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With the rapid development and application of container cloud computing-related technologies, more and more applications are being deployed to container cloud clusters. As an essential feature of container cloud platforms and cloud-native architecture, auto-scaling aims to automatically and quickly adjust the allocation of cloud resources according to the resource requirements of applications. Currently, widely used responsive auto-scaling methods, such as Kubernetes HPA, exhibit certain lags due to the startup time costs of containers and Pods. This lag makes it difficult to guarantee the service quality of applications when there is a sudden increase in online application load. This paper proposes a dynamic interval auto-scaling optimization method based on Informer time series prediction. By predicting online application load and dynamically determining the auto-scaling interval, sufficient resources are allocated to the application in advance. In the experiments conducted on the official World Cup forum load and Alibaba cluster CPU load, the Informer time series prediction algorithm demonstrated better long-sequence time series prediction capabilities compared to algorithms such as LSTM and RNN. In elastic scaling experiments, compared to Kubernetes HPA, the method proposed in this paper reduces the average application response time from 0.821 seconds to 0.692 seconds, and the SLA violation rate decreases from 18.277% to 9.157%. This indicates a significant improvement in the service quality metrics of online applications. Furthermore, the proposed method effectively maintains a balance between high CPU resource utilization and low application response time and SLA violation rate, which is something RNN-based elastic scaling method cannot achieve, as it can only reduce application response time and SLA violation rate by sacrificing CPU resource utilization.

Abstract Polycomb repressive complex 2 (PRC2) catalyzes the writing of the tri-methylated histone H3 at Lys27 (H3K27me3) epigenetic marker and suppresses the expression of genes, including tumor suppressors. The function of the complex can be partially antagonized by the SWI/SNF chromatin-remodeling complex. Previous studies have suggested that PRC2 is important for the proliferation of tumors with SWI/SNF loss-of-function mutations. In the present study, we have developed an EED-directed allosteric inhibitor of PRC2 termed BR0063, which exhibits anti-proliferative properties in a subset of solid tumor cell lines harboring mutations of the SWI/SNF subunits, SMARCA4 or ARID1A. Tumor cells sensitive to BR0063 exhibited several distinct phenotypes, including cell senescence, which was mediated by the up-regulation of CDKN2A/p16. Further experiments revealed that the expression of p16 was suppressed in the BR0063-resistant cells via DNA hypermethylation in the CpG island (CGI) promoter region, rather than via PRC2 occupancy. The expression of TET1, which is required for DNA demethylation, was found to be inversely correlated with p16 CGI methylation, and this may serve as a biomarker for the prediction of resistance to PRC2 inhibitors in SWI/SNF LOF tumors.

Abstract Background Chronic alcohol users often exhibit an increased minimum alveolar concentration (MAC) of sevoflurane, yet the specific mechanism remains unclear. It has been reported that ethanol exposure can upregulate the protein expression and enzyme activity of cytochrome P450 2E1 (CYP2E1). CYP2E1 is a key enzyme that converts 2–5% of sevoflurane into equimolar amounts of hexafluoroisopropanol (HFIP) and F−. This study aims to explore whether ethanol exposure could alter sevoflurane metabolism through CYP2E1 modulation, potentially explaining the increased MAC observed in alcohol users. Methods Eighty adult male Sprague-Dawley (SD) rats were randomly divided into two groups and received either 50% ethanol (dose: 3 g/kg) or 0.9% saline twice daily by gavage. After 1, 2, 3, and 4 weeks of gavage, ten rats were randomly selected from each group to undergo 1-hour anesthesia with 2.3% sevoflurane. Blood samples were collected after anesthesia to measure the concentration of free HFIP using gas chromatography. Additionally, the left lobe tissue of the liver was collected for the analysis of CYP2E1 protein expression by Western blot and CYP2E1 enzyme activity by colorimetric assay. Correlations between these parameters were analyzed using Pearson’s correlation. Results In the ethanol group, CYP2E1 expression, activity, and the concentration of free HFIP were significantly higher at all time points compared to the control group (P 0.05). Within-group comparisons indicated no significant changes in any of the parameters for the control group (P > 0.05). In the ethanol group, there was no difference in free HFIP concentration between the first and second weeks (P > 0.05), but a significant increase was observed in the third and fourth weeks (P

