Multiomics strategies for decoding seed dormancy breakdown in Paris polyphylla.

BACKGROUND: The disruption of seed dormancy is a complicated process and is controlled by various factors. Among these factors, membrane lipids and plant hormones are two of the most important ones. Paris polyphylla is an important Chinese herbaceous species, and the dormancy trait of its seed limits the cultivation of this herb. RESULTS: In this study, we investigate the global metabolic and transcriptomic profiles of Paris polyphylla during seed dormancy breaking. Widely targeted metabolomics revealed that lysophospholipids (lysoPLs) increased during P. polyphylla seed dormancy breaking. The expression of phospholipase A2 (PLA2), genes correlated to the production of lysoPLs, up-regulated significantly during this process. Abscisic acid (ABA) decreased dramatically during seed dormancy breaking of P. polyphylla. Changes of different GAs varied during P. polyphylla seeds dormancy breaking, 13-OH GAs, such as GA53 were not detected, and GA3 decreased significantly, whereas 13-H GAs, such as GA15, GA24 and GA4 increased. The expression of CYP707As was not synchronous with the change of ABA content, and the expression of most UGTs, GA20ox and GA3ox up-regulated during seed dormancy breaking. CONCLUSIONS: These results suggest that PLA2 mediated production of lysoPLs may correlate to the seed dormancy breaking of P. polyphylla. The conversion of ABA to ABA-GE catalysed by UGTs may be the main cause of ABA degradation. Through inhibition the expression of genes related to the synthesis of 13-OH GAs and up-regulation genes related to the synthesis of 13-H GAs, P. polyphylla synthesized more bioactive 13-H GA (GA4) to break its seed dormancy.

A transformer-based multi-features fusion model for prediction of conversion in mild cognitive impairment.

Mild cognitive impairment (MCI) is usually considered the early stage of Alzheimer's disease (AD). Therefore, the accurate identification of MCI individuals with high risk in converting to AD is essential for the potential prevention and treatment of AD. Recently, the great success of deep learning has sparked interest in applying deep learning to neuroimaging field. However, deep learning techniques are prone to overfitting since available neuroimaging datasets are not sufficiently large. Therefore, we proposed a deep learning model fusing cortical features to address the issue of fusion and classification blocks. To validate the effectiveness of the proposed model, we compared seven different models on the same dataset in the literature. The results show that our proposed model outperformed the competing models in the prediction of MCI conversion with an accuracy of 83.3% in the testing dataset. Subsequently, we used deep learning to characterize the contribution of brain regions and different cortical features to MCI progression. The results revealed that the caudal anterior cingulate and pars orbitalis contributed most to the classification task, and our model pays more attention to volume features and cortical thickness features.

The brain activation of anxiety disorders with emotional stimuli-an fMRI ALE meta-analysis.

Numerous studies have analyzed the state of brain activation about anxiety disorders under emotional stimuli. However, there is no meta-analysis to assess the commonality and specificity activation concerning different subtypes of anxiety. Here, we used ALE to assess this. 29 studies revealed increased bilateral amygdala, anterior cingulate gyrus, parahippocampal gyrus activation in anxiety disorders during emotional stimuli. Moreover, we observed decreased activations in the posterior cingulate, lingual gyrus, and precuneus. In sub-analysis, although different anxiety showed dissimilar activations, the principal activations were observed in limbic lobe, which might indicate the limbic circuit was the main neural reflection of anxiety symptoms.

Metabolically healthy obesity increases the prevalence of stroke in adults aged 40 years or older: Result from the China National Stroke Screening survey.

