Self-Supervised Learning for Generic Raman Spectrum Denoising.

Raman and surface-enhanced Raman scattering (SERS) spectroscopy are highly specific and sensitive optical modalities that have been extensively investigated in diverse applications. Noise reduction is demanding in the preprocessing procedure, especially for weak Raman/SERS spectra. Existing denoising methods require manual optimization of parameters, which is time-consuming and laborious and cannot always achieve satisfactory performance. Deep learning has been increasingly applied in Raman/SERS spectral denoising but usually requires massive training data, where the true labels may not exist. Aiming at these challenges, this work presents a generic Raman spectrum denoising algorithm with self-supervised learning for accurate, rapid, and robust noise reduction. A specialized network based on U-Net is established, which first extracts high-level features and then restores key peak profiles of the spectra. A subsampling strategy is proposed to refine the raw Raman spectrum and avoid the underlying biased interference. The effectiveness of the proposed approach has been validated by a broad range of spectral data, exhibiting its strong generalization ability. In the context of photosafe detection of deep-seated tumors, our method achieved signal-to-noise ratio enhancement by over 400%, which resulted in a significant increase in the limit of detection thickness from 10 to 18 cm. Our approach demonstrates superior denoising performance compared to the state-of-the-art denoising methods. The occlusion method further showed that the proposed algorithm automatically focuses on characterized peaks, enhancing the interpretability of our approach explicitly in Raman and SERS spectroscopy.

Raman spectroscopy analysis combined with computed tomography imaging to identify microsatellite instability in gastric cancers.

Accurate determination of microsatellite instability (MSI) status is critical for tailoring treatment approaches for gastric cancer patients. Existing clinical techniques for MSI diagnosis are plagued by problems of suboptimal time efficiency, high cost, and burdensome experimental requirements. Here, we for the first time establish the classification model of gastric cancer MSI status based on Raman spectroscopy. To begin with, we reveal that tumor heterogeneity-induced signal variations pose a prominent impact on MSI classification. To eliminate this issue, we develop Euclidean distance-based Raman Spectroscopy (EDRS) algorithm, which establishes a standard spectrum to represent the "most microsatellite stable" status. The similarity between each spectrum of tissues with the standard spectrum is calculated to provide a direct assessment on the MSI status. Compared to machine learning-algorithms including k-Nearest Neighbors, Random Forest, and Extreme Learning Machine, the EDRS method shows the highest accuracy of 94.6 %. Finally, we integrate the EDRS method with the clinical diagnostic modality, computed tomography, to construct an innovative joint classification model with good classification performance (AUC = 0.914, Accuracy = 94.6 %). Our work demonstrates a robust, rapid, non-invasive, and convenient tool in identifying the MSI status, and opens new avenues for Raman techniques to fit into existing clinical workflow.

Raman fiber-optic probe for rapid diagnosis of gastric and esophageal tumors with machine learning analysis or similarity assessments: a comparative study.

Gastric and esophageal cancers, the predominant forms of upper gastrointestinal malignancies, contribute significantly to global cancer mortality. Routine detection methods, including medical imaging, endoscopic examination, and pathological biopsy, often suffer from drawbacks such as low sensitivity and laborious and complex procedures. Raman spectroscopy is a non-invasive and label-free optical technique that provides highly sensitive biomolecular information to facilitate effective tumor identification. In this work, we report the use of fiber-optic Raman spectroscopy for the accurate and rapid diagnosis of gastric and esophageal cancers. Using a database of 14,000 spectra from 140 ex vivo tissue pieces of both tumor and normal tissue samples, we compare the random forest (RF) and our established Euclidean distance Raman spectroscopy (EDRS) model. The RF analysis achieves a sensitivity of 85.23% and an accuracy of 83.05% in diagnosing gastric tumors. The EDRS algorithm with improved diagnostic transparency further increases the sensitivity to 92.86% and accuracy to 89.29%. When these diagnostic protocols are extended to esophageal tumors, the RF and EDRS models achieve accuracies of 71.27% and 93.18%, respectively. Finally, we demonstrate that fewer than 20 spectra are sufficient to achieve good Raman diagnostic accuracy for both tumor tissues. This optimizes the balance between acquisition time and diagnostic performance. Our work, although conducted on ex vivo tissue models, offers valuable insights for in vivo in situ endoscopic Raman diagnosis of gastric and esophageal cancer lesions in the future. Our study provides a robust, rapid, and convenient method as a new paradigm in in vivo endoscopic medical diagnostics that integrates spectroscopic techniques and a Raman probe for detecting upper gastrointestinal malignancies.

