Citations

Study Design Retrospective Cohort. Objectives Craniovertebral instability can arise from various congenital or acquired conditions, but definitive management often requires craniocervical fusion. This study evaluates whether postoperative clivo-axial angle (CXA) can predict distal junctional failure in patients undergoing craniocervical fusion. While postoperative alignment of the head and cervical spine can be quantified via the CXA, it is unclear whether the CXA can predict distal junctional failure. Methods All patients undergoing craniocervical decompression and fusion (CCF) for craniovertebral instability between 2012 and 2023 at a single institution were identified. Patients experiencing adjacent segment disease (ASD) were identified and compared to those without said pathology based on CXA, Grabb-Oakes line (pBC2), and the presence of cervical kyphosis, cervical disc degeneration, or subluxation on pre- and postoperative imaging. Statistical analysis included Shapiro-Wilk tests for normality, followed by independent t-tests or Mann-Whitney U tests with FDR adjusted P values as appropriate, and univariable logistic regression. Results 71 patients were included, of whom ten (14.3%) developed distal junctional kyphosis and 3 (4.3%) developed cervical spondylolisthesis. The median postoperative CXA was significantly lower in non-ASD patients (144° [IQR: 140-148°] vs 158° [IQR: 153-162°]; P < .001) and logistic regression showed that each 10° degree increase in CXA was associated with a 6.7-fold higher odds of ASD. Conclusions We found craniocervical alignment, as assessed by CXA, independently predicted distal junctional disease in patients undergoing craniocervical fusion, possibly due to low preoperative CXA in these patients. Large increases in CXA may contribute to postoperative instrumentation failure.

Background: Patients presenting with non-ST elevation myocardial infarction (NSTEMI) are often admitted to medical wards. We aimed to evaluate the impact of the admitting department on long-term outcomes. Methods: Patients admitted to a large tertiary center were categorized according to the admission ward, either the intensive cardiac care unit (ICCU) or internal medicine department (IMD). We compared major adverse cardiovascular events (MACEs), a composite of all-cause death, recurrent myocardial infarction (MI), and revascularization, along with the individual components of MACE, between the two groups during a long-term follow-up. Results: A total of 11,779 NSTEMI patients were included, with 4522 admitted to the ICCU and 7257 to the department of internal medicine. Patients admitted to the ICCU had lower systolic blood pressure, higher troponin levels and lower left ventricular ejection fraction (LVEF) compared to those in the IMD group, indicating greater initial clinical severity. Although patients admitted to the ICCU experienced a significantly higher rate of in-hospital complications, there were no significant differences in the incidence of in-hospital deaths between the two groups. During 5-year follow-up, NSTEMI patients initially admitted to the ICCU had significantly lower rates of mortality and MACEs. The estimated hazard ratio for 5-year MACE and 5-year mortality rates for NSTEMI patients admitted to the IMD vs. those admitted to the ICCU were 2.03 (95% CI, 1.04–3.34) and 2.5 (95% CI, 1.10–4.38), respectively. Conclusions: NSTEMI patients admitted to the ICCU experienced lower long-term mortality and MACE rates. These findings support the management of NSTEMI patients in cardiac wards and warrant further research into the reasons for the improved outcome.

