Citations

ABSTRACT The novel HLA‐DQB1*02:02:01:28Q shows a point mutation in the intron 3 splice site.

Abstract Background Anti‐M antibodies were described as rarely eliciting hemolytic disease of the fetus and newborn (HDFN) in pregnancy. More recent literature, primarily from Asia, reports severe HDFN attributed to maternal anti‐M. The objective of this study is to determine if there is a difference in the frequency and/or severity of anti‐M HDFN among subjects who self‐report as Asian compared to non‐Asian. Methods Retrospective data were collected from 18 sites. Fetuses/neonates affected by HDFN were categorized as unaffected, mild, moderate, or severe based on clinical management and outcomes. Results Anti‐M was identified in 557 obstetric patients. Among 332 neonates with complete data for analysis, anti‐M was detected in isolation during the pregnancies of 210 (63.3%) neonates; 198 (94.3%) were unaffected by HDFN. The overall frequency of HDFN was 6.5% (2/31) in Asian patients and 5.6% (10/179) in non‐Asian patients. The frequency of severe HDFN was 6.5% (2/31) among Asian patients and 0.6% (1/179) among non‐Asian patients. Among non‐Asian patients, eight (4.5%) were mild and one (0.6%) was moderate. Of 63 pregnancies with anti‐M and ABO incompatibility, 6 (9.5%) were associated with mild HDFN and 57 (90.5%) were unaffected. Of 49 pregnancies with anti‐M and non‐ABO antibody(s), 6 (12.2%) were associated with mild HDFN and 43 (87.8%) were unaffected. One case (10.0%) of HDFN (mild) occurred in the presence of anti‐M, additional antibody(s), and ABO incompatibility. Conclusion In this study, severe anti‐M HDFN was rare in non‐Asian subjects. There is a signal that Asian patients are at greater risk for severe anti‐M HDFN.

The studies demonstrate the differential effects of the nicotinic acetylcholine receptors (nAChRs) regulating airway hyperreactivity (AHR) and remodeling in a mixed allergen model of asthma. We compared wild-type (WT) versus global α7nAChR knockout (Glα7KO) versus smooth muscle-specific α7nAChR KO (smα7KO) mice. We found that smα7KO improved lung function and AHR in mice challenged with MA and in the presence of nicotine, whereas MA-challenged Glα7KO in the presence of nicotine showed worsened AHR.

Dynamic fluid and solute transportation across the arachnoid barrier in the meninges of healthy participants was measured by tracking intravenous gadolinium-based contrast agent entering cerebrospinal fluid from dural blood vessels using dynamic dual-spin-echo perfusion MRI.

Abstract Isolated/idiopathic rapid-eye-movement (REM)-Sleep Behavior Disorder (iRBD) is characterized by dream enactment behaviors associated with loss of REM atonia. iRBD is in most cases a prodromal synucleinopathy, and emerging evidence suggests associations between RBD and other neurological and psychiatric conditions. In this study, we performed pathway-based polygenic risk score (PRS) and rare variant burden analyses to examine these potential associations. Pathway-specific PRS were constructed from genome-wide association study summary statistics of five neurodegenerative and seven psychiatric traits across 10 biologically relevant pathway categories, including a total of 279 pathways, in 1,573 iRBD cases and 16,022 controls from the International RBD Study Group and UK Biobank. Rare variant burden tests were performed in 1,264 iRBD cases and 2,581 controls. We identified multiple potential pathways indicating shared polygenic risk between RBD and both neurodegenerative and psychiatric disorders. Lewy body diseases and post-traumatic stress disorder had the most shared polygenic risk pathways in neurological and psychiatric disorders, respectively. Two pathways, the serotonin transport pathway and the chaperone-mediated autophagy pathway, showed the strongest association with iRBD, and gene-based rare variants analyses revealed five genes associated with iRBD: GBA1 , PLEKHM1 , LRP2 , P2RX1 , and HAP1. Subsequent analysis of these genes in Parkinson’s disease and dementia with Lewy bodies replicated several associations. Together, these findings provide novel insights into the shared genetic architecture underlying iRBD, neurodegenerative disorders, and psychiatric traits, with implications for early identification and mechanistic understanding.

Abstract Primary liver cancers, including hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), arise from the neoplastic transformation of hepatocytes and cholangiocytes, respectively. Loss or downregulation of PTEN, a tumor suppressor negatively regulating the PI3K/AKT pathway, is frequently observed in CCA and HCC. Notably, PTEN mutations are observed at nearly twice the frequency in combined CCA-HCC tumors than either HCC or CCA alone. Using lineage-specific liver-targeted PTEN-deficient mouse models, we demonstrate that PTEN loss drives cellular dedifferentiation and tumorigenesis, a process that is critically dependent on AKT2. Mechanistically, PTEN deficiency induces activation of NOTCH and upregulation of transcriptional factor SOX9, which plays a central role in tumor cell transformation. In parallel, PTEN loss increases SMAD4 expression and sensitizes the tumor cells to TGFβ signaling, with TGFβ treatment repressing SOX9 expression in tumor cells lacking PTEN. Together, our study defined a critical role for PTEN-AKT2 signaling in maintaining liver epithelial lineage fidelity and revealed how its disruption promotes the conversion of mature hepatocytes or cholangiocytes into liver cancer stem-like cells (LCSCs). Furthermore, we identify a PTEN-dependent crosstalk between NOTCH and TGFβ pathways that governs liver tumor development. Together, this work provides mechanistic insight into lineage plasticity in liver cancer with implications for pathway-directed therapy.

