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Photodynamic therapy (PDT) can produce high levels of reactive oxygen species (ROS) to kill tumor cells and induce antitumor immunity. However, intracellular antioxidant systems, including glutathione (GSH) system and thioredoxin (Trx) system, limit the accumulation of ROS, resulting in compromised PDT and insufficient immune stimulation. Herein, we designed a nanomedicine PtHPs co-loading photosensitizer pyropheophorbide a (PPa) and cisplatin prodrug Pt–COOH(IV) (Pt (IV)) based on hydroxyethyl starch (HES) to inhibit both GSH and Trx antioxidant systems and achieve potent PDT as well as antitumor immune responses. Specifically, HES-PPa and HES-Pt were obtained by coupling HES with PPa and Pt (IV), and assembled into nanoparticle PtHPs by emulsification method to achieve the purpose of co-delivery of PPa and Pt (IV). PtHPs improved PPa photostability while retaining PPa photodynamic properties. In vitro experiments showed that PtHPs reduced GSH, inhibited Trx system and had better cell-killing effect and ROS generation ability. Subcutaneous tumor models showed that PtHPs had good safety and tumor inhibition effect. Bilateral tumor models suggested that PtHPs promoted the release of damage-associated molecular patterns and the maturation of dendritic cells, induced T cell-mediated immune responses, and thus suppressed the growth of both primary and distal tumors. This study reports a novel platinum-based nanomedicine and provides a new strategy for boosting PDT therapy-mediated antitumor immunity by overcoming intrinsic antioxidant systems.

IntroductionLung cancer is the leading cause of cancer-related death worldwide. The treatment for lung cancer, particularly for non-small cell lung cancer (NSCLC), remains a clinical challenge. Cancer stem cells are vital for lung cancer development. This study aimed to determine the influence of the neuronally expressed developmentally downregulated 4-fibronectin leucine-rich transmembrane 2 (NEDD4-FLRT2) axis on cancer cell stemness in NSCLC.MethodsFLRT2 expression in NSCLC tissues and stem cells was investigated using western blot and RT-qPCR. The sphere formation assay and the abundance of stemness markers were employed to confirm the stemness of NSCLC stem cells. The CCK-8, colony formation, and Trans-well assays, as well as flow cytometry, were used to determine NSCLC stem cell growth, metastasis, and apoptosis, respectively. The Co-IP assay was used to confirm the binding between NEDD4 and FLRT2. Xenograft tumor mouse models were used to investigate tumorigenesis in vivo.ResultsHere, we reported that FLRT2 expression was reduced in NSCLC tissues, cells, and NSCLC stem cells. FLRT2 upregulation inhibited NSCLC stem cell proliferation, sphere formation, and drug resistance and promoted drug-resistant cell apoptosis. Furthermore, FLRT2 overexpression demonstrated antitumor effects in a xenograft tumor mouse model. Mechanically, FLRT2 was ubiquitinated and degraded by E3 ligase NEDD4. NEDD4 overexpression significantly abolished the inhibitory effects of FLRT2 on NSCLC stemness, as evidenced by in vitro and in vivo experiments. DiscussionThis study revealed that FLRT2 acted as a tumor suppressor by inhibiting cancer cell stemness in NSCLC. NEDD4 promoted ubiquitination degradation of FLRT2 protein. NEDD4 counteracted the inhibitory effects of FLRT2 on NSCLC stem cell tumorigenesis.

To achieve simple and reliable conversion from constant current (CC) charging mode to constant voltage (CV) charging mode in wireless power transfer systems, this paper proposes a hybrid topology equipped with a reconfigurable rectifier. An AC switch is adapted to simultaneously realize the change from half-bridge rectification to full-bridge rectification and achieve the shift from a topology with capacitors in the series compensated primary and secondary sides to a topology with capacitors in the series compensated primary side and the inductor-capacitor-inductor (LCL) compensated secondary side. Notably, communication between the transmitter and receiver sides is not necessary in the proposed method. Furthermore, the zero phase angle characteristic is successfully maintained in both charging modes. The experimental results obtained using the 48 V/0.7 A experimental prototype, which was built to validate the relevant theoretical analysis, show that output current/voltage can be achieved in CC/CV charging mode independent of load resistance. Notably, the maximum power transfer efficiency of the system during the charging process reached 95.33%.

Abstract Biomass‐derived carbon is a promising electrode material in energy storage devices. However, how to improve its low capacity and stability, and slow diffusion kinetics during lithium storage remains a challenge. In this research, we propose a “self‐assembly‐template” method to prepare B, N codoped porous carbon (BN‐C) with a nanosandwich structure and abundant pyridinic N‐B species. The nanosandwich structure can increase powder density and cycle stability by constructing a stable solid electrolyte interphase film, shortening the Li+ diffusion pathway, and accommodating volume expansion during repeated charging/discharging. The abundant pyridinic N‐B species can simultaneously promote the adsorption/desorption of Li+/PF6− and reduce the diffusion barrier. The BN‐C electrode showed a high lithium‐ion storage capacity of above 1140 mAh g−1 at 0.05 A g−1 and superior stability (96.5% retained after 2000 cycles). Moreover, owing to the synergistic effect of the nanosandwich structure and pyridinic N‐B species, the assembled symmetrical BN‐C//BN‐C full cell shows a high energy density of 234.7 W h kg−1, high power density of 39.38 kW kg−1, and excellent cycling stability, superior to most of the other cells reported in the literature. As the density functional theory simulation demonstrated, pyridinic N‐B shows enhanced adsorption activity for Li+ and PF6−, which promotes an increase in the capacity of the anode and cathode, respectively. Meanwhile, the relatively lower diffusion barrier of pyridinic N‐B promotes Li+ migration, resulting in good rate performance. Therefore, this study provides a new approach for the synergistic modulation of a nanostructure and an active site simultaneously to fabricate the carbon electrode material in energy storage devices.

Background Pneumoconiosis mostly combines pulmonary and cardiovascular diseases, among which pulmonary heart disease (PHD) is of major concern due to its significant impact on the survival of pneumoconiosis patients. White cell count (WCC), red cell distribution width (RDW) and platelet parameters are thought to affect inflammatory responses and may be predictors of various cardiovascular diseases. However, very few studies have focused on PHD.Objectives To examine the relationship between baseline complete blood count parameters (WCC, RDW, platelet parameters) and the risk of incident PHD in pneumoconiosis patients.Design A retrospective cohort study.Setting This was a single-centre, retrospective cohort study that used data from an Occupational Disease Hospital, Chengdu, Sichuan.Participants A total of 946 pneumoconiosis patients from January 2012 to November 2021 were included in the study. Female patients and patients who had PHD, coronary heart disease, hypertensive heart disease, cardiomyopathy, heart failure, oncological disease, multiple organ dysfunction, AIDS at baseline and follow-up time of less than 6 months were also excluded.Outcome measures We identified PHD according to the patient’s discharge diagnosis. We constructed Cox proportional hazard regression models to assess the HR of incident PHD in pneumoconiosis, as well as 95% CIs.Results In the multiple Cox proportional hazard regression analysis, platelet count (PLT) and plateletcrit (PCT) above the median at baseline were associated with an increased risk of PHD in pneumoconiosis with adjusted HR of 1.52 (95% CI 1.09 to 2.12) and 1.42 (95% CI 1.02 to 1.99), respectively.Conclusion Higher baseline PLT and PCT are associated with a higher risk of PHD in pneumoconiosis.

