Lastly, we consider the possible interactions of eosinophils with the enteric nervous system, and how this represents an exciting area for future research which may inform future therapeutic targets. In this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional prior constraints, in the context of multicenter images with heterogeneous characteristics. In cervical cancer, an additional challenge is the location of the tumor uptake near or even stuck to the bladder. PET datasets of 232 patients from five institutions were exploited. To avoid unreliable manual delineations, the ground truth was generated with a semi-automated approach a volume containing the tumor and excluding the bladder was first manually determined, then a well-validated, semi-automated approach relying on the Fuzzy locally Adaptive Bayesian (FLAB) algorithm was applied to generate the ground truth. Our model built on the U-Net architecture incorporates residual blocks with concurrent spatial squeeze and excitation modules, as well as learnable non-linear downsampling and upsampling blocks. 8-OH-DPAT research buy Experiments relied on cross-validation (four institutions for training and validation, and the fifth for testing). The model achieved good Dice similarity coefficient (DSC) with little variability across institutions (0.80 ± 0.03), with higher recall (0.90 ± 0.05) than precision (0.75 ± 0.05) and improved results over the standard U-Net (DSC 0.77 ± 0.05, recall 0.87 ± 0.02, precision 0.74 ± 0.08). Both vastly outperformed a fixed threshold at 40% of SUVmax (DSC 0.33 ± 0.15, recall 0.52 ± 0.17, precision 0.30 ± 0.16). In all cases, the model could determine the tumor uptake without including the bladder. Neither shape priors nor anatomical information was required to achieve efficient training. The proposed method could facilitate the deployment of a fully automated radiomics pipeline in such a challenging multicenter context.The proposed method could facilitate the deployment of a fully automated radiomics pipeline in such a challenging multicenter context. To test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[ F] fluoro-2-deoxy-D-glucose ([ F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC). One hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm. Radiomic features were extracted from the tumours and from regions drawn over the normal liver. Cox proportional hazard model was used to test statistical significance of clinical and radiomic features. Fivefold cross validation was used to tune the number of features. Seven different feature selection methods and four classifiers were tested. The models with the selected features were trained using bootstrapping and tested in data from each scanner independently. Reproducibility of radiomics features, clinical data added value ty across PET/CT devices. There is significant interest in the development of targeted alpha-particle therapies (TATs) for treatment of solid tumors. The metal chelator-peptide conjugate, DOTA-TATE, loaded with the β-particle emitting radionuclide Lu ([ Lu]Lu-DOTA-TATE) is now standard care for neuroendocrine tumors that express the somatostatin receptor 2 (SSTR2) target. A recent clinical study demonstrated efficacy of the corresponding [ Ac]Ac-DOTA-TATE in patients that were refractory to [ Lu]Lu-DOTA-TATE. Herein, we report the radiosynthesis, toxicity, biodistribution (BD), radiation dosimetry (RD), and efficacy of [ Ac]Ac-DOTA-TATE in small animal models of lung neuroendocrine neoplasms (NENs). [ Ac]Ac-DOTA-TATE was synthesized and characterized for radiochemical yield, purity and stability. Non-tumor-bearing BALB/c mice were tested for toxicity and BD. Efficacy was determined by single intravenous injection of [ Ac]Ac-DOTA-TATE into SCID mice-bearing human SSTR2 positive H727 and H69 lung NENs. RD was calculated using the BD data. [ Ac]Ac-DOTA-TATE was synthesized with 98% yield, 99.8% purity, and displayed 97% stability after 2days incubation in human serum at 37°C. All animals in the toxicity study appeared healthy 5months post injection with no indications of toxicity, except that animals that received ≥111kBq of [ Ac]Ac-DOTA-TATE had chronic progressive nephropathy. BD studies revealed that the primary route of elimination is by the renal route. RD calculations determined pharmacokinetics parameters and absorbed α-emission dosages from Ac and its daughters. For both tumor models, a significant tumor growth delay and time to experimental endpoint were observed following a single administration of [ Ac]Ac-DOTA-TATE relative to controls. These results suggest significant potential for the clinical translation of [ Ac]Ac-DOTA-TATE for lung NENs.These results suggest significant potential for the clinical translation of [225Ac]Ac-DOTA-TATE for lung NENs. Recent studies have demonstrated that cigarette smoking is related to changes in brain structure and function. However, few studies focus on functional brain differences between male chronic smokers and nonsmokers in both local spontaneous activity and whole-brain functional networks. Our study recruited 67 chronic smokers and 43 nonsmokers who underwent functional magnetic resonance imaging (fMRI) scans to investigate functional activity and connectivity alterations in chronic smokers. We used the mean fractional amplitude of the low-frequency fluctuation (mfALFF) and mean regional homogeneity (mReHo) methods to investigate resting-state spontaneous activity in chronic smokers and nonsmokers. The graph theoretical analysis (GTA) and network-based statistical (NBS) analysis were also used to investigate functional connectivity alterations. Compared with nonsmokers, chronic smokers exhibited higher activation in the reward system and portions of the prefrontal cortex but lower activation in the default mode networks (DMN) and visual-related regions.