Chinese patent medicine as adjuvant for mild-to-moderate active ulcerative colitis : a network meta-analysis of randomized controlled trials

Objective. To evaluate the effectiveness and safety of Chinese patent medicine for mild-to-moderate active ulcerative colitis (UC) using network meta-analysis (NMA). Methods. We systematically searched PubMed, Cochrane library, Embase, Sino-Med, China National Knowledge Infrastructure (CNKI), Wanfang, and Chinese Scientific Journal Database (VIP) databases to October, 2020. We included randomized controlled trials (RCTs) on Chinese patent medicine for mild-to-moderate active UC. The main analysis was complemented by network subanalyses and standard pairwise comparisons. Statistical heterogeneity, inconsistencies, and ranking probability were also evaluated. Results. The databases search identified 3222 citations, of which 33 RCTs involving 2971 patients met the inclusion criteria. A total of 15 Chinese patent medicines were analyzed. The overall quality of the included studies was low. Pairwise meta-analysis showed that Chinese patent medicine was superior to Mesalazine in improving disappearances of clinical symptoms, recurrence rate, and Mayo score. Based on decreases in adverse events, results from NMA showed that Xilei powder plus Mesalazine was more effective than other drugs. Other NMA results indicated that Danshen freeze-dried powder plus Mesalazine (RR: 0.13; 95% CI, 0.02–0.78) and Kangfuxin lotion plus Mesalazine (RR: 0.24; 95% CI, 0.07–0.57) were superior to Mesalazine in decreasing recurrence rate. Another NMA result indicated that Kangfuxin lotion plus Mesalazine (RR: 0.00; 95% CI, 0.00–0.02) and Zhi Kang capsule plus Mesalazine (RR: 0.00; 95% CI, 0.00–0.02) were superior to Mesalazine in increasing the disappearance of tenesmus. Conclusion. In the probability sorting, Xilei powder combined with Mesalazine ranked first for having the fewest adverse events, Maintaining Intestines Antidiarrheal Pills combined with Mesalazine ranked first for having the lowest recurrence rate, Xilei powder combined with Mesalazine ranked first for improving disappearance rate of mucopurulent bloody stool/abdominal pain, and Kangfuxin lotion combined with Mesalazine ranked first for improving the disappearance rate of diarrhea/tenesmus. However, there is a lack of direct comparisons among Chinese patent medicines for UC. More multiarm RCTs are needed in the future to provide direct comparative evidence

The progress of pulmonary artery denervation

Pulmonary arterial hypertension (PAH) is a chronic pulmonary vascular disease characterized by increased pulmonary arterial pressure and pulmonary arterioles remodeling. Some studies have discovered the relationship between sympathetic nerves (SNs) and pathogenesis of PAH. This review is aimed to illustrate the location and components of SNs in the pulmonary artery, along with different methods and effects of pulmonary artery denervation (PADN). Studies have shown that the SNs distributed mainly around the main pulmonary artery (MPA) and pulmonary artery (PA) bifurcation. And the SNs could be destroyed by three ways: the chemical way, the surgical way and the catheter-based way. PADN can significantly decrease pulmonary arterial pressure rapidly, improve hemodynamic varieties, and then palliate PAH. PADN has been recognized as a prospective and effective therapy for PAH patients, especially for those with medication-refractory PAH. However, further enlarged clinical studies are needed to confirm accurate distribution of SNs in the pulmonary artery and the efficacy of PADN

Multispectral LiDAR Point Cloud Segmentation for Land Cover Leveraging Semantic Fusion in Deep Learning Network

Multispectral LiDAR technology can simultaneously acquire spatial geometric data and multispectral wavelength intensity information, which can provide richer attribute features for semantic segmentation of point cloud scenes. However, due to the disordered distribution and huge number of point clouds, it is still a challenging task to accomplish fine-grained semantic segmentation of point clouds from large-scale multispectral LiDAR data. To deal with this situation, we propose a deep learning network that can leverage contextual semantic information to complete the semantic segmentation of large-scale point clouds. In our network, we work on fusing local geometry and feature content based on 3D spatial geometric associativity and embed it into a backbone network. In addition, to cope with the problem of redundant point cloud feature distribution found in the experiment, we designed a data preprocessing with principal component extraction to improve the processing capability of the proposed network on the applied multispectral LiDAR data. Finally, we conduct a series of comparative experiments using multispectral LiDAR point clouds of real land cover in order to objectively evaluate the performance of the proposed method compared with other advanced methods. With the obtained results, we confirm that the proposed method achieves satisfactory results in real point cloud semantic segmentation. Moreover, the quantitative evaluation metrics show that it reaches state-of-the-art

