Search

Abstract. Interpreting genes of interest is essential for identifying molecular mechanisms, but acquiring such information typically involves tedious manual retrieval. To streamline this process, the fanyi package offers tools to retrieve gene information from sources like National Center for Biotechnology Information (NCBI), significantly enhancing accessibility. Additionally, understanding the latest research advancements and sharing achievements are crucial for junior researchers. However, language barriers often restrict knowledge absorption and career development. To address these challenges, we developed the fanyi package, which leverages artificial intelligence (AI)-driven online translation services to accurately translate among multiple languages. This dual functionality allows researchers to quickly capture and comprehend information, promotes a multilingual environment, and fosters innovation in academic community. Meanwhile, the translation functions are versatile and applicable beyond biomedicine research to other domains as well. The fanyi package is freely available at https://github.com/YuLab-SMU/fanyi.

Abstract Background Cerebrospinal fluid (CSF) circulation is essential in removing metabolic wastes from the brain and is an integral component of the glymphatic system. Abnormal CSF circulation is implicated in neurodegenerative diseases. Low b-value magnetic resonance imaging quantifies the variance of CSF motion, or pseudodiffusivity. However, few studies have investigated the relationship between the spatial patterns of CSF pseudodiffusivity and cognition. Methods We introduced a novel technique, CSF-based spatial statistics (CBSS), to automatically quantify CSF pseudodiffusivity in each sulcus, cistern and ventricle. Using cortical regions as landmarks, we segmented each CSF region. We retrospectively analyzed a cohort of 93 participants with varying degrees of cognitive impairment. Results We identified two groups of CSF regions whose pseudodiffusivity profiles were correlated with each other: one group displaying higher pseudodiffusivity and near large arteries and the other group displaying lower pseudodiffusivity and away from the large arteries. The pseudodiffusivity in the third ventricle positively correlated with short-term memory (standardized slope of linear regression = 0.38, adjusted p

The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.

Abstract Background Good communication is an important professional attribute for radiologists. However, explorations of communication education and their outcomes in radiology residents are sparse. This scoping review aims to evaluate the existing literature on communication education for radiology residents, identify gaps in current practices, and suggest directions for future studies. Methods A scoping review following the six-step approach of Arksey and O’Malley was undertaken. We searched through PubMed, Embase, ERIC, and Web of Science databases, focusing on communication education in radiology residents. Results Sixteen of the 3096 identified articles were included in the analysis. Most studies (13/16) originated from the United States. The studies varied in study design, including quantitative, qualitative and mixed-methods approaches. The sample sizes of most studies were small to moderate, with more than half of the studies had fewer than 30 participants. The identified studies predominantly focused on communication with patients and healthcare professionals. The need for communication education, the efficacy of specific communication education programs, and the capability of some assessment tools for evaluating residents’ communication skills were investigated. Conclusions This scoping review reveals the gap between the need for communication education and the lack of comprehensive education programs in radiology residents globally. Future studies should develop tailored interventions and use reliable assessment tools, engaging more participants with extended follow-up periods, and expand the scope of communication training to include all relevant stakeholders.

