Genome-Wide Profiling of H3K27ac Identifies TDO2 as a Pivotal Therapeutic Target in Metabolic Associated Steatohepatitis Liver Disease.

H3K27ac has been widely recognized as a representative epigenetic marker of active enhancer, while its regulatory mechanisms in pathogenesis of metabolic dysfunction-associated steatotic liver disease (MASLD) remain elusive. Here, a genome-wide comparative study on H3K27ac activities and transcriptome profiling in high fat diet (HFD)-induced MASLD model is performed. A significantly enhanced H3K27ac density with abundant alterations of regulatory transcriptome is observed in MASLD rats. Based on integrative analysis of ChIP-Seq and RNA-Seq, TDO2 is identified as a critical contributor for abnormal lipid accumulation, transcriptionally activated by YY1-promoted H3K27ac. Furthermore, TDO2 depletion effectively protects against hepatic steatosis. In terms of mechanisms, TDO2 activates NF-κB pathway to promote macrophages M1 polarization, representing a crucial event in MASLD progression. A bovine serum albumin nanoparticle is fabricated to provide sustained release of Allopurinol (NPs-Allo) for TDO2 inhibition, possessing excellent biocompatibility and desired targeting capacity. Venous injection of NPs-Allo robustly alleviates HFD-induced metabolic disorders. This study reveals the pivotal role of TDO2 and its underlying mechanisms in pathogenesis of MASLD epigenetically and genetically. Targeting H3K27ac-TDO2-NF-κB axis may provide new insights into the pathogenesis of abnormal lipid accumulation and pave the way for developing novel strategies for MASLD prevention and treatment.

Canagliflozin attenuates post-resuscitation myocardial dysfunction in diabetic rats by inhibiting autophagy through the PI3K/Akt/mTOR pathway.

This study investigated the effects of canagliflozin on myocardial dysfunction after cardiac arrest and cardiopulmonary resuscitation in diabetic rats and the underlying mechanisms. Male rats with type 2 diabetes mellitus (T2DM) were subjected to a modified epicardial fibrillation model. Pretreatment with canagliflozin (10 mg/kg/day) for four weeks improved ATP levels, post-resuscitation ejection fraction, acidosis, and hemodynamics. Canagliflozin also reduced myocardial edema, mitochondrial damage and, post-resuscitation autophagy levels. In vitro analyses showed that canagliflozin significantly reduced reactive oxygen species and preserved mitochondrial membrane potential. Using the PI3K/Akt pathway inhibitor Ly294002, canagliflozin was shown to attenuate hyperautophagy and cardiac injury induced by high glucose and hypoxia-reoxygenation through activation of the PI3K/Akt/mTOR pathway. This study highlights the therapeutic potential of canagliflozin in post-resuscitation myocardial dysfunction in diabetes, providing new insights for clinical treatment and experimental research.

Construction and validation of a nomogram prediction model for the progression to septic shock in elderly patients with urosepsis.

Background: Septic shock is a clinical syndrome characterized by the progression of sepsis to a severe stage. Elderly patients with urosepsis in the intensive care unit (ICU) are more likely to progress to septic shock. This study aimed to establish and validate a nomogram model for predicting the risk of progression to septic shock in elderly patients with urosepsis. Methods: We extracted data from the Medical Information Mart for Intensive Care (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). The MIMIC-IV dataset was split into a training set for model development and an internal validation set to assess model performance. Further external validation was performed using a distinct dataset sourced from the eICU-CRD. Predictors were screened using least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression analyses. The evaluation of model performance included discrimination, calibration, and clinical usefulness. Results: The study demonstrated that the Glasgow Coma Scale (GCS), white blood count (WBC), platelet, blood urea nitrogen (BUN), calcium, albumin, congestive heart failure (CHF), and invasive ventilation were closely associated with septic shock in the training cohort. Nomogram prediction, utilizing eight parameters, demonstrated strong predictive accuracy with area under the curve (AUC) values of 0.809 (95 % CI 0.786-0.834), 0.794 (95 % CI 0.756-0.831), and 0.723 (95 % CI 0.647-0.801) in the training, internal validation, and external validation sets, respectively. Additionally, the nomogram demonstrated a promising calibration performance and significant clinical usefulness in both the training and validation sets. Conclusion: The constructed nomogram is a reliable and practical tool for predicting the risk of progression to septic shock in elderly patients with urosepsis. Its implementation in clinical practice may enhance the early identification of high-risk patients, facilitate timely and targeted interventions to mitigate the risk of septic shock, and improve patient outcomes.

