Characteristics of the salivary microbiota in cheilitis granulomatosa

Cheilitis granulomatosa (CG) is a disturbing and persistent idiopathic lip swelling. The cause and treatment has not been wholly elucidated. Some reports infer that CG is mainly associated with dental infection but no firm or reliable microbiological evidence has been provided for a causative organism. This study aimed to evaluate whether microorganisms contribute to the etiology of CG in order to inform appropriate treatment options in clinic. Unstimulated saliva was collected from 15 CG patients who were diagnosed clinically and pathologically and 15 healthy controls (HC). DNA was extracted from the precipitate of the centrifuged saliva for 16s rRNA high-throughput sequencing using the Miseq PE300 platform. The distribution of the microbiome between the two groups was compared. CG patients had a greater microbial flora that was more diverse than the HC. Prevotella, Alloprevotella, Porphyromonas, Actinomyces, Rothia, Fusobacterium, Haemophilus, and Aggregatibacter had a significantly higher abundance in CG patients. In contrast, Streptococcus and Campylobacter were the most abundant genera in HC with a mean relative abundance of 63% and 2%, respectively. The microbiological network indicated that most of the bacteria that were enriched at greater levels in CG patients were likely to be Prevotella, Actinomyces, and Rothia. These have been shown to co-exist with other bacteria. The composition and structure of bacterial communities in CG patients were different from HC. Most of the genera observed in CG patients were associated with periodontitis and pulp infection. These findings might be helpful in understanding the etiology of CG. Further study will be needed to confirm these findings and explore the underlying pathological mechanism

Benefits of high-dose intravenous immunoglobulin on mortality in patients with severe COVID-19: An updated systematic review and meta-analysis

BackgroundThe clinical benefits of high-dose intravenous immunoglobulin (IVIg) in treating COVID-19 remained controversial.MethodsWe systematically searched databases up to February 17, 2022, for studies examining the efficacy of IVIg compared to routine care. Meta-analyses were conducted using the random-effects model. Subgroup analysis, meta-regression, and trial series analysis w ere performed to explore heterogeneity and statistical significance.ResultsA total of 4,711 hospitalized COVID-19 patients (1,925 IVIg treated and 2786 control) were collected from 17 studies, including five randomized controlled trials (RCTs) and 12 cohort studies. The application of IVIg was not associated with all-cause mortality (RR= 0.89 [0.63, 1.26], P= 0.53; I2 = 75%), the length of hospital stays (MD= 0.29 [-3.40, 6.44] days, P= 0.88; I2 = 96%), the needs for mechanical ventilation (RR= 0.93 ([0.73, 1.19], P= 0.31; I2 = 56%), or the incidence of adverse events (RR= 1.15 [0.99, 1.33], P= 0.06; I2 = 20%). Subgroup analyses showed that overall mortality among patients with severe COVID-19 was reduced in the high-dose IVIg subgroup (RR= 0.33 [0.13, 0.86], P= 0.02, I2 = 68%; very low certainty).ConclusionsResults of this study suggest that severe hospitalized COVID-19 patients treated with high-dose IVIg would have a lower risk of death than patients with routine care.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021231040, identifier CRD42021231040

The mutation in splicing factor genes correlates with unfavorable prognosis, genomic instability, anti-tumor immunosuppression and increased immunotherapy response in pan-cancer

Splicing abnormality resulting from somatic mutations in key splicing factor genes (SFG) has been detected in various cancers. Hence, an in-depth study of splicing factor genes mutations’ impact on pan-cancer is meaningful. This study investigated associations of splicing factor genes mutations with clinical features, tumor progression phenotypes, genomic integrity, anti-tumor immune responses, and immunotherapy response in 12 common cancer types from the TCGA database. Compared to SFG-wildtype cancers, SFG-mutated cancers displayed worse survival prognosis, higher tumor mutation burden and aneuploidy levels, higher expression of immunosuppressive signatures, and higher levels of tumor stemness, proliferation potential, and intratumor heterogeneity (ITH). However, splicing factor genes-mutated cancers showed higher response rates to immune checkpoint inhibitors than splicing factor genes-wildtype cancers in six cancer cohorts. Single-cell data analysis confirmed that splicing factor genes mutations were associated with increased tumor stemness, proliferation capacity, PD-L1 expression, intratumor heterogeneity, and aneuploidy levels. Our data suggest that the mutation in key splicing factor genes correlates with unfavorable clinical outcomes and disease progression, genomic instability, anti-tumor immunosuppression, and increased immunotherapy response in pan-cancer. Thus, the splicing factor genes mutation is an adverse prognostic factor and a positive marker for immunotherapy response in cancer

Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery

In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, to improve the quality of the segmentation completion, we present two coupled discriminators and introduce an auxiliary 3D model pool for sampling authentic silhouettes as adversarial samples. In addition, we propose a two-path structure with a shared network to enhance the appearance recovery capability. By iteratively performing the segmentation completion and the appearance recovery, the results will be progressively refined. To evaluate our method, we present a dataset, the Occluded Vehicle dataset, containing synthetic and real-world occluded vehicle images. We conduct comparison experiments on this dataset and demonstrate that our model outperforms the state-of-the-art in tasks of recovering segmentation mask and appearance for occluded vehicles. Moreover, we also demonstrate that our appearance recovery approach can benefit the occluded vehicle tracking in real-world videos

A New Position and Speed Estimation Scheme for Position Control of PMSM Drives Using Low-Resolution Position Sensors

A new position control method for permanent magnet synchronous motor (PMSM) drive with a low-resolution encoder is proposed in this paper. Three binary Hall position sensors are utilized to realize a moderate-performance position control system for the consideration of economy and simplicity in servo application. Compared with sensorless control, the usage of binary Hall position sensors is a guarantee of both control performance and low cost. However, the low resolution of the Hall sensor will heavily deteriorate the accuracy of the position and speed calculation. Such drawback can be effectively minimized by using appropriate position and speed estimation schemes. With the help of polynomial fitting and state observer techniques, a solution is provided to realize semi-closed loop control by treating the position and speed estimators as separate systems. The performance can be improved (1) by proposing a polynomial fitting scheme with least squares method, high-resolution rotor-position predictor can be derived by fitting the predefined position data from binary Hall position sensors in a linear or quadratic manner; (2) by adopting the dual-sampling-rate observer, instantaneous speed can be estimated at each control cycle and the estimation error is corrected once a new measurement form the Hall arrives. Furthermore, a nonlinear position control algorithm is introduced to increase standstill stability. Extensive experimental results are given to demonstrate the feasibility of the proposed method and its superiority over conventional methods

Virtual Water Flows Embodied in International and Interprovincial Trade of Yellow River Basin: A Multiregional Input-Output Analysis

With the imminent need of regional environmental protection and sustainable economic development, the concept of virtual water is widely used to solve the problem of regional water shortage. In this paper, nine provinces, namely Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia, Shaanxi, Shanxi, Henan, and Shandong in the Yellow River Basin (YRB), are taken as the research objects. Through the analysis of input-output tables of 30 provinces in China in 2012, the characteristics of virtual water trade in this region are estimated by using a multi-regional input-output (MRIO) model. The results show that: (1) The YRB had a net inflow of 17.387 billion m³ of virtual water in 2012. In interprovincial trade, other provinces outside the basin export 21.721 billion m³ of virtual water into the basin. In international trade, the basin exports 4334 million m³ of virtual water to the international market. (2) There are different virtual flow paths in the basin. Shanxi net inputs virtual water by interprovincial trade and international trade, while Gansu and Ningxia net output virtual water by interprovincial trade and international trade. The other six provinces all net output virtual water through international trade, and obtain the net input of virtual water from other provinces outside the basin. (3) From the industrial structure of the provinces in the basin, the provinces with a relatively developed economy, such as Shandong and Shanxi, mostly import virtual water in the agricultural sector, while relatively developing provinces, such as Gansu and Ningxia, mostly import virtual water in the industrial sector. In order to sustain the overall high-quality development of the YRB, we propose the virtual water trade method to quantify the net flow of virtual water in each province and suggest the compensation responsibility of the virtual water net inflow area, and the compensation need of the virtual water net outflow area, in order to achieve efficient water resources utilization