Abstract Background Systemic inflammation and frailty have been implicated in osteoporosis (OP) and fracture risks; however, existing evidence remains limited and inconclusive. This study aimed to assess the associations of systemic inflammation and frailty phenotype with incident OP and fracture and to evaluate the mediating role of frailty phenotype. Methods The present study analysed data from the UK Biobank, a comprehensive and representative dataset encompassing over 500 000 individuals from the general population. Baseline peripheral blood cell counts were employed to calculate the systemic inflammation markers, including neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR) and systemic immune‐inflammation index (SII). Frailty phenotype was assessed using five criteria, defined as frail (≥3 items met), pre‐frail (1–2 items met) and non‐frail (0 items met). OP and fracture events were confirmed through participants' health‐related records. Multivariable linear and Cox regression models were utilized, along with mediation analysis. Results Increased systemic inflammation was associated with increased risks of OP and fracture. The corresponding hazard ratios and 95% confidence intervals (CIs) for OP risk per standard deviation increase in the log‐transformed NLR, PLR and SII were 1.113 (1.093–1.132), 1.098 (1.079–1.118) and 1.092 (1.073–1.111), and for fracture risk, they were 1.066 (1.051–1.082), 1.059 (1.044–1.075) and 1.073 (1.058–1.089), respectively. Compared with the non‐frail individuals, the pre‐frail and frail ones showed an elevated OP risk by 21.2% (95% CI: 16.5–26.2%) and 111.0% (95% CI: 98.1–124.8%), respectively, and an elevated fracture risk by 6.1% (95% CI: 2.8–9.5%) and 38.2% (95% CI: 30.7–46.2%), respectively. The systemic inflammation level demonstrated a positive association with frailty, with β (95% CI) of 0.034 (0.031–0.037), 0.026 (0.023–0.029) and 0.008 (0.005–0.011) in response to per standard deviation increment in log‐transformed SII, NLR and PLR, respectively. The frailty phenotype mediated the association between systemic inflammation and OP/fracture risk. Subgroup and sensitivity analyses confirmed the robustness of these findings. Conclusions Systemic inflammation and frailty phenotype are independently linked to increased risks of OP and fracture. The frailty phenotype partially mediates the association between systemic inflammation and osteoporotic traits. These results highlight the significance of interventions targeting systemic inflammation and frailty in OP and fracture prevention and management.

At room temperature, the conversion of greenhouse gases into valuable chemicals using metal-free catalysts for dry reforming of methane (DRM) is quite promising and challenging. Herein, we developed a novel covalent organic porous polymer (TPE-COP) with rapid charge separation of the electron–hole pairs for DRM driven by visible light at room temperature, which can efficiently generate syngas (CO and H2). Both electron donor (tris(4-aminophenyl)amine, TAPA) and acceptor (4,4′,4″,4‴-((1 E,1′E,1″E, 1‴E)-(ethene-1,1,2,2-tetrayltetrakis (benzene-4,1-diyl))tetrakis (ethene-2,1-diyl))tetrakis (1-(4-formylbenzyl)quinolin-1-ium), TPE-CHO) were existed in TPE-COP, in which the push–pull effect between them promoted the separation of photogenerated electron–hole, thus greatly improving the photocatalytic activity. Density functional theory (DFT) simulation results show that TPE-COP can form charge-separating species under light irradiation, leading to electrons accumulation in TPE-CHO unit and holes in TAPA, and thus efficiently initiating DRM. After 20 h illumination, the photocatalytic results show that the yields reach 1123.6 and 30.8 μmol g−1 for CO and H2, respectively, which are significantly higher than those of TPE-CHO small molecules. This excellent result is mainly due to the increase of specific surface area, the enhancement of light absorption capacity, and the improvement of photoelectron-generating efficiency after the formation of COP. Overall, this work contributes to understanding the advantages of COP materials for photocatalysis and fundamentally pushes metal-free catalysts into the door of DRM field.

