Multiple indicators of gut dysbiosis predict all-cause and cause-specific mortality in solid organ transplant recipients.

OBJECTIVE: Gut microbiome composition is associated with multiple diseases, but relatively little is known about its relationship with long-term outcome measures. While gut dysbiosis has been linked to mortality risk in the general population, the relationship with overall survival in specific diseases has not been extensively studied. In the current study, we present results from an in-depth analysis of the relationship between gut dysbiosis and all-cause and cause-specific mortality in the setting of solid organ transplant recipients (SOTR). DESIGN: We analysed 1337 metagenomes derived from faecal samples of 766 kidney, 334 liver, 170 lung and 67 heart transplant recipients part of the TransplantLines Biobank and Cohort-a prospective cohort study including extensive phenotype data with 6.5 years of follow-up. To analyze gut dysbiosis, we included an additional 8208 metagenomes from the general population of the same geographical area (northern Netherlands). Multivariable Cox regression and a machine learning algorithm were used to analyse the association between multiple indicators of gut dysbiosis, including individual species abundances, and all-cause and cause-specific mortality. RESULTS: We identified two patterns representing overall microbiome community variation that were associated with both all-cause and cause-specific mortality. The gut microbiome distance between each transplantation recipient to the average of the general population was associated with all-cause mortality and death from infection, malignancy and cardiovascular disease. A multivariable Cox regression on individual species abundances identified 23 bacterial species that were associated with all-cause mortality, and by applying a machine learning algorithm, we identified a balance (a type of log-ratio) consisting of 19 out of the 23 species that were associated with all-cause mortality. CONCLUSION: Gut dysbiosis is consistently associated with mortality in SOTR. Our results support the observations that gut dysbiosis is associated with long-term survival. Since our data do not allow us to infer causality, more preclinical research is needed to understand mechanisms before we can determine whether gut microbiome-directed therapies may be designed to improve long-term outcomes.

Enhanced breakage of the aggregates of nanoscale zero-valent iron via ball milling.

Aggregates of nanoscale zero-valent iron (nZVI) are commonly encountered for nZVI in aqueous solution, particularly during large-scale nZVI applications where nZVI is often in a highly concentrated slurry, and such aggregates lower nZVI mobility during its in-situ remediation applications. Herein, we report that the ball milling is an effective tool to break the nZVI aggregates and thereby improve the nZVI mobility. Results show that the milling (in just five minutes) can break the aggregates of a few tens of microns to less than one micron, which is one-tenth of the size that is acquired via the breakage using the mechanical mixing and ultrasonication. The milling breakage can also improve the efficacy of the chemical conditioning method that is commonly used for the nanoparticle stabilization and dispersion. The milling breakage is further optimized via a study of the milling operational factors including milling time, bead velocity, bead diameter, and chamber porosity, and an empirical equation is proposed combining the bead collision number during the milling. Mechanistic study shows that the high efficacy of the milling to break the aggregates can be explained by the small eddy created by the high shear rate produced by the close contact of the milling beads and may also relate to the direct mechanical pulverization effect. This study provides a high efficacy physical method to break the nanoparticle aggregates. The method can be used to improve the nZVI mobility performance by milling the nZVI slurry before its injection for in-situ remediation, and the milling may also replace the mechanical mixing during the nZVI stabilization via surface modification.

In-situ treatment of gaseous benzene in fixed-bed biofilter with polyurethane foam: Functional population response and benzene transformation pathway.

Volatile organic compounds emitted from landfills posed adverse effect on health. In this study, gaseous benzene was biologically treated using an in-situ biofilter without air pump. Its performance was investigated and the removal efficiency of benzene reached over 90 %. The decrease in the average benzene concentration was consistent with first-order reaction kinetics. Mycolicibacterium dominated the bacterial consortium (41-57 %) throughout the degradation. Annotation of genes by metagenomic analysis helped to deduce the degradation pathways (benzene degradation, catechol ortho-cleavage and meta-cleavage) and to reveal the contribution of different species to the degradation process. In total, 21 kinds of key genes and 13 enzymes were involved in the three modules of benzene transformation. Mycolicibacter icosiumassiliensis and Sphingobium sp. SCG-1 carried multiple functional genes critically involved in benzene biodegradation. These findings provide technical and theoretical support for the in-situ bioremediation of benzene-contaminated soil and waste gas reduction in landfills.

