Bimetallic MOFs-Derived Metal Oxides Co3O4/SnO2 Microspheres for Ultrahigh Response n-Butanol Gas Sensors.

The construction of p-n heterojunctions is expected to be one of the effective means to improve gas sensitivity. In this research, p-n heterojunctions are successfully constructed by metal oxides derived from metal-organic frameworks (MOFs). MOFs-derived bimetallic Co3O4/SnO2 microspheres are prepared by precipitation. Gas-sensing performance shows that the Co3O4/SnO2 sensor exhibits an extremely high response (Ra/Rg = 641) to 20 ppm of n-butanol at 200 °C, which is 19 times that of pristine SnO2. It can detect n-butanol gas at low concentrations, has good selectivity to alcohol gas, and reduces the interference of benzene gas. The improved gas sensitivity can be attributed to the formation of a stable heterojunction between Co3O4 and SnO2, resulting in a greater resistance change of Co3O4/SnO2. Co3O4/SnO2 inherits the characteristic of high specific surface area of MOFs, which provides abundant sites for the reaction of the target gas and oxygen molecules. Finally, the gas-sensing mechanism of the Co3O4/SnO2-based sensor is discussed in detail.

TGF-ß-induced lncRNA TBUR1 promotes EMT and metastasis in lung adenocarcinoma via hnRNPC-mediated GRB2 mRNA stabilization.

The transforming growth factor-ß (TGF-ß) signaling pathway is pivotal in inducing epithelial-mesenchymal transition (EMT) and promoting cancer metastasis. Long non-coding RNAs (lncRNAs) have emerged as significant players in these processes, yet their precise mechanisms remain elusive. Here, we demonstrate that TGF-ß-upregulated lncRNA 1 (TBUR1) is significantly activated by TGF-ß via Smad3/4 signaling in lung adenocarcinoma (LUAD) cells. Functionally, TBUR1 triggers EMT, enhances LUAD cell migration and invasion in vitro, and promotes metastasis in nude mice. Mechanistically, TBUR1 interacts with heterogeneous nuclear ribonucleoprotein C (hnRNPC) to stabilize GRB2 mRNA in an m6A-dependent manner. Clinically, TBUR1 is upregulated in LUAD tissues and correlates with poor prognosis, highlighting its potential as a prognostic biomarker and therapeutic target for LUAD. Taken together, our findings underscore the crucial role of TBUR1 in mediating TGF-ß-induced EMT and metastasis in LUAD, providing insights for future therapeutic interventions.

Epigenetic regulation by TET1 in gene-environmental interactions influencing susceptibility to congenital malformations.

The etiology of neural tube defects (NTDs) involves complex gene-environmental interactions. Folic acid (FA) prevents NTDs, but the mechanisms remain poorly understood and at least 30% of human NTDs resist the beneficial effects of FA supplementation. Here, we identify the DNA demethylase TET1 as a nexus of folate-dependent one-carbon metabolism and genetic risk factors post-neural tube closure. We determine that cranial NTDs in Tet1 -/- embryos occur at two to three times higher penetrance in genetically heterogeneous than in homogeneous genetic backgrounds, suggesting a strong impact of genetic modifiers on phenotypic expression. Quantitative trait locus mapping identified a strong NTD risk locus in the 129S6 strain, which harbors missense and modifier variants at genes implicated in intracellular endocytic trafficking and developmental signaling. NTDs across Tet1 -/- strains are resistant to FA supplementation. However, both excess and depleted maternal FA diets modify the impact of Tet1 loss on offspring DNA methylation primarily at neurodevelopmental loci. FA deficiency reveals susceptibility to NTD and other structural brain defects due to haploinsufficiency of Tet1. In contrast, excess FA in Tet1 -/- embryos drives promoter DNA hypermethylation and reduced expression of multiple membrane solute transporters, including a FA transporter, accompanied by loss of phospholipid metabolites. Overall, our study unravels interactions between modified maternal FA status, Tet1 gene dosage and genetic backgrounds that impact neurotransmitter functions, cellular methylation and individual susceptibilities to congenital malformations, further implicating that epigenetic dysregulation may underlie NTDs resistant to FA supplementation.