Abstract Background Neuropsychiatric symptoms (NPS) are prevalent in cognitively impaired individuals including Alzheimer’s disease (AD) dementia and mild cognitive impairment (MCI). Whereas several studies have reported the associations between NPS with AD pathologic biomarkers and cerebral small vessel disease (SVD), but it remains unknown whether AD pathology and SVD contribute to different sub-syndromes independently or aggravate same symptoms synergistically. Method We included 445 cognitively impaired individuals (including 316 MCI and 129 AD) with neuropsychiatric, cerebrospinal fluid (CSF) biomarkers (Aβ42, p-tau, and t-tau) and multi-model MRI data. Psychiatric symptoms were accessed by using the Neuropsychiatric Inventory (NPI). Visual assessment of SVD (white matter hyperintensity, microbleed, perivascular space, lacune) on MRI images was performed by experienced radiologist. Linear regression analyses were conducted to test the association between neuropsychiatric symptoms with AD pathology and CSVD burden after adjustment for age, sex, education, apolipoprotein E (APOE) ε4 carrier status, and clinical diagnosis. Results The NPI total scores were related to microbleed (estimate 2.424; 95% CI [0.749, 4.099]; P =0.005). Considering the sub-syndromes, the hyperactivity was associated with microbleed (estimate 0.925; 95% CI [0.115, 1.735]; P =0.025), whereas the affective symptoms were correlated to CSF level of Aβ42 (estimate -0.006; 95% CI [-0.011, -0.002]; P =0.005). Furthermore, we found the apathy sub-syndrome was associated with CSF t-tau/Aβ42 (estimate 0.636; 95% CI [0.078, 1.194]; P =0.041) and microbleed (estimate 0.693; 95% CI [0.046, 1.340]; P =0.036). In addition, we found a significant interactive effect between CSF t-tau/Aβ42 and microbleed (estimate 0.993; 95% CI [0.360, 1.626]; P =0.019) on severity of apathy sub-syndrome. Conclusion Our study showed that CSF Aβ42 was associated with affective symptoms, but microbleed was correlated with hyperactivity and apathy, suggesting the effect of AD pathology and SVD on different neuropsychiatric sub-syndromes.

Abstract We investigate the entanglement and Rényi entropies for the (1+1)-dimensional O(3) nonlinear sigma model using the tensor renormalization group method. The central charge is determined from the asymptotic scaling properties of both entropies. We also examine the consistency between the entanglement entropy and the nth-order Rényi entropy with n → 1.

We study the nonlinear coupled Kirchhoff system with purely Sobolev critical exponent. By using appropriate transformation, we get one equivalent system involving a critical Schrödinger system and an algebraic system. Through solving the critical Schrödinger system with a corresponding algebraic system, under suitable conditions we obtain the existence and classification of positive ground states for the Kirchhoff system in dimensions 3 and 4. Furthermore, for the degenerate case, we give a complete classification of positive ground states for the Kirchhoff system in any dimension. To the best of our knowledge, this paper is the first to give classification results for the ground states of Kirchhoff systems. The results in this paper partially extend and complement the main results established by Lü and Peng [Existence and asymptotic behavior of vector solutions for coupled nonlinear Kirchhoff-type system, J. Differ. Equ. 263 (2017) 8947–8978] considering the linearly coupled Kirchhoff system with subcritical exponent and some partial results established by Chen and Zou [Positive least energy solutions and phase separation for coupled Schrödinger equations with critical exponent, Arch. Ration. Mech. Anal. 205 (2012) 515–551; Positive least energy solutions and phase separation for coupled Schrödinger equations with critical exponent: higher dimensional case, Calc. Var. Partial Differ. Equ. 52 (2015) 423–467], where the authors considered the coupled purely critical Schrödinger system.

BackgroundThe relationship between epilepsy and risk of acute myocardial infarction (AMI) is not fully understood. Evidence from the Stockholm Heart Study indicates that the risk of AMI is increased in people with epilepsy. This study aims to analyze the temporal trends in prevalence, adverse clinical outcomes, and risk factors of AMI in patients with epilepsy (PWE).MethodsPatients aged 18 years or older, diagnosed with epilepsy with or without AMI and hospitalized from January 1, 2008, to December 31, 2017, were identified from the National Inpatient Sample (NIS) database. The Cochran–Armitage trend test and logistic regressions were conducted using SAS 9.4. Odds ratios (ORs) were generated for multiple variables.ResultsA total of 8,456,098 inpatients were eligible for our analysis, including 181,826 comorbid with AMI (2.15%). The prevalence of AMI diagnosis in PWE significantly increased from 1,911.7 per 100,000 hospitalizations in 2008 to 2,529.5 per 100,000 hospitalizations in 2017 (Ptrend

Concentrating electromagnetic (EM) heat losses on the surface for road microwave deicing have been used to increase the efficiency of surface microwave heating. Here, a numerical model to enhance the surface EM losses based on metamaterial absorbers (MMAs) was developed and validated using experiments with cement as the thermal medium. The model included structure optimization, analysis for characterization of the surface loss mechanism, and tested for determination of the surface heating efficiency. Using the optimized structures, the efficiency of surface microwave heating improved by fivefold when compared to conventional road structures. These effects were consistent across tested and simulated results. In summary, the potential of implementing MMAs for road microwave deicing is highlighted to improve surface microwave heating efficiency.

