Transplantation

Background. Fluid collections are common after kidney transplantation, but their prevalence at scheduled imaging and their management are inconsistent from canter to canter. We describe the cross sectional prevalence and management of perigraft collections, including an exploratory deepdrain subgroup. Methods. In this retrospective single center cohort, 146 consecutive adult kidney transplant recipients (Cleveland Clinic Weston Hospital, July 2024 to June 2025) were imaged on postoperative day 0/1 and at approximately 1 and 3 months. Routine deep drainage was avoided (n=124), 22 clinically selected recipients with perioperative anticoagulation received a deep drain. The primary outcome was cross sectional prevalence of a perigraft collection at each visit. Secondary outcomes were management among patients with a collection. Drain group comparisons were exploratory. Results. Collections were present in 29 of 146 recipients at postoperative day 0/1 (19.9%; 95% CI, 14.2 to 27.1), 71 of 146 at 1 month (48.6%; 95% CI, 40.7 to 56.7), and 34 of 146 at 3 months (23.3%; 95% CI, 17.2 to 30.8). Prevalence in the no drain group was 21.0%, 50.0%, and 23.4%, respectively; corresponding prevalence in the deep drain group was 13.6%, 40.9%, and 22.7%. Between group prevalence comparisons were not statistically significant at any time point. BMI was higher in the deep-drain group than in the no-drain group (30.04 vs 27.1 kg/m2; P=0.01). Among patients with a 1-month collection, management did not differ significantly between groups (any invasive management, 11.1% vs 16.1%; P=1.00). Among patients with a 3month collection, invasive management was more common in the deep drain group than in the no drain group (80.0% vs 13.8%; P=0.007). This comparison included only five deep-drain recipients and was confounded by clinical selection, perioperative anticoagulation, and BMI imbalance. Conclusions. Perigraft collection prevalence was highest at 1 month and lower at 3 months in this predominantly no drain cohort. Most collections were managed conservatively, and invasive treatment was uncommon in the no-drain group. The deep drain subgroup provides clinically relevant information about high risk recipients selected for drainage but cannot support causal conclusions regarding drain benefit or harm. Patient level linkage is required to distinguish persistence, resolution, and new collection formation.

Background Delayed graft function (DGF) is among the most used metrics in transplant medicine, yet its prevailing definition-dialysis requirement within the first seven postoperative days lacks granularity and fails to capture the dynamic nature of graft recovery. It reflects a treatment decision rather than graft biology, varies by centre practice, and offers no temporal information about the pace or pattern of recovery. This study proposes a trajectory-based framework using serial serum creatinine measurements across three biologically defined time windows to capture the dynamic nature of early renal recovery. Methods We retrospectively analysed 205 adult kidney transplant recipients at Cleveland Clinic Weston Hospital in 2025. Donor groups included living donors, donation after brain death (DBD), donation after circulatory death with normothermic regional perfusion (DCD-NRP), and DCD without NRP. Creatinine slopes were estimated for Days 1-7, 7-30, and 30-90 using ordinary least-squares regression. Logistic regression assessed the predictive performance of each trajectory window for DGF, adjusting for Kidney Donor Profile Index, cold ischemia time, BMI, and donor type. Results DCD non-NRP kidneys showed negligible early creatinine decline (slope +0.02) compared to DBD (-0.71) and DCD-NRP (-0.57) grafts during Days 1-7. During Days 7-30, DCD-NRP kidneys showed accelerated recovery and converged toward DBD-level trajectories, whereas DCD non-NRP grafts remained sluggish. Predictive modelling identified the Day 7-30 trajectory had the strongest discrimination for DGF, with AUC 0.83, pseudo-R2 0.25, and the lowest AIC (181.73), outperforming Days 1-7 (AUC 0.75) and 30-90 (AUC 0.79). DGF rates were 0% in living donors, 25.5% in DBD, 20.7% in DCD-NRP, and 58.9% in DCD non-NRP recipients. Conclusions A trajectory-based creatinine model provides more informative prognostic value than the conventional binary DGF definition The Day 7-30 window periods offers the highest discriminatory power, and normothermic regional perfusion appears to favourably shift DCD kidney recovery trajectories in a manner the binary definition fails to detect.

