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Abstract Background Hearing loss in children and adolescents is attracting increasing attention as a growing public health problem. This study aimed to analyze the burden of hearing loss in children and adolescents from 1990 to 2021 to provide a new basis for further epidemiological research, disease prevention, and public health policy development. Methods The prevalence and years lived with disability (YLDs) of hearing loss in children and adolescents from 1990 to 2021 at the global, regional, and national levels were extracted from the Global Burden of Disease 2021 database. The estimated annual percentage change (EAPC) was used to assess trends in prevalence and YLDs. Pearson correlation analysis was used to assess the relationships between sociodemographic index (SDI) and prevalence and YLDs. Results In 2021, approximately 97.83 million children and adolescents under the age of 20 years were affected by hearing loss globally, resulting in approximately 3.91 million YLDs. From 1990 to 2021, the prevalence rate increased from 3537 per 100,000 to 3711 per 100,000, with an EAPC of 0.15% (95% CI: 0.12–0.17). The burden of hearing loss was greater in low-middle-SDI region and low-SDI region. Mild hearing loss accounted for 62.1% of the total cases and was the predominant type of hearing loss. Otitis media was the leading preventable cause of hearing loss, with 46.9% of hearing loss attributed to otitis media. Moreover, in children under 5 years of age, 68.7% of hearing loss was attributed to otitis media. Pearson correlation analysis revealed that the prevalence rates and YLDs rates of hearing loss were negatively correlated with the SDI (R = -0.57, P

Objectives: Artificial intelligence (AI) with white light imaging (WLI) is not enough for detecting non-polypoid colorectal polyps and it still has high false positive rate (FPR). We developed AIs using blue laser imaging (BLI) and linked color imaging (LCI) to detect them with specific learning sets (LS). Methods: The contents of LS were as follows, LS (WLI): 1991 WLI images of lesion of 2-10 mm, LS (IEE): 5920 WLI, BLI, and LCI images of non-polypoid and small lesions of 2-20 mm. LS (IEE) was extracted from videos and included both in-focus and out-of-focus images. We designed three AIs as follows: AI (WLI) finetuned by LS (WLI), AI (IEE) finetuned by LS (WLI)+LS (IEE), and AI (HQ) finetuned by LS (WLI)+LS (IEE) only with images in focus. Polyp detection using a test set of WLI, BLI, and LCI videos of 100 non-polypoid or non-reddish lesions of 2-20 mm and FPR using movies of 15 total colonoscopy were analyzed, compared to 2 experts and 2 trainees. Results: The sensitivity for LCI in AI (IEE) (83%) was compared to that for WLI in AI (IEE) (76%: p=0.02), WLI in AI (WLI) (57%: p

Microglia-mediated neuroinflammation and synaptic damage contribute to the pathogenesis of major depressive disorder. Coeloglossum viride var. bracteatum extract (CE) has anti-inflammatory and neuroprotective effects. Therefore, we hypothesized that CE could inhibit the pathogenesis of depression. To test this hypothesis, we evaluated the antidepressant effects of CE in a lipopolysaccharide (LPS)-induced mouse model. We showed that CE ameliorated LPS-induced depressive-like phenotypes such as increased preference for sucrose, decreased immobility, and improved willingness to move in mice. Consistently, CE reduced the levels of pro-inflammatory cytokines TNF-α, IL-1β, and IL-6 in the brain. Mechanistically, CE transformed the phenotypic polarization of microglia by inhibiting the enhancement of aerobic glycolysis and improving oxidative phosphorylation, mediated by the HIF-1α/PKM2 signaling axis. CE reversed the reduction in synaptic proteins, dendritic spines, and neuronal loss. Thus, CE may alleviate LPS-induced depression through anti-inflammation, alteration of microglial energy metabolism and protection of synaptic plasticity, thus highlighting its potential as antidepressant.

BackgroundMetagenomic next-generation sequencing (mNGS) has been widely reported to identify pathogens in infectious diseases (IDs). In this work, we intended to investigate the diagnostic value and clinical acceptance of paired-samples mNGS as compared to the culture method.MethodsA total of 361 patients with suspected infection were retrospectively included. With reference to the clinical diagnosis, we compared the diagnostic performance and clinical acceptance in pathogen detection between mNGS and culture tests. Moreover, the pathogen concordance of paired blood and respiratory tract (RT) samples in mNGS assay was investigated.ResultsAmong 511 samples, 62.04% were shown to be pathogen positive by mNGS, and that for clinical diagnosis was 51.86% (265/511). When compared to culture assay (n = 428), mNGS had a significantly higher positivity rate (51.87% vs. 33.18%). With reference to the clinical diagnosis, the sensitivity of mNGS outperformed that of culture (89.08% vs. 56.72%). Importantly, mNGS exhibited a clinically accepted rate significantly superior to that of culture. In addition, the mNGS result from 53 paired blood and RT samples showed that most pairs were pathogen positive by both blood and RT, with pathogens largely being partially matched.ConclusionThrough this large-scale study, we further illustrated that mNGS had a clinically accepted rate and sensitivity superior to those of the traditional culture method in diagnosing infections. Moreover, blood and paired RT samples mostly shared partial-matched positive pathogens, especially for pathogens with abundant read numbers in RT, indicating that both blood and RT mNGS can aid the identification of pathogens for respiratory system infection.

Objectives: Metagenomic next-generation sequencing (mNGS) is a powerful tool for pathogen detection. The accuracy depends on both wet lab and dry lab procedures. The objective of our study was to assess the influence of read length and dataset size on pathogen detection. Methods: In this study, 43 clinical BALF samples, which tested positive via clinical mNGS and were consistent with the diagnosis, were subjected to re-sequencing on the Illumina NovaSeq 6000 platform. The raw re-sequencing data, consisting of 100 million (M) paired-end 150 bp (PE150) reads, were divided into simulated datasets with eight different data sizes (5 M, 10 M, 15 M, 20 M, 30 M, 50 M, 75 M, 100 M) and five different read lengths (single-end 50 bp (SE50), SE75, SE100, PE100, and PE150). Both Kraken2 and IDseq bioinformatics pipelines were employed to analyze the previously diagnosed pathogens in the simulated data. Detection of pathogens was based on read counts ranging from 1 to 10 and RPM values ranging from 0.2 to 2. Results: Our results revealed that increasing dataset sizes and read lengths can enhance the performance of mNGS in pathogen detection. However, a larger data sizes for mNGS require higher economic costs and longer turnaround time for data analysis. Our findings indicate 20 M reads being sufficient for SE75 mode to achieve high recall rates. Additionally, high nucleic acid loads in samples can lead to increased stability in pathogen detection efficiency, reducing the impact of sequencing strategies. The choice of bioinformatics pipelines had a significant impact on recall rates achieved in pathogen detection. Conclusions: Increasing dataset sizes and read lengths can enhance the performance of mNGS in pathogen detection but increase the economic and time costs of sequencing and data analysis. Currently, the 20 M reads in SE75 mode may be the best sequencing option.

Acute liver injury (ALI) is a prevalent clinical condition featured by liver inflammation and functional abnormalities resulting from various causes. Prolonged damage to liver cells can progress to chronic inflammation, fibrosis, and cirrhosis. Astragalin (AST), a natural flavonoid present in traditional medicinal and edible plants, exhibits anti-inflammatory and antioxidative properties, making it a potential therapeutic candidate for ALI. Using network pharmacology analysis, we explored the targets and pathways implicated in the regulation of AST against ALI. Our study using an ALI mouse model showed that AST effectively mitigates oxidative stress and inflammation induced by lipopolysaccharide (LPS), offering protection against LPS-induced ALI. The mechanism may involve inhibiting the tumor necrosis factor-α-driven necroptosis pathway and downregulating the hypoxia-inducible factor-1α/pyruvate kinase M2 signaling pathway, thereby suppressing enhanced glycolysis-mediated inflammatory response. Through the synergistic modulation of multiple targets and pathways, AST emerges as a promising therapeutic option for ALI, offering potential for clinical application.