Integration of protein sequence and protein-protein interaction data by hypergraph learning to identify novel protein complexes.

Protein-protein interactions (PPIs) are the basis of many important biological processes, with protein complexes being the key forms implementing these interactions. Understanding protein complexes and their functions is critical for elucidating mechanisms of life processes, disease diagnosis and treatment and drug development. However, experimental methods for identifying protein complexes have many limitations. Therefore, it is necessary to use computational methods to predict protein complexes. Protein sequences can indicate the structure and biological functions of proteins, while also determining their binding abilities with other proteins, influencing the formation of protein complexes. Integrating these characteristics to predict protein complexes is very promising, but currently there is no effective framework that can utilize both protein sequence and PPI network topology for complex prediction. To address this challenge, we have developed HyperGraphComplex, a method based on hypergraph variational autoencoder that can capture expressive features from protein sequences without feature engineering, while also considering topological properties in PPI networks, to predict protein complexes. Experiment results demonstrated that HyperGraphComplex achieves satisfactory predictive performance when compared with state-of-art methods. Further bioinformatics analysis shows that the predicted protein complexes have similar attributes to known ones. Moreover, case studies corroborated the remarkable predictive capability of our model in identifying protein complexes, including 3 that were not only experimentally validated by recent studies but also exhibited high-confidence structural predictions from AlphaFold-Multimer. We believe that the HyperGraphComplex algorithm and our provided proteome-wide high-confidence protein complex prediction dataset will help elucidate how proteins regulate cellular processes in the form of complexes, and facilitate disease diagnosis and treatment and drug development. Source codes are available at https://github.com/LiDlab/HyperGraphComplex.

Nomogram predictive model for in-hospital mortality risk in elderly ICU patients with urosepsis.

BACKGROUND: Urinary tract infection (UTI) is a common cause of sepsis. Elderly patients with urosepsis in intensive care unit (ICU) have more severe conditions and higher mortality rates owing to factors such as advanced age, immunosenescence, and persistent host inflammatory responses. However, comprehensive studies on nomograms to predict the in-hospital mortality risk in elderly patients with urosepsis are lacking. This study aimed to construct a nomogram predictive model to accurately assess the prognosis of elderly patients with urosepsis and provide therapeutic recommendations. METHODS: Data of elderly patients with urosepsis were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV 2.2 database. Patients were randomly divided into training and validation cohorts. A predictive nomogram model was constructed from the training set using logistic regression analysis, followed by internal validation and sensitivity analysis. RESULTS: This study included 1,251 patients. LASSO regression analysis revealed that the Glasgow Coma Scale (GCS) score, red cell distribution width (RDW), white blood count (WBC), and invasive ventilation were independent risk factors identified from a total of 43 variables studied. We then created and verified a nomogram. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) of the nomogram were superior to those of the traditional SAPS-II, APACHE-II, and SOFA scoring systems. The Hosmer-Lemeshow test results and calibration curves suggested good nomogram calibration. The IDI and NRI values showed that our nomogram scoring tool performed better than the other scoring systems. The DCA curves showed good clinical applicability of the nomogram. CONCLUSIONS: The nomogram constructed in this study is a convenient tool for accurately predicting in-hospital mortality in elderly patients with urosepsis in ICU. Improving the treatment strategies for factors related to the model could improve the in-hospital survival rates of these patients.

Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation.

