Risk factors associated with hospital mortality in non-surgical patients receiving extracorporeal membrane oxygenation and continuous renal replacement treatment: a retrospective analysis.

OBJECTIVES: The prognosis-predicting factors for non-surgical patients receiving continuous renal replacement therapy (CRRT) and extracorporeal membrane oxygenation (ECMO) remains limited. In this study, we aim to analyze prognosis-predicting factors in the non-surgical patients receiving these two therapies. METHODS: We retrospectively analyzed data from non-surgical patients with ECMO treatment from December 2013 until April 2023. Hospital mortality was primary endpoint of this study. The area under the curve and receiver operating characteristic curves were used to assess the sensitivity and specificity of mortality. The independent risk factors were identified by multivariate logistic regression. The prediction model was a nomogram, and decision curve analysis and the calibration plot were used to assess it. Using restricted cubic spline curves and Spearman correlation, the correlation analysis was performed. RESULTS: The model that incorporated CRRT duration and age surpassed the two variables alone in predicting hospital mortality in non-surgical patients with ECMO therapy (AUC value = 0.868, 95% CI = 0.779-0.956). Older age, CRRT implantation, and duration were independent risk factors for hospital mortality (all p < 0.05). The nomogram predicting outcomes model containing on CRRT implantation and duration was developed, and the consistency between the predicted probability and observed probability and clinical utility of the models were good. CRRT duration was negatively associated with hemoglobin concentration and positively associated with urea nitrogen and serum creatinine levels. CONCLUSION: Hospital mortality in non-surgical ECMO patients was found to be independently associated with older age, longer CRRT duration, and CRRT implantation.

Regulation of drought stress on nutrient cycle and metabolism of rhizosphere microorganisms in desert riparian forest.

Microbial communities in desert riparian forest ecosystems have developed unique adaptive strategies to thrive in harsh habitats shaped by prolonged exposure to abiotic stressors. However, the influence of drought stress on the functional and metabolic characteristics of soil rhizosphere microorganisms remains unknown. Therefore, this study aimed to investigate the effects of drought stress on soil biogeochemistry and metabolism and analyze the relationship between the biogeochemical cycle processes and network of differentially-expressed metabolites. Using metagenomics and metabolomics, this study explored the microbial functional cycle and differential metabolic pathways within desert riparian forests. The predominant biogeochemical cycles in the study area were the Carbon and Nitrogen cycles, comprising 78.90 % of C, N, Phosphorus, Sulfur and Iron cycles. Drought led to increased soil C fixation, reduced C degradation and methane metabolism, weakened denitrification, and decreased N fixation. Furthermore, drought can disrupt iron homeostasis and reduce its absorption. The differential metabolic pathways of drought stress include flavonoid biosynthesis, arachidonic acid metabolism, steroid hormone biosynthesis, and starch and sucrose degradation. Network analysis of functional genes and metabolism revealed a pronounced competitive relationship between the C cycle and metabolic network, whereas the Fe cycle and metabolic network promoted each other, optimizing resource utilization. Partial least squares analysis revealed that drought hindered the expression and metabolic processes and functional genes, whereas the rhizosphere environment facilitated metabolic expression and the functional genes. The rhizosphere effect primarily promoted metabolic processes indirectly through soil enzyme activities. The integrated multi-omics analysis further revealed that the effects of drought and the rhizosphere play a predominant role in shaping soil functional potential and the accumulation of metabolites. These insights deepen our comprehension of desert riparian forest ecosystems and offer strong support for the functionality of nutrient cycling and metabolite dynamics.

A greater negative impact of future climate change on vegetation in Central Asia: Evidence from trajectory/pattern analysis.