Ryegrass uptake behavior and forage risk assessment after exposing to soil with combined polycyclic aromatic hydrocarbons and cadmium.

Internalization of chemicals and the forage risks of ryegrass under the combined exposure to PAHs and Cd at environmental concentrations were studied here. The effect of soil pH was also concerned due to the widely occurred soil acidification and general alkali remediation for acidification soil. Unexpectedly, as same as the acid-treated group (pH 6.77), the alkali-treatment (pH 8.83) increased Cd uptake compared with original soil pH group (pH 7.92) for the reason of CdOH+ and CdHCO3+ formed in alkali-treated group. Co-exposure to PAHs induced more oxidative stress than Cd exposure alone due to PAHs aggregated in young root regions, such as root tips, and consequently, affecting the expression of Cd-transporters, destroying the basic structure of plant cells, inhibiting the energy supply for the transporters, even triggering programmed cell death, and finally resulting in decreased Cd uptake. Even under environmental concentrations, combined exposure caused potential risks derived from both PAHs and Cd. Especially, ryegrass grown in alkali-treated soil experienced an increased forage risks despite the soil meeting the national standards for Cd at safe levels. These comprehensive results reveal the mechanism of PAHs inhibiting Cd uptake, improve the understanding of bioavailability of Cd based on different forms, provide a theoretical basis to formulate the safety criteria, and guide the application of actual soil management.

The Effects of Heat Treatment on the Microstructure and Mechanical Properties of a Selective Laser Melted AlCoFeNi Medium-Entropy Alloy.

A single body-centered cubic (BCC)-structured AlCoFeNi medium-entropy alloy (MEA) was prepared by the selective laser melting (SLM) technique. The hardness of the as-built sample was around 32.5 HRC. The ultimate tensile strength (UTS) was around 1211 MPa, the yield strength (YS) was around 1023 MPa, and the elongation (El) was around 10.8%. A novel BCC + B2 + face-centered cubic (FCC) structure was formed after aging. With an increase in aging temperature and duration, the number of fine grains increased, and more precipitates were observed. After aging at 450 °C for 4 h, the formed complex polyphase structure significantly improved the mechanical properties. Its hardness, UTS, YS, and El were around 45.7 HRC, 1535 MPa, 1489 MPa, and 8.5%, respectively. The improvement in mechanical properties was mainly due to Hall-Petch strengthening, which was caused by fine grains, and precipitation strengthening, which was caused by an increase in precipitates after aging. Meanwhile, the FCC precipitates made the alloy have good toughness. The complex interaction of multiple strengthening mechanisms leads to a good combination of strength, hardness, and toughness.

Comprehensive assessment of regulatory T-cells-related scoring system for predicting the prognosis, immune microenvironment and therapeutic response in hepatocellular carcinoma.