Debate over the cardio-cerebrovascular risk associated with metabolically healthy obesity (MHO) continues. In this study we investigated the association of MHO with the risk of stroke among 221,114 individuals aged 40 years or older based on data from the China National Stroke Screening and Prevention Project (CNSSPP), a nationally representative cross-sectional study, during 2014 to 2015. Different metabolic health and obesity phenotypes were defined according to the Adult Treatment Panel III (ATP III) criteria, where obesity was defined as a body mass index (BMI) ≥28 kg/m2. Logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for stroke risk associated with different metabolically healthy phenotypes. BMI was used to estimate the mediation effect for metabolic abnormalities to stroke. Compared with the metabolically healthy non-obesity (MHNO) group, individuals with MHO (adjusted OR: 1.21, 95% CI: 1.10,1.33), metabolically unhealthy non-obesity (MUNO) (adjusted OR:1.41, 95% CI: 1.36,1.46), or metabolically unhealthy obesity (MUO) (adjusted OR: 1.70, 95% CI: 1.61,1.80) were found to have an increased risk of stroke. The findings were confirmed robustly by various sensitivity analyses and subgroup analyses. Furthermore, obesity and metabolic abnormalities had an additive interaction for stroke risk with an attributable proportion (AP) of 14.0% in females. BMI played a partial mediating role with the proportion of the effect (PE) at 11.1% in the relationship between metabolic abnormalities and stroke. This study strengthens the evidence that management and interventions in the MHO population may contribute to the primary prevention of stroke.

First Report of Root Rot Caused by Dactylonectria torresensis on Bletilla striata (Baiji) in Yunnan, China.

Bletilla striata (Thunb.) Rchb.f. (Orchidaceae family, known as Baiji in Chinese) is an endangered plant species with important medicinal value in China. Bletilla striata plants with symptoms of wilting, leaf yellowing and rotting on underground parts were found in Shizong (24.82822 N; 103.99084 E), Yunnan Province, China in July 2016. In the following years, this disease occurred and became prevalent when high temperature and high humidity prevailed in the fields from May to August. The incidence of the disease varied from 45 to 75%, with yield losses of 40 to 65% in different B. striata fields. To identify the causal agent of the disease, symptomatic vascular tissue fragments were soaked in 2% sodium hypochlorite for 2 min, rinsed twice with sterile distilled water, and then placed on 4% (w/v) potato dextrose agar (PDA) plates. The plates were incubated at 26°C in 12h light/dark for three days. Mycelia grown from the edges of the plant fragments were transferred to PDA plates and incubated at 26°C in 12h light/dark. After three days, hyphal tips were isolated from the edge of the colonies to PDA plates. Three hyphal-tip isolates from different plants were further studied. The colonies of these three isolates were dark red, with cottony mycelia of moderate density. Hyphae were transparent and branched. Numerous hyphae anastomosed frequently and formed hyphal coils. For further morphological analysis, sporulation was induced as described by Cabral et al. (2012) and Lombard et al. (2014). Macroconidia were abundant, 37.2 to 44.0 µm × 5.2 to 8.7 µm based on the measurement of 20 conidia from each isolate. Ascospores divided into two cells of equal size, ellipsoid to oblong-ellipsoid, 12.5 to 14.8 µm × 4.8 to 5.9 µm based on the measurement of 20 spores from each isolate. Conidiophores simple or complex, sporodochial. Simple conidiophores arising laterally or terminally from aerial mycelium, solitary to loosely aggregated, unbranched or sparsely brached, more or less cylindrical. These morphological characteristics were consistent with the description of Dactylonectria spp. by Cabral et al. (2012) and Lombard et al. (2014). From one isolate, the internal transcribed spacer (ITS) region of ribosomal DNA and the beta-tubulin (tub2) gene were amplified by polymerase chain reaction (PCR) using the primer pairs ITS1/ITS4 (White et al. 1990) and T1/Bt-2b (Cabral et al. 2012), respectively. PCR products were sequenced and deposited in GenBank with accession numbers MH458779 (ITS) and MH626485 (tub2). BLAST search revealed that both sequences showed 99 to 100% homology with the corresponding sequences of previously identified D. torresensis isolates. Specially, MH458779 shares 100% identity with the entire 463-base pair (bp) sequence of KP411806, the ITS sequence of a D. torresensis isolate identified from olive trees (Nigro et al. 2019); MH626485 shares 99% identity with the entire 320-bp sequence of KP411801, the tub2 sequence of the same olive tree isolate. In addition, the entire 609-bp sequence of MH626485 shares 99% identity with JF735478, the tub2 sequence of a D. torresensis isolate identified from grapevines (Cabral et al. 2012). To test the pathogenicity of the fungus, plants of B. striata in plastic pots filled with sterilized nursery soil were inoculated with each of the three isolates by placing a fungal-colonized wheat kernel adjacent to each health plant. Plants inoculated with noncolonized wheat kernels were used as controls. Plants in three pots (replicates), with one plant per pot, were inoculated by each isolate. The pots were maintained in a greenhouse with a 12h photoperiod at 25°C. Ten days after inoculation, black necrotic lesions identical to those observed in the field were evident on the roots of all inoculated plants. Using the same methods described above, fungi with identical morphologies as described above were isolated from lesions caused by each of the three isolates. The control plants remained healthy, and no fungus was re-isolated. This completed Koch's postulates. Based on the morphological characteristics and molecular identification, the pathogen was determined to be D. torresensis. To our knowledge, this is the first report of D. torresensis causing root rot of B. striata in Yunnan, China. It is important to further study the impacts of this new disease on B. striata production in China.

