The clinical significance of renal resistance index (RRI) and renal oxygen saturation (RrSO2) in critically ill children with AKI: a prospective cohort study.

OBJECTIVE: The purpose of this study was to look into the clinical significance of the renal resistance index (RRI) and renal oxygen saturation (RrSO2) in predicting the development of acute kidney injury (AKI) in critically ill children. A new non-invasive method for the early detection and prediction of AKI needs to develop. METHODS: Patients admitted to the pediatric intensive care unit (PICU) affiliated with the capital institute of pediatrics from December 2020 to March 2021 were enrolled consecutively. Data of clinical information, renal Doppler ultrasound, RrSO2, and hemodynamic index within 24 h of admission were prospectively collected. Patients were divided into two groups: the study group was AKI occurred within 72 h, while the control group did not. SPSS (version 25.0) was used to analyze the data, and P < 0.05 was considered a statistical difference. RESULTS: 1) A total of 66 patients were included in this study, and the incidence of AKI was 19.70% (13/66). The presence of risk factors (shock, tumor, severe infection) increased the incidence of AKI by three times. 2) Univariate analysis showed significant differences in length of hospitalization, white blood cells (WBC), C-reactive protein (CRP), renal resistance index (RRI), and ejection fraction (EF) between the study and control groups (P < 0.05). There were no significant differences in renal perfusion semi-quantitative score (P = 0.053), pulsatility index (P = 0.051), pediatric critical illness score (PCIS), and peripheral vascular resistance index (P > 0.05). 3) Receiver operating characteristic (ROC) curve showed that if RRI > 0.635, the sensitivity, specificity, and AUC for predicting AKI were 0.889, 0.552, and 0.751, respectively; if RrSO2 < 43.95%, the values were 0.615, 0.719 and 0.609, respectively; if RRI and RrSO2 were united, they were 0.889, 0.552, and 0.766, respectively. CONCLUSIONS: The incidence of AKI is high in PICU patients. And infection, RRI, and EF are risk factors for AKI in PICU patients. RRI and RrSO2 have certain clinical significance in the early prediction of AKI and may provide a new non-invasive method for early diagnosis and prediction of AKI.

Genome-wide analysis of Dof transcription factors and their response to cold stress in rice (Oryza sativa L.).

BACKGROUND: Rice (Oryza sativa L.) is a food crop for humans worldwide. However, temperature has an effect during the vegetative and reproductive stages. In high-latitude regions where rice is cultivated, cold stress is a major cause of yield loss and plant death. Research has identified a group of plant-specific transcription factors, DNA binding with one zinc fingers (DOFs), with a diverse range of functions, including stress signaling and stress response during plant growth. The aim of this study was to identify Dof genes in two rice subspecies, indica and japonica, and screen for Dof genes that may be involved in cold tolerance during plant growth. RESULTS: A total of 30 rice Dofs (OsDofs) were identified using bioinformatics and genome-wide analyses and phylogenetically analyzed. The 30 OsDOFs were classified into six subfamilies, and 24 motifs were identified based on protein sequence alignment. The chromosome locations of OsDofs were determined and nine gene duplication events were identified. A joint phylogenetic analysis was performed on DOF protein sequences obtained from four monocotyledon species to examine the evolutionary relationship of DOF proteins. Expression profiling of OsDofs from two japonica cultivars (Longdao5, which is cold-tolerant, and Longjing11, which is cold-sensitive) revealed that OsDof1 and OsDof19 are cold-inducible genes. We examined the seed setting rates in OsDof1- and OsDof19-overexpression and RNAi lines and found that OsDof1 showed a response to cold stress. CONCLUSIONS: Our investigation identified OsDof1 as a potential target for genetic breeding of rice with enhanced cold tolerance.

Can a Deep-learning Model for the Automated Detection of Vertebral Fractures Approach the Performance Level of Human Subspecialists?