INTRODUCTION: Regulatory T cells (Tregs) play important roles in tumor immunosuppression and immune escape. The aim of the present study was to construct a novel Tregs-associated biomarker for the prediction of tumour immune microenvironment (TIME), clinical outcomes, and individualised treatment in hepatocellular carcinoma (HCC). METHODS: Single-cell sequencing data were obtained from the three independent cohorts. Cox and LASSO regression were utilised to develop the Tregs Related Scoring System (TRSSys). GSE140520, ICGC-LIRI and CHCC cohorts were used for the validation of TRSSys. Kaplan-Meier, ROC, and Cox regression were utilised for the evaluation of TRSSys. The ESTIMATE, TIMER 2.0, and ssGSEA algorithm were utilised to determine the value of TRSSys in predicting the TIME. GSVA, GO, KEGG, and TMB analyses were used for mechanistic exploration. Finally, the value of TRSSys in predicting drug sensitivity was evaluated based on the oncoPredict algorithm. RESULTS: Comprehensive validation showed that TRSSys had good prognostic predictive efficacy and applicability. Additionally, ssGSEA, TIMER and ESTIMATE algorithm suggested that TRSSys could help to distinguish different TIME subtypes and determine the beneficiary population of immunotherapy. Finally, the oncoPredict algorithm suggests that TRSSys provides a basis for individualised treatment. CONCLUSIONS: TRSSys constructed in the current study is a novel HCC prognostic prediction biomarker with good predictive efficacy and stability. Additionally, risk stratification based on TRSSys can help to identify the TIME landscape subtypes and provide a basis for individualized treatment options.

[Retracted] microRNA­146b promotes neuroblastoma cell growth through targeting NUMB.

[This retracts the article DOI: 10.3892/etm.2020.8623.].

Characterization of Fatigue Damage in Hadfield Steel Using Acoustic Emission and Machine Learning-Based Methods.

Structural health monitoring (SHM) of fatigue cracks is essential for ensuring the safe operation of engineering equipment. The acoustic emission (AE) technique is one of the SHM techniques that is capable of monitoring fatigue-crack growth (FCG) in real time. In this study, fatigue-damage evolution of Hadfield steel was characterized using acoustic emission (AE) and machine learning-based methods. The AE signals generated from the entire fatigue-load process were acquired and correlated with fatigue-damage evolution. The AE-source mechanisms were discussed based on waveform characteristics and bispectrum analysis. Moreover, multiple machine learning algorithms were used to classify fatigue sub-stages, and the results show the effectiveness of classification of fatigue sub-stages using machine learning algorithms. The novelty of this research lies in the use of machine learning algorithms for the classification of fatigue sub-stages, unlike the existing methodology, which requires prior knowledge of AE-loading history and calculation of ∆K.

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis.

Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.

A pilot study on non-invasive in situ detection of phytochemicals and plant endogenous status using fiber optic infrared spectroscopy.

Traditional methods for assessing plant health often lack the necessary attributes for continuous and non-destructive monitoring. In this pilot study, we present a novel technique utilizing a customized fiber optic probe based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) with a contact force control unit for non-invasive and continuous plant health monitoring. We also developed a normalized difference mid-infrared reflectance index through statistical analysis of spectral features, enabling differentiation of drought and age conditions in plants. Our research aims to characterize phytochemicals and plant endogenous status optically, addressing the need for improved analytical measurement methods for in situ plant health assessment. The probe configuration was optimized with a triple-loop tip and a 3 N contact force, allowing sensitive measurements while minimizing leaf damage. By combining polycrystalline and chalcogenide fiber probes, a comprehensive wavenumber range analysis (4000-900 cm-1) was achieved. Results revealed significant variations in phytochemical composition among plant species, for example, red spinach with the highest polyphenolic content and green kale with the highest lignin content. Petioles displayed higher lignin and cellulose absorbance values compared to veins. The technique effectively monitored drought stress on potted green bok choy plants in situ, facilitating the quantification of changes in water content, antioxidant activity, lignin, and cellulose levels. This research represents the first demonstration of the potential of fiber optic ATR-FTIR probes for non-invasive and rapid plant health measurements, providing insights into plant health and advancements in quantitative monitoring for indoor farming practices, bioanalytical chemistry, and environmental sciences.

Acoustic Emission Monitoring of Fatigue Crack Growth in Hadfield Steel.