Abstract Objective The aim of the study was to investigate the 3.0 Tesla magnetic resonance imaging (MRI) features of Madelung’s deformity. Materials and methods The wrist MRI scans of 19 patients clinically diagnosed with Madelung’s deformity and 20 patients without deformity were consecutively selected from Beijing Jishuitan Hospital between April 2019 and December 2022 for observation, in the case group and control group, respectively. Multiple linear regression was used to analyze the factors affecting tilting angle and width of central disc (CD, also termed as triangular fibrocartilage, the main component of triangular fibrocartilage complex), while the chi-square test was used to compare the occurrences of CD (radial) attachment displacement, VL, and RTL. p

Synthetic aperture radar (SAR) and optical sensing are two important means of Earth observation. SAR-to-optical image translation (S2OIT) can integrate the advantages of both and assist SAR image interpretation under all-day and all-weather conditions. The existing S2OIT methods generally follow the generative adversarial networks paradigm, and encounter the problem of mode collapse, making them difficult to train. SAR and optical images have heterogeneous characteristics and large spectral differences, most of the existing methods do not focus on the color correlation between these two image domains, which leads to spectral distortion and detail errors in translation results. To address these issues, we propose a novel diffusion model capable of memorizing color and directly mapping between the SAR and optical image domains for S2OIT called CM-Diffusion. The color attention Brownian bridge diffusion structure is designed to learn the color correlation and translation between the SAR and optical image domains directly through the bidirectional diffusion process and color attention mechanism, avoiding the conditional information leverage. The color feature extraction module is constructed to provide color and semantic information for the diffusion model. Extensive experiments conducted on three benchmark datasets SEN1-2, QXS-SAROPT, and SEN12MS demonstrate that the proposed CM-Diffusion outperforms the state-of-the-art methods on both subjective and objective evaluation metrics.

Synthetic aperture radar (SAR) and optical sensing are two important means of Earth observation. SAR-to-optical image translation (S2OIT) can integrate the advantages of both and assist SAR image interpretation under all-day and all-weather conditions. The existing S2OIT methods generally follow a paired training paradigm, which is difficult when dealing with the unpaired S2OIT application scenarios. Moreover, the generator and discriminator in current S2OIT methods have insufficient scene memory for SAR images, resulting in regional landform deformation in the generated images. To address these issues, we propose a novel generative adversarial network capable of memorizing scene for unpaired S2OIT called MS-GAN. The cycle learning framework based on cycle generative adversarial network for unpaired S2OIT is designed to construct the translation mapping between unpaired SAR and optical images. The multiscale representation generator is constructed for multiscale fusion and utilization of scene features of SAR images. The proposed multireceptive field discriminator has the ability to enhance scene memory and generate higher quality optical images in different landforms. In addition, the designed subbands shrinkage denoising module can further suppress the effect of speckle noise in SAR images on the quality of the generated results. Extensive experiments conducted on three challenging datasets SEN1-2, WHU-SEN-City, and QXS-SAROPT demonstrate that the proposed MS-GAN outperforms the state-of-the-art methods on both subjective and objective evaluation metrics.

The lack of timely, high-resolution data on agricultural production is a major challenge in developing countries where such information can guide the allocation of scarce resources for food security, agricultural investment, and other objectives. While much research has suggested that remote sensing can potentially help address these gaps, few studies have indicated the immediate potential for large-scale estimations over both time and space. In this study we described a machine learning approach to estimate smallholder maize yield in Ethiopia, using well-measured and broadly distributed ground truth data and freely available spatiotemporal covariates from remote sensing. A neural networks model outperformed other algorithms in our study. Importantly, our work indicates that a model developed and calibrated on a previous year's data could be used to reasonably estimate maize yield in the subsequent year. Our study suggests the feasibility of developing national programs for the routine generation of broad-scale and high-resolution estimates of smallholder maize yield, including seasonal forecasts, on the basis of machine learning algorithms, well-measured ground control data, and currently existing time series satellite data.

Laser technology can be used to control weeds by irradiating the apical meristematic tissue (AMT) of weeds when they are still seedlings. Two factors are necessary for the successful large-scale implementation of this technique: the ability to accurately identify the apical meristematic tissue and the effectiveness of the localization algorithm used in the process. Based on this, this study proposes a lightweight weed AMT localization algorithm based on YOLO (look only once) instance segmentation. The YOLOv8n-seg network undergoes a lightweight design enhancement by integrating the FasterNet lightweight network as its backbone, resulting in the F-YOLOv8n-seg model. This modification effectively reduces the number of parameters and computational demands during the convolution process, thereby achieving a more efficient model. Subsequently, F-YOLOv8n-seg is combined with the connected domain analysis algorithm (CDA), yielding the F-YOLOv8n-seg-CDA model. This integration enables the precise localization of the AMT of weeds by calculating the center-of-mass coordinates of the connected domains. The experimental results indicate that the optimized model significantly outperforms the original model; the optimized model reduces floating-point computations by 26.7% and the model size by 38.2%. In particular, the floating-point calculation is decreased to 8.9 GFLOPs, and the model size is lowered to 4.2 MB. Comparing this improved model against YOLOv5s-seg and YOLOv10n-seg, it is lighter. Furthermore, it exhibits exceptional segmentation accuracy, with a 97.2% accuracy rate. Experimental tests conducted on five different weed species demonstrated that F-YOLOv8n-seg-CDA exhibits strong generalization capabilities. The combined accuracy of the algorithm for detecting these weeds was 81%. Notably, dicotyledonous weeds were detected with up to 94%. Additionally, the algorithm achieved an average inference speed of 82.9 frames per second. These results indicate that the algorithm is suitable for the real-time detection of apical meristematic tissues across multiple weed species. Furthermore, the experimental results demonstrated the impact of distinctive variations in weed morphology on identifying the location of the AMT of weeds. It was discovered that dicotyledonous and monocotyledonous weeds differed significantly in terms of the detection effect, with dicotyledonous weeds having significantly higher detection accuracy than monocotyledonous weeds. This discovery can offer novel insights and avenues for future investigation into the identification and location of the AMT of weeds.

Due to the similarity in wavelength between millimeter-wave (MMW) signals and raindrop diameters, rainfall induces significant attenuation and scattering effects that challenge the detection performance of MMW fuzes in rainy environments. To enhance the adaptability of frequency-modulated MMW fuzes in such conditions, the effects of rain on MMW signal attenuation and scattering are investigated. A mathematical model for the multipath echo signals of the fuze was developed. The Monte Carlo method was employed to simulate echo signals considering multiple scattering, and experimental validations were conducted. The results from simulations and experiments revealed that rainfall increases the bottom noise of the echo signal, with rain backscatter noise predominantly affecting the lower end of the echo signal spectrum. However, rain conditions below torrential levels did not significantly impact the detection of strong reflection targets at the high end of the spectrum. The modeling approach and findings presented offer theoretical support for designing MMW fuzes with improved environmental adaptability.

Abstract Detection of vector magnetic fields at nanoscale dimensions is critical in applications ranging from basic material science and fundamental physics to information storage and medical diagnostics. So far, nanoscale vector magnetic field sensing is achieved solely by exploiting a single nitrogen‐vacancy (NV) center in a diamond, by evaluating the Zeeman splitting of NV spin qubits by using the technique of an optically‐detected magnetic resonance. This protocol requires a complex optical setup and expensive detection systems to detect the photoluminescence light, which may limit miniaturization and scalability. Here, a simple approach with all‐electric operation to sensing a vector magnetic field at 200 × 200 nm2 dimensions is experimentally demonstrated, by monitoring a stochastic nanomagnet's transition probability from a metastable state, excited by a driving current due to spin‐orbit torque, to a settled state.