Background Studies of the 2018 United Network for Organ Sharing (UNOS) policy revision suggest improved early transplantation access for the sickest patients. It remains unknown whether these benefits vary as a function of time on the waitlist. Thus, we evaluated potential time-dependent effects of the 2018 heart allocation policy revision on waitlist outcomes. Methods Using UNOS data, we analyzed adult heart transplant candidates listed five years before (pre-policy) and after (post-policy) policy change, categorized as high-acuity (Status 1-3 or 1A) or low-acuity (Status 4-6 or 1B/old 2). Landmark analysis at weeks 0, 1, 2, 4, 8, 16, 24 using a competing risks framework was performed to assess transplantation, death/deterioration, and myocardial recovery. Sub-distribution and cause-specific hazard ratios were calculated. Absolute number and proportion of far-away transplants (donor distance [≥]500 nautical miles) were calculated at each timepoint. Results High-acuity patients experienced significantly higher risk of transplantation within 2 weeks post-policy change but equivocal risk by week 16. Low-acuity patients exhibited increased rate/risk of myocardial recovery and equal or improved death/deterioration risk at all timepoints; early transplantation cumulative incidence was increased. At all timepoints, both groups experienced significantly increased far-away donor use, highest immediately after listing and progressively declining over time. Conclusions The 2018 heart allocation policy change resulted in an initial appearance of time-dependent benefits among high-acuity, post-policy patients, but this was likely due to rapid transplantation of Status 1 and 2 patients from broader geographic sharing rather than a true temporal phenomenon. Post-policy, low-acuity patients experience benefits in all waitlist outcomes.

Background: Noninvasive biomarkers capable of detecting kidney allograft injury and distinguishing rejection from infection remain an important clinical need. Torque teno virus (TTV) reflects host immune status, but its utility in urine has not been well established. We evaluated urinary TTV, total cell-free DNA (cfDNA), cfDNA fragmentation, and BK polyomavirus (BKV) as potential biomarkers of kidney transplant rejection and BK polyomavirus-associated nephropathy (BKPyVAN). Methods: Urine samples from 334 kidney transplant recipients were analyzed in association with kidney biopsies performed within 30 days of sample collection. TTV and cfDNA were measured by multiplex quantitative PCR (qPCR), with a subset analyzed by digital PCR (dPCR), and BKV was quantified by qPCR. Associations with biopsy results were evaluated using group comparisons and nominal logistic regression. Changes associated with outcomes of consecutive biopsies were assessed by matched pairs analysis. Results: Among 544 unique urine samples, the urinary TTV/cfDNA ratio was significantly lower in rejection than in non-rejection samples (P<0.0001). Logistic regression adjusted for demographic factors confirmed an inverse association between the qPCR-derived TTV/cfDNA metric and rejection (adjusted odds ratio per delta Cq, 0.91; 95% CI, 0.88-0.94; P<0.00001). Significant differences were observed for all types of rejection. Association was confirmed by dPCR: higher TTV was associated with lower rejection odds, whereas higher cfDNA was associated with greater rejection odds. A nominal logistic model incorporating dPCR measurements achieved an AUROC of 0.76 (95% CI, 0.69-0.82). Matched pairs analysis showed a significant decrease in TTV/cfDNA when rejection developed and an increase following recovery from rejection, with no significant changes when biopsy status remained unchanged. BKV levels were strongly associated with BKPyVAN. No correlation between TTV and BKV was observed. Nominal logistic regression integrating TTV, cfDNA, and BKV measurements yielded AUROCs of 0.75, 0.97, and 0.73 for rejection, BKPyVAN, and non-rejection, respectively. Conclusions: A reduced urinary TTV/cfDNA ratio is associated with biopsy-defined kidney transplant rejection, and changes in the ratio correlate with transitions in biopsy results. Combined TTV, cfDNA, and BKV measurements showed differential associations with rejection and BKPyVAN, supporting further evaluation as complementary urinary biomarkers.

