Effects of the seedling tray overlapping for seed emergence mode on emergence characteristics and growth of rice seedlings.

Seedling mode plays a crucial role in the rice production process, as it significantly affects the growth and development of seedlings. Among the various seedling modes, the seedling tray overlapping for seed emergence mode (STOSE mode) has been demonstrated to be effective in enhancing seedling quality. However, the impact of this mode on the germination and growth of seeds with varying plumpness remains uncertain. To investigate the effect of the STOSE mode on seedling emergence characteristics, growth uniformity, and nutrient uptake of seeds with varying plumpness levels, we conducted a study using super early rice Zhongzao 39 (ZZ39) as the test material. The seeds were categorized into three groups: plumped, mixed, and unplumped. The results indicated that the STOSE mode significantly improved the seedling rate for all types of seeds in comparison to the seedling tray nonoverlapping for seed emergence mode (TSR mode). Notably, the unplumped seeds exhibited the most pronounced enhancement effect. The soluble sugar content of the seeds increased significantly after 2 days of sowing under the STOSE mode, whereas the starch content exhibited a significant decrease. Furthermore, the STOSE mode outperformed the TSR mode in several aspects including seedling growth uniformity, aboveground dry matter mass, root traits, and nutrient uptake. Overall, the STOSE mode not only promoted the germination and growth of plumped and mixed seeds but also had a more pronounced impact on unplumped seeds.

A Viscous-Biofluid Self-Pumping Organohydrogel Dressing to Accelerate Diabetic Wound Healing.

Viscous biofluids on wounds challenge conventional "water-absorbing" wound dressings in efficient drainage due to their poor fluidity, generally causing prolonged inflammation, anti-angiogenesis, and delayed wound closure. Herein, it is reported that a self-pumping organohydrogel dressing (SPD) with aligned hydrated hydrogel channels, prepared by a three-dimensional-templated wetting-enabled-transfer (3D-WET) polymerization process, can efficiently drain viscous fluids and accelerate diabetic wound healing. The asymmetric wettability of the hydrophobic-hydrophilic layers and aligned hydrated hydrogel channels enable unidirectional and efficient drainage of viscous fluids away from the wounds, preventing their overhydration and inflammatory stimulation. The organogel layer can adhere onto the skin around the wounds but can be easily detached from the wet wound area, avoiding secondary trauma to the newly formed tissues. Taking a diabetic rat model as an example, the SPD can significantly downregulate the inflammation response by ≈70.8%, enhance the dermal remodeling by ≈14.3%, and shorten wound closure time by about 1/3 compared with the commercial dressing (3M, Tegaderm hydrocolloid thin dressing). This study sheds light on the development of the next generation of functional dressings for chronic wounds involving viscous biofluids.

Novel Angiogenesis Role of GLP-1(32-36) to Rescue Diabetic Ischemic Lower Limbs via GLP-1R-Dependent Glycolysis in Mice.

BACKGROUND: Restoring the capacity of endothelial progenitor cells (EPCs) to promote angiogenesis is the major therapeutic strategy of diabetic peripheral artery disease. The aim of this study was to investigate the effects of GLP-1 (glucagon-like peptide 1; 32-36)-an end product of GLP-1-on angiogenesis of EPCs and T1DM (type 1 diabetes) mice, as well as its interaction with the classical GLP-1R (GLP-1 receptor) pathway and its effect on mitochondrial metabolism. METHODS: In in vivo experiments, we conducted streptozocin-induced type 1 diabetic mice as a murine model of unilateral hind limb ischemia to examine the therapeutic potential of GLP-1(32-36) on angiogenesis. We also generated Glp1r-/- mice to detect whether GLP-1R is required for angiogenic function of GLP-1(32-36). In in vitro experiments, EPCs isolated from the mouse bone marrow and human umbilical cord blood samples were used to detect GLP-1(32-36)-mediated angiogenic capability under high glucose treatment. RESULTS: We demonstrated that GLP-1(32-36) did not affect insulin secretion but could significantly rescue angiogenic function and blood perfusion in ischemic limb of streptozocin-induced T1DM mice, a function similar to its parental GLP-1. We also found that GLP-1(32-36) promotes angiogenesis in EPCs exposed to high glucose. Specifically, GLP-1(32-36) has a causal role in improving fragile mitochondrial function and metabolism via the GLP-1R-mediated pathway. We further demonstrated that GLP-1(32-36) rescued diabetic ischemic lower limbs by activating the GLP-1R-dependent eNOS (endothelial NO synthase)/cGMP/PKG (protein kinase G) pathway. CONCLUSIONS: Our study provides a novel mechanism with which GLP-1(32-36) acts in modulating metabolic reprogramming toward glycolytic flux in partnership with GLP-1R for improved angiogenesis in high glucose-exposed EPCs and T1DM murine models. We propose that GLP-1(32-36) could be used as a monotherapy or add-on therapy with existing treatments for peripheral artery disease. REGISTRATION: URL: www.ebi.ac.uk/metabolights/; Unique identifier: MTBLS9543.