OBJECTIVE: Sepsis-induced coagulopathy (SIC) is extremely common in individuals with sepsis, significantly associated with poor outcomes. This study attempted to develop an interpretable and generalizable machine learning (ML) model for early predicting the risk of 28-day death in patients with SIC. METHODS: In this retrospective cohort study, we extracted SIC patients from the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, and eICU-CRD database according to Toshiaki Iba's scale. And the overlapping in the MIMIC-IV was excluded for this study. Afterward, only the MIMIC-III cohort was randomly divided into the training set, and the internal validation set according to the ratio of 7:3, while the MIMIC-IV and eICU-CRD databases were considered the external validation sets. The predictive factors for 28-day mortality of SIC patients were determined using recursive feature elimination combined with tenfold cross-validation (RFECV). Then, we constructed models using ML algorithms. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), accuracy, sensitivity, specificity, negative predictive value, positive predictive value, recall, and F1 score. Finally, Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) were employed to provide a reasonable interpretation for the prediction results. RESULTS: A total of 3280, 2798, and 1668 SIC patients were screened from MIMIC-III, MIMIC-IV, and eICU-CRD databases, respectively. Seventeen features were selected to construct ML prediction models. XGBoost had the best performance in predicting the 28-day mortality of SIC patients, with AUC of 0.828, 0.913 and 0.923, the AUPRC of 0.807, 0.796 and 0.921, the accuracy of 0.785, 0.885 and 0.891, the F1 scores were 0.63, 0.69 and 0.70 in MIMIC-III (internal validation set), MIMIC-IV, and eICU-CRD databases. The importance ranking and SHAP analyses showed that initial SOFA score, red blood cell distribution width (RDW), and age were the top three critical features in the XGBoost model. CONCLUSIONS: We developed an optimal and explainable ML model to predict the risk of 28-day death of SIC patients 28-day death risk. Compared with conventional scoring systems, the XGBoost model performed better. The model established will have the potential to improve the level of clinical practice for SIC patients.

4-amino-2-trifluoromethyl-phenyl retinate alleviates lipopolysaccharide-induced acute myocardial injury through activation of the KLF4/p62 axis.

Ferroptosis plays a pivotal role in the pathological process of sepsis-induced cardiomyopathy (SIC). All-trans retinoic acid (ATRA) enhances the host immune response to lipopolysaccharides (LPS). This study investigated the role of 4-amino-2-trifluoromethyl-phenyl retinate (ATPR), a derivative of ATRA, in myocardial injury caused by sepsis. Male C57BL/6 mice were intraperitoneally injected with LPS to establish a sepsis model. H9c2 cells were stimulated by LPS to establish an injury model. We observed that ATPR improved myocardial injury in mice, which was presented in terms of an increased glutathione (GSH) level and reduced production of malondialdehyde (MDA), as well as an increased number of mitochondrial cristae and maintenance of the mitochondrial membrane integrity. ATPR improved cardiac function in the LPS-injured mice. It inhibited the inflammatory response as evidenced by the decreasing mRNA levels of TNF-α and IL-6. The elevated protein expression levels of Nrf2, SLC7A11, GPX4, and FTH1 in mice and H9c2 cells showed that ATPR inhibited ferroptosis. Immunoprecipitation of LPS-stimulated H9c2 cells demonstrated that ATPR increased the interaction between p62 and Keap1. ATPR upregulated the KLF4 and p62 protein expression. However, the inhibition of Nrf2 by ML385 reduced the protective effect of ATPR in LPS-treated H9c2 cells. Furthermore, we used siRNA to knock down KLF4 in H9c2 cells and found that the KLF4 knockdown eliminated the inhibition of ferroptosis by ATPR in H9c2 cells. Therefore, ATPR alleviates LPS-induced myocardial injury by inhibiting ferroptosis via the KLF4/p62 axis.

A combination of all-trans retinoic acid derivative and COX-2 inhibitor has anticancer effects in human pharyngeal carcinoma cells.

Backgrounds and aims: Carcinogenesis is characterized by an unlimited growth of cells exacerbated by Cox-2 overexpression. Cox-2 inhibitors have been proven effective in preventing and treating tumors. In our previous studies, we found that 4-Amino-2-Trifluoromethylphenyl Retinate (ATPR) induces cell apoptosis and inhibits cell proliferation to exhibit anti-cancer properties. The use of ATRA as well as Cox-2 inhibitors in clinical settings can cause adverse reactions. It is unknown what the effects and mechanisms of co-administration of ATPR and Cox-2 inhibitors are. Results: A combination of ATPR and Cox-2 inhibitors, Celecoxib, inhibited pharyngeal cancer cell proliferation in vitro and induced apoptosis. The cell cycle was arrested at G0/G1 by activating P53 and CDNA1. By activating MAPK/JNK pathways, ATPR and Celecoxib led to intrinsic and extrinsic apoptosis in pharyngeal cancer cells. ATPR/Celecoxib combined treatment suppressed tumor growth in the pharyngeal cancer cell-derived xenograft mouse model by increasing the number of apoptotic cells. The expression of the RARA and PTGS2 genes was significantly increased in tumor tissue compared to non-tumor tissue in the clinical analysis of the head and neck squamous cell carcinoma dataset. An association was found between this and the level of intrinsic apoptotic signals. Furthermore, a survival analysis conducted over a period of five years indicated that higher levels of RARA expression were associated with a better clinical outcome. Conclusion: ATPR and celecoxib inhibit the proliferation of cancer cells as well as induce apoptosis. Co-administration of ATPR and Cox-2 inhibitors has the potential to be a novel treatment plan for cancer.