In the context of global warming, vegetation changes exhibit various patterns, yet previous studies have focused primarily on monotonic changes, often overlooking the complexity and diversity of multiple change processes. Therefore, it is crucial to further explore vegetation dynamics and diverse change trajectories in this region under future climate scenarios to obtain a more comprehensive understanding of local ecosystem evolution. In this study, we established an integrated machine learning prediction framework and a vegetation change trajectory recognition framework to predict the dynamics of vegetation in Central Asia under future climate change scenarios and identify its change trajectories, thus revealing the potential impacts of future climate change on vegetation in the region. The findings suggest that various future climate scenarios will negatively affect most vegetation in Central Asia, with vegetation change intensity increasing with increasing emission trajectories. Analyses of different time scales and trend variations consistently revealed more pronounced downward trends. Vegetation change trajectory analysis revealed that most vegetation has undergone nonlinear and dramatic changes, with negative changes outnumbering positive changes and curve changes outnumbering abrupt changes. Under the highest emission scenario (SSP5-8.5), the abrupt vegetation changes and curve changes are 1.7 times and 1.3 times greater, respectively, than those under the SSP1-2.6 scenario. When transitioning from lower emission pathways (SSP1-2.6, SSP2-4.5) to higher emission pathways (SSP3-7.0, SSP5-8.5), the vegetation change trajectories shift from neutral and negative curve changes to abrupt negative changes. Across climate scenarios, the key climate factors influencing vegetation changes are mostly evapotranspiration and soil moisture, with temperature and relative humidity exerting relatively minor effects. Our study reveals the negative response of vegetation in Central Asia to climate change from the perspective of vegetation dynamics and change trajectories, providing a scientific basis for the development of effective ecological protection and climate adaptation strategies.

Highly Robust and Self-Adhesive Soft Strain Gauge via Interface Design Engineering.

Highly robust soft strain gauges are rapidly emerging as a promising candidate in the fields of vital signs and machine conditions monitoring. However, it is still a key challenge to achieve high-performance strain sensing in these sensors with mechanical/electrical robustness for long-term usage. The multilayer structural design of sensors enhances sensing performance while the interfacial connection of heterogeneous materials between different layers is weak. Herein, inspired by the efficient perception mechanism of scorpion slit sensilla with tough interface interconnections, the synergy of ultra-high electrical performance and mechanical robustness is successfully achieved via interface design engineering. The developed multilayer soft strain gauge (MSSG) exhibits a strain sensitivity beyond 105, a lower detection limit of 8.3 µm, a frequency resolution within 0.1 Hz, and cyclic stability over 63 000 strain cycles. Also, the tough interface improves the level of heterogeneous integration in the MSSG which allows to endure different stresses. Furthermore, an MSSG-based wireless strain monitoring system is developed that enables applications on different complex dynamic surfaces, including accurate identification of human throat activity and monitoring of rolling bearing conditions.

The impact of compound drought and heatwave events from 1982 to 2022 on the phenology of Central Asian grasslands.

In the context of global warming, the occurrence and severity of extreme events like atmospheric drought (AD) and warm spell duration index (WSDI) have increased, causing significant impacts on terrestrial ecosystems in Central Asia's arid regions. Previous research has focused on single extreme events such as AD and WSDI, but the effect of compound hot and dry events (CHWE) on grassland phenology in the arid regions of Central Asia remains unclear. This study utilized structural equation modeling (SEM) and the Pettitt breakpoint test to quantify the direct and indirect responses of grassland phenology (start of season - SOS, length of season - LOS, and end of season - EOS) to AD, WSDI, and CHWE. Furthermore, this research investigated the threshold of grassland phenology response to compound hot and dry events. The research findings indicate a significant increasing trend in AD, WSDI, and CHWE in the arid regions of Central Asia from 1982 to 2022 (0.51 day/year, P < 0.01; 0.25 day/year, P < 0.01; 0.26 day/year, P < 0.01). SOS in the arid regions of Central Asia showed a significant advancement trend, while EOS exhibited a significant advance. LOS demonstrated an increasing trend (-0.23 day/year, P < 0.01; -0.12 day/year, P < 0.01; 0.56 day/year). The temperature primarily governs the variation in SOS. While higher temperatures promote an earlier SOS, they also offset the delaying effect of CHWE on SOS. AD, temperature, and CHWE have negative impacts on EOS, whereas WSDI has a positive effect on EOS. AD exhibits the strongest negative effect on EOS, with an increase in AD leading to an earlier EOS. Temperature and WSDI are positively correlated with LOS, indicating that higher temperatures and increased WSDI contribute to a longer LOS. The threshold values for the response of SOS, EOS, and LOS to CHWE are 16.14, 18.49, and 16.61 days, respectively. When CHWE exceeds these critical thresholds, there are significant changes in the response of SOS, EOS, and LOS to CHWE. These findings deepen our understanding of the mechanisms by which extreme climate events influence grassland phenology dynamics in Central Asia. They can contribute to better protection and management of grassland ecosystems and help in addressing the impacts of global warming and climate change in practice.