Abstract. Background:. While type 2 diabetes mellitus (T2DM) is considered a putative causal risk factor for coronary artery disease (CAD), the intrinsic link underlying T2DM and CAD is not fully understood. We aimed to highlight the importance of integrated care targeting both diseases by investigating the phenotypic and genetic relationships between T2DM and CAD. Methods:. We evaluated phenotypic associations using data from the United Kingdom Biobank (N = 472,050). We investigated genetic relationships by leveraging genomic data conducted in European ancestry for T2DM, with and without adjustment for body mass index (BMI) (T2DM: Ncase/Ncontrol = 74,124/824,006; T2DM adjusted for BMI [T2DMadjBMI]: Ncase/Ncontrol = 50,409/523,897) and for CAD (Ncase/Ncontrol = 181,522/984,168). We performed additional analyses using genomic data conducted in multiancestry individuals for T2DM (Ncase/Ncontrol = 180,834/1,159,055). Results:. Observational analysis suggested a bidirectional relationship between T2DM and CAD (T2DM→CAD: hazard ratio [HR] = 2.12, 95% confidence interval [CI]: 2.01–2.24; CAD→T2DM: HR = 1.72, 95% CI: 1.63–1.81). A positive overall genetic correlation between T2DM and CAD was observed (rg = 0.39, P = 1.43 × 10–75), which was largely independent of BMI (T2DMadjBMI–CAD: rg = 0.31, P = 1.20 × 10–36). This was corroborated by six local signals, among which 9p21.3 showed the strongest genetic correlation. Cross-trait meta-analysis replicated 101 previously reported loci and discovered six novel pleiotropic loci. Mendelian randomization analysis supported a bidirectional causal relationship (T2DM→CAD: odds ratio [OR] = 1.13, 95% CI: 1.11–1.16; CAD→T2DM: OR = 1.12, 95% CI: 1.07–1.18), which was confirmed in multiancestry individuals (T2DM→CAD: OR = 1.13, 95% CI: 1.10–1.16; CAD→T2DM: OR = 1.08, 95% CI: 1.04–1.13). This bidirectional relationship was significantly mediated by systolic blood pressure and intake of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, with mediation proportions of 54.1% (95% CI: 24.9–83.4%) and 90.4% (95% CI: 29.3–151.5%), respectively. Conclusion:. Our observational and genetic analyses demonstrated an intrinsic bidirectional relationship between T2DM and CAD and clarified the biological mechanisms underlying this relationship.

In recent years, the use of WiFi Channel State Information (CSI) for Human Activity Recognition (HAR) has attracted widespread attention, thanks to its low cost and non-intrusive advantages. Previous research mostly used models based on Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN) for activity recognition. However, these methods fail to achieve good parallelism while learning global features and fine-grained features, so they often cannot achieve the ideal recognition effect or training speed. In light of this, we propose an ensemble deep learning model based on CNN and Transformer, ConTransEn. Specifically, we first use CNN to extract spatial features of the sequence, and then use Vision Transformer (ViT) to further extract temporal features. The Transformer introduces self-attention mechanism, enabling the model to fully consider information from other positions in the sequence, rather than being limited to the current input. Furthermore, due to the increased parallelism, Transformer has an advantage in training speed over RNN. In order to further improve the accuracy and robustness of the model, we adopt a bagging ensemble learning strategy, integrating the prediction results of multiple homogeneous base models using a soft voting method to obtain the final classification result. This ensemble learning method reduces the risk of model overfitting, and improves the overall accuracy and reliability of the model. We extensively evaluated the model on two publicly available datasets, and achieved excellent recognition results, indicating its good performance and robustness. The average recognition accuracy on the UT-HAR dataset reached 99.41%, surpassing existing solutions.

Semantic segmentation of airborne point clouds is crucial for 3D scene reconstruction and remote sensing in surveying applications. Current deep learning methods for point clouds primarily focus on effectively aggregating local neighborhood information. However, they often overlook the fusion of global context information and elevation features, which are vital for airborne point clouds. In this study, we propose Dense-LGEANet, a novel network with dense connected architecture and multiscale feature supervision based on our designed LGEA module. The key component of our LGEA module is the combination of the graph convolution block and the transformer block with elevation attention. It can effectively fuse local neighborhood information and global context information to improve the accuracy of semantic segmentation of airborne point cloud. Moreover, the designed dense connected network architecture can enhance the feature extraction capability for point cloud objects at different scales by facilitating interactions between multiple up-sampling and down-sampling layers. We have conducted multiple experiments on the public point cloud dataset. Experimental results show that our method can achieve an mIoU of 58.5% and an mF1 of 72.0% on the ISPRS Vaihingen 3D dataset, while an mIoU of 67.2% and an mF1 of 78.3% on the LASDU dataset. It demonstrates the superior performance of our network and the effectiveness of the proposed feature enhancement module and network architecture.

Abstract Objectives To explore the associations of social support, and cognitive activity with cognitive impairment incidence, and further examine the mediation effect of cognitive activity on the association between social support and cognitive impairment incidence based on a nationwide elderly Chinese cohort. Methods We collected the participants from an ongoing cohort of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A total of 9394 older adults aged 65 or more years and free of cognitive impairment who participated in the CLHLS between 2008 and 2018 were included. The information on social support and cognitive activity was collected through a questionnaire. The incident cognitive impairment cases were identified through the Mini-Mental State Examination scale (MMSE). Cox proportional hazard regression models were conducted to calculate the hazard ratios (HRs) and 95% confidence interval (CI) of social support and cognitive activity associated with cognitive impairment. We used casual mediation models to assess the indirect association of cognitive activities underlying the association between social support and cognitive impairment. Results The adjusted HRs (95% CI) of incident cognitive impairment were 0.956 (0.932 to 0.980), and 0.895 (0.859 to 0.933) associated with per 1 score increase in social support and cognitive activity score, respectively. Better adherence to social support was associated with a higher cognitive activity score (adjusted β = 0.046, 95% CI[0.032–0.060]). The baseline cognitive activity, as well as the mean cognitive activity at baseline and during the first follow-up wave, mediate the association between social support and the incidence of cognitive impairment, accounting for 11.4% and 12.6% of the total association, respectively. The participants who were aged 80 years or older, or those with mild daily functional limitations gained more benefits in the development of cognitive activity related to social support, leading to a reduction in the risks of cognitive impairment. Conclusion The results of this nationwide cohort provide consistent evidence linking social support, and cognitive activity to reduced risk of subsequent cognitive impairment incidence. These findings provide additional evidence to inform the social strategies to prevent cognitive impairment incidence in elderly people.

Cremastrae Pseudobulbus Pleiones Pseudobulbus (CPPP) is derived from the dried pseudobulb of the orchid family plants Cremastra appendiculata (D.Don) Makino, Pleione bulbocodioides (Franch.) Rolfe, or Pleione yunnanensis Rolfe, and has the properties of clearing heat, detoxification, resolving phlegm, and dispersing nodules. It is frequently used for the treatment of various malignant tumors in clinical practice, especially lung cancer. CPPP is divided into two commercial specifications in the market, Maocigu (MCG) and Bingqiuzi (BQZ). However, owing to a lack of appropriate research strategies, the active ingredients and molecular mechanisms involved have not yet been clarified. This study intended to discover the combination of effective anti-lung-cancer ingredients in CPPP and explore their potential mechanisms of action. In this study, UHPLC-MS fingerprints of MCG and BQZ were established separately. Inhibitory effects on the proliferative viability and migratory ability of A459 and H1299 cells were evaluated as pharmacodynamic indicators. GRA and BCA were used to determine spectrum–effect relationships. Next, the identification and analysis of components of drug-containing serum were performed using UHPLC-Q-Exactive Orbitrap MS. Then, the results of the two analyses were combined to jointly screen out the anti-lung-cancer candidate active monomers of CPPP, and their in vitro activities were verified. Afterward, all effective ingredient combinations of MCG (MCGC) and BQZ (BQZC) were prepared according to their contents in the original medicinal materials. Their anti-lung-cancer activities in vitro and in vivo were compared and verified. Finally, we used the human lung cancer cell line A549 and the Lewis tumor xenograft model to investigate how BQZC would influence autophagy and apoptosis processes and the mechanisms involved. Overall, 11 predominant anti-lung-cancer active ingredients from CPPP were screened. Next, MCGC and BQZC were prepared according to their contents in the original medicinal materials, respectively, and their anti-tumor effects were equivalent to those of the original materials in vitro and in vivo. We found that BQZC could inhibit lung cancer cell growth and induce protective autophagy and apoptosis in lung cancer cells by activating the AMPK–mTOR–ULK1/BMF signaling pathway. These results provide important evidence for the clinical application and deep development of CPPP against tumors.

