To develop a deep learning (DL) approach for automatic segmentation and accurate risk stratification in multiple myeloma (MM) using whole-body [18F]FDG PET/CT. This retrospective study included MM patients who underwent [18F]FDG PET/CT between August 2013 and December 2023. To automatically segment focal lesions, an nnU-Net architecture was trained using dual-channel PET/CT inputs. For diffuse/mixed patterns, the tumor was automatically identified via bone segmentation using liver SUVmedian thresholds. Subsequently, DL and radiomics features were extracted using Pyradiomics and the STU-Net encoder. Then a deep learning radiomics nomogram (DLRN) was constructed using the Cox proportional hazards model and evaluated using the calibration curve, the receiver operating characteristic (ROC) curve, the Kaplan-Meier curve, and decision curve analysis. The study included 345 patients (median age, 59 years [IQR, 35–67 years], 198 male). The nnU-Net achieved a median DSC of 0.64–0.77 for focal lesions segmentation across cohorts. The DLRN was constructed by integrating deep learning radiomics score (DLRS), lactate dehydrogenase (LDH), and β2-microglobulin (β2-MG). The DLRN achieved an area under ROC curve (AUC) of 0.87 (95

This study aimed to investigate the spatiotemporal correlation between tumor metabolism and cancer-associated fibroblast activity using dual-tracer PET/CT, and to explore their potential association with pathologic response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC). This retrospective analysis of a prospective trial (ChiCTR2100051599) included 48 patients with ESCC from February 2022 to August 2024. All patients underwent 68Ga-FAPI and 18F-FDG PET/CT before pre-nCRT (S1) and after nCRT (S2). Two primary metrics were used to quantify their spatial correlations by voxel-wise correlation analyses: the SUV Correlation Coefficient (SUV_R), reflecting static uptake concordance, and the Dose-Response Matrix Correlation Coefficient (DRM_R), reflecting concordance in treatment dose sensitivity. The conventional parameters derived from FAPI- and FDG- PET were also extracted: SUVmax, SUVmean, SUV_CV, SUVmaxratio, SUVmeanratio, DRMmax, DRMmedian, DRM_CV and the resistant tumor volume (V(DRM> 0.7)). The associations of these metrics with pathological tumor regression grade (TRG) were evaluated in an exploratory manner, except for DRM-related metrics. A strong voxel-wise correlation was observed between pre-nCRT FAPI and FDG uptake (S1 SUV_R = 0.80 ± 0.17), which was weaker after nCRT (S2 SUV_R = 0.51 ± 0.23). Two dose-response matrices showed a strong correlation (DRM_R = 0.82 ± 0.14). The bivariate Logistic model, integrating the post-treatment SUV_R and SUV_CV_FDG, significantly predicted pathological response (p = 0.007), achieving an optimization-corrected AUC of 0.78 (95

Fever of unknown origin (FUO) remains diagnostically challenging because of heterogeneous causes, non-specific clinical manifestations, and overlapping imaging findings. We developed and validated FUO-PETMamba, a PET maximum-intensity-projection (MIP)-based artificial intelligence framework for AI-assisted aetiological classification of FUO. This retrospective multicentre study included 681 patients with FUO who underwent baseline [18 F]FDG PET/CT, comprising one development cohort (n = 355) and two independent external validation cohorts (n = 195 and n = 131). FUO-PETMamba is a weakly supervised framework analysing PET MIP images generated from PET data. Model performance was assessed by discrimination, calibration, decision curve analysis (DCA). Attention-based visual explanations were generated using attention mechanisms and gradient-based activation mapping. A reader study assessed the potential assistive effect of model-predicted probabilities on physicians with different PET/CT experience. In the development cohort, FUO-PETMamba achieved AUCs of 0.838 for malignancy, 0.851 for infection, 0.914 for autoimmune disease, and 0.788 for miscellaneous causes. Corresponding AUCs were 0.809, 0.815, 0.805, and 0.849 in external validation cohort 1, and 0.808, 0.772, 0.701, and 0.927 in external validation cohort 2, respectively. Calibration and decision curve analysis suggested potential clinical benefit for the major aetiological categories, although performance varied across cohorts and classes. The miscellaneous category should be interpreted cautiously because of limited case numbers and low positive predictive value and F1 scores. In the reader study, AI assistance improved diagnostic accuracy for junior and intermediate physicians, whereas changes in senior-physician performance were variable. Post hoc attention-based visualisations highlighted clinically plausible hypermetabolic patterns and served as qualitative explanatory aids. FUO-PETMamba provides a PET MIP-based AI-assisted diagnostic support framework for aetiological classification of FUO across multicentre cohorts and may help reduce experience-dependent diagnostic variability after prospective validation.