BackgroundLiving donor kidney transplantation improves outcomes for eligible patients with kidney failure, yet living donation remains limited. Public awareness about the possibility of living donation may not be sufficient to translate into willingness to donate. This study evaluated knowledge, attitudes, personal exposure, perceived barriers, motivators, and financial considerations associated with willingness to participate in living kidney donation among adults in San Antonio, Texas. MethodsA cross-sectional community-based survey was conducted among 346 adults. The survey assessed demographic characteristics, knowledge about kidney transplantation and living donation, personal exposure to dialysis, transplantation, and living kidney donation, misconceptions regarding donation, motivating and discouraging factors, financial considerations, and willingness to become a living donor. Associations with willingness were evaluated using multivariable logistic regression models. ResultsAlthough 83.8% of participants knew that a healthy person could donate one kidney while living, only 68 participants (19.8%) expressed general willingness to become a living donor. Only 40.0% identified kidney transplantation as the best treatment for eligible patients with kidney failure. Demographic and socioeconomic factors were not independently associated with willingness, and individual misconceptions were not independently associated with willingness. In the integrated model, knowing a living kidney donor was associated with greater willingness (OR 2.82, 95% CI 1.33-5.95), whereas knowing a transplant recipient was associated with lower willingness (OR 0.42, 95% CI 0.19- 0.96). Helping someone in need was associated with greater willingness (OR 2.48, 95% CI 1.39-4.44), while concern about long-term donor health complications was associated with lesser willingness to donate (OR 0.48, 95% CI 0.26-0.90). Nearly half of respondents who answered the compensation question selected a monetary amount that could motivate donation. ConclusionsIn this community-based sample, awareness that living donation is possible did not translate into broad willingness to donate. Willingness appeared to be shaped by personal exposure, altruistic motivation, donor safety concerns and financial considerations. Community education should move beyond basic awareness and address donor safety, financial protection, living donor experiences, and the benefits of kidney transplantation.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSLung donor-recipient matching requires balancing recipient medical urgency with expected post-transplant benefit. Conventional prediction models estimate outcomes for historical donor-recipient pairs but do not directly compare alternative candidates for the same donor. We developed an offline contextual-bandit framework to assess the feasibility of learning donor-centered lung-matching policies from historical registry data. MethodsWe used linked United Network for Organ Sharing (UNOS)/Organ Procurement and Transplantation Network (OPTN) Standard Transplant Analysis and Research files to identify 8,529 lung transplant allocation events from 2023 through 2025. Each event included the historical recipient and nine date-matched, ABO-compatible pseudo-candidates. The reward combined 72-hour respiratory-support-free survival (weight, 0.75) with a normalized initial Composite Allocation Score waitlist medical-urgency score (weight, 0.25). We trained a pessimistic neural lower-confidence-bound contextual-bandit policy (NeuraLCB) and evaluated it on held-out donor events using matched-action evaluation and self-normalized inverse propensity scoring (SNIPS). ResultsThe reconstructed dataset contained 85,290 donor-candidate rows. In the held-out set of 1,706 allocation events, the observed clinician reward was 0.534 (95% CI, 0.518-0.549). NeuraLCB agreed with the historical recipient in 174 events (10.2%), with a matched-action reward of 0.616. The SNIPS-estimated value of the NeuraLCB policy was 0.649 (95% CI, 0.568-0.717), corresponding to a paired difference of +0.116 (95% CI, +0.035 to +0.181) relative to observed clinician reward. However, the effective sample size was 51.5 events, indicating limited overlap between the learned and estimated historical policies. ConclusionsThis study demonstrates the feasibility of constructing and evaluating an offline contextual-bandit framework for lung donor-recipient matching using registry data. The estimated policy value was higher than the observed historical reward, but this finding is exploratory because candidate sets were reconstructed and off-policy evaluation had limited overlap. Verified offer sets, time-stamped candidate data, explicit compatibility constraints, and prospective evaluation are required before clinical use.

The human T cell repertoire is generated through recombination of a multitude of T cell receptor gene segments, yielding a complex array of T cell clones. T cell receptor beta (TRB) V gene segment defined, T cell clonal frequency when organized in correspondence with the respective V gene segment positions on the TRB genomic locus yields a periodic, undulating curve in the spatial domain. Using the genomic distance from the TRB-D1 segment to the TRB-V1-29 segments, spectral analysis was performed utilizing Lomb-Scargle periodogram (modified Fourier analysis) to obtain spectral power curves quantifying the TRB V clonal frequencies from 12 allogeneic stem cell transplant donors (baseline) and recipients (<100 days or b/w 100-365) using a variety of analytic software. Patients either underwent HLA matched related bone marrow or HLA matched unrelated blood stem cell transplantation, using PT-CY for the former and ATG for the latter. Spectral Power curves revealed dominant spectral peaks at wavelengths ranging from 113-335 millicycles/kb in the 12 donors, with consistent frequency domain spectral patterns, supporting similar use of V segments across healthy individuals. PT-CY recipients had power spectra closely corresponding to the respective donors, however the ATG recipients had relatively dispersed spectra, with spectral centroid shifted towards higher frequencies compared to donors and a reciprocal decline in the low frequency indices. Spectral power was concentrated in the <3 kb and 3-12 kb wavelengths in both groups, consistent with the periodicity observed in the relative V gene segment usage across the repertoire. The analyses reported here demonstrate that the healthy SCT donors have a spectral signature occupying short to intermediate wavelegnths in the frequency domain, PT CY recipients recover with donor-like spectra on the average, however ATG recipients tend to shift towards higher frequencies. These findings are consistent with a normal organized distribution of TRB V segment usage in healthy individuals, with PT-CY preserving this organization more consistently than ATG. Spectral (Fourier) analysis of TRB sequencing data provides a repertoire wide summary of T cell clonal distribution.