Abstract The properties of rockfalls, such as the volume and geometry of the detached rock mass as well as the number and diameter of rock fragments, greatly affect their propagated behavior, and different strategies are required to mitigate rockfall hazards. Landquakes generated by rockfalls are regarded as good proxies for those properties. To explore the free‐fall impact dynamics of rockfalls, a series of free‐fall experiments on granular masses were designed and conducted with the characteristics of the generated landquakes versus initial variables being analyzed. The results show that the particle diameter is a dominant factor affecting landquakes, with the most significant effect on the mean frequency. The impact area is another key factor, with which both the maximum seismic amplitude and radiated seismic energy have strong positive correlations, while the mean frequency of landquakes shows a weak negative correlation. The maximum seismic amplitude appears to have a poor correlation with the numbers of layers of the free‐falling granular masses, and it was proven that the maximum seismic amplitude is determined by only the lower part of the granular masses. Quantitative relationships between the number of particles and seismic signal characteristics indicate that disintegration needs to be accounted for when landquakes are used to investigate the characteristics of a rockfall. Moreover, the frequency of landquakes decreases with a decrease in the vertical velocity and increase in the horizontal velocity during granular mass collapse.

Abstract Tertiary lymphoid structure (TLS) can predict the prognosis and sensitivity of tumors to immune checkpoint inhibitors (ICIs) therapy, whether it can be noninvasively predicted by radiomics in hepatocellular carcinoma with liver transplantation (HCC‐LT) has not been explored. In this study, it is found that intra‐tumoral TLS abundance is significantly correlated with recurrence‐free survival (RFS) and overall survival (OS). Tumor tissues with TLS are characterized by inflammatory signatures and high infiltration of antitumor immune cells, while those without TLS exhibit uncontrolled cell cycle progression and activated mTOR signaling by bulk and single‐cell RNA‐seq analyses. The regulators involved in mTOR signaling (RHEB and LAMTOR4) and S‐phase (RFC2, PSMC2, and ORC5) are highly expressed in HCC with low TLS. In addition, the largest cohort of HCC patients is studied with available radiomics data, and a classifier is built to detect the presence of TLS in a non‐invasive manner. The classifier demonstrates remarkable performance in predicting intra‐tumoral TLS abundance in both training and test sets, achieving areas under receiver operating characteristic curve (AUCs) of 92.9% and 90.2% respectively. In summary, the absence of intra‐tumoral TLS abundance is associated with mTOR signaling activation and uncontrolled cell cycle progression in tumor cells, indicating unfavorable prognosis in HCC‐LT.

To address the problems of complex environment, limited device computational resources and limited memory resources in the existing IoT, SELSTM, an intrusion detection system composed of NSENet and LSTM fusion based on SENet, is investigated. The NSENet part of the SELSTM system is based on the squeeze-and-excitation network (SENet). The lightweight computational modules NonLocal, SKConv and inverted residuals are fused into SE blocks, and self-attention of Nonlocal is used to improve the local receptive field of feature extraction. The channel attention and spatial attention of each part of the data are strengthened by the use of SKConv To enhance the adaptive convolution ability of the model and ensure the completeness of the information, the properties of the inverted residual structure are used to ensure that the gradient of the model decreases steadily without gradient explosion or disappearance. For the problem of data imbalance, the dataset is randomly resampled using the weight resampling technique to improve the balance of the dataset to ensure that the final detection effect of the model is more effective and generalized, while the data flow is divided into two parts for processing, and the model parameters are optimized using the model gradient optimizer consisting of the optimizer Lion and the optimization function Lookahead. The model extracts the spatial and temporal features of the data through multidimensional extraction to ensure the completeness of the data feature information in multiple dimensions, thus obtaining better detection results. The results of the experiments comparing the SELSTM model with other models on the intrusion dataset show that the intrusion detection model has a higher detection precision and accuracy than the traditional deep learning intrusion detection model, which indicates that the SELSTM has better detection performance properties and better practicality and effectiveness on IoT devices.