Melatonin Potentiates Sensitivity to 5-Fluorouracil in Gastric Cancer Cells by Upregulating Autophagy and Downregulating Myosin Light-Chain Kinase.

5-Fluorouracil is an effective chemotherapeutic drug for gastric cancer. However, the acquisition of chemotherapeutic resistance remains a challenge in treatment. Melatonin can enhance the therapeutic effect of 5-fluorouracil; however, the underlying mechanisms are not well understood. We investigated the effects of combinations of melatonin and 5-fluorouracil on the proliferation, migration and invasion of gastric cancer cells. Melatonin significantly potentiated the 5-fluorouracil-mediated inhibition of proliferation, migration and invasion in gastric cancer cells, which potentiates sensitivity to 5-FU by promoting the activation of Beclin-1-dependent autophagy and targeting the myosin light-chain kinase (MLCK) signaling pathway. Previous studies have shown that autophagy might be associated with the MLCK signaling pathway. The autophagy inhibitor, 3-methyladenine, effectively rescued the migratory and invasive capabilities of gastric cancer cells, while also reducing expression level of MLCK and the phosphorylation level of MLC. This indicates that autophagy is involved in tumor metastasis, which may be related to inhibition of the MLCK signaling pathway. Our findings indicate that melatonin can improve the effectiveness of 5-fluorouracil in gastric cancer and could be used as a supplemental agent in the treatment of gastric cancer with 5-fluorouracil.

Effects of long-term fertilization patterns on bacterial community structure and soil nutrients in dryland of yellow soil.

Understanding the responses of soil bacterial community to long-term fertilization in dryland of yellow soil could provide theoretical basis for establishing scientific fertilization system and cultivating healthy soil. Based on a 25-year long-term fertilization experiment on yellow soil, we collected soil samples from 0-20 cm layer under different fertilization treatments: no fertilization (CK), balanced application of N, P and K fertilizers (NPK), single application of organic fertilizer (M), combined application of constant organic and inorganic fertilizer (MNPK), and 1/2 organic fertilizer instead of 1/2 chemical fertilizer (MNP). Illumina MiSeq high-throughput sequencing technology was used to examine the effects of different fertilization patterns on soil bacterial community structure and soil nutrient content. The main driving factors of soil bacterial community were explored. The results showed that soil pH and organic matter content under treatments with organic fertilizer increased by 11.4%-13.5% and 28.8%-52.0%, respectively, compared to that under NPK treatment. Long-term fertilization did not affect soil bacterial α diversity, but significantly affected soil bacterial ß diversity. Compared with CK and NPK treatment, treatments of M, MNP, and MNPK significantly changed soil bacterial community structure, and increased the relative abundance of Fusobacteria and Anaerobes. Four fertilization treatments increased the relative abundance of Bacteroidetes, and decreased the relative abundance of Actinomyces and Campylobacter, compared to CK. Soil pH was the most important factor affecting soil bacterial community structure. Fertilization-stimulated rare microbial taxa (Pumilomyces and Anaerobes) were more sensitive to changes in different environmental factors and were the main drivers of the formation of community versatility. In conclusion, organic fertilizer improved soil properties and fertility and changed soil bacterial community structure, which are conducive to cultivating healthy soil.

Extraction of DNA from trace forensic samples with a modified lysis buffer and chitosan coated magnetic beads.