Background: The Lung Transplant Frailty Scale (LT-FS) is a validated binary measure of frailty in lung transplant candidates. We aimed to refine the LT-FS Base and Body Composition measures by developing a three-category classification to improve clinical risk stratification with serum albumin as a more clinically practical frailty biomarker. Methods: To develop the refined LT-FS, we applied a 4-step measure development method to a multicenter prospective cohort study of lung transplant candidates. Low albumin (<3.5 g/dL) replaced high serum C-reactive protein ([&ge;]10 mg/L) as the LT-FS biomarker. Cutpoints that balanced discrimination, risk prediction, and calibration were identified to define ''frail'', ''pre-frail'', and ''not frail'' categories. Next, we evaluated associations between the refined LT-FS and exercise capacity, patient-reported outcomes, and mortality using multivariate linear and Cox regression. Results: Among 419 participants, the refined LT-FS identified a graded increase in mortality risk across frailty categories. For example, compared with candidates who were not frail, pre-frailty and frailty by the LT-FS Body Composition were associated with a 2.9-fold (HR 2.9, 95% CI 1.2-6.9) and 7-fold (HR 6.9, 95% CI 2.6-18.2) higher risk of waitlist delisting or death, respectively. Pre-frailty and frailty by LT-FS Body Composition were associated with 2-fold (HR 2.1, 95% CI 1.2-3.8) and 3.6-fold (HR 3.6, 95% CI 1.8-7.3) higher risk of mortality after transplant. Relationships with exercise capacity and patient-reported disability were less consistent. Conclusions: A refined Lung Transplant Frailty Scale incorporating serum albumin demonstrates strong associations with waitlist and post-transplant mortality. The addition of a pre-frail category better captures the continuum of frailty and improves clinical risk stratification of lung transplant candidates.

Introduction Timely diagnosis of kidney allograft rejection is critical for post-transplant management, yet treatment decisions may need to be made empirically while awaiting the final pathology report. We previously demonstrated that biopsy transport medium (BTM) contains molecular signals reflective of allograft rejection. Building on these findings, we developed a rapid BTM qPCR workflow using a targeted gene panel for early molecular assessment of rejection. Methods Kidney transplant biopsies assessed at Leiden University Medical Center from April 2023 to November 2024 were eligible. A predefined gene panel was quantified in BTM by qPCR, while matched formalin-fixed paraffin-embedded tissues were profiled using Tempo-Seq. Elastic Net, Random Forest, and support vector machine models were developed to classify rejection and predict continuous histological indices directly from the BTM. Results A total of 134 paired BTM samples and biopsy tissues were analyzed. The best-performing BTM gene-panel models demonstrated good discrimination for rejection, with a median AUC of 0.80, and good calibration (median slope, 0.92; median intercept, 0.08). For continuous histological indices, the best-performing BTM gene-panel models achieved R square values of 0.33 for Activity, 0.31 for TCMR/TI, and 0.27 for AMR/MVI. Corresponding biopsy gene-panel models showed similar patterns across diagnostic outcomes. Conclusions The BTM qPCR workflow captures molecular features of kidney allograft rejection and enables prediction of rejection status along the spectrum of continuous activity indices. By providing same-day molecular information while definitive biopsy interpretation is pending, this rapid approach has the potential to complement conventional histopathological diagnostics and support early clinical decision-making.