Abstract Purpose To build an Mult-Task Learning (MTL) based Artificial Intelligence(AI) model that can simultaneously predict clinical stage, histology, grade and LNM for cervical cancer before surgery. Methods This retrospective and prospective cohort study was conducted from January 2001 to March 2014 for the training set and from January 2018 to November 2021 for the validation set at Beijing Chaoyang Hospital, Capital Medical University. Preoperative clinical information of cervical cancer patients was used. An Artificial Neural Network (ANN) algorithm was used to build the MTL-based AI model. Accuracy and weighted F1 scores were calculated as evaluation indicators. The performance of the MTL model was compared with Single-Task Learning (STL) models. Additionally, a Turing test was performed by 20 gynecologists and compared with this AI model. Results A total of 223 cervical cancer cases were retrospectively enrolled into the training set, and 58 cases were prospectively collected as independent validation set. The accuracy of this cervical cancer AI model constructed with ANN algorithm in predicting stage, histology, grade and LNM were 75%, 95%, 86% and 76%, respectively. And the corresponding weighted F1 score were 70%, 94%, 86%, and 76%, respectively. The average time consumption of AI simultaneously predicting stage, histology, grade and LNM for cervical cancer was 0.01s (95%CI: 0.01–0.01) per 20 patients. The mean time consumption doctor and doctor with AI were 581.1s (95%CI: 300.0-900.0) per 20 patients and 534.8s (95%CI: 255.0-720.0) per 20 patients, respectively. Except for LNM, both the accuracy and F-score of the AI model were significantly better than STL AI, doctors and AI-assisted doctors in predicting stage, grade and histology. (P

BackgroundPneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection.MethodThis study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6.ResultThe first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6.ConclusionOur neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.

Objective: Appropriate medical imaging is important for value-based care. We aim to evaluate the performance of generative pretrained transformer 4 (GPT-4), an innovative natural language processing model, providing appropriate medical imaging automatically in different clinical scenarios. Methods: Institutional Review Boards (IRB) approval was not required due to the use of nonidentifiable data. Instead, we used 112 questions from the American College of Radiology (ACR) Radiology-TEACHES Program as prompts, which is an open-sourced question and answer program to guide appropriate medical imaging. We included 69 free-text case vignettes and 43 simplified cases. For the performance evaluation of GPT-4 and GPT-3.5, we considered the recommendations of ACR guidelines as the gold standard, and then three radiologists analyzed the consistency of the responses from the GPT models with those of the ACR. We set a five-score criterion for the evaluation of the consistency. A paired t-test was applied to assess the statistical significance of the findings. Results: For the performance of the GPT models in free-text case vignettes, the accuracy of GPT-4 was 92.9%, whereas the accuracy of GPT-3.5 was just 78.3%. GPT-4 can provide more appropriate suggestions to reduce the overutilization of medical imaging than GPT-3.5 (t = 3.429, P = 0.001). For the performance of the GPT models in simplified scenarios, the accuracy of GPT-4 and GPT-3.5 was 66.5% and 60.0%, respectively. The differences were not statistically significant (t = 1.858, P = 0.070). GPT-4 was characterized by longer reaction times (27.1 s in average) and extensive responses (137.1 words on average) than GPT-3.5. Conclusion: As an advanced tool for improving value-based healthcare in clinics, GPT-4 may guide appropriate medical imaging accurately and efficiently.

Abstract Background The significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM. Aim Our study presents a novel and significant contribution by developing an interpretable fusion model that effectively integrates both free‐text medical record data and structured laboratory data to predict LM in postoperative CRC patients. Methods We used a robust dataset of 1463 patients and leveraged state‐of‐the‐art natural language processing (NLP) and machine learning techniques to construct a two‐layer fusion framework that demonstrates superior predictive performance compared to single modal models. Our innovative two‐tier algorithm fuses the results from different data modalities, achieving balanced prediction results on test data and significantly enhancing the predictive ability of the model. To increase interpretability, we employed Shapley additive explanations to elucidate the contributions of free‐text clinical data and structured clinical data to the final model. Furthermore, we translated our findings into practical clinical applications by creating a novel NLP score‐based nomogram using the top 13 valid predictors identified in our study. Results The proposed fusion models demonstrated superior predictive performance with an accuracy of 80.8%, precision of 80.3%, recall of 80.5%, and an F1 score of 80.8% in predicting LMs. Conclusion This fusion model represents a notable advancement in predicting LMs for postoperative CRC patients, offering the potential to enhance patient outcomes and support clinical decision‐making.