Research on risk factor filtering and rating of cold chain logistics from the perspective of root-state risk identification.

The natural attributes of perishable and vulnerable cold chain products make the cold chain have more risks than the general supply chain. The attribute characteristics and internal relations of various risks increase the complexity of risk analysis. The purpose of this paper is to study the horizontal and vertical assessment of various risk factors. The multi-dimensional risk measurement model is used to integrate the assessment of multiple risk factors of human, machine, environment, and management, and the cold chain risk management is discussed from the perspective of risk factor classification. The root-state risk identification (RSRI) method was used to identify potential risks. Based on the double standard filtering and multiple criteria, the filtering of irrelevant risks and screening of uncontrollable risks were evaluated, and the triangular fuzzy number method was used to quantitatively evaluate the controllable risk factors. A total of 223 potential risks, 18 important risks, and 6 key risks were identified, followed by inspection and quarantine reports, pesticide residues, improper loading and unloading operations, unqualified centralized environment, unqualified pre-cooling technology of carriages, and unreasonable storage temperature. According to the analysis results, targeted control measures are proposed to better prevent risks and reduce the probability of cold chain accidents. The traditional risk assessment method can only assess the impact of a single risk factor on the system. This assessment method overcomes this limitation and provides a new perspective for cold chain risk management. PRACTICAL APPLICATION: This study laid the foundation for further risk safety management of cold chain logistics.

Enhanced performance of EEG-based brain-computer interfaces by joint sample and feature importance assessment.

Electroencephalograph (EEG) has been a reliable data source for building brain-computer interface (BCI) systems; however, it is not reasonable to use the feature vector extracted from multiple EEG channels and frequency bands to perform recognition directly due to the two deficiencies. One is that EEG data is weak and non-stationary, which easily causes different EEG samples to have different quality. The other is that different feature dimensions corresponding to different brain regions and frequency bands have different correlations to a certain mental task, which is not sufficiently investigated. To this end, a Joint Sample and Feature importance Assessment (JSFA) model was proposed to simultaneously explore the different impacts of EEG samples and features in mental state recognition, in which the former is based on the self-paced learning technique while the latter is completed by the feature self-weighting technique. The efficacy of JSFA is extensively evaluated on two EEG data sets, i.e., SEED-IV and SEED-VIG. One is a classification task for emotion recognition and the other is a regression task for driving fatigue detection. Experimental results demonstrate that JSFA can effectively identify the importance of different EEG samples and features, leading to enhanced recognition performance of corresponding BCI systems.

Assessment of reproductive toxicity in adult zebrafish (Danio rerio) following sublethal exposure to penthiopyrad.