To evaluate the predictive value of dual-tracer 68Ga-FAPI and 18F-FDG PET/CT for pathologic tumor regression grade (TRG) and progression-free survival (PFS) in patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC) undergoing neoadjuvant chemoradiotherapy (nCRT). This retrospective analysis of a prospective trial (ChiCTR2100051599) included patients with LA-ESCC enrolled from February 2022 to August 2024. Patients received nCRT and underwent dual-tracer PET/CT at baseline (S1) and post-treatment (before esophagectomy, S2). Intensity-, volume-, and distribution-based PET features, including total lesion glycolysis, were extracted. LASSO regression was employed for feature selection. Eight models with different variable combinations were established by logistic regression and cox regression. Associations with TRG and PFS were evaluated using receiver operating characteristic (ROC) curves, Harrell’s C-index and Kaplan-Meier estimates. The models were compared pair-to-pair to identify the added value of imaging parameters. Forty-nine patients (mean age 65.8 ± 5.7 years) were included. TRG 0 was achieved in 29/49 (59.2

Abstract Background Mantle cell lymphoma (MCL) is a rare, biologically heterogeneous B-cell malignancy with highly variable outcomes. Existing prognostic tools are suboptimal. We developed an interpretable deep learning framework integrating baseline [18F]FDG PET/CT and electronic health record (EHR) data for individualized risk stratification. Methods In this multicenter study, 187 treatment-naïve MCL patients were analyzed. A mixture-of-experts (MoE) fusion network integrated multimodal representations from PET/CT and EHR data. Expert modules comprising vision encoders, radiomics extractors, and a medical language model were integrated through an attention-based gating mechanism to construct multimodal radiomic signatures (R-signatures) predictive of progression-free survival (PFS) and overall survival (OS). R-signatures were validated and incorporated with clinical and metabolic factors into multiparametric models. Deep learning model interpretability was evaluated using attention visualization, expert-level contributions and pathologic correlation. Results R-signatures robustly discriminated relapse (AUC = 0.893 training, 0.755 validation) and death (AUC = 0.804 and 0.844), and independently predicted adverse outcomes (PFS: HR = 27.70, P < 0.001; OS: HR = 6.86, P = 0.001). Multiparametric models integrating R-signatures with total lesion glycolysis, β2-microglobulin, WBC, and Ki-67 outperformed conventional indices (C-indices: PFS 0.892 training, 0.781 validation; OS 0.877 training, 0.862 validation). Time-dependent ROC analyses consistently showed AUCs approaching or exceeding 0.800. Calibration and decision curve analyses confirmed excellent agreement and superior clinical net benefit. Attention maps localized high-weighted regions to hypermetabolic tumor areas, with higher R-signature values in blastoid and pleomorphic variants versus classical histology (P = 0.028 and P = 0.010). Conclusions This interpretable PET/CT-EHR fusion framework substantially improves prognostic precision in MCL, providing a noninvasive, clinically translatable tool for risk-adapted management.

ABSTRACT Carbohydrates play essential roles throughout biology, making them key targets in biological research and drug development. While multivalent presentation of carbohydrates is widely recognized as crucial for high‐affinity target binding (the glycoside cluster effect), most existing methods focus on installing one glycosidic bond and lack robust strategies for attaching multiple carbohydrate units to a functional group. Here we report a mild, operationally simple protocol for the direct, stereoselective bis‐ S ‐glycosylation of dialkyl disulfide bonds in an open‐flask aqueous solution. This transformation installs two unprotected glycosyl units onto disulfide bonds, affording fully unprotected S ‐linked glycopeptides. The reaction employs bench‐stable, readily accessible glycosyl sulfinates as donors and tert ‐butyl hydroperoxide ( t BuOOH) as oxidant, forging cysteine‐glycosyl linkages via a radical pathway. Mixed disulfides derived from thiols partook in this reaction as well, allowing installation of one sugar unit onto peptide backbones. We applied this method to synthesize glycosylated peptides with liver‐targeting capabilities and to generate sugar‐linked affibody‐radionuclide conjugates. DFT calculations informed the reaction design and rationalized the observed selectivity of this transformation.