Evaluating the condition of a Hadfield steel crossing nose using existing inspection methods is subject to accessibility and geographical constraints. Thus, the use of conditional monitoring techniques to complement the existing inspection methods has become increasingly necessary. This paper focuses on the study of acoustic emission (AE) behaviour and its correlation with fatigue crack growth in Hadfield steel during bending fatigue tests. The probability density function for acoustic emission parameters was analysed based on the power law distribution. The results show that a sharp increase in the moving average and cumulative sum of the AE parameter can give early warning against the final failure of Hadfield steel. Two parts (Part 1 and Part 2) can be identified using the change in the slope of duration rate (dD/dN) vs. ΔK plot during the stable fatigue crack growth (FCG) process where Paris's law is valid. The fitted power law exponent of AE parameters is smaller in Part 2 than in Part 1. The novelty of this research lies in the use of the fitted power law distribution of AE parameters for monitoring fatigue damage evolution in Hadfield steel, unlike existing AE fatigue monitoring methodology, which relies solely on the analysis of AE parameter trends.

Fiber optic probe-based ATR-FTIR spectroscopy for rapid breast cancer detection: A pilot study.

Breast cancer diagnosis is crucial for timely treatment and improved outcomes. This paper proposes a novel approach for rapid breast cancer diagnosis using optical fiber probe-based attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy from 750 to 4000 cm-1 . The technique enables direct analysis of tissue samples, eliminating the need for microtome sectioning and staining, thus saving time and resources. By capturing molecular fingerprint information, various machine-learning models were used to analyze the spectroscopic data to classify cancerous and non-cancerous tissues accurately. Comparing deparaffinized and paraffinized samples reveals the impact of sample preparation and experimental methods. The study demonstrates a strong correlation between the cancerous nature of a sample and its ATR-FTIR spectrum, suggesting its potential for breast cancer diagnosis (sensitivity of 74.2% and specificity of 78.3%). The proposed approach holds promise for integration into clinical operations, providing a rapid method for preliminary breast cancer diagnosis.

Lipidomic Profiles of Lipid Biosynthesis in Oil Palm during Fruit Development.

The fruit of the oil palm (Elaeis guineensis Jacq.) has fleshy mesocarpic tissue rich in lipids. This edible vegetable oil is economically and nutritionally significant across the world. The core concepts of oil biosynthesis in oil palms remain to be researched as the knowledge of oil biosynthesis in plants improves. In this study, we utilized a metabolite approach and mass spectral analysis to characterize metabolite changes and identify the sequences of protein accumulation during the physiological processes that regulate oil synthesis during oil palm fruit ripening. Here, we performed a comprehensive lipidomic data analysis in order to understand the role of lipid metabolism in oil biosynthesis mechanisms. The experimental materials were collected from the mesocarp of oil palm (Tenera) at 95 days (early accumulation of fatty acid, first stage), 125 days (rapid growth of fatty acid accumulation, second stage), and 185 days (stable period of fatty acid accumulation, third stage) after pollination. To gain a clear understanding of the lipid changes that occurred during the growth of the oil palm, the metabolome data were found using principal component analysis (PCA). Furthermore, the accumulations of diacylglycerols, ceramides, phosphatidylethanolamine, and phosphatidic acid varied between the developmental stages. Differentially expressed lipids were successfully identified and functionally classified using KEGG analysis. Proteins related to the metabolic pathway, glycerolipid metabolism, and glycerphospholipid metabolism were the most significantly changed proteins during fruit development. In this study, LC-MS analysis and evaluation of the lipid profile in different stages of oil palm were performed to gain insight into the regulatory mechanisms that enhance fruit quality and govern differences in lipid composition and biosynthesis.

Anxiety, depression, and quality of life in postoperative non-small cell lung cancer patients under the intervention of cognitive-behavioral stress management.