The continuous increased stability of sediment dissolved organic matter implies ecosystem degradation of lakes in the cold and arid regions.

The characteristics of lake dissolved organic matter (DOM) pool and lake ecosystem interact, and studying the responses between sediment DOM characteristics and lake ecosystem changes may shed light on the inherent connection between ecosystem evolution and carbon biogeochemical cycles. Lakes in cold and arid regions are sensitive to changes and accumulate large amounts of carbon as DOM, which may provide a window into more explicit relationships between ecosystem evolution and changes in sediment DOM characteristics in time dimension. However, considerable blind spots exist in the responses between the sediment DOM and ecosystem evolution on time scale and the underlying mechanisms. In this study, multiple approaches were combined to investigate the relationship between the variation trend of sediment DOM characteristics and the evolution of fragile lake ecosystems across three different lake ecosystems in cold and arid regions of China. A strong positive relationship between sediment DOM stabilities, especially humification, and ecosystem degradation was found, consistent for the three lakes. Ultra-high-resolution mass spectrometry and structural equation modeling revealed that the changes of ecosystems affected sediment DOM stability through direct pathways (0.24), such as the contents of terrestrial DOM in lake DOM pool, and indirect pathways, including algae-mediated (0.43) and salinity-mediated pathways (0.22), which all increased the contents of refractory DOM in the lake DOM pool and sediments. Based on the fact that DOM stability changes could act on the ecosystem in turn, a possible positive feedback mechanism between ecosystem degradation and increased DOM stability was further inferred. These results suggested that the continuous increased stability of sediment DOM in may implies ecosystem degradation of lakes in the cold and arid regions. This study provides a new perspective for recognizing ecosystem evolution through sediment DOM and improves the understanding of the interaction of lake ecosystem evolution and the biogeochemical cycle of DOM.

Epidemiology and Triggers of Severe Perioperative Anaphylaxis: An 8-Year Single-Center Study.

OBJECTIVE: To determine the features, rescue measures, outcomes, re-allergic reactions, and independent risk factors associated with severe anaphylaxis during surgery. DESIGN: Instances of severe perioperative anaphylaxis were identified through perioperative electronic records, adverse event reporting records, and surveys of anesthesiologists. Confirmed cases were randomly matched 4:1 with control cases on the same operation day. Patient risk factors, surgery type, anesthetic technique, and perioperative medications, fluids, and blood transfusions were given in instances of severe perioperative anaphylaxis were compared with control cases. SETTING: A tertiary hospital in China. PATIENTS: All patients undergoing surgery and anesthesia in the operating room from January 2014 to February 2022. MEASUREMENTS: Incidence and the independent risk factors for severe perioperative anaphylaxis. MAIN RESULTS: Ninety-seven patients experienced severe perioperative allergic responses during the 266,033 surgeries performed, with an incidence rate of 3.6 per 10,000. Three of 97 anaphylaxis patients experienced a severe allergic reaction again during the second surgery. The nested case-control study revealed that the independent triggers during surgery were allergy history (odds ratio 5.23; 95% confidence interval [CI], 2.35-11.68; p < 0.001), cisatracurium use (odds ratio 5.03; 95% CI, 1.22-20.70; p < 0.001), hydroxyethyl starch 130/0.4 use (odds ratio 5.36; 95% CI, 2.99-9.60; p =0.025), and allogeneic plasma (odds ratio 11.02; 95% CI, 3.78-35.95; p < 0.001). CONCLUSIONS: Perioperative severe anaphylaxis is a rare but life-threatening complication. Previous allergic history, cisatracurium, hydroxyethyl starch 130/0.4, and allogeneic plasma may be the independent triggers. Early diagnosis of anaphylaxis and the timely administration of epinephrine are critical to allergic treatment. Avoiding exposure to allergens is effective for preventing severe allergic responses and the efficacy of glucocorticoids and antihistamines is controversial.

