Background and Purpose Type 2 diabetes is associated with an increased risk for dementia. This study investigated the global connectivity patterns in the brains of patients with type 2 diabetes using a functional MRI technique. Materials and Methods Forty patients and 43 age-, sex- and education-matched healthy controls underwent resting-state functional imaging in a 3T MR unit. Degree centrality, a commonly employed measurement of global connectivity, was computed for a full-brain exploration of the regions influenced by type 2 diabetes. We then examined the functional connectivity of each region using the seed-based approach. Finally, voxel-wise correlation analyses were performed to explore the relationship between the connectivity changes, cognitive performance and diabetes-related variables. Results Patients exhibited decreased degree centrality in the left lingual gyrus, and increased centrality in the right insula and dorsal anterior cingulate cortex (corrected P <0.05). The occipital network anchored in the lingual gyrus showed extensively reduced connectivity, while the network connectivity of insula and cingulate cortex (mostly included in the salience network) was significantly elevated (corrected P <0.05). Correlational analyses revealed that in the diabetic group, impaired visual memory and executive-function performance were correlated with occipital hypoconnectivity, while higher fasting plasma glucose levels and better executive functioning was related to anterior cingulate cortex hyperconnectivity (all corrected P values <0.05). Similar effects were not detected in the controls. Conclusion This preliminary study shows that network connectivity is altered in patients with type 2 diabetes, which may provide critical insights into the neural substrate of diabetes-related cognitive decline.

The clinical employment of cisplatin (cis‐diamminedichloroplatinum(II) (CDDP)) is largely constrained due to the non‐specific delivery and resultant serious systemic toxicity. Small‐sized biocompatible and biodegradable hollow mesoporous organosilica (HMOS) nanoparticles show superior advantages for targeted CDDP delivery but suffer from premature CDDP leakage. Herein, the smart use of a bimetallic Zn2+/Cu2+ co‐doped metal–organic framework (MOF) is made to block the pores of HMOS for preventing potential leakage of CDDP and remarkably increasing the loading capacity of HMOS. Once reaching the acidic tumor microenvironment (TME), the outer MOF can decompose quickly to release CDDP for chemotherapy against cancer. Besides, the concomitant release of dopant Cu2+ can deplete the intracellular glutathione (GSH) for increased toxicity of CDDP as well as catalyzing the decomposition of intratumoral H2O2 into highly toxic •OH for chemodynamic therapy (CDT). Moreover, the substantially reduced GSH can also protect the yielded •OH from scavenging and thus greatly improve the •OH‐based CDT effect. In addition to providing a hybrid HMOS@MOF nanocarrier, this study is also expected to establish a new form of TME‐unlocked nanoformula for highly efficient tumor‐specific GSH‐depletion‐enhanced synergistic chemotherapy/chemodynamic therapy.

Dynamic-Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been used widely for clinical applications. Pharmacokinetic modeling of DCE-MRI data that extracts quantitative contrast reagent/tissue-specific model parameters is the most investigated method. One of the primary challenges in pharmacokinetic analysis of DCE-MRI data is accurate and reliable measurement of the arterial input function (AIF), which is the driving force behind all pharmacokinetics. Because of effects such as inflow and partial volume averaging, AIF measured from individual arteries sometimes require amplitude scaling for better representation of the blood contrast reagent (CR) concentration time-courses. Empirical approaches like blinded AIF estimation or reference tissue AIF derivation can be useful and practical, especially when there is no clearly visible blood vessel within the imaging field-of-view (FOV). Similarly, these approaches generally also require magnitude scaling of the derived AIF time-courses. Since the AIF varies among individuals even with the same CR injection protocol and the perfect scaling factor for reconstructing the ground truth AIF often remains unknown, variations in estimated pharmacokinetic parameters due to varying AIF scaling factors are of special interest. In this work, using simulated and real prostate cancer DCE-MRI data, we examined parameter variations associated with AIF scaling. Our results show that, for both the fast-exchange-limit (FXL) Tofts model and the water exchange sensitized fast-exchange-regime (FXR) model, the commonly fitted CR transfer constant (Ktrans) and the extravascular, extracellular volume fraction (ve) scale nearly proportionally with the AIF, whereas the FXR-specific unidirectional cellular water efflux rate constant, kio, and the CR intravasation rate constant, kep, are both AIF scaling insensitive. This indicates that, for DCE-MRI of prostate cancer and possibly other cancers, kio and kep may be more suitable imaging biomarkers for cross-platform, multicenter applications. Data from our limited study cohort show that kio correlates with Gleason scores, suggesting that it may be a useful biomarker for prostate cancer disease progression monitoring.

An electronic density model was developed and used to identify a novel pyrrolotriazinone replacement for a quinazolinone, a commonly used moiety to impart selectivity in inhibitors for PI3Kγ and PI3Kδ. Guided by molecular docking, this new specificity piece was then linked to the hinge-binding region of the inhibitor using a novel cyclic moiety. Further structure–activity relationship optimization around the hinge region led to the discovery of candidate 26, a highly potent and selective PI3Kγ–PI3Kδ dual inhibitor with favorable drug metabolism and pharmacokinetic properties in preclinical species.