A Single-Cell Metabolic Profiling Characterizes Human Aging via SlipChip-SERS.

Metabolic dysregulation is a key driver of cellular senescence, contributing to the progression of systemic aging. The heterogeneity of senescent cells and their metabolic shifts are complex and unexplored. A microfluidic SlipChip integrated with surface-enhanced Raman spectroscopy (SERS), termed SlipChip-SERS, is developed for single-cell metabolism analysis. This SlipChip-SERS enables compartmentalization of single cells, parallel delivery of saponin and nanoparticles to release intracellular metabolites and to realize SERS detection with simple slipping operations. Analysis of different cancer cell lines using SlipChip-SERS demonstrated its capability for sensitive and multiplexed metabolic profiling of individual cells. When applied to human primary fibroblasts of different ages, it identified 12 differential metabolites, with spermine validated as a potent inducer of cellular senescence. Prolonged exposure to spermine can induce a classic senescence phenotype, such as increased senescence-associated ß-glactosidase activity, elevated expression of senescence-related genes and reduced LMNB1 levels. Additionally, the senescence-inducing capacity of spermine in HUVECs and WRL-68 cells is confirmed, and exogenous spermine treatment increased the accumulation and release of H2O2. Overall, a novel SlipChip-SERS system is developed for single-cell metabolic analysis, revealing spermine as a potential inducer of senescence across multiple cell types, which may offer new strategies for addressing ageing and ageing-related diseases.

Increasing the flexibility of the substrate binding pocket of Streptomyces phospholipase D can enhance its catalytic efficiency in soybean phosphatidylcholine.

The catalytic efficiency of Streptomyces klenkii phospholipase D (SkPLD) in soybean phosphatidylcholine (soy-PC) processing is constrained by its acyl chain specificity. To address this limitation, we engineered the substrate-binding pocket of SkPLD to increase its flexibility. The mutant P343A/Y383L exhibited a 7.14-fold increase in catalytic efficiency toward soy-PC compared to the wild type. This enhancement was attributed to improved substrate-binding pocket flexibility, as evidenced by the significantly higher specific activity of the mutant toward PCs with various acyl chains (58.20-327.76 U/mg vs. 13.56-76.67 U/mg). Monomolecular film experiments demonstrated that the P343A/Y383L mutant reduced the energy barrier for PC binding, facilitating favorable interactions with the soy-PC monolayer. Molecular dynamics simulations revealed that the mutant's increased flexibility allowed for easier diffusion and penetration into the soy-PC monolayer, while the non-polar amino acids in the substrate-binding pocket promoted rapid interactions with the acyl chains of PC, ultimately leading to enhanced catalytic activity.

Gut microbiota, circulating inflammatory proteins and sepsis: a bi-directional Mendelian randomization study.

Background: Gut microbiota is closely related to the occurrence and development of sepsis. However, the causal effects between the gut microbiota and sepsis, and whether circulating inflammatory proteins act as mediators, remain unclear. Methods: Gut microbiota, circulating inflammatory proteins, and four sepsis-related outcomes were identified from large-scale genome wide association studies (GWAS) summary data. Inverse Variance Weighted (IVW) was the primary statistical method. Additionally, we investigated whether circulating inflammatory proteins play a mediating role in the pathway from gut microbiota to the four sepsis-related outcomes. Results: There were 14 positive and 15 negative causal effects between genetic liability in the gut microbiota and four sepsis-related outcomes. Additionally, eight positive and four negative causal effects were observed between circulating inflammatory proteins and the four sepsis-related outcomes. Circulating inflammatory proteins do not act as mediators. Conclusions: Gut microbiota and circulating inflammatory proteins were causally associated with the four sepsis-related outcomes. However, circulating inflammatory proteins did not appear to mediate the pathway from gut microbiota to the four sepsis-related outcomes.

Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples.

The incidence of thyroid cancer is increasing worldwide. Fine-needle aspiration (FNA) cytology is widely applied with the use of extracted biological cell samples, but current FNA cytology is labor-intensive, time-consuming, and can lead to the risk of false-negative results. Surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms holds promise for cancer diagnosis. In this study, we develop a label-free SERS liquid biopsy method with machine learning for the rapid and accurate diagnosis of thyroid cancer by using thyroid FNA washout fluids. These liquid supernatants are mixed with silver nanoparticle colloids, and dispersed in quartz capillary for SERS measurements to discriminate between healthy and malignant samples. We collect Raman spectra of 36 thyroid FNA samples (18 malignant and 18 benign) and compare four classification models: Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The results show that the CNN algorithm is the most precise, with a high accuracy of 88.1%, sensitivity of 87.8%, and the area under the receiver operating characteristic curve of 0.953. Our approach is simple, convenient, and cost-effective. This study indicates that label-free SERS liquid biopsy assisted by deep learning models holds great promise for the early detection and screening of thyroid cancer.

Unraveling colorectal cancer prevention: The vitamin D - gut flora - immune system nexus.

Colorectal cancer (CRC) is one of the most common cancers diagnosed in the world. Although environmental and genetic factors play a major role in the pathogenesis of CRC, extensive research has suggested that vitamin D may play a pivotal role in the development of CRC. Vitamin D, primarily obtained through sunlight exposure, dietary sources, and supplements, has long been recognized for its essential functions in maintaining health, including immune regulation. This article delves into the intricate relationship between vitamin D, the immune system, gut flora, and the prevention of CRC. It presents a synthesis of epidemiological data, experimental studies, and clinical trials, highlighting the mechanisms by which vitamin D influences immune cell function, cytokine production, and inflammation. By enhancing the immune system's surveillance and anti-tumor activity, vitamin D may offer a promising avenue for CRC prevention. Furthermore, this comprehensive review delves into the prospective clinical applications of vitamin D supplementation and delineates the forthcoming avenues of research in this dynamic domain. Additionally, the paper tentatively outlines a spectrum of prophylactic impacts of vitamin D on CRC, emphasizing its significant potential in reducing CRC risk through shedding light on its mechanisms, encompassing antineoplastic mechanisms, influences on the immune system, and modulation of the gut microbiome.

Oppositely-charged silver nanoparticles enable selective SERS molecular enhancement through electrostatic interactions.

Label-free surface-enhanced Raman spectroscopy (SERS) has attracted extensive attention as an emerging technique for molecular phenotyping of biological samples. However, the selective enhancement property of SERS mediated by complicated interactions between substrates and analytes is unfavorable for molecular profiling. The electrostatic force is among the most dominating interactions that can cause selective adsorption of molecules to charged substrates. This means if only negatively- or positively-charged SERS substrates are applied, then considerable SERS information from a portion of analytes would be lost, hindering comprehensive SERS sensing. In this work, we utilize both negatively- and positively-charged colloidal silver (Ag) nanoparticles (NPs) to detect various charged molecules. The negatively-charged citrate-stabilized Ag and the positively-charged Ag prepared via a cetyltrimethyl-ammonium chloride-based charge reversal protocol have been adopted as SERS substrates. The Ag NPs are all relatively well-dispersed with good uniformity. After applying the oppositely-charged NPs to the detection of charged molecules, we find the SERS results explicitly demonstrate the electrostatically-driven SERS selective enhancement, which is further supported and clarified by molecular electrostatic potential calculations. Our work highlights the importance of developing SERS substrates modified with appropriate surface charges for various analytes, and enlightens us that potentially more molecular SERS information can be acquired from complex bio-samples using combinations of oppositely-charged substrates.