Chinese Gravimetry Augment and Mass Change Exploring Mission Status and Future

The satellite gravimetry technology effectively recovers the global Earth’s gravity field. Since 2000s, HL-SST satellite CHAMP, LL-SST satellite GRACE, Gravity Gradient Measurement (GGM) satellite GOCE have been launched successfully, producing some Earth’s gravity models solely from satellites data. However, the space and time resolution of the Earth’s gravity fields do not adequately satisfy scientific objectives. The main reason is that the gravimetry satellites are not enough and observation data insufficient. The paper outlines the current and future status of Chinese gravity satellite missions. The Chinese gravimetry satellite system, named Chinese Gravimetry augment and Mass change exploring mission (ChiGaM), successfully launched in Dec. 2021 after four years of production and over a year of calibration and valiation. The accelerometer, K-band ranging system and the three stellar sensors, among others, were precisely calibrated and trimmed. The satellite mass center was determined and coordinated with the proof center of accelerometer with an accuracy 100μm. The inter-satellite ranging system and BDS/GPS receiver operate together seamlessly. The range and range rate noise is less than 3μm/Hz1/2 and 1μm/s/Hz1/2, respectively, in band of 0.025~0.1Hz. The electrostatic suspension accelerometer is working well. Its high-sensitive axis noise level is 3×10-10 m/s2/Hz1/2 near the frequency 0.1Hz, and 1×10-9 m/s2/Hz1/2 for the less-sensitive axis. Meanwhile the BDS/GPS receiver is used to achieve data for precise orbit determination, yielding an orbit result with accuracy better than 2cm. When compared with KBR observations, the RMS of the bias is less than 1mm. Besides above mission, next gravimetric satellite is being developed now. TQ-2 mission is designed as a totally experimental satellite for gravitational wave detection at low Earth orbit, which can detect the Earth’s gravity field simultaneously. The Bender-type mission is considered the most promising configuration for TQ-2 and consists of two polar satellites and two inclined satellites. Due to the extra observations at the east-west direction derived from the inclined satellite pair, significant improvements has been made in detecting temporal signals with higher accuracy and spatial-temporal resolution. To achieve the scientific goal, the ACC MBW can shift from 0.001~0.1Hz to 0.004~0.1Hz for ACC, and the LRI MBW can shift from 0.01~1Hz to 0.1~1Hz. For future research, a gravimetric potential survey using cold-atomic-clock based on the general relativity theory, cold atom gradiometer should be pursued. Gravimetric technologies should be mined and researched, and the gravimetry satellite constellation should be developed, so as to improve the time resolution and space resolution for meeting the requirements of geophysics, geodesy, earthquake, water resources environment, oceanography, etc

A Novel Inflammatory and Nutritional Prognostic Scoring System for Nonpathological Complete Response Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

Background. It has been demonstrated that inflammatory and nutritional variables are associated with poor breast cancer survival. However, some studies do not include these variables due to missing data. To investigate the predictive potential of the INPS, we constructed a novel inflammatory-nutritional prognostic scoring (INPS) system with machine learning. Methods. This retrospective analysis included 249 patients with malignant breast tumors undergoing neoadjuvant chemotherapy (NAC). After comparing seven potent machine learning models, the best model, Xgboost, was applied to construct an INPS system. K-M survival curves and the log-rank test were employed to determine OS and DFS. Univariate and multivariate analyses were carried out with the Cox regression model. Additionally, we compared the predictive power of INPS, inflammatory, and standard nutritional variables using the Z test. Results. After comparing seven machine learning models, it was determined that the XGBoost model had the best OS and DFS performance (AUC=0.865 and 0.771, respectively). For overall survival (OS, cutoff value=0.3917) and disease-free survival (cutoff value=0.4896), all patients were divided into two groups by the INPS. Those with low INPS had higher 5-year OS and DFS rates (77.2% vs. 50.0%, P<0.0001; and 59.6% vs. 32.1%, P<0.0001, respectively) than patients with high INPS. For OS and DFS, the INPS exhibited the highest AUC compared to the other inflammatory and nutritional variables (AUC=0.615, P=0.0003; AUC=0.596, P=0.0003, respectively). Conclusion. The INPS was an independent predictor of OS and DFS and exhibited better predictive ability than BMI, PNI, and MLR. For patients undergoing NAC for nonpCR breast cancer, INPS was a crucial and comprehensive biomarker. It could also forecast individual survival in breast cancer patients with low HER-2 expression

Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning

Abstract Background Develop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients. Methods From June 2016 to August 2022, 1005 breast cancer patients were included in this retrospective study. Univariate and multivariate analyses were performed using logistic regression. Six ML models were introduced, and their performance was compared. Results NSLNM occurred in 338 (33.6%) of 1005 patients. The best ML model was XGBoost, whose average area under the curve (AUC) based on 10-fold cross-verification was 0.722. It performed better than the nomogram, which was based on logistic regression (AUC: 0.764 vs. 0.706). Conclusions The ML model XGBoost can well predict NSLNM in breast cancer patients