Two-pore channel 2 is required for soluble adenylyl cyclase-dependent regulation of melanosomal pH and melanin synthesis.

Melanosomal pH is important for the synthesis of melanin as the rate-limiting enzyme, tyrosinase, is very pH-sensitive. The soluble adenylyl cyclase (sAC) signaling pathway was recently identified as a regulator of melanosomal pH in melanocytes; however, the melanosomal proteins critical for sAC-dependent regulation of melanosomal pH were undefined. We now systematically examine four well-characterized melanosomal membrane proteins to determine whether any of them are required for sAC-dependent regulation of melanosomal pH. We find that OA1, OCA2, and SLC45A2 are dispensable for sAC-dependent regulation of melanosomal pH. In contrast, TPC2 activity is required for sAC-dependent regulation of melanosomal pH and melanin synthesis. In addition, activation of TPC2 by NAADP-AM rescues melanosomal pH alkalinization and reduces melanin synthesis following pharmacologic or genetic inhibition of sAC signaling. These studies establish TPC2 as a critical melanosomal protein for sAC-dependent regulation of melanosomal pH and pigmentation.

Secretome profiling reveals acute changes in oxidative stress, brain homeostasis, and coagulation following short-duration spaceflight.

As spaceflight becomes more common with commercial crews, blood-based measures of crew health can guide both astronaut biomedicine and countermeasures. By profiling plasma proteins, metabolites, and extracellular vesicles/particles (EVPs) from the SpaceX Inspiration4 crew, we generated "spaceflight secretome profiles," which showed significant differences in coagulation, oxidative stress, and brain-enriched proteins. While >93% of differentially abundant proteins (DAPs) in vesicles and metabolites recovered within six months, the majority (73%) of plasma DAPs were still perturbed post-flight. Moreover, these proteomic alterations correlated better with peripheral blood mononuclear cells than whole blood, suggesting that immune cells contribute more DAPs than erythrocytes. Finally, to discern possible mechanisms leading to brain-enriched protein detection and blood-brain barrier (BBB) disruption, we examined protein changes in dissected brains of spaceflight mice, which showed increases in PECAM-1, a marker of BBB integrity. These data highlight how even short-duration spaceflight can disrupt human and murine physiology and identify spaceflight biomarkers that can guide countermeasure development.

Peripapillary atrophy area predicts the decrease of macular choroidal thickness in young adults during myopia progression.

OBJECTIVE: This study aimed to investigate the influence of peripapillary atrophy (PPA) area and axial elongation on the longitudinal changes in macular choroidal thickness (ChT) in young individuals with myopia. METHODS AND ANALYSIS: In this longitudinal investigation, 431 eyes-342 categorised as non-high myopia (non-HM) and 89 as HM-were examined for 2 years. Participants were examined with swept-source optical coherence tomography. The macular ChT, PPA area and axial length (AL) were measured at baseline and follow-up visits. Multiple regression analysis was performed to identify factors associated with ChT changes. The areas under the receiver operating characteristic curves were analysed to ascertain the predictive capacity of the PPA area and axial elongation for the reduction in macular ChT. RESULTS: Initial measurements revealed that the average macular ChT was 240.35±56.15 µm in the non-HM group and 198.43±50.27 µm in the HM group (p<0.001). It was observed that the HM group experienced a significantly greater reduction in average macular ChT (-7.35±11.70 µm) than the non-HM group (-1.85±16.95 µm, p=0.004). Multivariate regression analysis showed that a greater reduction of ChT was associated with baseline PPA area (ß=-26.646, p<0.001) and the change in AL (ß=-35.230, p<0.001). The combination of the baseline PPA area with the change in AL was found to be effective in predicting the decrease in macular ChT, with an area under the curve of 0.741 (95% CI 0.694 to 0.787). CONCLUSION: Over 2 years, eyes with HM exhibit a more significant decrease in ChT than those without HM. Combining the baseline PPA area with the change in AL could be used to predict the decrease of macular ChT.

Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.