Profiling membrane glycerolipids during γ-ray-induced membrane injury.

BACKGROUND: γ-rays are high-energy radiation that cause a range of random injuries to plant cells. Most studies on this issue have focused on γ-ray-induced nucleotide damage and the production of reactive oxygen species in cells, so little is known about the glycerolipid metabolism during γ-rays induced membrane injury. Using an ESI-MS/MS-based lipidomic method, we analysed the lipidome changes in wild-type and phospholipase D (PLD)δ- and α1-deficient Arabidopsis after γ-ray treatment. The aim of this study was to investigate the role of PLD-mediated glycerolipid metabolism in γ-ray-induced membrane injury. RESULTS: The ion leakage of Arabidopsis leaves after 2885-Gy γ-ray treatment was less than 10%. High does γ-ray treatment could induce the accumulation of intracellular reactive oxygen species (ROS). Inhibition of PLDα1 caused severe lipid degradation under γ-ray treatment. γ-ray-induced glycerolipid degradation mostly happened in chloroplastidic lipids, rather than extraplastidic ones. The levels of lysophosphatidylcholine (lysoPC) and lysophosphatidylethanolamine (lysoPE) were maintained in the WS ecotypes during γ-ray treatments, while increased significantly in the Col ecotype treated with 1100 Gy. After 210- and 1100-Gy γ-ray treatments, the level of lysophosphatidylglycerol (lysoPG) decreased significantly in the four genotypes of Arabidopsis. CONCLUSIONS: γ-ray-induced membrane injury may occur via an indirect mechanism. The degradation of distinct lipids is not synchronous, and that interconversions among lipids can occur. During γ-ray-induced membrane injury, the degradation of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) may be mediated by PLDζ1 or phospholipase A1. The degradation of phosphatidylglycerol was not mediated by PLA, PLDδ or PLDα1, but by phospholipase C or other PLDs. γ-rays can decrease the double-bond index and increase the acyl chain length in membrane lipids, which may make membranes more rigid and further cause injury in membranes.

Glycerolipidome responses to freezing- and chilling-induced injuries: examples in Arabidopsis and rice.