Purpose: Predictors of ‘imminent’ risk of second hip fracture are unknown. The aims of the study were to explore strength of hip areal bone mineral density (aBMD), and muscle area and density for predicting second hip fracture at different time intervals. Methods: Data of the Chinese Second Hip Fracture Evaluation were analyzed, a longitudinal study to evaluate the risk of second hip fracture (of the contralateral hip) by using CT images obtained immediately after first hip fracture. Muscle cross-sectional area and density were measured of the gluteus maximus (G.MaxM) and gluteus medius and minimus (G.Med/MinM) and aBMD of the proximal femur at the contralateral unfractured side. Patients were followed up for a median time of 4.5 years. Separate Cox models were used to predict second hip fracture risk at different time intervals after first event adjusted for age, sex, BMI and diabetes. Results: The mean age of subjects with imminent (within 1st or 2nd year) second hip fracture was 79.80 ± 5.16 and 81.56 ± 3.64 years. In the 1st year after the first hip fracture, femoral neck (FN) aBMD predicted second hip fracture (HR 5.88; 95 % CI, 1.32–26.09). In the remaining years of follow-up after 2nd year, muscle density predicted second hip fracture (G.MaxM HR 2.13; 95 % CI, 1.25–3.65,G.Med/MinM HR 2.10; 95 % CI, 1.32–3.34). Conclusions: Our results show that femoral neck aBMD is an important predictor for second hip fracture within the first year and therefore suggest supports the importance concept of early and rapid-acting bone-active drugs to increase hip BMD. In addition, the importance of muscle density predicting second hip fracture after the second year suggest post hip fracture rehabilitation and exercise programs could also be important to reduce muscle fatty infiltration.

In the environment of smoke and suspended particles, the accurate detection of targets is one of the difficulties for frequency-modulated continuous-wave (FMCW) laser fuzes to work properly in harsh conditions. To weaken and eliminate the significant influence caused by the interaction of different systems in the photon transmission process and the smoke particle environment, it is necessary to increase the amplitude of the target echo signal to improve the signal-to-noise ratio (SNR), which contributes to enhancing the detection performance of the laser fuze for the ground target in the smoke. Under these conditions, the particle transmission of photons in the smoke environment is studied from the perspective of three-dimentional (3D) collisions between photons and smoke particles, and the modeling and Unity3D simulation of FMCW laser echo signal based on 3D particle collision is conducted. On this basis, a laser fuze structure based on multiple channel beam emission is designed for the combined effect of particle features from different systems and its impact on the target characteristics is researched. Simulation results show that the multiple channel laser emission enhances the laser target echo signal amplitude and also improves the anti-interference ability against the combined effects of multiple particle features compared with the single channel. Through the validation based on the laser prototype with four-channel beam emitting, the above conclusions are supported by the experimental results. Therefore, this study not only reveals the laser target properties under the 3D particle collision perspective, but also reflects the reasonableness and effectiveness of utilizing the target characteristics in the 3D particle collision mode to enhance the detection performance of FMCW laser fuze in the smoke.

Abstract Background Mortality following hip fracture is high and incompletely understood. We hypothesize that hip musculature size and quality are related to mortality following hip fracture. This study aims to investigate the associations of hip muscle area and density from hip CT with death following hip fracture as well as assess the dependence of this association on time after hip fracture. Methods In this secondary analysis of the prospectively collected CT images and data from the Chinese Second Hip Fracture Evaluation, 459 patients were enrolled between May 2015 and June 2016 and followed up for a median of 4.5 years. Muscle cross‐sectional area and density were measured of the gluteus maximus (G.MaxM) and gluteus medius and minimus (G.Med/MinM) and aBMD of the proximal femur. The Goutallier classification (GC) was used for qualitatively assessing muscle fat infiltration. Separate Cox models were used to predict mortality risk adjusted for covariates. Results At the end of the follow‐up, 85 patients were lost, 81 patients (64% women) had died, and 293 (71% women) survived. The mean age of non‐surviving patients at death (82.0 ± 8.1 years) was higher than that of the surviving patients (74.4 ± 9.9 years). The Parker Mobility Score and the American Society of Anesthesiologists scores of the patients that died were respectively lower and higher compared to the surviving patients. Hip fracture patients received different surgical procedures, and no significant difference in the percentage of hip arthroplasty was observed between the dead and the surviving patients (P = 0.11). The cumulative survival was significantly lower for patients with low G.MaxM area and density and low G.Med/MinM density, independent of age and clinical risk scores. The GC grades were not associated with the mortality after hip fracture. Muscle density of both G.MaxM (adj. HR 1.83; 95% CI, 1.06–3.17) and G.Med/MinM (adj. HR 1.98; 95% CI, 1.14–3.46) was associated with mortality in the 1st year after hip fracture. G.MaxM area (adj. HR 2.11; 95% CI, 1.08–4.14) was associated with mortality in the 2nd and later years after hip fracture. Conclusion Our results for the first time show that hip muscle size and density are associated with mortality in older hip fracture patients, independent of age and clinical risk scores. This is an important finding to better understand the factors contributing to the high mortality in older hip fracture patients and to develop better future risk prediction scores that include muscle parameters.

ObjectiveThe objective of this study is to explore the prevalence and attributable burden of diet high in processed meat (DHIPM) in global, regional, and national level due to the burden caused by unhealthy dietary pattern worldwide.DesignCross-sectional study.Materials and designAll the data involved in this research were obtained from Global Burden of Diseases Study 2019. DisMod-MR 2.1, a Bayesian meta-regression tool, was used to estimate the prevalence, which was measured by summary exposure value (SEV) and attributable burden of DHIPM. The Spearman rank order correlation method was performed to measure the correlation between sociodemographic index (SDI) and the prevalence as well as attributable burden. The estimated annual percentage change (EAPC) was calculated to demonstrate the temporal trends.ResultsGlobally, there were 304.28 thousand deaths and 8556.88 disability-adjusted life years (DALYs) caused by DHIPM in 2019 and increased by 34.63 and 68.69%, respectively. The prevalence had decreased slightly from 1990 to 2019, however increased in most regions and countries, especially in middle SDI regions, despite the implicitly high prevalence in high SDI regions. Countries with higher SDI values were facing higher prevalence and attributable burden of DHIPM while developing countries were observed with severer temporal trends. Compared with women, men had suffered from lower exposure level however graver attributable burden of DHIPM in the past three decades.ConclusionThe progress of continuous urbanization allowed increasingly severe prevalence and attributable burden of DHIPM, thus the challenge to alleviate this trend was acute. Effective measures such as education on beneficial dietary pattern and supplement on healthy food were urgently required, especially in developing regions and countries.

Context Toddalolactone, the main component of Toddalia asiatica (L.) Lam. (Rutaceae), has anticancer, antihypertension, anti-inflammatory, and antifungal activities.Objective This study investigated the metabolic characteristics of toddalolactone.Materials and methods Toddalolactone metabolic stabilities were investigated by incubating toddalolactone (20 μM) with liver microsomes from humans, rabbits, mice, rats, dogs, minipigs, and monkeys for 0, 30, 60, and 90 min. The CYP isoforms involved in toddalolactone metabolism were characterized based on chemical inhibition studies and screening assays. The effects of toddalolactone (0, 10, and 50 µM) on CYP1A1 and CYP3A5 protein expression were investigated by immunoblotting. After injecting toddalolactone (10 mg/kg), in vivo pharmacokinetic profiles using six Sprague–Dawley rats were investigated by taking 9-time points, including 0, 0.25, 0.5, 0.75, 1, 2, 4, 6 and 8 h.Results Monkeys showed the greatest metabolic capacity in CYP-mediated and UGT-mediated reaction systems with short half-lives (T1/2) of 245 and 66 min, respectively, while T1/2 of humans in two reaction systems were 673 and 83 min, respectively. CYP1A1 and CYP3A5 were the major CYP isoforms involved in toddalolactone biotransformation. Induction of CYP1A1 protein expression by 50 μM toddalolactone was approximately 50% greater than that of the control (0 μM). Peak plasma concentration (Cmax) for toddalolactone was 0.42 μg/mL, and Tmax occurred at 0.25 h post-dosing. The elimination t1/2 was 1.05 h, and the AUC0–t was 0.46 μg/mL/h.Conclusions These findings demonstrated the significant species differences of toddalolactone metabolic profiles, which will promote appropriate species selection in further toddalolactone studies.