The causal role of gut microbiota in susceptibility of Long COVID: a Mendelian randomization study.

Background: Long COVID is a major challenge facing the public. Gut microbiota is closely related to Long COVID. However, the causal effects between gut microbiota and Long COVID remains unclear. Methods: Using summary statistics from Genome-Wide Association Studies (GWAS), Mendelian randomization (MR) analyses were performed to investigate the relationship between gut microbiota and Long COVID. The primary statistical method employed was Inverse Variance Weighted (IVW). Sensitivity analyses were then conducted to evaluate the reliability of the findings and account for potential confounding variables. Finally, a reverse MR analysis was conducted to examine potential associations between Long COVID and genetically predicted gut microbiota compositions. Results: There were 2 positive and 1 negative causal effect between gut microbiota and Long COVID. Meta-analysis results show that genus Parasutterella (OR = 1.145, 95%CI = 1.035 ∼ 1.266, P = 0.008) and genus Oscillospira (OR = 1.425, 95%CI = 1.235 ∼ 1.645, P < 0.001) significantly increased the risk of Long COVID. And genus Eisenbergiella (OR = 0.861, 95%CI = 0.785 ∼ 0.943, P = 0.001) significantly decreased the risk of Long COVID. Neither the pleiotropy nor the heterogeneity was observed. Reverse causal effect does not hold. Conclusion: Our research has provided genetic evidence that establishes multiple causal relationships between the gut microbiota and Long COVID, supporting the role of the gut microbiota in Long COVID. It is possible that different taxa play a role in the development of Long COVID. The causal relationships identified in this study require further investigation.

Hepatic artery infusion chemotherapy combined with camrelizumab plus rivoceranib for hepatocellular carcinoma with portal vein tumor thrombosis: a multicenter propensity score-matching analysis.

BACKGROUND: Portal vein tumor thrombosis (PVTT) signifies late-stage hepatocellular carcinoma (HCC) with high-risk progression and poor prognosis. As a standard treatment, sorafenib monotherapy has limited the efficacy in managing HCC with PVTT. Currently, both hepatic arterial infusion chemotherapy (HAIC) and the combination of camrelizumab and rivoceranib have shown favorable survival benefits for advanced HCC, surpassing the standard sorafenib treatment. In this study, we investigate the safety and efficacy of HAIC combined with camrelizumab and rivoceranib in treating HCC patients with PVTT. METHODS: From January 2020 to December 2021, HCC patients with PVTT, who received either a triple regime of HAIC combined with camrelizumab and rivoceranib or a dual regime of camrelizumab and rivoceranib as their first-line treatment, were reviewed for eligibility at four hospital centers in China. To balance any intergroup differences, propensity score matching (PSM) was applied. The aim of this study is to compare the efficacy of the dual and triple combination treatment regimens based on survival prognosis and tumor response and evaluate the safety based on the occurrence of adverse reactions. RESULT: In this study, a total of 411 patients who received either the triple treatment regime (HAIC combined with camrelizumab plus rivoceranib, referred to as the HAICCR group, n = 292) or the dual treatment regime (camrelizumab combined with rivoceranib, referred to as the CR group, n = 119) between January 2020 and December 2021 were included. The results showed that the HAICCR group exhibited significantly better overall survival (mOS: 19.60 months vs. 11.50 months, p < 0.0001) and progression-free survival (mPFS: 10.0 months vs. 5.6 months, p < 0.0001) compared to the CR group in the overall cohort. Moreover, the HAICCR group also had a significantly higher ORR (objective response rate, 55.5% vs. 42.0%, p = 0.013) and DCR (disease control rate, 89.0% vs. 79.0%) compared to the CR group. After PSM, a final matched cohort of 83 pairs was obtained, and the survival benefits were consistent in this cohort as well (mOS: 18.70 months vs. 11.0 months, p < 0.0001; mPFS: 10.0 months vs. 5.6 months, p < 0.0001). However, there was no significant difference in the ORR between the triple and dual combination regimes. Univariate and multivariate analysis showed that CTP (Child-Turcotte-Pugh) stage, ALBI (albumin-bilirubin index) grade, tumor number, and treatment regime were significant risk factors affecting overall survival, while AFP (α-fetoprotein) level, tumor number, metastasis, and treatment regime were significant risk factors affecting progression-free survival. As for safety, hypertension and hand-foot syndrome were the two most common adverse reactions in both groups, with no significant difference in the occurrence of adverse reactions between the two groups (p < 0.05). CONCLUSION: In the context of advanced HCC patients with PVTT, the combination regime of HAIC and camrelizumab plus rivoceranib demonstrates more excellent capacity for prolonging survival and offers a well-tolerated safety compared to the CR dual therapy approach. This triple regime represents a therapeutic modality of broad prospects and vast potential for HCC patients with PVTT.