Abstract Motivation Multiplex immunofluorescence (MxIF) enables the quantification of multiple protein markers at a single-cell level while preserving spatial information, offering a powerful tool for studying tissue microenvironments. However, the flexibility in MxIF panel design poses challenges in standardizing cell phenotyping. Results We present SpaFlow, an efficient, customizable pipeline for unsupervised clustering and classification of MxIF data, implemented using Nextflow. SpaFlow performs quality control, clustering, and postclustering analysis on segmented and quantified MxIF data, facilitating reproducible and scalable analyses across various computing platforms. The SpaFlow pipeline integrates three clustering and classification packages—Seurat, SCIMAP, and CELESTA—each providing unique methodologies for identifying cell types based on phenotypic markers. A novel “meta-clustering” approach condenses clusters across multiple regions of interest into common meta-clusters, streamlining the cell-type identification process in large datasets. SpaFlow’s robust quality control steps, including signal summation and cell density filtering, mitigate artifacts that may impact clustering accuracy. We demonstrate the utility of SpaFlow in a case study involving 297 ovarian tumor cores, where SpaFlow successfully identified biologically meaningful cell populations, including tumor-infiltrating lymphocytes, efficiently and rapidly. Additionally, SpaFlow’s reproducibility is validated using serial tonsil sections, confirming its capability to consistently identify distinctive cell populations across matched ROIs. Availability and implementation SpaFlow is freely available with detailed documentation and examples at https://github.com/dimi-lab/SpaFlow.

TPS788 Background: MSLN is a desirable candidate for targeted therapy given its limited expression in normal tissues and its overexpression in several malignancies, including PANC, mesothelioma, non-small cell lung, and ovarian cancers (TCGA 2022). MSLN-targeted CAR T and T-cell receptor therapies have shown promising clinical activity; however, on-target, off-tumor toxicity, including fatal events, have occurred (1-3). Autologous logic-gated Tmod CAR T therapies address the challenges of on-target, off-tumor toxicity by combining 2 CARs: activating and blocking receptors. The activator recognizes a tumor-associated antigen present on the surface of both tumor and normal cells; the blocker prevents CAR T activity when it binds HLA-A*02, which is present in normal cells and often lost in tumor cells. Thus, in patients with tumor-associated HLA-A*02 LOH, the blocker prevents on-target, off-tumor toxicity on normal cells due to retained HLA-A*02 expression (4). HLA-A*02 LOH is observed in various solid tumors, including ~20% of PANC (5). Two autologous CAR T therapies are currently under investigation: A2B530, targeting carcinoembryonic antigen, in the EVEREST-1 trial, and A2B694, targeting MSLN, in the EVEREST-2 trial, described herein. A2B694 may provide a therapeutic window to treat patients with PANC and other MSLN-expressing solid tumors exhibiting HLA-A*02 LOH due to its unique discriminatory mechanism. Methods: EVEREST-2 (NCT06051695) is a first-in-human, phase 1/2, open-label, nonrandomized study to evaluate the safety and efficacy of A2B694 for recurrent unresectable, locally advanced, or metastatic cancers with MSLN expression, including PANC. Eligible patients must have exhausted options of potentially curative therapy and have recurrent disease. Enrollment to EVEREST-2 occurs through the prescreening study BASECAMP-1 (NCT04981119), in which patients with tumor-associated HLA-A*02 LOH are identified via next-generation sequencing (Tempus AI, Inc) and leukapheresis product is collected. A2B694 is manufactured from cryopreserved T cells when clinically appropriate for patients. The primary objective of phase 1 is to evaluate the safety and tolerability of A2B694 and identify the recommended phase 2 dose (RP2D). The dose-expansion phase will confirm RP2D and collect biomarker data to further characterize A2B694. Phase 2 will assess the primary endpoint overall response rate per RECIST v1.1 and secondary endpoints progression-free survival and overall survival. The first patient was dosed on April 24, 2024 and dose escalation is ongoing. 1. Beatty, et al. Gastroenterology . 2018. 2. Haas, et al. Mol Ther . 2023. 3. Hong, et al. ESMO 2021. Abstr 9590. 4. Hamburger, et al. Mol Immunol . 2020. 5. Hecht, et al. J Clin Oncol . 2022. Clinical trial information: NCT06051695 .