IntroductionSepsis-induced acute lung injury (SALI) is a critical illness with high mortality, and pulmonary microvascular endothelial cells (PMECs) barrier dysfunction is a well-documented pathogenesis of SALI. The current study aimed to investigate the underlying mechanism of Reduning (RDN) in the treatment of SALI.MethodsNetwork pharmacology and molecular dynamics simulation (MDS) were used to confirm the possibility of key active components of RDN combining with AKT1. Hematoxylin-eosin staining (HE) and immunohistochemistry (IHC) were used to investigate the effect of RDN in vivo. Immunofluorescence (IF) and co-immunoprecipitation (CoIP) were used to investigate the relationship between mammalian target of rapamycin (mTOR) and Bax in PMECs. ELISA was used to test the level of TNF-α. Flow cytometry was used to detect apoptosis. JC-1 and electron microscopy were used to evaluate mitochondrial damage. The results showed that RDN likely alleviated SALI via targeting AKT1.ResultsIn vivo, RDN could evidently decrease the expression levels of apoptosis-related proteins, alleviate mitochondrial damage, reduce lung tissue edema, down-regulate the level of TNF-α in the serum, and improve the mortality of sepsis in mice. In vitro, RDN had a significant effect on reducing the level of apoptosis-related proteins and cell apoptosis rate, while also mitigated mitochondrial damage. Furthermore, RDN could effectively lower the level of Bax in PMECs and increase the level of mTOR both in vivo and in vitro. Notably, mTOR has the ability to directly bind to Bax, and RDN can enhance this binding capability.DiscussionRDN could attenuate SALI through reducing apoptosis of PMECs, which is a promising therapeutic strategy for SALI prevention.

Abstract Background Patients with a first hip fracture are at high risk of fracturing their other hip. Despite this, preventive therapy is often not given. Because little is known about specific risk factors of a second hip fracture, we investigated the association with areal bone mineral density (aBMD), muscle size, and density. We also investigated whether muscle parameters predict the risk of a contralateral fracture independently of aBMD. Methods Three groups were included, one without hip fracture (a subcohort of the China Action on Spine and Hip Status study), one with a first, and one with a second hip fracture. Subjects with fractures were recruited from the longitudinal Chinese Second Hip Fracture Evaluation (CSHFE). Computed tomography scans of CSHFE patients, which were obtained immediately following their first fracture, were used to measure cross‐sectional area and density of the gluteus maximus (G.MaxM) and gluteus medius and minimus (G.Med/MinM) muscles. Computed tomography X‐ray absorptiometry was used to measure aBMD of the contralateral femur. Median follow‐up time to second fracture was 4.5 years. Cox proportional hazards models were used to compute hazard ratios (HR) of second hip fracture risk in subjects with a first hip fracture. Multivariate logistic regressions were used to compare odds ratios (OR) for the risk of a first and second hip fracture. Results Three hundred and one participants (68.4 ± 6.1 years, 64% female) without and 302 participants (74.6 ± 9.9 years, 71% female) with a first hip fracture were included in the analysis. Among the latter, 45 (79.2 ± 7.1 years) sustained a second hip fracture. ORs for first hip fracture were significant for aBMD and muscle size and density. ORs for a second fracture were smaller by a factor of 3 to 4 and no longer significant for femoral neck (FN) aBMD. HRs for predicting second hip fracture confirmed the results. G.Med/MinM density (HR, 1.68; CI, 1.20–2.35) and intertrochanter aBMD (HR, 1.62; CI, 1.13–2.31) were the most significant. FN aBMD was not significant. G.Med/MinM density remained significant for predicting second hip fracture after adjustment for FN (HR, 1.66; Cl, 1.18–2.30) or total hip aBMD (HR, 1.50; 95% Cl, 1.04–2.15). Conclusions Density of the G.Med/MinM muscle is an aBMD independent predictor of the risk of second hip fracture. Intertrochanteric aBMD is a better predictor of second hip fracture than FN and total hip aBMD. These results may trigger a paradigm shift in the assessment of second hip fracture risk and prevention strategies.

In this study, current-induced partial magnetization-based switching was realized through the spin–orbit torque (SOT) in single-layer L10 FePt with a perpendicular anisotropy (Ku⊥) of 1.19 × 107 erg·cm−3 (1 erg·cm−3 = 0.1 J·m−3), and its corresponding SOT efficiency (βDL) was 8 × 10−6 Oe·(A·cm−2)−1 (1 Oe = 79.57747 A·m−1), which is several times higher than that of the traditional Ta/CoFeB/MgO structure reported in past work. The SOT in the FePt films originated from the structural inversion asymmetry in the FePt films since the dislocations and defects were inhomogeneously distributed within the samples. Furthermore, the FePt grown on MgO with a granular structure had a larger effective SOT field and efficiency than that grown on SrTiO3 (STO) with a continuous structure. The SOT efficiency was found to be considerably dependent on not only the sputtering temperature-induced chemical ordering but also the lattice mismatch-induced evolution of the microstructure. Our findings can provide a useful means of efficiently electrically controlling a magnetic bit that is highly thermally stable via SOT.

Abstract The emergence of new highly pathogenic and drug-resistant influenza strains urges the development of novel therapeutics for influenza A virus (IAV). Here, we report the discovery of an anti-IAV microbial metabolite called APL-16-5 that was originally isolated from the plant endophytic fungus Aspergillus sp. CPCC 400735. APL-16-5 binds to both the E3 ligase TRIM25 and IAV polymerase subunit PA, leading to TRIM25 ubiquitination of PA and subsequent degradation of PA in the proteasome. This mode of action conforms to that of a proteolysis targeting chimera which employs the cellular ubiquitin-proteasome machinery to chemically induce the degradation of target proteins. Importantly, APL-16-5 potently inhibits IAV and protects mice from lethal IAV infection. Therefore, we have identified a natural microbial metabolite with potent in vivo anti-IAV activity and the potential of becoming a new IAV therapeutic. The antiviral mechanism of APL-16-5 opens the possibility of improving its anti-IAV potency and specificity by adjusting its affinity for TRIM25 and viral PA protein through medicinal chemistry.

Background: Thrombocytopenia is a complication after liver transplantation. This study’s aims were to evaluate the role of CYP3A5 genotypes on tacrolimus-induced thrombocytopenia after orthotopic liver transplantation. Methods: In this retrospective case–control study, data from 100 patients who underwent deceased-donor liver transplantation (DDLT) were divided into CYP3A5*3 genotype (donor/recipient) tacrolimus fast- (A*/A*, n = 22), intermediate- (A*/GG, n = 20; GG/A*, n = 31) and slow-metabolizer (GG/GG, n = 27) groups. Platelet count changes and prognosis for 180 days after surgery were compared. Results: Platelet counts declined significantly after DDLT, especially on postoperative day (POD) 3, and continued at low levels for a week thereafter in all groups. In the GG/GG group, platelet counts on POD3 (50.29 ± 5.44 × 109/L) were the lowest among the groups (A*/A*, 71.00 ± 6.22 × 109/L; A*/GG, 57.95 ± 6.21 × 109/L; GG/A*, 75.90 ± 5.56 × 109/L) (p = 0.006). Compared with the A*/A* genotype, tacrolimus nadir levels were significantly higher in GG/GG genotype patients, who also exhibited a higher incidence of hemorrhage (22.2%, p = 0.011). A combination of a nadir blood concentration of tacrolimus ≥ 4.74 ng/mL and spleen size ≥ 165.5 mm was a risk factor for increased thrombocytopenia after DDLT on POD3, with an AUC of 0.735 (sensitivity, 77.2%; specificity, 41.7%). Conclusions: A high blood concentration of tacrolimus after the early stage of DDLT is a major risk factor for hemorrhage. For the CYP3A5 genotype (GG/GG), controlling the blood concentration of tacrolimus below the target concentration until POD3 can avoid thrombocytopenia-related complications.