Abstract Background Glymphatic dysfunction is a crucial pathway for dementia. Alzheimer’s disease (AD) pathologies co-existing with cerebral small vessel disease (CSVD) is the most common pathogenesis for dementia. We hypothesize that AD pathologies and CSVD could be associated with glymphatic dysfunction, contributing to cognitive impairment. Method Participants completed with amyloid PET, diffusion tensor imaging (DTI), and T2 fluid-attenuated inversion-recovery (FLAIR) sequences were included from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). White matter hyperintensities (WMH), the most common CSVD marker, was evaluated from T2FLAIR images and represented the burden of CSVD. Amyloid PET was used to assess Aβ aggregation in the brain. We used diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, the burden of enlarged perivascular spaces (PVS), and choroid plexus volume to reflect glymphatic function. The relationships between WMH burden/Aβ aggregation and these glymphatic markers as well as the correlations between glymphatic markers and cognitive function were investigated. Furthermore, we conducted mediation analyses to explore the potential mediating effects of glymphatic markers in the relationship between WMH burden/Aβ aggregation and cognition. Results One hundred and thirty-three participants along the AD continuum were included, consisting of 40 CN − , 48 CN + , 26 MCI + , and 19 AD + participants. Our findings revealed that there were negative associations between whole-brain Aβ aggregation (r = − 0.249, p = 0.022) and WMH burden (r = − 0.458, p

Abstract Light-harvesting is an indispensable process in photosynthesis, and researchers have been exploring various structural scaffolds to create artificial light-harvesting systems. However, achieving high donor/acceptor ratios for efficient energy transfer remains a challenge as excitons need to travel longer diffusion lengths within the donor matrix to reach the acceptor. Here, we report a polymeric supramolecular column-based light-harvesting platform inspired by the natural light-harvesting of purple photosynthetic bacteria to address this issue. The supramolecular column is designed as a discotic columnar liquid crystalline polymer and acts as the donor, with the acceptor intercalated within it. The modular columnar design enables an ultrahigh donor/acceptor ratio of 20000:1 and an antenna effect exceeding 100. Moreover, the spatial confinement within the supramolecular columns facilitates control over the energy transfer process, enabling dynamic full-color tunable emission for information encryption applications with spatiotemporal regulation security.

Abstract Background Seeking COVID-19 information promotes individuals to adopt preventive behaviors, including wearing a mask, social distancing, staying away from risky places, and washing hands. This study aims to investigate which information and sources individuals relied on in seeking COVID-19 information and further examine their roles in individuals’ adoption of preventive behaviors. Methods Through a statistical analysis of 1027 valid responses from citizens in different Chinese cities in 2022 to the self-designed items in an online survey, this study identified individuals’ preferred information sources and content on COVID-19. Regarding the information sources and content, the study used multiple regression analysis to examine their associations with individuals’ preventive behaviors, and further applied fuzzy-set qualitative comparative analysis (fsQCA) to explore their configurations that increase the likelihood of individuals adopting preventive behaviors. Results Individuals preferred information about the newest prevention and control policies, precautions and treatment, and symptoms from the sources of workplace and community, social media, and social live streaming services. Additionally, individuals’ preventive behaviors were positively related to the workplace and community (β = 0.202, p

Abstract Although generally superior, hybrid approaches for correcting errors in third-generation sequencing (TGS) reads, using next-generation sequencing (NGS) reads, mistake haplotype-specific variants for errors in polyploid and mixed samples. We suggest HERO, as the first “hybrid-hybrid” approach, to make use of both de Bruijn graphs and overlap graphs for optimal catering to the particular strengths of NGS and TGS reads. Extensive benchmarking experiments demonstrate that HERO improves indel and mismatch error rates by on average 65% (27 $$\sim$$ ∼ 95%) and 20% (4 $$\sim$$ ∼ 61%). Using HERO prior to genome assembly significantly improves the assemblies in the majority of the relevant categories.