Bi-Parametric Magnetic Resonance Imaging Analysis of Biochemical Recurrence of Prostate Cancer after Radical Surgery and Its Predictive Value: A Retrospective Study.

OBJECTIVE: This study aimed to analyse the characteristics of biochemical recurrence after radical prostatectomy via bi-parametric magnetic resonance imaging. METHODS: A total of 200 patients with radical prostatectomy admitted to our hospital from January 2016 to January 2021 were retrospectively enrolled as observation objects. According to whether there was biochemical recurrence after surgery, the patients were divided into the abnormal group (n = 62) and normal group (n = 138). Clinical data, encapsulation infiltration, seminal vesicle infiltration and prostate imaging report and data system (PI-RADS) were collected and compared between the two groups. Propensity score matching (PSM) was used to balance the baseline data of the two groups. Student's t-test and Chi-square test were used to analyse the data. RESULTS: PSM was performed in a 1:1 ratio, and a total of 72 patients were included in the abnormal and normal groups. The baseline data of the patients in each group were not statistically significant. The incidence of extraperitoneal invasion and seminal vesicle invasion was higher in the abnormal group than in the normal group, and we observed a significant difference in PI-RADS scores between the two groups (p < 0.05). Extracapsular invasion, seminal vesicle invasion, PI-RADS score and biochemical recurrence were significantly correlated (p < 0.05). The PI-RADS score has a high value for predicting biochemical recurrence, with an area under the curve value of 0.824, sensitivity of 0.667, specificity of 0.861 and Youden index of 0.528. CONCLUSIONS: Bi-parametric magnetic resonance imaging has a high predictive value in biochemical recurrence after radical prostatectomy, which can provide reference for early intervention measures.

Novel perspectives on autophagy-oxidative stress-inflammation axis in the orchestration of adipogenesis.

Adipose tissue, an indispensable organ, fulfils the pivotal role of energy storage and metabolism and is instrumental in maintaining the dynamic equilibrium of energy and health of the organism. Adipocyte hypertrophy and adipocyte hyperplasia (adipogenesis) are the two primary mechanisms of fat deposition. Mature adipocytes are obtained by differentiating mesenchymal stem cells into preadipocytes and redifferentiation. However, the mechanisms orchestrating adipogenesis remain unclear. Autophagy, an alternative cell death pathway that sustains intracellular energy homeostasis through the degradation of cellular components, is implicated in regulating adipogenesis. Furthermore, adipose tissue functions as an endocrine organ, producing various cytokines, and certain inflammatory factors, in turn, modulate autophagy and adipogenesis. Additionally, autophagy influences intracellular redox homeostasis by regulating reactive oxygen species, which play pivotal roles in adipogenesis. There is a growing interest in exploring the involvement of autophagy, inflammation, and oxidative stress in adipogenesis. The present manuscript reviews the impact of autophagy, oxidative stress, and inflammation on the regulation of adipogenesis and, for the first time, discusses their interactions during adipogenesis. An integrated analysis of the role of autophagy, inflammation and oxidative stress will contribute to elucidating the mechanisms of adipogenesis and expediting the exploration of molecular targets for treating obesity-related metabolic disorders.

