Citations

Introduction Alzheimer’s disease and related dementias (ADRD) are conditions with progressive cognitive decline. Still, people living with late-stage ADRD (PLWD) have been reported to exhibit transient recovery of communication or behavioural abilities that had seemingly been lost. These lucid episodes (LEs) are underinvestigated and poorly understood. This study aims to advance scientific understanding of the incidence, prevalence and predictors of LEs and assess from family caregivers if LEs are associated with changes they make in care planning or experiences with burden, distress or grief. Methods This study recruited 545 caregivers from five ADRD-related registries in the USA. Eligibility included caregivers over 18 years who currently provide care to someone with moderate to very severe ADRD and can complete online questionnaires. Using a longitudinal observational study design, consented caregivers will be assessed monthly for 1 year using online questionnaires that inquire about witnessing LEs. If witnessed, the context and content of the LE is reported. Changes in caregiver decision-making about care for the PLWD, and caregiver burden, distress and grief are assessed at baseline, 6 and 12 months. Analysis Analysis of baseline data will assess descriptive aspects of LEs that are currently unknown (eg, prevalence, content, antecedents, duration). Longitudinal analysis will examine the incidence of LEs, characteristics of PLWD and caregivers that are predictors of episodes, and the associations between LEs and caregiver outcomes (eg, burden, distress, grief). Ethics and dissemination This study is being conducted in accordance with all Federal Policies for the Protection of Human Subjects. The protocol has been approved by the Mayo Clinic Institutional Review Board (ID 22-006861). Findings will be presented at scientific conferences and disseminated through journal publications and outreach efforts with collaborating partners invested in brain health and caregiver support.

Abstract Nephrocalcinosis provides a nidus for stone formation and is strongly linked to renal injury and chronic kidney disease. Although the crucial role of macrophages in the formation and progression of calcium oxalate (CaOx) crystals has long been widely recognized, finding effective immunotherapies for nephrocalcinosis remains a challenge. In this study, we described an innovative macrophage‐based method that delivers interleukin‐10 (IL‐10) into the kidney, reduces the deposition of CaOx crystals, and alleviates renal injury in a mouse model of glyoxylate‐induced CaOx. Compared with recombinant IL‐10 direct injection, the macrophage‐based method has the advantages of biocompatibility and sustaining action. We found that the transplantation of engineered macrophages via the tail vein significantly reduced the volume of crystals in the kidney, thereby alleviating the kidney injury caused by crystal. In mechanistic studies, IL‐10‐secreting macrophages inhibited crystal formation and promoted crystal clearance by promoting macrophage M2 polarization, exerting a protective effect on renal tissue. Our data suggest that macrophage‐based delivery of IL‐10 to the kidney can be a potential treatment method for nephrocalcinosis.

Abstract Teclistamab is an anti-CD3-/B-cell maturation antigen (BCMA) bispecific antibody approved for use in relapsed/refractory multiple myeloma. We undertook a retrospective study of post-approval, real-world outcomes with teclistamab in the US Multiple Myeloma Immunotherapy Consortium. Among 509 patients, 89% would have been ineligible for the MajesTEC-1 trial, primarily due to prior BCMA-directed therapy, cytopenias, or diminished performance status. Cytokine release syndrome occurred in 54% (1.4% grade ≥3) and immune effector cell–associated neurotoxicity syndrome in 11% (2.2% grade ≥3), with no fatal events. Infections occurred in 42% and contributed to death in 5%. Partial response (PR) or better was achieved in 53% and very good PR or better in 45%. With 10.1 months of median follow-up, the estimated median progression-free survival (PFS) was 5.8 months and 12-month overall survival was 61%. Independent predictors of less than very good PR and shorter PFS included BCMA-directed chimeric antigen receptor T-cell therapy in the previous 9 months, high disease burden, lymphopenia, and elevated ferritin. Significance: T cell–engaging bispecific antibodies such as teclistamab represent an important new treatment modality for multiple myeloma and other blood cancers. This study evaluates the real-world safety and efficacy of teclistamab, including its activity in populations not represented in the initial phase I/II study, and identifies clinical variables associated with treatment response. See related commentary by Zweegman et al., p. 542

ABSTRACTTissue‐specific gene manipulation using Cre/loxP or Flp/frt recombination systems is a cornerstone of genetically engineered mouse models. In this study, we aim to develop a novel hepatocyte‐specific, tamoxifen‐inducible Flp mouse line. BAC (bacterial artificial chromosome)‐Alb(albumin)‐FlpER2(estrogen receptor ligan binding domain) was developed by inserting IRES‐FlpER2 cDNA between the translation stop codon and 3′‐UTR of the mouse albumin gene in a bacterial artificial chromosome. Upon tamoxifen induction in mice crossed with reporter lines, western blotting, immunohistochemistry, immunofluorescence staining, and X‐gal staining (5‐bromo‐4‐chloro‐3‐indolyl‐β‐D‐galactopyranoside Staining) were used to verify the recombination efficiency and specificity of this mouse model. Recombination was highly efficient and specific in hepatocytes, with no recombination detected in intrahepatic cholangiocytes or other organs in this mouse model. We generated a new tamoxifen‐induced hepatocyte‐specific mouse model with highly efficient recombination specifically in hepatocytes, and this model can be used to generate tumor model lines.

