Lung cancers are highly heterogeneous and resistant to available therapeutic agents, with a five-year survival rate of less than 15%. Despite significant advances in our knowledge of the genetic alterations and aberrations in lung cancer, it has been difficult to determine the basis of lung cancer's heterogeneity and drug resistance. Cancer stem cell model has attracted a significant amount of attention in recent years as a viable explanation for the heterogeneity, drug resistance, dormancy, recurrence and metastasis of various tumors. At the same time, cancer stem cells have been relatively less characterized in lung cancers. This review summarizes the current understanding of lung cancer stem cells, including their molecular features and signaling pathways that drive their stemness. This review also discusses the prognosis of lung cancer and its relationship with lung cancer stem cell, in an effort to eradicate these cells to combat lung cancer.
ObjectiveTo summarize the latest progress of parathyroid gland identification in thyroid surgery, and to provide some reference for improving the clinical efficacy.MethodThe literatures about the identification of parathyroid gland in thyroid surgery in recent years were collected to make an review.ResultsThere were many methods for identifying parathyroid gland in thyroid surgery, such as naked eye identification method, intraoperative frozen section, intraoperative staining identification method, intraoperative optical identification method, intraoperative parathyroid hormone assay, γ-detector, and histological identification, each method had its own advantages and disadvantages.ConclusionThe identification of parathyroid gland does not only depend on a certain method, but also require surgeons to enhance their ability to distinguish parathyroid gland.
ObjectiveTo investigate the status of knowledge, attitude, and practice of patient identification in nurses, and provide a basis for clinical managers to carry out targeted training.MethodsA total of 3 696 nurses of tertiary, secondary, and primary hospitals in Guizhou Province were recruited and investigated for the status of knowledge, attitude, and practice of patient identification with a questionnaire by using convenient sampling in May 2019.ResultsThe scores of identification knowledge, attitude, and practice of the 3 696 nurses were 47.87±6.10, 27.39±3.15, and 57.19±4.86, respectively. Logistic regression analysis showed that the higher the educational level was, the higher the score of nurses’ knowledge of patient identification was [odds ratio (OR)=1.592, 95% confidence interval (CI) (1.084, 2.338), P=0.018]; the higher the personal monthly income was, the more positive the nurses’ attitude towards patient identification was [OR=1.570, 95%CI (1.005, 2.453), P=0.048].ConclusionsThe general situation of patient identification in nurses is good, but there are still differences among nurses with different characteristics. It is suggested that managers should pay special attention to the training of nurses with low educational level and low income, make them master the knowledge of patient identification, at the same time, improve their enthusiasm and standardize their behavior, so as to ensure the safety of patients.
ObjectiveTo introduce a new method for identifying intersegmental planes during thoracoscopic segmentectomy using pulmonary circulation single-blocking in the target segment. MethodsTo retrospectively analyze the clinical data of 83 patients who underwent thoracoscopic pulmonary segmentectomy from January 2019 to March 2020 using the pulmonary circulation single-blocking method. There were 33 males and 50 females, with a median age of 54 (46-65) years, and they were divided into a single vein group (SVG, n=31) and a single artery group (SAG, n=52), and the clinical data of two groups were compared. ResultsThe intersegmental planes were identified successfully in both groups and there were no statistically significant differences between the two groups in terms of intersegmental plane management (P=0.823), operating time (P=0.786), intraoperative blood loss (P=0.775), chest drainage time (P=0.659), postoperative hospital stay (P=0.824) or the incidence of postoperative complications (P=1.000). ConclusionThe use of pulmonary circulation single-blocking for intersegmental plane identification during thoracoscopic segmentectomy is safe and feasible, and the intersegmental plane can be satisfactorily identified by the single-blocking of arteries or veins.
Objective To propose a lightweight end-to-end neural network model for automated Korotkoff sound phase recognition and subsequent blood pressure (BP) measurement, aiming to improve measurement accuracy and population adaptability. Methods We developed a streamlined architecture integrating depthwise separable convolution (DSConv), multi-head attention (MHA), and bidirectional gated recurrent unit (BiGRU). The model directly processes Korotkoff sound time-series signals to identify auscultatory phases. Systolic BP (SBP) and diastolic BP (DBP) were determined using Phase Ⅰ and PhaseⅤdetections, respectively. Given the clinical relevance of phase Ⅳ for specific populations (e.g., children and pregnant women, denoted as DBPIV), BP values from this phase were also recorded. Results The study enrolled 106 volunteers with 70 males, 36 females at mean age of (40.0±12.0) years. The model achieved 94.25% phase recognition accuracy. Measurement errors were (0.1±2.5) mm Hg (SBP), (0.9±3.4) mm Hg (DBPIV), and (0.8±2.6) mm Hg (DBP). Conclusion Our method enables precise phase recognition and BP measurement, demonstrating potential for developing population-adaptive blood pressure monitoring systems.