Construction of a Prognostic Model for Hepatocellular Carcinoma Based on Macrophage Polarization-Related Genes.

Background: The progression of hepatocellular carcinoma (HCC) is related to macrophage polarization (MP). Our aim was to identify genes associated with MP in HCC patients and develop a prognostic model based on these genes. Results: We successfully developed a prognostic model consisting of six MP-related genes (SCN4A, EBF3, ADGRB2, HOXD9, CLEC1B, and MSC) to calculate the risk score for each patient. Patients were then classified into high- and low-risk groups based on their median risk score. The performance of the MP-related prognostic model was evaluated using Kaplan-Meier and ROC curves, which yielded favorable results. Additionally, the nomogram demonstrated good clinical effectiveness and displayed consistent survival predictions with actual observations. Gene Set Enrichment Analysis (GSEA) revealed enrichment of pathways related to KRAS signaling downregulation, the G2M checkpoint, and E2F targets in the high-risk group. Conversely, pathways associated with fatty acid metabolism, xenobiotic metabolism, bile acid metabolism, and adipogenesis were enriched in the low-risk group. The risk score positively correlated with the number of invasion-related genes. Immune checkpoint expression differed significantly between the two groups. Patients in the high-risk group exhibited increased sensitivity to mitomycin C, cisplatin, gemcitabine, rapamycin, and paclitaxel, while those in the low-risk group showed heightened sensitivity to doxorubicin. These findings suggest that the high-risk group may have more invasive HCC with greater susceptibility to specific drugs. IHC staining revealed higher expression levels of SCN4A in HCC tissues. Furthermore, experiments conducted on HepG2 cells demonstrated that supernatants from cells with reduced SCN4A expression promoted M2 macrophage polarization marker, CD163 in THP-1 cells. Reduced SCN4A expression induced HCC-related genes, while increased SCN4A expression reduced their expression in HepG2 cells. Conclusion: The MP-related prognostic model comprising six MPRGs can effectively predict HCC prognosis, infer invasiveness, and guide drug therapy. SCN4A is identified as a suppressor gene in HCC.

Grassland productivity in arid Central Asia depends on the greening rate rather than the growing season length.

Climate change has induced substantial impact on the gross primary productivity (GPP) of terrestrial ecosystems by affecting vegetation phenology. Nevertheless, it remains unclear which among the mean rates of grass greening (RG), yellowing (RY), and the length of growing season (LOS) exhibit stronger explanatory power for GPP variations, and how RG and RY affect GPP variations under warming scenarios. Here, we explored the relationship between RG, RY, LOS, and GPP in arid Central Asia (ACA) from 1982 to 2019, elucidating the response mechanisms of RG, RY, and GPP to the mean temperature (TMP), vapor pressure deficit (VPD), precipitation (PRE), and soil moisture (SM). The results showed that the multi-year average length of greening (LG) in ACA was 22.7 days shorter than that of yellowing (LY) and the multi-year average GPP during LG (GPPlg) was 38.28 g C m-2 d -1 more than that of during LY (GPPly). RG and RY were positively correlated with GPPlg and GPPly, although the degree of correlation between RG and GPPlg was higher than that between RY and GPPly. Increases in RG and RY contributed to an increase in GPPlg (55.44 % of annual GPP) and GPPly (35.44 % of annual GPP). The correlation between RG and GPPlg was the strongest (0.49), followed by RY and GPPly (0.33), and LOS and GPP was the weakest (0.21). TMP, VPD, PRE, and SM primarily affected GPP by influencing RG and RY, rather than direct effects. The positive effects of TMP during LG (TMPlg), PRE during LG (PRElg), and SM during LG (SMlg) facilitated increases in RG and GPPlg, and higher VPD during LY (VPDly) and lower PRE during LY (PREly) accelerated increases in RY. Our study elucidated the impact of vegetation growth rate on GPP, thus providing an alternate method of quantifying the relationship between vegetation phenology and GPP.