Penthiopyrad (PO), a succinate dehydrogenase inhibitor (SDHI) fungicide, poses a potential risk to fish. Here, we investigated the adverse effects of PO on endocrine regulation and reproductive capacity in zebrafish during a 21-d sublethal exposure to PO concentrations ranging from 0.02 to 2.00 mg/L. Following exposure to PO (0.20 and 2.00 mg/L), female-specific effects including follicle necrosis, structural disturbance of the yolk follicle, fusion of cortical follicles appeared in ovarian tissue of adult females, which led to a significant reduction in fertility. Correspondingly, 0.20 and 2.00 mg/L PO led to a marked reduction in the GSI values of females, and 2.00 mg/L PO caused a 31% decline in the proportion of perinucleolar oocytes (PCO) in oocytes. In addition, testosterone (T) level was obviously suppressed and 17ß-estradiol (E2) level was increased in females after exposure to 2.00 mg/L PO. Male zebrafish treated with 0.20 and 2.00 mg/L of PO exhibited significant interstitial enlargement, edema in the testes, and reduced diameter of seminiferous tubules, along with a thinner basement membrane. The effects of PO on males were associated with significant increase in E2 level, suggesting that PO has an estrogenic effect on male fish. Greater E2 levels in serum were further supported by increased transcription levels of genes linked to the hypothalamic-pituitary-gonad-liver (HPGL) axis. Notably, transcription levels of cyp19a, er2b, era, and cyp19b was remarkably increased, exhibiting a clear link with variations in E2 levels. Overall, the present study demonstrates that PO induces reproductive impairment in zebrafish by promoting steroidogenesis.

Comparative Transcriptome Combined with Morphophysiological Analyses Revealed Carotenoid Biosynthesis for Differential Chilling Tolerance in Two Contrasting Rice (Oryza sativa L.) Genotypes.

Early spring cold spells can lead to leaf chlorosis during the rice seedling greening process. However, the physiological and molecular mechanisms underlying the rice greening process under low-temperature conditions remain unknown. In this study, comparative transcriptome and morphophysiological analyses were performed to investigate the mechanisms mediating the responses of the Koshihikari (Kos) and Kasalath (Kas) rice cultivars to chilling stress. According to their growth-related traits, electrolyte leakage, and chlorophyll fluorescence parameters, Kos was more tolerant to low-temperature stress than Kas. Moreover, chloroplast morphology was more normal (e.g., oval) in Kos than in Kas at 17 °C. The comparative transcriptome analysis revealed 610 up-regulated differentially expressed genes that were common to all four comparisons. Furthermore, carotenoid biosynthesis was identified as a critical pathway for the Kos response to chilling stress. The genes in the carotenoid biosynthesis pathway were expressed at higher levels in Kos than in Kas at 17 °C, which was in accordance with the higher leaf carotenoid content in Kos than in Kas. The lycopene ß-cyclase and lycopene ε-cyclase activities increased more in Kos than in Kas. Additionally, the increases in the violaxanthin de-epoxidase and carotenoid hydroxylase activities in Kos seedlings resulted in the accumulation of zeaxanthin and lutein and mitigated the effects of chilling stress on chloroplasts. These findings have clarified the molecular mechanisms underlying the chilling tolerance of rice seedlings during the greening process.

Lipidomics Profiling Reveals Serum Phospholipids Associated with Albuminuria in Early Type 2 Diabetic Kidney Disease.

Early screening and administration of DKD are beneficial for renal outcomes of type 2 diabetic patients. However, the current early diagnosis using the albuminuria/creatine ratio (ACR) contains limitations. This study aimed to compare serum lipidome variation between type 2 diabetes and early DKD patients with increased albuminuria through an untargeted lipidomics method to explore the potential lipid biomarkers for DKD identification. 92 type 2 diabetic patients were enrolled and divided into two groups: DM group (ACR < 3 mg/mmol, n = 49) and early DKD group (3 mg/mmol ≤ ACR < 30 mg/mmol, n = 43). Fasting serum was analyzed through an ultraperformance liquid mass spectrometry tandem chromatography system (LC-MS). Orthogonal partial least-squares discriminant analysis (OPLS-DA) and univariate and multivariate analysis were performed to filter differentially depressed lipids. Receiver operating characteristic (ROC) curves were used to estimate the diagnostic capability of potential lipid biomarkers. We found that serum phospholipids including phosphatidylserine (PS), sphingomyelin (SM), and phosphatidylcholine (PC) were significantly upregulated in the DKD group and were highly correlated with the ACR. In addition, a panel of two phospholipids including PS(27:0)-H and PS(30:2e)-H showed good performance to help clinical lipids in early DKD identification, which increased the area under the curve (AUC) from 0.568 to 0.954. The study exhibited the serum lipidome variation in early DKD patients, and the increased phospholipids might participate in the development of albuminuria. The panel of PS(27:0)-H and PS(30:2e)-H could be a potential biomarker for DKD diagnosis.