Histopathological studies have identified Aβ and phosphorylated tau proteins in the post-mortem retina of patients with Alzheimer's disease (AD). Retinal and choroidal alterations have been found in patients with posterior cortical atrophy (PCA) compared with healthy controls. However, the relationship between retinal and choroidal changes and in vivo AD pathology in PCA due to AD (AD-PCA) remains poorly understood. Our aim was to explore the association between retinal parameters and the PET and plasma biomarkers in AD-PCA. This cross-sectional study included patients diagnosed with AD-PCA, confirmed by Aβ-PET, from the West China Hospital ( n  = 25, mean age 60.35 ± 6.16, 32% male). Retinal and choroidal structural and microvascular parameters were assessed using swept-source optical coherence tomography (SS-OCT) and angiography (SS-OCTA). Volume-weighted global Aβ SUVR and four meta-ROIs SUVRs corresponding to the stages of Aβ deposition from early (A1) to late (A4), were calculated to quantify Aβ burden in Aβ-PET. In a subcohort who also received tau-PET ( n  = 14), volume-weighted tau SUVR values were quantified in the medial temporal lobe (MTL) and neocortex (representing Braak stages V and VI). Partial correlation analyses were performed to assess correlations between retinal parameters, and Aβ-PET, tau-PET or plasma biomarkers (Aβ42, Aβ40, pTau181, and pTau217) adjusting for sex and age. False discovery rate (FDR) method was applied for multiple comparisons. The vessel density of the superficial vascular plexus (SVP) was inversely correlated with both global Aβ burden (r=-0.463, p  = 0.042) and Aβ burden across stages (A1, r=-0.469, p  = 0.042; A2, r=0.573, p  = -0.021; A3, r=-0.426, p  = 0.042; A4, r=-0.445, p  = 0.042). Lower thickness of the retinal inner nuclear layer was associated with higher tau burden in both the MTL (r=-0.641, p  = 0.024) and neocortex (r=-0.684, p  = 0.014). While overall retinal thickness was negatively correlated only with the neocortical tau SUVR (r=-0.675, p  =  0.016). Additionally, SVP vessel density was positively correlated with the plasma Aβ42/40 ratio (r=0.518, p  = 0.023). Decreased choroidal thickness was associated with higher levels of both plasma pTau217 (r=-0.701, p  <0.001) and pTau181 (r=-0.483, p  = 0.042). Retinal structural and microvascular biomarkers may provide valuable insights into early amyloid and tau deposition, as well as the biological progression of AD-PCA.