BACKGROUND: Glycerolipids are the principal constituent of cellular membranes; remodelling of glycerolipids plays important roles in temperature adaptation in plants. Temperate plants can endure freezing stress, but even chilling at above-zero temperatures can induce death in tropical species. However, little is known about the differences in glycerolipid response to low temperatures between chilling-sensitive and freezing-tolerant plants. Using ESI-MS/MS-based lipidomic analysis, we compared the glycerolipidome of chilling (4 and 10 °C)-treated rice with that of freezing (-6 and -12 °C)-treated Arabidopsis, both immediately after these low-temperature treatments and after a subsequent recovery culture period. RESULTS: Arabidopsis is a 16:3 plant that harbours both eukaryotic and prokaryotic-type lipid synthesis pathways, while rice is an 18:3 plant that harbours only the eukaryotic lipid synthesis pathway. Arabidopsis contains higher levels of galactolipids than rice and has a higher double bond index (DBI). Arabidopsis contains lower levels of high melting point phosphatidylglycerol (PG) molecules and has a lower average acyl chain length (ACL). Marked phospholipid degradation occurred during the recovery culture period of non-lethal chilling treated rice, but did not occur in non-lethal freezing treated Arabidopsis. Glycerolipids with larger head groups were synthesized more in Arabidopsis than in rice at sub-lethal low-temperatures. Levels of phosphatidic acid (PA) and phosphatidylinositol (PI) rose in both plants after low-temperature treatment. The DBI and ACL of total lipids did not change during low-temperature treatment. CONCLUSIONS: A higher DBI and a lower ACL could make the membranes of Arabidopsis more fluid at low temperatures. The ability to synthesize glycerolipids containing a larger head group may correlate with low-temperature tolerance. The low-temperature-induced increase of PA may play a dual role in plant responses to low temperatures: as a lipid signal that initiates tolerance responses, and as a structural molecule that, on extensive in large accumulation, could damage the integrity of membranes. Changes in ACL and DBI are responses of plants to long-term low temperature.

Associations between physical activity and all-cause and cardiovascular mortality in adults with type 2 diabetes mellitus: A prospective cohort study from NHANES 2007-2018.

AIMS: To investigate the dose-response association between physical activity and all-cause and cardiovascular mortality in adults with type 2 diabetes mellitus and the effects of replacing sedentary behavior with physical activity. METHODS: 4808 adults with type 2 diabetes mellitus were included in NHANES 2007-2018. Cox proportional hazards models were used to calculate hazard ratios and 95% confidence intervals. Isotemporal substitution analyses were further to determine the possible benefit of replacing sedentary time. RESULTS: During a median follow-up of 6.58 years, 902 deaths occurred, including 290 deaths from cardiovascular disease. Compared with the inactive group, the low-active and high-active groups were associated with declined risks of all-cause mortality [HRs (95% CIs) 0.64 (0.50, 0.83); 0.60 (0.50, 0.73), respectively] and cardiovascular mortality [0.50 (0.29, 0.88); 0.54 (0.39, 0.76)), respectively]. Dose-response analysis showed a significant U-shaped curve between physical activity and all-cause and cardiovascular mortality. Replacing 30 min/day of sedentary time with physical activity was substantially linked to a reduced risk of 8-32% mortality. CONCLUSION: A high level of PA of 40.52 and 31.66 MET-h/week was respectively related to the lowest risk of all-cause and cardiovascular mortality. Replacing sedentary time with physical activity could benefit the type 2 diabetes mellitus population.

Disrupted dynamic brain functional connectivity in male cocaine use disorder: Hyperconnectivity, strongly-connected state tendency, and links to impulsivity and borderline traits.

Cocaine use is a major public health problem with serious negative consequences at both the individual and societal levels. Cocaine use disorder (CUD) is associated with cognitive and emotional impairments, often manifesting as alterations in brain functional connectivity (FC). This study employed resting-state functional magnetic resonance imaging (rs-fMRI) to examine dynamic FC in 38 male participants with CUD and 31 matched healthy controls. Using group spatial independent component analysis (group ICA) combined with sliding window approach, we identified two recurring distinct connectivity states: the strongly-connected state (state 1) and weakly-connected state (state 2). CUD patients exhibited significant increased mean dwell and fraction time in state 1, and increased transitions from state 2 to state 1, demonstrated significant strongly-connected state tendency. Our analysis revealed abnormal FC patterns that are state-dependent and state-shared in CUD patients. This study observed hyperconnectivity within the default mode network (DMN) and between DMN and other networks, which varied depending on the state. Furthermore, after adjustment for multiple comparisons, we found significant correlations between these altered dynamic FCs and clinical measures of impulsivity and borderline personality disorder. The disrupted FC and repetitive effects of precuneus and angular gyrus across correlations suggested that they might be the important hub of neural circuits related behaviorally and mentally in CUD. In summary, our study highlighted the potential of these disrupted FC as neuroimaging biomarkers and therapeutic targets, and provided new insights into the understanding of the neurophysiologic mechanisms of CUD.

