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Abstract Background and Aims To elaborate on the development and characteristics of trauma orthopedic robots and their real curative effect in a clinical application through the collection and analysis of relevant literature and reported clinical results. Method We conducted the Embase, ScienceDirect, Pubmed, Medline, Wanfang, CNKI, and VIP search of the literature on robotic‐assisted surgery in trauma orthopedics in China. We combined search terms with “robotic surgery/artificial intelligence surgery/navigation surgery,” “trauma/trauma orthopedics,” and “China/Chinese.” The exclusion criteria were: (1) articles in languages other than English or Chinese, (2) articles focused on other topics other than robotic‐assisted surgery in trauma orthopedics of China, (3) article types were not clinical studies (reviews, basic research, etc.), and (4) articles were not included in the Chinese core journals or science citation index. Authors, type of surgery, robot type, and clinical research results were recorded and analyzed. Results There were three categories of surgical robots in the clinical application of trauma orthopedics (TiRobot, electromagnetic navigation surgical robots, and small medical robots developed by Beijing Jishuitan Hospital). In terms of blood loss, the fluoroscopy time, and fluoroscopy frequency, most studies found that the robot group was significantly better than the traditional group. Conclusions Robot‐assisted surgery has obvious advantages in accuracy, stability, and reducing intraoperative radiation exposure, but there is no final conclusion about functional recovery.

Quinoa (Chenopodium quinoa Willd.), an Andean native crop, is increasingly popular around the world due to its high nutritional content and stress tolerance. The production and the popularity of this strategic global food are greatly restricted by many limiting factors, such as seed pre-harvest sprouting, bitter saponin, etc. To solve these problems, the underlying mechanism of seed maturation in quinoa needs to be investigated. In this study, based on the investigation of morphological characteristics, a quantitative analysis of its global proteome was conducted using the combinational proteomics of tandem mass tag (TMT) labeling and parallel reaction monitoring (PRM). The proteome changes related to quinoa seed maturation conversion were monitored to aid its genetic improvement. Typical changes of morphological characteristics were discovered during seed maturation, including mean grain diameter, mean grain thickness, mean hundred-grain weight, palea, episperm color, etc. With TMT proteomics analysis, 581 differentially accumulated proteins (DAPs) were identified. Functional classification analysis and Gene Ontology enrichment analysis showed that most DAPs involved in photosynthesis were downregulated, indicating low levels of photosynthesis. DAPs that participated in glycolysis, such as glyceraldehyde-3-phosphate dehydrogenase, pyruvate decarboxylase, and alcohol dehydrogenase, were upregulated to fulfill the increasing requirement of energy consumption during maturation conversion. The storage proteins, such as globulins, legumins, vicilins, and oleosin, were also increased significantly during maturation conversion. Protein–protein interaction analysis and function annotation revealed that the upregulation of oleosin, oil body-associated proteins, and acyl-coenzyme A oxidase 2 resulted in the accumulation of oil in quinoa seeds. The downregulation of β-amyrin 28-oxidase was observed, indicating the decreasing saponin content, during maturation, which makes the quinoa “sweet”. By the PRM and qRT-PCR analysis, the expression patterns of most selected DAPs were consistent with the result of TMT proteomics. Our study enhanced the understanding of the maturation conversion in quinoa. This might be the first and most important step toward the genetic improvement of quinoa.

Intravesical bacillus Calmette-Guerin (BCG) instillation is recommended as an adjuvant therapy for intermediate-risk and high-risk non-muscle invasive bladder cancer (NMIBC) after transurethral resection of bladder tumor (TURBt) with nearly 70% reoccurrence. In the present study, we investigated the dynamics of peripheral purified protein derivative (PPD)-specific immune responses along the treatment. Intravesical BCG instillation caused a significant increase in peripheral PPD-specific IFN-γ release of NMIBC patients, when compared to those receiving chemo-drug instillation. Through a follow-up study, we detected rapid increase in PPD-specific IFN-γ, IL-2, and IL-17A producing CD4+ and CD8+ T cells in the induction phase. Interestingly, the frequencies of PPD-specific IFN-γ and IL-2 producing CD4+ and CD8+ T cells decreased dramatically after induction treatment and were restored after BCG re-instillation, whereas IL-17A-producing T cells remained at the maintenance phase. However, we only observed that the percentages of peripheral CD8+ T cells were significantly higher in BCG responder patients than those in BCG refractory patients at the baseline with the potential of predicting the recurrence. A more dramatic increase in PPD-specific IFN-γ and IL-2 producing CD4+ and CD8+ T cells after one and two dose BCG instillations was observed in refractory NMIBC patients. Therefore, regional BCG instillation induced transient peripheral PPD-specific T cell responses, which could be restored through repetitive BCG instillation. Higher proportions of peripheral CD8+ T cells at baseline were associated with better responses to BCG instillation for the prevention of recurrence of bladder cancer.