AbstractContext Levodopa combined with traditional Chinese medicine has a synergistic effect on Parkinson’s disease (PD). Recently, we demonstrated that Nardostachys jatamansi (D. Don) DC. [syn. Patrinia jatamansi D.Don, N. grandiflora DC.] (Valerianaceae) (NJ) can alleviate PD.Objective To explore the synergistic effect of NJ combined with levodopa against PD.Materials and methods The PD model was established by injecting rotenone. Eighty-four Sprague-Dawley rats were randomly divided into seven groups: sham, model, different doses of NJ (0.31, 0.62, or 1.24 g/kg) combined with levodopa (25 mg/kg), and levodopa alone (25 and 50 mg/kg) groups. The synergistic effect of the combination was investigated by pharmacodynamic investigation and detection of expression of nuclear factor erythro2-related factor 2 (Nrf2) and NLR family proteins containing Pyrin-related domain 3 (NLRP3) pathways.Results Compared with the model group, NJ + levodopa (1.24 g/kg + 25 mg/kg) increased the moving distance of PD rats in the open field (2395.34 ± 668.73 vs. 1501.41 ± 870.23, p

Abstract In the early stages of diabetic retinopathy (DR), diabetes-related hyperglycemia directly inhibits the AKT signaling pathway by increasing oxidative stress or inhibiting growth factor expression, which leads to retinal cell apoptosis, nerve proliferation and fundus microvascular disease. However, due to compensatory vascular hyperplasia in the late stage of DR, the vascular endothelial growth factor (VEGF)/phosphatidylinositol 3 kinase (PI3K)/AKT cascade is activated, resulting in opposite levels of AKT regulation compared with the early stage. Studies have shown that many factors, including insulin, insulin-like growth factor-1 (IGF-1), VEGF and others, can regulate the AKT pathway. Disruption of the insulin pathway decreases AKT activation. IGF-1 downregulation decreases the activation of AKT in DR, which abrogates the neuroprotective effect, upregulates VEGF expression and thus induces neovascularization. Although inhibiting VEGF is the main treatment for neovascularization in DR, excessive inhibition may lead to apoptosis in inner retinal neurons. AKT pathway substrates, including mammalian target of rapamycin (mTOR), forkhead box O (FOXO), glycogen synthase kinase-3 (GSK-3)/nuclear factor erythroid 2-related factor 2 (Nrf2), and nuclear factor kappa-B (NF-κB), are a research focus. mTOR inhibitors can delay or prevent retinal microangiopathy, whereas low mTOR activity can decrease retinal protein synthesis. Inactivated AKT fails to inhibit FOXO and thus causes apoptosis. The GSK-3/Nrf2 cascade regulates oxidation and inflammation in DR. NF-κB is activated in diabetic retinas and is involved in inflammation and apoptosis. Many pathways or vital activities, such as the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) and mitogen-activated protein kinase (MAPK) signaling pathways, interact with the AKT pathway to influence DR development. Numerous regulatory methods can simultaneously impact the AKT pathway and other pathways, and it is essential to consider both the connections and interactions between these pathways. In this review, we summarize changes in the AKT signaling pathway in DR and targeted drugs based on these potential sites.

A method based on data fusion is proposed for detection of fire in dense bus ducts in buildings. The environmental temperature data, infrared image data and gas concentration data are used in the fire warning system. The features of temperature data and gas concentration data are extracted by statistical method, the features of infrared image are extracted by Faster RCNN (Region Convolutional Neural Networks), then three types of features are fused and inputted into the SVM (Support Vector Machine) model to detect fire early. The experimental results show that this method has high recognition rate and good robustness.

Abstract Background This study aims to comprehensively investigate the phenotypic and genetic relationships between four common lipids (high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; total cholesterol, TC; and triglycerides, TG), chronic kidney disease (CKD), and estimated glomerular filtration rate (eGFR). Methods We first investigated the observational association of lipids (exposures) with CKD (primary outcome) and eGFR (secondary outcome) using data from UK Biobank. We then explored the genetic relationship using summary statistics from the largest genome-wide association study of four lipids (N = 1,320,016), CKD (Ncase = 41,395, Ncontrol = 439,303), and eGFR(N = 567,460). Results There were significant phenotypic associations (HDL-C: hazard ratio (HR) = 0.76, 95%CI = 0.60–0.95; TG: HR = 1.08, 95%CI = 1.02–1.13) and global genetic correlations (HDL-C: $${r}_{g}$$ r g = − 0.132, P = 1.00 × 10–4; TG: $${r}_{g}$$ r g = 0.176; P = 2.66 × 10–5) between HDL-C, TG, and CKD risk. Partitioning the whole genome into 2353 LD-independent regions, twelve significant regions were observed for four lipids and CKD. The shared genetic basis was largely explained by 29 pleiotropic loci and 36 shared gene-tissue pairs. Mendelian randomization revealed an independent causal relationship of genetically predicted HDL-C (odds ratio = 0.91, 95%CI = 0.85–0.98), but not for LDL-C, TC, or TG, with the risk of CKD. Regarding eGFR, a similar pattern of correlation and pleiotropy was observed. Conclusions Our work demonstrates a putative causal role of HDL-C in CKD and a significant biological pleiotropy underlying lipids and CKD in populations of European ancestry. Management of low HDL-C levels could potentially benefit in reducing the long-term risk of CKD. Graphical Abstract

Abstract Background Despite epidemiological evidence associating gallstone disease (GSD) with cardiovascular disease (CVD), a dilemma remains on the role of cholecystectomy in modifying the risk of CVD. We aimed to characterize the phenotypic and genetic relationships between GSD and two CVD events – stroke and coronary artery disease (CAD). Methods We first performed a meta-analysis of cohort studies to quantify an overall phenotypic association between GSD and CVD. We then investigated the genetic relationship leveraging the largest genome-wide genetic summary statistics. We finally examined the phenotypic association using the comprehensive data from UK Biobank (UKB). Results An overall significant effect of GSD on CVD was found in meta-analysis (relative risk [RR] = 1.26, 95% confidence interval [CI] = 1.19–1.34). Genetically, a positive shared genetic basis was observed for GSD with stroke ( $${r}_{g}$$ r g =0.16, P = 6.00 × 10–4) and CAD ( $${r}_{g}$$ r g =0.27, P = 2.27 × 10–15), corroborated by local signals. The shared genetic architecture was largely explained by the multiple pleiotropic loci identified in cross-phenotype association study and the shared gene-tissue pairs detected by transcriptome-wide association study, but not a causal relationship (GSD to CVD) examined through Mendelian randomization (MR) (GSD-stroke: odds ratio [OR] = 1.00, 95%CI = 0.97–1.03; GSD-CAD: OR = 1.01, 95%CI = 0.98–1.04). After a careful adjustment of confounders or considering lag time using UKB data, no significant phenotypic effect of GSD on CVD was detected (GSD-stroke: hazard ratio [HR] = 0.95, 95%CI = 0.83–1.09; GSD-CAD: HR = 0.98, 95%CI = 0.91–1.06), further supporting MR findings. Conclusions Our work demonstrates a phenotypic and genetic relationship between GSD and CVD, highlighting a shared biological mechanism rather than a direct causal effect. These findings may provide insight into clinical and public health applications.

