Does "National Civilized City" policy mitigate air pollution in China? A spatial Durbin difference-in-differences analysis.

"National Civilized City" (NCC) is regarded as China's highest honorary title and most valuable city brand. To win and maintain the "golden city" title, municipal governments must pay close attention to various key appraisal indicators, mainly environmental ones. In this study we verify whether cities with the title are more likely to mitigate SO2 pollution. We adopt the spatial Durbin difference-in-differences (DID) model and use panel data of 283 Chinese cities from 2003 to 2018 to analyze the local (direct) and spillover effects (indirect) of the NCC policy on SO2 pollution. We find that SO2 pollution in Chinese cities is not randomly distributed in geography, suggesting the existence of spatial spillovers and possible biased estimates. Our study treats the NCC policy as a quasi-experiment and incorporates spatial spillovers of NCC policy into a classical DID model to verify this assumption. Our findings show: (1) The spatial distribution of SO2 pollution represents strong spatial spillovers, with the most highly polluted regions mainly situated in the North China Plain. (2) The Moran's I test results confirms significant spatial autocorrelation. (3) Results of the spatial Durbin DID models reveal that the civilized cities have indeed significantly mitigated SO2 pollution, indicating that cities with the honorary title are acutely aware of the environment in their bid to maintain the golden city brand. As importantly, we notice that the spatial DID term is also significant and negative, implying that neighboring civilized cities have also mitigated their own SO2 pollution. Due to demonstration and competition effects, neighboring cities that won the title ostensibly motivates local officials to adopt stringent policies and measures for lowering SO2 pollution and protecting the environment in competition for the golden title. The spatial autoregressive coefficient was significant and positive, indicating that SO2 pollution of local cities has been deeply affected by neighbors. A series of robustness check tests also confirms our conclusions. Policy recommendations based on the findings for protecting the environment and promoting sustainable development are proposed.

A mix & act liposomes of phospholipase A2-phosphatidylserine for acute brain detoxification by blood‒brain barrier selective-opening.

In the treatment of central nervous system disease, the blood-brain barrier (BBB) is a major obstruction to drug delivery that must be overcome. In this study, we propose a brain-targeted delivery strategy based on selective opening of the BBB. This strategy allows some simple bare nanoparticles to enter the brain when mixed with special opening material; however, the BBB still maintains the ability to completely block molecules from passing through. Based on the screening of BBB opening and matrix delivery materials, we determined that phospholipase A2-catalyzed 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoserine liposomes can efficiently carry drugs into the brain immediately. At an effective dose, this delivery system is safe, especially with its effect on the BBB being reversible. This mix & act delivery system has a simple structure and rapid preparation, making it a strong potential candidate for drug delivery across the BBB.

Wi-CHAR: A WiFi Sensing Approach with Focus on Both Scenes and Restricted Data.

Significant strides have been made in the field of WiFi-based human activity recognition, yet recent wireless sensing methodologies still grapple with the reliance on copious amounts of data. When assessed in unfamiliar domains, the majority of models experience a decline in accuracy. To address this challenge, this study introduces Wi-CHAR, a novel few-shot learning-based cross-domain activity recognition system. Wi-CHAR is meticulously designed to tackle both the intricacies of specific sensing environments and pertinent data-related issues. Initially, Wi-CHAR employs a dynamic selection methodology for sensing devices, tailored to mitigate the diminished sensing capabilities observed in specific regions within a multi-WiFi sensor device ecosystem, thereby augmenting the fidelity of sensing data. Subsequent refinement involves the utilization of the MF-DBSCAN clustering algorithm iteratively, enabling the rectification of anomalies and enhancing the quality of subsequent behavior recognition processes. Furthermore, the Re-PN module is consistently engaged, dynamically adjusting feature prototype weights to facilitate cross-domain activity sensing in scenarios with limited sample data, effectively distinguishing between accurate and noisy data samples, thus streamlining the identification of new users and environments. The experimental results show that the average accuracy is more than 93% (five-shot) in various scenarios. Even in cases where the target domain has fewer data samples, better cross-domain results can be achieved. Notably, evaluation on publicly available datasets, WiAR and Widar 3.0, corroborates Wi-CHAR's robust performance, boasting accuracy rates of 89.7% and 92.5%, respectively. In summary, Wi-CHAR delivers recognition outcomes on par with state-of-the-art methodologies, meticulously tailored to accommodate specific sensing environments and data constraints.