348 Background: Neoadjuvant chemoimmunotherapy has shown promise in resectable esophageal squamous cell carcinoma (ESCC), yet its efficacy remains limited by relatively low pathologic complete response (pCR) rates. To address this, we proposed reprogramming the tumor immune microenvironment through the addition of low-dose radiotherapy, aiming to synergize with anti-PD-1 immunotherapy without increasing treatment-related toxicity. Methods: This phase II clinical trial was designed to enroll 30 participants. The key inclusion criteria for this study were as follows: Histologically (pathologically) confirmed thoracic esophageal squamous cell carcinoma with clinical stage: cT1b-cT2N1-2M0 or cT3-cT4aN0-2M0. The primary endpoint was pCR rate. During the neoadjuvant therapy phase, patients underwent two cycles of low-dose radiotherapy followed by chemotherapy and immunotherapy, with each cycle lasting 21 days. Patients were allocated into three treatment groups (4Gy/2f, 6Gy/3f, and 8Gy/4f). Tislelizumab, nab-paclitaxel and carboplatin were administered concurrently on the day following radiotherapy completion. Patients were scheduled for esophagectomy 6-8 weeks after the second neoadjuvant session. Blood and tumor tissue samples were collected before and after neoadjuvant therapy. Tumor tissue samples were collected for multi-omics sequencing. Blood samples were analyzed for ctDNA. Results: A total of 30 patients were included for final analysis, with 10 patients in each sub-group (4Gy/2f, 6Gy/3f, and 8Gy/4f) The results demonstrated that the pCR rate in the 8Gy/4f group was 70% (7/10), which was higher than that in the 4Gy/2f group (10%; p = 0.011) and the 6Gy/3f group (50%; p = 0.371). Furthermore, the ctDNA clearance rates in both the 8Gy/4f group (7/10) and the 6Gy/3f group (6/8) were numerically higher than that in the 4Gy/2f group (2/7), with P-values of 0.160 and 0.120, respectively. Among the 30 patients, 23 experienced treatment-related adverse events (TRAEs) of any grade, with the majority being mild (grade 1). Conclusions: The 8Gy/4f radiotherapy followed by chemoimmunotherapy significantly increased the anti-PD-1 immunotherapy efficacy without increasing the treatment-related adverse events or postoperative complications, highlighting promise as an effective neoadjuvant treatment modality. Clinical trial information: ChiCTR2400084438 . Baseline, pathological, and perioperative data of patients. All patients 4Gy/2f 6Gy/3f 8Gy/4f Number of Patients 30 10 10 10 Sex Female 6 2 1 3 Male 24 8 9 7 Age ≥65 14 4 4 6 <60 16 6 6 4 cTNM I 1 0 0 1 II 10 3 5 2 III 10 4 2 4 IV 9 3 3 3 ypTNM I 21 6 8 7 II 1 0 1 0 IIIA 1 0 0 1 IIIB 6 3 1 2 IV 1 1 0 0 pCR Yes 13 1 5 7 No 17 9 5 3 Pre-treatment ctDNA Positive 25 7 8 10 Negative 5 3 2 0 Post-treatment ctDNA Positive 10 5 2 3 Negative 20 5 8 7 Adverse Event (CTCAE) I 12 5 4 3 II 6 1 3 2 III 5 3 0 2 Perioperative Complication (Clavien-Dindo) I 3 1 1 0 II 8 1 5 2 III 1 1 0 0

Marginal zone lymphoma (MZL) exhibits substantial clinical heterogeneity, necessitating accurate whole-body tumor burden assessment and early risk stratification. However, conventional [18F]FDG PET/CT is limited in both anatomical staging and in vivo biological characterization. This prospectively recruiting study conducted a head-to-head comparison of the CXCR4-targeted tracer [68Ga]Ga-PentixaFor and [18F]FDG in treatment-naïve patients to evaluate their complementary value in improving staging accuracy and their association with early treatment response. Forty-one treatment-naïve MZL patients prospectively underwent dual-tracer PET/CT prior to therapy. Lesion detection rates, stage migration, and management modifications were systematically assessed. The prognostic value of baseline imaging parameters for interim treatment response was also analyzed. [68Ga]Ga-PentixaFor demonstrated significantly higher lesion detection rates than [18F]FDG, particularly for gastric (72.0

To develop and validate a prognostic imaging biomarker derived from baseline [¹⁸F]FDG PET/CT using tabular deep learning for prediction of progression of disease within 24 months (POD24) and survival risk stratification in patients with follicular lymphoma (FL). This retrospective multicenter study included 309 patients with newly diagnosed FL (grades 1-3a) from five independent medical centers. Tumor volumes segmented from baseline [¹⁸F]FDG PET and CT images were used to extract high-throughput radiomic features. Five conventional machine learning algorithms and four advanced tabular deep learning models were developed and compared. The predictive output of the GAMformer model was defined as the deep learning score (DLS). The DLS was integrated with clinical variables and PET metabolic parameters to construct a multiparametric model in the training cohort. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis, and further validated in validation cohort. During a median follow-up of 44 months, POD24 occurred in 55 patients. The DLS demonstrated strong predictive performance for POD24 (training AUC = 0.857; validation AUC = 0.753). The multiparametric model further improved discrimination, achieving AUCs of 0.882 in the training cohort and 0.797 in the validation cohort, outperforming FLIPI, FLIPI-2, and PRIMA-PI. Calibration showed good agreement, and decision curve analysis indicated higher net clinical benefit. The DLS stratified survival risk (P < 0.05) and remained predictive of survival and POD24 across histologic grades. The DLS derived from baseline [¹⁸F]FDG PET/CT enables POD24 prediction and accurate survival risk stratification in FL, supporting its potential role in precision management.