Improving brain age prediction with anatomical feature attention-enhanced 3D-CNN.

Currently, significant progress has been made in predicting brain age from structural Magnetic Resonance Imaging (sMRI) data using deep learning techniques. However, despite the valuable structural information they contain, the traditional engineering features known as anatomical features have been largely overlooked in this context. To address this issue, we propose an attention-based network design that integrates anatomical and deep convolutional features, leveraging an anatomical feature attention (AFA) module to effectively capture salient anatomical features. In addition, we introduce a fully convolutional network, which simplifies the extraction of deep convolutional features and overcomes the high computational memory requirements associated with deep learning. Our approach outperforms several widely-used models on eight publicly available datasets (n = 2501), with a mean absolute error (MAE) of 2.20 years in predicting brain age. Comparisons with deep learning models lacking the AFA module demonstrate that our fusion model effectively improves overall performance. These findings provide a promising approach for combining anatomical and deep convolutional features from sMRI data to predict brain age, with potential applications in clinical diagnosis and treatment, particularly for populations with age-related cognitive decline or neurological disorders.

An attention-based multi-modal MRI fusion model for major depressive disorder diagnosis.

Objective.Major depressive disorder (MDD) is one of the biggest threats to human mental health. MDD is characterized by aberrant changes in both structure and function of the brain. Although recent studies have developed some deep learning models based on multi-modal magnetic resonance imaging (MRI) for MDD diagnosis, the latent associations between deep features derived from different modalities were largely unexplored by previous studies, which we hypothesized may have potential benefits in improving the diagnostic accuracy of MDD.Approach.In this study, we proposed a novel deep learning model that fused both structural MRI (sMRI) and resting-state MRI (rs-fMRI) data to enhance the diagnosis of MDD by capturing the interactions between deep features extracted from different modalities. Specifically, we first employed a brain function encoder (BFE) and a brain structure encoder (BSE) to extract the deep features from fMRI and sMRI, respectively. Then, we designed a function and structure co-attention fusion (FSCF) module that captured inter-modal interactions and adaptively fused multi-modal deep features for MDD diagnosis.Main results.This model was evaluated on a large cohort and achieved a high classification accuracy of 75.2% for MDD diagnosis. Moreover, the attention distribution of the FSCF module assigned higher attention weights to structural features than functional features for diagnosing MDD.Significance.The high classification accuracy highlights the effectiveness and potential clinical of the proposed model.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and immunotherapy response in bladder cancer.

BACKGROUND: T cells have been proven to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in bladder cancer (BLCA). METHODS: Single-cell RNA-sequencing (scRNA-seq) data were downloaded from the gene expression omnibus (GEO) database to screen T-cell marker genes. Bulk RNA-sequencing data and clinical information from BLCA patients were downloaded from the cancer genome atlas (TCGA) database to develop a prognosis signature. We analyzed the association of different risk groups with survival analysis, gene set enrichment analysis (GSEA), tumor mutational burden (TMB), and immunotherapy response. RESULTS: Based on 192 T-cell marker genes identified by scRNA-seq analysis, we constructed a prognostic signature containing 7 genes in the training cohort, which was further validated in the testing cohort and GEO cohort. The areas under the receiver operating characteristic curve at 1-, 3-, and 5 years were 0.734, 0.742 and 0.726 in the training cohort, 0.697, 0.671 and 0.670 in the testing cohort, 0.702, 0.665 and 0.629 in the GEO cohort, respectively. In addition, we constructed a nomogram based on clinical factors and the risk score of the signature. The low-risk group exhibited higher immune-related pathways, immune cell infiltration and TMB levels. Importantly, immunophenotype score and immunotherapy cohort (IMvigor210) analyses showed that the low-risk group had better immunotherapy response and prognosis. CONCLUSIONS: Our study reveals a novel prognostic signature based on T-cell marker genes, which provides a new target and theoretical support for BLCA patients.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and therapeutic response in lung squamous cell carcinoma.