The trace amounts of human tissue cells or body fluids left at the crime scene are often mixed with inhibitors such as rust, pigments, and humic acid. The extraction of the DNA from the trace cells is crucial for the investigation of cases. Usually, specially designed magnetic nanoparticles were chosen by the case investigators to enrich and elute DNA, which was then used for polymerase chain reaction (PCR) and short tandem repeat (STR) analysis. The traditional approach often had the following problems, such as low DNA enrichment efficiency, possible DNA breakage, and complex operations. Here, the 1%(w/v) of chitosan (75% deacetylation degree) was used to modify the 50 nm magnetic nanoparticles to gain the Chitosan@Beads, which theoretically carried positively charged in the pH = 5 of lysis buffer so as to adsorb negatively charged DNA through electrostatic interactions. The XPS and FT-IR results demonstrated that chitosan was successfully attached to the surface of magnetic nanoparticles. A set of simulated samples, containing 20 mg/µL of humic acid, pigments, iron ions (Fe2+, Fe3+), and the coexistence of the above three substances, were prepared to simulate the case scene. Human bronchial epithelial cells were mixed with the 200 µL of the above simulated samples for DNA extraction. 400 µL of lysis buffer, 20 µL of proteinase K (10 mg/mL) and 20 µL of Chitosan@Beads solution (20 mg/mL) was used for cell disruption and DNA enrichment. The extraction sensitivity of Chitosan@Beads was confirmed to be 10 cells, superior to commercial reagent kits. The Chitosan@Beads@DNA can directly use for "In-situ PCR" with elution-free operations. The STR loci rate of DNA extracted by Chitosan@Beads was around 97.9%, higher than the commercial kit (66.7%). In short, we foresee here developed novel Chitosan@Beads and modified lysis buffer could provide a new model for the DNA extraction of forensic trace evidence.

MiR-29c alleviates hyperglycemia-induced inflammation via targeting TGF-ß in cardiomyocytes.

This study aims to investigate whether miR-29c is involved in regulating transforming growth factor-ß (TGF-ß) mediated inflammation in diabetic cardiomyopathy (DCM). Our data showed increased inflammation and oxidative stress in diabetic myocardium together with decrease of miR-29c and elevation of TGF-ß expression. In vitro experiments, we transfected miR-29c mimic and antagomir into HL-1 cells to explore the effect of miR-29c on inflammation in hyperglycemic conditions. Overexpression of miR-29c down-regulated the elevated TNF-α level, ROS production and NADPH oxidase activity which caused by high glucose. However, above changes were reversed by miR-29c antagomir. Interestingly, TGF-ß protein rather than mRNA expression was changed significantly after transfection with miR-29c mimic, indicating that the modulation of TGF-ß mediated by miR-29c was at the posttranslational level. Meanwhile, we found that 3'-UTR of TGF-ß was the direct target of miR-29c confirmed by dual-luciferase assay. In conclusion, our study revealed that miR-29c could alleviate hyperglycemic-induced inflammation and ROS production via targeting TGF-ß in cardiomyocytes, which provides a potential target for the treatment of DCM.

Influence of long-term fertilization on soil aggregates stability and organic carbon occurrence characteristics in karst yellow soil of Southwest China.

Current research has long focused on soil organic carbon and soil aggregates stability. However, the effects of different long-term fertilization on the composition of yellow soil aggregates and the characteristics of the occurrence of organic carbon in the karst region of Southwest China are still unclear. Based on a 25-year long-term located experiment on yellow soil, soil samples from the 0-20 cm soil layer were collected and treated with different fertilizers (CK: unfertilized control; NPK: chemical fertilizer; 1/4 M + 3/4 NP: 25% chemical fertilizer replaced by 25% organic fertilizer; 1/2 M + 1/2 NP: 50% chemical fertilizer replaced by organic fertilizer; and M: organic fertilizer). In water-stable aggregates, soil aggregates stability, total organic carbon (TOC), easily oxidized organic carbon (EOC), carbon preservation capacity (CPC), and carbon pool management index (CPMI) were analyzed. The findings demonstrated that the order of the average weight diameter (MWD), geometric mean diameter (GWD), and macro-aggregate content (R0.25) of stable water aggregates was M > CK > 1/2M +1/2NP > 1/4M +3/4NP> NPK. The MWD, GWD, and R0.25 of NPK treatment significantly decreased by 32.6%, 43.2%, and 7.0 percentage points, respectively, compared to CK treatment. The order of TOC and EOC content in aggregates of different particle sizes was M > 1/2M +1/2NP > 1/4M +3/4NP> CK > NPK, and it increased as the rate of organic fertilizer increased. In macro-aggregates and bulk soil, the CPC of TOC (TOPC) and EOC (EOPC), as well as CPMI, were arranged as M > 1/2M +1/2NP > 1/4M +3/4NP> CK > NPK, but the opposite was true for micro-aggregates. In bulk soil treated with organic fertilizer, the TOPC, EOPC, and CPMI significantly increased by 27.4%-53.8%, 29.7%-78.1%, 29.7-82.2 percentage points, respectively, compared to NPK treatment. Redundancy analysis and stepwise regression analysis show that TOC was the main physical and chemical factor affecting the aggregates stability, and the TOPC in micro-aggregates has the most direct impact. In conclusion, the primary cause of the decrease in SOC caused by the long-term application of chemical fertilizer was the loss of organic carbon in macro-aggregates. An essential method to increase soil nutrient supply and improve yellow soil productivity was to apply an organic fertilizer to increase aggregates stability, storage and activity of SOC in macro-aggregates.