Abstract Prospective inter-relationships among biomarkers were unexplored, which may provide mechanistic insights into diseases. We investigated the longitudinal associations of BMI change with trajectories of biomarkers related to cardiometabolic or breast cancer risk. A longitudinal study was conducted among 444 healthy women between 2019 to 2021. Cross‑lagged path analysis was used to examine the temporal relationships among BMI, cardiometabolic risk score (CRS), and obesity‑related proteins score (OPS) of breast cancer. Linear mixed-effect models were applied to investigate associations of time-varying BMI with biomarker-based risk score trajectories. Baseline BMI was associated with subsequent change of breast cancer predictors (P = 0.03), and baseline CRS were positively associated with OPS change (P

Abstract Biochar with a highly accessible specific surface area can display a higher performance when it is used as the cathode of lithium-ion capacitors. Facing the complex composition and diversity of biomass precursors, there is a lack of a universally applicable method to construct hierarchical porous biochar controllably. In this work, a multi-stage activation strategy combining the feature of different activation methods is proposed for this target. To confirm the porous characteristic in prepared samples, N2 adsorption–desorption and transmission electron microscope were used. As the optimal sample, BC-P3K4S had the highest specific surface area of 3583.3 m2 g−1. Evaluated as the electrode for a lithium-ion capacitor, BC-P3K4S displayed a capacity of 139.1 mAh g−1 at 0.1 A g−1. After coupling it with pre-lithiated hard carbon, the full device exhibited a high energy density of 129.3 W h kg−1 at 153 W kg−1. The work outlined herein offers some insights into the preparation of hierarchical porous biochar from complex biomass by multistep activation method. Graphical Abstract

BackgroundThe incidence of prostate cancer is increasing in older males globally. Age, ethnicity, and family history are identified as the well-known risk factors for prostate cancer, but few modifiable factors have been firmly established. The objective of this study was to identify and evaluate various factors modifying the risk of prostate cancer reported in meta-analyses of prospective observational studies and mendelian randomization (MR) analyses.Methods and findingsWe searched PubMed, Embase, and Web of Science from the inception to January 10, 2022, updated on September 9, 2023, to identify meta-analyses and MR studies on prostate cancer. Eligibility criteria for meta-analyses were (1) meta-analyses including prospective observational studies or studies that declared outcome-free at baseline; (2) evaluating the factors of any category associated with prostate cancer incidence; and (3) providing effect estimates for further data synthesis. Similar criteria were applied to MR studies. Meta-analysis was repeated using the random-effects inverse-variance model with DerSimonian-Laird method. Quality assessment was then conducted for included meta-analyses using AMSTAR-2 tool and for MR studies using STROBE-MR and assumption evaluation. Subsequent evidence grading criteria for significant associations in meta-analyses contained sample size, P values and 95% confidence intervals, 95% prediction intervals, heterogeneity, and publication bias, assigning 4 evidence grades (convincing, highly suggestive, suggestive, or weak). Significant associations in MR studies were graded as robust, probable, suggestive, or insufficient considering P values and concordance of effect directions. Finally, 92 selected from 411 meta-analyses and 64 selected from 118 MR studies were included after excluding the overlapping and outdated studies which were published earlier and contained fewer participants or fewer instrument variables for the same exposure. In total, 123 observational associations (45 significant and 78 null) and 145 causal associations (55 significant and 90 null) were categorized into lifestyle; diet and nutrition; anthropometric indices; biomarkers; clinical variables, diseases, and treatments; and environmental factors. Concerning evidence grading on significant associations, there were 5 highly suggestive, 36 suggestive, and 4 weak associations in meta-analyses, and 10 robust, 24 probable, 4 suggestive, and 17 insufficient causal associations in MR studies. Twenty-six overlapping factors between meta-analyses and MR studies were identified, with consistent significant effects found for physical activity (PA) (occupational PA in meta: OR = 0.87, 95% CI: 0.80, 0.94; accelerator-measured PA in MR: OR = 0.49, 95% CI: 0.33, 0.72), height (meta: OR = 1.09, 95% CI: 1.06, 1.12; MR: OR = 1.07, 95% CI: 1.01, 1.15, for aggressive prostate cancer), and smoking (current smoking in meta: OR = 0.74, 95% CI: 0.68, 0.80; smoking initiation in MR: OR = 0.91, 95% CI: 0.86, 0.97). Methodological limitation is that the evidence grading criteria could be expanded by considering more indices.ConclusionsIn this large-scale study, we summarized the associations of various factors with prostate cancer risk and provided comparisons between observational associations by meta-analysis and genetically estimated causality by MR analyses. In the absence of convincing overlapping evidence based on the existing literature, no robust associations were identified, but some effects were observed for height, physical activity, and smoking.

The semantic segmentation task is an essential issue in various fields, including land cover classification and cultural heritage investigation. The CNN and Transformer have been widely utilized in semantic segmentation tasks due to notable advancements in deep learning technologies. However, these methodologies may not fully account for remote sensing images' distinctive attributes, including the large intra-class variation and the small inter-class variation. Driven by it, we propose a category attention guided network (CAGNet). Initially, a local feature extraction module is devised to cater to striped objects and features at different scales. Then, we propose a novel concept of category attention for remote sensing images as a feature representation of category differences between pixels. Meanwhile, we designed the Transformer-based and CNN-based category attention guided modules to integrate the proposed category attention into the global scoring functions and local category feature weights, respectively. The network is designed to give more attention to the category features by updating these weights during the training process. Finally, a feature fusion module is developed to integrate global, local, and category multi-scale features and contextual information. A series of extensive experiments along with ablation studies on the UAVid, Vaihingen, and Potsdam datasets indicate that our network outperforms existing methods, including those based on CNN and Transformer.

Large-signal stability of direct current (DC) microgrids (MGs) are required to be seriously considered when more power converters are penetrated. For DC MGs suffering from large disturbances, the power electronics at both source and load sides are operated within the voltage and current limits to ensure the stability and safety. However, no existing methods have been developed to address the non-conservative stability analysis of a DC MGs with physical variable constraints. To this end, a comprehensive is developed in this paper to analyze the large-signal stability of typical DC MGs with voltage/current and pulse-width modulation duty cycle constraints, with a low level of conservatism. First, the system-level model of a DC MG is established as the basis of stability analysis. Then, a workflow based on the Lyapunov theory and numerical integration is developed to evaluate the large signal stability with physical variable constraints. The developed stability analysis method has less conservatism than the existing methods with only one moderate computational cost. The effectiveness of the proposed strategy is validated by case studies based on a single-bus direct current microgrid.

Cuproptosis-based cancer nanomedicine has received widespread attention recently. However, cuproptosis nanomedicine against pancreatic ductal adenocarcinoma (PDAC) is severely limited by cancer stem cells (CSCs), which reside in the hypoxic stroma and adopt glycolysis metabolism accordingly to resist cuproptosis-induced mitochondria damage. Here, we leverage hyperbaric oxygen (HBO) to regulate CSC metabolism by overcoming tumor hypoxia and to augment CSC elimination efficacy of polydopamine and hydroxyethyl starch stabilized copper-diethyldithiocarbamate nanoparticles (CuET@PH NPs). Mechanistically, while HBO and CuET@PH NPs inhibit glycolysis and oxidative phosphorylation, respectively, the combination of HBO and CuET@PH NPs potently suppresses energy metabolism of CSCs, thereby achieving robust tumor inhibition of PDAC and elongating mice survival importantly. This study reveals novel insights into the effects of cuproptosis nanomedicine on PDAC CSC metabolism and suggests that the combination of HBO with cuproptosis nanomedicine holds significant clinical translation potential for PDAC patients.