The direct utilization of low-light images hinders downstream visual tasks. Traditional low-light image enhancement (LLIE) methods, such as Retinex-based networks, require image pairs. A spiking-coding methodology called intensity-to-latency has been used to gradually acquire the structural characteristics of an image. convLSTM has been used to connect the features. This study introduces a simplified DCENet to achieve unsupervised LLIE as well as the spiking coding mode of a spiking neural network. It also applies the comprehensive coding features of convLSTM to improve the subjective and objective effects of LLIE. In the ablation experiment for the proposed structure, the convLSTM structure was replaced by a convolutional neural network, and the classical CBAM attention was introduced for comparison. Five objective evaluation metrics were compared with nine LLIE methods that currently exhibit strong comprehensive performance, with PSNR, SSIM, MSE, UQI, and VIFP exceeding the second place at 4.4% (0.8%), 3.9% (17.2%), 0% (15%), 0.1% (0.2%), and 4.3% (0.9%) on the LOL and SCIE datasets. Further experiments of the user study in five non-reference datasets were conducted to subjectively evaluate the effects depicted in the images. These experiments verified the remarkable performance of the proposed method.

The operational parameters of plant protection unmanned aerial vehicles (UAVs) significantly impact spraying effectiveness, but the underlying mechanism remains unclear. This paper conducted a full factorial experiment with varying flight speeds, heights, and nozzle flow rates to collect parameter space data. Using the Kriging surrogate model, we characterized this parameter space and subsequently optimized the average deposition rate and coefficient of variation by employing a variable crossover (mutation) probability multi-objective genetic algorithm. In the obtained Pareto front, the average sedimentation rate is no less than 46%, with a maximum of 56.08%, and the CV coefficient is no more than 13.91%, with a minimum of only 8.42%. These optimized parameters enhance both the average deposition rate and spraying uniformity compared to experimental data. By employing these optimized parameters in practical applications, a balance between the maximum average deposition rate and minimum coefficient of variation can be achieved during UAV spraying, thereby reducing pesticide usage, promoting sustainable agriculture, and mitigating instances of missed spraying and re-spraying.

ABSTRACTHelicobacter pylori (H. pylori) has been regarded as a definite carcinogenic bacterium for gastric cancer (GC). This multi-omics research was designed to investigate the genetic, microbial, and metabolic changes of GC patients when they are infected with H. pylori. We first mined The Cancer Genome Atlas Stomach Adenocarcinoma (STAD) data to identify the key genes and critical pathways in H. pylori-positive individuals with GC compared to H. pylori-negative individuals with GC. Then, fresh stool samples were collected from GC individuals screened for eligibility, and we analyzed the microbial changes and metabolite alterations between H. pylori-positive and H. pylori-negative GC individuals. Finally, we tried to explore the interaction between key gut flora and metabolite changes in GC patients infected with H. pylori. We identified three genes (GCG, APOA1, and IGFBP1) with significant relevance to H. pylori infection, and the survival monogram based on the three H. pylori-related genes showed good predictive ability for overall survival among GC individuals. 16S rRNA sequencing showed that the abundance of Escherichia-Shigella, Bacteroides, Enterococcus, and Lactobacillus was upregulated in GC cases with H. pylori at the level of genus. There exists a great difference in alpha and beta diversity between H. pylori group and non-H. pylori group. The untargeted metabolome analysis identified 295 significant fecal metabolites, and the levels of penitrem E, auberganol, stercobilinogen, and lys thr are upregulated in the H. pylori group. Finally, correlation analysis showed that there exists a significant correlation between the fecal metabolites and gut bacterial strains. This is the first clinical research to investigate the difference between GC patients with H. pylori and GC patients without H. pylori via multi-omics analysis. 16S rRNA sequencing along with untargeted metabolomics demonstrated decreased microbial diversity and metabolic dysregulation in gastric carcinoma individuals with H. pylori infection.IMPORTANCEThis is the first clinical research to systematically expound the difference between gastric cancer (GC) individuals with Helicobacter pylori and GC individuals without H. pylori from the perspective of multi-omics. This clinical study identified significant genes, microbes, and fecal metabolites, which exhibited nice power for differentiating GC individuals with H. pylori infection from GC individuals without H. pylori infection. This study provides a crucial basis for a better understanding of eradication therapy among the GC population.