We report 3-year follow-up results from TRANSFORM comparing lisocabtagene maraleucel (liso-cel) versus standard of care (SOC) for second-line primary refractory/early relapsed (≤12 months) large B-cell lymphoma (LBCL). Adults eligible for autologous stem cell transplantation (N = 184) were randomly assigned 1:1 to liso-cel (100 × 10 6 chimeric antigen receptor–positive T cells) or SOC. Results are reported descriptively. With a median follow-up of 33.9 months, median (95% CI) event-free survival was 29.5 months (9.5 to not reached [NR]) for liso-cel versus 2.4 months (2.2 to 4.9) for SOC (hazard ratio [HR], 0.375; 95% CI, 0.259 to 0.542). Median progression-free survival was NR (12.6-NR) for liso-cel versus 6.2 months (4.3-8.6) for SOC (HR, 0.422; 95% CI, 0.279 to 0.639) with 36-month rates of 51% versus 26.5%. Median overall survival (OS) was NR for both arms (HR, 0.757; 95% CI, 0.481 to 1.191), with 66% of patients crossing over to receive liso-cel; 36-month OS rate was 63% for liso-cel versus 52% for SOC. OS HR (0.566 [95% CI, 0.359 to 0.895]) favored liso-cel when accounting for the treatment effect of crossover. Safety results were consistent with previous reports. At 3-year follow-up, liso-cel confirmed superior, more durable efficacy versus SOC with a favorable safety profile and no new safety signals. These data support liso-cel as an effective second-line treatment with curative potential for relapsed/refractory LBCL.

Importance Tumor-infiltrating lymphocytes (TILs) are a provocative biomarker in melanoma, influencing diagnosis, prognosis, and immunotherapy outcomes; however, traditional pathologist-read TIL assessment on hematoxylin and eosin–stained slides is prone to interobserver variability, leading to inconsistent clinical decisions. Therefore, development of newer TIL scoring approaches that produce more reliable and consistent readouts is important. Objective To evaluate the analytical and clinical validity of a machine learning algorithm for TIL quantification in melanoma compared with traditional pathologist-read methods. Design, Setting, and Participants This multioperator, global, multi-institutional prognostic study compared TIL scoring reproducibility between traditional pathologist-read methods and an artificial intelligence (AI)-driven approach. The study was conducted using retrospective cohorts of patients with melanoma between January 2022 and June 2023 across 45 institutions, with tissue evaluated by participants from academic, clinical, and research institutions. Participants were selected to ensure diverse expertise and professional backgrounds. Main Outcomes and Measures Intraclass correlation coefficient (ICC) values were calculated for the manual and AI-assisted arms using log-transformed data. Kendall W values were calculated for Clark scores (brisk = 3, nonbrisk = 2, and sparse = 1). Reliabilities of ICC and W values were classified as moderate (0.40-0.60), good (0.61-0.80), or excellent (>0.80). AI TIL measurements were dichotomized using the 16.6 and median cutoffs. Univariable and multivariable Cox regression analyses assessed the prognostic value of TIL scores adjusted for clinicopathologic variables. Results There were 111 patients with melanoma in the independent testing cohort (median [range] age at diagnosis, 61.0 [25.0-87.0] years; 56 [50.5%] male) who contributed melanoma whole tissue sections. A total of 98 participants evaluated TILs on 60 hematoxylin and eosin–stained melanoma tissue sections. All 40 participants in the manual arm were pathologists, while the AI-assisted arm included 11 pathologists and 47 nonpathologists (scientists). The AI algorithm demonstrated superior reproducibility, with ICCs higher than 0.90 for all machine learning TIL variables, significantly outperforming manual assessments (ICC, 0.61 for AI-derived stromal TILs vs Kendall W , 0.44 for manual Clark TIL scoring). AI-based TIL scores showed prognostic associations with patient outcomes (n = 111) using the median cutoff approach with a hazard ratio (HR) of 0.45 (95% CI, 0.26-0.80; P = .005), and using the cutoff of 16.6, with an HR of 0.56 (95% CI, 0.32-0.98; P = .04). Conclusions and Relevance In this prognostic study of TIL quantification in melanoma, the AI algorithm demonstrated superior reproducibility and prognostic associations compared with traditional methods. Although the retrospective nature of the cohorts limits demonstration of clinical utility, the publicly available dataset and open-source AI tool offer a foundation for future validation and integration into melanoma management.