AbstractBackgroundRecent advances in automatic face recognition have increased the risk that de‐identified research imaging data could be re‐identified from face imagery in brain scans.MethodAn ADNI committee of independent imaging experts evaluated 11 published techniques for face‐deidentification (“de‐facing”) and selected four algorithms (FSL‐UK Biobank, HCP/XNAT, mri_reface, and BIC) for formal testing using 183 longitudinal scans of 61 racially and ethnically diverse ADNI participants, evaluated by their facial feature removal on 3D rendered surfaces (confirming sufficient privacy protection) and by comparing measurements from ADNI routine image analyses on unmodified vs. de‐faced images (confirming negligible side effects on analyses).ResultEffects of the de‐facing methods on FreeSurfer at UCSF (Figure 1‐top), ASHS at University of Pennsylvania (Figure 1 bottom‐left), and longitudinal atrophy at Mayo Clinic (Figure 1 bottom‐right) were comparable with each other and sufficiently small vs. unmodified scans to recommend de‐facing in ADNI‐4. The FSL and mri_reface methods were more thorough in removing facial features, partly because these also removed ears (Figure 2‐top). mri_reface was recommended for ADNI‐4 primarily because fewer (zero) images had modified brain (Figure 2‐bottom), and ADNI leadership approved de‐facing with mri_reface. ADNI performed a second validation of its latest version on another diverse sample of 100 ADNI‐3 participants. These analyses included: boundary shift interval at University College London, where average differences in measurements were all <0.05%; cortical white matter hyperintensities at UC Davis, where they found “minimum impact” (R>0.97); and others.ConclusionADNI‐4 de‐faces all indicated image types (Table 1) before subsequent pre‐processing, analyses, and public release. Sites upload images to the central repository (LONI), where they are queued to the MRI Core at Mayo Clinic for de‐facing. Image types requiring de‐facing are de‐faced using mri_reface. Trained analysts inspect de‐faced images to confirm complete removal of the face and complete non‐modification of the brain, and any flagged images are re‐processed manually until issues are resolved. De‐faced images are converted back to DICOM and re‐uploaded for further distribution. Image types not requiring de‐facing are indicated to proceed directly to subsequent steps. In summary, ADNI‐4 has adopted mri_reface because it successfully de‐faces without substantially affecting brain measurements.

AbstractBackgroundRecent advances in automatic face recognition have increased the risk that de‐identified research imaging data could be re‐identified from face imagery in brain scans.MethodAn ADNI committee of independent imaging experts evaluated 11 published techniques for face‐deidentification (“de‐facing”) and selected four algorithms (FSL‐UK Biobank, HCP/XNAT, mri_reface, and BIC) for formal testing using 183 longitudinal scans of 61 racially and ethnically diverse ADNI participants, evaluated by their facial feature removal on 3D rendered surfaces (confirming sufficient privacy protection) and by comparing measurements from ADNI routine image analyses on unmodified vs. de‐faced images (confirming negligible side effects on analyses).ResultEffects of the de‐facing methods on FreeSurfer at UCSF (Figure 1‐top), ASHS at University of Pennsylvania (Figure 1 bottom‐left), and longitudinal atrophy at Mayo Clinic (Figure 1 bottom‐right) were comparable with each other and sufficiently small vs. unmodified scans to recommend de‐facing in ADNI‐4. The FSL and mri_reface methods were more thorough in removing facial features, partly because these also removed ears (Figure 2‐top). mri_reface was recommended for ADNI‐4 primarily because fewer (zero) images had modified brain (Figure 2‐bottom), and ADNI leadership approved de‐facing with mri_reface. ADNI performed a second validation of its latest version on another diverse sample of 100 ADNI‐3 participants. These analyses included: boundary shift interval at University College London, where average differences in measurements were all <0.05%; cortical white matter hyperintensities at UC Davis, where they found “minimum impact” (R>0.97); and others.ConclusionADNI‐4 de‐faces all indicated image types (Table 1) before subsequent pre‐processing, analyses, and public release. Sites upload images to the central repository (LONI), where they are queued to the MRI Core at Mayo Clinic for de‐facing. Image types requiring de‐facing are de‐faced using mri_reface. Trained analysts inspect de‐faced images to confirm complete removal of the face and complete non‐modification of the brain, and any flagged images are re‐processed manually until issues are resolved. De‐faced images are converted back to DICOM and re‐uploaded for further distribution. Image types not requiring de‐facing are indicated to proceed directly to subsequent steps. In summary, ADNI‐4 has adopted mri_reface because it successfully de‐faces without substantially affecting brain measurements.