Selective conversion of CO2 to isobutane-enriched C4 alkanes over InZrOx-Beta composite catalyst.

Direct conversion of CO2 to a single specific hydrocarbon with high selectivity is extremely attractive but very challenging. Herein, by employing an InZrOx-Beta composite catalyst in the CO2 hydrogenation, a high selectivity of 53.4% to butane is achieved in hydrocarbons (CO free) under 315 °C and 3.0 MPa, at a CO2 conversion of 20.4%. Various characterizations and DFT calculation reveal that the generation of methanol-related intermediates by CO2 hydrogenation is closely related to the surface oxygen vacancies of InZrOx, which can be tuned through modulating the preparation methods. In contrast, the three-dimensional 12-ring channels of H-Beta conduces to forming higher methylbenzenes and methylnaphthalenes containing isopropyl side-chain, which favors the transformation of methanol-related intermediates to butane through alkyl side-chain elimination and subsequent methylation and hydrogenation. Moreover, the catalytic stability of InZrOx-Beta in the CO2 hydrogenation is considerably improved by a surface silica protection strategy which can effectively inhibit the indium migration.

USP9X expression is functionally related to laryngeal cancer.

An increasing number of studies have shown that USP9X is closely related to cancer. However, its role in carcinogenesis and progression of laryngeal cancer has not yet been investigated. In this study, we found that USP9X was upregulated in laryngeal cancer tissues. The expression of USP9X was significantly correlated with degree of laryngeal cancer differentiation and lymphatic metastasis. USP9X knockdown led to a decrease in the ability of proliferation, migration, and invasion of FaDu cells. The proportion of FaDu apoptotic cells increased by interfering with the endogenous expression of USP9X. We speculated that inhibiting USP9X might induce apoptosis in FaDu cells by downregulating Mcl-1 and upregulating Bax protein expression. Our findings for the first time suggest the expression level and trend of USP9X in laryngeal cancer tissue and USP9X may plays an important role in promoting the occurrence and progression of laryngeal cancer. USP9X may be a potential target for intervention in treatment of laryngeal cancer.

Melatonin Alleviates Hyperglycemia-Induced Cardiomyocyte Apoptosis via Regulation of Long Non-Coding RNA H19/miR-29c/MAPK Axis in Diabetic Cardiomyopathy.

Recent studies revealed that non-coding RNAs (ncRNAs) play a crucial role in pathophysiological processes involved in diabetic cardiomyopathy (DCM) that contribute to heart failure. The present study was designed to further investigate the anti-apoptotic effect of melatonin on cardiomyocytes in diabetic conditions, and to elucidate the potential mechanisms associated with ncRNAs. In animal models, we induced diabetes in SD rats by single intraperitoneal injection of streptozotocin (STZ) solution (55 mg/kg) at 18:00 in the evening, after a week of adaptive feeding. Our results indicate that melatonin notably alleviated cardiac dysfunction and cardiomyocyte apoptosis. In the pathological situation, lncRNA H19 level increased, along with a concomitant decrease in miR-29c level. Meanwhile, melatonin significantly downregulated lncRNA H19 and upregulated miR-29c levels. In our in vitro experiments, we treated H9c2 cells with high-concentration glucose medium (33 mM) to simulate the state of diabetes. It was verified that positive modulation of miR-29c and inhibition of lncRNA H19, as well as mitogen-activated protein kinase (MAPK) pathways, distinctly attenuated apoptosis in high-glucose-treated H9c2 cells. A luciferase activity assay was conducted to evaluate the potential target sites of miR-29c on lncRNA H19 and MAPK13. LncRNA H19 silencing significantly downregulated the expression of miR-29c target gene MAPK13 by inducing miR-29c expression. Most importantly, our results show that melatonin alleviated apoptosis by inhibiting lncRNA H19/MAPK and increasing miR-29c level. Our results elucidate a novel protective mechanism of melatonin on diabetic cardiomyocyte apoptosis, which involved the regulation of lncRNA H19/miR-29c and MAPK pathways, providing a promising strategy for preventing DCM in diabetic patients.

