Citations

Study Design Narrative Review. Objectives To summarize the scientific contributions generated from the AO Spine Knowledge Forum Tumor (AOSKFT) databases, focusing on primary spine tumors, and highlight key findings, research trends, and future directions. Methods Data from the Primary Tumor Retrospective (PT-Retro) and Primary Tumor Research Outcome Network (PTRON) registries were analyzed. The nineteen studies included were peer-reviewed manuscripts focused on primary spine tumors, excluding abstracts, book chapters, systematic reviews, and metastatic studies. Results The PT-Retro registry compiled data from 1495 patients across 18 primary tumor histologies, offering insights into recurrence, survival, and treatment paradigms. Key findings emphasize the importance of Enneking-appropriate (EA) resection in improving survival and reducing recurrence in tumors such as chordoma, chondrosarcoma, and osteosarcoma. Genetic markers, including hTERT promoter mutations and rs2305089 SNP, were linked to prognosis in specific histologies. Benign tumors, such as giant cell tumors and aneurysmal bone cysts, demonstrated variable outcomes with different surgical approaches and selective arterial embolization. Conclusions The AOSKFT registries have significantly advanced knowledge in primary spine tumor management, emphasizing preoperative staging, surgical margins, and multidisciplinary approaches. International, multicentric registries are essential for studying rare diseases like primary spine tumors, enabling robust data collection, improved statistical power, and broader applicability of findings across diverse clinical settings. Ongoing prospective data collection through PTRON will further refine evidence-based care for these rare and challenging conditions.

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen's kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure 1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure 1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure 2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure 2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

SUMMARY Artificial intelligence (AI)-based methods are increasingly critical in peptide drug discovery, but their applications are limited in a narrow scope such as antimicrobial peptides. For indications where peptide therapy inherently excels, such as metabolism, endocrinology, and tissue regeneration, AI-based pipeline for therapeutic peptide discovery is long awaited but still unmet. Here, we propose a deep learning-based pipeline, Deepeptide, capable of identifying therapeutic oligopeptides for various metabolism-related indications. Leveraging the intrinsical relationship of ‘disease indication — biological processes — molecular functions’, Deepeptide discovers oligopeptides with indication-ameliorating-related molecular functions as lead candidates for indication of interest. Deepeptide was applied in five representative indications of metabolism, endocrinology, and tissue regeneration: angiogenesis, lipid metabolism, osteogenesis, glucose metabolism, and anti-angiogenesis. Overall, 62% of the identified oligopeptide candidates demonstrated significant bioactivity in vitro, with most of them showing comparable potency to the first-line drugs. Notably, the heptapeptide AP7 exhibited angiogenic potency comparable to VEGF in excisional wound splinting mouse model by promoting cell migration rather than proliferation, and hexapeptide TP6 showed significant dual-efficacy against hyperlipidemia and obesity in high-fat diet mice by inhibiting lipid synthesis and regulating gut microbiota. These findings highlight the potential and generalizability of Deepeptide in therapeutic oligopeptide discovery for metabolic diseases.

The success of living donor liver transplantation (LDLT) has bridged the growing gap between organ demand and availability in patients with liver failure or liver cancer. Safety of the living liver donor (LLD) is paramount, and astute intraoperative management plays a critical role in ensuring optimal outcomes for them. Given that intraoperative surgical and anesthesia practices vary widely across centers, the International Liver Transplantation Society (ILTS) and the International Living Donor Liver Transplantation Group (iLDLT Group) convened a global consensus conference in Toronto in March 2025 to develop guidelines for intraoperative management of the LLD. This manuscript details the recommendations on intraoperative considerations for the safety of LLD developed by Working Group II of the Consensus Scientific Committee, using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system for assessment of recommendations, and the Danish model of consensus. Working Group II focused on intraoperative monitoring, fluid and ventilation strategies, surgical techniques for vascular and biliary division, surgeon and center-specific experience, and intraoperative crises planning. These guidelines aim to support both established and emerging LDLT teams to ensure high-quality and safe intraoperative care for the LLD.

Abstract Multiple lines of evidence suggest that calorie restriction may slow biological processes related to aging and extend lifespan. To determine whether calorie restriction affects mitochondrial function, we treated fibroblasts from adult donors with serum samples obtained from participants of the CALERIE (Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy) study. The CALERIE study enrolled healthy, non-obese middle-aged (average 38y.o) participants to examine the effects of long-term calorie restriction. We studied the serum samples of 177 participants (calorie restriction CR = 121, ad libitum AL = 56) who had specimens available at two timepoints (12 and 24 months). We assessed the basal and maximal mitochondrial respiration of fibroblasts treated with dilute serum for 24 hours. Our results show that the baseline basal and maximal mitochondrial respiration of fibroblasts is positively correlated with the adiposity of the serum donor (R = 0.112, P = 0.137; R = 0.183, P = 0.015, respectively). Interestingly, treatment of fibroblasts with serum collected after 12 months of calorie restriction causes a decrease in both basal and maximal mitochondrial respiration (P = 0.1854; P = 0.0623, respectively). Fibroblasts treated with serum collected after 24 months of calorie restriction exhibited a further reduction in both basal and maximal mitochondrial respiration (P = 0.0174; P = 0.0092, respectively). These findings suggest that the serum of individuals on long term calorie restriction diet comprises factors that can reduce oxidative phosphorylation. This reduction in bioenergetic capacity may align with the theory that calorie restriction leads to a cellular energy-conserving state, which could have implications for cellular aging and metabolism.