378 Background: RATIONALE-305 (NCT03777657) enrolled the highest number of pts with PM among similar trials and stratified pts with advanced GC/GEJC by presence of PM. We used statistical modeling to evaluate the impact of varying PM levels on outcomes with TIS+CT. Methods: We randomized adults with GC/GEJC (1:1) to TIS or placebo (PBO) + CT. Primary endpoints: OS in intent-to-treat (ITT) population and pts with programmed death-ligand 1 (PD-L1) Tumor Area Positivity (TAP) score ≥5%. We weighted pts using the method of moments to match a hypothetical population to ITT, with the same distribution of sex, primary tumor location, PD-L1 (TAP score <5% vs ≥5%), number of metastatic sites (0-2 vs 3+), and liver metastasis. PM representation was set from 20% to 45%. Weighted OS, PFS, and objective response rate (ORR) were estimated. Results: Of 997 pts enrolled, 434 (43.5%) had PM. Among pts with PD-L1 TAP scores ≥5% (54.8%), 39.7% had PM, and among pts with scores ≥1% (88.9%), 43.6% had PM (data cutoff Feb 28, 2024). Fewer pts with (+) PM had PD-L1 high tumors (TAP score ≥5%: 50.0%) and more had primary stomach tumors (86.6%) compared to those without (-) PM (58.4% and 75.5%). TIS+CT improved OS, PFS, and ORR in ITT and in pts +/- PM vs PBO+CT (Table). Across 20%–45% PM, TIS+CT showed consistent benefit, with stable HRs for OS and PFS (20% and 45% shown). TIS+CT showed survival benefits in pts with PM and PD-L1 TAP score ≥5% (OS: HR=0.71; PFS: HR=0.65) and ≥1% (OS: HR=0.79; PFS: HR=0.79) vs PBO+CT. ORR was higher with TIS+CT vs PBO+CT in pts + PM and PD-L1 TAP scores ≥5% (47.3% vs 30.8%) and ≥1% (44.2% vs 33.2%). Conclusions: Our analysis showed that lowering PM representation marginally improved OS and ORR, underscoring the poor prognosis of peritoneal disease. In RATIONALE-305, TIS+CT provides clinically meaningful response and survival benefits irrespective of PM, with higher responses in PD-L1+ GC/GEJC. Clinical trial information: NCT03777657 . Arm(95% CI) ITT + PM - PM 20%+ PM 45%+ PM OS, months TIS+CT 15.0 (13.6, 16.5) 12.3 (10.6, 14.3) 17.3 (15.0, 20.3) 15.4 (13.9, 18.0) 15.0 (13.5, 16.5) PBO+CT 12.9 (12.1, 14.1) 11.8 (10.5, 13.0) 14.0 (12.6, 16.0) 13.1 (12.3, 14.5) 12.8 (12.1, 14.1) HR 0.79 (0.69, 0.91) 0.78 (0.64, 0.96) 0.79 (0.65, 0.95) 0.81 (0.69, 0.95) 0.79 (0.69, 0.90) PFS, months TIS+CT 6.9 (5.7, 7.2) 5.8 (5.6, 7.3) 7.0 (5.7, 8.4) 6.9 (5.7, 7.3) 6.9 (5.7, 7.2) PBO+CT 6.2 (5.6, 6.9) 5.7 (5.3, 6.9) 6.9 (5.7, 7.1) 6.5 (5.6, 7.0) 6.2 (5.6, 6.9) HR 0.79 (0.68, 0.91) 0.80 (0.64, 0.98) 0.77 (0.64, 0.94) 0.79 (0.67, 0.94) 0.79 (0.69, 0.91) ORR, % TIS+CT 47.3 (42.9, 51.8) 43.6 (37.0, 50.5) 50.2 (44.2, 56.2) 48.0(42.9, 53.1) 47.3 (42.8, 51.8) PBO+CT 40.5 (36.2, 45.0) 32.2 (26.0, 39.0) 46.8 (40.9, 52.8) 43.1(38.1, 48.3) 40.3(36.0, 44.8) ORR difference, % 6.8 (0.6, 12.9) 11.4 (2.3, 20.3) 3.4 (-4.9, 11.6) 4.9 (-2.2, 11.9) 6.9 (0.8, 13.0)

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.

PURPOSE Manual extraction of treatment outcomes from unstructured oncology clinical notes is a significant challenge for real-world evidence (RWE) generation. This study aimed to develop and evaluate a robust natural language processing (NLP) system to automatically extract cancer treatments and their associated RECIST-based response categories (complete response, partial response, stable disease, and progressive disease) from non–small cell lung cancer (NSCLC) clinical notes. METHODS This retrospective NLP development and validation study used a corpus of 250 NSCLC oncology notes from University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center, annotated by physician experts. An end-to-end NLP pipeline was designed, integrating a rule-based module for entity extraction (treatments and responses) and a machine learning module using biomedical clinical bidirectional encoder representations from transformers for relation classification. The system's performance was evaluated on a held-out test set, with partial external validation for relation extraction on a Mayo Clinic data set. RESULTS The NLP system achieved high overall accuracy. On the UPMC test set (64 notes), the relation classification model attained an area under the receiver operating characteristic curve of 0.94 and an F1 score of 0.92 for linking treatments with documented responses. The rule-based entity extraction demonstrated a macro-averaged F1 score of 0.87 (precision 0.98, recall 0.81). Although precision was high for chemotherapy and most response types (1.00), recall for cancer surgery was 0.45. External validation at Mayo Clinic showed moderate relation extraction F1 scores (range: 0.51-0.64). CONCLUSION The proposed NLP system can reliably extract structured treatment and response information from unstructured NSCLC oncology notes with high accuracy. This automated approach can assist in abstracting critical cancer treatment outcomes from clinical narrative text, thereby streamlining real-world data analysis and supporting the generation of RWE in oncology.