Search

AIM: To assess the stability of the tear film and the characteristics of corneal nerves in patients with Parkinson's disease(PD).METHODS: This cross-sectional observational study included 72 PD patients and 50 healthy controls. Disease severity was determined using the Hoehn-Yahr(H-Y)scale, dividing patients into mild and moderate PD groups. Dry eye symptoms were evaluated via the ocular surface disease index(OSDI)questionnaire, while tear secretion was quantified using the Schirmer I test. Ocular surface damage was assessed through staining scores, and comprehensive ocular examinations were performed utilizing the LipiView ocular surface interferometer and an ocular surface analyzer. Corneal nerve parameters were examined using corneal confocal microscopy in conjunction with automated analysis software ACCMetrics, with correlations drawn between these parameters, PD course, and severity.RESULTS: PD patients exhibited significantly elevated OSDI scores, indicative of more pronounced dry eye symptoms compared to the control group(F=70.290, P

Objective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators. Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022.With the positive/negative ratio of 1:3, 928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases.These 1 243 patients were randomly divided into the modeling group (n=869) and the validation group (n=374) in a ratio of 7:3 using the R language setting code.Preoperative clinical indicators (basic information, medical history, relevant scale score, surgical information and results of laboratory tests) and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms, k-nearest neighbor (KNN), support vector machine linear (SVM), logic regression (LR) and extreme gradient boosting (XGBoost).In the validation group, receiver operating characteristic (ROC) curve, precision-recall curve (P-R), decision curve analysis (DCA) curve, Kappa value, sensitivity, specificity, Brier score, and F1 score were applied to evaluate the efficacy of model. Results The model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm.The area under the receiver operating characteristic curve (AUROC) was 0.916(95%CI: 0.883~0.949), area under the precision-recall curve (AUPRC) was 0.841, Brier score was 0.097, and sensitivity and specificity was 78.95% and 87.10%, respectively. Conclusion The postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury.

Hepatocellular carcinoma (HCC) is one of the most prevalent and deadly cancers in the world, which is frequently diagnosed at a late stage. HCC patients have a poor prognosis due to the lack of an efficacious therapeutic strategy. Approved drug repurposing is a way for accelerating drug discovery and can significantly reduce the cost of drug development. Carfilzomib (CFZ) is a second-generation proteasome inhibitor, which is highly efficacious against multiple myeloma and has been reported to possess potential antitumor activities against multiple cancers. However, the underlying mechanism of CFZ on HCC is still unclear. Here, we show that CFZ inhibits the proliferation of HCC cells through cell cycle arrest at the G2/M phase and suppresses the migration and invasion of HCC cells by inhibiting epithelial-mesenchymal transition. We also find that CFZ promotes reactive oxygen species production to induce endoplasmic reticulum (ER) stress and activate JNK/p38 MAPK signaling in HCC cells, thus inducing cell death in HCC cells. Moreover, CFZ significantly inhibits HCC cell growth in a xenograft mouse model. Collectively, our study elucidates that CFZ impairs mitochondrial function and activates ER stress and JNK/p38 MAPK signaling, thus inhibiting HCC cell and tumor growth. This indicates that CFZ has the potential as a therapeutic drug for HCC.

Glioma is a highly malignant brain tumor with a grim prognosis. Genetic factors play a role in glioma development. While some susceptibility loci associated with glioma have been identified, the risk loci associated with prognosis have received less attention. This study aims to identify risk loci associated with glioma prognosis and establish a prognostic prediction model for glioma patients in the Chinese Han population.