Background:The clinical management of small (<20 mm), nonfunctional, nonmetastatic pancreatic neuroendocrine neoplasms (panNENs) relies heavily on accurate pathological grading, as Grade 1 (G1) tumors are often managed with active surveillance, whereas Grade 2 (G2) tumors warrant surgical resection. Currently, limited biopsy sampling may misrepresent the true tumor grade due to intratumoral heterogeneity, and conventional imaging lacks grading accuracy. Although 68Ga-DOTA0-Tyr3-octreotate (68Ga-DOTATATE) and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) can reflect tumor differentiation and aggressiveness, their grading efficacy-especially for small, localized panNENs-remains controversial. Therefore, this study evaluates the ability of dual-tracer PET imaging to accurately grade these specific lesions to guide optimal management decisions. Methods:A retrospective study was conducted between January 2019 and January 2025 on patients with histologically confirmed panNENs. All 34 patients (with 41 lesions) underwent 68Ga-DOTATATE PET imaging, and a subset of 19 patients (with 24 lesions) also underwent 18F-FDG PET imaging. The maximum standardized uptake values (denoted as G-SUV for 68Ga-DOTATATE and F-SUV for 18F-FDG) were measured in lesions with surgically confirmed histological grades. Statistical analyses were performed using the Mann-Whitney U test for continuous variables and the Chi-squared test for categorical variables. The Spearman correlation coefficient was used to evaluate both the strength and direction of the bivariate relationships. The optimal cutoff for distinguishing G1 from G2 was determined by receiver operating characteristic (ROC) analysis. Additionally, the clinical utility of visual assessment was evaluated. Results:No significant differences in G-SUV or the G-SUV/F-SUV ratio were found between G1 and G2 (P=0.355 and P=0.861, respectively). In contrast, F-SUV was significantly lower in G1 than in G2 (P=0.003), with an optimal cutoff value of 2.5, yielding a sensitivity of 73% and a specificity of 89%. Although visual assessment of neither 68Ga-DOTATATE PET nor 18F-FDG PET could distinguish G1 from G2 (P=0.548 and P=0.102, respectively), 18F-FDG PET demonstrated a high positive predictive value (89%) when using pancreatic background as the reference. Conclusions:68Ga-DOTATATE PET could not be used to predict small panNENs grade. In contrast, 18F-FDG PET showed potential, and if validated by prospective data, it might help in selecting appropriate treatment options for patients with panNENs.

PET/MR combines MR imaging's structural detail with PET's functional and molecular insights, offering a powerful tool for pediatric nervous disorders. It is particularly valuable in epilepsy for surgical planning and treatment guidance, with emerging applications in autoimmune, neuroinflammatory, and neurodevelopmental conditions. This review highlights recent advances and case examples demonstrating its clinical utility in nonmalignant pediatric nervous disorders.

A 57-year-old woman with follicular lymphoma (FL) presented with diffuse cutaneous and subcutaneous involvement on baseline 18 F-FDG PET/CT. Following therapy, cutaneous uptake largely resolved; however, follow-up PET/CT demonstrated new, widespread confluent hypermetabolic lymphadenopathy, indicating systemic progression. This striking post-therapy redistribution highlights the importance of serial whole-body PET/CT in atypical FL.

To develop and validate a multimodal deep learning framework that integrates clinical metadata with [18F]FDG PET/CT imaging to resolve overlapping metabolic phenotypes. The primary objective is the histological subtyping of non-small cell lung cancer (NSCLC), utilizing binary clinical staging (early vs. advanced) strategically as an auxiliary regularization task. A multi-center surgical NSCLC cohort (n = 780) was partitioned into a development set (n = 675) and an independent external test set (n = 105). The framework first utilized a 3D Transformer for bounding-box-based tumor localization. Subsequently, a multi-task network employed Feature-wise Linear Modulation (FiLM) to dynamically inject clinical metadata into the visual backbone. For histological subtyping of adenocarcinoma versus squamous cell carcinoma, in the validation cohort, the proposed multimodal framework achieved the highest area under the receiver operating characteristic curve (AUC) of 0.894 (95