The Failure Mechanism of Micro Thermoelectric Devices under the Action of the Temperature Field.

The micro thermoelectric device (m-TED) boasts features such as adjustable volume, straightforward structure, and precise, rapid temperature control, positioning it as the only current solution for managing the temperature of microelectronic systems. It is extensively utilized in 5G optical modules, laser lidars, and infrared detection. Nevertheless, as the size of the m-TED diminishes, the growing proportion of interface damages the device's operational reliability, constraining the advancement of the m-TED. In this study, we used commercially available bismuth telluride materials to construct the m-TED. The device's reliability was tested under various temperatures: -40, 85, 125, and 150 °C. By deconstructing and analyzing the devices that failed during the tests, we discovered that the primary cause of device failure was the degradation of the solder layer. Moreover, we demonstrated that encapsulating the device with polydimethylsiloxane (PDMS) could effectively delay the deterioration of its performance. This study sparks new insights into the service reliability of m-TEDs and paves the way for further optimizing device interface design and enhancing the device manufacturing process.

Urticaria in infants: A single-institution retrospective study.

Urticaria in infants can cause significant anxiety in parents, especially if a trigger cannot be identified. In a retrospective study of 246 infants seen for urticaria of unknown etiology at Boston Children's Hospital, 88.2% had resolution of urticaria within 6 weeks. The etiology of urticaria was ultimately established in 62.6% (72/115) of acute urticaria and 12.5% (2/16) of chronic urticaria cases with follow-up data. Pediatric healthcare providers can counsel families that while etiology of urticaria is never determined in over 40% of infants, symptoms are most likely to resolve spontaneously.

UBR5 forms ligand-dependent complexes on chromatin to regulate nuclear hormone receptor stability.

Nuclear hormone receptors (NRs) are ligand-binding transcription factors that are widely targeted therapeutically. Agonist binding triggers NR activation and subsequent degradation by unknown ligand-dependent ubiquitin ligase machinery. NR degradation is critical for therapeutic efficacy in malignancies that are driven by retinoic acid and estrogen receptors. Here, we demonstrate the ubiquitin ligase UBR5 drives degradation of multiple agonist-bound NRs, including the retinoic acid receptor alpha (RARA), retinoid x receptor alpha (RXRA), glucocorticoid, estrogen, liver-X, progesterone, and vitamin D receptors. We present the high-resolution cryo-EMstructure of full-length human UBR5 and a negative stain model representing its interaction with RARA/RXRA. Agonist ligands induce sequential, mutually exclusive recruitment of nuclear coactivators (NCOAs) and UBR5 to chromatin to regulate transcriptional networks. Other pharmacological ligands such as selective estrogen receptor degraders (SERDs) degrade their receptors through differential recruitment of UBR5 or RNF111. We establish the UBR5 transcriptional regulatory hub as a common mediator and regulator of NR-induced transcription.

Negatively charged phospholipids doped liposome delivery system for mRNA with high transfection efficiency and low cytotoxicity.

Messenger RNA (mRNA) has become one of the most potential drugs in recent years. However, efficient and safe delivery of fragile and easily degradable mRNA is a major challenge. Appropriate delivery system (DS) determines the final effect of mRNA. Cationic lipids play a crucial and decisive role in the entire DS, but also cause huge biosafety problems due to the high toxicity. In this study, a new DS for mRNA delivery that combines negatively charged phospholipids was developed in order to neutralize the positive charge and thus increase the safety. Further, the factors affecting mRNA transfection from cell to animal were investigated. The mRNA DS with optimum condition of lipid composition, proportions, structure, and transfection time was synthesized. Adding an appropriate amount of the anionic lipid to liposomes could increase the safety while maintaining the original transfection efficiency. For transporting mRNA in vivo, requirements regarding the mRNA encapsulation and releasing rate should be further considered to optimize DS design and preparation.