Lithium (Li) metal is a potential anode material for high energy density batteries. However, issues such as lithium dendrite growth, interface instability, poor cycling stability and large volume expansion limit the application of lithium metal anode. Aiming at the problem of dendritic growth and volume expansion, a semi-confined hierarchical porous carbon (HPC) material with a large specific surface area was prepared by template method. The high specific area of HPC electrode can reduce the local current density of Li deposition, and the rich pore structure can restrict the Li deposition in it, thus inhibiting the dendritic growth and alleviating the volume expansion. Li‖HPC battery can cycle for more than 250 cycles at a current density of 1.0 mA/cm2 and a deposition capacity of 1.0 mAh/cm2, maintaining a Coulombic efficiency of 97.6%. Li@HPC‖lithium iron phosphate a deposition capacity of (LiFePO4) full cell has a specific capacity of 93.6 mAh/g after 100 cycles at 0.5 C, which is higher than that of Li@Cu‖LiFePO4 full cell (60.8 mAh/g) with an increase of by 32.8 mAh/g.

BACKGROUND Chameleon diamond is a type of color diamond with photochromic and thermochromic phenomenon. If the chameleon diamond is placed in a dark room or heated for a long time, it can change from greenish yellow to fancy yellow, and return to its original color when it cools. Previous studies on chameleon diamond focus mainly on spectral characteristics. Through testing a large number of samples, the gemological and spectral characteristics of chameleon diamond are summarized. The characteristics of chameleon diamond, include a persistent yellow phosphorescence, a 480nm absorption band, and mainly the A-aggregate of nitrogen. A model to explain the thermochromic and photochromic phenomenon of chameleon diamond was proposed. The characteristics of chameleon diamond discovered by previous studies are also found in a few yellow diamonds. Diamonds colored by the 480nm band show yellow fluorescence and phosphorescence under short wave ultraviolet light, low nitrogen content, high concentrations of defects related to hydrogen and nickel, but no obvious thermochromic and photochromic phenomena. Previous studies of the chameleon diamond and similar yellow diamonds were conducted independently and did not compare the two similar diamonds. OBJECTIVES To ascertain the difference of spectral characteristics and defect types between chameleon diamonds and similar yellow diamonds, and then analyze the causes of thermochromic phenomenon of chameleon diamond to gain a deeper understanding of the possible structure of the center responsible for the chameleon effect. METHODS Firstly, two chameleon diamond samples and four similar yellow diamond samples were collected to observe the color changes of the diamonds before and after heating. Top illumination and scattering illumination were used to observe the internal characteristics and color distribution of the samples. The fluorescence and phosphorescence characteristics of diamonds at long wave (365nm) and short wave (254nm) were observed by ordinary fluorescent lamp. The fluorescence and phosphorescent characteristics and growth structure of the samples under ultra-violet light were observed by DiamondViewTM. Secondly, in order to investigate the causes of thermochromism in chameleon diamond, the absorption characteristics of diamond samples in the range of ultraviolet visible light were collected before and after heating and compared with those of similar yellow diamonds. The differences in absorption spectra between chameleon diamond and similar yellow diamond were analyzed, and the causes of thermochromic phenomena were explored. Finally, an infrared spectrometer was used to collect the infrared spectrum of diamond samples and analysis of the types and impurity elements of diamond samples was conducted. Through the comparative analysis of the infrared spectrum of samples, the differences in the types and contents of nitrogen and hydrogen impurities between chameleon diamond and similar yellow diamond were obtained. The laser Raman spectrometer was used to collect PL spectra of the samples at liquid nitrogen temperature by using 473nm, 532nm and 830nm lasers. By comparing the PL characteristics of chameleon diamond and similar yellow diamond, the differences between the two defects were obtained. RESULTS The color distribution of the 6 diamond samples is uniform. After heating, the chameleon diamond samples change color from yellow-green to yellow, while similar yellow diamonds have no obvious color change. Under long and short wavelength UV light, the samples are yellow fluorescence without phosphorescence. The performance of the samples in this study is consistent under long and short wavelength ultraviolet light, but it is different from the previous study results that the chameleon diamond has persistent yellow phosphorescence, which further indicates that the chameleon effect is not directly related to the phosphorescence. Under ultra-violet light, the samples have green fluorescence with irregular patterns which are markedly similar to diamonds colored by the 480nm band. Chameleon diamonds show moderate green phosphorescence. All the samples have C defect absorption continuum combined with 480nm absorption band. The specific wide absorption band of 650-800nm is obvious in chameleon diamond. After being heated, the 480nm and 650-800nm absorption band of chameleon diamond completely disappears. However, the 480nm absorption band of similar yellow diamonds is weakened or has no obvious change. Therefore, the poor thermal stability of 480nm and 650-800nm absorption band is responsible for the thermochromic phenomenon of chameleon diamonds. The infrared absorption characteristics of chameleon diamond mainly show A-defect absorption. Isolated nitrogen and hydrogen-related features can be seen, in addition to the characteristic 1430cm−1 wide absorption, which has a different origin than the platelet-related 1430cm−1 feature in Cape diamond. The similar yellow diamond has higher nitrogen content, and the degree of nitrogen aggregation is higher, and does not show any type Ib character. The broadband emission of all samples occurs mainly as one band centered at 700nm, this band consists of a dozen of peaks from 595nm to 725nm in steps of about 10nm. Additionally sharp peaks at 753, 771, 799, 818, 838, 845nm are present in the chameleon diamond samples. This spectrum is virtually identical to that reported previously for diamonds containing the 480nm absorption band. Chameleon diamonds and similar yellow diamonds all contain nitrogen-vacancy, nickel-nitrogen related defects, but the amount of defects is different. Chameleon diamonds cannot be effectively distinguished based on the PL spectral. The PL spectrum analysis shows that the nickel impurity is responsible for the green fluorescence of the samples under ultra-violet light. CONCLUSIONS This study focuses on the comparative analysis of gemology, UV-visible absorption, infrared absorption and photoluminescence characteristics of chameleon diamond and its similar yellow diamond. Chameleon diamond is type Ia diamond with low concentration of A defect, and shows some type Ib character, such as 1240cm−1 absorption. Chameleon diamond has 480nm and 650-800nm absorption bands, that are responsible for the chameleon effect. The characteristics of chameleon diamond in this study are consistent with those in previous studies. Absorption bands of 650-800nm and absorption peaks of 1430cm−1 (unrelated to platelet) are the main differences between the chameleon diamond and similar yellow diamond sample. It is inferred that the absorption band of 650-800nm and the absorption peak of 1430cm−1 are related to the center causing the thermochromic phenomenon. Although similar yellow diamond samples also have a 480nm center, their thermal stability is different from that of chameleon diamonds. The reason for this phenomenon is unknown, therefore further studies are needed.