Objective: Cognitive-behavioral stress management (CBSM) is a psychotherapy that helps patients cognize and manage stress to improve mental health and quality of life. This study aimed to explore the influence of CBSM on anxiety, depression, and quality of life in non-small cell lung cancer (NSCLC) patients. Methods: In total, 172 NSCLC patients who received tumor resection were randomized 1:1 into the usual care (UC) group (N = 86) and CBSM group (N = 86) to receive 10-week UC and CBSM interventions. Moreover, all participants attended a 6-month follow-up. Results: Hospital Anxiety and Depression Scales (HADS)-anxiety score at 3rd month (M3) (P = 0.015) and 6th month (M6) (P = 0.018), HADS-depression score at M3 (P = 0.040) and M6 (P = 0.028), and depression rate at M6 (P = 0.035) were descended in CBSM group compared to UC group. Besides, depression severity was reduced at M6 (P = 0.041) in CBSM group compared to UC group, but anxiety severity only showed a decreased trend (P = 0.051). Additionally, Quality of Life Questionnaire-Core 30 (QLQ-C30) global health status score and QLQ-C30 function score at 1st month (M1), M3, and M6 were elevated (all P < 0.05), while QLQ-C30 symptoms score was declined at M1 (P = 0.031) and M3 (P = 0.014) in CBSM group compared to UC group. Notably, the efficacy of CBSM was impressive in patients with baseline depression or undergoing adjuvant therapy. Conclusion: CBSM is a feasible intervention that effectively improves mental health and quality of life in postoperative NSCLC patients.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review.

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Label-free detection of MiRNA biomarkers using broadband multi-resonant infrared metasurfaces for early breast cancer diagnosis.

Breast cancer is the most prevalent cancer globally. Early detection is crucial and can be achieved by detecting cancer biomarkers in blood, such as circulating miRNAs (microRNAs). In this study, we present a label-free detection method based on broadband multi-resonant infrared metasurface for surface-enhanced infrared absorption (SEIRA) spectroscopy to detect miRNAs. The SEIRA resonances were optimized to match the miRNA biomarker fingerprint regions in the range of 800 to 2000 cm-1 and 2800 to 3500 cm-1, resulting in a simulated resonance enhancement of up to 103 times. Nine patient samples (six cancerous and three non-cancerous) were measured using SEIRA multi-well sensor chips. A novel analysis method, SEIRA-AR, was also developed to benchmark the results against industry standards, such as quantitative reverse transcription polymerase chain reaction (RT-qPCR) and next-generation sequencing (NGS). Our results showed an excellent linear correlation with a Pearson's r value of up to 0.99 and an R Squared value of up to 0.98. This study represents the first use of a SEIRA sensor for biomarker detection on clinical breast cancer samples and introduces an analysis method that produces results comparable to industry standards. Our findings pave the way for routine cancer diagnosis in the future. Additionally, the method discussed can be generalized to other biosensing activities involving two-step binding processes with complementary molecule-capturing agents.

Redox ferrocenylseleno compounds modulate longitudinal and transverse relaxation times of FNPs-Gd MRI contrast agents for multimodal imaging and photo-Fenton therapy.