ELABELA/APJ Axis Prevents Diabetic Glomerular Endothelial Injury by Regulating AMPK/NLRP3 Pathway.

ELABELA (ELA), a recently discovered peptide, is highly expressed in adult kidneys and the endothelium system. It has been identified as a novel endogenous ligand for the apelin receptor (APJ). This study aims to investigate the role of ELA in diabetic glomerular endothelial pyroptosis and its underlying mechanism. Initially, a significant decrease in ELA mRNA levels was observed in the renal cortex of db/db mice and high glucose-treated glomerular endothelial cells (GECs). It was also found that ELA deficiency in ELA+/- mice significantly accelerated diabetic glomerular injury, as shown by exacerbated glomerular morphological damage, increased serum creatine and blood urea nitrogen, and elevated 24-h urinary albumin excretion. In addition, in vivo overexpression of ELA prevented diabetic glomerular injury, reduced von Willebrand factor expression, restored endothelial marker CD31 expression, and attenuated the production of adhesive molecules such as intercellular adhesion molecule-1 and vascular cell adhesion molecule-1. Furthermore, in vitro studies confirmed that treatment with ELA inhibited GEC injury by regulating the NOD-like receptor protein 3 (NLRP3) inflammasome, as indicated by blocking NLRP3 inflammasome formation, decreasing cleaved Caspase-1 production, and inhibiting interleukin-1ß and interleukin-18 production. Moreover, in vitro experiments demonstrated that the protective effects of ELA in GECs during hyperglycemia were diminished by inhibiting adenosine monophosphate-activated protein kinase (AMPK) using Compound C or by APJ deficiency. Taken together, this study provides the first evidence that ELA treatment could prevent diabetic glomerular endothelial injury, which is partly mediated by the regulation of the AMPK/NLRP3 signaling pathway. Therefore, pharmacologically targeting ELA may serve as a novel therapeutic strategy for diabetic kidney disease.

SIFIAE: An adaptive emotion recognition model with EEG feature-label inconsistency consideration.

BACKGROUND: A common but easily overlooked affective overlap problem has not been received enough attention in electroencephalogram (EEG)-based emotion recognition research. In real life, affective overlap refers to the current emotional state of human being is sometimes influenced easily by his/her historical mood. In stimulus-evoked EEG collection experiment, due to the short rest interval in consecutive trials, the inner mechanisms of neural responses make subjects cannot switch their emotion state easily and quickly, which might lead to the affective overlap. For example, we might be still in sad state to some extent even if we are watching a comedy because we just saw a tragedy before. In pattern recognition, affective overlap usually means that there exists the feature-label inconsistency in EEG data. NEW METHODS: To alleviate the impact of inconsistent EEG data, we introduce a variable to adaptively explore the sample inconsistency in emotion recognition model development. Then, we propose a semi-supervised emotion recognition model for joint sample inconsistency and feature importance exploration (SIFIAE). Accordingly, an efficient optimization method to SIFIAE model is proposed. RESULTS: Extensive experiments on the SEED-V dataset demonstrate the effectiveness of SIFIAE. Specifically, SIFIAE achieves 69.10%, 67.01%, 71.50%, 73.26%, 72.07% and 71.35% average accuracies in six cross-session emotion recognition tasks. CONCLUSION: The results illustrated that the sample weights have a rising trend in the beginning of most trials, which coincides with the affective overlap hypothesis. The feature importance factor indicated the critical bands and channels are more obvious compared with some models without considering EEG feature-label inconsistency.

Semi-supervised regression with adaptive graph learning for EEG-based emotion recognition.

