Quality in screening and measuring blood pressure in China's primary health care: a national cross-sectional study using unannounced standardized patients.

Background: This study aims to evaluate primary care providers' adherence to the standard of measuring blood pressure for people aged 35 or above during their initial visit, as per Chinese guidelines, and to identify factors affecting their practices. Methods: We developed 11 standardized patients (SP) cases as tracer conditions to evaluate primary care, and deployed trained SPs for unannounced visits to randomly selected providers in seven provinces of China. The SPs used a checklist based on guidelines to record whether and how blood pressure was measured. Data were analyzed descriptively and regression analysis was performed to examine the association between outcomes and factors such as provider, patient, facility, and clinical case characteristics. Findings: The SPs conducted 1201 visits and found that less than one-third of USPs ≥35 had their blood pressure measured. Only 26.9% of migraine and 15.4% of diabetes cases received blood pressure measurements. Additionally, these measurements did not follow the proper guidelines and recommended steps. On average, 55.6% of the steps were followed with few providers considering influencing factors before measurement and only 6.0% of patients received both-arm measurements. The use of wrist sphygmomanometers was associated with poor blood pressure measurement. Interpretation: In China, primary care hypertension screening practices fall short of guidelines, with infrequent initiation of blood pressure measurements and inadequate adherence to proper measurement steps. To address this, priority should be placed on adopting, implementing, and upholding guidelines for hypertension screening and measurement. Funding: National Natural Science Foundation of China, Swiss Agency for Development and Cooperation, Doctoral Fund Project of Inner Mongolia Medical University, China Postdoctoral Science Foundation.

Association between late gadolinium enhancement and outcome in dilated cardiomyopathy: A meta-analysis.

BACKGROUND: The prognostic value of late gadolinium enhancement (LGE) derived from cardiovascular magnetic resonance (CMR) is well studied, and several new metrics of LGE have emerged. However, some controversies remain; therefore, further discussion is needed, and more precise risk stratification should be explored. AIM: To investigate the associations between the positivity, extent, location, and pattern of LGE and multiple outcomes in dilated cardiomyopathy (DCM). METHODS: PubMed, Ovid MEDLINE, and Cochrane Library were searched for studies that investigated the prognostic value of LGE in patients with DCM. Pooled hazard ratios (HRs) and 95% confidence intervals were calculated to assess the role of LGE in the risk stratification of DCM. RESULTS: Nineteen studies involving 7330 patients with DCM were included in this meta-analysis and covered a wide spectrum of DCM, with a mean left ventricular ejection fraction between 21% and 50%. The meta-analysis revealed that the presence of LGE was associated with an increased risk of multiple adverse outcomes (all-cause mortality, HR: 2.14; arrhythmic events, HR: 5.12; and composite endpoints, HR: 2.38; all P < 0.001). Furthermore, every 1% increment in the extent of LGE was associated with an increased risk of all-cause mortality. Analysis of a subgroup revealed that the prognostic value varied based on different location and pattern of LGE. Additionally, we found that LGE was a stronger predictor of arrhythmic events in patients with greater left ventricular ejection fraction. CONCLUSION: LGE by CMR in patients with DCM exhibited a substantial value in predicting adverse outcomes, and the extent, location, and pattern of LGE could provide additional information for risk stratification.

Genome-Wide Identification and Preliminary Functional Analysis of BAM (ß-Amylase) Gene Family in Upland Cotton.

The ß-amylase (BAM) gene family encodes important enzymes that catalyze the conversion of starch to maltose in various biological processes of plants and play essential roles in regulating the growth and development of multiple plants. So far, BAMs have been extensively studied in Arabidopsis thaliana (A. thaliana). However, the characteristics of the BAM gene family in the crucial economic crop, cotton, have not been reported. In this study, 27 GhBAM genes in the genome of Gossypium hirsutum L (G. hirsutum) were identified by genome-wide identification, and they were divided into three groups according to sequence similarity and phylogenetic relationship. The gene structure, chromosome distribution, and collinearity of all GhBAM genes identified in the genome of G. hirsutum were analyzed. Further sequence alignment of the core domain of glucosyl hydrolase showed that all GhBAM family genes had the glycosyl hydrolase family 14 domain. We identified the BAM gene GhBAM7 and preliminarily investigated its function by transcriptional sequencing analysis, qRT-PCR, and subcellular localization. These results suggested that the GhBAM7 gene may influence fiber strength during fiber development. This systematic analysis provides new insight into the transcriptional characteristics of BAM genes in G. hirsutum. It may lay the foundation for further study of the function of these genes.