Abstract HER2 belongs to the human epidermal growth factor receptor tyrosine kinase family. Its overexpression or hyperactivation is a leading cause for multiple types of cancers. HER2 functions mainly through dimerization with other family members, such as EGFR. However, the molecular details for heterodimer assembly have not been completely understood. Here, we report cryo-EM structures of the EGF- and epiregulin-bound EGFR/HER2 ectodomain complexes at resolutions of 3.3 Å and 4.5 Å, respectively. Together with the functional analyses, we demonstrate that only the dimerization arm of HER2, but not that of EGFR, is essential for their heterodimer formation and signal transduction. Moreover, we analyze the differential membrane dynamics and transient interactions of endogenous EGFR and HER2 molecules in genome-edited cells using single-molecule live-cell imaging. Furthermore, we show that the interaction with HER2 could allow EGFR to resist endocytosis. Together, this work deepens our understanding of the unique structural properties and dynamics of the EGFR/HER2 complex.

Regular exercise reduces the risk of malignancy and decreases the recurrence of cancer. However, the mechanisms behind this protection remain to be elucidated. Natural killer (NK) cells are lymphocytes of the innate immune system, which play essential roles in immune defense and effectively prevent cancer metastasis. Physical exercise can increase the activity of NK cells. Interleukin-15 (IL-15) is the best-studied cytokine activator of NK cells, and it was shown to have many positive functional effects on NK cells to improve antitumor responses. The aim of this study was to clarify the possible important mechanisms behind endurance exercise-induced changes in NK cell function, which may be highly correlated with IL-15. An animal model was used to study IL-15 expression level, tumor volume, cancer cell apoptosis, and NK cell infiltration after treadmill exercise. Although IL-15 was highly expressed in skeletal muscle, treadmill exercise further elevated IL-15 levels in plasma and muscle (P

Branching fractions (BFs) of Sm ii for 71 lines from 12 excited levels ranging from 31,638.79 to 35,463.91 cm ^−1 were determined for the first time based on the Fourier transform spectra available from the National Solar Observatory database. New transition probabilities and oscillator strengths for these lines were derived by combining the determined BFs with reliable lifetimes measured using a time-resolved laser-induced fluorescence technique. Furthermore, BFs for 38 lines from five levels included in earlier studies were also determined for comparisons. The new results reported in this work will be useful in many fields, especially for astrophysics.

BackgroundIt is well known that the occurrence and development of ovarian cancer are closely related to the patient’s weight and various endocrine factors in the body.AimMendelian randomization (MR) was used to analyze the bidirectional relationship between insulin related characteristics and ovarian cancer.MethodsThe data on insulin related characteristics are from up to 5567 diabetes free patients from 10 studies, mainly including fasting insulin level, insulin secretion rate, peak insulin response, etc. For ovarian cancer, UK Biobank data just updated in 2021 was selected, of which the relevant gene data was from 199741 Europeans. Mendelian randomization method was selected, with inverse variance weighting (IVW) as the main estimation, while MR Pleiotropy, MR Egger, weighted median and other methods were used to detect the heterogeneity of data and whether there was multi validity affecting conclusions.ResultsAmong all insulin related indicators (fasting insulin level, insulin secretion rate, peak insulin response), the insulin secretion rate was selected to have a causal relationship with the occurrence of ovarian cancer (IVW, P < 0.05), that is, the risk of ovarian cancer increased with the decrease of insulin secretion rate. At the same time, we tested the heterogeneity and polymorphism of this indicator, and the results were non-existent, which ensured the accuracy of the analysis results. Reverse causal analysis showed that there was no causal effect between the two (P>0.05).ConclusionThe impairment of the insulin secretion rate has a causal effect on the risk of ovarian cancer, which was confirmed by Mendel randomization. This suggests that the human glucose metabolism cycle represented by insulin secretion plays an important role in the pathogenesis of ovarian cancer, which provides a new idea for preventing the release of ovarian cancer.

Due to the variability of fluid properties and saturation of reservoirs, large differences in formation temperature and pressure, and the diversity of rock and mineral compositions, the petrophysical response of reservoirs is often complex. This study explored a new method of reservoir fluid identification and evaluation based on the complex resistivity response characteristics of sandstone reservoirs under different measurement conditions. The complex resistivity of the five sandstone samples was measured under normal temperature and pressure and variable pressure, temperature, and formation conditions and under different oil saturations. Furthermore, the reservoir was comprehensively analyzed and evaluated based on the mineral composition, porosity, and permeability parameters. The results show that the resistivity of the sandstone increases logarithmically with pressure and oil saturation but decreases logarithmically with temperature and depth. The polarizability decreases slightly with increasing pressure and increases slightly with increasing temperature. With increasing depth, the polarizability decreases obviously, and with increasing oil saturation, the polarizability decreases moderately. Under different measurement conditions, the complex resistivity data for the sandstone reservoir and the IP parameters extracted through inversion are regular. The results of this study provide a new method for the identification and evaluation of complex reservoir fluids and have important reference value.