Background:The C-X-C motif chemokine receptor 4 (CXCR4)-targeted tracer, 68Ga-pentixafor, has shown promise for imaging hematologic malignancies including mucosa-assisted lymphoid tissue (MALT) lymphoma involving lymph nodes (LNs). Its physiological uptake patterns in gastric LN stations remain undefined. This retrospective study aimed to evaluate gastric LN positivity in individuals without known malignancy, establish region-specific references, and explore factors influencing uptake. Methods:Data of patients with hyperaldosteronism who underwent 68Ga-pentixafor positron emission tomography/computed tomography (PET/CT) or PET/magnetic resonance (MR) were retrospectively analyzed. Gastric LNs were categorized as perigastric [1-6], near-extragastric [7-9], far-extragastric [10-12], and distant [13-16]. Positive LNs were those with increased 68Ga-pentixafor uptake which was higher than that of the blood pool. Reference ranges for maximum standardized uptake value (SUVmax) and LN-to-blood pool SUV ratio (LBR) of each LN station were established. Laboratory data including complete blood count, liver function, hepatitis B virus (HBV) serology, and derived inflammatory indices, were analyzed to identify factors associated with tracer uptake. Results:A total of 122 patients were included. Physiologically increased uptake was rare in perigastric LNs, only one (0.8%) in LN station 3. In near-extragastric groups, 8a (43.4%) and 8p (22.1%) showed notable uptake. Among far-extragastric LNs, LN station 12p had the highest positivity (86.1%), with a median SUVmax of 4.16 and a LBR of 2.23, respectively. LN station 16a2 had a moderate positivity rate (20.5%). Correlation analysis indicated correlation between the LBR in extragastric groups (LN station 9, 12a, 12b, and 12p) and liver function test parameters (P<0.05), while distant LNs uptake showed no significant laboratory associations except that with alanine aminotransferase (ALT). Conclusions:Physiological 68Ga-pentixafor uptake in gastric LNs is station-specific, notably high in 12p, 8a, and 8p. LN evaluation should integrate uptake and morphology, and larger studies comparing benign and malignant cases are warranted.

Relapsed/refractory classical Hodgkin lymphoma (R/R cHL) remains clinically challenging due to substantial heterogeneity in relapse risk. Reliable, non-invasive tools for improved relapse risk stratification are urgently needed to guide individualized therapeutic strategies. In this multicenter retrospective study, 161 patients with R/R cHL from five institutions were included (training cohort: n = 102; validation cohort: n = 59). Clinical and metabolic covariates were assessed at the time of relapsed/refractory disease and baseline 18F-FDG PET/CT before salvage treatment. We developed a deep learning-based Mixture-of-Experts (MoE) framework that integrates four medical foundation models (PET-Diffusion, SAM-Med2D, MedCLIP, and RadFM) to derive a quantitative imaging biomarker (MoEScore) from baseline 18F-FDG PET/CT. A multiparametric model incorporating MoEScore and independent clinical/metabolic predictors was constructed and evaluated using discrimination, calibration, and clinical utility analyses. Model interpretability was assessed using attention visualization, ablation analysis, and pathological correlation. MoEScore demonstrated predictive performance (AUC: 0.861 in training; 0.783 in validation) and remained independently associated with relapse (HR = 11.18, 95

Poly(ADP-ribose) polymerase (PARP)-targeted positron emission tomography (PET) imaging has emerged as a valuable approach for identifying PARP inhibitor (PARPi) nonresponders, monitoring treatment response, and predicting prognosis. However, clinical translation of current tracers, such as [18F]FTT and [18F]PARPi, has been hampered by complex radiosynthesis and a high background signal. By leveraging the advantages of the photocatalyzed 18F-fluorination strategy and analyzing the interaction mode between Olaparib and PARP-1, we developed six novel PARP PET tracers. By finely tuning the physicochemical properties of these tracers with various electron-donating hydroxyl groups, effective direct 18F-deoxyfluorination, improved tumor uptake, and higher tumor-to-background ratios were achieved. All tracers were efficiently obtained with a high radiochemical purity. An in vitro experiment confirmed sufficient specific PARP binding of [18F]8a, [18F]8b, and [18F]8d, among which [18F]8a exhibited high tumor uptake (SUVmax = 0.22 ± 0.02) and superior tumor-to-muscle ratio (3.71 ± 0.18) at 1 h postinjection in U87MG tumor-bearing mice on PET/CT images, along with acceptable stability. Collectively, these findings highlight [18F]8a as a promising PARP PET tracer, combining improved synthetic accessibility with robust imaging performance, and underscore its potential for clinical translation.