OBJECTIVE: Artificial intelligence (AI) holds enormous potential for noninvasively identifying patients with rectal cancer who could achieve pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT). We aimed to conduct a meta-analysis to summarize the diagnostic performance of image-based AI models for predicting pCR to nCRT in patients with rectal cancer. METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search of PubMed, Embase, Cochrane Library, and Web of Science was performed from inception to July 29, 2023. Studies that developed or utilized AI models for predicting pCR to nCRT in rectal cancer from medical images were included. The Quality Assessment of Diagnostic Accuracy Studies-AI was used to appraise the methodological quality of the studies. The bivariate random-effects model was used to summarize the individual sensitivities, specificities, and areas-under-the-curve (AUCs). Subgroup and meta-regression analyses were conducted to identify potential sources of heterogeneity. Protocol for this study was registered with PROSPERO (CRD42022382374). RESULTS: Thirty-four studies (9933 patients) were identified. Pooled estimates of sensitivity, specificity, and AUC of AI models for pCR prediction were 82% (95% CI: 76-87%), 84% (95% CI: 79-88%), and 90% (95% CI: 87-92%), respectively. Higher specificity was seen for the Asian population, low risk of bias, and deep-learning, compared with the non-Asian population, high risk of bias, and radiomics (all P < 0.05). Single-center had a higher sensitivity than multi-center (P = 0.001). The retrospective design had lower sensitivity (P = 0.012) but higher specificity (P < 0.001) than the prospective design. MRI showed higher sensitivity (P = 0.001) but lower specificity (P = 0.044) than non-MRI. The sensitivity and specificity of internal validation were higher than those of external validation (both P = 0.005). CONCLUSIONS: Image-based AI models exhibited favorable performance for predicting pCR to nCRT in rectal cancer. However, further clinical trials are warranted to verify the findings.

Ocular trauma from Lawn mower accidents: Clinical insights, visual outcomes and microbial profiles.

AIMS: To characterize the clinical features of ocular trauma resulting from lawn mower, identify determinants of unfavorable final visual acuity (FVA), and assess the spectrum of microbial in posttraumatic endophthalmitis. METHODS: This retrospective study enrolled patients who experienced ocular trauma due to lawn mower at Zhongshan Ophthalmic Center from January 2013 to August 2021. Demographics, clinical features, isolated microorganisms, risk factors influencing reduced visual acuity, treatment regimens, and utilization of eyewear were collected. RESULTS: The study included 140 participants (140 eyes) (49.47 ± 12.03 years, 95% male). The predominant injury manifestations were penetrating globe injuries (75.7%) and intraocular foreign bodies (51.4%). Endophthalmitis occurred in 35 cases (25%) and Bacillus cereus (23.5%) was the primary pathogen, followed by Staphylococcus epidermidis (11.8%) and Streptococcus species (11.8%). Following the initial assessment, where 77.9% of patients had initial visual acuity (IVA) at grade IV (ranging from light perception to 4/200) and only 0.7% attained grade I (better than 20/40), post-treatment results revealed that 5.7% achieved FVA at grade I, with a concurrent decrease in patients with grade IV vision to 64.3%. Multivariate logistic regression revealed that injury protection (p < 0.001, OR = 0.237, 95% CI = 0.126-0.446), IVA (p = 0.001, OR = 4.102, 95% CI = 1.730-9.729), and retinal detachment (p = 0.042, OR = 8.105, 95% CI = 1.075-61.111) were significant independent risk factors impacting FVA. CONCLUSION: Lawn mower often cause severe ocular injuries, with high-velocity metal foreign bodies that can lead to infections, most commonly caused by Bacillus cereus. Correct use of protective gear, initial vision assessment, and detecting retinal detachment are crucial for visual prognosis.

Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study.

