Nanoparticulate drug delivery systems (Nano-DDSs) have emerged as possible solution to the obstacles of anticancer drug delivery. However, the clinical outcomes and translation are restricted by several drawbacks, such as low drug loading, premature drug leakage and carrier-related toxicity. Recently, pure drug nano-assemblies (PDNAs), fabricated by the self-assembly or co-assembly of pure drug molecules, have attracted considerable attention. Their facile and reproducible preparation technique helps to remove the bottleneck of nanomedicines including quality control, scale-up production and clinical translation. Acting as both carriers and cargos, the carrier-free PDNAs have an ultra-high or even 100% drug loading. In addition, combination therapies based on PDNAs could possibly address the most intractable problems in cancer treatment, such as tumor metastasis and drug resistance. In the present review, the latest development of PDNAs for cancer treatment is overviewed. First, PDNAs are classified according to the composition of drug molecules, and the assembly mechanisms are discussed. Furthermore, the co-delivery of PDNAs for combination therapies is summarized, with special focus on the improvement of therapeutic outcomes. Finally, future prospects and challenges of PDNAs for efficient cancer therapy are spotlighted.

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Purpose Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging has been used for the evaluation of pediatric epilepsy. However, the epileptic focus is very difficult to be identified by visual assessment since it may present either hypo- or hyper-metabolic abnormality with unclear boundary. This study aimed to develop a novel symmetricity-driven deep learning framework of PET imaging for the identification of epileptic foci in pediatric patients with temporal lobe epilepsy (TLE). Methods We retrospectively included 201 pediatric patients with TLE and 24 age-matched controls who underwent 18F-FDG PET-CT studies. 18F-FDG PET images were quantitatively investigated using 386 symmetricity features, and a pair-of-cube (PoC)-based Siamese convolutional neural network (CNN) was proposed for precise localization of epileptic focus, and then metabolic abnormality level of the predicted focus was calculated automatically by asymmetric index (AI). Performances of the proposed framework were compared with visual assessment, statistical parametric mapping (SPM) software, and Jensen-Shannon divergence-based logistic regression (JS-LR) analysis. Results The proposed deep learning framework could detect the epileptic foci accurately with the dice coefficient of 0.51, which was significantly higher than that of SPM (0.24, P < 0.01) and significantly (or marginally) higher than that of visual assessment (0.31–0.44, P = 0.005–0.27). The area under the curve (AUC) of the PoC classification was higher than that of the JS-LR (0.93 vs. 0.72). The metabolic level detection accuracy of the proposed method was significantly higher than that of visual assessment blinded or unblinded to clinical information (90% vs. 56% or 68%, P < 0.01). Conclusion The proposed deep learning framework for 18F-FDG PET imaging could identify epileptic foci accurately and efficiently, which might be applied as a computer-assisted approach for the future diagnosis of epilepsy patients. Trial registration NCT04169581. Registered November 13, 2019 Public site: https://clinicaltrials.gov/ct2/show/NCT04169581

Macroautophagy/autophagy is known to be important for intracellular quality control in the lens. GJA8 is a major gap junction protein in vertebrate lenses. Mutations in GJA8 cause cataracts in humans. The well-known cataractogenesis mechanism is that mutated GJA8 leads to abnormal assembly of gap junctions, resulting in defects in intercellular communication among lens cells. In this study, we observed that ablation of Gja8b (a homolog of mammalian GJA8) in zebrafish led to severe defects in organelle degradation, an important cause of cataractogenesis in developing lens. The role of autophagy in organelle degradation in lens remains disputable. Intriguingly, we also observed that ablation of Gja8b induced deficient autophagy in the lens. More importantly, in vivo treatment of zebrafish with rapamycin, an autophagy activator that inhibits MAPK/JNK and MTORC1 signaling, stimulated autophagy in the lens and relieved the defects in organelle degradation, resulting in the mitigation of cataracts in gja8b mutant zebrafish. Conversely, inhibition of autophagy by treatment with the chemical reagent 3-MA blocked these recovery effects, suggesting the important roles of autophagy in organelle degradation in the lens in gja8b mutant zebrafish. Further studies in HLE cells revealed that GJA8 interacted with ATG proteins. Overexpression of GJA8 stimulated autophagy in HLE cells. These data suggest an unrecognized cataractogenesis mechanism caused by ablation of Gja8b and a potential treatment for cataracts by stimulating autophagy in the lens.