Prematurity and exposure to melamine-contaminated formula were associated with urinary stones. Affected children lacked typical signs and symptoms of urolithiasis.

Renal‐clearable nanoparticles have made it possible to overcome the toxicity by nonspecific accumulation in healthy tissues/organs due to their highly efficient clearance characteristics. However, their tumor uptake is relatively low due to the short blood circulation time and rapid body elimination. Here, this problem is addressed by developing renal‐clearable nanoparticles by controlled coating of sub‐6 nm CuS nanodots (CuSNDs) on doxorubicin ladened mesoporous silica nanoparticles (pore size ≈6 nm) for multimodal application. High tumor uptake of the as‐synthesized nanoparticles (abbreviated as MDNs) is achieved due to the longer blood circulation time. The MDNs also show excellent performance in bimodal imaging. Moreover, the MDNs demonstrated a photothermally sensitive drug release and pronounced synergetic effects of chemo‐photothermal therapy, which were confirmed by two different tumor models in vivo. A novel key feature of the proposed synthesis is the use of renal‐clearable CuSNDs and biodegradable mesoporous silica nanoparticles which also are renal‐clearable after degradation. Therefore, the MDNs would be rapidly degraded and excreted in a reasonable period in living body and avoid long‐term toxicity. Such biodegradable and clearable single‐compartment theranostic agents applicable in highly integrated multimodal imaging and multiple therapeutic functions may have substantial potentials in clinical practice.

Purpose To establish an 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) mathematical prediction model to improve the diagnosis of solitary pulmonary nodules (SPNs). Materials and Methods We retrospectively reviewed 177 consecutive patients who underwent 18F-FDG PET/CT for evaluation of SPNs. The mathematical model was established by logistic regression analysis. The diagnostic capabilities of the model were calculated, and the areas under the receiver operating characteristic curve (AUC) were compared with Mayo and VA model. Results The mathematical model was y = exp⁡(x)/[1 + exp⁡(x)], x = −7.363 + 0.079 × age + 1.900 × lobulation + 1.024 × vascular convergence + 1.530 × pleural retraction + 0.359 × the maximum of standardized uptake value (SUVmax). When the cut-off value was set at 0.56, the sensitivity, specificity, and accuracy of our model were 86.55%, 74.14%, and 81.4%, respectively. The area under the receiver operating characteristic curve (AUC) of our model was 0.903 (95% confidence interval (CI): 0.860 to 0.946). The AUC of our model was greater than that of the Mayo model, the VA model, and PET (P < 0.05) and has no difference with that of PET/CT (P > 0.05). Conclusion The mathematical predictive model has high accuracy in estimating the malignant probability of patients with SPNs.