Developing a feasible way to feature longitudinal (T1) and transverse (T2) relaxation performance of contrast agents for magnetic resonance imaging (MRI) is important in cancer diagnosis and therapy. Improved accessibility to water molecule is essential for accelerating the relaxation rate of water protons around the contrast agents. Ferrocenyl compounds have reversible redox property for modulating the hydrophobicity/hydrophilicity of assemblies. Thus, they could be the candidates that can change water accessibility to the contrast agent surface. Herein, we incorporated ferrocenylseleno compound (FcSe) with Gd3+-based paramagnetic UCNPs, to obtain FNPs-Gd nanocomposites using T1-T2 MR/UCL trimodal imaging and simultaneous photo-Fenton therapy. When the surface of NaGdF4:Yb,Tm UNCPs was ligated by FcSe, the hydrogen bonding between hydrophilic selenium and surrounding water molecules accelerated their proton exchange to initially endow FNPs-Gd with high r1 relaxivity. Then, hydrogen nuclei from FcSe disrupted the homogeneity of the magnetic field around the water molecules. This facilitated T2 relaxation and resulted in enhanced r2 relaxivity. Notably, upon the near-infrared light-promoted Fenton-like reaction in the tumor microenvironment, hydrophobic ferrocene(II) of FcSe was oxidized into hydrophilic ferrocenium(III), which further increased the relaxation rate of water protons to obtain r1 = 1.90±0.12 mM-1 s-1 and r2 = 12.80±0.60 mM-1 s-1. With an ideal relaxivity ratio (r2/r1) of 6.74, FNPs-Gd exhibited high contrast potential of T1-T2 dual-mode MRI in vitro and in vivo. This work confirms that ferrocene and selenium are effective boosters that enhance the T1-T2 relaxivities of MRI contrast agents, which could provide a new strategy for multimodal imaging-guided photo-Fenton therapy of tumors. STATEMENT OF SIGNIFICANCE: T1-T2 dual-mode MRI nanoplatform with tumor-microenvironment-responsive features has been an attractive prospect. Herein, we designed redox ferrocenylseleno compound (FcSe) modified paramagnetic Gd3+-based UCNPs, to modulate T1-T2 relaxation time for multimodal imaging and H2O2-responsive photo-Fenton therapy. Selenium-hydrogen bond of FcSe with surrounding water molecules facilitated water accessibility for fast T1 relaxation. Hydrogen nucleus in FcSe perturbed the phase coherence of water molecules in an inhomogeneous magnetic field and thus accelerated T2 relaxation. In tumor microenvironment, FcSe was oxidized into hydrophilic ferrocenium via NIR light-promoted Fenton-like reaction which further increased both T1 and T2 relaxation rates; Meanwhile, the released toxic •OH performed on-demand cancer therapy. This work confirms that FcSe is an effective redox mediate for multimodal imaging-guided cancer therapy.

Differential analysis of transcriptomic and metabolomic of free fatty acid rancidity process in oil palm (Elaeis guineensis) fruits of different husk types.

Introduction: Oil palm is the world's highest yielding oil crop and its palm oil has high nutritional value, making it an oilseed plant with important economic value and application prospects. After picking, oil palm fruits exposed to air will gradually become soft and accelerate the process of fatty acid rancidity, which will not only affect their flavor and nutritional value, but also produce substances harmful to the human body. As a result, studying the dynamic change pattern of free fatty acids and important fatty acid metabolism-related regulatory genes during oil palm fatty acid rancidity can provide a theoretical basis for improving palm oil quality and extending its shelf life. Methods: The fruit of two shell types of oil palm, Pisifera (MP) and Tenera (MT), were used to study the changes of fruit souring at different times points of postharvesting, combined with LC-MS/MS metabolomics and RNA-seq transcriptomics techniques to analyze the dynamic changes of free fatty acids during fruit rancidity, and to find out the key enzyme genes and proteins in the process of free fatty acid synthesis and degradation according to metabolic pathways. Results and discussion: Metabolomic study revealed that there were 9 different types of free fatty acids at 0 hours of postharvest, 12 different types of free fatty acids at 24 hours of postharvest, and 8 different types of free fatty acids at 36 hours of postharvest. Transcriptomic research revealed substantial changes in gene expression between the three harvest phases of MT and MP. Combined metabolomics and transcriptomics analysis results show that the expression of SDR, FATA, FATB and MFP four key enzyme genes and enzyme proteins in the rancidity of free fatty acids are significantly correlated with Palmitic acid, Stearic acid, Myristic acid and Palmitoleic acid in oil palm fruit. In terms of binding gene expression, the expression of FATA gene and MFP protein in MT and MP was consistent, and both were expressed higher in MP. FATB fluctuates unevenly in MT and MP, with the level of expression growing steadily in MT and decreasing in MP before increasing. The amount of SDR gene expression varies in opposite directions in both shell types. The above findings suggest that these four enzyme genes and enzyme proteins may play an important role in regulating fatty acid rancidity and are the key enzyme genes and enzyme proteins that cause differences in fatty acid rancidity between MT and MP and other fruit shell types. Additionally, differential metabolite and differentially expressed genes were present in the three postharvest times of MT and MP fruits, with the difference occurring 24 hours postharvest being the most notable. As a result, 24 hours postharvest revealed the most obvious difference in fatty acid tranquility between MT and MP shell types of oil palm. The results from this study offer a theoretical underpinning for the gene mining of fatty acid rancidity of various oil palm fruit shell types and the enhancement of oilseed palm acid-resistant germplasm cultivation using molecular biology methods.