Marine deep Ordovician reservoirs are significantly controlled by strike-slip fault zones, which govern reservoir fluid evolution during various activity periods. Such fluid evolution elucidates the process underpinning ultra-deep oil and gas accumulation and delineates the pivotal role of strike-slip fault zones in hydrocarbon aggregation. This method can improve the understanding of the mechanism of hydrocarbon accumulation in deep to ultradeep carbonate rocks. The findings indicate that the Ordovician reservoirs in the northern thrust fault zone of the Fuman Oilfield predominantly exhibit two stages of calcite vein formation. The distribution patterns of rare earth elements and Sr isotope characteristics suggest that both stages of vein formation were sourced from Middle to Lower Ordovician marine strata, with no evidence of oxidizing fluid infiltration. This indicates that late-stage oil and gas charging in deep-ultradeep formations has good sealing properties. In these calcite veins, early-, middle-, and late-stage fluid inclusions were primarily entrapped. By examining the development of primary oil inclusions and combining the U‒Pb isotope data of host minerals, this study confirms the occurrence of three stages of oil and gas charging in the deep Ordovician strata of the northern thrust fault zone in the Fuman Oilfield. These stages correspond to approximately 459 ± 7.2 Ma (mid-Caledonian), 348 ± 18 Ma (early Permian), and 268 Ma (late Permian). The key accumulation period of oil and gas reservoirs in the study area is the middle and late Caledonian, and there is a good correspondence between oil and gas charging and fault activity.

Glass-metal hetero-bonding through optical epoxy adhesive is frequently used in opto-mechanics and requires high stability. However, the bonding stability is difficult to achieve and predict in massive use cases, where dozens to thousands of metal pads are bonded to mount large optics. Here a universal adhesive bonding technology was proposed and evaluated through reliability analyses based on a limited number of sample tests. Specimens were prepared and tested via standard procedures and equipment; afterward, the lower strength limits were calculated with high reliability. Nominal tensile (6.7 MPa) and shear (4.3 MPa) strength limits at 99.95% reliability were expected for ongoing 30 m aperture telescopes. This study may improve the quality of opto-mechanical interfaces and reduce the risk on constructing extremely large telescopes.

ObjectivesThe altered neuromelanin in substantia nigra pars compacta (SNpc) is a valuable biomarker in the detection of early-stage Parkinson’s disease (EPD). Diagnosis via visual inspection or single radiomics based method is challenging. Thus, we proposed a novel hybrid model that integrates radiomics and deep learning methodologies to automatically detect EPD based on neuromelanin-sensitive MRI, namely short-echo-time Magnitude (setMag) reconstructed from quantitative susceptibility mapping (QSM).MethodsIn our study, we collected QSM images including 73 EPD patients and 65 healthy controls, which were stratified into training-validation and independent test sets with an 8:2 ratio. Twenty-four participants from another center were included as the external validation set. Our framework began with the detection of the brainstem utilizing YOLO-v5. Subsequently, a modified LeNet was applied to obtain deep learning features. Meanwhile, 1781 radiomics features were extracted, and 10 features were retained after filtering. Finally, the classified models based on radiomics features, deep learning features, and the hybrid of both were established through machine learning algorithms, respectively. The performance was mainly evaluated using accuracy, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The saliency map was used to visualize the model.ResultsThe hybrid feature-based support vector machine (SVM) model showed the best performance, achieving ACC of 96.3 and 95.8% in the independent test set and external validation set, respectively. The model established by hybrid features outperformed the one radiomics feature-based (NRI: 0.245, IDI: 0.112). Furthermore, the saliency map showed that the bilateral “swallow tail” sign region was significant for classification.ConclusionThe integration of deep learning and radiomic features presents a potent strategy for the computer-aided diagnosis of EPD. This study not only validates the accuracy of our proposed model but also underscores its interpretability, evidenced by differential significance across various anatomical sites.