Image_1_Prediction of lymph node metastasis in patients with breast invasive micropapillary carcinoma based on machine learning and SHapley Additive exPlanations framework.png

AbstractBackground and purpose: Machine learning (ML) is applied for outcome prediction and treatment support. This study aims to develop different ML models to predict risk of axillary lymph node metastasis (LNM) in breast invasive micropapillary carcinoma (IMPC) and to explore the risk factors of LNM.MethodsFrom the Surveillance, Epidemiology, and End Results (SEER) database and the records of our hospital, a total of 1547 patients diagnosed with breast IMPC were incorporated in this study. The ML model is built and the external validation is carried out. SHapley Additive exPlanations (SHAP) framework was applied to explain the optimal model; multivariable analysis was performed with logistic regression (LR); and nomograms were constructed according to the results of LR analysis.ResultsAge and tumor size were correlated with LNM in both cohorts. The luminal subtype is the most common in patients, with the tumor size ConclusionsThe Xgboost model outperforms the traditional LR-based nomogram model in predicting the LNM of IMPC patients. Combined with SHAP, it can more intuitively reflect the influence of different variables on the LNM. The tumor size was the most important risk factor of LNM for breast IMPC patients. The prediction score obtained by the Xgboost model could be a good indicator for LNM.</p

The archaeal KEOPS complex possesses a functional Gon7 homolog and has an essential function independent of the cellular t6A modification level

Abstract Kinase, putative Endopeptidase, and Other Proteins of Small size (KEOPS) is a multisubunit protein complex conserved in eukaryotes and archaea. It is composed of Pcc1, Kae1, Bud32, Cgi121, and Gon7 in eukaryotes and is primarily involved in N6‐threonylcarbamoyl adenosine (t6A) modification of transfer RNAs (tRNAs). Recently, it was reported that KEOPS participates in homologous recombination (HR) repair in yeast. To characterize the KEOPS in archaea (aKEOPS), we conducted genetic and biochemical analyses of its encoding genes in the hyperthermophilic archaeon Saccharolobus islandicus. We show that aKEOPS also possesses five subunits, Pcc1, Kae1, Bud32, Cgi121, and Pcc1‐like (or Gon7‐like), just like eukaryotic KEOPS. Pcc1‐like has physical interactions with Kae1 and Pcc1 and can mediate the monomerization of the dimeric subcomplex (Kae1‐Pcc1‐Pcc1‐Kae1), suggesting that Pcc1‐like is a functional homolog of the eukaryotic Gon7 subunit. Strikingly, none of the genes encoding aKEOPS subunits, including Pcc1 and Pcc1‐like, can be deleted in the wild type and in a t6A modification complementary strain named TsaKI, implying that the aKEOPS complex is essential for an additional cellular process in this archaeon. Knock‐down of the Cgi121 subunit leads to severe growth retardance in the wild type that is partially rescued in TsaKI. These results suggest that aKEOPS plays an essential role independent of the cellular t6A modification level. In addition, archaeal Cgi121 possesses dsDNA‐binding activity that relies on its tRNA 3ʹ CCA tail binding module. Our study clarifies the subunit organization of archaeal KEOPS and suggests an origin of eukaryotic Gon7. The study also reveals a possible link between the function in t6A modification and the additional function, presumably HR

Image_2_Prediction of lymph node metastasis in patients with breast invasive micropapillary carcinoma based on machine learning and SHapley Additive exPlanations framework.png

AbstractBackground and purpose: Machine learning (ML) is applied for outcome prediction and treatment support. This study aims to develop different ML models to predict risk of axillary lymph node metastasis (LNM) in breast invasive micropapillary carcinoma (IMPC) and to explore the risk factors of LNM.MethodsFrom the Surveillance, Epidemiology, and End Results (SEER) database and the records of our hospital, a total of 1547 patients diagnosed with breast IMPC were incorporated in this study. The ML model is built and the external validation is carried out. SHapley Additive exPlanations (SHAP) framework was applied to explain the optimal model; multivariable analysis was performed with logistic regression (LR); and nomograms were constructed according to the results of LR analysis.ResultsAge and tumor size were correlated with LNM in both cohorts. The luminal subtype is the most common in patients, with the tumor size ConclusionsThe Xgboost model outperforms the traditional LR-based nomogram model in predicting the LNM of IMPC patients. Combined with SHAP, it can more intuitively reflect the influence of different variables on the LNM. The tumor size was the most important risk factor of LNM for breast IMPC patients. The prediction score obtained by the Xgboost model could be a good indicator for LNM.</p