A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer

Method and application of information sharing throughout the emergency rescue process based on 5G and AR wearable devices

Abstract The 2022 Winter Olympics were held in the three competition zones of Beijing, Yanqing and Zhangjiakou, China. The venues of this Winter Olympics were scattered and the terrain was complex. Moreover, the medical resources of Hebei and Beijing were relatively unbalanced. In the medical security of major events, the connection between first aid and in-hospital processes is of the utmost importance to rescue quality. 5th generation mobile network (5G) applications in medical scenarios are on the rise. It would be of great relevance to fully use 5G’s low-latency and high-speed features to share the process information of patients, ambulance personnel, and the destination hospital’s rescue team at emergency scenes and in transportation, improving rescue efficiency. This paper proposes a system scheme of cross-institutional emergency health information sharing based on 5G and augmented reality wearable devices. It also integrates the construction method of monitoring and other sign data sharing, in addition to testing the proposed scheme’s service quality in 5G environments. In the deployment area of the 5G emergency medical rescue information sharing scheme for the Beijing Winter Olympic Games, we selected two designated medical support institutions for testing. The test adopted a combination of fixed-point and driving tests to experiment on the service data, voice service, and streaming media indicators. The 5G signal's coverage rate was close to 100%, the standalone connection's success rate was 100%, and the drop rate was 0. The average downlink rate of multiple scenarios was 620mbps, and the average uplink rate of 5G was over 71.8mbps, which is higher than the average 5G level in China. The downlink rate was more than 20 times larger than the 4th generation mobile network (4G) rate. This study’s proposed scheme demonstrates the importance of 5G applications in emergency response and support, in addition to providing a suitable scheme for the integration of 5G networks in the medical scene

Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system

Abstract To improve the hospital's ability to proactively detect infectious diseases, a knowledge-based infectious disease monitoring and decision support system was established based on real medical records and knowledge rules. The effectiveness of the system was evaluated using interrupted time series analysis. In the system, a monitoring and alert rule library for infectious diseases was generated by combining infectious disease diagnosis guidelines with literature and a real medical record knowledge map. The system was integrated with the electronic medical record system, and doctors were provided with various types of real-time warning prompts when writing medical records. The effectiveness of the system's alerts was analyzed from the perspectives of false positive rates, rule accuracy, alert effectiveness, and missed case rates using interrupted time series analysis. Over a period of 12 months, the system analyzed 4,497,091 medical records, triggering a total of 12,027 monitoring alerts. Of these, 98.43% were clinically effective, while 1.56% were invalid alerts, mainly owing to the relatively rough rules generated by the guidelines leading to several false alarms. In addition, the effectiveness of the system's alerts, distribution of diagnosis times, and reporting efficiency of doctors were analyzed. 89.26% of infectious disease cases could be confirmed and reported by doctors within 5 min of receiving the alert, and 77.6% of doctors could complete the filling of 33 items of information within 2 min, which is a reduction in time compared to the past. The timely reminders from the system reduced the rate of missed cases by doctors; the analysis using interrupted time series method showed an average reduction of 4.4037% in the missed-case rate. This study proposed a knowledge-based infectious disease decision support system based on real medical records and knowledge rules, and its effectiveness was verified. The system improved the management of infectious diseases, increased the reliability of decision-making, and reduced the rate of underreporting

Nearest Neighbor Classifier Embedded Network for Active Learning

Deep neural networks (DNNs) have been widely applied to active learning. Despite of its effectiveness, the generalization ability of the discriminative classifier (the softmax classifier) is questionable when there is a significant distribution bias between the labeled set and the unlabeled set. In this paper, we attempt to replace the softmax classifier in deep neural network with a nearest neighbor classifier, considering its progressive generalization ability within the unknown sub-space. Our proposed active learning approach, termed nearest Neighbor Classifier Embedded network (NCE-Net), targets at reducing the risk of over-estimating unlabeled samples while improving the opportunity to query informative samples. NCE-Net is conceptually simple but surprisingly powerful, as justified from the perspective of the subset information, which defines a metric to quantify model generalization ability in active learning. Experimental results show that, with simple selection based on rejection or confusion confidence, NCE-Net improves state-of-the-arts on image classification and object detection tasks with significant margins

Modular Hydrogel Vaccine for Programmable and Coordinate Elicitation of Cancer Immunotherapy

