Citations

Cholera toxin (CTx) is a potent enterotoxin and widely used mucosal adjuvant, yet the acute host response to CTx itself, in the absence of a co-administered antigen, remains vague. In this study, we integrated physiological and metabolic phenotyping with intestinal transcriptomics, epithelial imaging, immune profiling, hepatic gene-expression analysis, and paired microbiome profiling to characterize the response to a single oral CTx exposure in mice. Within 24 h, CTx remodeled immune-cell composition in both epithelial and lamina propria compartments, with responses shaped by sex and genetic background. Recurrent changes included increased epithelial group 3 innate lymphoid cells, reduced lamina propria RORγt+ regulatory T cells, and decreased epithelial ILC1/NK cells in males of both strains. CTx also increased villus DCLK1+ tuft cells and reduced MUC2 immunoreactivity. Jejunal transcriptomics revealed induction of inflammatory defense, glycolytic, autophagy, and lipid-utilization programs together with suppression of proliferative and mitochondrial programs, consistent with coordinated stress adaptation. These local changes were accompanied by rapid restructuring of the fecal microbial community and by systemic hypometabolism, despite markedly different hypothermic responses across strains. CTx also elicited a selective hepatic stress and metabolic program, including conserved suppression of Cyp7a1 across sexes and strains. Together, these findings show that CTx alone rapidly reorganizes the intestinal immune, epithelial, metabolic, and microbial environment while suppressing whole-body metabolism. This work provides an integrated framework for understanding the acute host response to CTx and for interpreting its widespread use as a mucosal adjuvant.

Abstract Recent evidence implicates somatic mutations in disease-associated genes across multiple neurodegenerative disorders, including the identification of two somatic TARDBP variants as a putative cause of frontotemporal lobar degeneration with TDP-43 pathology type C (FTLD-TDP type C). However, these findings remain anecdotal. We performed single-nucleus amplicon sequencing of 39,800 neurons from the superior temporal gyrus of sporadic FTLD-TDP type C patients (34,738 neurons, 52 individuals) and non-demented controls (5,062 neurons, 26 individuals) using the Mission Bio Tapestri platform and investigated somatic variants in TARDBP and other FTLD-TDP-associated genes in a case-control setting. We uncovered an extensive landscape of ultra-low-frequency (<1%) somatic mutations across all analysed genes, including TARDBP . Rare somatic occurrences of known ALS/FTD-associated germline TARDBP variants, including p.N267S and p.M337V, were identified exclusively in patients. Among all targeted genes, TARDBP had the highest proportion of neurons harbouring a somatic variant, and this mutational burden was lower in individuals who died at an older age. Strikingly, the C-terminal domain of TDP-43, where most ALS/FTD germline pathogenic variants reside, exhibited a lower neuronal mutational burden than its other domains. This suggests that neurons harbouring damaging mutations may undergo progressive loss prior to autopsy, leaving only rare survivors at frequencies too low to detect by bulk or small-scale single-cell approaches. Together, our findings provide an unprecedented resolution into the somatic mutational landscape of post-mitotic neurons in FTLD-TDP type C and reveal a gene-specific layer of neuronal mosaicism that current single-cell WGS studies fail to detect.

Background/Objectives: Glioblastoma surveillance after surgery and chemoradiation remains challenging because MRI findings of tumor recurrence can overlap with pseudo-progression, treatment-related effects, and postoperative tissue changes. Methods: We developed a seven-channel postoperative MRI framework using a deep learning transfer method to support non-invasive glioblastoma treatment-effect assessment. The model used T1 contrast-enhanced, FLAIR, T1, RSI-Cell, ADC, T2, and cerebral blood flow volumes from 124 postoperative glioblastoma patients (164 MRI timepoints) as input. Images were processed using a 3D ResNet18 encoder pretrained on 588 postoperative glioma samples and fine-tuned using task-specific classification heads. The cohort comprised 124 patients contributing 164 postoperative MRI timepoints, all acquired at 3T on scanners from a single vendor. Performance was evaluated with nested five-fold cross-validation stratified and assigned at the patient level, so that all timepoints from a given patient fell in one-fold and the training epoch was selected on an inner split rather than on the fold being reported. Because some of the clinical labels were incomplete, the number of evaluable timepoints differed by task (recurrence versus pseudo-progression, 164; MGMT, 99; short-term survival, 139). The whole procedure was repeated under three independent random seeds and results are reported as the mean and standard deviation across seeds. Results: The strongest clinical endpoint was recurrence versus pseudo-progression, where nested cross-validation across three random seeds gave a pooled out-of-fold AUC of 0.935 (SD = 0.014), area under the precision-recall curve of 0.973, balanced accuracy of 0.880, sensitivity of 0.917, and specificity of 0.843. No other endpoint reached reliable discrimination. Radiation decision reached an AUC of 0.658 (SD = 0.039), while MGMT promoter methylation (AUC = 0.532, SD = 0.082) and short-term survival (AUC = 0.514, SD = 0.027) were indistinguishable from chance. Conclusions: In this single-center proof-of-concept study, postoperative MRI successfully distinguished tumor recurrence from pseudo-progression. However, the models did not reliably predict the other three outcomes related to molecular status, treatment planning, and prognosis. Overall, the model learned imaging features specifically associated with recurrence, rather than a more general representation of the tumor that can predict many different clinical outcomes. These findings support technical feasibility for a single endpoint rather than clinical readiness, for which external multi-center validation is required.