Abstract Introduction The distribution of voxel‐ and connection‐based white matter hyperintensity (WMH) patterns in early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD), as well as factors associated with these patterns, remain unclear. Method We analyzed the WMH distribution patterns in EOAD and LOAD at the voxel and connection levels, each compared with their age‐matched cognitively unimpaired participants. Linear regression assessed the independent effects of amyloid and vascular risk factors on WMH distribution patterns in both groups. Results Patients with EOAD showed increased WMH burden in the posterior region at the voxel level, and in occipital region tracts and visual network at the connection level, compared to controls. LOAD exhibited extensive involvement across various brain areas in both levels. Amyloid accumulation was associated WMH distribution in the early‐onset group, whereas the late‐onset group demonstrated associations with both amyloid and vascular risk factors. Discussion EOAD showed posterior‐focused WMH distribution pattern, whereas LOAD was with a wider distribution. Amyloid accumulation was associated with connection‐based WMH patterns in both early‐onset and late‐onset groups, with additional independent effects of vascular risk factors in late‐onset group. Highlights Both early‐onset Alzheimer's disease (EOAD) and late‐onset AD (LOAD) showed increased white matter hyperintensity (WMH) volume compared with their age‐matched cognitively unimpaired participants. EOAD and LOAD exhibited distinct patterns of WMH distribution, with EOAD showing a posterior‐focused pattern and LOAD displaying a wider distribution across both voxel‐ and connection‐based levels. In both EOAD and LOAD, amyloid accumulation was associated with connection‐based WMH patterns, with additional independent effects of vascular risk factors observed in LOAD.

ObjectivePrevious research has partially revealed distinct gut microbiota in ankylosing spondylitis (AS). In this study, we performed non-targeted fecal metabolomics in AS in order to discover the microbiome–metabolome interface in AS. Based on prospective cohort studies, we further explored the impact of the tumor necrosis factor inhibitor (TNFi) on the gut microbiota and metabolites in AS.MethodsTo further understand the gut microbiota and metabolites in AS, along with the influence of TNFi, we initiated a prospective cohort study. Fecal samples were collected from 29 patients with AS before and after TNFi therapy and 31 healthy controls. Metagenomic and metabolomic experiments were performed on the fecal samples; moreover, validation experiments were conducted based on the association between the microbiota and metabolites.ResultsA total of 7,703 species were annotated using the metagenomic sequencing system and by profiling the microbial community taxonomic composition, while 50,046 metabolites were identified using metabolite profiling. Differential microbials and metabolites were discovered between patients with AS and healthy controls. Moreover, TNFi was confirmed to partially restore the gut microbiota and the metabolites. Multi-omics analysis of the microbiota and metabolites was performed to determine the associations between the differential microbes and metabolites, identifying compounds such as oxypurinol and biotin, which were correlated with the inhibition of the pathogenic bacteria Ruminococcus gnavus and the promotion of the probiotic bacteria Bacteroides uniformis. Through experimental studies, the relationship between microbes and metabolites was further confirmed, and the impact of these two types of microbes on the enterocytes and the inflammatory cytokine interleukin-18 (IL-18) was explored.ConclusionIn summary, multi-omics exploration elucidated the impact of TNFi on the gut microbiota and metabolites and proposed a novel therapeutic perspective: supplementation of compounds to inhibit potential pathogenic bacteria and to promote potential probiotics, therefore controlling inflammation in AS.