Electroencephalogram (EEG) signals are widely used in the field of emotion recognition since it is resistant to camouflage and contains abundant physiological information. However, EEG signals are non-stationary and have low signal-noise-ratio, making it more difficult to decode in comparison with data modalities such as facial expression and text. In this paper, we propose a model termed semi-supervised regression with adaptive graph learning (SRAGL) for cross-session EEG emotion recognition, which has two merits. On one hand, the emotional label information of unlabeled samples is jointly estimated with the other model variables by a semi-supervised regression in SRAGL. On the other hand, SRAGL adaptively learns a graph to depict the connections among EEG data samples which further facilitates the emotional label estimation process. From the experimental results on the SEED-IV data set, we have the following insights. 1) SRAGL achieves superior performance compared to some state-of-the-art algorithms. To be specific, the average accuracies are 78.18%, 80.55%, and 81.90% in the three cross-session emotion recognition tasks. 2) As the iteration number increases, SRAGL converges quickly and optimizes the emotion metric of EEG samples gradually, leading to a reliable similarity matrix finally. 3) Based on the learned regression projection matrix, we obtain the contribution of each EEG feature, which enables us to automatically identify critical frequency bands and brain regions in emotion recognition.

2-DG Re-Normalized IFN-γ Production in T Cells Excluding TEMRA Cells from Patients with Aplastic Anemia.

Aplastic anemia (AA) is a T cell immune mediated autoimmune disease in which cytokines, particularly IFN-γ are pathogenesis factors. Glucose metabolism is closely related to effector functions of activated T cells, such as IFN-γ production. The characteristics of glucose metabolism and whether interfering with glucose metabolism could affect T cells produce IFN-γ ability in AA patients remains unknown. In this study, we examined the characteristics of glucose metabolism in T cells from AA patients and the effects of the glucose metabolism inhibitor 2-deoxy-D-glucose (2-DG) on the ability of T cell production IFN-γ. Our data demonstrated abnormal glucose metabolism in stimulated T cells from AA patients, mainly reflected by increased glucose uptake and lactate secretion. In addition, EM and TEMRA cells exhibit higher glucose uptake in patients with AA compared with healthy individuals. Moreover, the frequency of IFN-γ+ was reduced by 2-DG in T cell from AA patients. Unexpectedly, 2-DG re-normalized the frequency of IFN-γ+ in other T cell subsets, except for in the TEMRA. In conclusion, our study reveals for the first time the existence of enhanced aerobic glycolysis in T cells from AA patients, and different T cell subsets exhibit different extent glucose uptake requirements. Aerobic glycolysis regulation may be a potential therapeutic strategy for aberrant T cell immunity. Moreover, TEMRA may have specific metabolic abnormalities, which should receive more attention in future targeted immune metabolism research.

Efficacy of acupuncture for cardiopulmonary cerebral resuscitation: A systematic review and meta-analysis.

Background: Cerebral resuscitation is one of the main therapeutic aims in the treatment of cardiac arrest (CA) patients who experience a return of spontaneous circulation (ROSC). However, the therapeutic effects of current treatments are not ideal. The purpose of this study was to evaluate the efficacy of neurological function of acupuncture combined with conventional cardiopulmonary cerebral resuscitationthe (CPCR) for patients after ROSC. Methods: Seven electronic databases and other related websites were searched to identify studies on acupuncture combined with conventional CPCR for patients after ROSC. R software was used to conduct a meta-analysis, and the outcomes that could not be pooled were analyzed using a descriptive analysis. Results: Seven RCTs involving 411 participants who had experienced ROSC were eligible for inclusion. The main acupoints were Neiguan (PC6), Shuigou (DU26), Baihui (DU20), Yongquan (KI1), and Sanyinjiao (SP6). Compared to conventional CPCR, acupuncture combined with conventional CPCR led to significantly higher Glasgow Coma Scale (GCS) scores on day 3 (mean difference (MD)=0.89, 95% CI: 0.43, 1.35, I2 = 0%), day 5 (MD = 1.21, 95% CI: 0.27, 2.15; I2 = 0%), and day 7 (MD = 1.92, 95% CI: 1.35, 2.50; I2 = 0%). Conclusion: Acupuncture-assisted conventional CPCR may have a potential role in improving neurological function in CA patients after ROSC, but the certainty of evidence is very low and more high-quality studies are required. Protocol registration: This review was registered at the International Prospective Registry of Systematic Reviews (PROSPERO): CRD42021262262.