Background: Previous deep learning models have been proposed to predict the pathological complete response (pCR) and axillary lymph node metastasis (ALNM) in breast cancer. Yet, the models often leveraged multiple frameworks, required manual annotation, and discarded low-quality images. We aimed to develop an automated and reusable deep learning (AutoRDL) framework for tumor detection and prediction of pCR and ALNM using ultrasound images with diverse qualities. Methods: The AutoRDL framework includes a You Only Look Once version 5 (YOLOv5) network for tumor detection and a progressive multi-granularity (PMG) network for pCR and ALNM prediction. The training cohort and the internal validation cohort were recruited from Guangdong Provincial People's Hospital (GPPH) between November 2012 and May 2021. The two external validation cohorts were recruited from the First Affiliated Hospital of Kunming Medical University (KMUH), between January 2016 and December 2019, and Shunde Hospital of Southern Medical University (SHSMU) between January 2014 and July 2015. Prior to model training, super-resolution via iterative refinement (SR3) was employed to improve the spatial resolution of low-quality images from the KMUH. We developed three models for predicting pCR and ALNM: a clinical model using multivariable logistic regression analysis, an image model utilizing the PMG network, and a combined model that integrates both clinical and image data using the PMG network. Findings: The YOLOv5 network demonstrated excellent accuracy in tumor detection, achieving average precisions of 0.880-0.921 during validation. In terms of pCR prediction, the combined modelpost-SR3 outperformed the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.833 vs 0.822 vs 0.806 vs 0.790 vs 0.712, all p < 0.05) in the external validation cohort (KMUH). Consistently, the combined modelpost-SR3 exhibited the highest accuracy in ALNM prediction, surpassing the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.825 vs 0.806 vs 0.802 vs 0.787 vs 0.703, all p < 0.05) in the external validation cohort 1 (KMUH). In the external validation cohort 2 (SHSMU), the combined model also showed superiority over the clinical and image models (0.819 vs 0.712 vs 0.806, both p < 0.05). Interpretation: Our proposed AutoRDL framework is feasible in automatically predicting pCR and ALNM in real-world settings, which has the potential to assist clinicians in optimizing individualized treatment options for patients. Funding: National Key Research and Development Program of China (2023YFF1204600); National Natural Science Foundation of China (82227802, 82302306); Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (JNU1AF-CFTP-2022-a01201); Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); and Postdoctoral Science Foundation of China (2022M721349).

Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of structurally identified and yet-undefined metabolites across tissue cryosections. While numerous software packages enable pixel-by-pixel imaging of individual metabolites, the research community lacks a discovery tool that images all metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs informs discovery of unanticipated molecules contributing to shared metabolic pathways, uncovers hidden metabolic heterogeneity across cells and tissue subregions, and indicates single-timepoint flux through pathways of interest. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling and instrument drift, markedly enhances spatial image resolution, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent tool to enhance knowledge obtained from current spatial metabolite profiling technologies.

An immobilized composite microbial material combined with slow release agents enhances oil-contaminated groundwater remediation.

Microbial remediation of oil-contaminated groundwater is often limited by the low temperature and lack of nutrients in the groundwater environment, resulting in low degradation efficiency and a short duration of effectiveness. In order to overcome this problem, an immobilized composite microbial material and two types of slow release agents (SRA) were creatively prepared. Three oil-degrading bacteria, Serratia marcescens X, Serratia sp. BZ-L I1 and Klebsiella pneumoniae M3, were isolated from oil-contaminated groundwater, enriched and compounded, after which the biodegradation rate of the Venezuelan crude oil and diesel in groundwater at 15 °C reached 63 % and 79 %, respectively. The composite microbial agent was immobilized on a mixed material of silver nitrate-modified zeolite and activated carbon with a mass ratio of 1:5, which achieved excellent oil adsorption and water permeability performance. The slow release processes of spherical and tablet SRAs (SSRA, TSRA) all fit well with the Korsmeyer-Peppas kinetic model, and the nitrogen release mechanism of SSRA N2 followed Fick's law of diffusion. The highest oil removal rates by the immobilized microbial material combined with SSRA N2 and oxygen SRA reached 94.9 % (sand column experiment) and 75.1 % (sand tank experiment) during the 45 days of remediation. Moreover, the addition of SRAs promoted the growth of oil-degrading bacteria based on microbial community analysis. This study demonstrates the effectiveness of using immobilized microbial material combined with SRAs to achieve a high efficiency and long-term microbial remediation of oil contaminated shallow groundwater.