Raman spectroscopy for esophageal tumor diagnosis and delineation using machine learning and the portable Raman spectrometer.

Esophageal cancer is one of the leading causes of cancer-related deaths worldwide. The identification of residual tumor tissues in the surgical margin of esophageal cancer is essential for the treatment and prognosis of cancer patients. But the current diagnostic methods, either pathological frozen section or paraffin section examination, are laborious, time-consuming, and inconvenient. Raman spectroscopy is a label-free and non-invasive analytical technique that provides molecular information with high specificity. Here, we report the use of a portable Raman system and machine learning algorithms to achieve accurate diagnosis of esophageal tumor tissue in surgically resected specimens. We tested five machine learning-based classification methods, including k-Nearest Neighbors, Adaptive Boosting, Random Forest, Principal Component Analysis-Linear Discriminant Analysis, and Support Vector Machine (SVM). Among them, SVM shows the highest accuracy (88.61 %) in classifying the esophageal tumor and normal tissues. The portable Raman system demonstrates robust measurements with an acceptable focal plane shift of up to 3 mm, which enables large-area Raman mapping on resected tissues. Based on this, we finally achieve successful Raman visualization of tumor boundaries on surgical margin specimens, and the Raman measurement time is less than 5 min. This work provides a robust, convenient, accurate, and cost-effective tool for the diagnosis of esophageal cancer tumors, advancing toward Raman-based clinical intraoperative applications.

Adaptive Clustering for Point Cloud.

The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied to large-scale scenes. Therefore, this paper proposes an adaptive clustering segmentation method. In this method, the threshold for clustering points within the point cloud is calculated using the characteristic parameters of adjacent points. After completing the preliminary segmentation of the point cloud, the segmentation results are further refined according to the standard deviation of the cluster points. Then, the cluster points whose number does not meet the conditions are further segmented, and, finally, scene point cloud data segmentation is realized. To test the superiority of this method, this study was based on point cloud data from a park in Guilin, Guangxi, China. The experimental results showed that this method is more practical and efficient than other methods, and it can effectively segment all ground objects and ground point cloud data in a scene. Compared with other segmentation methods that are easily affected by parameters, this method has strong robustness. In order to verify the universality of the method proposed in this paper, we test a public data set provided by ISPRS. The method achieves good segmentation results for multiple sample data, and it can distinguish noise points in a scene.

Regulating the Electrode-Electrolyte Interfaces of Lithium-High Nickel Batteries via a Multifunctional Additive.

Lithium metal batteries with high nickel ternary (LiNixCoyMn1-x-yO2, x ≥ 0.8) as the cathode hold the promise to meet the demand of next-generation high energy density batteries. However, the unsatisfactory stability of electrode-electrolyte interfaces limits their practical applications. In this work, N-methyl-N-trimethylsilyltrifluoroacetamide (NMTFA) is suggested as a new functional electrolyte additive to stabilize the Li∥LiNi0.9Co0.05Mn0.05O2 chemistry by forming robust and effective electrode-electrolyte interphases, namely the anode-electrolyte interphase (AEI, or conventionally called SEI) and cathode-electrolyte interphase (CEI). The NMTFA-derived SEI/CEI greatly enhances the battery performance that a capacity retention of 82.1% after 200 cycles at 1C charge/discharge is achieved, significantly higher than that without NMTFA addition (52.5%). Moreover, the NMTFA also improves the thermal stability of the electrolyte and inhibits the hydrolysis of LiPF6. This work provides new clues for the optimization of electrolyte formulation for lithium-high nickel batteries through modulating interfaces.