Background: The clinical course of acute leukemias and related hematologic neoplasms is defined by aggressive clonal proliferation, severe hematopoietic disruption, and recurrent molecular alterations. Although serial bone marrow biopsies, multiparameter flow cytometry, and next-generation sequencing remain standard surveillance modalities, procedural invasiveness, high costs, and operational turnaround times limit their frequency. Minimally invasive, scalable biomarkers integrating routine hematologic indices, targeted gene expression, circulating oncoprotein levels, and patient compliance metrics may provide an accessible framework for dynamic monitoring. Methods: In this longitudinal cohort study of 66 patients across 272 serial encounters (discrete follow-up intervals: days 0, 14, 41, 71, and 78), we assessed the prognostic value of peripheral complete blood counts (white blood cell count [WBC], hemoglobin [Hb]), molecular indicators (FLT3, NPM1, and DNMT3A transcript levels; targeted soluble FLT3 quantification), and medication adherence. The primary clinical endpoint was longitudinal progression to critical deterioration or disease-related mortality. Generalized linear mixed-effects models (GLMM) with patient-specific random intercepts were constructed to account for intra-subject repeated measures. Predictors were standardized per 1-SD increment. Model performance was assessed via the Akaike Information Criterion (AIC) and the Concordance index (C-index). Results: Leukocyte expansion correlated inversely with hemoglobin concentration (r=-0.877, p<0.001), reflecting bone marrow infiltration and secondary erythroid suppression. Multivariable mixed-effects modeling confirmed that elevated WBC count (adjusted odds ratio [aOR] = 1.52 per 1-SD increase; 95% CI: 1.24-1.86; p<0.001) and reduced medication adherence (aOR = 0.58; 95% CI: 0.44-0.76; p<0.001) independently predicted adverse clinical transition. Integrating targeted molecular markers (FLT3 expression and soluble protein levels) with routine hematologic indices and adherence demonstrated superior calibration and discrimination over single-domain models ({Delta}AIC[&ge;]116; Harrells C-index = 0.81). Conclusions: Readily accessible peripheral hematologic indices, targeted molecular quantification, and medication adherence provide complementary prognostic information regarding disease activity. Integrating these low-burden parameters into clinical workflows offers an efficient, scalable paradigm for early risk stratification in hematologic malignancies.

Defining molecular patterns underlying pediatric end-stage liver disease and its treatment with liver transplantation requires rich datasets spanning a multitude of diagnoses and transplant outcomes. Here, we describe the spatial molecular landscape of approximately five million liver cell profiles from 98 liver tissue samples. The samples span donor liver, used here as a reference tissue, and end-stage diseases including biliary atresia, progressive familial intrahepatic cholestasis, hepatoblastoma, acute liver failure, and gestational alloimmune liver disease. Post-transplant liver allograft biopsies include those without and with T-cell-mediated and antibody-mediated rejection, mixed pathology, and outcome groups from immunosuppression withdrawal studies. Samples were profiled using the CosMx Human Universal Cell Characterization RNA Panel (1K) and the CosMx Human 6K Discovery Panel (6K). Preliminary exploratory analyses include cellular composition and gene abundance and variability across clinical cohorts. A twelve-gene signature comprising the genes with the highest variance implicates acute-phase response genes (e.g., SAA1) and profibrotic genes (e.g., COL1A1) as a shared link across these otherwise distinct conditions. This injury-repair-remodeling gene network warrants further investigation through higher-resolution, zone-specific spatial mapping to help clarify disease pathogenesis.

Background: Induced pluripotent stem cell (iPSC)-derived kidney organoids hold great promise for disease modeling, drug screening, and regenerative medicine. However, since these organoids are synthetic constructs, it is essential to evaluate how faithfully their constituent cell populations recapitulate the corresponding cell types in the native kidney. Single-cell transcriptomic label-transfer tools, which assign cell-type labels by integrating cells with well-annotated reference data and classifying them, are increasingly used for this purpose. Yet the reliability of the resulting labels - particularly for immature or partially differentiated cells that might have no clear in vivo counterpart - has not been systematically addressed. Methodology: To quantify the reliability of cell-type labels assigned by label-transfer algorithms, we introduce two complementary robustness measures for each cell type - the classification stability score, which assesses label stability under simulated Poisson noise, and the prediction specificity score, which assesses how well the predicted cell-type label probabilities match the assigned labels. We apply this methodology to scRNA-seq datasets from the human fetal kidney and four iPSC-derived kidney organoid protocols, using a mouse fetal kidney dataset as a reference. Results: We find that label-transfer robustness scores are generally high in most human fetal kidney cell types, except for cells of immature epithelial structures. In the organoid-derived cell subpopulations, robustness scores are high for the cap mesenchyme but vary for epithelial lineages according to each protocol's specific differentiation strategy. These observations are consistent across both classification stability and prediction specificity scores, which are generally well correlated with each other. We further validate our approach through a simulated cell-removal experiment, confirming that the proposed measures correctly identify unreliable classifications. Significance: These robustness measures provide a generalizable framework for assessing the reliability of cell-type label transfer in single-cell transcriptomic datasets, and can be extended to other biomedical settings that rely on automated single-cell classification.