Maltol inhibits oxygen glucose deprivation­induced chromatinolysis in SH­SY5Y cells by maintaining pyruvate level.

Maltol, a chemical isolated from ginseng root, has shown treatment effects on several pathological processes including osteoarthritis, diabetic peripheral neuropathy and liver fibrosis. Nevertheless, its effect on ischemia­induced neuron death remains elusive. In the present study, the treatment effect of maltol on ischemia­induced neuron damage was investigated by using oxygen and glucose deprivation (OGD) model in SH­SY5Y cells. In vitro studies revealed that maltol protected SH­SY5Y cells against OGD­induced chromatinolysis by inhibiting two reactive oxygen species (ROS)­regulated pathways. One was DNA double­strand breaks and the other was nuclear translocation of apoptosis inducing factor. Mechanistically, maltol not only inhibited OGD­induced depletion of glutathione and cysteine by maintaining cystine/glutamate antiporter (xCT) level, but also abrogated OGD­induced catalase downregulation. Meanwhile, maltol also alleviated OGD­induced inactivation of mTOR by attenuating OGD­induced depletion of adenosine triphosphate and pyruvate and downregulation of pyruvate kinase M2, indicating that maltol inhibited the glycolysis dysfunction caused by OGD. Considering that activated mammalian target of the rapamycin (mTOR) could lead to enhanced xCT expression and decreased catalase degradation by autophagy, these findings indicated that maltol attenuated OGD­induced ROS via inhibition of mTOR inactivation by maintaining pyruvate level. Taken together, it was demonstrated that maltol prevented OGD­induced chromatinolysis in SH­SY5Y cells via inhibiting pyruvate depletion.

Construction and validation of a novel and superior protein risk model for prognosis prediction in esophageal cancer.

Esophageal cancer (EC) is recognized as one of the most common malignant tumors in the word. Based on the biological process of EC occurrence and development, exploring molecular biomarkers can provide a good guidance for predicting the risk, prognosis and treatment response of EC. Proteomics has been widely used as a technology that identifies, analyzes and quantitatively acquires the composition of all proteins in the target tissues. Proteomics characterization applied to construct a prognostic signature will help to explore effective biomarkers and discover new therapeutic targets for EC. This study showed that we established a 8 proteins risk model composed of ASNS, b-Catenin_pT41_S45, ARAF_pS299, SFRP1, Vinculin, MERIT40, BAK and Atg4B via multivariate Cox regression analysis of the proteome data in the Cancer Genome Atlas (TCGA) to predict the prognosis power of EC patients. The risk model had the best discrimination ability and could distinguish patients in the high- and low-risk groups by principal component analysis (PCA) analysis, and the high-risk patients had a poor survival status compared with the low-risk patients. It was confirmed as one independent and superior prognostic predictor by the receiver operating characteristic (ROC) curve and nomogram. K-M survival analysis was performed to investigate the relationship between the 8 proteins expressions and the overall survival. GSEA analysis showed KEGG and GO pathways enriched in the risk model, such as metabolic and cancer-related pathways. The high-risk group presented upregulation of dendritic cells resting, macrophages M2 and NK cells activated, downregulation of plasma cells, and multiple activated immune checkpoints. Most of the potential therapeutic drugs were more appropriate treatment for the low-risk patients. Through adequate analysis and verification, this 8 proteins risk model could act as a great prognostic evaluation for EC patients and provide new insight into the diagnosis and treatment of EC.