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Chemoresistance is a major obstacle to prolonging pancreatic ductal adenocarcinoma (PDAC) patient survival. TET1 is identified as the most important epigenetic modification enzyme that facilitates chemoresistance in cancers. However, the chemoresistance mechanism of TET1 in PDAC is unknown. This study aimed to determine the role of TET1 in the chemoresistance of PDAC. TET1-associated chemoresistance in PDAC was investigated in vitro and in vivo. The clinical significance of TET1 was analyzed in 228 PDAC patients by tissue microarray profiling. We identified that TET1 downregulation is caused by its promoter hypermethylation and correlates with poor survival in PDAC patients. In vitro and in vivo functional studies performed by silencing or overexpressing TET1 suggested that TET1 is able to suppress epithelial-mesenchymal transition (EMT) and sensitize PDAC cells to 5FU and gemcitabine. Then RNA-seq, whole genome bisulfite sequencing (WGBS) and ChIP-seq were used to explore the TET1-associated pathway, and showed that TET1 promotes the transcription of CHL1 by binding and demethylating the CHL1 promoter, which consequently inhibits the Hedgehog pathway. Additionally, inhibiting Hedgehog signaling by CHL1 overexpression or the Hedgehog pathway inhibitor, GDC-0449, reversed the chemoresistance induced by TET1 silencing. Regarding clinical significance, we found that high TET1 and high CHL1 expression predicted a better prognosis in resectable PDAC patients. In summary, we demonstrated that TET1 reverses chemoresistance in PDAC by downregulating the CHL1-associated Hedgehog signaling pathway. PDAC patients with a high expression levels of TET1 and CHL1 have a better prognosis.

Visual detection based on nanozymes has great potential for the rapid detection of metabolites in clinical analysis or home-based health management. In this work, iron and nitrogen co-doped carbon dots (Fe,N-CDs) were conveniently synthesized as a nanozyme for the visual detection of glucose (Glu) or cholesterol (Chol). Using inexpensive and readily available precursors, Fe,N-CDs with peroxidase-like activity were conveniently prepared through a simple hydrothermal method. Co-doping of Fe and N atoms enhanced the catalytic activity of the nanozyme. The nanozyme had a low Michaelis constant (Km) of 0.23 mM when hydrogen peroxide (H2O2) was used as the substrate. Free radical trapping experiments revealed that the reactive oxygen species (ROS) generated in the nanozyme-catalyzed process were superoxide anion radicals (•O2−), which can oxidize colorless 3,3′,5,5′-tetramethylbenzidine (TMB) to generate blue oxidation product (ox-TMB) with characteristics absorbance at 652 nm. Based on this mechanism, a colorimetric sensor was constructed to detect H2O2 ranging from 0.1 μM to 200 μM with a detection limit (DL) of 75 nM. In the presence of glucose oxidase (Gox) or Chol oxidase (Chox), Glu or Chol was oxidized, respectively, and generated H2O2. Based on this, indirect detection of Glu and Chol was realized with linear detection ranges of 5–160 μM and 2–200 μM and DLs of 2.8 μM and 0.8 μM, respectively. A paper-based visual detection platform was fabricated using Fe,N-CDs as nanozyme ink to prepare testing paper by inkjet printing. Using a smartphone to record the RGB values of the testing paper after the reaction, visual detection of Glu and Chol can be achieved with linear detection ranges of 5–160 μM (DL of 3.3 μM) and 2–200 μM (DL of 1.0 μM), respectively.

Medical wearables, non-invasive devices that measure physiological biomarkers, are potentially disruptive and powerful tools to promote health equity at scale. Here we describe our experiences designing, validating, and deploying wearable sensors in vulnerable patient populations to improve health outcomes. Wearable devices can provide personalised medicine at the point of need, potentially increasing access to health services and therefore improving health equity. Here the authors discuss their experiences developing wearable devices for vulnerable patient populations, including neonates and pregnant individuals.