Integrative analysis of proteomics and lipidomic profiles reveal the fat deposition and meat quality in Duroc × Guangdong small spotted pig.

Introduction: This study aims to explore the important factors affecting the characteristics of different parts of pork. Methods: Lipidomics and proteomics methods were used to analyze DAL (differential lipids) and DAPs (differential proteins) in five different parts (longissimus dorsi, belly meat, loin, forelegs and buttocks) of Duhua pig (Duroc × Guangdong small spotted pig), to identify potential pathways affecting meat quality, investigating fat deposition in pork and its lipid-protein interactions. Results: The results show that TG (triglyceride) is the lipid subclass with the highest proportion in muscle, and the pathway with the most significantly enriched lipids is GP. DAP clustered on several GO terms closely related to lipid metabolism and lipogenesis (lipid binding, lipid metabolism, lipid transport, and lipid regulation). In KEGG analysis, there are two main DAP aggregation pathways related to lipid metabolism, namely Fatty acid degradation and oxidative phosphorylation. In PPI analysis, we screened out 31 core proteins, among which NDUFA6, NDUFA9 and ACO2 are the most critical. Discussion: PC (phosphatidylcholine) is regulated by SNX5, THBS1, ANXA7, TPP1, CAVIN2, and VDAC2 in the phospholipid binding pathway. TG is regulated by AUH/HADH/ACADM/ACADL/HADHA in the lipid oxidation and lipid modification pathways. Potential biomarkers are rich in SFA, MUFA and PUFA respectively, the amounts of SFA, MUFA and PUFA in the lipid measurement results are consistent with the up- and down-regulation of potential biomarker lipids. This study clarified the differences in protein and lipid compositions in different parts of Duhua pigs and provided data support for revealing the interactions between pork lipids and proteins. These findings provide contributions to the study of intramuscular fat deposition in pork from a genetic and nutritional perspective.

A deep learning-based approach for fully automated segmentation and quantitative analysis of muscle fibers in pig skeletal muscle.

Muscle fiber properties exert a significant influence on pork quality, with cross-sectional area (CSA) being a crucial parameter closely associated with various meat quality indicators, such as shear force. Effectively identifying and segmenting muscle fibers in a robust manner constitutes a vital initial step in determining CSA. This step is highly intricate and time-consuming, necessitating an accurate and automated analytical approach. One limitation of existing methods is their tendency to perform well on high signal-to-noise ratio images of intact, healthy muscle fibers but their lack of validation on more complex image datasets featuring significant morphological changes, such as the presence of ice crystals. In this study, we undertake the fully automatic segmentation of muscle fiber microscopic images stained with myosin adenosine triphosphate (mATPase) activity using a deep learning architecture known as SOLOv2. Our objective is to efficiently derive accurate measurements of muscle fiber size and distribution. Tests conducted on actual images demonstrate that our method adeptly handles the intricate task of muscle fiber segmentation, yielding quantitative results amenable to statistical analysis and displaying reliability comparable to manual analysis.

Predictive Value of Magnetic Resonance Imaging Radiomics Combined with Serum PSA for the Extracapsular Extension of Prostate Cancer Prediction.