Association of radiotherapy with thoracic vertebral fractures in esophageal squamous cell carcinoma: A retrospective cohort study.

To investigate the association between radiotherapy (RT) and thoracic vertebral fractures in esophageal squamous cell carcinoma (ESCC) and explore the risk factors of thoracic vertebral fracture in ESCC who underwent RT. This retrospective cohort study including 602 consecutive ESCC patients examined the association between RT and thoracic vertebral fractures using multivariable Cox proportional hazard models and relevant risk factors of thoracic vertebral fractures based on clinical and RT parameters in patients with ESCC. Followed for a median follow-up of 24 months, 54 patients had thoracic vertebral fractures. The multivariable analysis revealed RT as an independent risk factor after adjusting for clinical risk factors. Univariable analyses associated a 5-Gy increase in vertebral dose to single vertebrae and a 1-time increase in RT fraction with higher risk of vertebral fracture. Adding RT factors (vertebral dose and fraction) and mean vertebral hounsfield unit to the Cox models containing conventional clinical risk factors significantly improved the χ2 value for predicting vertebral fractures (all P < .001). This study revealed RT, as well as increased vertebral dose and RT fractions, as a significant, consistent, and strong vertebral fracture predictor in ESCC. Combined vertebral dose, RT fractions, and vertebral hounsfield unit provided optimal risk stratification for ESCC patients.

Tailoring the d-band electronic structure of deficient LaMn0.3Co0.7O3-δ perovskite nanofibers for boosting oxygen electrocatalysis in Zn-Air batteries.

The development and design of efficient bifunctional electrocatalysts towards oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are crucial for rechargeable Zinc-air batteries (ZABs). Optimizing the d-band structure of active metal center in perovskite oxides is an effective method to enhance ORR/OER activity by accelerating the rate-determining step. Herein, we report a deficient method to optimize the d-band structure of Co ions in LaMn0.3Co0.7O3-δ (LMCO-2) perovskite nanofibers, which regulates the mutual effect between B-site Co ions and reactive oxygen intermediates. It is proved by experiment and theoretical calculation that the d-band center (Md) of transition metal ions in LMCO-2 is moved up and the electron filling number of eg orbital in B site is 1.01, thus leading to the reduction of Gibbs free energy required for ORR rate-determining step (OH*→H2O*) to 0.22 eV and promoting reaction proceeds. In this manner, LMCO-2 showed good bifunctional oxygen electrocatalytic activity, with a half-wave potential of 0.71 V vs. RHE. Furthermore, the high specific capacity of 811.54 mAh g-1 and power density of 326.56 mW cm-2 were obtained by using LMCO-2 as the cathode catalyst for ZABs. This study proved the feasibility of d-band structure regulation to enhance the electrocatalytic activity of perovskite oxides.

Improving eye care quality through brief verbal intervention on optometry service provider by using unannounced standardized patient with refractive error: study protocol for a randomized controlled trial.