Abstract Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Most studies on drug addiction degree are made based on statistical scales, addicts' account, and subjective judgement of rehabilitation doctors. No objective, quantified evaluation has been made. This paper uses devises the synchronous bimodal signal collection and experimentation paradigm with electroencephalogram (EEG) and forehead high-density near-infrared spectroscopy (NIRS) device. The drug addicts are classified into mild, moderate and severe groups with reference to the suggestions of researchers and medical experts. Data of 45 drug addicts (mild: 15; moderate: 15; and severe: 15) is collected, and then used to design an addiction degree testing algorithm based on decision fusion. The algorithm is used to classify mild, moderate and severe addiction. This paper pioneers to use two types of Convolutional Neural Network (CNN) to abstract the EEG and NIR data of drug addicts, and introduces batch normalization to CNN, thus accelerating training process, reducing parameter sensitivity, and enhancing system robustness. The characteristics output by two CNNs are transformed into dimensions. Two new characteristics are assigned with a weight of 50% each. The data is used for decision fusion. In the networks, 27 subjects are used as training sets, 9 as validation sets, and 9 as testing sets. The 3-class accuracy remains to be 63.15%, preliminarily justifying this method as an effective approach to measure drug addiction degree. And the method is ready to use, objective, and offers results in real time.

Taking different types of addictive drugs such as methamphetamine, heroin, and mixed drugs causes brain functional Changes. Based on the prefrontal functional near-infrared spectroscopy, this study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings. Hemoglobin concentrations of 30 drug users (10 on methamphetamine, 10 on heroin, and 10 on mixed type) were collected. For these three types of individuals, the convolutional neural networks (CNN) was designed to classify eight Brodmann areas and the entire prefrontal area, and the average accuracy of the three classifications on each functional area was obtained. As a result, the classification accuracy was lower on the left side than on the right in the dorsolateral prefrontal cortex (DLPFC) of the drug users, while it was higher on the left than on the right in the ventrolateral prefrontal cortex (VLPFC), frontopolar prefrontal cortex (FPC), and orbitofrontal cortex (OFC). Then the differences in eight functional areas between the three types of individuals were statistically analyzed, and results showed significant differences in the right VLPFC and right OFC.

Drug addiction can cause abnormal brain activation changes, which are the root cause of drug craving and brain function errors. This study enrolled drug abusers to determine the effects of different drugs on brain activation. A functional near-infrared spectroscopy (fNIRS) device was used for the research. This study was designed with an experimental paradigm that included the induction of resting and drug addiction cravings. We collected the fNIRS data of 30 drug users, including 10 who used heroin, 10 who used Methamphetamine, and 10 who used mixed drugs. First, using Statistical Analysis, the study analyzed the activations of eight functional areas of the left and right hemispheres of the prefrontal cortex of drug addicts who respectively used heroin, Methamphetamine, and mixed drugs, including Left/Right-Dorsolateral prefrontal cortex (L/R-DLPFC), Left/Right-Ventrolateral prefrontal cortex (L/R-VLPFC), Left/Right-Frontopolar prefrontal cortex (L/R-FPC), and Left/Right Orbitofrontal Cortex (L/R-OFC). Second, referencing the degrees of activation of oxyhaemoglobin concentration (HbO[Formula: see text], the study made an analysis and got the specific activation patterns of each group of the addicts. Finally, after taking out data which are related to the addicts who recorded high degrees of activation among the three groups of addicts, and which had the same channel numbers, the paper classified the different drug abusers using the data as the input data for Convolutional Neural Networks (CNNs). The average three-class accuracy is 67.13%. It is of great significance for the analysis of brain function errors and personalized rehabilitation.

