outgrowths from SC-α cells have not been explored. This study identifies that engrafted SC-α cells generate outgrowths of similar composition to SC-β cell outgrowths. Both cell types share outgrowth-driving populations marked by SOX9, CDX2, or SOX2. Single-cell RNA sequencing was used to reveal an enrichment in cell-adhesion signaling events in outgrowth-driving populations. Small-molecule inhibition of the Notch pathway was insufficient to disrupt all three outgrowth-driving populations. A comprehensive disruption of cell-adhesion signaling via single-cell dispersion and reaggregation is found to reduce the outgrowth propensity in engrafted SC-α and SC-β cells. Together, these results suggest that disrupting residual progenitor cells with SC-α and SC-β cell clusters can enhance the safety profile of these cell therapy products for T1D therapy.

Three-dimensional (3D) anatomic modeling derived from high-resolution medical imaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), has been increasingly adopted in preclinical testing and device development. This white paper describes a cardiac-specific workflow that integrates 3D printing and silicone molding for support device development and procedural simulation. Patient-derived computed tomography angiography data were segmented using FDA-cleared medical modeling software to isolate the left ventricular anatomy and were further processed in computer-aided design (CAD) to ensure accurate physiological wall thickness and structural fidelity. Material jetting 3D printing was performed on a Stratasys J750 using material distributions designed to mimic the mechanical properties of myocardium, thereby approximating myocardial compliance. In parallel, stereolithography apparatus molds were designed from the left ventricle CAD model to cast transparent, pliable left ventricular models in Sorta-Clear™ 18 silicone. The 3D-printed models preserved intricate morphological detail and were suitable for mechanical manipulation and device deployment studies, whereas silicone models offered tunable mechanical properties, transparency for visualization, and durability for repeated use. Together, these complementary modalities provided rapid manufacturing capability and application-relevant physical representation. Case-specific parameters, strengths, and limitations of both models in enhancing patient care and device testing are highlighted, with relevance to heart failure applications. Current knowledge gaps, workflow and integration challenges, and future opportunities are identified, positioning this work as a reference framework for continued innovation in anatomic modeling. Within the collaborative framework of Mayo Clinic’s Anatomic Modeling Unit and Simulation Center, this integrated modeling workflow demonstrates the value of multidisciplinary collaboration between engineers and clinicians. Clinically, these patient-specific left ventricular models may enable pre-procedural device sizing and positioning and may support simulation of mechanical circulatory support (MCS) deployment while identifying possible anatomic constraints prior to intervention. This workflow has direct applicability in advanced heart failure patients undergoing MCS support, such as the Impella axillary MCS device or the durable LVAD, with potential to reduce procedural uncertainty while reducing complications and improving peri-procedural outcomes. Additionally, these models also serve as high-accuracy educational tools, enabling trainees and multidisciplinary care teams to visualize and possibly rehearse procedural steps while gaining hands-on experience in a risk-free environment.

Disseminated cutaneous coccidioidomycosis (DCC) is an uncommon manifestation of Coccidioides infection resulting from hematogenous spread to the skin. While recurrence after treatment discontinuation has been reported in 17 to 50 percent of cases, associated frequency, timing, and risk factors are not well defined. We conducted a retrospective review of biopsy-proven or probable DCC cases between January 2008 and March 2024, and investigated for evidence of recurrence. Demographic, clinical, and treatment data were abstracted, including antifungal regimen, adherence, immune status, and coccidioidal titers. A total of 45 subjects met the inclusion criteria, including 27 immunocompetent and 18 immunosuppressed patients. Eleven (24.4%) experienced one or more recurrences, totaling 22 recurrences; 19 of these (86.4%) occurred at previously affected sites. Ten immunocompetent patients (37.0%) had 21 total recurrences, while one immunosuppressed patient (5.6%) experienced a single recurrence. Median antifungal-free interval before recurrence was 14 months (range, 1–96), and 10 recurrences (90.9%) occurred while off antifungal therapy. Ten patients underwent initial surgical excision, with four (40.0%) experiencing a total of 11 recurrences afterwards. DCC recurrence was common, mostly among immunocompetent individuals not on suppressive antifungal therapy, and frequently presented with multiple recurrences. Recurrences were almost always at prior lesion sites, often years after treatment discontinuation.

ABSTRACT In LGI1-linked animal models of inherited autosomal dominant lateral temporal lobe epilepsy, increased neuronal excitability is accompanied by modifications in the AMPA/NMDA receptor ratio and a large decrease in Kv 1 type potassium channels. However, the mechanism which links the absence of LGI1 to reduced expression of key neuronal ion channels is unknown. We observed multiple conserved canonical ganglioside-binding domains (GBDs) within human LGI1, mainly located in the EPTP domain. We show that GT1b is co-captured from native rat brain extracts by human LGI1 antibodies and, using SPR analysis, that recombinant full length LGI1 interacted with liposomes containing GT1b and GM1, but not GM3, lyso-lactosylceramide, phosphatidylserine or phosphatidylcholine. The ganglioside binding capacity of GBD peptide sequences exposed at the surface of LGI1 were confirmed using SPR and Langmuir film balance. Our data suggest that LGI1 interacts with gangliosides and may be involved in organizing lipid membrane platforms accommodating functional protein complexes. The loss of LGI1 could destabilize these platforms and contribute to reduced expression of key ion channels in Lgi1 -/- mice.

Abstract Generative artificial intelligence (AI) is rapidly populating medical records with synthetic or partially AI-generated content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability of medical data at population scale. By analysing more than 800,000 synthetic data points across clinical text generation, vision–language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architectures. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence. Models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies that can be easily integrated into existing clinical workflows, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.

ABSTRACT Background LINS1 , the human homolog of the Drosophila segment polarity gene, encodes a key regulator of the Wingless/Wnt signaling pathway. While numerous genes have been implicated in intellectual disability (ID), only a limited number have been conclusively associated with autosomal recessive intellectual disability (ARID). Variants in LINS1 have been identified as one such cause. Methods In this study, we employed exome sequencing (ES) to investigate the genetic basis of ID in two consanguineous Iranian families. This variant was confirmed by Sanger sequencing, and segregation analysis supported its pathogenicity. Additionally, in silico analyses were conducted to explore the protein–protein interaction network of LINS1 and its functional connections to ID‐associated proteins. Results We identified a novel homozygous missense variant in LINS1 (c.1354G>C), leading to an alanine‐to‐proline substitution (p.Ala452Pro) in exon six. Conclusion Our findings provide new molecular and clinical insights into the role of LINS1 in ARID, expanding the genetic landscape of ID. This discovery has significant implications for genetic counseling and prenatal diagnosis, aiding in the identification of at‐risk couples.