BACKGROUND: Improper refractive correction can be harmful to eye health, aggravating the burden of vision impairment. During most optometry clinical consultations, practitioner-patient interactions play a key role. Maybe it is feasible for patients themselves to do something to get high-quality optometry. But the present empirical research on the quality improvement of eye care needs to be strengthened. The study aims to test the effect of the brief verbal intervention (BVI) through patients on the quality of optometry service. METHODS: This study will take unannounced standardized patient (USP) with refractive error as the core research tool, both in measurement and intervention. The USP case and the checklist will be developed through a standard protocol and assessed for validity and reliability before its full use. USP will be trained to provide standardized responses during optical visits and receive baseline refraction by the skilled study optometrist who will be recruited within each site. A multi-arm parallel-group randomized trial will be used, with one common control and three intervention groups. The study will be performed in four cities, Guangzhou and three cities in Inner Mongolia, China. A total of 480 optometry service providers (OSPs) will be stratified and randomly selected and divided into four groups. The common control group will receive USP usual visits (without intervention), and three intervention groups will separately receive USP visits with three kinds of BVI on the patient side. A detailed outcome evaluation will include the optometry accuracy, optometry process, patient satisfaction, cost information and service time. Descriptive analysis will be performed for the survey results, and the difference in outcomes between interventions and control providers will be compared and statistically tested using generalized linear models (GLMs). DISCUSSION: This research will help policymakers understand the current situation and influencing factors of refractive error care quality, and then implement precise policies; at the same time, explore short and easy interventions for patients to improve the quality of optometry service. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2200062819. Registered on August 19, 2022.

Validity of patients' online reviews at direct-to-consumer teleconsultation platforms: a protocol for a cross-sectional study using unannounced standardised patients.

INTRODUCTION: As direct-to-consumer teleconsultation (hereafter referred to as 'teleconsultation') has gained popularity, an increasing number of patients have been leaving online reviews of their teleconsultation experiences. These reviews can help guide patients in identifying doctors for teleconsultation. However, few studies have examined the validity of online reviews in assessing the quality of teleconsultation against a gold standard. Therefore, we aim to use unannounced standardised patients (USPs) to validate online reviews in assessing both the technical and patient-centred quality of teleconsultations. We hypothesise that online review results will be more consistent with the patient-centred quality, rather than the technical quality, as assessed by the USPs. METHODS AND ANALYSIS: In this cross-sectional study, USPs representing 11 common primary care conditions will randomly visit 253 physicians via the three largest teleconsultation platforms in China. Each physician will receive a text-based and a voice/video-based USP visit, resulting in a total of 506 USP visits. The USP will complete a quality checklist to assess the proportion of clinical practice guideline-recommended items during teleconsultation. After each visit, the USP will also complete the Patient Perception of Patient-Centeredness Rating. The USP-assessed results will be compared with online review results using the intraclass correlation coefficient (ICC). If ICC >0.4 (p<0.05), we will assume reasonable concordance between the USP-assessed quality and online reviews. Furthermore, we will use correlation analysis, Lin's Coordinated Correlation Coefficient and Kappa as supplementary analyses. ETHICS AND DISSEMINATION: This study has received approval from the Institutional Review Board of Southern Medical University (#Southern Medical Audit (2022) No. 013). Results will be actively disseminated through print and social media, and USP tools will be made available for other researchers. TRIAL REGISTRATION: The study has been registered at the China Clinical Trials Registry (ChiCTR2200062975).

Evaluation on leachability of heavy metals from tailings: risk factor identification and cumulative influence.

The leachability of heavy metals (HMs) in tailings is significantly affected by multivariate factors associated with environmental conditions. However, the leaching patterns of HMs in molybdenum (Mo) tailings due to environmental change and cumulative influences of multi-leaching factors remain unclear. The leaching behaviors of HMs in Mo tailings were studied through static leaching tests. The key leaching factors were discussed via simulating acid rain leaching scenario in terms of global and local environmental conditions. The potential risk factors were identified, and their cumulative influences on the leachability of HMs were evaluated with boosted regression trees (BRT) and generalized additive model (GAM) analyses. Environmental factors showed interactive effects on the leachability of HMs in tailings. The leachability of HMs in tailings decreased significantly with the interaction of increasing liquid/solid (L/S) ratio and pH. Rebound of leachability was observed with high L/S ratio (> 60) and long-time leaching (> 30 h). L/S ratio and pH were the most sensitive factors to the leachability of HMs with the corresponding contribution of 40.8% and 27.1%, respectively, followed by leaching time and temperature (~ 16%). The total contribution of global climate-associated factors, i.e., L/S ratio, leaching time, and temperature to the leachability of HMs was up to 70%, while leachate pH shared the other 30%. With the increase of persistent heavy rain in summer globally, As and Cd were found to having higher leaching risks than the other HMs in tailings, although an obvious decrease in their leachability was obtained due to the improvement of acid rain pollution in China. The study provides a valuable method for the identification of potential risk factors and their associations with the leaching behaviors of HMs in tailings under the background of obvious improvement on acid rain pollution in China and global climate change.