Effects of visceral adipose tissue on anti-tumour necrosis factor-α in Crohn's disease.

Background: It remains unclear whether visceral adipose tissue (VAT) can predict the response of patients with Crohn's disease (CD) to anti-tumour necrosis factor-α (anti-TNF-α) therapy. Objectives: This study aimed to investigate whether VAT predicts the efficacy of infliximab (IFX) for different sites of CD and its relationship with serum TNF-α levels and IFX serum trough concentration. Design: This is a multicentre retrospective study. Methods: Patients with CD treated with IFX from January 2014 to January 2021 were included. The perimeter of the visceral adipose area was obtained by a Computed Tomography (CT) scan. Participants were classified according to the lesion site (L1, L2, and L3) and visceral fat area. The participants were divided into colon-uninvolved non-visceral obesity (L1-VATL), colon-uninvolved visceral obesity (L1-VATH), colon-involved non-visceral obesity (L2 + L3-VATL), and colon involved visceral obesity (L2 + L3-VATH) groups. The end points of this study were set as disease remission status at 6 and 12 months. Results: The final cohort included 140 patients. Regarding efficacy at 6 and 12 months, there was a significant difference between L1-VATL (73.8% versus 36.8%, p = 0.006) and L1-VATH (81.0% versus 47.4%, p = 0.008) groups. In the analysis of serum TNF-α levels and IFX serum trough concentrations, there was a significant difference between L1-VATL and L1-VATH (59.5 pg/mL versus 236.0 pg/mL, pTNF-α = 0.006), (10.0 µg/mL versus 0.4 µg/mL, pIFX = 0.000), and L1-VATH and L2 + L3-VATH (78.7 pg/mL versus 118.6 pg/mL, pTNF-α = 0.031), (0.4 µg/mL versus 6.40 µg/mL, pIFX = 0.017). Conclusion: In L1 patients, the VAT level predicted the efficacy of IFX, with high VAT values indicating poor efficacy. The VAT level may be a useful radiological marker to predict the efficacy of IFX in patients with various types of CD.

The efficacy and mechanism of vonoprazan-containing triple therapy in the eradication of Helicobacter pylori.

Purpose: Vonoprazan (VPZ) produces a strong acid-inhibitory effect, which can potentially eradicate Helicobacter Pylori (H. pylori). We aimed to assess whether a 14-day VPZ-containing triple therapy was safe and effective in the Chinese population and the potential mechanism. Methods: Enrolled patients confirmed to be infected with H. pylori were randomly divided into four groups: VPZ + doxycycline + furazolidone, VPZ + doxycycline + amoxicillin, esomeprazole (EPZ) + bismuth + doxycycline + furazolidone, and EPZ + colloidal bismuth + doxycycline + amoxicillin for 14 days. The eradication rate, medication adherence, and incidence of adverse events (AEs) were evaluated. Inhibition of H. pylori by VPZ and EPZ in vitro was assessed. H. pylori treated with appropriate concentrations of VPZ and EPZ were sequenced by transcriptome analysis to explore the antibacterial mechanism. Results: A higher eradication rate were observed in VPZ-containing triple therapy. No obvious differences were observed in medication adherence or the incidence of AEs. VPZ had no direct inhibitory effect on H. pylori, whereas EPZ directly inhibited H. pylori may through downregulated genes related to the ribosome. Conclusion: In the Chinese population, 14-day VPZ-containing triple therapy was safe and more effective and can be used clinically as first-line H. pylori treatment. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT05097846.

Risk assessment based on dose-responsive and time-responsive genes to build PLS-DA models for exogenously induced lung injury.