Cancer immunotherapy is an increasingly successful strategy for treating patients with advanced or conventionally drug-resistant cancers. T cells have been proved to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in lung squamous cell carcinoma (LUSC). In this study, we first performed a comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data from the gene expression omnibus (GEO) database to identify 72 T-cell marker genes. Subsequently, we constructed a 5-gene prognostic signature in the training cohort based on the T-cell marker genes from the cancer genome atlas (TCGA) database, which was further validated in the testing cohort and GEO cohort. The areas under the receiver operating characteristic curve at 1-, 3-, and 5-years were 0.614, 0.713 and 0.702 in the training cohort, 0.669, 0.603 and 0.645 in the testing cohort, 0.661, 0.628 and 0.590 in the GEO cohort, respectively. Furthermore, we created a highly reliable nomogram to facilitate clinical application. Gene set enrichment analysis showed that immune-related pathways were mainly enriched in the high-risk group. Tumor immune microenvironment indicated that high-risk group exhibited higher immune score, stromal score, and immune cell infiltration levels. Moreover, genes of the immune checkpoints and human leukocyte antigen family were all overexpressed in high-risk group. Drug sensitivity revealed that low-risk group was sensitive to 8 chemotherapeutic drugs and high-risk group to 4 chemotherapeutic drugs. In short, our study reveals a novel prognostic signature based on T-cell marker genes, which provides a new target and theoretical support for LUSC patients.

Aberrant functional network topology and effective connectivity in burnout syndrome.

OBJECTIVE: Recent neuroimaging studies have demonstrated that burnout is linked to specific anatomical and functional abnormalities in the brain. However, topological alterations of brain networks are not yet characterized in burnout. METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) was performed on 32 female participants with burnout and 30 matched healthy controls. Subsequently, we employed graph theoretical and network-based statistic (NBS) methods to analyze the functional connectivity. We further explored the causal influences between brain regions using the Granger Causal Analysis. Finally, partial correlation analyses were conducted between clinical scores and the altered network properties as well as connectivity metrics. RESULTS: Both the participants with burnout and healthy controls displayed a small-world organization. However, participants with burnout showed increased characteristic path length and decreased global efficiency. Corresponding local changes were mainly distributed in the visual network (2/3,66.67%). With the network-based statistic (NBS) approach, significantly decreased effective connectivities were observed mainly between the visual network and the right hippocampus. In addition, characteristic path length and nodal local efficiency of the left fusiform gyrus showed a significant negative correlation with depression severity. CONCLUSIONS: The present psychopathological findings reflect the disrupted global integration of the functional network related to the traits of participants with burnout. SIGNIFICANCE: These findings deliver novel insights from a full network perspective into the brain mechanisms of burnout.

Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures.

Background: Immunotherapy has changed the therapeutic landscape of cervical cancer (CC), but has durable anti-tumor activity only in a subset of patients. This study aims to comprehensively analyze the tumor immune microenvironment (TIME) of CC and to mine biomarkers related to immunotherapy and prognosis. Methods: The Cancer Genome Atlas (TCGA) data was utilized to identify heterogeneous immune subtypes based on survival-related immune cell signatures (ICSs). ICSs prognostic model was constructed by Cox regression analyses, and immunohistochemistry was conducted to verify the gene with the largest weight coefficient in the model. Meanwhile, the tumor immune infiltration landscape was comprehensively characterized by ESTIMATE, CIBERSORT and MCPcounter algorithms. In addition, we also analyzed the differences in immunotherapy-related biomarkers between high and low-risk groups. IMvigor210 and two gynecologic tumor cohorts were used to validate the reliability and scalability of the Risk score. Results: A total of 291 TCGA-CC samples were divided into two ICSs clusters with significant differences in immune infiltration landscape and prognosis. ICSs prognostic model was constructed based on eight immune-related genes (IRGs), which showed higher overall survival (OS) rate in the low-risk group (P< 0.001). In the total population, time-dependent receiver operating characteristic (ROC) curves displayed area under the curve (AUC) of 0.870, 0.785 and 0.774 at 1-, 3- and 5-years. Immunohistochemical results showed that the expression of the oncogene (FKBP10) was negatively correlated with the degree of differentiation and positively correlated with tumor stage, while the expression of tumor suppressor genes (S1PR4) was the opposite. In addition, the low-risk group had more favorable immune activation phenotype and higher enrichment of immunotherapy-related biomarkers. The Imvigor210 and two gynecologic tumor cohorts validated a better survival advantage and immune efficacy in the low-risk group. Conclusion: This study comprehensively assessed the TIME of CC and constructed an ICSs prognostic model, which provides an effective tool for predicting patient's prognosis and accurate immunotherapy.