Quantitative trait locus mapping and identification of candidate genes for resistance to Verticillium wilt in four recombinant inbred line populations of Gossypium hirsutum.

Improving resistance to Verticillium wilt is of great significance for achieving high and stable yields of Upland cotton (Gossypium hirsutum). To deeply understand the genetic basis of cotton resistance to Verticillium wilt, Verticillium wilt-resistant Upland Lumianyan 28 and four Verticillium wilt-susceptible Acala cotton cultivars were used to create four recombinant inbred line (RIL) populations of 469 families through nested hybridization. Phenotypic data collected in five stressful environments were used to select resistant and sensitive lines and create a mixed pool of extreme phenotypes for BSA-seq. A total of 8 QTLs associated with Verticillium wilt resistance were identified on 4 chromosomes, of which qVW-A12-5 was detected simultaneously in the RIL populations and in one of the RIL populations and was identified for the first time. According to the sequence comparison and transcriptome analysis of candidate genes in the QTL interval between parents and pools, 4 genes were identified in the qVW-A12-5 interval. qRT-PCR of parental and phenotypically extreme lines revealed that Gh_CPR30 was induced by and may be a candidate gene for resistance to Verticillium wilt in G. hirsutum. Furthermore, VIGS technology revealed that the disease severity index (DSI) of the Gh_CPR30-silenced plants was significantly higher than that of the control. These results indicate that the Gh_CPR30 gene plays an important role in the resistance of G. hirsutum to Verticillium wilt, and the study provides a molecular basis for analyzing the molecular mechanism underlying G. hirsutum resistance to Verticillium wilt.

Intelligent injury prediction for traumatic airway obstruction.

Airway obstruction is one of the crucial causes of death in trauma patients during the first aid. It is extremely challenging to accurately treat a great deal of casualties with airway obstruction in hospitals. The diagnosis of airway obstruction in an emergency mostly relies on the medical experience of physicians. In this paper, we propose the feature selection approach genetic algorithm-mean decrease impurity (GA-MDI) to effectively minimize the number of features as well as ensure the accuracy of prediction. Furthermore, we design a multi-modal neural network, called fully convolutional network with squeeze-and-excitation and multilayer perceptron (FCN-SE + MLP), to help physicians to predict the severity of airway obstruction. We validate the effectiveness of the proposed feature selection approach and multi-modal model on the emergency medical database from the Chinese General Hospital of the PLA. The experimental results show that GA-MDI outperforms the existing feature selection algorithms, while it is also validated that the model FCN-SE + MLP can effectively and accurately achieve the prediction of the severity of airway obstruction, which can assist clinicians in making treatment decisions for airway obstruction casualties.

Development and Validation of a Dynamic Prediction Model for Massive Hemorrhage in Trauma.

Objectives: Early warning prediction of massive hemorrhages can greatly reduce mortality in trauma patients. This study aimed to develop and validate dynamic prediction models for massive hemorrhage in trauma patients. Methods: Based on vital signs (e.g., heart rate, respiratory rate, pulse pressure, and peripheral oxygen saturation) time-series data and the gated recurrent unit algorithm, we characterized a group of models to flexibly and dynamically predict the occurrence of massive hemorrhages in the subsequent T hours (where T = 1, 2, and 3). Models were evaluated in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the curve (AUC). Results: Results show that of the 2205 trauma patients selected for model development, a total of 265 (12.02%) had a massive hemorrhage. The AUCs of the model in the 1-h-group, 2-h-group, and 3-h-group were 0.763 (95% CI: 0.708-0.820), 0.775 (95% CI: 0.728-0.823), and 0.756 (95% CI: 0.715-0.797), respectively. Finally, the models were used in a web calculator and information system for the hospital emergency department. Conclusions: This study developed and validated a group of dynamic prediction models based on vital sign time-series data and a deep-learning algorithm to assist medical staff in the early diagnosis and dynamic prediction of a future massive hemorrhage in trauma.