BackgroundSepsis is an organ dysfunction syndrome caused by the body’s dysregulated response to infection. Yet, due to the heterogeneity of this disease process, the diagnosis and definition of sepsis is a critical issue in clinical work. Existing methods for early diagnosis of sepsis have low specificity.AimsThis study evaluated the diagnostic and predictive values of pyroptosis-related genes in normal and sepsis patients and their role in the immune microenvironment using multiple bioinformatics analyses and machine-learning methods.MethodsPediatric sepsis microarray datasets were screened from the GEO database and the differentially expressed genes (DEGs) associated with pyroptosis were analyzed. DEGs were then subjected to multiple bioinformatics analyses. The differential immune landscape between sepsis and healthy controls was explored by screening diagnostic genes using various machine-learning models. Also, the diagnostic value of these diagnosis-related genes in sepsis (miRNAs that have regulatory relationships with genes and related drugs that have regulatory relationships) were analyzed in the internal test set and external test.ResultsEight genes (CLEC5A, MALT1, NAIP, NLRC4, SERPINB1, SIRT1, STAT3, and TLR2) related to sepsis diagnosis were screened by multiple machine learning algorithms. The CIBERSORT algorithm confirmed that these genes were significantly correlated with the infiltration abundance of some immune cells and immune checkpoint sites (all P

Introduction: The increasing mortality in patients with sepsis-induced pulmonary fibrosis owes to a lack of effective treatment options. This study aims to explore the possibility and possible targets of Reduning in the prevention of sepsis-related pulmonary fibrosis.Methods: The active components and targets of Reduning were searched and screened from the database and analysis platform of traditional Chinese medicine (TCM) system pharmacology. GeneCards, human genome database, DisGeNET database, and the OMIM database were checked to determine the targets associated with sepsis-induced pulmonary fibrosis. DAVID Bioinformatics Resources 6.8 was used for GO and KEGG enrichment analysis to predict its possible signaling pathways and explore its molecular mechanism. The protein–protein interaction (PPI) network was used to identify key active components and core targets. Molecular docking technology was applied to screen the complexes with stable binding of key active components and core targets. Molecular dynamics simulations were used to verify the binding stability and molecular dynamics characteristics of the complexes. The protective effect of RDN on sepsis-induced pulmonary fibrosis was verified by in vitro and in vivo experiments.Results: There were 319 shared targets between sepsis-induced pulmonary fibrosis and RDN. GO enrichment analysis showed that they mainly regulated and participated in the positive regulation of kinase activity, mitogen-activated protein kinase (MAPK) cascade, and protein phosphorylation. KEGG enrichment analysis showed that they were mainly enriched in the mitogen-activated protein kinase cascade signaling pathway, the calcium signaling pathway, the apoptosis pathway, and other signaling pathways. The results of molecular docking and molecular dynamics simulations showed that the active components, stigmasterol, beta-sitosterol, and quercetin, had good binding activities with ERBB2, and they exhibited good stability. Molecular validation experiments confirmed RDN could alleviate lung fibrosis induced by cecum ligation and puncture (CLP), in parallel with the inhibition of the ERBB2-p38 MAPK pathway in mouse alveolar macrophages (AMs).Discussion: Reduning may prevent sepsis-induced pulmonary fibrosis by regulating the ERBB2-p38 MAPK signaling pathway, which provides a possibility for the prevention of sepsis-induced pulmonary fibrosis with traditional Chinese medicine.

PurposeAKT1 is an important target in sepsis acute lung injury (SALI). The current study was aim to construct a high-throughput screening (HTS) system based on the ChemDiv database (https://www.chemdiv.com/complete-list/) and use the system to screen for AKT1 activation agents, which may provide clues for the research and development of new drugs to treat SALI.MethodsBased on the existing X-ray structure of AKT1 and known AKT activators, a large-scale virtual HTS was performed on the ChemDiv database of small molecules by the cascade docking method and demonstrated both accuracy and screening efficiency. Molecular docking and molecular dynamics simulations were used to assess the stability and binding characteristics of the identified small-molecule compounds. The protective effect of the new highly selective compound on SALI were verified both in vitro and in vivo experiments.ResultsThe small-molecule compound 7460-0250 was screened out as a specific activator of AKT1. Molecular validation experiments confirmed that compound 7460-0250 specifically promoted the phosphorylation of AKT1 and down-regulated the LPS-induced apoptosis of human umbilical vein endothelial cells (HUVECs) by activating the AKT-mTOR pathway. Up-regulated mTOR was detected to directly interact with Bax to reduce apoptosis. In vivo, compound 7460-0250 could improved survival rate and alleviated lung injury of sepsis mice induced by cecum ligation and puncture (CLP), parallel with the activation of the AKT-mTOR pathway.ConclusionSmall-molecule compound 7460-0250 was successfully screened and confirmed as a highly selective AKT1 activator, which is a critical target in the development of new therapeutics for SALI.

Background: Sepsis is one of the main causes of death in critically ill patients with high morbidity and mortality. Circular RNAs (CircRNAs) are aberrantly expressed, and play significant regulatory roles in many diseases. However, the expression profiles and functions of circRNAs in sepsis have not yet been fully clarified.Methods: Our present study performed an RNA sequencing (RNA-seq) analysis to assess the expression profiles of circRNAs in vitro. We applied the quantitative real-time polymerase chain reaction (RT-qPCR) to verify the RNA-seq results. The analyses of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the competitive endogenous RNA (ceRNA) regulatory networks, were performed to explore the potential mechanism in sepsis. And then, significantly up-regulated differentially expressed (DE) circRNA, hsa_circ_0074158, was selected for further study. Hsa_circ_0074158 was silenced to investigate its regulatory function in sepsis, and the barrier function was also examined in vitro. Endothelial cell junctions were valued using Vascular endothelial cadherin (VE-cadherin), which was detected by immunofluorescence staining. We measured endothelial permeability by transendothelial electrical resistance (TEER) and fluorescein isothiocyanate (FITC)-dextran extravasation.Results: In total, 203 significantly DE circRNAs, including 77 up-regulated and 126 down-regulated, were identified. In vitro, the RT-qPCR assay showed that the expression pattern of hsa_circ_0074158, hsa_circ_RSBN1L_11059, hsa_circ_0004188, and hsa_circ_0005564 were consistent with the results from RNA-seq analysis. The expression of hsa_circ_0074158 detected by RT-qPCR in vivo was also consistent with the RNA-seq results. The ceRNA networks, GO enrichment, and the KEGG pathway analyses revealed that circRNAs may be related to the barrier function in sepsis. The immunofluorescence assay showed that the suppression of hsa_circ_0074158 expression significantly enhanced the expression of VE-cadherin, which was suppressed in lipopolysaccharide (LPS)-induced sepsis. Additionally, hsa_circ_0074158 knockdown could partially reverse the LPS-induced TEER reduction and FITC-dextran extravasation elevation in sepsis.Conclusion: In conclusion, we have found DE circRNAs could serve as potential biomarkers and therapeutic targets for sepsis. Hsa_circ_0074158 plays a vital role in sepsis and is related to the disruption of the endothelial barrier.

Abstract Analog arithmetic operations are the most fundamental mathematical operations used in image and signal processing as well as artificial intelligence (AI). In‐memory computing (IMC) offers a high performance and energy‐efficient computing paradigm. To date, in‐memory analog arithmetic operations with emerging nonvolatile devices are usually implemented using discrete components, which limits the scalability and blocks large scale integration. Here, a prototypical implementation of in‐memory analog arithmetic operations (summation, subtraction and multiplication) is experimentally demonstrated, based on in‐memory electrical current sensing units using spin‐orbit torque (SOT) devices. The proposed structures for analog arithmetic operations are smaller than the state‐of‐the‐art complementary metal oxide semiconductor (CMOS) counterparts by several orders of magnitude. Moreover, data to be processed and computing results can be locally stored, or the analog computing can be done in the nonvolatile SOT devices, which are exploited to experimentally implement the image edge detection and signal amplitude modulation with a simple structure. Furthermore, an artificial neural network (ANN) with SOT devices based synapses is constructed to realize pattern recognition with high accuracy of ≈95%.

The molecular mechanism of Reduning (RDN) in the treatment of sepsis was analyzed based on network pharmacology. The system pharmacology method was administered to search the active ingredients and targets of RDN, identify the sepsis-related genes, and determine the targets of RDN in the treatment of sepsis. Cytoscape was used to build a “drug component-target” network to screen key compounds. A protein-protein interaction (PPI) network was constructed using STRING, and core targets were revealed through topological analysis. 404 shared targets of RDN and sepsis were introduced into DAVID Bioinformatics Resources 6.8 for GO and KEGG enrichment analysis to predict their possible signaling pathways and explore their molecular mechanisms. GO enrichment analysis highlighted that they were largely related to protein phosphorylation, inflammatory reaction, and positive regulation of mitogen-activated protein kinase (MAPK) cascade. KEGG enrichment analysis outlined that they were enriched in PI3K-AKT signaling pathway, calcium signaling pathway, rhoptry-associated protein 1 (Rap1) signaling pathway, and advanced glycation end products and receptors for advanced glycation end products (AGE-RAGE) signaling pathway. Molecular biological validation results exposed that RDN could significantly improve the protein expression of p-AKT and p-PI3K, alleviate apoptosis-related proteins expression level and decrease apoptosis rate in LPS-induced HUVECs. In conclusion, it was illustrated that RDN could considerably constrain LPS-induced apoptosis by activating the PI3K-AKT signaling pathway, which advocated a basis for fundamental mechanism research and clinical application of RDN in the treatment of sepsis.