Abstract INTRODUCTION The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is evaluating lifestyle interventions in older adults at risk for cognitive decline and dementia. Here we characterize the baseline data set of the POINTER Imaging ancillary study. METHODS Participants underwent health and cognitive assessments and neuroimaging with multimodal positron emission tomography (PET) (beta‐amyloid [Aβ] and tau) and magnetic resonance imaging (MRI). Framingham risk score (FRS) was used to quantify cardiovascular disease (CVD) risk. RESULTS A total of 1052 participants (31% from underrepresented ethnoracial groups) were enrolled. Compared to Aβ−, Aβ+ (29%) participants were older, had higher apolipoprotein E ( APOE ) ε4 carriage rate and white matter hyperintensity volume, and greater temporal tau. FRS was related to MRI measures, but not AD biomarkers. FRS and tau had independent effects on cognition. DISCUSSION In this heterogenous, at‐risk cohort, CVD risk was related to more abnormal brain structure and poorer cognition, representing a putative non‐AD (Alzheimer's disease) pathway to brain injury and cognitive decline. Highlights The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) cohort is enriched for cardiovascular disease (CVD) and poor lifestyle POINTER Imaging collected multimodal neuroimaging data in this unique, at‐risk cohort Amyloid burden was related to age, apolipoprotein E ( APOE ) ε4 carriage, and measures of disease progression Associations between amyloid and tau, and tau and cognition, were relatively weak CVD risk and tau pathology were independently related to memory

We previously showed that both reactive oxygen species-inducing chemotherapy and some genetic subtypes of B precursor acute lymphoblastic leukaemia (ALL) can induce bone marrow (BM) mesenchymal stromal cells (MSC) to become cancer associated fibroblasts (CAFs). Exposure of MSC to ALL-cell conditioned media (CM) is sufficient to generate CAFs - direct cell-cell contact is not required (Burt, Dey et al, 2024). We showed that CAFs can lead to chemoresistance via transfer of mitochondria to ALL cells through tunnelling nanotubes (Burt, Dey et al 2019). We noted that CAFs in the ALL BM had features of senescence, prompting the current study in which we aimed to investigate the link between senescence and CAFs in ALL. We used 3 genetic subtypes of ALL; BCR::ABL1 (SD1 cells, representing “adult” ALL) , ETV6::RUNX1 (REH cells, representing “childhood” ALL) and KMT2A::AFF1 (SEM cells, known from our previous work NOT to induce CAFs). We exposed HS27a stromal cells to the ALL CM at a number of timepoints over a period of three weeks and characterised senescence using qPCR, cytochemical B-galactosidase assays, flow cytometry and immunofluorescence (IF). Cytokine arrays were performed to quantify the senescence associated secretory phenotype (SASP) components and assess the complexity of SASP components in all three ALL subtypes. A more prolonged exposure (&gt; 10 days) to CM was required for senescence to develop in contrast to CAF formation which occurs early (2-3 days) in HS27a MSC cells. Senescence marker expression peaked at around day 11 to day 14 of CM exposure before subsiding to lower levels around day 21. On day 14, 70% of HS27a cells treated with SD1 CM were positive for beta galactosidase compared to below 25% positive for REH and SEM cells. Cell cycle arrest was demonstrated by P16 and Ki67 IF. 95% of SD1 CM treated HS27a cells were positive for P16 compared to less than 10% in RPMI control. Likewise, only 29% of HS27a exposed to SD1 CM were Ki67 positive compared to 66% in the RPMI control. Expression levels of other senescence-associated markers such as SERPINE1, SERPINB2, SOD2, NFKB1 were quantified by qPCR and were all significantly (p&lt;0.001) upregulated in SD1 CM treated HS27a (2 to 40 fold) compared to SEM CM (-2 to 5 fold) and REH CM (-0.5 to 1.0) treated HS27a. SASP associated cytokines IL6 (1000-5000pg/ml), IL8 (2500-5000pg/ml), CXCL10 (500-1500pg/ml), CCL5 (1500-2250pg/ml), IL10 (25-50pg/ml), were significantly (p&lt;0.0001) upregulated in the SD1 CM treated HS27a compared to controls and other ALL cell line exposed HS27a. Finally, HMGB2 and LMNB1 genes, loss of which is associated with senescence were significantly (p&lt;0.001) downregulated in the SD1 CM (-2.5 to 0.5 fold) treated HS27a compared to controls. In view of the prominence of senescence in SD1-exposed HS27a cells, we tested the effects of two different senolytic drugs venetoclax (BCL2i) and navitoclax (BCL2i/BCLXLi) in vitro. Briefly, HS27a cells were exposed to SD1 CM for 11 days, and after washing, increasing concentrations of the drugs were added for up to 72 hrs followed by washing, fixing and DAPI-staining and counting. HS27a exposed to SD1 CM was highly sensitive to low dose treatment of navitoclax (0.5uM) with 80% cell death 48 hrs post treatment. HS27a cells exposed to SD1 CM were less sensitive to venetoclax with only 50% cell death observed at higher doses (5uM) even after 72 hrs post treatment. Further senolytic drug screening experiments are ongoing. Our data suggest that BCR::ABL1 but not ETV6::RUNX1 or KMT2A::AFF1 ALL cells, can induce paracrine senescence in HS27a MSC cells. We suggest that paracrine senescence in the stromal microenvironment may be a component of treatment response in B-ALL. ^^ and ** contributed equally