Specific TCR profiles predict clinical outcome of adjuvant EGFR-TKIs for resected EGFR-mutant non-small cell lung cancer.

BACKGROUND: ADJUVANT-CTONG1104 reported a favorable survival outcome from adjuvant gefitinib treatment over chemotherapy in EGFR-mutant non-small cell lung cancer (NSCLC) patients. However, heterogeneous benefit from EGFR-TKIs and chemotherapy demands further biomarker exploration for patient selection. Previously, we identified certain TCR sequences with predictive value for adjuvant therapies from the CTONG1104 trial and found a relationship between the TCR repertoire and genetic variations. It remains unknown which TCR sequences could further enhance the prediction for only adjuvant EGFR-TKI. METHODS: In this study, 57 tumor and 12 tumor-adjacent samples, respectively, from gefitinib-treated patients in the CTONG1104 were collected for TCR ß gene sequencing. We attempted to constitute a predictive model for prognosis and favorable adjuvant EGFR-TKI outcome for patients with early-stage NSCLC and EGFR mutations. RESULTS: The TCR rearrangements demonstrated significant prediction for overall survival (OS). A combined model of high frequent Vß7-3Jß2-5 and Vß24-1Jß2-1 with lower frequent Vß5-6Jß2-7 and Vß28Jß2-2 constituted the best value for predicting OS (P < 0.001; Hazard Ratio [HR] = 9.65, 95% confidence interval [CI]: 2.27 to 41.12) or DFS (P = 0.02; HR = 2.61, 95% CI: 1.13 to 6.03). In Cox regression analyses, when multiple clinical data were included, the risk score remained an independent prognostic predictor for OS (P = 0.003; HR = 9.49; 95% CI: 2.21 to 40.92) and DFS (P = 0.015; HR = 3.13; 95% CI: 1.25 to 7.87). CONCLUSIONS: In this study, a predictive model was constituted with specific TCR sequences for prognosis prediction and gefitinib benefit in the ADJUVANT-CTONG1104 trial. We provide a potential immune biomarker for EGFR-mutant NSCLC patients who might benefit from an adjuvant EGFR-TKI.

Spatial position is a key determinant of N-glycan functionality of the scavenger receptor cysteine-rich domain of human hepsin.

The scavenger receptor cysteine-rich (SRCR) domain is a key constituent in diverse proteins. N-glycosylation is important in protein expression and function. In the SRCR domain of different proteins, N-glycosylation sites and functionality vary substantially. In this study, we examined the importance of N-glycosylation site positions in the SRCR domain of hepsin, a type II transmembrane serine protease involved in many pathophysiological processes. We analysed hepsin mutants with alternative N-glycosylation sites in the SRCR and protease domains using three-dimensional modelling, site-directed mutagenesis, HepG2 cell expression, immunostaining, and western blotting. We found that the N-glycan function in the SRCR domain in promoting hepsin expression and activation on the cell surface cannot be replaced by alternatively created N-glycans in the protease domain. Within the SRCR domain, the presence of an N-glycan in a confined surface area was essential for calnexin-assisted protein folding, endoplasmic reticulum (ER) exiting, and zymogen activation of hepsin on the cell surface. Hepsin mutants with alternative N-glycosylation sites on the opposite side of the SRCR domain were trapped by ER chaperones, resulting in the activation of the unfolded protein response in HepG2 cells. These results indicate that the spatial N-glycan positioning in the SRCR domain is a key determinant in the interaction with calnexin and subsequent cell surface expression of hepsin. These findings may help to understand the conservation and functionality of N-glycosylation sites in the SRCR domains of different proteins.

Increased IFN-γ+ and TNF-α+ mucosal-associated invariant T cells in patients with aplastic anemia.