Background: Chronic kidney disease (CKD) imposes persistent metabolic stress, yet whether metabolic organization can be preserved under sustained renal stress remains unclear. Essential trace elements support redox defense, mitochondrial function, and intermediary metabolism, and their homeostasis is disturbed in CKD; however, single-element supplementation has yielded heterogeneous results. We examined whether a defined multi-element formulation preserves metabolic resilience in CKD. Methods: In a randomized, double-blind, placebo-controlled trial, 80 maintenance dialysis patients received standard care alone (n = 40) or plus a trace element cocktail (TEX, 20 mL/day; n = 40). Longitudinal trajectories were analyzed with linear mixed-effects models; endpoint plasma metabolomics and fecal metagenomics were profiled in subsets. A 5/6 nephrectomy mouse model was used to quantify plasma tryptophan-pathway branches and renal spatial metabolism by MALDI imaging mass spectrometry. Results: TEX was well tolerated, with no treatment-related safety signal, and was associated with modestly more favorable creatinine and eGFR trajectories. Supplementation constrained, rather than erased, CKD-associated metabolic displacement (median |Cohen's d|, 0.25 versus 2.09 for disease effects), selectively realigning circulating metabolism toward the healthy state. The host kynurenine branch responded most strongly, whereas microbial indole metabolism and gut community structure changed little. Renal metabolic remodeling was region-specific, with preservation of selected tubular and antioxidant features. Conclusions: Trace element supplementation preserved metabolic resilience in CKD, limiting metabolic displacement without complete restoration. Preservation of metabolic organization may represent a complementary dimension of therapeutic response in CKD. Trial registration: International Traditional Medicine Clinical Trial Registry, ITMCTR2026000263 (registered 3 February 2026).

Cardiomyocyte maturation spans embryogenesis through postnatal life and involves coordinated transcriptional transitions that are disrupted in disease and incompletely recapitulated by stem cell-derived cardiomyocytes. However, no unified framework exists for assigning absolute transcriptional maturation age across datasets, platforms, and experimental conditions. Here, we developed CMAC (Cardiomyocyte Maturation Age Clock), integrating 18 murine single-cell RNA-sequencing datasets spanning embryonic and postnatal timepoints, into a batch-invariant probabilistic atlas using single cell variational inference (scVI) with a 100-dimensional latent representation. Analysis of the developmental trajectory revealed highly non-uniform transcriptional change, with 63.9% of the total transcriptional journey completed by birth and the neonatal transition representing the largest single transcriptional step. An XGBoost regressor trained on the CMAC latent space predicted absolute chronological age with a leave-one-dataset-out mean absolute error (MAE) of 4.09 days (median 3.27 days), Pearson r = 0.903, and Spearman {rho} = 0.908 across 18 datasets and six sequencing platforms, outperforming linear regression (MAE = 6.18 days) and PCA-based representations (MAE = 6.35 days). In an independent P14 cohort comprising 98,163 cardiomyocytes from six biological replicates, CMAC achieved a mouse-level MAE of 0.33 days, with all six mice predicted within 1.1 days of chronological age. Application of CMAC to a previously published PGC1/{beta} double-knockout model with established delayed cardiomyocyte maturation independently recapitulated this phenotype, quantifying maturation deficits of 4.77 to 8.60 days across P7 to P28, with the largest deficit at P14. Together, these results establish CMAC as a quantitative, deep learning framework for assigning absolute transcriptional maturation age and quantifying deviations from normal cardiomyocyte development.