Assessing the causal associations of sleep apnea with mental health and socioeconomic status: a bidirectional two-sample Mendelian randomization.

OBJECTIVE: Traditional observational research has suggested a connection between socioeconomic position, mental health, and sleep apnea (SA), but the specifics of this connection are still unclear. Using the Mendelian randomization approach, we intended to evaluate the potential causal link between mental health, socioeconomic status, and SA. METHODS: Our research employed summary statistics data from large-scale genome-wide association studies (GWAS) on mental health, socioeconomic status, and SA. In the main study, the connection between mental health, socioeconomic status, and SA was examined using the inverse variance weighted approach. In addition, as a supplement, we also used other Mendelian randomization methods, including MR Egger, weighted median, simple mode, and weighted mode. RESULTS: The primary analysis showed that educational attainment, including longer years of schooling, college or university degree, and higher intelligence was associated with a lower risk of SA (OR = 0.750, 95%CI = 0.653-0.862; OR = 0.558, 95%CI = 0.423-0.735; OR = 0.871, 95%CI = 0.760-0.999, respectively), while social deprivation was associated with a higher risk of SA (OR = 1.821, 95%CI = 1.075-3.085). And the income was not associated with the risk of sleep apnea (OR = 0.877, 95%CI = 0.682-1.129). In mental health exposure, major depressive disorder was associated with a higher risk of sleep apnea (OR = 1.196, 95%CI = 1.015-1.409), while attention-deficit hyperactivity disorder, bipolar disorder, and schizophrenia were not associated with the risk of sleep apnea (OR = 1.064, 95%CI = 0.958-1.181; OR = 1.030, 95%CI = 0.942-1.127; OR = 0.990, 95%CI = 0.957-1.025, respectively). Reverse MR analysis failed to find a causal effect from SA on mental health and socioeconomic status. CONCLUSIONS: This MR investigation offers proof of a possible causal relationship between SA, socioeconomic level, and mental health.

High-Throughput Calculations for Screening d- and p-Block Single-Atom Catalysts toward Li2 S/Na2 S Decomposition Guided by Facile Descriptor beyond Brønsted-Evans-Polanyi Relationship.

Single-atom catalysts (SACs) are promising cathode materials for addressing issues faced by lithium-sulfur batteries. Considering the ample chemical space of SACs, high-throughput calculations are efficient strategies for their rational design. However, the high throughput calculations are impeded by the time-consuming determination of the decomposition barrier (Eb ) of Li2 S. In this study, the effects of bond formation and breakage on the kinetics of SAC-catalyzed Li2 S decomposition with g-C3 N4 as the substrate are clarified. Furthermore, a new efficient and easily-obtained descriptor Li─S─Li angle (ALi─S─Li ) of adsorbed Li2 S, different from the widely accepted thermodynamic data for predicting Eb , which breaks the well-known Brønsted-Evans-Polanyi relationship, is identified. Under the guidance of ALi─S─Li , several superior SACs with d- and p-block metal centers supported by g-C3 N4 are screened to accelerate the sulfur redox reaction and fix the soluble lithium polysulfides. The newly identified descriptor of ALi─S─Li can be extended to rationally design SACs for Na─S batteries. This study opens a new pathway for tuning the performance of SACs to catalyze the decomposition of X2 S (X = Li, Na, and K) and thus accelerate the design of SACs for alkaline-chalcogenide batteries.

Structural Analysis and Novel Mechanism of Enteromorpha prolifera Sulfated Polysaccharide in Preventing Type 2 Diabetes Mellitus.