BACKGROUND: Aplastic anemia (AA) is known as an autoimmune disease in which T cell activation is aberrant. It has been reported that unconventional T cells, mucosal-associated invariant T (MAIT) cells, play an important role in several autoimmune diseases, but it is unclear if they are involved in AA. METHODS: In this study, we for the first time analyzed the proportions, phenotypes, and cytokine properties of MAIT cells in AA by flow cytometry. RESULTS: We found that the percentage of circulating MAIT cells was generally higher for CD3+ , CD8+ , and CD8- T cells in AA patients compared with healthy individuals. Moreover, the percentage of IL-18Rα-, NKG2D-, IFN-γ-, and TNF-α- positive MAIT cells was also significantly higher in AA patients. In addition, the percentage of IFN-γ+ CD3+ or TNF-α+ CD8- MAIT cells had a significant negative correlation with the absolute neutrophil count. CONCLUSIONS: We present the first observation of MAIT cells in patients with AA. MAIT cells are associated with a higher frequency of IFN-γ and TNF-α production and may contribute to the pathogenesis of AA.

High expression of LOC541471, GDAP1, SOD1, and STK25 is associated with poor overall survival of patients with acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is an aggressive heterogeneous hematological malignancy with remarkably heterogeneous outcomes. This study aimed to identify potential biomarkers for AML risk stratification via analysis of gene expression profiles. METHODS: RNA sequencing data from 167 adult AML patients in the Cancer Genome Atlas (TCGA) database were obtained for overall survival (OS) analysis, and 52 bone marrow (BM) samples from our clinical center were used for validation. Additionally, siRNA was used to investigate the role of prognostic genes in the apoptosis and proliferation of AML cells. RESULTS: Co-expression of 103 long non-coding RNAs (lncRNAs) and mRNAs in the red module that were positively correlated with European Leukemia Network (ELN) risk stratification and age was identified by weighted gene co-expression network analysis (WGCNA). After screening by uni- and multivariate Cox regression, Kaplan-Meier survival, and protein-protein interaction analysis, four genes including the lncRNA LOC541471, GDAP1, SOD1, and STK25 were incorporated into calculating a risk score from coefficients of the multivariate Cox regression model. Notably, GDAP1 expression was the greatest contributor to OS among the four genes. Interestingly, the risk score, ELN risk stratification, and age were independent prognostic factors for AML patients, and a nomogram model constructed with these factors could illustrate and personalize the 1-, 3-, and 5-year OS rates of AML patients. The calibration and time-dependent receiver operating characteristic curves (ROCs) suggested that the nomogram had a good predictive performance. Furthermore, new risk stratification was developed for AML patients based on the nomogram model. Importantly, knockdown of LOC541471, GDPA1, SOD1, or STK25 promoted apoptosis and inhibited the proliferation of THP-1 cells compared to controls. CONCLUSIONS: High expression of LOC541471, GDAP1, SOD1, and STK25 may be biomarkers for risk stratification of AML patients, which may provide novel insight into evaluating prognosis, monitoring progression, and designing combinational targeted therapies.

Identification of TCR rearrangements specific for genetic alterations in EGFR-mutated non-small cell lung cancer: results from the ADJUVANT-CTONG1104 trial.

Tumor response T cells, which have specific T cell receptor (TCR) rearrangements in tumor-infiltrating lymphocytes, determine their ability to interact with the mutation-derived neoantigens presented by antigen-presenting cells. Little is known about the genetic alterations related to specific TCR clones in non-small cell lung cancer (NSCLC) patients who have an epidermal growth factor receptor (EGFR) mutation. In this study, tumor tissues were collected from 101 patients with stage II/III resectable NSCLC with an EGFR mutation (57 patients were treated with gefitinib and 44 were treated with chemotherapy) in the ADJUVANT-CTONG1104 trial for high-throughput TCRß V region and exome sequencing. Ten clonal TCRs were associated with EGFR exon 19 deletion (del), EGFR exon 21 mutation (L858R), RB1 alteration, TP53 exon 4/5 missense mutation, TP53 nonsense mutation, or copy number gains in NKX2-1 and CDK4. Among the TCRs, there was frequent use of Vß20-1Jß2-3 specifically for EGFR exon 19 del or Vß9Jß2-1 specifically for EGFR exon 21 mutation (L858R), and these were significantly associated with favorable overall survival (OS) for NSCLC patients harboring EGFR exon 19 del or exon 21 L858R, particularly in the adjuvant gefitinib setting. Moreover, in comparison with the chemotherapy-preferable (CP) group, higher frequencies of Vß20-1Jß2-3 and Vß9Jß2-1 were found in the highly TKI-preferable (HTP) or TKI-preferable (TP) groups. Altogether, we identified ten TCR rearrangements specific for genetic alterations in NSCLC. Importantly, high abundance Vß20-1Jß2-3 or Vß9Jß2-1 may be an immune biomarker for guiding adjuvant gefitinib decisions for NSCLC patients harboring EGFR exon 19 del or EGFR exon 21 L858R.