Abstract Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, with personalized medicine, systems biology, and biomedical applications. The application of MS-based proteomics advances our understanding of protein function, cellular signaling, and complex biological systems. MS data analysis is a critical process that includes identifying and quantifying proteins and peptides and then exploring their biological functions in downstream analyses. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets with DIA-NN preinstalled. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analyses. ProtPipe provides downstream analyses, including protein and peptide differential abundance identification, pathway enrichment analysis, protein–protein interaction analysis, and major histocompatibility complex (MHC)–peptide binding affinity analysis. ProtPipe generates annotated tables and visualizations by performing statistical post-processing and calculating fold changes between predefined pairwise conditions in an experimental design. It is an open-source, well-documented tool available at https://github.com/NIH-CARD/ProtPipe, with a user-friendly web interface.

Abstract INTRODUCTION A key goal of the Alzheimer's Disease NeuroImaging Initiative (ADNI) positron emission tomography (PET) Core is to harmonize quantification of β‐amyloid (Aβ) and tau PET image data across multiple scanners and tracers. METHODS We developed an analysis pipeline (Berkeley PET Imaging Pipeline, B‐PIP) for ADNI Aβ and tau PET images and applied it to PET data from other multisite studies. Steps include image pre‐processing, refacing, magnetic resonance imaging (MRI)/PET co‐registration, visual quality control (QC), quantification of tracer uptake, and standardization of Aβ and tau standardized uptake value ratios (SUVrs) across tracers. RESULTS Measurements from 10,105 cross‐sectional and longitudinal Aβ and tau PET scans acquired in several studies between 2010 and 2024 can be processed, harmonized, and directly merged across tracers and cohorts. DISCUSSION The B‐PIP developed in ADNI is a scalable image harmonization approach used in several observational studies and clinical trials that facilitates rigorous Aβ and tau PET quantification and data sharing. Highlights Quantitative results from ADNI Aβ and tau PET data are generated using a rigorous, scalable image processing pipeline This pipeline has been applied to PET data from several other large, multisite studies and trials Quantitative outcomes are harmonizable across studies and are shared with the scientific community