Idiopathic normal pressure hydrocephalus (iNPH) often co-occurs with beta-amyloid deposition, both of which share a common pathophysiology involving glymphatic dysfunction. This study aimed to investigate the clinical impact of beta-amyloid co-pathology and the effects of glymphatic dysfunction on beta-amyloid deposition in patients with iNPH. Patients diagnosed with probable iNPH ( n  = 60; 28 with A+, positive amyloid PET; 32 with A-, negative amyloid PET) and Alzheimer's disease (AD) ( n  = 30, A+T+N+) were enrolled from prospective cohorts at the West China Hospital of Sichuan University. All participants underwent neuropsychological tests, magnetic resonance imaging and 18 F-AV45 PET. The choroid plexus volume/estimated total intracranial volume (CPV/eTIV) and 18 F-AV45 PET standard uptake value ratio (SUVR) were calculated based on automatic segmentation to evaluate the glymphatic function and amyloid burden. ANOVA and Kruskal-Wallis test were used for group comparison between iNPH with positive amyloid PET and other groups, and Bonferroni correction was used for post-hoc analysis. Multivariate generalized linear models were constructed to analyze the association between CPV/eTIV and 18 F-AV45 PET SUVR in patients with iNPH and AD. Patients with iNPH A+ exhibited the highest CPV/eTIV compared to the patients with iNPH A- ( p  = 0.006). Patients with iNPH A+ showed lower MMSE score compared to iNPH A- (P Bon =0.013) and AD (P Bon =0.039), while no significant difference was found between iNPH A- and AD. In iNPH patients, higher CPV/eTIV were associated with higher 18 F-AV45 PET SUVR in temporal ( p  = 0.007), parietal ( p  = 0.002), and occipital lobes ( p  = 0.004); however, no association was observed in patients with AD in these regions. Our study demonstrated that beta-amyloid co-pathology may exacerbate the cognitive symptoms of iNPH. Moreover, glymphatic dysfunction might play a distinct role in promoting beta-amyloid deposition in iNPH compared to Alzheimer's disease.

An 11-year-old boy with relapsed, poorly differentiated left adrenal neuroblastoma presented with multiple bone metastases, demonstrated by an 18 F-MFBG PET/CT with a Curie score of 4, despite prior multimodal therapy. He was subsequently enrolled in a phase 1 dose-escalation trial of 211 At-MABG-targeted alpha therapy. Follow-up 18 F-MFBG PET/CT revealed the resolution of most lesions and markedly decreased tracer uptake at the residual site, reducing his posttreatment Curie score to 1. This case highlights the therapeutic potential, safety, and feasibility of repeated, low-dose 211 At-MABG-targeted alpha therapy for pediatric relapsed neuroblastoma.

Bicyclic boronates are being developed as inhibitors targeting both metallo-β-lactamases (MBLs) and serine-β-lactamases (SBLs) to combat the growing clinical challenge of carbapenem resistance. There is, however, poor knowledge of how they work in an in vivo context, in particular regarding selectivity and distribution. We here describe a synthetic and assay framework for discovering fluorine-containing bicyclic boronates (F-boronates) that target clinically relevant MBLs/SBLs and enable real-time positron emission tomography (PET) imaging. We identified 19F-boronates that are potent dual inhibitors of MBLs/SBLs or selective MBL inhibitors, and prepared three structurally distinct 18F-boronates suitable for PET imaging in mice. The PET imaging results revealed similar distribution patterns for the three 18F-boronates, with notable accumulation in the bladder and some presence in the brain. Notably, one probe (18FB2-9) effectively detected mouse infection sites caused by both MBL- and SBL-producing pathogens, while 18FB4-2 selectively imaged MBL-producing pathogens. The 19F-boronates displayed substantial potential to restore meropenem efficacy in mouse infection models. These results are of interest with respect to boronate β-lactamase inhibitor and antibiotic discovery research, in particular for treatment of urinary tract infections.