Venetoclax and azacitidine (ven-aza) is a standard of care therapy for newly diagnosed acute myeloid leukemia (AML) patients unfit to receive intensive therapy, and may also be appropriate for certain fit patients. Models predicting response to ven-aza currently focus predominantly on genetics. Here, we present two multimodal fusion models: a custom ResNet-34-based model and a pathology foundation model fine-tuned for bone marrow smear (BMS) images that combine clinical and genetic variables with whole slide images. Training on 193 newly diagnosed AML patients who received ven-aza, we obtained an area-under-the-receiver-operating-characteristic (AUROC) of 0.79 and 0.82 for the ResNet-based and fine-tuned foundation models in a heldout test set, and 0.76 and 0.73 in an external validation set, predicting best response to this therapy. Sensitivity analysis showed that fusing image data with clinical and genetic variables outperformed training on either modality alone. In univariable and multivariable analyses, image-based classification was an independent predictive marker, second only in effect size to IDH1/2 mutation status, outperforming other genetic alterations. Image-based explainability showed high model attention on myeloblasts, suggesting previously unrecognized cell-specific patterns associated with response. Our findings suggest BMS images complement genetic biomarkers to refine outcome prediction in ven-aza treatment and incentivize AI-supported outcome prediction, including bone marrow morphology.

Background: In 2026, the US Food and Drug Administration (FDA) identified uveitis as a potential signal of serious risk for the B-cell maturation antigen (BCMA) x CD3 bispecific antibodies teclistamab and elranatamab. Uveitis in this setting may reflect opportunistic infection or immune-mediated inflammation, which require opposite management. Methods: We analyzed FDA Adverse Event Reporting System (FAERS) reports from July 2020 to June 2026 (8,690,601 reports after deduplication) using a case-non-case design, calculating reporting odds ratios (RORs) and the information component for primary-suspect reports. Comparators were belantamab mafodotin, talquetamab and CD3 bispecific antibodies used outside myeloma. Every primary-suspect uveitis report (n = 76) was classified as infectious, probable immune-mediated (hereafter immune-mediated) or indeterminate by a physician blinded to a rule-based algorithm. Results: Uveitis was reported in 18 of 2,995 teclistamab and 29 of 1,691 elranatamab reports (ROR 4.10 [95% CI 2.58-6.52] and 11.85 [8.21-17.12]). Of these 47 cases, 20 were infectious (18 cytomegalovirus chorioretinitis; median onset 92 days), and infection-specified uveitis was disproportionately reported for both agents and for CD3 bispecific antibodies used outside myeloma. Thirteen were immune-mediated, arose early (median onset 4 days) and were co-reported with cytokine release syndrome in six; immune-mediated uveitis showed a signal only for elranatamab (8 cases; ROR 3.59, 1.79-7.18), not for teclistamab or any comparator. Six of the eight elranatamab cases originated from Japan. Agreement between physician and algorithm was 98.7%. Conclusions: The FDA uveitis signal appears to comprise a late infectious uveitis shared by most T-cell-redirecting bispecific antibodies examined and an early immune-mediated uveitis that showed a signal only with elranatamab. These hypothesis-generating findings support prompt ophthalmological assessment of new visual symptoms and exclusion of cytomegalovirus retinitis before corticosteroids are started.

Xp11.2 translocation RCC [Xp11.2 tRCC]) is an underdiagnosed and aggressive subtype of RCC with few specific or targeted therapies. The transmembrane glycoprotein CD44 is an emerging target in many advanced malignancies, and with its ligand OPN (SPP1), is a crucial driver of cancer progression, stemness, metastasis, and immune suppression. Here we show that common TFE3-fusions [ including SFPQ-TFE3, PRCC-TFE3, ASPSCR1-TFE3, and NONO-TFE3] are associated with upregulated expression of CD44 and SPP1, as observed in multiple human and murine bulk transcriptomic studies and a murine tRCC snRNA-Seq dataset. CD44 and/or SPP1 protein expression were also upregulated in murine models of transgenic tRCC kidneys and urine specimens, patient-derived cell lines and human tRCC cases, by immunoblotting and/or IHC. Transient deletion of CD44 was associated with profound and specific suppression of tRCC cell line growth, with decreased mTOR signaling. These data suggest that CD44 and/or SPP1 may potentially drive tumorigenesis in tRCC.