BackgroundThe relationship between type 2 diabetes mellitus (T2DM) and gallstone disease (GSD) have been incompletely understood. We aimed to investigate their phenotypic and genetic associations and evaluate the biological mechanisms underlying these associations.MethodsWe first evaluated the phenotypic association between T2DM and GSD using data from the UK Biobank (n>450,000) using a prospective observational design. We then conducted genetic analyses using summary statistics from a meta-analysis of genome-wide association studies of T2DM, with and without adjusting for body mass index (BMI) (Ncase=74,124, Ncontrol=824,006; T2DMadjBMI: Ncase=50,409, Ncontrol=523,897) and GSD (Ncase=43,639, Ncontrol=506,798).ResultsA unidirectional phenotypic association was observed, where individuals with T2DM exhibited a higher GSD risk (hazard ratio (HR)=1.39, P

Abstract Background Both depression and breast cancer (BC) contribute to a substantial global burden of morbidity and mortality among women, and previous studies have observed a potential depression-BC link. We aimed to comprehensively characterize the phenotypic and genetic relationships between depression and BC. Methods We first evaluated phenotypic association using longitudinal follow-up data from the UK Biobank (N = 250,294). We then investigated genetic relationships leveraging summary statistics from the hitherto largest genome-wide association study of European individuals conducted for depression (N = 500,199), BC (N = 247,173), and its subtypes based on the status of estrogen receptor (ER + : N = 175,475; ER − : N = 127,442). Results Observational analysis suggested an increased hazard of BC in depression patients (HR = 1.10, 95%CIs = 0.95–1.26). A positive genetic correlation between depression and overall BC was observed ( $${r}_{g}$$ r g = 0.08, P = 3.00 × 10–4), consistent across ER + ( $${r}_{g}$$ r g = 0.06, P = 6.30 × 10–3) and ER − subtypes ( $${r}_{g}$$ r g = 0.08, P = 7.20 × 10–3). Several specific genomic regions showed evidence of local genetic correlation, including one locus at 9q31.2, and four loci at, or close, to 6p22.1. Cross-trait meta-analysis identified 17 pleiotropic loci shared between depression and BC. TWAS analysis revealed five shared genes. Bi-directional Mendelian randomization suggested risk of depression was causally associated with risk of overall BC (OR = 1.12, 95%Cis = 1.04–1.19), but risk of BC was not causally associated with risk of depression. Conclusions Our work demonstrates a shared genetic basis, pleiotropic loci, and a putative causal relationship between depression and BC, highlighting a biological link underlying the observed phenotypic relationship; these findings may provide important implications for future studies aimed reducing BC risk.

Undesirable meteorological conditions are typical aviation flight safety threats. Although most meteorological radar and flight augmentation computers have avionic system wind shear alarms, the preferred approach is that pilots avoid unsafe wind shear events. Therefore, effective pilot competency evaluations are needed to assess pilots’ abilities to deal with these events. This study developed a wind shear operation competency model that includes observable behavior indicators, sub-task decomposition, and competency check items. An adapted competency model and a quantitative data-driven competency evaluation criteria optimization method were then developed using three-dimensional competency feature modeling, after which wind shear simulation flight training data were used to verify the effectiveness of the proposed method. The competency assessment grades were significantly correlated with the results from experienced examiners with a 93.33% evaluation accuracy. The proposed quantitative data-driven competency assessment method can provide effective pilot competency assessments for a range of aviation meteorological threats.

Radiometric correction is one of the most important preprocessing parts of unmanned aerial vehicle (UAV) multispectral remote sensing data analysis and application. In this article, a back propagation (BP) neural network-based radiometric correction method (BPNNRCM) considering optimal parameters was proposed. First, we used different UAV multispectral sensors (K6 equipped on the DJI M600, D-MSPC2000 equipped on the FEIMA D2000) to collect training, validation, testing and cross-validation data. Second, the radiometric correction results of BP neural network with different input variables and hidden layer node number were compared to select the best combination of input parameters and hidden layer node number. Finally, the radiometric correction accuracy and robustness of BP neural network considering the optimal parameters were verified. When the number of nodes in the input layer was five (digital number, UAV sensor height, wavelength, solar altitude angle, and temperature) and the number of nodes in the hidden layer was eight, the BP neural network had the best comprehensive performance in training time of train set and accuracy of validation/test set. In the aspect of accuracy and robustness, the absolute errors of test and cross-validation images' surface reflectance obtained by the BPNNRCM were all less than 0.054. The BPNNRCM had smaller average absolute error (0.0141), mean squared error (0.0003), mean absolute error (0.0141) and mean relative error (7.1%) comparing with empirical line method and radiative transfer model. In general, the research results of this article prove the feasibility and prospect of BPNNRCM for radiometric correction of UAV multispectral images.

Summary: Little is known regarding the long-term adverse effects of COVID-19 on female-specific cancers, nor the shared genetic influences underlying these conditions. We performed a comprehensive genome-wide cross-trait analysis to investigate the shared genetic architecture between COVID-19 (infection, hospitalization, and critical illness) with three female-specific cancers (breast cancer (BC), epithelial ovarian cancer (EOC), and endometrial cancer (EC)). We identified significant genome-wide genetic correlations with EC for both hospitalization (rg = 0.19, p = 0.01) and critical illness (rg = 0.29, p = 3.00 × 10−4). Mendelian randomization demonstrated no valid association of COVID-19 with any cancer of interest, except for suggestive associations of genetically predicted hospitalization (ORIVW = 1.09, p = 0.04) and critical illness (ORIVW = 1.06, p = 0.04) with EC risk, none withstanding multiple correction. Cross-trait meta-analysis identified 20 SNPs shared between COVID-19 with BC, 15 with EOC, and 5 with EC; and transcriptome-wide association studies revealed multiple shared genes. Findings support intrinsic links underlying these complex traits, highlighting shared mechanisms rather than causal associations.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has exerted a profound influence on humans. Increasing evidence shows that immune response is crucial in influencing the risk of infection and disease severity. Observational studies suggest an association between COVID‐19 and immunoglobulin G (IgG) N-glycosylation traits, but the causal relevance of these traits in COVID-19 susceptibility and severity remains controversial.MethodsWe conducted a two-sample Mendelian randomization (MR) analysis to explore the causal association between 77 IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity using summary-level data from genome-wide association studies (GWAS) and applying multiple methods including inverse-variance weighting (IVW), MR Egger, and weighted median. We also used Cochran’s Q statistic and leave-one-out analysis to detect heterogeneity across each single nucleotide polymorphism (SNP). Additionally, we used the MR-Egger intercept test, MR-PRESSO global test, and PhenoScanner tool to detect and remove SNPs with horizontal pleiotropy and to ensure the reliability of our results.ResultsWe found significant causal associations between genetically predicted IgG N-glycosylation traits and COVID-19 susceptibility, hospitalization, and severity. Specifically, we observed reduced risk of COVID-19 with the genetically predicted increased IgG N-glycan trait IGP45 (OR = 0.95, 95% CI = 0.92–0.98; FDR = 0.019). IGP22 and IGP30 were associated with a higher risk of COVID-19 hospitalization and severity. Two (IGP2 and IGP77) and five (IGP10, IGP14, IGP34, IGP36, and IGP50) IgG N-glycosylation traits were causally associated with a decreased risk of COVID-19 hospitalization and severity, respectively. Sensitivity analyses did not identify any horizontal pleiotropy.ConclusionsOur study provides evidence that genetically elevated IgG N-glycosylation traits may have a causal effect on diverse COVID-19 outcomes. Our findings have potential implications for developing targeted interventions to improve COVID-19 outcomes by modulating IgG N-glycosylation levels.

Image matching is a key prerequisite for image fusion. Currently, deep learning methods had shown great potential in matching. However, these methods mainly focus on optical images or area-based matching and their performance highly relies on the diversity of the training sets. This paper makes two major contributions to the synthetic aperture radar (SAR)-optical image feature matching task. First, we create a high-quality open SAR and optical patch dataset called SOPatch, which contains more than 650000 matching patch pairs. SOPatch is generated from a variety of satellite images (Sentinel-1, Sentinel-2, Gaofen-3, etc.) and contains a rich set of features (mountains, lakes, buildings, farmland, bare land, etc.), which can provide robustness to sensor and localization changes and train a model with good generalization ability. Similar to the HPatches (Balntas et al., 2017), we also add position and rotation noise to make the dataset more realistic and robust. Second, a local descriptor for SAR-optical matching called SODescNet is proposed. We first weaken the speckle noise of SAR by multiscale dilated convolution and channel attention mechanisms and then use DenseNet-CSP (cross stage partial) as the backbone for feature description. To reduce the difficulty of network training, we first use a Siamese network for pre-training and then fine-tune it via a pseudo-Siamese network. Extensive experiments show that our SODescNet is comparable or superior to state-of-the-arts.