Prognostic characteristics of immune subtypes associated with acute myeloid leukemia and their identification in cell subsets based on single-cell sequencing analysis.

Immune genes play an important role in the development and progression of acute myeloid leukemia (AML). However, the role of immune genes in the prognosis and microenvironment of AML remains unclear. In this study, we analyzed 151 AML patients in the TCGA database for relevant immune cell infiltration. AML patients were divided into high and low immune cell infiltration clusters based on ssGSEA results. Immune-related pathways, AML pathways and glucose metabolism pathways were enriched in the high immune cell infiltration cluster. Then we screened the differential immune genes between the two immune cell infiltration clusters. Nine prognostic immune genes were finally identified in the train set by LASSO-Cox regression. We constructed a model in the train set based on the nine prognostic immune genes and validated the predictive capability in the test set. The areas under the ROC curve of the train set and the test set for ROC at 1, 3, 5 years were 0.807, 0.813, 0.815, and 0.731, 0.745, 0.830, respectively. The areas under ROC curve of external validation set in 1, 3, and 5 years were 0.564, 0.619, and 0.614, respectively. People with high risk scores accompanied by high TMB had been detected with the worst prognosis. Single-cell sequencing analysis revealed the expression of prognostic genes in AML cell subsets and pseudo-time analysis described the differentiation trajectory of cell subsets. In conclusion, our results reveal the characteristics of immune microenvironment and cell subsets of AML, while it still needs to be confirmed in larger samples studies. The prognosis model constructed with nine key immune genes can provide a new method to assess the prognosis of AML patients.

Germplasm resources of three wood plant species enriched with nervonic acid.

Nervonic acid (NA) is a very-long-chain monounsaturated fatty acid with pharmaceutical and nutraceutical functions that plays an important role in treating several neurological disorders. One major source of NA is plant seed oil. Here we report fatty acid profiles of seeds and germplasm diversity of six plant species, including three woody plants with high amounts of NA-enriched seed oil, Malania oleifera, Macaranga adenantha, and M. indica. M. oleifera had the largest seed (average 7.40 g single seed), highest oil content (58.71%), and highest NA level (42.22%). The germplasm diversity of M. oleifera is associated with its habitat but not elevation. Seeds of M. adenantha contained higher NA levels (28.41%) than M. indica (21.77%), but M. indica contained a significantly higher oil content (29.22%) and seed yield. M. adenantha germplasm varied among populations, with one population having seeds with high oil content (22.63%) and NA level (37.78%).Although M. indica grow naturally at a range of elevations, no significant differences were detected between M. indica populations. These results suggest that M. indica and M. oleifera have greater potential as a source of NA, which will contribute to constructing a germplasm resource nursery and establishing a selection and breeding program to improve the development of NA-enriched plants.

Comprehensive Analysis of N6-Methyladenosine-Related Long Noncoding RNA Prognosis of Acute Myeloid Leukemia and Immune Cell Infiltration.