A prediction model for massive hemorrhage in trauma: a retrospective observational study.

BACKGROUND: Massive hemorrhage is the main cause of preventable death after trauma. This study aimed to establish prediction models for early diagnosis of massive hemorrhage in trauma. METHODS: Using the trauma database of Chinese PLA General Hospital, two logistic regression (LR) models were fit to predict the risk of massive hemorrhage in trauma. Sixty-two potential predictive variables, including clinical symptoms, vital signs, laboratory tests, and imaging results, were included in this study. Variable selection was done using the least absolute shrinkage and selection operator (LASSO) method. The first model was constructed based on LASSO feature selection results. The second model was constructed based on the first vital sign recordings of trauma patients after admission. Finally, a web calculator was developed for clinical use. RESULTS: A total of 2353 patients were included in this study. There were 377 (16.02%) patients with massive hemorrhage. The selected predictive variables were heart rate (OR: 1.01; 95% CI: 1.01-1.02; P<0.001), pulse pressure (OR: 0.99; 95% CI: 0.98-0.99; P = 0.004), base excess (OR: 0.90; 95% CI: 0.87-0.93; P<0.001), hemoglobin (OR: 0.95; 95% CI: 0.95-0.96; P<0.001), displaced pelvic fracture (OR: 2.13; 95% CI: 1.48-3.06; P<0.001), and a positive computed tomography scan or positive focused assessment with sonography for trauma (OR: 1.62; 95% CI: 1.21-2.18; P = 0.001). Model 1, which was developed based on LASSO feature selection results and LR, displayed excellent discrimination (AUC: 0.894; 95% CI: 0.875-0.912), good calibration (P = 0.405), and clinical utility. In addition, the predictive power of model 1 was better than that of model 2 (AUC: 0.718; 95% CI: 0.679-0.757). Model 1 was deployed as a public web tool ( http://82.156.217.249:8080/ ). CONCLUSIONS: Our study developed and validated prediction models to assist medical staff in the early diagnosis of massive hemorrhage in trauma. An open web calculator was developed to facilitate the practical application of the research results.

Robust C-PdNi-CNF Sandwich-Structured Catalyst for Suzuki Reactions and Experimental Study on the Mechanism.

The stability of metal nanoparticles is one of the key issues for their catalytic applications. In this study, we fabricated a sandwich structure to protect the metal nanoparticles. A carbon layer was used to wrap the PdNi nanoparticles on the carbon fiber, and the whole preparation process was simple and green. Electron transfer occurs between the carbon layer and the metal nanoparticles, making the two more closely combined. As a catalyst for the Suzuki reaction, it exhibits highly efficient catalysis and excellent stability. The calculated TOF reaches 18 662 h-1. After nine cycles, there was almost no decrease in performance. Additionally, we found that the addition of iodobenzene into the chlorobenzene reaction system could significantly improve the chlorobenzene conversion, and we proved that the catalyst has fine activity and stability with a bright future in commercial applications. The possible catalytic mechanism of Suzuki reaction was proposed based on experimental results. This study provides a simple and green method to prepare encapsulated metal nanoparticle catalysts and gives a deep insight into Suzuki reaction.

[Research on grading prediction model of traumatic hemorrhage volume based on deep learning].

