Accurate detection and early diagnosis of oral diseases such as dental caries and periodontitis, can be potentially achieved by detecting the secretion of volatile sulfur compounds (VSCs) in oral cavities. Current diagnostic approaches for VSCs can detect the existence and concentrations, yet are not capable of locating the dental lesion sites. Herein, the development of a unique approach for accurately locating dental lesion sites using a fluorescent mouthguard consisting of the zinc oxide–poly(dimethylsiloxane) (ZnO‐PDMS) nanocomposite to detect the local release of VSCs is reported. The ZnO‐PDMS mouthguard displays a highly sensitive and selective response to VSCs, and exhibits high fluorescent stability, good biocompatibility, and low biological toxicity in normal physiological environments. Then, the wearable ZnO‐PDMS mouthguard is demonstrated to be able to identify the precise locations of lesion sites in human subjects. Combined with image analysis, the mouthguards successfully uncover the precise locations of dental caries, allowing convenient screening of hidden dental lesion sites that are oftentimes omitted by dentists. Due to low cost, long‐term stability, and good patient compliance, the proposed wearable mouthguard is suitable for large‐scale production and enables widely applicable, preliminary yet accurate screening of dental lesions prior to dental clinics and routine physical examinations.

Saliva contains important personal physiological information that is related to some diseases, and it is a valuable source of biochemical information that can be collected rapidly, frequently, and without stress. In this article, we reported a new and simple localized surface plasmon resonance (LSPR) substrate composed of polyaniline (PANI)-gold hybrid nanostructures as an optical sensor for monitoring the pH of saliva samples. The overall appearance and topography of the substrates, the composition, and the wettability of the LSPR surfaces were characterized by optical and scanning electron microscope (SEM) images, infrared spectra, and contact angles measurement, respectively. The PANI-gold hybrid substrate readily responded to the pH. The response time was very short, which was 3.5 s when the pH switched from 2 to 7, and 4.5 s from 7 to 2. The changes of visible-near-infrared (NIR) spectra of this sensor upon varying pH in solution showed that—for the absorption at given wavelengths of 665 nm and 785 nm—the sensitivities were 0.0299 a.u./pH (a.u. = arbitrary unit) with a linear range of pH = 5–8 and 0.0234 a.u./pH with linear range of pH = 2–8, respectively. By using this new sensor, the pH of a real saliva sample was monitored and was consistent with the parallel measurements with a standard laboratory method. The results suggest that this novel LSPR sensor shows great potential in the field of mobile healthcare and home medical devices, and could also be modified by different sensitive materials to detect various molecules or ions in the future.

BackgroundWith the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke and its risk factors and conducts high-risk population interventions for people aged above 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, smoking, lack of exercise, apparently overweight and family history of stroke. People with more than two risk factors or history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk levels for people with unknown values in fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in statistical results at the national level. In this paper, we use 2017 national stroke screening data to develop stroke risk classification models based on machine learning algorithms to improve the classification efficiency.MethodFirstly, we construct training set and test sets and process the imbalance training set based on oversampling and undersampling method. Then, we develop logistic regression model, Naïve Bayesian model, Bayesian network model, decision tree model, neural network model, random forest model, bagged decision tree model, voting model and boosting model with decision trees to classify stroke risk levels.ResultThe recall of the boosting model with decision trees is the highest (99.94%), and the precision of the model based on the random forest is highest (97.33%). Using the random forest model (recall: 98.44%), the recall will be increased by about 2.8% compared with the method currently used, and several thousands more people with high risk of stroke can be identified each year.ConclusionModels developed in this paper can improve the current screening method in the way that it can avoid the impact of unknown values, and avoid unnecessary rescreening and intervention expenditures. The national stroke screening program can choose classification models according to the practice need.

Current chemotherapy strategies used in clinic appear with lots of disadvantages due to the low targeting effects of drugs and strong side effects, which significantly restricts the drug potency, causes multiple dysfunctions in the body, and even drives the emergence of diseases. Immunotherapy has been proved to boost the body’s innate and adaptive defenses for more effective disease control and treatment. As a trace element, selenium plays vital roles in human health by regulating the antioxidant defense, enzyme activity, and immune response through various specific pathways. Profiting from novel nanotechnology, selenium nanoparticles have been widely developed to reveal great potential in anticancer, antibacterial, and anti-inflammation treatments. More interestingly, increasing evidence has also shown that functional selenium nanoparticles can be applied for potential immunotherapy, which would achieve more effective treatment efficiency as adjunctive therapy strategies for the current chemotherapy. By directly interacting with innate immune cells, such as macrophages, dendritic cells, and natural killer cells, selenium nanoparticles can regulate innate immunity to intervene disease developments, which were reported to boost the anticancer, anti-infection, and anti-inflammation treatments. Moreover, selenium nanoparticles can also activate and recover different T cells for adaptive immunity regulations to enhance their cytotoxic to combat cancer cells, indicating the potential of selenium nanoparticles for potential immunotherapy strategy development. Here, aiming to enhance our understanding of the potential immunotherapy strategy development based on Se NPs, this review will summarize the immunological regulation effects of selenium nanoparticles and the application of selenium nanoparticle-based immunotherapy strategies. Furthermore, we will discuss the advancing perspective of selenium nanoparticle-based potential immunotherapy as a kind of novel adjunctive therapy to enhance the efficiency of current chemotherapies and also introduce the current obstacles for the development of selenium nanoparticles for potential immunotherapy strategy development. This work is expected to promote the future research on selenium nanoparticle-assisted immunotherapy and finally benefit the more effective disease treatments against the threatening cancer and infectious and chronic diseases.

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