Objective To investigate the clinical significance of visual identification and intraoperative neuromonitoring of recurrent laryngeal nerve (RLN) during thyroidectomy. Methods Totally 1 664 patients underwent thyroidectomy with RLN protection from January 2009 to December 2009 were included in this study, in which 1 447 cases were protected by visual identification only, and 217 complex thyroidectomy cases were protected by visual identification and intraoperative monitoring. Results By the “multisites, three steps” RLN exposure method, 1 417 cases （85.16%） were successfully recognized and the recognition time was (3.57±1.26) min. The recognition time in the rest 30 complex cases (2.07%) without intraoperative neuromonitoring was (17.02±5.48) min. By this method, the temporary RLN injury occurred in 23 cases (1.54%) and 15 cases (65.22%) recovered within 2 weeks. In patients undewent intraoperative neuromonitoring, the recognition rate was 100% (217/217) and recognition time was (2.18±0.67) min. The temporary RLN injury occurred in 4 cases (1.84%) and 3 cases (75.00%) recovered within 2 weeks. All temporary RLN injuries recovered within 1 month and no persistent RLN injury occurred. Conclusions Conventional visual identification can reduce the RLN injury, but not meet the needs of the RLN protection during complex thyroidectomy. The combination of visual identification and intraoperative neuromonitoring can further improve the recognition rate and shorten the recovery time of vocal cord dyskinesia.
ObjectiveTo summarize the differences between Budd-Chiari syndrome (BCS) and hepatic veno-occlusive disease (HVOD). MethodsBased on the current reports about BCS and HVOD, combined with the authors' clinical experience, a review was performed for the 2 kinds of diseases. ResultsBCS and HVOD were both post-hepatic portal hypertension symptoms, and both would result in liver cirrhosis in the late phase. According to the different causes of 2 kinds of diseases clinically, and the corresponding clinical characteristics, most cases can be confirmed by the preliminary judgment. As for the cases without clear diagnosis, corresponding imaging examinations may be helpful, but the final diagnosis depended on the pathologic examination after liver biopsy. ConclusionThere are some differences on the cause, clinical characteristic, and characteristic of images between the BCS and HVOD, that all of them contribute to differential diagnosis.
ObjectiveTo explore value of ultrasound real-time elastography (RTE) technology for identification of benign and malignant solid thyroid nodules.MethodsA retrospective analysis was performed on 125 patients with thyroid nodules who underwent ultrasound RTE in this hospital from February 2018 to August 2019. All patients underwent RTE on the basis of conventional ultrasound. The ultrasound elasticity contrast index (ECI) was used as the evaluation index and the pathological examination result was used as the gold standard. The receiver operating characteristic (ROC) curve analysis was used to evaluate the value of ECI in the identification of benign and malignant solid thyroid nodules. Logistic regression analysis was used to analyze the influencing factors of ECI.ResultsAmong the 125 patients with solid thyroid nodules, 51 were malignant nodules, 74 were benign nodules. The ECI value of patients with benign thyroid nodules was lower than that of patients with malignant nodules (2.71±0.83 versus 3.42±1.14, t=–4.030, P<0.001). The result of ROC analysis showed that the cutoff value of ECI to distinguish benign and malignant solid thyroid nodules was 3.07, area under curve of ROC was 0.806 [95%CI (0.717, 0.894), P<0.001], sensitivity was 80.3%, specificity was 70.4%. The multivariate logistic regression analysis showed that the thyroid nodules with diffuse lesions, calcification, and maximum nodule diameter ≥1 cm were the risk factors for elevated ECI values (P<0.05). For the solid thyroid nodules without diffuse lesions, without calcification, and maximum nodule diameter <1 cm, ECI had the higher sensitivity, specificity, accuracy, and positive predictive value for the differential diagnosis of benign and malignant thyroid nodules (all exceed 80%), but these indexes were lower (under 60%) for the differential diagnosis of solid thyroid nodules with diffuse diseases, with calcification, and maximum nodule diameter ≥1 cm.ConclusionsECI obtained by ultrasound RTE can be used to differentiate solid thyroid nodules from benign ones. The presence or absence of diffuse lesions, calcification, and maximum nodule diameter are the influencing factors for ECI to differentiate solid thyroid nodules. In clinical diagnosis, it should be paid attention to the comprehensive analysis of the above factors.
In this paper, the research has been conducted by the Microsoft kinect for windows v2 for obtaining the walking trajectory data from hemiplegic patients, based on which we achieved automatic identification of the hemiplegic gait and sorted the significance of identified features. First of all, the experimental group and two control groups were set up in the study. The three groups of subjects respectively completed the prescribed standard movements according to the requirements. The walking track data of the subjects were obtained straightaway by Kinect, from which the gait identification features were extracted: the moving range of pace, stride and center of mass (up and down/left and right). Then, the bayesian classification algorithm was utilized to classify the sample set of these features so as to automatically recognize the hemiplegia gait. Finally, the random forest algorithm was used to identify the significance of each feature, providing references for the diagnose of disease by ranking the importance of each feature. This thesis states that the accuracy of classification approach based on bayesian algorithm reaches 96%; the sequence of significance based on the random forest algorithm is step speed, stride, left-right moving distance of the center of mass, and up-down moving distance of the center of mass. The combination of step speed and stride, and the combination of step speed and center of mass moving distance are important reference for analyzing and diagnosing of the hemiplegia gait. The results may provide creative mind and new references for the intelligent diagnosis of hemiplegia gait.
In order to develop safe training intensity and training methods for the passive balance rehabilitation training system, we propose in this paper a mathematical model for human standing balance adjustment based on T-S fuzzy identification method. This model takes the acceleration of a multidimensional motion platform as its inputs, and human joint angles as its outputs. We used the artificial bee colony optimization algorithm to improve fuzzy C-means clustering algorithm, which enhanced the efficiency of the identification for antecedent parameters. Through some experiments, the data of 9 testees were collected, which were used for model training and model results validation. With the mean square error and cross-correlation between the simulation data and measured data, we concluded that the model was accurate and reasonable.