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Intubated general anesthesia and single-lung ventilation are considered mandatory for conventional thoracoscopic surgery. Non-intubated thoracoscopic thymectomy is technically challenging. The aim of this article was to present the initial results of non-intubated subxiphoid-subcostal thoracoscopic thymectomy (NI-STT) under LMA management for patients with thymic tumor or myasthenia gravis (MG) and to investigate the feasibility and safety of the procedure. A retrospective analysis of patients undergoing NI-STT for thymic tumor or MG at our department from January 2017 to January 2020 was performed. The clinical characteristics and perioperative outcomes of the patients were reviewed and analyzed. A total of 61 patients were received NI-STT in this analysis, of which 19 patients with MG undergone an extended thymectomy and the rest (n=42) undergone a partial thymectomy. The anesthetic induction duration, surgical duration and global operating room duration were 24.83±12.27 min, 118.75±32.49 min and 173.51±41.80 min, respectively. The lowest SpO2 and peak EtCO2 during operation were 96.15±2.93 mmHg and 41.79±7.53 mmHg, respectively. The mean duration of chest drainage and postoperative hospital stays were 1.87 days, and 2.91 days, respectively. Three cases had sore throat and irritable cough and two cases suffered nausea and vomiting occurred. one patient suffered from an atrial fibrillation, two patients experienced pneumonia, and one patient suffered wound infection, respectively. There were no phrenic nerve paralysis and mortality occurred in the study group. The postoperative pain was low on 1,3, 7, 14, 30, 90 and 180 postoperative days. NI-STT was a technically safe and feasible approach for treating thymic tumors or MG. It could be an alternative to intubated single-lung ventilation for thymectomy in selected patients.

Medicinal plants have a variety of values and are an important source of new drugs and their lead compounds. They have played an important role in the treatment of cancer, AIDS, COVID-19 and other major and unconquered diseases. However, there are problems such as uneven quality and adulteration. Therefore, it is of great significance to find comprehensive, efficient and modern technology for its identification and evaluation to ensure quality and efficacy. In this study, deep learning, which is superior to conventional identification techniques, was extended to the identification of the part and region of the medicinal plant Paris polyphylla var. yunnanensis from the perspective of spectroscopy. Two pattern recognition models, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM), were established, and the overall discrimination performance of the three types of models was compared. In addition, we also compared the effects of different sample sizes on the discriminant performance of the models for the first time to explore whether the three models had sample size dependence. The results showed that the deep learning model had absolute superiority in the identification of medicinal plant. It was almost unaffected by factors such as data type and sample size. The overall identification ability was significantly better than the PLS-DA and SVM models. This study verified the superiority of the deep learning from examples, and provided a practical reference for related research on other medicinal plants.

With the development of information technology, educational data mining is increasingly widely used in colleges and universities. We explored the relationship between students’ personal situations, course performance, and final graduation destination and examined the relationship between various course performances. We analyzed the data of 1572 software college students from 2011 to 2014. Firstly, based on the students’ academic performance and personal information, we predicted the students’ future after graduation. Secondly, we used random forest to mine features or courses that influenced employment. Thirdly, we used the Apriori algorithm to investigate the implicit relationship between students’ scores in different courses. Experiments showed that students’ employment direction can be predicted based on student information, and there was a correlation between course scores, which could provide reasonable guidance for students’ learning and employment and assist university educators.

Background The widespread use of low-dose chest CT screening has improved the detection of early lung adenocarcinoma. Radical surgery is the best treatment strategy for patients with early lung adenocarcinoma; however, some patients present with postoperative recurrence and poor prognosis. Through this study, we hope to establish a model that can identify patients that are prone to recurrence and have poor prognosis after surgery for early lung adenocarcinoma. Materials and Methods We screened prognostic and relapse-related genes using The Cancer Genome Atlas (TCGA) database and the GSE50081 dataset from the Gene Expression Omnibus (GEO) database. The GSE30219 dataset was used to further screen target genes and construct a risk prognosis signature. Time-dependent ROC analysis, calibration degree analysis, and DCA were used to evaluate the reliability of the model. We validated the TCGA dataset, GSE50081, and GSE30219 internally. External validation was conducted in the GSE31210 dataset. Results A novel four-gene signature (INPP5B, FOSL2, CDCA3, RASAL2) was established to predict relapse-related survival outcomes in patients with early lung adenocarcinoma after surgery. The discovery of these genes may reveal the molecular mechanism of recurrence and poor prognosis of early lung adenocarcinoma. In addition, ROC analysis, calibration analysis and DCA were used to verify the genetic signature internally and externally. Our results showed that our gene signature had a good predictive ability for recurrence and prognosis. Conclusions We established a four-gene signature and predictive model to predict the recurrence and corresponding survival rates in patients with early lung adenocarcinoma after surgery. These may be helpful for reforumulating post-operative consolidation treatment strategies.

Paris polyphylla var. yunnanensis, as perennial plants, its quality is closely related to growth period. Different harvest years determine the dry matter accumulation of its medicinal parts and the dynamic accumulation of active ingredients, as well as its economic value and medicinal value. Therefore, it is necessary to establish a systematic evaluation method for the identification and evaluation of P. polyphylla var. yunnanensis with different growth years. Deep learning has a powerful ability in recognition. This study extends it to the identification analysis of medicinal plants from the perspective of spectrum. For the first time, two-dimensional correlation spectroscopy (2DCOS) based on the attenuated total reflection Fourier transformed infrared spectroscopy (ATR-FTIR) combined with residual neural network (Resnet) was used to identify growth years. 525 samples were collected, 4725 2DCOS images were drawn, and the dry matter accumulation in rhizomes of different growth years and different sampling sites were briefly analyzed. The results show that the eight-year-old P. polyphylla var. yunnanensis in Dali has higher economic value and medicinal value. The synchronous 2DCOS models based on ATR-FTIR can realize the identification of growth years with accuracy of 100%. Synchronous 2DCOS are more suitable for the identification of medicinal plants with complex systems. 2DCOS images with different colors and second derivative processing cannot optimize the modeling results. In summary, the method we established is innovative and feasible. It not only solved the identification of growth years, expanded the application field of deep learning, but could also be extended to further research on other medicinal plants.

Spectral analysis has the characteristics of fast and nondestructive. In order to conform to the development of the times, a practical method beyond the traditional spectral analysis was established. For the first time, the two-dimensional correlation spectroscopy (2DCOS) images of Fourier-transform mid-infrared spectroscopy combined with the Residual Neural Network (ResNet) was used for the identification and analysis of 12 Paris species, and the second derivative 2DCOS rarely involved in previous researchers was established. Besides, the fusion strategy of 2DCOS images based on feature bands was first proposed for modeling analysis. From the results, (1) 2DCOS combined with ResNet can successfully identify 12 Paris species. (2) 2DCOS is a powerful tool for identification, whether it is used for image visual analysis or modeling analysis. (3) Compared with asynchronous 2DCOS, synchronous 2DCOS is more suitable for the identification and analysis of complex mixed systems such as traditional Chinese medicine. (4) The modeling based on feature bands fusion strategy of 2DCOS has better model performance and is also suitable for the analysis of small samples. To sum up, what we proposed is an innovative and feasible method with wide applicability, which can not only solve the problem of identifying Paris, provide ideas and methods for the selection of spectral types and feature bands, but also provide practical reference for the research in analytical chemistry and other related fields.