INTRODUCTION: Evidence on the comparative real-world effectiveness of terlipressin vs midodrine plus octreotide (MO) for hepatorenal syndrome-acute kidney injury (HRS-AKI) in the United Kingdom and the United States is limited. METHODS: Using individual-level chart review data for patients across the United Kingdom (2013–2017) and the United States (2016–2019), an indirect treatment comparison was conducted comparing the efficacy of terlipressin (UK cohort) with MO (US cohort). Covariate balancing propensity scoring matched the cohorts on baseline serum creatinine (SCr), presence of encephalopathy and/or ascites, albumin use and duration, age, and sex. The primary endpoint was HRS reversal, defined as achieving SCr ≤1.5 mg/dL by the last day of treatment. RESULTS: At treatment initiation, 90.2% of UK patients received terlipressin (194/215), while 89.2% of US patients received MO (140/157). Concomitant albumin was administered in 67.9% of UK and 98.7% of US patients. In a covariate balancing propensity score-adjusted cohort, HRS reversal was achieved in 53.2% of terlipressin-treated patients (the United Kingdom, weighted effective sample size of 75) compared with 16.9% of MO-treated patients (the United States, n = 89) (adjusted mean difference (95% CI) 36.3% (22.4, 50.2), P < 0.0001). In adjusted analysis, individuals treated with terlipressin experienced an overall reduction in SCr at completion of treatment (SCr decrease 1.00 mg/dL vs increase of 0.08 mg/dL for MO-treated patients, P < 0.0001). DISCUSSION: HRS-AKI treatment and outcomes differ between the United Kingdom and the United States, attributed to the historical standard of care MO in the United States. In adjusted analyses, real-world use of terlipressin was more effective than MO at improving kidney function and achieving HRS-AKI reversal.

Parkinson’s Disease (PD) is a neurodegenerative disorder in which gait abnormalities serve as key indicators of motor impairment and disease progression. Although wearable sensor-based gait analysis has advanced, existing methods still face challenges in modeling multi-sensor spatial relationships, extracting adaptive multi-scale temporal features, and effectively integrating time–frequency information. To address these issues, this paper proposes a multi-sensor gait neural network that integrates biomechanical priors with time–frequency collaborative learning for the automatic assessment of PD gait severity. The framework consists of three core modules: (1) BGS-GAT (Biomechanics-Guided Graph Attention Network), which constructs a sensor graph based on plantar anatomy and explicitly models inter-regional force dependencies via graph attention; (2) AMS-Inception1D (Adaptive Multi-Scale Inception-1D), which employs dilated convolutions and channel attention to extract multi-scale temporal features adaptively; and (3) TF-Branch (Time–Frequency Branch), which applies Real-valued Fast Fourier Transform (RFFT) and frequency-domain convolution to capture rhythmic and high-frequency components, enabling complementary time–frequency representation. Experiments on the PhysioNet multi-channel foot pressure dataset demonstrate that the proposed model achieves 0.930 in accuracy and 0.925 in F1-score for four-class severity classification, outperforming state-of-the-art deep learning models.

Abstract Background Plasma biomarkers offer improved accessibility compared to established diagnostic modalities for Alzheimer's disease (AD). However, current plasma biomarker candidates have not been tested comprehensively in diverse populations, and represent only a fraction of pathways dysregulated in AD. This study seeks to address these knowledge gaps through untargeted plasma biomarker discovery in African Americans (AA), an underrepresented group in AD research despite having a higher risk of developing dementia compared to non‐Hispanic Whites (NHW). Method Untargeted proteomics were generated using the SomaScan 7k platform on plasma samples from AA study participants clinically diagnosed as AD dementia ( n = 181) or cognitively unimpaired (CU; n = 142). Machine learning was implemented to identify a set of plasma proteins to distinguish between AD dementia and CU. Predictive models were constructed by elastic net regression optimized with nested cross‐validation on 6,853 proteins and evaluated by receiver operating characteristic analysis. The generalizability of plasma protein predictors was evaluated in an external Alzheimer's Disease Research Center cohort of 369 NHW and AA study participants diagnosed as AD dementia or CU. Result Thirty‐six proteins were selected as predictors. Proteins selected by machine learning were enriched for processes previously associated with AD including synaptic dysfunction, inflammation, vascular dysfunction, and amyloid processing. The 36 plasma proteins alone achieved an area under the curve (AUC) of 0.94 to classify AD dementia in the discovery cohort, representing a 22% improvement over a model including age and sex. The 36 proteins were validated in an external cohort also achieving AUC=0.94, a 35% improvement over age and sex. Conclusion Biomarker discovery combining untargeted proteomics and machine learning identified a set of plasma proteins that could serve as a novel and highly accurate diagnostic plasma biomarker panel for AD. Work is underway to assess the association of the predictive panel with p ‐tau217, medical comorbidities, and social/structural determinants of health.