Abstract Objective The validity of progression-free survival (PFS) as a surrogate for overall survival (OS) in the antibody-drug conjugate (ADCs) era remains uncertain. We evaluated the association between treatment effects on PFS and OS in randomized trials of ADCs for metastatic breast cancer (mBC) and explored the surrogate threshold effect (STE). Methods PubMed, Embase, Cochrane, and ClinicalTrials.gov were searched for published randomized controlled phase II–IV trials evaluating ADCs in mBC and reporting hazard ratios (HRs) for both PFS and OS. A meta-epidemiological approach was used. The primary metric was the ratio of hazard ratios (rHR = HR_PFS/ HR_OS), with rHR &lt; 1 indicating a larger effect on PFS. Trial-level associations were assessed using weighted linear regression (R²) and Spearman’s correlation. Results Twenty-one trials (n = 21; 18 phase III) evaluating nine ADCs were included. The pooled rHR was 0.79 (95% CI, 0.73–0.86), indicating a larger relative effect on PFS than OS. Numerically, the largest discrepancies were observed in triple-negative disease (rHR 0.72) and trials allowing crossover (rHR 0.71). Concordant statistical significance between endpoints occurred in 76% of the trials, whereas 24% showed a significant PFS benefit without OS improvement. The trial-level association between PFS and OS was moderate (R² = 0.62; ρ = 0.67). Conclusion Treatment effects on PFS consistently exceeded those on OS, although effect directions were highly concordant between endpoints. These findings provide an exploratory framework to contextualize the relationship between PFS and OS in ADC trials, requiring further validation in diverse clinical settings.

Objective Epidemiologic estimates for age of systemic lupus erythematosus (SLE) diagnosis are limited, particularly for men and racial and ethnic subpopulations in the United States. Leveraging the Centers for Disease Control and Prevention (CDC) National Lupus Registry network of population‐based SLE registries, meta‐analyses were performed estimating age‐specific incidence of SLE by sex, race, and ethnicity. Methods The CDC network of SLE registries includes five state‐based registries, plus a sixth Indian Health Service registry. Incidence periods spanned calendar years 2002‐2018. Registries provided age‐specific incidence of SLE, fulfilling the American College of Rheumatology (ACR) SLE classification criteria, per 100,000 person‐years, stratified by sex, race, and ethnicity for the meta‐analyses. Results Incidence rates among women were highest for those aged 30‐39 (12.7, 95%CI: 9.5‐16.8), while rates among men were highest for those aged 60‐69 (2.1, 95%CI: 1.3‐3.4). Black women aged 20‐39 had the highest SLE incidence rates (23.6, 95%CI: 20.7‐26.8). Among women diagnosed with SLE before age 20, incidence was highest among Asian (6.5, 95%CI: 2.8‐15.2) individuals. Among women diagnosed with SLE after age 60, incidence was highest among American Indian/Alaska Native (9.4, 95%CI: 3.4‐15.4) individuals. Weighted percentages show 10.0% of those with SLE were diagnosed before age 20, while 15.1% were diagnosed after age 60. The largest proportion of individuals with SLE, 22.1%, were diagnosed between 30‐39 years. Conclusions This study provides comprehensive sex‐ and race‐specific estimates of age at SLE diagnosis. The substantial later in life SLE incidence among men and disease burden in pediatric and older populations should raise awareness in considering SLE in the differential diagnosis in previously underrecognized groups.