BACKGROUND: Vertebral fractures are the most common osteoporotic fractures in older individuals. Recent studies suggest that the performance of artificial intelligence is equal to humans in detecting osteoporotic fractures, such as fractures of the hip, distal radius, and proximal humerus. However, whether artificial intelligence performs as well in the detection of vertebral fractures on plain lateral spine radiographs has not yet been reported. QUESTIONS/PURPOSES: (1) What is the accuracy, sensitivity, specificity, and interobserver reliability (kappa value) of an artificial intelligence model in detecting vertebral fractures, based on Genant fracture grades, using plain lateral spine radiographs compared with values obtained by human observers? (2) Do patients' clinical data, including the anatomic location of the fracture (thoracic or lumbar spine), T-score on dual-energy x-ray absorptiometry, or fracture grade severity, affect the performance of an artificial intelligence model? (3) How does the artificial intelligence model perform on external validation? METHODS: Between 2016 and 2018, 1019 patients older than 60 years were treated for vertebral fractures in our institution. Seventy-eight patients were excluded because of missing CT or MRI scans (24% [19]), poor image quality in plain lateral radiographs of spines (54% [42]), multiple myeloma (5% [4]), and prior spine instrumentation (17% [13]). The plain lateral radiographs of 941 patients (one radiograph per person), with a mean age of 76 ± 12 years, and 1101 vertebral fractures between T7 and L5 were retrospectively evaluated for training (n = 565), validating (n = 188), and testing (n = 188) of an artificial intelligence deep-learning model. The gold standard for diagnosis (ground truth) of a vertebral fracture is the interpretation of the CT or MRI reports by a spine surgeon and a radiologist independently. If there were any disagreements between human observers, the corresponding CT or MRI images would be rechecked by them together to reach a consensus. For the Genant classification, the injured vertebral body height was measured in the anterior, middle, and posterior third. Fractures were classified as Grade 1 (< 25%), Grade 2 (26% to 40%), or Grade 3 (> 40%). The framework of the artificial intelligence deep-learning model included object detection, data preprocessing of radiographs, and classification to detect vertebral fractures. Approximately 90 seconds was needed to complete the procedure and obtain the artificial intelligence model results when applied clinically. The accuracy, sensitivity, specificity, interobserver reliability (kappa value), receiver operating characteristic curve, and area under the curve (AUC) were analyzed. The bootstrapping method was applied to our testing dataset and external validation dataset. The accuracy, sensitivity, and specificity were used to investigate whether fracture anatomic location or T-score in dual-energy x-ray absorptiometry report affected the performance of the artificial intelligence model. The receiver operating characteristic curve and AUC were used to investigate the relationship between the performance of the artificial intelligence model and fracture grade. External validation with a similar age population and plain lateral radiographs from another medical institute was also performed to investigate the performance of the artificial intelligence model. RESULTS: The artificial intelligence model with ensemble method demonstrated excellent accuracy (93% [773 of 830] of vertebrae), sensitivity (91% [129 of 141]), and specificity (93% [644 of 689]) for detecting vertebral fractures of the lumbar spine. The interobserver reliability (kappa value) of the artificial intelligence performance and human observers for thoracic and lumbar vertebrae were 0.72 (95% CI 0.65 to 0.80; p < 0.001) and 0.77 (95% CI 0.72 to 0.83; p < 0.001), respectively. The AUCs for Grades 1, 2, and 3 vertebral fractures were 0.919, 0.989, and 0.990, respectively. The artificial intelligence model with ensemble method demonstrated poorer performance for discriminating normal osteoporotic lumbar vertebrae, with a specificity of 91% (260 of 285) compared with nonosteoporotic lumbar vertebrae, with a specificity of 95% (222 of 234). There was a higher sensitivity 97% (60 of 62) for detecting osteoporotic (dual-energy x-ray absorptiometry T-score ≤ -2.5) lumbar vertebral fractures, implying easier detection, than for nonosteoporotic vertebral fractures (83% [39 of 47]). The artificial intelligence model also demonstrated better detection of lumbar vertebral fractures compared with detection of thoracic vertebral fractures based on the external dataset using various radiographic techniques. Based on the dataset for external validation, the overall accuracy, sensitivity, and specificity on bootstrapping method were 89%, 83%, and 95%, respectively. CONCLUSION: The artificial intelligence model detected vertebral fractures on plain lateral radiographs with high accuracy, sensitivity, and specificity, especially for osteoporotic lumbar vertebral fractures (Genant Grades 2 and 3). The rapid reporting of results using this artificial intelligence model may improve the efficiency of diagnosing vertebral fractures. The testing model is available at http://140.113.114.104/vght_demo/corr/. One or multiple plain lateral radiographs of the spine in the Digital Imaging and Communications in Medicine format can be uploaded to see the performance of the artificial intelligence model. LEVEL OF EVIDENCE: Level II, diagnostic study.

Molecular cloning, identification of GSTs family in sunflower and their regulatory roles in biotic and abiotic stress.