OBJECTIVE: To develop a grading prediction model of traumatic hemorrhage volume based on deep learning and assist in predicting traumatic hemorrhage volume. METHODS: A retrospective observational study was conducted based on the experimental data of pig gunshot wounds in the time-effect assessment database for experiments on war-traumatized animals constructed by the General Hospital of the Chinese People's Liberation Army. The hemorrhage volume data of the study population were extracted, and the animals were divided into 0-300 mL, 301-600 mL, and > 600 mL groups according to the hemorrhage volume. Using vital signs indexes as the predictive variables and hemorrhage volume grading as the outcome variable, trauma hemorrhage volume grading prediction models were developed based on four traditional machine learning and ten deep learning methods. Using laboratory test indexes as predictive variables and hemorrhage volume grading as outcome variables, trauma hemorrhage volume grading prediction models were developed based on the above fourteen methods. The effect of the two groups of models was evaluated by accuracy and area under the receiver operator characteristic curve (AUC), and the optimal models in the two groups were mixed to obtain hybrid model 1. Feature selection was conducted according to the genetic algorithm, and hybrid model 2 was constructed according to the best feature combination. Finally, hybrid model 2 was deployed in the animal experiment database system. RESULTS: Ninety-six traumatic animals in the database were enrolled, including 27 pigs in the 0-300 mL group, 40 in the 301-600 mL group, and 29 in the > 600 mL group. Among the fourteen models based on vital signs indexes, fully convolutional network (FCN) model was the best [accuracy: 60.0%, AUC and 95% confidence interval (95%CI) was 0.699 (0.671-0.727)]. Among the fourteen models based on laboratory test indexes, recurrent neural network (RNN) model was the best [accuracy: 68.9%, AUC (95%CI) was 0.845 (0.829-0.860)]. After mixing the FCN and RNN models, the hybrid model 1, namely RNN-FCN model was obtained, and the performance of the model was improved [accuracy: 74.2%, AUC (95%CI) was 0.847 (0.833-0.862)]. Feature selection was carried out by genetic algorithm, and the hybrid model 2, namely RNN-FCN∗ model, was constructed according to the selected feature combination, which further improved the model performance [accuracy: 80.5%, AUC (95%CI) was 0.880 (0.868-0.893)]. The hybrid model 2 contained ten indexes, including mean arterial pressure (MAP), hematocrit (HCT), platelet count (PLT), lactic acid, arterial partial pressure of carbon dioxide (PaCO2), Total CO2, blood sodium, anion gap (AG), fibrinogen (FIB), international normalized ratio (INR). Finally, the RNN-FCN∗ model was deployed in the database system, which realized automatic, continuous, efficient, intelligent, and grading prediction of hemorrhage volume in traumatic animals. CONCLUSIONS: Based on deep learning, a grading prediction model of traumatic hemorrhage volume was developed and deployed in the information system to realize the intelligent grading prediction of traumatic animal hemorrhage volume.

Genome-wide analysis of serine carboxypeptidase-like protein (SCPL) family and functional validation of Gh_SCPL42 unchromosome conferring cotton Verticillium der Verticillium wilt stress in Gossypium hirsutum.

BACKGROUND: Serine carboxypeptidase-like protein (SCPL) plays an important role in response to stress in plant. However, our knowledge of the function of the SCPL gene family is limited. RESULTS: In this study, a comprehensive and systematic analysis of SCPL gene family was conducted to explore the phylogeny and evolution of the SCPL gene in Gossypium hirsutum. The phenotype and molecular mechanism of silencing of the Gh_SCPL42 under Verticillium wilt stress was also studied. Our results showed that 96 SCPL genes were observed in genome of G. hirsutum, which distributed on 25 chromosomes and most of them were located in the nucleus. The phylogenetic tree analysis showed that members of SCPL gene family can be divided into three subgroups in G. hirsutum, which are relatively conservative in evolution. SCPL gene has a wide range of tissue expression types in G. hirsutum. Promoter analysis showed that the most cis-acting elements related to MeJA and ABA were contained. Through RNA-seq combined with genotyping, it was found that 11 GhSCPL genes not only had significant expression changes during Verticillium wilt stress but also had differential SNPs in the upstream, downstream, exonic or intronic regions. The expression of these 11 genes in the resistant (Zhongzhimian 2) and susceptible (Junmian 1) materials was further analyzed by qRT-PCR, it was found that 6 genes showed significant expression differences in the two materials. Among them, Gh_SCPL42 has the most obvious expression change. Furthermore, virus-induced gene silencing (VIGS) showed necrosis and yellowing of leaves and significantly higher disease severity index (DSI) and disease severity rate (DSR) values in VIGS plants than in control silenced Gh_SCPL42 plants. Moreover, the expression levels of genes related to the SA and JA pathways were significantly downregulated. These results show that Gh_SCPL42 might improve resistance to Verticillium wilt through the SA and JA pathways in G. hirsutum. CONCLUSION: In conclusion, our findings indicated that Gh_SCPL42 gene plays an important role in resistance to Verticillium wilt in cotton. It was provided an important theoretical basis for further research on the function of SCPL gene family and the molecular mechanism of resistance to Verticillium wilt in cotton.