Cadmium (Cd), a highly toxic heavy metal for crops in China, poses a significant threat to rice cultivation. It is crucial to identify the genotypes with robust resistance to heavy metals, including Cd, in rice. The experiment was conducted to examine the mitigation effect of silicon (Si) on Cd toxicity levels in Se-enriched Z3055B and non-Se-enriched G46B rice genotypes. A basal dose of Si improved the growth and the quality of rice significantly by reducing the Cd content in rice roots, stems, leaves and grains and increased the yield, biomass and selenium (Se) content of brown rice in both genotypes. Additionally, Se content in brown rice and polished rice was notably higher in Se-enriched rice than in non-Se-enriched rice, with the highest amount at 0.129 mg/kg and 0.085 mg/kg, respectively. The results demonstrated that a basal fertilizer concentration of 30 mg/kg of Si was more effective in reducing Cd transport from roots to shoots in Se-enriched rice than in non-Se-enriched rice genotypes. Therefore, it can be concluded that Se-enriched rice genotypes are a viable option for food crop production in Cd-contaminated areas.

The channel model optimization algorithm plays a critical role in novel communication method research. Wireless sensor connections using capacitive coupling communication inside a metal cabinet such as spacecraft are an emerging communication technology. However, channel modeling along with optimization methods have not been systematically investigated. In this paper, a modified artificial immune algorithm (MAIA) was developed to optimize a few tens of model parameters for the capacitive coupling communication channel within a metal cabinet. The mathematical model of the communication channel was derived from the equivalent circuit model by analyzing the capacitive coupling electric field distribution. Unknown parameters in the model were optimally estimated by adopting MAIA with the objective of minimizing the root mean square error (RMSE) between the model computed data and simulation or experimental data. The proposed scheme enhanced the convergence performance by incorporating the artificial bee colony (ABC) algorithm, modifying the strategies of immune operations and introducing a similarity detection step. Validation results showed that the frequency response of the optimized model matched well with the simulation and experimental data, verifying the feasibility and robustness of the proposed MAIA. Compared with three other state-of-the-art ABC algorithms and three enhanced intelligent algorithms, it was demonstrated that the proposed algorithm performed better with respect to convergence speed and accuracy. The study provided a multiparameter channel model estimation solution for capacitive coupling communication within a metal cabinet research.

Astrocytes are the major glia cells in the central nervous system (CNS). Increasing evidence indicates that more than to be safe-guard and supporting cells for neurons, astrocytes play a broad spectrum of neuroprotective and pathological functions. Thus, they are compelling models to decipher mechanistic insights of glia cells to CNS insults and for the development of drugs. Edaravone is a free radical scavenger with the capacity to eliminate hydroxyl radicals and lipid peroxides. In this study, we examined the neuroprotective effects of edaravone in rat astrocytes challenged by hydrogen peroxide (H2O2) or bacterial lipopolysaccharides (LPS), respectively. We discovered that edaravone attenuated H2O2-induced oxidative stress by reactivating the Akt signaling axis and antagonistically restoring the expression of apoptosis associated regulators such as Bcl-2 and Caspase-3. Consistently, inhibition of Akt signaling by LY294002 attenuated the anti-oxidative activity of edaravone. In addition, edaravone mitigated LPS-induced morphological changes in astrocytes and alleviated the inflammatory activation and expression of TNF-α, IL-1β, IL-6 and NOS2. In summary, our data suggested that edavarone effectively protects astrocytes from oxidative stress or infectious insults, which may pave a new avenue for its application in preclinical research and human disease therapeutics.

Background: One of the benefits of online education is the potential reduction in carbon emissions through the decrease in travel to attend a university in person. We estimated the savings in CO2 emissions of an international cohort of master’s students who studied fully online from their home countries, rather than travelling to the UK and living there while attending university. Methods: The city and country of residence of a cohort of students who first enrolled in the fully online Peoples-uni/Manchester Metropolitan University Master of Public Health programme between the second semester of 2011 and the first semester 2013 were recorded. Total difference in emissions was calculated by adding the estimated aviation emissions between Manchester, UK and the cities where students resided, to the difference in per capita emissions between the country of origin and the UK for the time that the student would have spent in Manchester, based on the semester in which they first enrolled. Results: 128 students enrolled from 70 cities in 30 countries. 93 students were from a range of African countries and 18 from the Indian sub-continent. Flights to and from Manchester were estimated to have accounted for 114,553kg of CO2 and living in Manchester for the duration of their course compared with staying in the home country would have been equivalent to 854,904kg of CO2. The combined net savings was 969,457kg of CO2. Conclusions: A small cohort of overseas students, largely from Africa and India, studied online rather than attending university in the UK. The likely saving by this small cohort of nearly a million kg of CO 2 emissions offers an indication of the potential environmental benefits of offering university education online to overseas students.

Background: One of the benefits of online education is the potential reduction in carbon emissions through the decrease in travel to attend a university in person. We estimated the savings in CO2 emissions of an international cohort of master’s students who studied fully online from their home countries, rather than travelling to the UK and living there while attending university. Methods: The city and country of residence of a cohort of students who first enrolled in the fully online Peoples-uni/Manchester Metropolitan University Master of Public Health programme between the second semester of 2011 and the first semester 2013 were recorded. Total difference in emissions was calculated by adding the estimated aviation emissions between Manchester, UK and the cities where students resided, to the difference in per capita emissions between the country of origin and the UK for the time that the student would have spent in Manchester, based on the semester in which they first enrolled. Results: 128 students enrolled from 55 cities in 31 countries. 93 students were from a range of African countries and 18 from the Indian sub-continent. Flights to and from Manchester were estimated to have accounted for 114,553kg of CO2 and living in Manchester for the duration of their course compared with staying in the home country would have been equivalent to 854,904kg of CO2. The combined net savings was 969,457kg of CO2. Conclusions: A small cohort of overseas students, largely from Africa and India, studied online rather than attending university in the UK. The likely saving by this small cohort of nearly a million kg of CO 2 emissions offers an indication of the potential environmental benefits of offering university education online to overseas students.

Background. The present study aimed to investigate the prognostic value of serum ferritin in critically ill patients with sepsis by using the MIMIC-IV database. Methods. Data were extracted from the MIMIC-IV database. Adult patients who met the sepsis-3 criteria and had the test of ferritin were included. Patients were divided into subgroups according to the initial serum ferritin. The association between initial serum ferritin and in-hospital mortality was performed by using Lowessregression, logistic regression, and ROC analysis. Subgroup analysis was used to search for the interacting factors and verify the robustness of the results. Results. Analysis of the 2,451 patients revealed a positive linear relationship between serum ferritin and in-hospital mortality. Patients with high-ferritin had a higher risk of in-hospital mortality, but no significant association was found in the low-ferritin subgroup compared with those whose ferritin was in the normal reference range. Serum ferritin had moderate predictive power for in-hospital mortality (AUC = 0.651), with an optimal cut-off value of 591.5 ng/ml. Ferritin ≥591.5 ng/ml acted as an independent prognostic predictor of in-hospital mortality, which increased the risk of in-hospital mortality by 119%. Our findings were still robust in subgroup analysis, and acute kidney injury and anemia were considered interactive factors. Conclusion. High-level serum ferritin was an independent prognostic marker for the prediction of mortality in patients with sepsis. Further high-quality research is needed to confirm the relationship between ferritin and the prognosis of septic patients.