Glutathione-S-transferase (GST) genes exist widely in plants and play major role in metabolic detoxification of exogenous chemical substances and oxidative stress. In this study, 14 sunflower GST genes (HaGSTs) were identified based on the sunflower transcriptome database that we had constructed. Full-length cDNA of 14 HaGTSs were isolated from total RNA by reverse transcription PCR (RT-PCR). Sunflower was received biotic stress (Sclerotinia sclerotiorum) and abiotic stress (NaCl, low-temperature, drought and wound). GST activity was measured by using the universal substrate. The results showed that most of the HaGSTs were up-regulated after NaCl and PEG6000-induced stresses, while a few HaGSTs were up-regulated after S. sclerotiorum, hypothermia and wound-induced stressed, and there was correlation between the changes of GST activity and the expression of HaGSTs, indicating that HaGSTs may play regulatory role in the biotic and abiotic stress responses. 14 HaGSTs from sunflower were identified, and the expression of HaGSTs were tissue-specific and played regulatory roles in both stress and abiotic stress.

The effect of childhood trauma on complex posttraumatic stress disorder: the role of self-esteem.

Background: Due to the short time that complex posttraumatic stress disorder (CPTSD) has been an independent diagnosis, few studies have explored the role that self-esteem might play in the relationship between childhood trauma and CPTSD.Objective: The current study aimed to explore the impact of childhood trauma on CPTSD and the role of self-esteem in this relationship.Methods: Study 1 involved a questionnaire survey in which a total of 360 young Chinese adults with childhood trauma participated. Study 2 used experimental research by manipulating short-term self-esteem to explore the effect of self-esteem on CPTSD. A total of 80 young Chinese adults with childhood trauma participated.Results: The results of Study 1 showed that childhood trauma positively predicts adulthood CPTSD, while self-esteem partially mediates the relationship between the two. It also found that self-esteem has a greater mediating effect on the disordered self-organization (DSO) dimension of CPTSD than on the PTSD dimension. In Study 2, participants in the high manipulated-self-esteem group reported fewer CPTSD symptoms than those in the low manipulated-self-esteem group.Conclusion: Overall, this study emphasized the role of self-esteem in CPTSD among individuals with a childhood trauma history. In practice, we provided a potential optimization direction for CPTSD clinical treatment, suggesting a method of self-esteem reconstruction.

Self-esteem mediates the relationship between childhood trauma and CPTSD.Short-term self-esteem decrease may contribute to CPTSD symptoms.Self-esteem has greater effects on disordered self-organization than PTSD symptoms.

Foliar spraying of exogenous uniconazole (S3307) at the flowering stage as an effective method to resist low-temperature stress on mung bean [Vigna radiata (L.) Wilczek].

Low temperature is one of the major constraints on agricultural productivity worldwide and is likely to further increase. Several adaptations and mitigation strategies are required to cope with low-temperature stress. Uniconazole (S3307) could play a significant role in the alleviation of abiotic stress in plants. In this study, the effects of S3307 on the reactive oxygen species (ROS) and antioxidant metabolism were studied in the leaves of mung bean [Vigna radiata (L.) Wilczek]. The experimental results showed that the low-temperature induced accumulation of superoxide anion (O2-) production rate, and malonaldehyde (MDA) contents. Increased proline content and enzymatic antioxidants, including superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD), were found to alleviate oxidative damage under low temperatures. While, S3307 could reduce O2- production rate and MDA contents and increase the activities of SOD, POD, and CAT, slowed the decrease in ascorbic acid (AsA), dehydroascorbic acid (DHA), glutathione (GSH), and oxidized glutathione (GSSG), and promoted increase in soluble sugars (SS), soluble proteins (SP), and proline (Pro) content under low-temperature. At the same time, low temperature leads to lower 100 grain weight and number of grains per plant, which eventually causes yield reduction decreased. Foliar spraying of S3307 could alleviate the yield loss caused by low temperature, and the increase of S3307 treatment was 5.1%-12.5% and 6.3%-32.9% for the two varieties, respectively, compared with CK. In summary, exogenous S3307 pretreatment enhances plant tolerance to low-temperature by improving the antioxidant enzyme activities, increased non-enzymatic antioxidants content, and decreased O2- production rate and MDA contents and inducing alterations in endogenous S3307, and reduce the decrease in mung bean yield.

Genome-wide analysis of maize PR-1 gene family and expression profiles induced by plant hormones and fungal phytopathogens.

OBJECTIVE: In order to find similarity of the protein X in maize with other species we performed a BLASTP search to identify the maize ZmPR-1 family genes. METHODS: We used a BLASTP search to identify the maize ZmPR-1 family genes that may show similarities between the protein X in maize and other species. RESULTS: A total of 17 ZmPR-1 genes were identified and these genes were unevenly distributed on 8 chromosomes of maize. All ZmPR-1 gene predicted proteins contained a conserved CAP domain, according to the results of multiple sequence alignment and gene structure analysis. Phylogenetic tree analysis of a total of 85 PR-1 protein sequences from maize, sorghum, rice and Arabidopsis showed that the PR-1 family proteins were divided into four categories, and the maize ZmPR-1 was closely related to sorghum PR-1. In the promoter of maize ZmPR-1 gene, hypothetical cis-elements related to fungal induction, defense stress response, plant hormones, low temperature and drought response were detected. Microarray data analysis showed that ZmPR-1 displayed a tissue-specific expression pattern at different developmental stages, and responded to the infections of five maize pathogens. In addition, we further verified that four ZmPR-1 genes (ZmPR-1-5, 12, 14 and 16) were not only significantly up-regulated after Setosphearia turcica infection, but also affected by exogenous cues such as SA, ABA, MeJA and H2O2. CONCLUSION: The ZmPR-1 family may be important in plant disease resistance. This study's data provide important clues for future research on the function of ZmPR-1 family genes.