Increased risk of premature cardiovascular disease in systemic lupus erythematosus (SLE) is not well understood, but in animal models, the Janus kinase inhibitor tofacitinib improves related phenotypes. Here the authors report a Phase 1 double-blind randomized trial that shows tofacitinib is safe and well tolerated in in patients with SLE.

Purpose: We aim to develop and validate PET/ CT image-based radiomics to determine the Ki-67 status of high-grade serous ovarian cancer (HGSOC), in which we use the metabolic subregion evolution to improve the prediction ability of the model. At the same time, the stratified effect of the radiomics model on the progression-free survival rate of ovarian cancer patients was illustrated.Materials and methods: We retrospectively reviewed 161 patients with HGSOC from April 2013 to January 2019. 18F-FDG PET/ CT images before treatment, pathological reports, and follow-up data were analyzed. A randomized grouping method was used to divide ovarian cancer patients into a training group and validation group. PET/ CT images were fused to extract radiomics features of the whole tumor region and radiomics features based on the Habitat method. The feature is dimensionality reduced, and meaningful features are screened to form a signature for predicting the Ki-67 status of ovarian cancer. Meanwhile, survival analysis was conducted to explore the hierarchical guidance significance of radiomics in the prognosis of patients with ovarian cancer.Results: Compared with texture features extracted from the whole tumor, the texture features generated by the Habitat method can better predict the Ki-67 state (p < 0.001). Radiomics based on Habitat can predict the Ki-67 expression accurately and has the potential to become a new marker instead of Ki-67. At the same time, the Habitat model can better stratify the prognosis (p < 0.05).Conclusion: We found a noninvasive imaging predictor that could guide the stratification of prognosis in ovarian cancer patients, which is related to the expression of Ki-67 in tumor tissues. This method is of great significance for the diagnosis and treatment of ovarian cancer.

Fusarium ear rot (FER) caused by Fusarium verticillioides is one of the most common diseases affecting maize production worldwide. FER results in severe yield losses and grain contamination with health-threatening mycotoxins. Although most studies to date have focused on comprehensive analysis of gene regulation in maize during defense responses against F. verticillioides infection, less is known about the role of microRNAs (miRNAs) in this process. We used deep sequencing to compare small RNA libraries from the maize kernels of susceptible (N6) or resistant (BT-1) inbred lines from uninfected plants and upon F. verticillioides infection. We found that pathogen exposure was accompanied by dynamic alterations in expression levels of multiple miRNAs, including new members of previously annotated miRNA families. A combination of transcriptomic, degradomic, and bioinformatics analyses revealed that F. verticillioides-responsive miRNAs and their potential target genes displayed opposite expression patterns in the susceptible and resistant genotypes. Functional category analysis uncovered preferential enrichment of the pathogen-responsive miRNAs and their targets in the phenylpropanoid metabolic processes, plant–pathogen interactions, and plant phytohormone signal transduction pathways. Furthermore, transgenic maize plants overexpressing miR408b exhibited reduced resistance to F. verticillioides infection in a susceptible maize line. These findings provide new insights into the regulatory roles of miRNAs in maize immunity against FER and new resources for breeding disease resistance into maize.

Background: The mechanism of DNA damage repair plays an important role in many solid tumors represented by cervical cancer.Purpose: The purpose of this study was to explore the effect of DNA damage repair-related genes on immune function of patients with cervical cancer, and to establish and evaluate a prognosis model based on DNA damage repair-related genes.Methods: In the study, we analyzed the genes related to DNA damage and repair, and obtained two subtypes (F1 and F2). We selected two groups of samples for different selection, and studied which pathways were enriched expression. For different subtypes, the immune score was explored to explain immune infiltration. We got the key genes through screening, and established the prognosis model through the key genes. These 11 key genes were correlated with the expression of common Clusters of Differentiation (CD) genes in order to explore the effects of these genes on immunity.Results: Through the Least absolute shrinkage and selection operator (LASSO) method, we screened 11 genes from 232 candidate genes as the key genes for the prognosis score. Through the Kaplan-Meier method, four genes (HAP1, MCM5, RNASEH2A, CETN2) with significant prognostic significance were screened into the final model, forming a Nomogram with C-index of 0.716 (0.649–1.0).Conclusion: In cervical cancer, DNA damage repair related genes and immune cell infection characteristics have certain association, and DNA damage repair related genes and immune cell infection characteristics can effectively predict the prognosis.