Objective To develop and validate a machine learning prediction model for postoperative respiratory failure (PRF) in patients after non-cardiothoracic surgery based on intraoperative indicators. Methods A total of 705 patients undergoing non-cardiothoracic surgery in our hospital from January 2014 to June 2019 were enrolled, and then 565 patients of them were assigned in the training set (including 128 cases of PRF) and 140 patients into the test set (35 cases of PRF). Another 164 patients undergoing non-cardiothoracic surgery at West China Hospital from May 2019 to January 2020 and Zhongshan Hospital from June 2019 to December 2019 were assigned into the validation set (41 cases of PRF). Nineteen intraoperative indicators were extracted, and 6 machine learning algorithms, such as gradient boosting model (GBM), generalize linear model (GLM), k-nearest neighbor (KNN), naive bayes (NB), neural network (NNET), and support vector machine linear (SVM) were used to develop and test the models and were verified in the validation set. The best model was screened out by comparing the performance of each model, and finally, the web page prediction model was established. Results GBM obtained the best performance, with an accuracy of 76.2%(95%CI: 69.0%~82.5%), an area under the subject curve (AUC) of 0.794 (95%CI: 0.707~0.882), an area under the precision-recall curve (AUPRC) of 0.641, and a Brier score of 0.169. Conclusion The model developed based on GBM algorithm is of higher generalization, accuracy, and clinical utility, and helps avoid overfitting. The developed web page prediction model (http://150.158.55.139) can provide a new dynamic evaluation method for PRF and quantify surgical risk.