N6-Methyladenosine-related long noncoding RNAs play an essential role in many cancers' development. However, the relationship between m6A-related lncRNAs and acute myelogenous leukemia (AML) prognosis remains unclear. We systematically analyzed the association of m6A-related lncRNAs with the prognosis and tumor immune microenvironment (TME) features using the therapeutically applicable research to generate effective treatment (TARGET) database. We screened 315 lncRNAs associated with AML prognosis and identified nine key lncRNAs associated with m6A by the LASSO Cox analysis. A model was established based on these nine lncRNAs and the predictive power was explored in The Cancer Genome Atlas (TCGA) database. The areas under the ROC curve of TARGET and TCGA databases for ROC at 1, 3, and 5 years are 0.701, 0.704, and 0.696, and 0.587, 0.639, and 0.685, respectively. The nomogram and decision curve analysis (DCA) showed that the risk score was more accurate than other clinical indicators in evaluating patients' prognoses. The clusters with a better prognosis enrich the AML pathways and immune-related pathways. We also found a close correlation between prognostic m6A-related lncRNAs and tumor immune cell infiltration. LAG3 expression at the immune checkpoint was lower in the worse prognostic cluster. In conclusion, m6A-related lncRNAs partly affected AML prognosis by remodeling the TME and affecting the anticarcinogenic ability of immune checkpoints, especially LAG3 inhibitors. The prognostic model constructed with nine key m6A-related lncRNAs can provide a method to assess the prognosis of AML patients in both adults and children.

Plant adaptation to frequent alterations between high and low temperatures: remodelling of membrane lipids and maintenance of unsaturation levels.

One major strategy by which plants adapt to temperature change is to decrease the degree of unsaturation of membrane lipids under high temperature and increase it under low temperature. We hypothesize that this strategy cannot be adopted by plants in ecosystems and environments with frequent alterations between high and low temperatures, because changes in lipid unsaturation are complex and require large energy inputs. To test this hypothesis, we used a lipidomics approach to profile changes in molecular species of membrane glycerolipids in two plant species sampled from alpine screes and in another two plant species grown in a growth chamber, with the temperature cycling daily between heat and freezing. We found that six classes of phospholipid and two classes of galactolipid showed significant changes, but the degree of unsaturation of total lipids and of three lysophospholipid classes remained unchanged. This pattern of changes in membrane lipids was distinct from that occurring during slow alterations in temperature. We propose two types of model for the adaptation of plants to temperature change: (1) remodelling of membrane lipids but maintenance of the degree of unsaturation are used to adapt to frequent temperature alterations; and (2) both remodelling and changes in the degree of unsaturation to adapt to infrequent temperature alterations.

Better Medications Adherence Lowers Cardiovascular Events, Stroke, and All-Cause Mortality Risk: A Dose-Response Meta-Analysis.

AIMS: We investigated the association between vascular medication adherence, assessed by different methods, and the risk of cardio-cerebrovascular events and all-cause mortality. METHODS: A meta-analysis with a systematic search of PubMed, Web of Science, EMBASE, and Cochrane databases from inception date to 21 June 2021 was used to identify relevant studies that had evaluated the association between cardiovascular medication adherence levels and cardiovascular events (CVEs), stroke, and all-cause mortality risks. Pooled relative risks (RRs) and 95% confidence intervals (CIs) were calculated using a random-effects meta-analysis. Restricted cubic splines were used to model the dose-response association. RESULTS: We identified 46 articles in the dose-response meta-analysis. The dose-response analysis indicated that a 20% increment in cardiovascular medication, antihypertensive medication, and lipid-lowering medication adherence level were associated with 9% (RR: 0.91, 95% CI 0.88-0.94), 7% (RR 0.93, 95% CI: 0.84-1.03), and 10% (RR 0.90, 95% CI: 0.88-0.92) lowers risk of CVEs, respectively. The reduced risk of stroke respectively was 16% (RR: 0.84, 95% CI: 0.81-0.87), 17% (RR 0.83, 95% CI: 0.78-0.89), and 13% (RR 0.87, 95% CI: 0.84-0.91). The reduced risk of all-cause mortality respectively was 10% (RR: 0.90, 95% CI: 0.87-0.92), 12% (RR 0.88, 95% CI: 0.82-0.94), and 9% (RR 0.91, 95% CI: 0.89-0.94). CONCLUSIONS: A better medication adherence level was associated with a reduced risk of cardio-cerebrovascular events and all-cause mortality.