An interpretable machine learning model based on contrast-enhanced CT parameters for predicting treatment response to conventional transarterial chemoembolization in patients with hepatocellular carcinoma.

OBJECTIVE: To explore the potential of pre-therapy computed tomography (CT) parameters in predicting the treatment response to initial conventional TACE (cTACE) in intermediate-stage hepatocellular carcinoma (HCC) and develop an interpretable machine learning model. METHODS: This retrospective study included 367 patients with intermediate-stage HCC who received cTACE as first-line therapy from three centers. We measured the mean attenuation values of target lesions on multi-phase contrast-enhanced CT and further calculated three CT parameters, including arterial (AER), portal venous (PER), and arterial portal venous (APR) enhancement ratios. We used logistic regression analysis to select discriminative features and trained three machine learning models via 5-fold cross-validation. The performance in predicting treatment response was evaluated in terms of discrimination, calibration, and clinical utility. Afterward, a Shapley additive explanation (SHAP) algorithm was leveraged to interpret the outputs of the best-performing model. RESULTS: The mean diameter, ECOG performance status, and cirrhosis were the important clinical predictors of cTACE treatment response, by multiple logistic regression. Adding the CT parameters to clinical variables showed significant improvement in performance (net reclassification index, 0.318, P < 0.001). The Random Forest model (hereafter, RF-combined model) integrating CT parameters and clinical variables demonstrated the highest performance on external validation dataset (AUC of 0.800). The decision curve analysis illustrated the optimal clinical benefits of RF-combined model. This model could successfully stratify patients into responders and non-responders with distinct survival (P = 0.001). CONCLUSION: The RF-combined model can serve as a robust and interpretable tool to identify the appropriate crowd for cTACE sessions, sparing patients from receiving ineffective and unnecessary treatments.

Biomolecular condensates create phospholipid-enriched microenvironments.

Proteins and RNA can phase separate from the aqueous cellular environment to form subcellular compartments called condensates. This process results in a protein-RNA mixture that is chemically different from the surrounding aqueous phase. Here, we use mass spectrometry to characterize the metabolomes of condensates. To test this, we prepared mixtures of phase-separated proteins and extracts of cellular metabolites and identified metabolites enriched in the condensate phase. Among the most condensate-enriched metabolites were phospholipids, due primarily to the hydrophobicity of their fatty acyl moieties. We found that phospholipids can alter the number and size of phase-separated condensates and in some cases alter their morphology. Finally, we found that phospholipids partition into a diverse set of endogenous condensates as well as artificial condensates expressed in cells. Overall, these data show that many condensates are protein-RNA-lipid mixtures with chemical microenvironments that are ideally suited to facilitate phospholipid biology and signaling.

Association between adjuvant chemotherapy and survival in stage I gastric cancer patients after curative resection.

Background: The efficacy of adjuvant chemotherapy (AC) on survival outcomes of patients with stage I gastric cancer (GC) after curative resection remains controversial. We aimed to determine whether these patients would benefit from AC. Methods: This retrospective study included patients with pathologically confirmed stage I GC who underwent curative resection between November 2010 and December 2020. Patients were divided into AC and non-AC groups, then a 1:1 propensity score matching (PSM) analysis was performed to minimize the selection bias. Potential risk factors including age, pN stage, pT stage, lymphovascular invasion, perineural invasion, tumor size, histological type, and carcinoembryonic antigen level were used as matching covariates. The recurrence-free survival (RFS) and disease-specific survival (DSS) were compared between groups using the Kaplan-Meier method. Results: A total of 902 consecutive patients were enrolled and 174 (19.3%) patients were treated with AC. PSM created 123 pairs of patients. Before PSM, patients receiving AC had lower 10-year RFS rates (90% vs 94.6%, P = 0.035) than those who did not receive AC; the two groups had similar 10-year DSS rates (93.8% vs 95.0%, P = 0.240). After PSM, there were no statistical differences in the 10-year RFS (90.9% vs 93.0%, P = 0.507) or DSS rates (93.5% vs 93.6%, P = 0.811) between the two groups. Similar results were found in the stage IA and IB subgroups. Moreover, these findings were not affected by AC cycles. Conclusions: The addition of AC could not provide survival benefits for patients with stage I GC after surgery and follow-up is thus recommended. However, large-scale randomized clinical trials are required.