Abstract Immunotherapy holds great promise for the treatment of malignant cancer. However, the lack of sufficient tumor neoantigens and incomplete dendritic cell (DC) maturation compromise the efficacy of immunotherapy. Here, a modular hydrogel‐based vaccine capable of eliciting a powerful and sustained immune response is developed. Briefly, CCL21a and ExoGM‐CSF+Ce6 (tumor cell‐derived exosomes with granulocyte‐macrophage colony‐stimulating factor (GM‐CSF) mRNA encapsulated inside and sonosensitizer chlorin e6 (Ce6) incorporated in the surface) are mixed with nanoclay and gelatin methacryloyl, forming the hydrogel designated as CCL21a/ExoGM‐CSF+Ce6@nanoGel. The engineered hydrogel releases CCL21a and GM‐CSF with a time gap. The earlier released CCL21a diverts the tumor‐draining lymph node (TdLN) metastatic tumor cells to the hydrogel. Consequently, the trapped tumor cells in the hydrogel, in turn, engulf the Ce6‐containing exosomes and thus are eradicated by sonodynamic therapy (SDT), serving as the antigen source. Later, together with the remnant CCL21a, GM‐CSF produced by cells engulfing ExoGM‐CSF+Ce6 continuously recruits and provokes DCs. With the two programmed modules, the engineered modular hydrogel vaccine efficiently inhibits tumor growth and metastasis via diverting TdLN metastatic cancer to hydrogel, killing the trapped tumor cells, and eliciting prolonged and powerful immunotherapy in an orchestrated manner. The strategy would open an avenue for cancer immunotherapy

Microbial and Protease Fermentation of Mao-Tai Lees Alters Nutritional Composition and Promotes In Vitro Intestinal Proteolysis

Mao-tai lees (ML) is a by-product produced in the process of Mao-tai liquor production and contains high levels of crude protein, starch and fiber, and large yield. Thus, the ML has the potential to become feedstuffs alternatives in livestock production. The present study evaluated the nutritional values of ML and fermented ML (FML), including the first stage (FML I; microbial fermentation), the second stage (FMTL II; microbial fermentation), and the final stage (FFML; microbial fermentation with proteases), and explored their effects on in vitro intestinal fermentation. The results showed that the FFML had higher contents of acid detergent fiber, acid detergent lignin, crude fiber, crude protein, neutral detergent fiber, starch, Vitamin B2, B6, and B12, whereas the FML II presented higher contents of calcium, copper, iron, manganese, magnesium, and Vitamin B1 compared with the other groups. Compared with the ML, the total free amino acids (FAAs) and total bioamine contents were higher in the FML II and FFML and had lower total hydrolyzed amino acids and total other free organic acids contents, among which the FFML had higher total FAAs and total bioamine contents. The FMLs had lower n-6:n-3 PUFA ratio compared with the ML; however, the FFML had lower n-6:n-3 PUFA ratio than the other groups. Furthermore, the FFML had higher concentrations of 1,7-diaminoheptane, isobutyrate, isovalerate, putrescine, and spermidine in vitro fermentation, suggesting that the FFML had greater proteolysis than the other groups. Collectively, these findings suggest that microbial fermentation with proteases could alter the nutritional composition and promote in vitro intestinal proteolysis of ML, which may be an effective way for promoting the protein utilization of ML. The study provides an effective potential strategy to develop novel feedstuff alternatives

Plants as highly diverse sources of construction wood, handicrafts and fibre in the Heihe valley (Qinling Mountains, Shaanxi, China): the importance of minor forest products

Abstract Background Chinese rural communities living among species-rich forests have little documentation on species used to make handicrafts and construction materials originating from the surrounding vegetation. Our research aimed at recording minor wood uses in the Heihe valley in the Qinling mountains. Methods We carried out 37 semi-structured interviews in seven villages. Results We documented the use of 84 species of plants. All local large canopy trees are used for some purpose. Smaller trees and shrubs which are particularly hard are selectively cut. The bark of a few species was used to make shoes, hats, steamers and ropes, but this tradition is nearly gone. A few species, mainly bamboo, are used for basket making, and year-old willow branches are used for brushing off the chaff during wheat winnowing. Conclusions The traditional use of wood materials documented suggests that some rare and endangered tree species may have been selectively cut due to their valuable wood, e.g. Fraxinus mandshurica and Taxus wallichiana var. chinensis. Some other rare species, e.g. Dipteronia sinensis, are little used and little valued

Pubescenosides E–K, Seven New Triterpenoid Saponins from the Roots of Ilex pubescens and Their Anti-Inflammatory Activity

Seven new triterpenoid saponins (1–7), together with three known ones (8–10), were isolated from Ilex pubescens. Elucidation of their structures was performed based on high-resolution electrospray ionisation mass spectrometry (HR-ESI-MS), infrared spectra (IR), and nuclear magnetic resonance (NMR) spectroscopic data. The anti-inflammatory activity of the isolates toward lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages was investigated. The results demonstrated that compounds 3, 5, and 6 inhibited the expression of inducible nitric oxide synthase (iNOS) protein in comparison with LPS stimulation in RAW264.7 cells