Excess agricultural nitrogen, mainly from manure and chemical fertilizers, is a primary source of nutrient pollution and presents serious environmental threats to natural ecosystems and human health. Improvements in nitrogen-use efficiency in crop production are critical for addressing the triple challenge of food insecurity, environmental degradation, and climate change. Approaches such as sustainable intensification that stress technological innovations have received the most attention. But science-based cropland use planning, a promising complementary approach, has so far been largely overlooked. Here we develop a spatially integrated economic-ecological modeling method to assess this previously unexplored potential for improving the economic-environmental performance of crop production by examining the seasonal and spatial implications of cropland and fertilizer use in Bangladesh. In doing so, we aim to make the modeling method accessible to researchers and practitioners interested in achieving the dual goal of food production and environmental sustainability for countries that are characterized by seasonal and spatial variations in crop mix and cropping practices. - The modeling method combines economic and ecological models to explore trade-offs between food security and environmental sustainability. - The modeling method considers both spatial and seasonal dimensions when measuring trade-offs. - The modeling method is highly suitable for adaptation to study topics concerning food security-ecosystem service trade-offs or to inform the design of related models.

(1) Background: Both sepsis acute lung injury (SALI) and non-small-cell lung cancer (NSCLC) are life-threatening diseases caused by immune response disorders and inflammation, but the underlining linking mechanisms are still not clear. This study aimed to detect the shared gene signature and potential molecular process between SALI and NSCLC. (2) Methods: RNA sequences and patient information on sepsis and NSCLC were acquired from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to build a co-expression network associated with sepsis and NSCLC. Protein–protein interaction (PPI) analysis of shared genes was intuitively performed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The involvement of EZH2 in the tumor immune microenvironment (TIME) and sepsis immune microenvironment (IME) was assessed by R software. Western blot, flow cytometry, and other in vitro assays were performed to further confirm the function and mechanism of EZH2 in NSCLC and SALI. (3) Results: WGCNA recognized three major modules for sepsis and two major modules for NSCLC, and there were seven shared genes identified for the two diseases. Additionally, the hub gene EZH2 was screened out. It was shown that EZH2 was closely related to the IME in the two diseases. In the validation assay, our data showed that EZH2 was expressed at a higher level in peripheral blood mononuclear cells (PBMCs) of septic patients than those of healthy donors (HDs), and EZH2 was also expressed at a higher level in lipopolysaccharide (LPS)-induced PBMCs and non-small cell lung cancer (A549) cells. EZH2 inhibitor (GSK343) downregulated the proliferation ability of A549 cells in a concentration-dependent manner, parallel with the decreased expression level of PD-L1. Similarly, GSK343 inhibited PD-L1 protein expression and downregulated the level of proinflammatory factors in LPS-induced PBMCs. In the co-culture system of PBMCs and human type II alveolar epithelial cells (ATIIs), the addition of GSK343 to PBMCs significantly downregulated the apoptosis of LPS-induced ATIIs. (4) Conclusions: This study illustrated that EZH2 inhibition could ameliorate A549 cell proliferation and LPS-induced ATII apoptosis in parallel with downregulation of PD-L1 protein expression, which provided new insights into molecular signaling networks involved in the pathogenetics of SALI and NSCLC.

Objectives The measurement of visceral fat (VF) is clinically important for the identification of individuals at high risk of visceral obesity-related health conditions. Bioelectrical impedance analysis (BIA) is a widely available and frequently used body composition assessment method, but there have been few validation studies for the measurement of VF. This validation study investigated agreement between BIA and CT for the assessment of VF in adults.Design Cross-sectional study.Setting Between 2015 and 2016 in China.Participants A total of 414 adults (119 men and 295 women) aged 40–82 years.Primary and secondary outcome measures CT-visceral fat area (VFA) was derived at the L2-3 and umbilicus level and VFA cut-offs for visceral obesity applied. BIA measurements of visceral fat level were compared with CT VFA findings using scatter plots and receiver operator characteristic (ROC) curves.Results Scatter plots showed poor agreement between BIA and CT-derived visceral fat measurements in both sexes (R=0.387–0.636). ROC curves gave optimum figures for sensitivity and specificity of 65% and 69% in women and 76% and 70% in men, respectively, for BIA to discriminate between adults with normal levels of VF and those with visceral obesity determined by CT.Conclusion BIA has limited accuracy for the assessment of VF in adults in practice when compared with the criterion method.

Background: One of the benefits of online education is the potential reduction in carbon emissions through the decrease in travel to attend a university in person. We estimated the savings in CO2 emissions of an international cohort of master’s students who studied fully online from their home countries, rather than travelling to the UK and living there while attending university. Methods: The city and country of residence of a cohort of students who first enrolled in the fully online Peoples-uni/Manchester Metropolitan University MPH programme between the second semester of 2011 and the first semester 2013 were recorded. We estimated the aviation emissions between Manchester, UK and the cities where students reside, and subtracted the per capita emissions for the country of origin from the per capita emissions for the UK over the time that the student would have spent in Manchester as a full-time student, based on the semester in which they first enrolled. Results: 128 students enrolled from 55 cities in 31 countries. 93 students were from a range of African countries and 18 from the Indian sub-continent. Flights to and from Manchester were estimated to have accounted for 114,553kg of CO2 and living in Manchester for the duration of their course compared with staying in the home country would have been equivalent to 854,904kg of CO2. The combined net savings was 969,457kg of CO2. Conclusions: A small cohort of overseas students, largely from Africa and India, studied online rather than attending university in the UK. The likely saving by this small cohort of nearly a million kg of CO 2 emissions offers an indication of the potential environmental benefits of offering university education online to overseas students.

We previously demonstrated the antioxidant activity of Coeloglossum viride var. bracteatum extract (CE) in rat cortical neurons and in mice with chemically induced cognitive impairment. In this work, we established a staurosporine (STS)-induced toxicity model to decipher the neuroprotective mechanisms of CE. We found that CE protected cell viability and neurite integrity in STS-induced toxicity by restoring the levels of FGF2 and its associated PI3K/Akt signaling axis. LY294002, a pan-inhibitor of PI3K, antagonized the activity of CE, although its-mediated restoration of FGF2 was unaffected. In addition, CE restored levels of Bcl-2/Caspase-3, PKCα/CaM pathway, and Dnmt3a and Dnmt3b, two methyltransferases that contribute to de novo DNA methylation. The Dnmts inhibitor 5-azacytidine impaired CE-mediated restoration of Dnmt3 or CaM, as well as the transition of DNA methylation status on the Dnmt3 promoter. These results reveal potential mechanisms that could facilitate the study and application of CE as a neuroprotective agent.

Abstract Background Histoplasmosis is one of the invasive fungal infections and presents with symptoms mainly in the lungs. Disseminated histoplasmosis (DH) is rare and its lesions in the gastrointestinal tract are even uncommon. The concomitant involvement of the upper and lower gastrointestinal tract has never been described in the immunocompetent individuals. Case presentation A 44-year-old immunocompetent Chinese man presented with fever, hepatosplenomegaly, fungal esophagitis and protuberant lesions with central depression and erosion along the mucous membrane of the colon. The patient was diagnosed as disseminated histoplasmosis by gastrointestinal endoscopy. Conclusions Histoplasmosis should be taken caution in patients with fever and hepatosplenomegaly. Actions should be taken to avoid its disseminated infection associated high mortality.

Summary: A novel method based on voxel-based morphometry was proposed to investigate the average volumetric bone mineral density (vBMD) of femoral head nail tract in patients treated with intramedullary nails—proximal femoral nail antirotation (PFNA) and gamma nail (GN). The results showed that there was no significant difference in average vBMD between the two groups. Background: For unstable intertrochanteric fractures, poor bone quality might be one of the most important causes of cut-out complications in the femoral head during surgical treatment. Bone quality is generally regarded as an equivalent of BMD. Thus, we develop a novel voxel-based morphometry–based method to quantify vBMD of the femoral head nail tract. Methods: Automatic calculation of average vBMD of nail tracts requires three main steps. First, we built a standard nail tract in a proximal femur template. Then, we mapped the proximal femur structure of each patient to the template by B-spline and Demons registration so that the anatomical positions of the proximal femur of all patients spatially corresponded to the standard template. Finally, we calculated and visualized the average vBMD distribution of the nail tract of all patients. To verify the feasibility of the method, we enrolled 75 patients (52 women and 23 men) with hip fractures to our study to compare measurements. The root mean square of the standard deviation (RMSSD) was calculated, and the coefficient of variation (CV) of the RMSSD (CV-RMSSD) was used to evaluate the reproducibility of intraoperator and interscan measurements. The Mann–Whitney U test was used to compare the average vBMD of nail tracts for the PFNA and GN. Results: The CV-RMSSD of intraoperator measurements ranged from 1.0% to 2.0%, and the CV-RMSSD of interscan measurements ranged from 3.6% to 4.5%. There was no significant difference in the average vBMD between patients with PFNAs and those with GNs (p > 0.05). Conclusions: The proposed method is reproducible for determining the average vBMD, which may provide a reference index for selection of appropriate intramedullary nails for individual patients. The current choice of intramedullary nail based on the experience of a surgeon may be biased. The translational potential of this article: A novel method was proposed to measure the spatial average vBMD of nail tracts, which has good potential to provide a reference index for surgeons to choose appropriate implants. Keywords: Fracture, Intramedullary nails, Nail tract, Proximal femur, Volumetric bone mineral density