Genome-Wide Association Analysis Reveals the Genetic Basis of Iron-Deficiency Stress Tolerance in Maize.

Iron (Fe) is an essential trace element for almost all organisms and is often the major limiting nutrient for normal growth. Fe deficiency is a worldwide agricultural problem, which affects crop productivity and product quality. Understanding the Fe-deficiency response in plants is necessary for improving both plant health and the human diet. In this study, Fe-efficient (Ye478) and Fe-inefficient maize inbred lines (Wu312) were used to identify the genotypic difference in response to low Fe stress during different developmental stages and to further determine the optimal Fe-deficient Fe(II) supply level which leads to the largest phenotypic difference between Ye478 and Wu312. Then, genome-wide association analysis was performed to further identify candidate genes associated with the molecular mechanisms under different Fe nutritional statuses. Three candidate genes involved in Fe homeostasis of strategy II plants (strategy II genes) were identified, including ZmDMAS1, ZmNAAT1, and ZmYSL11. Furthermore, candidate genes ZmNAAT1, ZmDMAS1, and ZmYSL11 were induced in Fe-deficient roots and shoots, and the expression of ZmNAAT1 and ZmDMAS1 responded to Fe deficiency more in shoots than in roots. Beyond that, several genes that may participate in Fe homeostasis of strategy I plants (strategy I genes) were identified, which were either encoding Fe transporters (ZmIRT1 and ZmZIP4), or acting as essential ethylene signal transducers (ZmEBF1). Interestingly, ZmIRT1, ZmZIP4, and ZmEBF1 were significantly upregulated under low Fe stress, suggesting that these genes may be involved in Fe-deficiency tolerance in maize which is considered as strategy II plant. This study demonstrates the use of natural variation in the association population to identify important genes associated with Fe-deficiency tolerance and may further provide insights for understanding the molecular mechanism underlying the tolerance to Fe-deficiency stress in maize.

Mapping of the Quantitative Trait Loci and Candidate Genes Associated With Iron Efficiency in Maize.

Iron (Fe) is a mineral micronutrient for plants, and Fe deficiency is a major abiotic stress in crop production because of its low solubility under aerobic and alkaline conditions. In this study, 18 maize inbred lines were used to preliminarily illustrate the physiological mechanism underlying Fe deficiency tolerance. Then biparental linkage analysis was performed to identify the quantitative trait loci (QTLs) and candidate genes associated with Fe deficiency tolerance using the recombinant inbred line (RIL) population derived from the most Fe-efficient (Ye478) and Fe-inefficient (Wu312) inbred lines. A total of 24 QTLs was identified under different Fe nutritional status in the Ye478 × Wu312 RIL population, explaining 6.1-26.6% of phenotypic variation, and ten candidate genes were identified. Plants have evolved two distinct mechanisms to solubilize and transport Fe to acclimate to Fe deficiency, including reduction-based strategy (strategy I) and chelation-based strategy (strategy II), and maize uses strategy II. However, not only genes involved in Fe homeostasis verified in strategy II plants (strategy II genes), which included ZmYS1, ZmYS3, and ZmTOM2, but also several genes associated with Fe homeostasis in strategy I plants (strategy I genes) were identified, including ZmFIT, ZmPYE, ZmILR3, ZmBTS, and ZmEIN2. Furthermore, strategy II gene ZmYS1 and strategy I gene ZmBTS were significantly upregulated in the Fe-deficient roots and shoots of maize inbred lines, and responded to Fe deficiency more in shoots than in roots. Under Fe deficiency, greater upregulations of ZmYS1 and ZmBTS were observed in Fe-efficient parent Ye478, not in Fe-inefficient parent Wu312. Beyond that, ZmEIN2 and ZmILR3, were found to be Fe deficiency-inducible in the shoots. These findings indicate that these candidate genes may be associated with Fe deficiency tolerance in maize. This study demonstrates the use of natural variation to identify important Fe deficiency-regulated genes and provides further insights for understanding the response to Fe deficiency stress in maize.

Association of Blood Glucose Level and Glycemic Variability With Mortality in Sepsis Patients During ICU Hospitalization.