Progesterone induces meiosis through two obligate co-receptors with PLA2 activity.

The steroid hormone progesterone (P4) regulates multiple aspects of reproductive and metabolic physiology. Classical P4 signaling operates through nuclear receptors that regulate transcription. In addition, P4 signals through membrane P4 receptors (mPRs) in a rapid nongenomic modality. Despite the established physiological importance of P4 nongenomic signaling, its detailed signal transduction remains elusive. Here, using Xenopus oocyte maturation as a well-established physiological readout of nongenomic P4 signaling, we identify the lipid hydrolase ABHD2 (α/ß hydrolase domain-containing protein 2) as an essential mPRß co-receptor to trigger meiosis. We show using functional assays coupled to unbiased and targeted cell-based lipidomics that ABHD2 possesses a phospholipase A2 (PLA2) activity that requires both P4 and mPRß. This PLA2 activity bifurcates P4 signaling by inducing mPRß clathrin-dependent endocytosis and producing lipid messengers that are G-protein coupled receptors agonists. Therefore, P4 drives meiosis by inducing the ABHD2 PLA2 activity that requires both mPRß and ABHD2 as obligate co-receptors.

Storage reliability prediction of electromechanical components based on virtual manufacturing and testing.

Electromechanical components (EMCs) such as relays and contactors have been used extensively in industrial and military areas. The storage reliability of these EMCs has a direct impact on the reliability of the system that contains them. However, during the design phase, it is difficult to predict the storage reliability of EMCs because of few failure rate data of parts, as well as limited testing time and budgets. To address these problems, a virtual Manufacturing and testing method is proposed in this paper, so as to simulate the storage degradation process of batch EMCs. By considering the influence of the quality screening process in the manufacturing process, as well as the unit-to-unit variability of EMCs on the storage degradation paths and the overall life distribution of batch products, the storage failure distribution function is obtained, based on Wiener process. At the same time, the distribution of the diffusion coefficient in the degradation model and the failure distribution model is quantified by introducing testing data of related products as priori information, so as to reflect the uncertainty of the storage degradation process of EMCs. A case study of an electromagnetic relay is carried out to illustrate the effectiveness of the proposed approach.

The future of green transportation: evaluating the impact of innovation in hybrid electric vehicles relating technologies on carbon dioxide emissions in Asia's top knowledge-based economies.

The significant contribution of the transportation sector to carbon dioxide emissions (CO2e) has become a developing concern for legislators and environmental experts. Innovation in hybrid electric vehicle-related technologies (IHVRTs) has been identified as a possible strategy for reducing CO2e in the transportation industry. Even though IHVRTs have the potential to reduce CO2e, there are insufficient studies on their impact in the top three Asian knowledge-based economies (Japan, South Korea, and Japan). This study attempts to address this gap in the literature by investigating the association between innovation in IHVRTs and CO2e in the top three Asian knowledge-based economies, with independent variables gross domestic product per capita (GDPPC), economic complexity (ECC), renewable energy consumption (RNEC), and financial development (FD). The model's coefficients are estimated using the augmented mean group, which considers cross-country dependencies and country-specific effects. The empirical findings indicate that IHVRTs have a substantial negative effect on CO2e. In addition, FD has a favorable relationship with CO2e, whereas ECC has a negative relationship with CO2e. The results also demonstrated that RNEC reduces CO2e, whereas the GDPPC reduces CO2e. The policy implications of the results imply an urgent need for additional investment in IHVRTs and a transition towards more environmentally conscious and less ecologically damaging economic activity.