BackgroundPregnancy to postpartum (PtP) applications (apps) are becoming more common tools to document everything from pregnancy and delivery to nutrient allocation, life taboos, and infant medical examinations. However, the dependability, quality, and efficacy of these apps remain unclear. This study examined the features and functions of mobile PtP care apps accessible in China and the United States and to identify the major gaps that need to be addressed.MethodsApps were selected by searching the Apple App Store and Android Markets (in the US and China) for the terms “pregnancy” and “postpartum” in Chinese and English. The apps' security, quality, and effectiveness were investigated, and chi-square tests and analysis of variance were performed to examine the differences in characteristics between apps available in the US and China.ResultsA total of 84 mobile PtP care apps (45 from the US and 39 from China) were included. A total of 89.7% (35/39) of Chinese mobile apps did not provide safety statements or supporting evidence. The objective app quality ratings for Chinese and US apps were 3.20 ± 0.48 (mean ± standard deviation) and 3.56 ± 0.45, respectively (p > 0.05). A greater number of Chinese apps provided app-based monitoring functions, namely recording fetal size (n = 18, 46.2% in China vs. n = 3, 6.7% in the US), contractions (n = 11, 28.2% in China vs. n = 0, 0% in the US), pregnancy weight (n = 11, 28.2% in China vs. 0, 0% in the US), and pregnancy check-up reminders (n = 10, 25.6% in China vs. n = 0, 0% in the US). Meanwhile, a greater number of US apps provided exercise modules, namely pregnancy yoga (n = 2, 5.1% in China vs. n = 21, 46.7% in the US), pregnancy workouts (n = 2, 5.1% in China vs. n = 13, 28.9% in the US), and pregnancy meditation (n = 0, 0% in China vs. 10, 22.2% in the US) (p < 0.01). A medium security risk was identified for 40% (18/45) of apps in the US and 82.1% (32/39) of apps in China (p < 0.01).ConclusionsThe functionality and characteristics of in-store mobile apps for PtP care varied between China and the US. Both countries' apps, particularly Chinese apps, encountered issues related to a lack of evidence-based information, acceptable content risk, and program evaluations. Both countries' apps lacked proper mental health care functions. The findings suggest that the design of app features should be enhanced in both countries, and increased interaction between app creators and users is recommended.

As a kind of clean energy, shale gas has attracted much attention, and the exploration and development potential of shale gas resources in the middle and deep layers is huge. However, due to the changeable geological and burial conditions, complex geophysical responses are formed. Therefore, studying the characteristics of reservoir rock minerals and their complex resistivity response characteristics is helpful to deepen the understanding of the electrical characteristics of shale gas reservoirs and provide theoretical basis and physical basis for exploration and development. The study is based on shale samples from the Longmaxi Formation to the Wufeng Formation of a shale gas well in southern Sichuan, China, and the mineral composition and complex resistivity of shale are measured. Through inversion of complex resistivity model, four IP parameters, namely zero-frequency resistivity, polarizability, time constant and frequency correlation coefficient, are extracted, and the relationship between mineral components of rock samples and IP parameters is analyzed. It is found that the polarizability gradually increases and the resistivity gradually decreases with the increase in borehole depth. With the increase in pyrite content, the polarization increases and the resistivity decreases. The corresponding relational model is established, and it is found that the polarizability is highly sensitive to the characteristic mineral pyrite, which provides more effective data support for the subsequent deep shale gas exploration.