Optical images are rich in spectral information, but difficult to acquire under all-weather conditions, while SAR images can overcome adverse meteorological conditions, but geometric distortion and speckle noise will reduce the quality of SAR images and thus make image interpretation more challenging. Therefore, transforming SAR images to optical images to assist SAR image interpretation will bring opportunities for SAR image application. With the advancement of deep learning technology, the ability of SAR-to-optical transformation has been greatly improved. However, most of the current mainstream transformation methods do not consider the imaging characteristics of SAR images, and there will be failures such as noisy color spots and regional landform deformation in the generated optical images. Moreover, since the SAR image itself does not contain color information, there also exist many color errors in these results. Aiming at the above problems, Sar2color, an end-to-end general SAR-to-optical transformation model, is proposed based on a conditional generative adversarial network (CGAN). The model uses DCT residual block to reduce the effect of coherent speckle noise on the generated optical images, and constructs the Light atrous spatial pyramid pooling (Light-ASPP) module to mitigate the negative effect of geometric distortion on the generation of optical images. These two designs ensure the precision of texture details when the SAR image is transformed into an optical image, and use the correct color memory block (CCMB) to improve the color accuracy of transformation results. Towards the Sar2color model, we have carried out evaluations on the homologous heterogeneous SAR image and optical image pairing dataset SEN1-2. The experimental results show that, compared with other mainstream transformation models, Sar2color achieves the state-of-the-art effect on all three objective and one subjective evaluation metrics. Furthermore, we have carried out various ablation experiments, and the results show the effectiveness of each designed module of Sar2color.

The laser transmitter and photoelectric receiver are the core modules of the detector in a laser proximity fuse, whose performance variability can affect the accuracy of target detection and identification. In particular, there is no study on the effect of detector’s component performance variability on frequency-modulated continuous-wave (FMCW) laser fuse under smoke interference. Therefore, based on the principles of particle dynamic collision, ray tracing, and laser detection, this paper builds a virtual simulation model of FMCW laser transmission with the professional particle system of Unity3D, and studies the effect of performance variability of laser fuse detector components on the target characteristics under smoke interference. Simulation results show that the difference in the performance of the fuse detector components causes the amplitude variation and peak migration of the beat signal spectrum, and the change in the visibility of the smoke can also affect the results, which indicates that the factors affecting the signal-to-noise ratio (SNR) of the echo signal are related to the smoke interference and performance variability of the detector. The proposed simulation model is supported by experimental results, which reflect the reliability of the proposed findings. Therefore, this study can be used for the optimization of the parameters in the laser fuse antismoke interference to avoid false alarms.

Background and study aims Colorectal cancers (CRC) with deep submucosal invasion (T1b) could be metastatic lesions. However, endoscopic images of T1b CRC resemble those of mucosal CRCs (Tis) or with superficial invasion (T1a). The aim of this study was to develop an automatic computer-aided diagnosis (CAD) system to identify T1b CRC based on plain endoscopic images. Patients and methods In two hospitals, 1839 non-magnified plain endoscopic images from 313 CRCs (Tis 134, T1a 46, T1b 56, beyond T1b 37) with sessile morphology were extracted for training. A CAD system was trained with the data augmented by rotation, saturation, resizing and exposure adjustment. Diagnostic performance was assessed using another dataset including 44 CRCs (Tis 23, T1b 21) from a third hospital. CAD generated a probability level for T1b diagnosis for each image, and > 95 % of probability level was defined as T1b. Lesions with at least one image with a probability level > 0.95 were regarded as T1b. Primary outcome is specificity. Six physicians separately read the same testing dataset. Results Specificity was 87 % (95 % confidence interval: 66–97) for CAD, 100 % (85–100) for Expert 1, 96 % (78–100) for Expert 2, 61 % (39–80) for both gastroenterology trainees, 48 % (27–69) for Novice 1 and 22 % (7–44) for Novice 2. Significant differences were observed between CAD and both novices (P = 0.013, P = 0.0003). Other diagnostic values of CAD were slightly lower than of the two experts. Conclusions Specificity of CAD was superior to novices and possibly to gastroenterology trainees but slightly inferior to experts.

Aim: The objective of our study was to investigate the epidemiologic characteristics, prognostic factors and survival in patients with primary hepatic lymphoma (PHL).Methods: PHL patients diagnosed between 1983 and 2015 were identified from the SEER database. The temporal trend in PHL incidence was assessed using joinpoint regression software. Overall survival(OS) and disease-specific survival (DSS) was evaluated using the Kaplan-Meier method and log-rank test. Univariate and multivariate Cox regression analysis was performed to identify the independent prognostic factors for OS and DSS. Nomograms to predict survival possibilities were constructed based on the identified independent prognostic factors.Results: A total of 1,182 patients were identified with PHL. The mean age was 61.7 ± 17.1 years with a male to female of 1.6:1. Diffuse large B-cell lymphoma (59.8%) was the most common histological subtype. The incidence of PHL steadily increasing by an annual percentage change (APC) of 2.6% (95% CI 2.0–3.2, P < 0.05). The 1-, 5-, and 10-year OS rates were 50.85, 39.6, and 30.4%, respectively, and the corresponding DSS rates were 55.3, 47.9, and 43.3%, respectively. Multivariate Cox regression analysis revealed that age, sex, race, marital status, histological subtype, surgery, and chemotherapy were independent prognostic factors for survival. Nomograms specifically for DLBCL were constructed to predict 1-, 5-, and 10-year OS and DSS possibility, respectively. The concordance index (C-index) and calibration plots showed the established nomograms had robust and accurate performance.Conclusion: PHL were rare but the incidence has been steadily increasing over the past four decades. Survival has improved in recent years. Surgery or chemotherapy could provide better OS and DSS. The established nomograms specifically for DLBCL were robust and accurate in predicting 1-, 5-, and 10-year OS and DSS.

Low crop yields in Sub-Saharan Africa are associated with low fertilizer use. To better understand patterns of, and opportunities for, fertilizer use, location specific fertilizer price data may be relevant. We compiled local market price data for urea fertilizer, a source of inorganic nitrogen, in 1729 locations in eighteen countries in two regions (West and East Africa) from 2010-2018 to understand patterns in the spatial variation in fertilizer prices. The average national price was lowest in Ghana (0.80 USD kg-1), Kenya (0.97 USD kg-1), and Nigeria (0.99 USD kg-1). Urea was most expensive in three landlocked countries (Burundi: 1.51, Uganda: 1.49, and Burkina Faso: 1.49 USD kg-1). Our study uncovers considerable spatial variation in fertilizer prices within African countries. We show that in many countries this variation can be predicted for unsampled locations by fitting models of prices as a function of longitude, latitude, and additional predictor variables that capture aspects of market access, demand and environmental conditions. Predicted within-country urea price variation (as a fraction of the median price) was particularly high in Kenya (0.77-1.12), Nigeria (0.83-1.34), Senegal (0.73-1.40), Tanzania (0.90-1.29) and Uganda (0.93-1.30), but much lower in Burkina Faso (0.96-1.04), Burundi (0.95-1.05), and Togo (0.94-1.05). The correlation coefficient of the country level models was between 0.17 to 0.83 (mean 0.52) and the RMSE varies from 0.005 to 0.188 (mean 0.095). In 10 countries, predictions were at least 25% better than a null-model that assumes no spatial variation. Our work indicates new opportunities for incorporating spatial variation in prices into efforts to understand the profitability of agricultural technologies across rural areas in Sub-Saharan Africa.