BackgroundPancreatic neuroendocrine tumors (PanNETs) are indolent pancreatic tumors derived from neuroendocrine cells in pancreatic islets. To date, reliable predictors for identifying patients at high risk for recurrence after curative cancer resection are lacking. We aimed to determine independent predictors for high-risk PanNETs and patient outcomes after surgery.MethodsWe analyzed relevant clinicopathological parameters in 319 consecutive patients of derivation cohort 1 and 106 patients of validation cohort 2 who underwent pancreatectomy and were diagnosed with PanNETs. Association of tumor characteristics with recurrence-free survival (RFS) and overall survival (OS) was evaluated using Cox regression.ResultsPanNET grade 3 (G3), pancreatic duct dilatation, and perineural invasion were independent prognostic factors for RFS and were significantly associated with early recurrence (within 1.5 years) of PanNETs after curative resection (P = 0.019, P < 0.001, and P < 0.001, respectively). Using these factors, we established a novel risk factor panel (R-panel), which predicted early recurrence (P < 0.001, HR = 15.02, 95% CI 5.76–39.19). Predictive accuracy of this R-panel was favorable, with a C-index of 0.853, higher than AJCC TNM staging (0.713). We further built an integrated staging system combining R-panel scoring and TNM staging, which improved predictive probability of TNM staging. Finally, we showed that adjuvant therapy with long-acting somatostatin analogs (SSAs) significantly reduced postoperative recurrence (P < 0.001) and prolonged long-term survival (P = 0.021) in patients with the above risk factors.ConclusionWe identified a novel risk factor panel, which includes PanNET G3, pancreatic duct dilatation, and perineural invasion; this panel predicted early recurrence of PanNETs after curative resection. Patients with these risk factors can benefit from adjuvant therapy with SSAs.

In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster–Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the frame of discernment (FOD). Compared with some other uncertainty measures in Dempster–Shafer framework, the new measure focuses on the uncertain information represented by not only the mass function, but also the scale of the FOD, which means less information loss in information processing. After that, a new multi-sensor data fusion approach based on the weighted belief entropy is proposed. The rationality and superiority of the new multi-sensor data fusion method is verified according to an experiment on artificial data and an application on fault diagnosis of a motor rotor.

We propose a decaying cold dark matter model to explain the excess of electron recoil observed at the XENON1T experiment. In this scenario, the daughter dark matter from the parent dark matter decay easily obtains velocity large enough to saturate the peak of the electron recoil energy around 2.5 keV, and the observed signal rate can be fulfilled by the parent dark matter with a mass of order 10–200 MeV and a lifetime larger than the age of Universe. We verify that this model is consistent with experimental limits from dark matter detections, Cosmic microwave background and large scale structure experiments.

In numerous developing nations, the pervasive practice of crop residue incineration is a principal contributor to atmospheric contamination in agricultural operations. This study examines the repercussions of such biomass combustion on air quality during the autumnal harvest season, utilizing data acquired from satellite-based remote sensing of fire events and air pollution measurements. Employing wind direction information alongside difference-in-difference and fixed-effects methodologies, this investigation rectifies estimation inaccuracies stemming from the non-random distribution of combustion occurrences. The empirical findings reveal that agricultural residue burning precipitates an elevation in average PM2.5 and PM10 concentrations by approximately 27 and 22 μg/m 3 during the autumnal incineration period, respectively. Furthermore, air pollution attributed to residue burning in prominent grain-producing regions exceeds the national average by approximately 40%. By integrating economic paradigms into agri-environmental inquiries, this study offers novel insights and substantiation of the environmental expenditures engendered by crop residue burning, juxtaposed with extant meteorological and ecological research findings.

We consider the scenario of composite Higgs with partial compositeness to address the muon g-2 anomaly. We show that this anomaly is resolved by one-loop correction due to composite muon partners with mass scale of order TeVs and large Yukawa coupling to composite Higgs, which is different from interpretations of vectorlike lepton models. We present parameter space by imposing indirect constraints from precise measurements on Higgs, Z and oblique electroweak parameters. We analyze direct constraints in light of both Drell–Yan and Higgs-associated productions of the composite muon partners at high-luminosity LHC. It turns out that the parameter regions with the lighter composite muon mass below ∼325 GeV can be excluded at 2σ order by the Drell–Yan processes.