Gb_ANR-47 Enhances the Resistance of Gossypium barbadense to Fusarium oxysporum f. sp. vasinfectum (FOV) by Regulating the Content of Proanthocyanidins.

Anthocyanidin reductase (ANR) is an important regulator of flavonoid metabolism, and proanthocyanidins, the secondary metabolites of flavonoids, play an important role in the response of plants to pathogenic stress. Therefore, in this study, the expression analysis of the ANR gene family of Gossypium barbadense after inoculation with Fusarium oxysporum f. sp. vasinfectum (FOV) was performed at different time points. It was found that Gb_ANR-47 showed significant differences in the disease-resistant cultivar 06-146 and the susceptible cultivar Xinhai 14, as well as in the highest root expression. It was found that the expression of Gb_ANR-47 in the resistant cultivar was significantly higher than that in the susceptible cultivar by MeJA and SA, and different amounts of methyl jasmonate (MeJA) and salicylic acid (SA) response elements were found in the promoter region of Gb_ANR-47. After silencing GbANR-47 in 06-146 material by VIGS technology, its resistance to FOV decreased significantly. The disease severity index (DSI) was significantly increased, and the anthocyanin content was significantly decreased in silenced plants, compared to controls. Our findings suggest that GbANR-47 is a positive regulator of FOV resistance in Gossypium barbadense. The research results provide an important theoretical basis for in-depth analysis of the molecular mechanism of GbANR-47 and improving the anti-FOV of Gossypium barbadense.

Comparison of two surgical approaches for acute type A aortic dissection: hybrid debranching versus total arch replacement.

BACKGROUND: The goal of this study was to determine the clinical outcomes of total arch replacement with frozen elephant trunk surgery and hybrid debranching surgery for acute type A aortic dissection patients. METHODS: From January 2017 to December 2019, the clinical data of acute type A aortic dissection patients were retrospectively collected and analyzed. There were 142 patients underwent total arch replacement with frozen elephant trunk surgery and 35 patients underwent hybrid debranching surgery. RESULT: The age, the body mass index and the renal insufficiency of patients in the hybrid group were higher than those in the total arch replacement (TAR) group (all P < 0.01). The operation time, the cardiopulmonary bypass time and the aortic occlusion time of patients in the TAR group were significantly longer than those in the hybrid group (all P < 0.01). Patients in the debranching group had shorter ventilator-assisted breathing time, shorter postoperative hospital stay time and shorter intensive care unit (ICU) stay time. The incidence of pulmonary infection and transient neurological dysfunction were lower, and the transfusions of red blood cells and plasma during the perioperative period were smaller. The survival rates at 2 years were 91.9% and 85.9% in the TAR and hybrid groups, respectively. CONCLUSION: Hybrid debranching operation is a safe and effective method for acute type A aortic dissection. Compared with TAR surgery, hybrid debranching surgery has the characteristics of less trauma, rapid recovery and lower incidence of complication.

Binding characteristics of copper onto biochar-derived DOM using general, heterospectral and moving-window two-dimensional correlation analyses.