BackgroundWe aimed to develop and validate a new nomogram for predicting the risk of intracranial hemorrhage (ICH) in patients with acute ischemic stroke (AIS) after intravenous thrombolysis (IVT).MethodsA retrospective study enrolled 553 patients with AIS treated with IVT. The patients were randomly divided into two cohorts: the training set (70%, n = 387) and the testing set (30%, n = 166). The factors in the predictive nomogram were filtered using multivariable logistic regression analysis. The performance of the nomogram was assessed based on the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and decision curve analysis (DCA).ResultsAfter multivariable logistic regression analysis, certain factors, such as smoking, National Institutes of Health of Stroke Scale (NIHSS) score, blood urea nitrogen-to-creatinine ratio (BUN/Cr), and neutrophil-to-lymphocyte ratio (NLR), were found to be independent predictors of ICH and were used to construct a nomogram. The AUC-ROC values of the nomogram were 0.887 (95% CI: 0.842–0.933) and 0.776 (95% CI: 0.681–0.872) in the training and testing sets, respectively. The AUC-ROC of the nomogram was higher than that of the Multicenter Stroke Survey (MSS), Glucose, Race, Age, Sex, Systolic blood Pressure, and Severity of stroke (GRASPS), and stroke prognostication using age and NIH Stroke Scale-100 positive index (SPAN-100) scores for predicting ICH in both the training and testing sets (p < 0.05). The calibration plot demonstrated good agreement in both the training and testing sets. DCA indicated that the nomogram was clinically useful.ConclusionsThe new nomogram, which included smoking, NIHSS, BUN/Cr, and NLR as variables, had the potential for predicting the risk of ICH in patients with AIS after IVT.

Zhang et al. identify Cystatin C as a new heparan sulfate (HS) binding protein, however only at acidic pH (≤6.5). They further identify two isolated HS binding motifs on Cystatin C that are required to come together to form a complete HS-binding site, which is a unique property among known HS binding proteins.

The fermionic hierarchical equations of motion (HEOM) approach has found wide application in the exploration of open quantum systems, and extensive efforts have been committed to improving its efficiency and accuracy in practical calculations. In this work, by scrutinizing the stationary-state and dynamic properties of Kondo-correlated quantum impurity systems, we show that the strength of Kondo correlation induced by the system-environment entanglement primarily determines the converged hierarchical truncation tier of the HEOM method. This complements the rule of thumb regarding the positive correlation between the height of hierarchy and system-environment coupling strength. These insights will provide useful guidelines for developing a more sophisticated fermionic HEOM method for the investigation of many-body open quantum systems. [ABSTRACT FROM AUTHOR]

In this study, analysis of the chemical constituents and bioactivities of the unpolar fractions [petroleum ether (PE) and chloroform (C)] of fruits and leaves of Alpinia oxyphylla Miq. were carried out, as well as the bioactivities of the main compounds nootkatone and valencene. From PE and C fractions of the fruits, and PE fraction of the leaves, 95.80%, 59.30%, and 82.11% of the chemical constituents respectively were identified by GC-MS. Among these identified compounds, nootkatone was the main compound in all of three fractions, while valencene was the second main compound in the PE fractions of the fruits and leaves. The bioactivities results showed that all of the fractions and the major compound nootkatone showed tyrosinase inhibitory, as well as inhibitory effect on NO production in LPS-stimulated RAW264.7 cells. While valencene only presented inhibitory activity on NO production in RAW264.7 cells. The critical genes involved in nootkatone biosynthesis in A. oxyphylla were identified from the public transcriptome datasets, and protein sequences were preliminarily analyzed. Our studies develop the usage of the unpolar fractions of A. oxyphylla, especially its leaves as the waste during its production, and meanwhile provide the gene resources for nootkatone biosynthesis. [ABSTRACT FROM AUTHOR]