Hydrocortisone, ascorbic acid, and thiamine (HAT) for sepsis and septic shock: a meta-analysis with sequential trial analysis.

BACKGROUND: Sepsis is a primary global health threat and costs a lot, requiring effective and affordable treatments. We performed this meta-analysis to explore the treatment of hydrocortisone, ascorbic acid, and thiamine (HAT) in sepsis and septic shock. METHODS: We searched Ovid MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials from inception to August 14, 2021. We included randomized controlled trials (RCTs) that evaluated the HAT treatments in sepsis and septic shock. The primary outcome was the change in SOFA score over the 72 h. The second outcomes were the hospital, and 28-/30-day mortality, the duration of vasopressors, PCT clearance, hospital length of stay (LOS), and ICU LOS. We performed a subgroup analysis and a trial sequential analysis (TSA). The Der Simonian-Laird random-effects models were used to report the pooled risk ratios (RR) or mean difference (MD) with confidence intervals (CI). RESULTS: Nine RCTs, enrolling 1427 patients of sepsis and septic shock treated with HAT (717) or only standard care (710), were included. There was a significant difference between the two groups in the change in SOFA score over the first 72 h (MD 0.65, 95% CI 0.30 to 1.00), the duration of vasopressors (MD - 18.16, 95% CI - 25.65 to - 10.68) and the PCT clearance (MD 14.54, 95% CI 0.64 to 28.43). In addition, there was no significant difference in the hospital mortality (RR 1.07, 95% CI 0.85 to 1.34), the 28-/30-day mortality (RR 0.96, 95% CI 0.80 to 1.15), the hospital LOS (MD 0.78, 95% CI - 0.30 to 1.86), and ICU LOS (MD 0.12, 95% CI - 0.53 to 0.78). CONCLUSIONS: The HAT combination improves the SOFA score in the first 72 h and reduces the duration of vasopressors in patients with sepsis. Given the minor mean difference of the change in SOFA score, the mortality benefit has not been observed. TRIAL REGISTRATION: PROSPERO, CRD42020203166.

Expression profile analysis of maize in response to Setosphaeria turcica.

Northern corn leaf blight (NCLB), caused by the hemibiotrophic fungal pathogen Setosphaeria turcica, is one of the major foliar diseases of maize. The use of resistant cultivars is the most effective, economical, and environmentally friendly means to control NCLB. At present, the molecular mechanisms of maize resistance to S. turcica is not clear. Elucidating the molecular resistance mechanisms of maize response to S. turcica would aid breeding for a maize variety with fungal tolerance. In this study, maize leaves before and after infection with S. turcica were sequenced by RNA-seq, and 5903 differentially expressed genes (DEGs) were screened. Among them, 950 and 2245 genes were up-regulated 12 h and 60 h (samples H12 and H60, respectively) after infection, 752 and 1956 genes were down-regulated in H12 and H60, respectively. The GO and KEGG enrichment analysis of the DEGs showed that the GO and Pathway with the most annotation sequences were closely related to plant resistance. The expression of eight randomly selected DEGs was analyzed using qRT-PCR, and expression was consistent with the RNA-seq data. The expression patterns of four categories of genes were analyzed namely, genes involved in plant and pathogen interactions, transcription factors related to plant stress-tolerance, genes related to plant hormones and plant antioxidant. Many resistant signaling pathways were initiated such as the MAPK signal transduction pathway and the expression of multiple antioxidant-related genes [Peroxidase (POD), Catalase (CAT), Glutathione-S-transferase (GST) and Superoxide Dismutase (SOD)] following S. turcica infection. Many disease resistance signal transduction pathways and defense response pathways were induced following maize infection by S. turcica, suggesting a multiple gene network system. To the best of our knowledge, this is the first time that RNA-seq technology has been used to perform transcription analysis of maize in response to S. turcica stress. Taken together, these data provide novel and valuable information that will help understand the resistance mechanism in maize against S. turcica and locate candidate genes related to maize resistance against S. turcica.