Xenobiotics can easily harm human lungs owing to the openness of the respiratory system. Identifying pulmonary toxicity remains challenging owing to several reasons: 1) no biomarkers for pulmonary toxicity are available that might help to detect lung injury; 2) traditional animal experiments are time-consuming; 3) traditional detection methods solely focus on poisoning accidents; 4) analytical chemistry methods hardly achieve universal detection. An in vitro testing system able to identify the pulmonary toxicity of contaminants from food, the environment, and drugs is urgently needed. Compounds are virtually infinite, whereas toxicological mechanisms are countable. Therefore, universal methods to identify and predict the risks of contaminants can be designed based on these well-known toxicity mechanisms. In this study, we established a dataset based on transcriptome sequencing of A549 cells upon treatment with different compounds. The representativeness of our dataset was analyzed using bioinformatics methods. Artificial intelligence methods, namely partial least squares discriminant analysis (PLS-DA) models, were employed for toxicity prediction and toxicant identification. The developed model predicted the pulmonary toxicity of compounds with a 92 % accuracy. These models were submitted to an external validation using highly heterogeneous compounds, which supported the accuracy and robustness of our developed methodology. This assay exhibits universal potential applications for water quality monitoring, crop pollution detection, food and drug safety evaluation, as well as chemical warfare agent detection.

A fast-acting brain-targeted nano-delivery system with ultra-simple structure for brain emergency poisoning rescue.

Treatment for acute brain conditions remains a major challenge owing to the unavailability of antidotes, especially for organophosphorus compounds, exposure to which leads to rapid death. Despite recent advances in brain-targeted nano delivery systems (BTNDS), the traditional ones which have been developed will likely not lead to the quick release of an antidote, which is essential to counteract fast neurotoxic effects. Herein, we present a BTNDS using thermosensitive liposomes, without the need for functionalization, to obtain a platform for brain-targeted delivery, which has a simple structure and thus can be easily synthesized and scaled-up. The brain-targeting effect of BTNDS was amplified by phospholipase A2 (PLA2), an inflammatory biomarker. The combination of PLA2 and BTNDS significantly improved brain targeting, leading to an excellent emergency rescue effect - 83- and 4.8-fold better cerebral AChE reactivation response and survival time, respectively. These findings provide a promising strategy to generate a facile, druggable, and effective BTNDS.

Contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) MASTER: A novel deep learning-based system in pancreatic mass diagnosis.

BACKGROUND AND AIMS: Distinguishing pancreatic cancer from nonneoplastic masses is critical and remains a clinical challenge. The study aims to construct a deep learning-based artificial intelligence system to facilitate pancreatic mass diagnosis, and to guide EUS-guided fine-needle aspiration (EUS-FNA) in real time. METHODS: This is a prospective study. The CH-EUS MASTER system is composed of Model 1 (real-time capture and segmentation) and Model 2 (benign and malignant identification). It was developed using deep convolutional neural networks and Random Forest algorithm. Patients with pancreatic masses undergoing CH-EUS examinations followed by EUS-FNA were recruited. All patients underwent CH-EUS and were diagnosed both by endoscopists and CH-EUS MASTER. After diagnosis, they were randomly assigned to undergo EUS-FNA with or without CH-EUS MASTER guidance. RESULTS: Compared with manual labeling by experts, the average overlap rate of Model 1 was 0.708. In the independent CH-EUS video testing set, Model 2 generated an accuracy of 88.9% in identifying malignant tumors. In clinical trial, the accuracy, sensitivity, and specificity for diagnosing pancreatic masses by CH-EUS MASTER were significantly better than that of endoscopists. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were respectively 93.8%, 90.9%, 100%, 100%, and 83.3% by CH-EUS MASTER guided EUS-FNA, and were not significantly different compared to the control group. CH-EUS MASTER-guided EUS-FNA significantly improved the first-pass diagnostic yield. CONCLUSION: CH-EUS MASTER is a promising artificial intelligence system diagnosing malignant and benign pancreatic masses and may guide FNA in real time. TRIAL REGISTRATION NUMBER: NCT04607720.

Uneven phosphoric acid interfaces with enhanced electrochemical performance for high-temperature polymer electrolyte fuel cells.