A water-soluble polysaccharide (EP) was purified from edible algae Enteromorpha prolifera. Gel permeation chromatography (GPC), ion chromatography (IC), and fourier transform infrared (FT-IR) were performed to characterize its structure. EP was defined as a low molecular weight (6625 Da) composed of rhamnose, glucose, glucuronic acid, xylose, galactose, arabinose, and mannose. Moreover, it was a sulfated polysaccharide with a degree of substitution (DS) of 1.48. Then, the high-fat diet/streptozotocin (HFD/STZ) induced diabetic mouse model was established to support evidence for a novel hypoglycemic mechanism. Results showed that blood glucose (47.32%), liver index (7.65%), epididymal fat index (16.86%), serum total cholesterol (26.78%) and triglyceride (37.61%) in the high-dose EP (HEP) group were significantly lower than those in the HFD group. Noticeably, the content of liver glycogen in the HEP group was significantly higher (62.62%) than that in the HFD group, indicating the promotion of glycogen synthesis. These beneficial effects were attributed to significantly increased protein kinase B (AKT) phosphorylation and its downstream signaling response. Further studies showed that diabetic mice exhibited excessive O-GlcNAcylation level and high expression of O-linked ß-D-N-acetylglucosamine transferase (OGT), which were decreased by 62.21 and 30.43% in the HEP group. This result suggested that EP had a similar effect to OGT inhibitors, which restored AKT phosphorylation and prevented pathoglycemia. This work reveals a novel hypoglycemic mechanism of EP, providing a theoretical basis for further studies on its pharmacological properties in improvement of T2DM.

Insight into the High-Efficiency Benzo(a)pyrene Degradation Ability of Pseudomonas benzopyrenica BaP3 and Its Application in the Complete Bioremediation of Benzo(a)pyrene.

Polycyclic aromatic hydrocarbons (PAHs) are common carcinogens. Benzo(a)pyrene is one of the most difficult high-molecular-weight (HMW) PAHs to remove. Biodegradation has become an ideal method to eliminate PAH pollutants from the environment. The existing research is mostly limited to low-molecular-weight PAHs; there is little understanding of HMW PAHs, particularly benzo(a)pyrene. Research into the biodegradation of HMW PAHs contributes to the development of microbial metabolic mechanisms and also provides new systems for environmental treatments. Pseudomonas benzopyrenica BaP3 is a highly efficient benzo(a)pyrene-degrading strain that is isolated from soil samples, but its mechanism of degradation remains unknown. In this study, we aimed to clarify the high degradation efficiency mechanism of BaP3. The genes encoding Rhd1 and Rhd2 in strain BaP3 were characterized, and the results revealed that rhd1 was the critical factor for high degradation efficiency. Molecular docking and enzyme activity determinations confirmed this conclusion. A recombinant strain that could completely mineralize benzo(a)pyrene was also proposed for the first time. We explained the mechanism of the high-efficiency benzo(a)pyrene degradation ability of BaP3 to improve understanding of the degradation mechanism of highly toxic PAHs and to provide new solutions to practical applications via synthetic biology.

GSDMD promotes neutrophil extracellular traps via mtDNA-cGAS-STING pathway during lung ischemia/reperfusion.

Lung ischemia/reperfusion injury (LIRI) is a complex pathophysiological process, with the histopathological hallmark of neutrophils migrating into the lungs. Neutrophil extracellular traps (NETs) have been suggested to exert a critical role in the pathogenesis of inflammation and infection in humans and animals, while the exact functions and underlying mechanisms of NETs in LIRI remain insufficiently elucidated. In this study, we investigated the role of pore-forming protein gasdermin D (GSDMD) on NETs release in LIRI induced by lung ischemia/reperfusion (I/R). We found that disulfiram, a GSDMD inhibitor, dramatically reduced NETs release and pathological injury in lung I/R in vivo and in vitro. Additionally, GSDMD caused mitochondrial DNA (mtDNA) leaking into the neutrophil cytosol, and then the cytoplasmic mtDNA activated the cGAS-STING signaling pathway and stimulated NETs formation in lung I/R. Furthermore, inhibition of cGAS/STING pathway could inhibit cytosol mtDNA mediated NETs formation.