Timely and remote biomarker detection is highly desired in personalized medicine and health protection but presents great challenges in the devices reported so far. Here, we present a cost-effective, flexible and self-powered sensing device for H₂S biomarker analysis in various application scenarios based on the structure of galvanic cells. The sensing mechanism is attributed to the change in electrode potential resulting from the chemical adsorption of gas molecules on the electrode surfaces. Intrinsically stretchable organohydrogels are used as solid-state electrolytes to enable stable and long-term operation of devices under stretching deformation or in various environments. The resulting open-circuit sensing device exhibits high sensitivity, low detection limit, and excellent selectivity for H₂S. Its application in the non-invasive halitosis diagnosis and identification of meat spoilage is demonstrated, emerging great commercial value in portable medical electronics and food security. A wireless sensory system has also been developed for remote H₂S monitoring with the participation of Bluetooth and cloud technologies. This work breaks through the shortcomings in the traditional chemiresistive sensors, offering a direction and theoretical foundation for designing wearable sensors catering to other stimulus detection requirements.

Early diagnosis of tumor metastasis is crucial for clinical treatment. Artificial intelligence (AI) has shown great promise in the field of medicine. We therefore aimed to evaluate the diagnostic accuracy of AI algorithms in detecting tumor metastasis using medical radiology imaging. We searched PubMed and Web of Science for studies published from January 1, 1997, to January 30, 2020. Studies evaluating an AI model for the diagnosis of tumor metastasis from medical images were included. We excluded studies that used histopathology images or medical wave-form data and those focused on the region segmentation of interest. Studies providing enough information to construct contingency tables were included in a meta-analysis. We identified 2620 studies, of which 69 were included. Among them, 34 studies were included in a meta-analysis with a pooled sensitivity of 82% (95% CI 79–84%), specificity of 84% (82–87%) and AUC of 0·90 (0·87–0·92). Analysis for different AI algorithms showed a pooled sensitivity of 87% (83–90%) for machine learning and 86% (82–89%) for deep learning, and a pooled specificity of 89% (82–93%) for machine learning, and 87% (82–91%) for deep learning. AI algorithms may be used for the diagnosis of tumor metastasis using medical radiology imaging with equivalent or even better performance to health-care professionals, in terms of sensitivity and specificity. At the same time, rigorous reporting standards with external validation and comparison to health-care professionals are urgently needed for AI application in the medical field. College students' innovative entrepreneurial training plan program .

SARS-CoV-2 has resulted in numerous cases of Coronavirus Disease 2019 (COVID-19) worldwide. In addition to fever and respiratory symptoms, digestive symptoms also are observed in some patients with COVID-19. Angiotensin-converting enzyme 2 (ACE2) was reported to be the receptor for SARS-CoV-2. The aim of this study was to comprehensively investigate the digestive symptoms that occur in COVID-19 patients, and the potential pathogenic route of the SARS-CoV-2 infection in digestive tract organs (from the oral cavity to the gastrointestinal tract). We investigated the digestive symptoms of 48 patients with COVID-19 and explored ACE2 expression in digestive tract and lung cancers, based on a series of bulk and single-cell RNA sequencing data obtained from public databases. We found that 25% (12/48) of the patients with COVID-19 suffered from digestive symptoms, among which pharyngalgia (7/48) was the most common manifestation, followed by diarrhea (3/48), anorexia (3/48), and nausea (1/48). The bulk tissue RNA sequencing analysis indicated that digestive tract organs had higher ACE2 expression levels compared to the lung, and the expression of ACE2 in the lung increased with age. Single-cell RNA-Seq results showed that the ACE2-positive-cell ratio in digestive tract organs was significantly higher compared to the lung. ACE2 expression was higher in tumor cells compared to normal control (NC) tissues. While in gastric tissues, ACE2 expression gradually increased from chronic gastritis to metaplasia, to early cancer. Our data might provide a theoretical basis for screening the SARS-CoV-2 susceptible population and for the clinical classification of treatment of patients with COVID-19.

Necroptosis is a recently discovered form of programmed cell death (PCD) having necrotic-like morphology. However, its presence and potential impact with respect to head and neck squamous cell carcinoma (HNSCC) are still unknown. The aim of this study was to reveal the necroptosis status and its clinicopathological relevance in HNSCC and to establish an in vitro model. We first analyzed the level of p-MLKL, MLKL, and tumor necrosis in HNSCC patient tissues as well as their correlation with clinicopathological features. Results showed that approximately half of the tumor necrosis can be attributed to necroptosis, and the extent of necroptosis is an independent prognostic marker for patient’s overall survival and progression-free survival. Then we established and thoroughly verified an in vitro model of necroptosis in two HNSCC cell lines using combined treatment of TNF-α, Smac mimetic and zVAD-fmk (TSZ). At last, we adopted this model and demonstrated that necroptosis can promote migration and invasion of HNSCC cells by releasing damage-associated molecular patterns. In conclusion, our study unveiled the necroptotic status in HNSCC for the first time and provided a novel in vitro model of necroptosis in two HNSCC cell lines. In addition, our results indicated that necroptosis may be a potential cancer promoter in HNSCC. This study may serve as the foundation for future researches of necroptosis in HNSCC.