Study region: The Yangtze River basin. Study focus: Machine learning techniques have been widely applied to streamflow prediction at short lead times, and increasing numbers of predictors are introduced to improve predictive skills. However, the effect different predictors and their combinations at long lead times remains to be explored. In this study, the Long Short-Term Memory (LSTM) model is used to forecast the daily streamflow over the Yangtze River during flood seasons at lead times of 1–30 days, by using different combinations of streamflow, precipitation, soil moisture, and evapotranspiration as predictors. The effects of different predictors on daily streamflow and high/low flows, together with the difference between short and long lead times, are investigated. New hydrological insights for the region: The experiment using antecedent streamflow as predictor has a high (0.58–0.99) Kling-Gupta efficiency (KGE) at 1–7days lead times, but KGE decreases rapidly as the lead time increases. Adding precipitation predictor increases the KGE of daily streamflow by 0.09–0.25 and corrects negative/positive biases in high/low flows at long lead times (> 20 days). Soil moisture and evapotranspiration predictors have added values to daily streamflow forecasting at long lead times by further improving the KGE by 0.04–0.11, but exert minor influences on streamflow extremes. Thus, how to incorporate multiple hydrometeorological predictors in LSTM model to improve the forecasts of extremes at long leads is still a challenge.

IntroductionTo compare the accuracy of Artificial Intelligent Breast Ultrasound (AIBUS) with hand-held breast ultrasound (HHUS) in asymptomatic women and to offer recommendations for screening in regions with limited medical resources.Methods852 participants who underwent both HHUS and AIBUS were enrolled between December 2020 and June 2021. Two radiologists, who were unaware of the HHUS results, reviewed the AIBUS data and scored the image quality on a separate workstation. Breast imaging reporting and data system (BI-RADS) final recall assessment, breast density category, quantified lesion features, and examination time were evaluated for both devices. The statistical analysis included McNemar’s test, paired t-test, and Wilcoxon test. The kappa coefficient and consistency rate were calculated in different subgroups.ResultsSubjective satisfaction with AIBUS image quality reached 70%. Moderate agreements were found between AIBUS with good quality images and HHUS for the BI-RADS final recall assessment (κ = 0.47, consistency rate = 73.9%) and breast density category (κ = 0.50, consistency rate = 74.8%). The lesions measured by AIBUS were statistically smaller and deeper than those measured by HHUS (P < 0.001), though they were not significant in clinical diagnosis (all < 3 mm). The total time required for the AIBUS examination and image interpretation was 1.03 (95% CI (0.57, 1.50)) minutes shorter than that of HHUS per case.ConclusionModerate agreement was obtained for the description of the BI-RADS final recall assessment and breast density category. With image quality comparable to that of HHUS, AIBUS was superior for the efficiency of primary screening.

With the development of multi-beam Light Detection and Ranging (LiDAR) sensors, fast and accurate LiDAR-based localization has become a crucial issue in robotics and autonomous driving. However, balancing accuracy and efficiency remains challenging in existing methods. In this paper, we propose a super-fast LiDAR global localization approach that can achieve state-of-the-art (SOTA) accuracy with superior efficiency. Our method leverages template descriptors to capture structural environments and approximates the vehicle’s position via map candidate points. Additionally, we create an offline map database to evenly simulate vehicle orientations. We design a loss function to improve localization accuracy. We extensively evaluated the proposed method in public KITTI outdoor sequences and self-collected indoor datasets. The experimental results show that our approach can run at close to 100 frames per second (FPS) on a single-thread CPU, which is much faster than current SOTA methods. Our average absolute translation errors (ATEs) are 0.20m (indoor) and 0.44m (outdoor), and the average localization success rates are 93% (indoor) and 90% (outdoor). The average localization success rates can exceed 97% in large outdoor scenarios with fine-tuned parameters. The source code will be available in https://github.com/ShiPC-AI.

Cordyceps sinensis (C. sinensis) is a widely used and highly valuable traditional Chinese medicine. Several dipeptides have been detected in C. sinensis, but current scientific knowledge of its chemical makeup remains limited. In this study, an improved approach that integrates offline two-dimensional liquid chromatography (2D LC) separation, precursor ion list, library screening, and diagnostic ion filtering was established to systematically screen and characterize dipeptides in C. sinensis. Offline 2D LC integrating hydrophilic interaction LC and reverse phase separations was established to eliminate interference and identify the target dipeptides. A library containing the potential 400 dipeptides was created, and a precursor ion list with all theoretical precursor ions was adopted to trigger the MS/MS scan with high sensitivity. To identify dipeptides, the type and connection sequence of amino acids were determined according to the product ions. Ile and Leu residues were differentiated for the first time according to the characteristic ion at m/z 69.07. Ultimately, 170 dipeptides were identified or tentatively characterized from C. sinensis, and most are reported for the first time in this species herein. In addition, the identified dipeptides were also applied for discrimination among the three Cordyceps species, and 11 markers were identified. The obtained results provide a deeper understanding of the chemical basis of C. sinensis.

Abstract Background Studies investigating breast cancer polygenic risk score (PRS) in Chinese women are scarce. The objectives of this study were to develop and validate PRSs that could be used to stratify risk for overall and subtype-specific breast cancer in Chinese women, and to evaluate the performance of a newly proposed Artificial Neural Network (ANN) based approach for PRS construction. Methods The PRSs were constructed using the dataset from a genome-wide association study (GWAS) and validated in an independent case-control study. Three approaches, including repeated logistic regression (RLR), logistic ridge regression (LRR) and ANN based approach, were used to build the PRSs for overall and subtype-specific breast cancer based on 24 selected single nucleotide polymorphisms (SNPs). Predictive performance and calibration of the PRSs were evaluated unadjusted and adjusted for Gail-2 model 5-year risk or classical breast cancer risk factors. Results The primary PRSANN and PRSLRR both showed modest predictive ability for overall breast cancer (odds ratio per interquartile range increase of the PRS in controls [IQ-OR] 1.76 vs 1.58; area under the receiver operator characteristic curve [AUC] 0.601 vs 0.598) and remained to be predictive after adjustment. Although estrogen receptor negative (ER−) breast cancer was poorly predicted by the primary PRSs, the ER− PRSs trained solely on ER− breast cancer cases saw a substantial improvement in predictions of ER− breast cancer. Conclusions The 24 SNPs based PRSs can provide additional risk information to help breast cancer risk stratification in the general population of China. The newly proposed ANN approach for PRS construction has potential to replace the traditional approaches, but more studies are needed to validate and investigate its performance.

BackgroundDespite the increased development and use of mobile health (mHealth) devices during the COVID-19 pandemic, there is little knowledge of willingness of the Chinese people to use mHealth devices and the key factors associated with their use in the post–COVID-19 era. Therefore, a more comprehensive and multiangle investigation is required. ObjectiveWe aimed to probe Chinese attitudes regarding the use of mHealth and analyze possible associations between the attitude of willingness to use mHealth devices and some factors based on the socioecological model. MethodsA survey was conducted using quota sampling to recruit participants from 148 cities in China between June 20 and August 31, 2022. Data from the survey were analyzed using multiple stepwise regression to examine the factors associated with willingness to use mHealth devices. Standardized regression coefficients (β) and 95% CIs were calculated using multiple stepwise regression. ResultsThe survey contained a collection of 21,916 questionnaires and 21,897 were valid questionnaires, with a 99.91% effective response rate. The median score of willingness to use mHealth in the post–COVID-19 era was 70 points on a scale from 0 to 100. Multiple stepwise regression results showed that the female gender (β=.03, 95% CI 1.04-2.35), openness personality trait (β=.05, 95% CI 0.53-0.96), higher household per capita monthly income (β=.03, 95% CI 0.77-2.24), and commercial and multiple insurance (β=.04, 95% CI 1.77-3.47) were factors associated with the willingness to use mHealth devices. In addition, people with high scores of health literacy (β=.13, 95% CI 0.53-0.68), self-reported health rating (β=.22, 95% CI 0.24-0.27), social support (β=.08, 95% CI 0.40-0.61), family health (β=.03, 95% CI 0.03-0.16), neighbor relations (β=.12, 95% CI 2.09-2.63), and family social status (β=.07, 95% CI 1.19-1.69) were more likely to use mHealth devices. ConclusionsOn the basis of the theoretical framework of socioecological model, this study identified factors specifically associated with willingness of the Chinese people to use mHealth devices in the post–COVID-19 era. These findings provide reference information for the research, development, promotion, and application of future mHealth devices.