Background Circulating atherogenic index of plasma (AIP) levels has been proposed as a novel biomarker for dyslipidemia and as a predictor of insulin resistance (IR) risk. However, the association between AIP and the incidence of new-onset stroke, particularly in individuals with varying glucose metabolism status, remains ambiguous. Methods A total of 8727 participants aged 45 years or older without a history of stroke from the China Health and Retirement Longitudinal Study (CHARLS) were included in this study. The AIP was calculated using the formula log [Triglyceride (mg/dL) / High-density lipoprotein cholesterol (mg/dL)]. Participants were divided into four groups based on their baseline AIP levels: Q1 (AIP ≤ 0.122), Q2 (0.122 < AIP ≤ 0.329), Q3 (0.329 < AIP ≤ 0.562), and Q4 (AIP > 0.562). The primary endpoint was the occurrence of new-onset stroke events. The Kaplan–Meier curves, multivariate Cox proportional hazard models, and Restricted cubic spline analysis were applied to explore the association between baseline AIP levels and the risk of developing a stroke among individuals with varying glycemic metabolic states. Results During an average follow-up of 8.72 years, 734 participants (8.4%) had a first stroke event. The risk for stroke increased with each increasing quartile of baseline AIP levels. Kaplan–Meier curve analysis revealed a significant difference in stroke occurrence among the AIP groups in all participants, as well as in those with prediabetes mellitus (Pre-DM) and diabetes mellitus (DM) (all P values < 0.05). After adjusting for potential confounders, the risk of stroke was significantly higher in the Q2, Q3, and Q4 groups than in the Q1 group in all participants. The respective hazard ratios (95% confidence interval) for stroke in the Q2, Q3, and Q4 groups were 1.34 (1.05–1.71), 1.52 (1.19–1.93), and 1.84 (1.45–2.34). Furthermore, high levels of AIP were found to be linked to an increased risk of stroke in both pre-diabetic and diabetic participants across all three Cox models. However, this association was not observed in participants with normal glucose regulation (NGR) ( p  > 0.05). Restricted cubic spline analysis also demonstrated that higher baseline AIP levels were associated with higher hazard ratios for stroke in all participants and those with glucose metabolism disorders. Conclusions An increase in baseline AIP levels was significantly associated with the risk of stroke in middle-aged and elderly individuals, and exhibited distinct characteristics depending on the individual’s glucose metabolism status.

The nitrate ion (NO3−) is a typical pollutant in environmental samples, posing a threat to the aquatic ecosystem and human health. Therefore, rapid and accurate detection of NO3− is crucial for both the aquatic sciences and government regulations. Here we report the fabrication of an amino-functionalized, vertically ordered mesoporous silica film (NH2-VMSF) confining localized copper nanoparticles (CuNPs) for the electrochemical detection of NO3−. NH2-VMSF-carrying amino groups possess an ordered perpendicular nanochannel structure and ultrasmall nanopores, enabling the confined growth of CuNPs through the electrodeposition method. The resulting CuNPs/NH2-VMSF-modified indium tin oxide (ITO) electrode (CuNPs/NH2-VMSF/ITO) combines the electrocatalytic reduction ability of CuNPs and the electrostatic attraction capacity of NH2-VMSF towards NO3−. Thus, it is a rapid and sensitive electrochemical method for the determination of NO3− with a wide linear detection range of 5.0–1000 μM and a low detection limit of 2.3 μM. Direct electrochemical detection of NO3− in water samples (tap water, lake water, seawater, and rainwater) with acceptable recoveries ranging from 97.8% to 109% was performed, demonstrating that the proposed CuNPs/NH2-VMSF/ITO sensor has excellent reproducibility, regeneration, and anti-interference abilities.