BACKGROUND: The impact of covert cerebrovascular disease on falls in the general population is not well-known. Here, we determine the time to a first fall following incidentally detected covert cerebrovascular disease during a clinical neuroimaging episode. METHODS: This longitudinal cohort study assessed computed tomography (CT) and magnetic resonance imaging from 2009 to 2019 of patients aged >50 years registered with Kaiser Permanente Southern California which is a healthcare organization combining health plan coverage with coordinated medical services, excluding those with before stroke/dementia. We extracted evidence of incidental covert brain infarcts (CBI) and white matter hyperintensities/hypoattenuation (WMH) from imaging reports using natural language processing. We examined associations of CBI and WMH with falls requiring medical attention, using Cox proportional hazards regression models with adjustment for 12 variables including age, sex, ethnicity multimorbidity, polypharmacy, and incontinence. RESULTS: We assessed 241 050 patients, mean age 64.9 (SD, 10.42) years, 61.3% female, detecting covert cerebrovascular disease in 31.1% over a mean follow-up duration of 3.04 years. A recorded fall occurred in 21.2% (51 239/241 050) during follow-up. On CT, single fall incidence rate/1000 person-years (p-y) was highest in individuals with both CBI and WMH on CT (129.3 falls/1000 p-y [95% CI, 123.4–135.5]), followed by WMH (109.9 falls/1000 p-y [108.0–111.9]). On magnetic resonance imaging, the incidence rate was the highest with both CBI and WMH (76.3 falls/1000 p-y [95% CI, 69.7–83.2]), followed by CBI (71.4 falls/1000 p-y [95% CI, 65.9–77.2]). The adjusted hazard ratio for single index fall in individuals with CBI on CT was 1.13 (95% CI, 1.09–1.17); versus magnetic resonance imaging 1.17 (95% CI, 1.08–1.27). On CT, the risk for single index fall incrementally increased for mild (1.37 [95% CI, 1.32–1.43]), moderate (1.57 [95% CI, 1.48–1.67]), or severe WMH (1.57 [95% CI, 1.45–1.70]). On magnetic resonance imaging, index fall risk similarly increased with increasing WMH severity: mild (1.11 [95% CI, 1.07–1.17]), moderate (1.21 [95% CI, 1.13–1.28]), and severe WMH (1.34 [95% CI, 1.22–1.46]). CONCLUSIONS: In a large population with neuroimaging, CBI and WMH are independently associated with greater risks of an index fall. Increasing severities of WMH are associated incrementally with fall risk across imaging modalities.

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTS-PEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.

Objective: To develop and validate a risk score for predicting post-transplant recurrence in perihilar cholangiocarcinoma (pCCA) patients. Background: pCCA is an aggressive malignancy with a poor prognosis. Although neoadjuvant chemoradiation (CRT) followed by liver transplantation (LT) offers a potential cure for patients with unresectable, early-stage de novo or primary sclerosing cholangitis (PSC)-associated pCCA, post-transplant recurrence negatively impacts survival. Currently, no risk stratification tools exist. Methods: This retrospective study included consecutive patients with unresectable de novo or PSC-associated pCCA who underwent neoadjuvant CRT followed by LT at Mayo Clinic (1993–2024). Predictors of post-transplant recurrence were identified using multivariable Cox regression with LASSO variable selection. The Perihilar Cholangiocarcinoma Risk Estimation of Tumor Recurrence after Transplant (PRETREAT) risk score was created by assigning points from model coefficients. Score performance was evaluated by discrimination (c-statistic), internally validated with 10-fold cross-validation, and externally validated in a separate cohort. Survival outcomes were compared across 3 PRETREAT risk groups and between 2 time periods. Results: This study included 399 patients (development: 301; validation: 98). The 5-year cumulative incidence of post-transplant recurrence was 29.7% in the development cohort and 27.6% in the validation cohort. Multivariable analysis identified 4 independent predictors: macroscopic residual tumor on explant [hazard ratio (HR): 12.4], vascular encasement (HR: 2.18), lymphovascular invasion (HR: 2.04), and radial tumor diameter (HR: 1.02/mm). The PRETREAT score (0–22), based on these factors, demonstrated excellent performance in both internal and external validation (C-index 0.83 and 0.85, respectively). Patients were stratified into low-risk (0–7), moderate-risk (8–15), and high-risk (16–22) groups. The 5-year recurrence-free survival rates significantly differed across low-risk, moderate-risk, and high-risk groups (89.0%, 38.3%, and 15.4%, respectively; P<0.001). Over the last decade, overall survival improved significantly only in moderate-risk patients (70.4% vs 46.9%, P=0.024), without significant improvement in recurrence-free survival. Conclusions: The PRETREAT score provides a valuable tool for guiding post-transplant management of pCCA, enabling risk-stratified surveillance, and laying the foundation for future clinical trials.