Ultrahigh mass transport resistance and excessive coverage of the active sites introduced by phosphoric acid (PA) are among the major obstacles that limit the performance of high-temperature polymer fuel cells, especially compared to their low-temperature counterparts. Here, an alternative strategy of electrode design with fibrous networks is developed to optimize the redistribution of acid within the electrode. Via structural tailoring with varied electrospinning parameters, uneven migration of PA with dispersed droplets is observed, subverting the immersion model of conventional porous electrode. Combining with experimental and calculation results, the microscaled uneven PA interfaces could not only provide extra diffusion pathways for oxygen but also minimize the thickness of PA layers. This electrode architecture demonstrates enhanced electrochemical performance of oxygen reduction within the PA phase, resulting in a 28% enhancement of the maximum power density for the optimally designed electrode as cathode compared to that of a conventional one.

Quantitative and qualitative analyses of metal ions in food and water by using a multicolor sensor array and chemometrics.

Rapid and accurate detection of toxic metal ions is the key to combating food contamination and environmental pollution. In sensor arrays, gold nanoparticles play a crucial role in monitoring metal ions based on surface plasmon resonance. However, identifying metal ions with unknown concentrations in a complex system through this assay is difficult because of its monotonous color change and weak anti-interference ability. To overcome these limitations, a sensitive, flexible, low-cost, and multicolor sensor array was designed herein. The applicability of the sensor array for the qualitative and quantitative analyses of metal ions in food and water was also verified. The developed sensor array could classify 14 metal ions (Cu2+, Fe2+, Fe3+, Mn2+, Ni2+, Zn2+, Cd2+, Cr3+, Co2+, Ba2+, K+, Tl+, Pb2+, and Hg2+) of unknown concentration with an accuracy of 100%. In addition, partial least squares models were established to quantify Tl+, Pb2+, and Hg2+ in water and rice samples, with square correlation coefficients (R2) of 0.9991, 0.9742, and 0.9731, respectively. This method can be used for accurate quantitative and qualitative analyses of heavy metal ions in water and food.

CT radiomics identifying non-responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer.

BACKGROUND AND PURPOSE: Early detection of non-response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT. MATERIALS AND METHODS: Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non-response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application. RESULTS: This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non-responders and 101 responders) and 64 patients in the validation cohort (21 non-responders and 43 responders). For predicting non-response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance. CONCLUSION: Pretreatment CT radiomics achieved satisfying performance in predicting non-response to nCRT and could be helpful to assist in treatment planning for patients with LARC.

An in vitro nerve agent brain poisoning transwell model for convenient and accurate antidote evaluation.

Nerve agent (NA) can inhibit acetylcholinesterase (AChE) causing seriously injury at extremely low doses. However, the cruel reality is that the lack of effective cerebral antidotes for treatment of NA poisoning. There is an urgent requirement for the large-scale evaluation and screening of antidotes. An effective NA antidote should include two characteristics: a) to permeate the blood-brain barrier (BBB); 2) to reactivate the inhibited AChE in brain. Existing methods for evaluating reactivators in vitro can only examine the reactivation effect, while the current Transwell model can only evaluate the drug penetration performance for crossing the barrier. In this work, brain microvascular endothelial cells (RBMECs) were inoculated to establish a Transwell model. AChE, NAs and antidotes of reactivators were added into the different chambers to simulate central poisoning and peripheral drug administration. This method can evaluate the reactivation ability and brain penetration ability of compounds at same time, which is a rapidly and accurately way for drug preliminary screening. In addition to small-molecule drugs, a liposomal nanoantidote loaded with the reactivator Asoxime chloride (HI-6)was prepared. This nanoantidote show high reactivation rate against the NA (sarin), evaluated by both this modified model in vitro and animal test, gaining the consistence results.