OBJECTIVE: We aimed to investigate the value of magnetic resonance imaging (MRI) radiomics combined with serum prostate-specific antigen (PSA) in predicting the extracapsular extension (ECE) of prostate cancer. METHODS: In total, 213 patients with prostate cancer admitted to our hospital from May 2021 to April 2023 were retrospectively enrolled as observation subjects. Based on the presence or absence of extracapsular extension, the patients were divided into occurrence (n = 70) and non-occurrence (n = 143) groups. The clinical data, PSA levels, Prostate Imaging Reporting and Data System (PI-RADS®), and MRI-ECE scores of the two groups were compared. RESULTS: In total, 80 patients were included in the occurrence (n = 40) and non-occurrence groups (n = 40), and no statistical significance was observed in the baseline data of the two groups. Preoperative PSA levels were significantly higher in the occurrence group than in the non-occurrence group, and the PI-RADS and MRI-ECE scores of each group differed significantly (p < 0.05). The area under the curve (AUC) for the combined determination of PSA levels and PI-RADS and MRI-ECE scores was 0.900, which was significantly higher than the AUC for the individual determination of the mentioned indicators (p < 0.05). CONCLUSIONS: The combination of MRI radiomics and PSA can accurately predict the extracapsular extension of prostate cancer; Thus, it is a favorable reference for subsequent precise diagnosis and treatment.

YBX1 Promotes Esophageal Squamous Cell Carcinoma Progression via m5C­Dependent SMOX mRNA Stabilization.

The modification and recognition of 5-methylcytosine (m5C) are involved in the initiation and progression of various tumor types. However, the precise role and potential mechanism of Y-box-binding protein 1 (YBX1) in esophageal squamous cell carcinoma (ESCC) remains unclear. Here, it is found that YBX1 is frequently upregulated in ESCC compared with matched nontumor tissues. Gain- and loss-of-function assays show that YBX1 promoted the proliferation and metastasis of ESCC cells both in vitro and in vivo. Functional studies revealed that NOP2/Sun RNA methyltransferase family member 2 (NSUN2) is a critical RNA methyltransferase that facilitates YBX1-mediated ESCC progression. Mechanistically, integrated analysis based on RNA immunoprecipitation sequencing (RIP-seq) and m5C methylated RNA immunoprecipitation and sequencing (MeRIP-seq) assays identified spermine oxidase (SMOX) as a target gene containing an m5C site in its coding sequence (CDS) region, which coincided well with the binding site of YBX1. Overexpression of SMOX-WT but not SMOX-Mut partially restored the proliferation and invasion ability of ESCC cells curbed by YBX1 knockdown. Moreover, YBX1 activated the mTORC1 signaling pathway by stabilizing SMOX mRNA. The study reveals that YBX1 promotes ESCC development by stabilizing SMOX mRNA in an m5C-dependent manner, thus providing a valuable therapeutic target for ESCC.

Predictive Value of Magnetic Resonance Imaging Radiomics Combined with Serum PSA for the Extracapsular Extension of Prostate Cancer Prediction

Objective: We aimed to investigate the value of magnetic resonance imaging (MRI) radiomics combined with serum prostate-specific antigen (PSA) in predicting the extracapsular extension (ECE) of prostate cancer. Methods: In total, 213 patients with prostate cancer admitted to our hospital from May 2021 to April 2023 were retrospectively enrolled as observation subjects. Based on the presence or absence of extracapsular extension, the patients were divided into occurrence (n = 70) and non-occurrence (n = 143) groups. The clinical data, PSA levels, Prostate Imaging Reporting and Data System (PI-RADS®), and MRI-ECE scores of the two groups were compared. Results: In total, 80 patients were included in the occurrence (n = 40) and non-occurrence groups (n = 40), and no statistical significance was observed in the baseline data of the two groups. Preoperative PSA levels were significantly higher in the occurrence group than in the non-occurrence group, and the PI-RADS and MRI-ECE scores of each group differed significantly (p < 0.05). The area under the curve (AUC) for the combined determination of PSA levels and PI-RADS and MRI-ECE scores was 0.900, which was significantly higher than the AUC for the individual determination of the mentioned indicators (p < 0.05). Conclusions: The combination of MRI radiomics and PSA can accurately predict the extracapsular extension of prostate cancer; Thus, it is a favorable reference for subsequent precise diagnosis and treatment. (AU)

Characterization of the Effects of Low-Sodium Salt Substitution on Sensory Quality, Protein Oxidation, and Hydrolysis of Air-Dried Chicken Meat and Its Molecular Mechanisms Based on Tandem Mass Tagging-Labeled Quantitative Proteomics.