Addressing the overheating fault detection and alarming of insulated busways in buildings, a system based on sensing volatile gases generated by the thermal degradation of the busduct insulation layer was proposed. By monitoring the concentration of volatile gases in the environment, the overheating fault of the busducts can be found early. The thermal degradation process of the busway insulating layer is analyzed, and the pyrolysis characteristic gas at low temperature is studied. The experimental platform has been built, by which the relation between the concentration of volatile gases and temperature of the insulated layer has been studied. By testing, the concentration of volatile organic compounds (VOCs) is proposed as the basis for judging the overheating fault in the alarming system. With the collected samples for training and testing, the AdaBoost classifier is used to identify the overheating fault. Finally, the design of the overheating fault alarming system is given.

Abstract Background Surgery remains the major treatment for early breast cancer (BC), but surgery itself is also a trauma which might induce alterations in lipid metabolism. The aim of this study was to investigate the changes in lipid profiles and to explore factors associated with lipid changes pre- and postoperation. Methods We retrospectively analyzed the pre- and postoperative serum lipid profiles of 1934 BC patients. Results The levels of triglycerides (TG) (p 24 (34.92%) had a higher incidence of dyslipidemia than patients with BMI ≤ 24 (24.84%; p = 0.001). Moreover, the magnitudes of the TG increase (p

During the COVID-19 pandemic, little is known about parental hesitancy to receive the COVID-19 vaccine for preschool children who are the potential vaccinated population in the future. The purpose of this mixed-method study was to explore the factors influencing Chinese parents’ decision to vaccinate their children aged 3–6 years old against COVID-19. In July 2021, we conducted semi-structured interviews (n = 19) and a cross-sectional survey (n = 2605) with parents of kindergarten children in an urban-rural combination pilot area in China. According to the qualitative study, most parents were hesitant to vaccinate their children with the COVID-19 vaccine. In the quantitative study, we found that three-fifths of 2605 participants were unwilling to vaccinate their children against COVID-19. Furthermore, the main predictors of parents’ intention to vaccinate their children were fathers, lower level of education, and positive attitudes toward vaccination. Based on our findings, targeted health education techniques may be able to boost childhood COVID-19 immunization rates.

In order to solve the problem of power transmission efficiency reduction resulting from misalignment in the Wireless Power Transfer (WPT) system for Autonomous Underwater Vehicle (AUVs), a novel coupling structure with strong tolerance to misalignment is proposed. A solenoid coil is selected as the transmitting coil, and the receiving coil is composed of dual combined planar coils. The WPT system can still maintain stable output under uncertain axial misalignments for AUVs. The magnetic field distribution of the proposed magnetic coupling structure is analyzed theoretically, and the distance between the coils in the dual combined planar receiving coil is optimized. The theoretical analysis shows that the proposed solenoid-dual combined planar coils coupling structure can effectively maintain a stable mutual inductance between the transmitting coil and receiving coil under different axial misalignments compared with solenoid-unipolar planar coil coupling structure. An S-S resonant compensated WPT experimental prototype is built to verify the output characteristics of the proposed magnetic coupling structure. Compared to the magnetic coupler with the unipolar planar coil, it is validated by experiment that the proposed magnetic coupler substantially enhances the stability of power transmission efficiency and output power when axial misalignment occurs. The power transmission efficiency decreases by 6.74% when axial misalignment increases from 0 to 40 mm in saltwater. Meanwhile, the variation of output power is less than 4.15%.

Most lakes have undergone significant changes on the Tibetan Plateau in recent decades, affecting water resources on the Tibetan Plateau and its surrounding areas. In this paper, we investigated the variations of 25 lakes in five sub-regions on the Tibetan Plateau from 1972 to 2019 based on SRTM DEM data and Landsat imagery. We used a method to derive lake-levels based on DEM and lake boundaries delineated from Landsat imagery, and then calculated the changes in lake area, level, and volume in 1972 to 2019. We also analyzed the potential impacts of temperature, precipitation, glacial and permafrost melting in lake changes during this period. The results show that the lakes tended to shrink until 2010 in southern and western plateau, after which they began to expand gradually but the overall trend is still shrinking. Limited meltwater from glaciers and permafrost and low precipitation are the main reasons for their shrinkage. The lakes in the central plateau, northwest plateau and northeast plateau tend to expand overall. The reason for the expansion of the lakes is not only precipitation but also the melting of glaciers and permafrost. Overall, the lake changes have gone through 3 phases, namely a slight decrease during 1972–2000, a rapid increase during 2000–2010, and a slowdown in the last decade (2010–2019). Multiple factors such as temperature, precipitation, the state of glaciers and permafrost have contributed to the changes in the lake.

Viral hepatitis is a major global public health problem that affects hundreds of millions of people and is associated with significant morbidity and mortality. Five biologically unrelated hepatotropic viruses account for the majority of the global burden of viral hepatitis, including hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV), and hepatitis E virus (HEV). Omics is defined as the comprehensive study of the functions, relationships and roles of various types of molecules in biological cells. The multi-omics analysis has been proposed and considered key to advancing clinical precision medicine, mainly including genomics, transcriptomics and proteomics, metabolomics. Overall, the applications of multi-omics can show the origin of hepatitis viruses, explore the diagnostic and prognostics biomarkers and screen out the therapeutic targets for viral hepatitis and related diseases. To better understand the pathogenesis of viral hepatitis and related diseases, comprehensive multi-omics analysis has been widely carried out. This review mainly summarizes the applications of multi-omics in different types of viral hepatitis and related diseases, aiming to provide new insight into these diseases.

Despite the significance of hydrogen spillover on metal-supported electrocatalysts for hydrogen evolution, fundamental understandings on such a process are insufficient. Here the authors show that small work function difference between metal and support facilitates hydrogen spillover and enhances activity.

Abstract There is strong evidence to suggest that obesity-related proteins play a key role in pathways that are related to breast cancer. In this study, we aimed to establish a robust obesity-related protein score (ORPS) that could be used to assess breast cancer risk. Based on evidence from high-quality systematic reviews and population studies, we selected nine such proteins that are stable in vitro, and measured their circulating concentrations by ELISA in a case–control study conducted in Chengdu, Sichuan, China, with 279 breast cancer cases and 260 healthy controls. Two obesity-related protein scores (ORPS) were calculated using a three-step method, with linear-weighted summation, and the one with a larger area under the curve was chosen for further evaluation. As a result, ORPS (PS5pre or PS4post) was positively correlated with breast cancer risk (premenopausal: OR≤63 VS >63 3.696, 95% CI 2.025–6.747; postmenopausal: OR≤38 VS >38 7.100, 95% CI 3.134–16.084), and represented a better risk predictor among obese women compared to non-obese in pre- and postmenopausal women. Among different molecular subtypes, ORPS was positively correlated with Luminal breast cancer, with additionally positive association with triple-negative breast cancer in premenopausal women. The ORPS might be a potential marker of breast cancer risk among Chinese women.

Robust fitting is a basic technique and has been widely applied in photogrammetry and remote sensing, such as geometric correction. As known, typical robust estimators (include M-estimators, S-estimators, MM-estimators, etc.) often fail when outlier rate is higher than 50%, even if the outliers are uniformly distributed. In this article, we propose simple yet effective estimators, called adaptive M-estimators (AM-estimators). They are still robust under 80% of outliers. The proposed AM-estimators are very important supplements of M-estimators. Different from M-estimators, we use a varying parameter (shape-control parameter) instead of the original constant parameter in the weight function. The shape-control parameter decreases along with iterations in iteratively reweighted least squares, namely, AM-estimators are optimized in a coarse-to-fine manner. We adapt the proposed estimators into classical remote sensing and photogrammetry tasks, including mismatch removal, camera orientation (or called perspective-n-point), and point set registration to demonstrate their powers. Extensive synthetic and real experiments show that AM-estimators are superior to M-estimators, S-estimators, MM-estimators, and RANSAC-type methods. The source code of AM-estimators will be publicly available at https://ljy-rs.github.io/web.