Objective To explore the feasibility of constructing prediction models of postoperative sepsis mortality risk based on machine learning in patients undergoing abdominal surgery. Methods A case-control trial was designed and conducted on the patients diagnosed with sepsis after abdominal surgery from Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database, and 90-day mortality was defined as the primary endpoint event after hospitalization. The dataset was ramdomly split into training (70%) and test (30%) datasets according to wether diagnosed with postopertive sepsis or not. On the training dataset, logistic regression (LR), gradient boosting decision tree (GBDT), random forest (RF), support vector machine (SVM) and adaptive boosting (AdaBoost) were used to develop the prediction model for death. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, accuracy and F1 score were used for model evaluation on the test dataset. Results A total of 986 patients were finally analyzed, of whom 251 patients (25.5%) died within 90 d after hospitalization. The AUC values of LR, GBDT, RF, SVM and AdaBoost prediction models were 0.852, 0.903, 0.921, 0.940 and 0.906, respectively. The model based on SVM yielded the best AUC value, higher differentiation and better prediction performance, while LR performed the worst among them. Conclusion The performances of the prediction model of postoperative sepsis mortality based on GBDTT, RF, SVM and AdaBoost are all better than that of traditional LR model, which may help to assist clinical decision making and improve adverse outcomes.

Objective To explore the application of deep learning in automatic classification of anemia with conjunctival images as input. Methods The conjunctival images of 284 patients undergoing elective surgery in the Department of Anesthesiology of the First Affiliated Hospital of Army Medical University from March 18 to April 26, 2021 were collected and analyzed prospectively. The images divided into 2 types: normal and anemia according to the corresponding hemoglobin concentration. Four deep learning algorithms, including InceptionV3, ResNet50V2, EfficientNetV2B0 and DenseNet121, were used to construct a prediction model for anemia. The performance of the model was evaluated by receiver operating characteristic (ROC) curve with accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Results The area under ROC curve (AUC) was 0.709 (95%CI: 0.643~0.769), 0.661 (95%CI: 0.594~0.725), 0.670 (95%CI: 0.603~0.733), and 0.695 (95%CI: 0.628~0.756), respectively for the 4 deep learning algorithms. The InceptionV3 model showed superior predictive performance on the test set, with an AUC value of 0.709 (95%CI: 0.643~0.769), an accuracy of 0.695, a sensitivity of 0.750, a specificity of 0.412, a positive predictive value of 0.707 and a negative predictive value of 0.629. Based on the optimal algorithm, a network service application which can be used for online prediction of anemia was developed (http://150.158.58.4). Conclusion Our model, which is established based on deep learning algorithm with conjunctiva image as input, has a good performance on fast and automatic prediction for anemia. The InceptionV3model has better comprehensive prediction performance.

Objective To establish a risk model for predicting postoperative cognitive dysfunction (POCD) after non-cardiac surgery using preoperative indicators based on machine learning algorithm. Methods A case-control study was designed, and conducted on the POCD patients after non-cardiac surgery in the medical big data platform of our hospital from January 2014 to January 2019. Finally, 92 patients were included in the POCD group. According to surgical type and age matched of the POCD group, another 276 patients who did not develop POCD after surgery and discharged from the hospital during the same time period from the same big data platform were assigned into the non-POCD group at a ratio of 1∶3. At the same time, these 368 patients were randomly divided into modeling group (n=259) and validation group (n=109) at a ratio of 7∶3. After data preprocessing and feature selection of preoperative clinical indicators (general data, relevant scoring scales, surgical-related data and results of preoperative laboratory tests), the risk prediction model of POCD for non-cardiac surgery was established based on 3 machine learning algorithms, that is, logistic regression (LR), support vector machine (SVM) and Decision Tree. The model efficacy was evaluated by sensitivity, specificity, F1 score, and the area under the receiver operating characteristic curve (AUC). Results The SVM algorithm was the best model among the 3 machine learning algorithms to predict the risk of POCD after non-cardiac surgery. The AUC value of the model in the validation group was 0.957 (95%CI: 0.905~1.000), with a sensitivity and specificity of 92.6% and 98.8%, respectively. Conclusion A prediction model of POCD after non-cardiac surgery is successfully established based on machine learning algorithm. This model shows good predictive performance for POCD. [Key words] machine learning , prediction model , postoperative cognitive dysfunction,