Abstract Background More than 85% of Alzheimer's disease (AD) donor brains exhibit some degree of cerebral amyloid angiopathy (CAA), which is characterized predominantly by Aβ40 deposits in the brain vasculature and can cause blood brain barrier (BBB) leakage. CAA is associated with risk of infarcts, cerebral hemorrhages, and cognitive decline. Brain cell type‐specific mechanisms that influence CAA pathology, as well as related measures such as Aβ40 biochemical measures and tight junction proteins, an indicator of BBB integrity, are still elusive. Method We performed single‐nucleus RNA sequencing (snRNAseq) of temporal cortex tissue from 79 AD donors, with varying levels of CAA co‐pathology. Measurements of soluble, insoluble and membrane‐associated fractions of brain Aβ40 and tight junction protein (claudin‐5, occludin) levels were available for these donors. We correlated these measures with cell proportions. We also conducted differential gene expression and analyses for intercellular communications and regulatory networks. Result We identified 25 clusters and annotated them with major brain cell types. There were 3 oligodendrocyte, 7 excitatory and 7 inhibitory neuronal, 2 microglial, 2 astrocytic, and 1 each for endothelial, pericytic, fibroblast, and oligodendrocyte precursor cell clusters. We found that higher CAA and Aβ40 levels correlate with reduced neuronal and increased microglial, astrocytic and vascular cell proportions. Increased tight junction proteins correlate with increased inhibitory neuronal and reduced microglial, astrocytic and vascular cell proportions. Among all clusters, one microglial, both astrocytic and the pericytic clusters have greater number of genes significantly associated with CAA, Aβ40 and tight junction protein levels. Most significant genes have negative associations with CAA and membrane‐bound Aβ40 and positive with tight junction protein measures. Using these genes as input for intercellular communications analysis, we prioritized ligand‐target interactions from microglia or astrocytes to pericytes. Some of the prioritized targets are transcription factors in pericytes. Using regulon analysis, we identified genes downstream of these transcription factors that are also significantly associated with lower CAA and membrane‐bound Aβ40 and higher tight junction protein levels. Conclusion We revealed glia‐to‐pericytes intercellular interactions and pericytic gene regulatory networks that are potentially perturbed by CAA and BBB breakdown. Future directions include analyzing external snRNAseq datasets and performing functional validations.

Abstract Background Aging and dementia are generally associated with lower metabolism in gray matter on FDG‐PET and have been widely studied. However, white matter (WM) metabolic changes remain understudied. We investigated the metabolic changes in WM associated with aging and dementia and their relationships with established Alzheimer's Disease (AD) biomarkers and cognitive outcomes. Method We analyzed data from 3,169 participants (72 ± 9 years; 53% male; 65% cognitively unimpaired) enrolled in the Mayo Clinic Study of Aging (MCSA) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). All participants underwent baseline MRI, amyloid‐PET, and FDG‐PET scans, as well as neuropsychological evaluations, with at least one clinical follow‐up. Voxel‐wise multiple regression analysis identified WM metabolic changes associated with global cognition in cognitively unimpaired MCSA participants ( n = 1,754). Univariate analyses and structural equation models were applied to elucidate the driving factors underlying the WM metabolic changes in MCSA data. Multiple regression and linear mixed‐effects models evaluated the explanatory utility of WM metabolism for baseline global cognition and cognitive decline in both cohorts. In a subset of MCSA participants, we evaluated neurite density index from diffusion MRI as a surrogate of WM health and glial fibrillary acidic protein (GFAP) as a surrogate of neuroinflammation to understand underlying mechanisms. Result We identified an atypical WM signature (AWM‐Signature) in the anterior and superior corona radiata, where glucose metabolism increased as cognitive performance worsened (Figure 1). Hypermetabolism in AWM‐Signature was related to increased age, amyloidosis, vascular disease, and neuroinflammation, as well as decreasing WM neurite density in the same regions (Figure 2). Hypermetabolism in AWM‐Signature regions predicted faster future cognitive decline, even after accounting for demographics, amyloid, and vascular disease metrics. The results observed in the AWM‐Signature regions were consistently in the opposite direction of the associations seen with gray matter and typical WM regions. Conclusion We found unexpected metabolism increases in corona radiata, a hub that carries significant portion of the brain's neural traffic, with aging and disease which likely indicates a compensatory response. These changes may be useful cognitive resilience measures and require further investigation to understand their role in maintaining cognitive function.