Dissolved organic matter released from biochar (biochar-derived DOM, BDOM) could dominate the environmental behavior and fate of trace metals by forming BDOM-metal complexes. Here general, heterospectral as well as moving-window (MW) two-dimensional correlation spectroscopy (2DCOS) analyses of synchronous fluorescence and Fourier transform infrared spectra were employed to explore the heterogeneous binding characteristics between sludge BDOM and Cu(II). The results revealed that Cu-BDOM binding first occurred in the fulvic-like (368-380 nm), then humic-like (428 nm) fluorescent fractions, followed by infrared groups of phenolic hydroxyl groups, carboxylate, COH of polysaccharide groups, CC of aromatic carbon, CH of aliphatics and COC of aliphatic ethers. The binding affinity of the hydrophilic groups was stronger than that of hydrophobic groups in BDOM towards Cu(II). Fluorescence components in BDOM played a decisive role in the binding of Cu(II) with trace concentration (1 µM), while infrared functional groups made a substantial contribution in the complexation of Cu(II) with higher concentration (10-100 µM). The concentration of final configuration transformation point (11.7 µmol/mg in this study) by MW2DCOS analysis was suggested as an actual binding threshold that was helpful for evaluating their environmental behaviors.

The single nucleotide polymorphism rs1814521 in long non-coding RNA ADGRG3 associates with the susceptibility to silicosis: a multi-stage study.

BACKGROUND: This study aimed to evaluate the correlation between long non-coding RNA (lncRNA)-related single nucleotide polymorphisms (SNPs) and susceptibility to silicosis. METHODS: First, RNA-sequencing (RNA-seq) data were comprehensively analyzed in the peripheral blood lymphocytes of eight participants (four silicosis cases and four healthy controls) exposed to silica dust to identify differentially expressed lncRNAs (DE-lncRNAs). The functional SNPs in the identified DE-lncRNAs were then identified using several databases. Finally, the association between functional SNPs and susceptibility to silicosis was evaluated by a two-stage case-control study. The SNPs of 155 silicosis cases and 141 healthy silica-exposed controls were screened by genome-wide association study (GWAS), and the candidate SNPs of 194 silicosis cases and 235 healthy silica-exposed controls were validated by genotyping using the improved Mutiligase Detection Reaction (iMLDR) system. RESULTS: A total of 76 DE-lncRNAs were identified by RNA-seq data analysis (cut-offs: fold change > 2 or fold change < 0.5, P < 0.05), while 127 functional SNPs among those 76 DE-lncRNAs were identified through multiple public databases. Furthermore, five SNPs were found to be significantly correlated with the risk of silicosis by GWAS screening (P < 0.05), while the results of GWAS and iMLDR validation indicated that the variant A allele of rs1814521 was associated with a reduced risk of silicosis (OR = 0.76, 95% CI = 0.62-0.94, P = 0.011). CONCLUSION: The presence of the SNP rs1814521 in the lncRNA ADGRG3 is associated with susceptibility to silicosis. Moreover, ADGRG3 was found to be lowly expressed in silicosis cases. The underlying biological mechanisms by which lncRNA ADGRG3 and rs1814521 regulate the development of silicosis need further study.

Early warning model for death of sepsis via length insensitive temporal convolutional network.

Sepsis is a life-threatening systemic syndrome characterized by various biological, biochemical, and physiological abnormalities. Due to its high mortality, identifying sepsis patients with high risk of in-hospital death early and accurately will help doctors make optimal clinical decisions and reduce the mortality of sepsis patients. In this paper, we propose a length insensitive TCN-based model to predict sepsis patient's death risk in the future k hours, which is the first work for sepsis death risk early warning model only based on vital signs time series to our best knowledge. Furthermore, we design residual connections between temporal residual blocks to improve the prediction performance and stability especially on short input sequences. We validate and evaluate our model on two freely-available datasets, i.e., MIMIC-IV and eICU, from which 16,520 and 29,620 patients are selected respectively. The experiment results show that our model outperforms LSTM and other machine learning methods, as it has the highest sensitivity and Youden index in almost all cases. Meanwhile, the Youden index of the TCN-based model only slightly decreases by 0.0233 and 0.0307 when the time range of the input sequence changes from 24 to 4 h for k equal to 6 and 12, respectively.