Objective To establish a prediction model of risk for postoperative cognitive dysfunction (POCD) after total knee replacement (TKR) by Bayesian network (BN) algorithm and investigate its predictive performance. Methods A case-control trial was conducted on 1 260 inpatients who underwent TKR from January 2017 to December 2021 in the Department of Joint Surgery of our hospital. Their main diagnosis of inclusion was severe osteoarthritis of left/right knee joint. They were 240 cases of male (19.0%) and 1 020 cases of female (81.0%), at an average age of 66.73±8.46 (23~79) years and a mean body mass index (BMI) of 25.08±5.08 kg/m2. The POCD patients (n=71) after surgery (from the end of surgery to discharge) were randomly divided into A1 group and B1 group at a ratio of 7∶3, and those without POCD (1 189 cases) were also randomly divided into A2 group and B2 group at a same ratio. The patients from A1 and A2 groups were combined together as training set, and those out of B1 and B2 groups as test set. Thirty-six indexes related to perioperative anesthesia decision, disease outcome and length of stay in TKR were selected as nodes, and the probability distribution model diagram of each node was established by using BN algorithm to predict the probability of risk for POCD, so as to minimize the length of stay and promote the maximum recovery of patients. Results The prediction model of risk for POCD after TKR was established based on BN algorithm. The area value under receiver operating characteristic curve (ROC-AUC) of the training set was 0.966 1 (95% CI: 0.954 1~0.978 4), and the ROC-AUC value of the test set was 0.897 4 (95% CI: 0.867 2~0.928 5), with an accuracy of 96.43% (95%CI: 0.951 1~0.976 4) and 93.44% (95% CI: 0.909 2~0.959 6), respectively. Conclusion Our risk prediction model for POCD after TKR based on BN algorithm has good performance and high accuracy

ObjectiveTo explore the effects of anxiety and depression on Internet gaming disorder in medical students， and to evaluate the moderating effect of gender， so as to provide references for the prevention and intervention of Internet gaming disorder in medical students.MethodsIn November 2021， a total of 11 771 medical students from a medical college in Sichuan province participated in an online survey through Wenjuanxing platform. Participants completed the Self-rating Anxiety Scale （SAS）， Self-rating Depression Scale （SDS） and the nine-item Internet Gaming Disorder Scale-Short Form （IGDS9-SF）. Then a hierarchical multiple regression analysis was conducted to discuss the moderating role of gender in the relationships between anxiety， depression and Internet gaming disorder in medical students.Results①Males scored lower on SDS （t=-8.302， P

With the rapid development of renewable energy (RE) and the continuous progress of smart grid, the energy consumption characteristic design of modern power system is developing towards a more flexible and economical microgrid (MG). Meanwhile, the high participation of multi-agent integrated energy in MG brings challenges to the efficient and economic operation of the system. Aiming at the complex capacity configuration of MG with multi-functional interaction, a combined capacity optimization method for a typical independent MG including photovoltaic (PV), wind turbines (WT), diesel generators (DG) and battery energy storage system (BESS) is proposed. On the one hand, the models of PV, WT, DG, BESS and electric vechicle (EV) charging load are established. On the other hand, the EV charging load are considered, and the capacity configuration is optimized to minimize the cost, reduce greenhouse gas emissions and energy waste. The results show that the combination of PV-WT-BESS-DG is not only more reliable but also more economical. This study can provide support for the optimal allocation of MG capacity.

Pancreatic cancer is a malignant tumor with high incidence, rapid progression and low survival rate. It is in urgent need of new targets for precise treatment or improvement of prognosis. In recent years, it has been found that trophoblast cell surface antigen 2 (TROP2) is highly expressed in a variety of malignant tumors and participates in the progress of proliferation, migration and adhesion of malignant tumor cells through cell surface receptor signals. In this paper, the expression of TROP2 in pancreatic cancer, the signaling pathway involved in the mediation of TROP2 and the research progress of antitumor drugs targeting TROP2 were summarized, which provided some references for the mechanism of targeting TROP2 in the treatment of pancreatic cancer and the improvement of prognosis of patients.