The effects of low-sodium salt mixture substitution on the sensory quality, protein oxidation, and hydrolysis of air-dried chicken and its molecular mechanisms were investigated based on tandem mass tagging (TMT) quantitative proteomics. The composite salt formulated with 1.6% KCl, 0.8% MgCl2, and 5.6% NaCl was found to improve the freshness and texture quality scores. Low-sodium salt mixture substitution significantly decreased the carbonyl content (1.52 nmol/mg), surface hydrophobicity (102.58 µg), and dimeric tyrosine content (2.69 A.U.), and significantly increased the sulfhydryl content (74.46 nmol/mg) and tryptophan fluorescence intensity, suggesting that protein oxidation was inhibited. Furthermore, low-sodium salt mixture substitution significantly increased the protein hydrolysis index (0.067), and cathepsin B and L activities (102.13 U/g and 349.25 U/g), suggesting that protein hydrolysis was facilitated. The correlation results showed that changes in the degree of protein hydrolysis and protein oxidation were closely related to sensory quality. TMT quantitative proteomics indicated that the degradation of myosin and titin as well as changes in the activities of the enzymes, CNDP2, DPP7, ABHD12B, FADH2A, and AASS, were responsible for the changes in the taste quality. In addition, CNDP2, ALDH1A1, and NMNAT1 are key enzymes that reduce protein oxidation. Overall, KCl and MgCl2 composite salt substitution is an effective method for producing low-sodium air-dried chicken.

Exploring Tunable Properties, Solvent-Modulated Dynamics, and Novel C(sp3)-H Activation Mechanisms in Electron Donor-Acceptor Complexes.

Electron donor-acceptor (EDA) complex photochemistry has emerged as a vibrant area in visible-light-mediated synthetic radical chemistry. However, theoretical insights into the reaction mechanisms remain limited. Our study investigates the influence of solvent polarity and halogen atom types on radical reaction pathways in EDA complexes. We demonstrate that solvent polarity modulates the charge transfer and spatial arrangement within EDA complexes, thereby influencing their stability and reaction kinetics. Iodide ions play a crucial role in facilitating free radical generation and stabilizing reaction intermediates. Different halogen atom types exhibit distinct effects on radical reactions. Variations in radical concentration and solvent environment further affect the pathway selectivity. Additionally, light conditions influence the free radical generation and pathway selectivity. Our findings enhance the understanding of EDA complex photochemistry and radical reactions, offering insights for organic synthesis and photochemistry applications.

High-Fidelity, Low-Hysteresis Bionic Flexible Strain Sensors for Soft Machines.

Stretchable flexible strain sensors based on conductive elastomers are rapidly emerging as a highly promising candidate for popular wearable flexible electronic and soft-mechanical sensing devices. However, due to the intrinsic limitations of low fidelity and high hysteresis, existing flexible strain sensors are unable to exploit their full application potential. Herein, a design strategy for a successive three-dimensional crack conductive network is proposed to cope with the uncoordinated variation of the output resistance signal arising from the conductive elastomer. The electrical characteristics of the sensor are dominated by the successive crack conductive network through a greater resistance variation and a concise sensing mechanism. As a result, the developed elastomer bionic strain sensors exhibit excellent sensing performance in terms of a smaller overshoot response, a lower hysteresis (∼2.9%), and an ultralow detection limit (0.00179%). What's more, the proposed strategy is universal and applicable to many conductive elastomers with different conductive fillers (including 0-D, 1-D, and 2-D conductive fillers). This approach improves the sensing signal accuracy and reliability of conductive elastomer strain sensors and holds promising potential for various applications in the fields of e-skin and soft robotic systems.