Background: There was considerable debate regarding the effect of mean blood glucose (MBG) and glycemic variability (GV) on the mortality of septic patients. This retrospective cohort study aimed to assess the association between MBG and GV with ICU mortality of sepsis patients and to explore the optimal MBG range. Methods: Sepsis patients were enrolled from the Medical Information Mart for Intensive Care IV database (MIMIC-IV). MBG and glycemic coefficient of variation (GluCV) were, respectively, calculated to represent the overall glycemic status and GV during ICU stay. The associations between MBG, GluCV, and ICU mortality of the septic patients were assessed by using multivariate logistic regression in different subgroups and the severity of sepsis. Restricted cubic splines evaluated the optimal MBG target. Results: A total of 7,104 adult sepsis patients were included. The multivariate logistic regression results showed that increased MBG and GluCV were significantly correlated with ICU mortality. The adjusted odds ratios were 1.14 (95% CI 1.09-1.20) and 1.05 (95% CI 1.00-1.12). However, there was no association between hyperglycemia and ICU mortality among diabetes, liver disease, immunosuppression, and hypoglycemia patients. And the impact of high GluCV on ICU mortality was not observed in those with diabetes, immunosuppression, liver disease, and non-septic shock. The ICU mortality risk of severe hyperglycemia (≧200 mg/dl) and high GluCV (>31.429%), respectively, elevated 2.30, 3.15, 3.06, and 2.37, 2.79, 3.14-folds in mild (SOFA ≦ 3), middle (SOFA 3-7), and severe group (SOFA ≧ 7). The MBG level was associated with the lowest risk of ICU mortality and hypoglycemia between 120 and 140 mg/dl in the subgroup without diabetes. For the diabetic subset, the incidence of hypoglycemia was significantly reduced when the MBG was 140-190 mg/dl, but a glycemic control target effectively reducing ICU mortality was not observed. Conclusion: MBG and GluCV during the ICU stay were associated with all-cause ICU mortality in sepsis patients; however, their harms are not apparent in some particular subgroups. The impact of hyperglycemia and high GV on death increased with the severity of sepsis. The risk of ICU mortality and hypoglycemia in those with no pre-existing diabetes was lower when maintaining the MBG in the range of 120-140 mg/dl.

Identification of Quantitative Trait Loci Associated With Iron Deficiency Tolerance in Maize.

Iron (Fe) is a limiting factor in crop growth and nutritional quality because of its low solubility. However, the current understanding of how major crops respond to Fe deficiency and the genetic basis remains limited. In the present study, Fe-efficient inbred line Ye478 and Fe-inefficient inbred line Wu312 and their recombinant inbred line (RIL) population were utilized to reveal the physiological and genetic responses of maize to low Fe stress. Compared with the Fe-sufficient conditions (+Fe: 200 µM), Fe-deficient supply (-Fe: 30 µM) significantly reduced shoot and root dry weights, leaf SPAD of Fe-efficient inbred line Ye478 by 31.4, 31.8, and 46.0%, respectively; decreased Fe-inefficient inbred line Wu312 by 72.0, 45.1, and 84.1%, respectively. Under Fe deficiency, compared with the supply of calcium nitrate (N1), supplying ammonium nitrate (N2) significantly increased the shoot and root dry weights of Wu312 by 37.5 and 51.6%, respectively; and enhanced Ye478 by 23.9 and 45.1%, respectively. Compared with N1, N2 resulted in a 70.0% decrease of the root Fe concentration for Wu312 in the -Fe treatment, N2 treatment reduced the root Fe concentration of Ye478 by 55.8% in the -Fe treatment. These findings indicated that, compared with only supplying nitrate nitrogen, combined supply of ammonium nitrogen and nitrate nitrogen not only contributed to better growth in maize but also significantly reduced Fe concentration in roots. In linkage analysis, ten quantitative trait loci (QTLs) associated with Fe deficiency tolerance were detected, explaining 6.2-12.0% of phenotypic variation. Candidate genes considered to be associated with the mechanisms underlying Fe deficiency tolerance were identified within a single locus or QTL co-localization, including ZmYS3, ZmPYE, ZmEIL3, ZmMYB153, ZmILR3 and ZmNAS4, which may form a sophisticated network to regulate the uptake, transport and redistribution of Fe. Furthermore, ZmYS3 was highly induced by Fe deficiency in the roots; ZmPYE and ZmEIL3, which may be involved in Fe homeostasis in strategy I plants, were significantly upregulated in the shoots and roots under low Fe stress; ZmMYB153 was Fe-deficiency inducible in the shoots. Our findings will provide a comprehensive insight into the physiological and genetic basis of Fe deficiency tolerance.