In order to enhance the flexibility and reliability of multi-energy microgrids with ice storage systems,integrate with distributed generations and energy storage systems effectively,an autonomous cooperative control is proposed in this paper,which consists of control of alternating current (AC) and direct current (DC) microgrids and interlinking converters (ICs). Slack terminals in AC and DC microgrids adopt P - f and P - V droop control respectively. The control of the IC with AC microgrid consists of interlinking power control loop,virtual synchronous control and inner voltage control. The control of the IC with DC microgrid includes interlinking power control and phase shift control. With the proposed method,it is possible to achieve followings. When electric energy is abundant,ice storage,storage battery and other devices absorb electric energy adaptively according to their rated capacity and bear the energy storage energy reasonably. At peak load,the ice storage device is put into operation preferentially,and other energy storage devices is used for other cooling loads,equivalent electrical loads and electrical loads,which is reasonably borne according to the rated capacity ratio. The effectiveness of the proposed control is verified by simulation. It should be noted that the proposed method improves the stability and controllability of the whole system,which provides a novel idea for system-level controller design for multi-energy microgrid.

With the integration of intelligent algorithm into the supply chain process, the fficiency of supply chain planning has been further improved through automatic prediction and decision-making. Although intelligent algorithms are developing, their challenges including real-time nature of supply chain planning and the complexity of scenarios hinder their true potential. In this study, we proposed an improved genetic algorithm (GA)-long short-term memory (LSTM) neural network prediction algorithm to solve various optimization planning problems for the supply chain from suppliers to production enterprises. Specifically, to determine stable suppliers, we first constructed the technique for order preference by similarity to ideal solution (TOPSIS) model to quantitatively evaluate each supplier, and the rationality of the index weight of the TOPSIS algorithm can be enhanced by the entropy method. Finally, the GA and LSTM were used to solve the decision-making and planning problem in raw material supply chain. Our results indicate that the algorithm we proposed can not only efficiently solve the decision planning problem in the raw material supply chain, but it also reasonably analyzes the suppliers quantitatively.

Music is closely related to human life and is an important way for people to express their feelings in life. Deep neural networks have played a significant role in the field of music processing. There are many different neural network models to implement deep learning for audio processing. For general neural networks, there are problems such as complex operation and slow computing speed. In this paper, we introduce Long Short-Term Memory (LSTM), which is a circulating neural network, to realize end-to-end training. The network structure is simple and can generate better audio sequences after the training model. After music generation, human voice conversion is important for music understanding and inserting lyrics to pure music. We propose the audio segmentation technology for segmenting the fixed length of the human voice. Different notes are classified through piano music without considering the scale and are correlated with the different human voices we get. Finally, through the transformation, we can express the generated piano music through the output of the human voice. Experimental results demonstrate that the proposed scheme can successfully obtain a human voice from pure piano Music generated by LSTM.

This paper proposes a POI displacement estimation method based on the functional optical fiber sensor and the phase modulation principle to improve the POI displacement estimation accuracy. First, the relation between the object deformation and the optic fiber lightwave phase is explained; the measurement principle of functional optical fiber sensor based on the heterodyne interference principle and its layout optimization method is proposed, and a POI displacement estimation model is presented based on the data approach. Secondly, a beam is taken as the simulation object, the optimal position and length of the optical fiber sensor are determined based on its simulation data. Finally, the experimental device is designed to verify the effectiveness of the POI displacement estimation method based on the optic fiber sensors. The frequency-domain plot of the signals shows that the optical fiber sensors can express the flexible deformation of the analyzed object well. The POI displacement estimation model with the fiber optic sensor signals as one of the inputs is constructed. Through estimating the test data, the error using the optical fiber sensor-based POI displacement estimation method proposed in this paper reduces by more than 61% compared to the rigid body-based assumption estimation method.