BMP9-ID1 Pathway Attenuates N6-Methyladenosine Levels of CyclinD1 to Promote Cell Proliferation in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a highly lethal malignant neoplasm, and the involvement of bone morphogenetic protein 9 (BMP9) has been implicated in the pathogenesis of liver diseases and HCC. Our goal was to investigate the role of BMP9 signaling in regulating N6-methyladenosine (m6A) methylation and cell cycle progression, and evaluate the therapeutic potential of BMP receptor inhibitors for HCC treatment. We observed that elevated levels of BMP9 expression in tumor tissues or serum samples from HCC patients were associated with a poorer prognosis. Through in vitro experiments utilizing the m6A dot blotting assay, we ascertained that BMP9 reduced the global RNA m6A methylation level in Huh7 and Hep3B cells, thereby facilitating their cell cycle progression. This effect was mediated by an increase in the expression of the inhibitor of DNA-binding protein 1 (ID1). Additionally, using methylated RNA immunoprecipitation qPCR(MeRIP-qPCR), we showed that the BMP9-ID1 pathway promoted CyclinD1 expression by decreasing the m6A methylation level in the 5' UTR of mRNA. This occurred through the upregulation of the fat mass and obesity-associated protein (FTO) in Huh7 and Hep3B cells. In our in vivo mouse xenograft models, we demonstrated that blocking the BMP receptor with LDN-212854 effectively suppressed HCC growth and induced global RNA m6A methylation. Overall, our findings indicate that the BMP9-ID1 pathway promotes HCC cell proliferation by down-regulating the m6A methylation level in the 5' UTR of CyclinD1 mRNA. Targeting the BMP9-ID1 pathway holds promise as a potential therapeutic strategy for treating HCC.

Identification of CD8+ T-cell exhaustion signatures for prognosis in HBV-related hepatocellular carcinoma patients by integrated analysis of single-cell and bulk RNA-sequencing.

BACKGROUND: HBV infection is the leading risk factor for HCC. HBV infection has been confirmed to be associated with the exhaustion status of CD8+ T cells and immunotherapeutic efficacy in HCC. In this study, we aimed to investigate the prognostic value of the CD8+ T-cell exhaustion signature and immunotherapy response in patients with HBV-related HCC. METHODS: We identified different clusters of HBV-related HCC cells by single-cell RNA sequencing (scRNA-seq) and identified CD8+ T-cell exhaustion-related genes (TERGs) by pseudotime analysis. We conducted differential expression analysis and LASSO Cox regression to detect genes and construct a CD8+ T-cell exhaustion index (TEI). We next combined the TEI with other clinicopathological factors to design a prognostic nomogram for HCC patients. We also analysed the difference in the TEI between the non-responder and responder groups during anti-PD-L1 therapy. In addition, we investigated how HBV induces CD8+ T lymphocyte exhaustion through the inhibition of tyrosine metabolism in HCC using gene set enrichment analysis and RT‒qPCR. RESULTS: A CD8+ T-cell exhaustion index (TEI) was established with 5 TERGs (EEF1E1, GAGE1, CHORDC1, IKBIP and MAGOH). An AFP level > 500 ng, vascular invasion, histologic grade (G3-G4), advanced TNM stage and poor five-year prognosis were related to a higher TEI score, while HBV infection was related to a lower TEI score. Among those receiving anti-PD-L1 therapy, responders had lower TEIs than non-responders did. The TEI also serves as an independent prognostic factor for HCC, and the nomogram incorporating the TEI, TNM stage, and vascular invasion exhibited excellent predictive value for the prognosis in HCC patients. RT‒qPCR revealed that among the tyrosine metabolism-associated genes, TAT (tyrosine aminotransferase) and HGD (homogentisate 1,2 dioxygenase) were expressed at lower levels in HBV-HCC than in non-HBV HCC. CONCLUSION: Generally, we established a novel TEI model by comprehensively analysing the progression of CD8+ T-cell exhaustion, which shows promise for predicting the clinical prognosis and potential immunotherapeutic efficacy in HBV-related HCC patients.