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OBJECTIVES: Digestive endoscopy is an important diagnostic and therapeutic tool for digestive system diseases. The artificial intelligence (AI)-assisted system in endoscopy (hereinafter referred to as AI in digestive endoscopy) has broad application prospects in the field of digestive endoscopy. The trust and acceptance of endoscopic subjects are the cornerstone of the research, application, and promotion of AI in digestive endoscopy. Currently, the tools for measuring the acceptance of AI in digestive endoscopy by subjects are limited at home and abroad. This study aims to develop a scale for measuring the acceptance of AI in digestive endoscopy by subjects, then to evaluate its reliability and validity. METHODS: By conducting literature research, an item pool and dimensions were constructed, and a preliminary scale was constructed using Delphi method. Through the first stage of the survey on the subjects, the reliability and validity of the scale were tested, and the revised scale was used for the second stage of survey on the subjects to further verify the structural validity of the scale. RESULTS: The acceptance scale for AI in digestive endoscopy included 11 items in 3 dimensions: accuracy, ethics, benefit and willingness. In the first stage of the survey, 351 valid questionnaires were collected, and the Cronbach's α was 0.864. The correlation coefficient between the total score of the scale and the score of the test item was 0.636, and the Kaiser-Meyer-Olkin (KMO) value in exploratory factor analysis was 0.788. In the second stage of the survey, 335 valid questionnaires were collected, and in confirmatory factor analysis, the χ2/df was 3.774, while the root mean squared error of approximation (RMSEA) was 0.091. CONCLUSIONS: Acceptance scale for AI in digestive endoscopy by subjects developed in this study has good reliability and validity.

Association between caregiver ability and quality of life for people with inflammatory bowel disease: The mediation effect of positive feelings of caregivers.

Inflammatory bowel disease (IBD) is an incurable digestive disease. Since patients have to live with it, improving patients' quality of life is important. Caregiver's positive feelings and closeness may have a positive effect on patients' quality of life. We hypothesized that caregiver's positive feeling affected patient's quality of life through caregiver's caring ability, and closeness might be the upstream of this chain. In this study, we conducted a single-center cross-sectional survey by questionnaire in China to tested the hypothesis. A total of 181 patient-caregiver pairs were included. The short version of the IBD questionnaire (SIBDQ), the twelve-item short-form health survey (SF-12), the positive aspects of caregiving (PAC) and Capacity Scale of caregivers were used to collect data. All the data were collected in one interview. Spearman correlation and Bootstrap method were used to analyze the data. Mediation analysis results indicated that caregiver's caring ability mediated the association between caregiver's positive feelings and patients' quality of life (p < 0.01), which explained 34.1% of the total variation of patients' quality of life. Mediation analysis results also revealed that patient-evaluated or caregiver-evaluated closeness had a positive effect on patients' quality of life through caregiver's positive feeling and caregiver's caring ability (p < 0.05), which explained 2.1 and 2.3% of the variation of patients' quality of life. Caregiver's positive feelings were related to caregivers' quality of life (p < 0.01), but there was no significant association between caregivers' ability and caregivers' quality of life. In summary, our model revealed that caregiver's positive feeling affected patients' quality of life through caregiver's caring ability.

Through-focus EUV multilayer defect compensation considering optical proximity correction.

Extreme ultraviolet (EUV) multilayer defects result in the degradation of through-focus imaging quality. The optical proximity effect is another crucial factor that degrades the imaging quality. Both the impacts of the defects and the optical proximity effects could be mitigated by modifying the original mask patterns. A heuristic-based defect compensation method considering optical proximity correction and through-focus optimization is proposed in this paper. The edge of the mask pattern and the insertion of sub-resolution assist features (SRAFs) are optimized by covariance matrix adaptation evolution strategy (CMA-ES) to compensate for the degradation of the imaging quality with a certain defocus range. New encoding strategies for the edge pixels of the mask pattern and the SRAFs are proposed and utilized in this paper to ensure the manufacturability of the mask and the efficiency of the optimization at the same time. The rigorous database approach based on the scattering matrix is adopted to simulate the mask diffraction spectrum efficiently. Simulations verify that the through-focus imaging quality of both the defective masks with bump defects and pit defects could be obviously improved by the proposed defect compensation method.