Kidney disease affects over 850 million people and leads to degradation of the kidney cortical compartments and kidney failure. Interstitial fibrosis and tubular atrophy (IFTA) is a universal feature of chronic kidney disease, yet the sequence of tubulointerstitial matrix protein (matrisome) changes that underlie the development and progression of IFTA are not fully understood. In particular, post-translational modifications which may stabilize key matrisome components and result in irreversible and functionally detrimental interstitial fibrosis or tubular basement membrane thickening, have yet to be characterized. Post-translational modifications (e.g., acetylation) have been shown to contribute to kidney diseases, yet they have not been investigated in chronic kidney disease progression. Herein, we established feasibility of matrisome protein quantification in the tubulointerstitium, with concurrent matrisome protein post-translational modification quantification, in archived diagnostic human kidney biopsies, using laser capture microdissection, liquid chromatography tandem mass spectrometry (LC-MS/MS), and bioinformatics analysis. In this pilot study, we focused on two conditions associated with IFTA progression: diabetic nephropathy and kidney transplantation. We examined both healthy and chronically damaged tubulointerstitium in two human kidney biopsies, one representing each condition. We identified signature protein markers of tubulointerstitium (e.g., UMOD, SLC12A1) in abundance, confirming sample composition, and observed uniformity in protein abundance across replicates from the same biopsies. We found increases in certain collagen isoforms (COL1A1, COL6A3) and other ancillary proteins in areas of IFTA. We also successfully quantified post-translational modifications, confirming that hydroxylproline was the most abundant modification in healthy tubulointerstitium from the kidney transplant biopsy, and that hydroxyllysine was also highly abundant and could be confidently quantitated on two collagen isoforms (COL1A1, COL4A2). Lastly, we performed power analysis to determine the required sample size to achieve significant power and false discovery rate. Several proteins exhibited large Hedge effect sizes in the tubulointerstitium (ANPEP, PRODH2), indicating their potential usefulness as fibrosis signatures. Our study is a first step toward comprehensive characterization of tubulointerstitial matrisome changes, including post-translational modifications seen with IFTA progression. Subsequent studies will potentially reveal new molecular markers of key early events in chronic kidney disease progression, and new therapeutic targets for mitigating this process. Data are available via ProteomeXchange identifier PXD079025.

Metabolic disturbance is a key feature in acute kidney injury (AKI) and chronic kidney disease (CKD). The kidney highly depends on fatty acid metabolism; how renal cells rewire metabolism upon AKI and CKD transition remained unclear. Here, we combine spatial transcriptomics with biochemical analyses to characterize metabolic changes and intercellular interactions during AKI to CKD transition. Using aristolochic acid model of CKD, we spatio-temporally correlated changes in structure and function with metabolic profile during AKI to CKD transition. Surprisingly, keto-gluconeogenic metabolic axis was highly enriched in healthy kidney and paralleled injury phase transitions. Intercellular interaction analysis associated a subtype of macrophage with the drift to chronicity. Online scRNAseq datasets analysis confirmed altered keto-gluconeogenic metabolic pathways in ischemia-reperfusion injured mouse kidneys and AKI and CKD human kidney biopsies compared to healthy controls. This study identifies a metabolic axis transcriptionally mirroring renal homeostasis and injury responses, and tubulo-interstitial interactions that can be targeted to mitigate AKI to CKD transition.

Abstract Endothelial cell (EC) injury is central to the pathogenesis of various renal disorders, highlighting the critical importance of vascular repair for restoring organ health. However, due to limited access to human tissue during active regeneration, the exact reparative processes remain largely unknown. This study aimed to investigate regeneration mechanisms specifically within the glomerular microvasculature by utilizing a selective endothelial cell injury (ECI) model in mice.Lineage tracing identified the origin of regenerating ECs. To uncover the transcriptional dynamics driving these reparative changes, we combined single-cell RNA sequencing and Xenium spatial transcriptomics (ST), mapping distinct glomerular EC (gEC) subpopulations across different phases of injury and recovery. Apold1 emerged as a key regulatory candidate within this process. To functionally validate this gene's specific role during the regenerative phase, Apold1-deficient (Apold1-/-) mice underwent the ECI model. After showing that gECs regenerate primarily from local, pre-existing renal ECs, our transcriptomic analysis identified 10 distinct gEC subpopulations and revealed a complex temporal pattern of regeneration. This process begins with the expression of genes associated with inflammation, followed sequentially by endothelial cell (EC) migration, the activation of immediate early genes, vascular remodeling and cellular proliferation. Within this dynamic process, the EC-specific gene Apold1 was identified as a key regulatory candidate. Apold1-/- mice exhibited impaired endothelial regeneration, characterized by increased endothelial activation and greater immune cell infiltration seven days post-ECI. To our knowledge, this is the first study to elucidate the spatiotemporal mechanisms of glomerular endothelial cell regeneration. By identifying local gECs as the primary source of repair, and stablishing the change in their expression pattern during regeneration, as well as identifying Apold1 as an intrinsic regulator, we have laid the groundwork for future research on renal microvascular disorders.