PurposeWe aim to compare the radiomic features and parameters on 2-deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. We also hope to explore the biologic significance of selected radiomic features.Materials and MethodsWe conducted a retrospective cohort study, first using the 18F-FDG PET/CT images and clinical data from 100 patients with endometrial cancer to construct a training group (70 patients) and a test group (30 patients). The metabolic parameters and radiomic features of each tumor were compared between patients with and without Lynch syndrome. An independent cohort of 23 patients with solid tumors was used to evaluate the value of selected radiomic features in predicting the expression of the programmed cell death 1 (PD1), using 18F-FDG PET/CT images and RNA-seq genomic data.ResultsThere was no statistically significant difference in the standardized uptake values on PET between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. However, there were significant differences between the 2 groups in metabolic tumor volume and total lesion glycolysis (p < 0.005). There was a difference in the radiomic feature of gray level co-occurrence matrix entropy (GLCMEntropy; p < 0.001) between the groups: the area under the curve was 0.94 in the training group (sensitivity, 82.86%; specificity, 97.14%) and 0.893 in the test group (sensitivity, 80%; specificity, 93.33%). In the independent cohort of 23 patients, differences in GLCMEntropy were related to the expression of PD1 (rs =0.577; p < 0.001).ConclusionsIn patients with endometrial cancer, higher metabolic tumor volumes, total lesion glycolysis values, and GLCMEntropy values on 18F-FDG PET/CT could suggest a higher risk for Lynch syndrome. The radiomic feature of GLCMEntropy for tumors is a potential predictor of PD1 expression.

Abstract Nanostructured manganese oxides, e.g. MnO2, have shown laccase-like catalytic activities, and are thus promising for pollutant oxidation in wastewater treatment. We have systematically compared the laccase-like reactivity of manganese oxide nanomaterials of different crystallinity, including α-, β-, γ-, δ-, and ɛ-MnO2, and Mn3O4, with 2,2′-azinobis-(3-ethylbenzthiazoline-6-sulfonate) (ABTS) and 17β-estradiol (E2) as the probing substrates. The reaction rate behaviors were examined with regard to substrate oxidation and oxygen reduction to evaluate the laccase-like catalysis of the materials, among which γ-MnO2 exhibits the best performance. Cyclic voltammetry (CV) was employed to assess the six MnOx nanomaterials, and the results correlate well with their laccase-like catalytic activities. The findings help understand the mechanisms of and the factors controlling the laccase-like reactivity of different manganese oxides nanomaterials, and provide a basis for future design and application of MnOx-based catalysts.

Tumor suppressor p53 prevents cell transformation by inducing apoptosis and other responses. Homozygous TP53 deletion occurs in various types of human cancers for which no therapeutic strategies have yet been reported. TCGA database analysis shows that the TP53 homozygous deletion locus mostly exhibits co-deletion of the neighboring gene FXR2, which belongs to the Fragile X gene family. Here, we demonstrate that inhibition of the remaining family member FXR1 selectively blocks cell proliferation in human cancer cells containing homozygous deletion of both TP53 and FXR2 in a collateral lethality manner. Mechanistically, in addition to its RNA-binding function, FXR1 recruits transcription factor STAT1 or STAT3 to gene promoters at the chromatin interface and regulates transcription thus, at least partially, mediating cell proliferation. Our study anticipates that inhibition of FXR1 is a potential therapeutic approach to targeting human cancers harboring TP53 homozygous deletion.

Tetrabromobisphenol A (TBBPA) is one of the most widely used brominated flame retardants and has attracted more and more attention. In this work, the parent TBBPA with an initial concentration of 100 mg/L was completely removed after 6 min of ozonation at pH 8.0, and alkaline conditions favored a more rapid removal than acidic and neutral conditions. The presence of typical anions and humic acid did not significantly affect the degradation of TBBPA. The quenching test using isopropanol indicated that direct ozone oxidation played a dominant role during this process. Seventeen reaction intermediates and products were identified using an electrospray time-of-flight mass spectrometer. Notably, the generation of 2,4,6-tribromophenol was first observed in the degradation process of TBBPA. The evolution of reaction products showed that ozonation is an efficient treatment for removal of both TBBPA and intermediates. Sequential transformation of organic bromine to bromide and bromate was confirmed by ion chromatography analysis. Two primary reaction pathways that involve cleavage of central carbon atom and benzene ring cleavage concomitant with debromination were thus proposed and further justified by calculations of frontier electron densities. Furthermore, the total organic carbon data suggested a low mineralization rate, even after the complete removal of TBBPA. Meanwhile, the acute aqueous toxicity of reaction solutions to Photobacterium Phosphoreum and Daphnia magna was rapidly decreased during ozonation. In addition, no obvious difference in the attenuation of TBBPA was found by ozone oxidation using different water matrices, and the effectiveness in natural waters further demonstrates that ozonation can be adopted as a promising technique to treat TBBPA-contaminated waters.