Immunotherapy with immune checkpoint inhibitor (ICI) drugs is gradually becoming a hot topic in cancer treatment. To comprehensively evaluate the safety and efficacy of ICI drugs, we employed the Bayesian model and conducted a network meta-analysis in terms of progression-free survival (PFS), overall survival (OS) and severe adverse events (AEs). Our study found that treatment with ipilimumab was significantly worse than standard therapies in terms of PFS, whereas treatment with cemiplimab significantly improved PFS. The results also indicated that cemiplimab was the best choice for PFS. Treatment with nivolumab, pembrolizumab and nivolumab plus ipilimumab significantly improved OS compared to standard therapies. In terms of OS, cemiplimab was found to be the best choice, whereas avelumab was the worst. In terms of severe AEs, atezolizumab, avelumab, durvalumab, nivolumab, and pembrolizumab all significantly reduced the risk of grade 3 or higher AEs compared to standard therapy. The least likely to be associated with severe AEs were as follows: cemiplimab, avelumab, nivolumab, atezolizumab, and camrelizumab, with nivolumab plus ipilimumab to be the worst. Therefore, different ICI drug therapies may pose different risks in terms of PFS, OS and severe AEs. Our study may provide new insights and strategies for the clinical practice of ICI drugs.

Abstract Background To investigate the regional and age-specific distribution of AIDS/HIV in China from 2004 to 2017 and to conduct time series analysis of the epidemiological trends. Method Using official surveillance data from publicly accessible database of the national infectious disease reporting system, we described long-term patterns of incidence and death in AIDS/HIV, analyzed age group and regional epidemic characteristics, and established Autoregressive Integrated Moving Average (ARIMA) models for time series analysis. Result The incidence and death of AIDS/HIV have increased rapidly from 2004 to 2017, with significant difference regarding age groups and provincial regions (a few provinces appear as hot spots). With goodness-of-fit criteria and using data from 2004 to 2015, ARIMA (0,1,3) × (2,0,0), ARIMA (3,1,0) × (1,0,1), and ARIMA (0,1,2) × (2,0,0) were chosen as the optimal model for the incidence of AIDS, HIV, and combined; ARIMA (0,1,3) × (1,0,0) was chosen as the optimal model for the death of AIDS, HIV, and combined. ARIMA models robustly predicted the incidence and death of AIDS/HIV in 2016 and 2017. Conclusion A focused intervention strategy targeting specific regions and age groups is essential for the prevention and control of AIDS/HIV. ARIMA models function as data-driven and evidence-based methods to forecast the trends of infectious diseases and formulate public health policies.

Background Burn patients are prone to infection as well as immunosuppression, which is a significant cause of death. Currently, there is a lack of prognostic biomarkers for immunosuppression in burn patients. This study was conducted to identify immune-related genes that are prognosis biomarkers in post-burn immunosuppression and potential targets for immunotherapy. Methods We downloaded the gene expression profiles and clinical data of 213 burn patients and 79 healthy samples from the Gene Expression Omnibus (GEO) database. Immune infiltration analysis was used to identify the proportion of circulating immune cells. Functional enrichment analyses were carried out to identify immune-related genes that were used to build miRNA-mRNA networks to screen key genes. Next, we carried out correlation analysis between immune cells and key genes that were then used to construct logistic regression models in GSE77791 and were validated in GSE19743. Finally, we determined the expression of key genes in burn patients using quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results A total of 745 differently expressed genes were screened out: 299 were up-regulated and 446 were down-regulated. The number of Th-cells (CD4+) decreased while neutrophils increased in burn patients. The enrichment analysis showed that down-regulated genes were enriched in the T-cell activation pathway, while up-regulated genes were enriched in neutrophil activation response in burn patients. We screened out key genes (NFATC2, RORA, and CAMK4) that could be regulated by miRNA. The expression of key genes was related to the proportion of Th-cells (CD4+) and survival, and was an excellent predictor of prognosis in burns with an area under the curve (AUC) value of 0.945. Finally, we determined that NFATC2, RORA, and CAMK4 were down-regulated in burn patients. Conclusion We found that NFATC2, RORA, and CAMK4 were likely prognostic biomarkers in post-burn immunosuppression and potential immunotherapeutic targets to convert Th-cell dysfunction.

BackgroundThe aim of this study was to investigate the status of serum lipids during endocrine therapy.MethodsWe retrospectively analysed lipid profiles during the 5-year treatment of 1487 consecutive postoperative BC patients. Lipid parameters included triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL-C) and high-density lipoprotein (HDL-C). Those biomarkers were measured at baseline and 1, 2, 3, 4 and 5 years following the initiation of endocrine therapy.ResultsFor premenopausal BC patients, LDL levels rapidly decreased at 1 year in the tamoxifen (TAM) group compared with baseline levels (p

In order to meet the demands of desirable efficiency, transformerless DC/DC equipment with great voltage step-down are inevitable needed. This research offers a unique type of high-frequency, high-voltage-gain DC/DC converter, which comprises a switched capacitor (SC) converter and a buck converter. Thanks to the transformation of a two-stage converter to a single-stage converter, it has a considerable ratio of step-down voltage transformation and a reasonable duty cycle. In addition, it can permit low voltage stress on the switches. The simple control method and easy driving circuit implementation makes it scalable for high-power-level devices. Low cost can be realized as fewer components are needed. Under all operational circumstances, total soft-charging and low equipment voltage stresses are accomplished. Compared to those classic high-voltage-gain converters, the proposed converter exhibits merits of higher efficiency, higher flexibility, lower ripples, and lower costs. A comprehensive analysis is carried out for the converter’s steady-state operations. With a 1 MHz switching frequency, a 900 W prototype of a 20-time converter is constructed, with a peak efficiency of 92.5%. Simulations and experiments verify the effectiveness of the theoretical investigation of the converter’s operation.

In this paper, a wireless power transfer (WPT) system with a compact planar magnetic coupler for an autonomous underwater vehicle (AUV) is proposed. A passive induction (PI) coil is integrated into the circular transmitter (Tx) coil to build a uniform magnetic field (UMF), which can guarantee the stable output of the WPT system under uncertain radial and axial misalignments for AUV. Based on normalized magnetic induction intensity distribution analysis, a UMF constructing method with a PI coil is given, aiming to eliminate the fluctuation of magnetic field intensity, and the PI coil design principles and flow chart are obtained. The theoretical analysis shows the proposed integrated coil can effectively enhance the radial misalignment tolerance compared with a conventional circular spiral coil. The zero-phase angle (ZPA) input condition can be achieved by adjusting the series capacitor connected with the Tx coil in S-S compensation topology. Experimental results show that the proposed magnetic coupler containing an integrated coil significantly improves the stability of output power and power transfer efficiency within the possible radial and axial misalignments compared with a conventional coupler. It was demonstrated that the output power changes less than 5.5% and the power transfer efficiency maintains at approximately 84.5% in arbitrary radial positions within the possible working region with an axial transfer distance of 50 mm in saltwater.

Background: The association between induced abortion and birth control methods (including oral contraceptives and intrauterine devices) and breast cancer may vary among countries, due to the different usage and frequency of birth control methods and induced abortion among countries. A better understanding of this association may help in determining safer birth control methods for Chinese women. Methods: A case-control study was conducted with a total of 794 cases and 805 controls. Standardized questionnaires were used to collect information on demographic characteristics, exposure to induced abortion, birth control methods, and other risk factors for breast cancer. Multivariate logistic regression was conducted to explore the association between birth control methods and breast cancer. Results: Multivariate logistic regression analyses showed that having a history of medical abortions, ≥3 surgical abortions, or both medical and surgical abortions was associated with an increased risk of breast cancer in post-menopausal women (odds ratio [OR] 2.48; 95% confidence interval [CI], 1.14–5.40). Pre-menopausal women who had used intra-uterine devices (IUDs) for more than 20 years tended to have a lower breast cancer risk than other age-matched pre-menopausal women (OR 0.41; 95% CI, 0.25–0.68). Both pre-menopausal and post-menopausal women who had