Liraglutide Improves the Angiogenic Capability of EPC and Promotes Ischemic Angiogenesis in Mice under Diabetic Conditions through an Nrf2-Dependent Mechanism.

The impairment in endothelial progenitor cell (EPC) functions results in dysregulation of vascular homeostasis and dysfunction of the endothelium under diabetic conditions. Improving EPC function has been considered as a promising strategy for ameliorating diabetic vascular complications. Liraglutide has been widely used as a therapeutic agent for diabetes. However, the effects and mechanisms of liraglutide on EPC dysfunction remain unclear. The capability of liraglutide in promoting blood perfusion and angiogenesis under diabetic conditions was evaluated in the hind limb ischemia model of diabetic mice. The effect of liraglutide on the angiogenic function of EPC was evaluated by cell scratch recovery assay, tube formation assay, and nitric oxide production. RNA sequencing was performed to assess the underlying mechanisms. Liraglutide enhanced blood perfusion and angiogenesis in the ischemic hindlimb of db/db mice and streptozotocin-induced type 1 diabetic mice. Additionally, liraglutide improved tube formation, cell migration, and nitric oxide production of high glucose (HG)-treated EPC. Assessment of liraglutide target pathways revealed a network of genes involved in antioxidant activity. Further mechanism study showed that liraglutide decreased the production of reactive oxygen species and increased the activity of nuclear factor erythroid 2-related factor 2 (Nrf2). Nrf2 deficiency attenuated the beneficial effects of liraglutide on improving EPC function and promoting ischemic angiogenesis under diabetic conditions. Moreover, liraglutide activates Nrf2 through an AKT/GSK3ß/Fyn pathway, and inhibiting this pathway abolished liraglutide-induced Nrf2 activation and EPC function improvement. Overall, these results suggest that Liraglutide represents therapeutic potential in promoting EPC function and ameliorating ischemic angiogenesis under diabetic conditions, and these beneficial effects relied on Nrf2 activation.

Bioinspired and Low-Power 2D Machine Vision with Adaptive Machine Learning and Forgetting.

Natural intelligence has many dimensions, with some of its most important manifestations being tied to learning about the environment and making behavioral changes. In primates, vision plays a critical role in learning. The underlying biological neural networks contain specialized neurons and synapses which not only sense and process visual stimuli but also learn and adapt with remarkable energy efficiency. Forgetting also plays an active role in learning. Mimicking the adaptive neurobiological mechanisms for seeing, learning, and forgetting can, therefore, accelerate the development of artificial intelligence (AI) and bridge the massive energy gap that exists between AI and biological intelligence. Here, we demonstrate a bioinspired machine vision system based on a 2D phototransistor array fabricated from large-area monolayer molybdenum disulfide (MoS2) and integrated with an analog, nonvolatile, and programmable memory gate-stack; this architecture not only enables dynamic learning and relearning from visual stimuli but also offers learning adaptability under noisy illumination conditions at miniscule energy expenditure. In short, our demonstrated "all-in-one" hardware vision platform combines "sensing", "computing", and "storage" to not only overcome the von Neumann bottleneck of conventional complementary metal-oxide-semiconductor (CMOS) technology but also to eliminate the need for peripheral circuits and sensors.