BACKGROUND AND OBJECTIVES Neonatal tracheal intubation is a high-risk procedure, and multiple attempts are associated with adverse events. At our tertiary neonatal intensive care unit, the baseline first-attempt success rate was 45%, which we aimed to increase to over 55% through a quality improvement (QI) initiative. METHODS A multidisciplinary team conducted a QI project using the define-measure-analyze-improve-control framework. Root cause analysis identified poor glottic visualization and patient instability as primary drivers of failure. Plan-do-study-act cycles were carried out (April 2023–July 2024), followed by a sustainability phase (August 2024–May 2025). Key interventions included the phased implementation of video laryngoscopy (VL) as the primary intubation device and the standardization of oxygenation during laryngoscopy using nasal high flow. First-attempt success was tracked using statistical process control, and bimonthly VL use was monitored as a process measure. RESULTS A total of 642 intubations were analyzed. By control chart analysis, the first-attempt success rate increased from a baseline of 45% to a sustained process mean of 61% during the intervention and sustainability phases. VL use increased from 0% at baseline to 70% to 90% during the sustainability phase. Success rates improved across all clinician types and locations. Notably, first-attempt success in infants weighing less than 1000 g increased from 38% at baseline to 62% during the intervention phase. CONCLUSION A systematic QI initiative focused on VL and standardized oxygenation improved and sustained neonatal intubation success rates, exceeding the project aim. The maintenance of high-success rates confirms the durability of these safety improvements.

Abstract Glioblastoma (GBM) remains the most lethal primary brain cancer due to its remarkable metabolic plasticity and therapeutic resistance. Here, we identify cholesterol dependency as a therapeutically exploitable vulnerability in GBM using two FDA-approved drugs: the H1 histamine antagonist clemastine and the retinoid X receptor agonist bexarotene. Combined treatment induces potent synergistic anti-tumor activity across patient-derived glioma models, suppressing proliferation, stemness, and survival at sub-IC50 concentrations. Mechanistically, this therapy disrupts cholesterol biosynthesis, transport, and homeostasis, triggering endoplasmic reticulum stress and activation of the unfolded protein response, ultimately leading to autophagy and apoptotic cell death. Orthotopic patient-derived glioma models recapitulate these mechanisms in vivo, where local intracranial administration significantly reduces tumor progression and prolongs survival using fourfold lower doses than systemic intraperitoneal delivery. Single-cell RNA sequencing revealed activation of regeneration and plasticity programs, accompanied by immune microenvironment remodeling and enhanced inflammatory signaling. Importantly, syngeneic models preserved immune cell composition, supporting future integration with immunotherapeutic strategies. Together, these findings establish cholesterol dysregulation–induced metabolic collapse as a promising therapeutic approach for GBM.

The NCCN Guidelines for Survivorship offer guidance for health care providers who care for survivors of adult-onset cancer. These guidelines include screening, evaluation, and treatment recommendations for common physical and psychosocial problems resulting from cancer and its treatment and provide a framework for care coordination. They also present guidance for helping cancer survivors to enhance their wellness and maintain a healthy lifestyle. This article summarizes the panel’s current recommendations and recent updates regarding anxiety, depression, distress, and trauma in cancer survivors.

Accurate estimation of Alzheimer’s disease (AD) severity from structural magnetic resonance imaging (MRI) remains difficult, as disease-associated anatomical alterations are often subtle and publicly available datasets are typically too small to support robust deep learning model training. This feasibility study sought to determine how much Alzheimer’s disease spectrum-related information could be extracted from a small structural MRI cohort using a deliberately lightweight two-dimensional convolutional neural network (2D CNN), and whether transfer learning improves model performance. This study was intended as a methodological proof of concept rather than the development of a clinically deployable diagnostic tool. Structural scans and Clinical Dementia Rating (CDR) labels from the OASIS-1 dataset were filtered to 214 subjects: 124 cognitively normal (CN), 65 with mild cognitive impairment (MCI; CDR = 0.5), and 25 with AD-level impairment (CDR ≥ 1). A compact 2D CNN trained from scratch and a transfer learning model (frozen ImageNet MobileNetV2 features) were evaluated on four binary tasks (CN vs. AD, MCI vs. AD, CN vs. MCI, and CN vs. any impairment) under identical pre-processing and subject-level repeated 5-fold cross-validation (10 repeats), with the decision threshold tuned only on an inner split. Discrimination was summarized by ROC-AUC with 95% confidence intervals (CIs), permutation tests against chance, and per-task sensitivity and specificity. The from-scratch CNN recovered only a broad normal-versus-impaired signal (CN vs. any impairment AUC 0.59) and was at chance on adjacent-stage tasks (MCI vs. AD 0.41; CN vs. MCI 0.51). Transfer learning improved every task: CN vs. AD AUC 0.745 (95% CI 0.730–0.763), CN vs. any impairment 0.642, CN vs. MCI 0.601, and MCI vs. AD 0.599. On an independent OASIS-2 cohort, the transfer learning CN vs. AD model retained AUC 0.748. In this small-data regime, transfer learning recovers substantially more Alzheimer-spectrum signals than a from-scratch CNN, but performance remains modest because it is bounded by CDR-based, non-biomarker-confirmed labels, suggesting the model separates CDR-defined cognitive-status groups rather than detecting AD pathology.