Epithelioid hemangioendothelioma (EHE) is a rare low-grade malignant vascular tumor. It mainly occurs in the liver, lungs, bones, and other parts of the body. Reports of epithelioid hemangioendothelioma in the parotid gland are rare in both domestic and international literature. Here, we present a case report of a parotid gland epithelioid hemangioendothelioma, including its complete clinical course and imaging findings, to improve the diagnosis and treatment of this unusual disease.Case presentationThe patient, a 75-year-old female, presented with a swelling around the right ear for 2 months and pain for 20 days. Enhanced MRI of the parotid gland revealed a well-defined, round mass with homogeneous signal intensity. The mass showed low signal intensity on T1-weighted imaging, high signal intensity on T2-weighted imaging, nodular low signal intensity within, significant high signal intensity on DWI sequence, low signal intensity on ADC sequence, and heterogeneous enhancement in the arterial phase after intravenous injection of Gd-DTPA. Nodular non-enhancing low signal intensity was observed internally, and slight clearance was seen in the venous phase. The initial diagnosis before surgery was a benign lesion, but after histopathological and immunohistochemical examination, it was confirmed as epithelioid hemangioendothelioma.InterventionComplete tumor resection was performed.ResultsThe patient experienced a favorable recovery, with meticulous follow-up conducted for up to 1 year revealing no signs of recurrence or metastasis. Continued patient surveillance is ongoing to substantiate and validate the long-term efficacy of the treatment.ConclusionDue to the extreme rarity of parotid gland epithelioid hemangioendothelioma, it often leads to a high misdiagnosis rate. The most common misdiagnosis is salivary gland lymphoma, followed by epithelioid hemangiosarcoma. When the lesion is multifocal, fusiform, with internal necrosis, and shows punctate low signal intensity on T2-weighted imaging, significant enhancement in the arterial phase, particularly with more pronounced peripheral enhancement, and persistent enhancement in the venous and delayed phases, epithelioid hemangioendothelioma should be considered. However, the current clinical diagnosis of epithelioid hemangioendothelioma still primarily relies on immunohistochemical methods.

Morphine is a widely used opioid for treatment of pain. The attendant problems including morphine tolerance and morphine dependence pose a major public health challenge. In recent years, there has been increasing interest in the gastrointestinal microbiota in many physiological and pathophysiological processes. The connectivity network between the gut microbiota and the brain is involved in multiple biological systems, and bidirectional communication between them is critical in gastrointestinal tract homeostasis, the central nervous system, and the microbial system. Many research have previously shown that morphine has a variety of effects on the gastrointestinal tract, but none have determined the function of intestinal microbiota in morphine tolerance. This study reviewed the mechanisms of morphine tolerance from the perspective of dysregulation of microbiota-gut-brain axis homeostasis, by summarizing the possible mechanisms originating from the gut that may affect morphine tolerance and the improvement of morphine tolerance through the gut microbiota.

Background: Arterial pulsation has been suggested as a key driver of paravascular cerebrospinal fluid flow, which is the foundation of glymphatic clearance. However, whether intracranial arterial pulsatility is associated with glymphatic markers in humans has not yet been studied. Methods: Seventy-three community participants were enrolled in the study. 4D phase-contrast magnetic resonance imaging (MRI) was used to quantify the hemodynamic parameters including flow pulsatility index (PIflow) and area pulsatility index (PIarea) from 13 major intracerebral arterial segments. Three presumed neuroimaging markers of the glymphatic system were measured: including dilation of perivascular space (PVS), diffusivity along the perivascular space (ALPS), and volume fraction of free water (FW) in white matter. We explored the relationships between PIarea, PIflow, and the presumed glymphatic markers, controlling for related covariates. Results: PIflow in the internal carotid artery (ICA) C2 segment (OR, 1.05; 95 % CI, 1.01–1.10, per 0.01 increase in PI) and C4 segment (OR, 1.05; 95 % CI, 1.01–1.09) was positively associated with the dilation of basal ganglia PVS, and PIflow in the ICA C4 segment (OR, 1.06, 95 % CI, 1.02–1.10) was correlated with the dilation of PVS in the white matter. ALPS was associated with PIflow in the basilar artery (β, -0.273, p, 0.046) and PIarea in the ICA C2 (β, -0.239, p, 0.041) and C7 segments (β, -0.238, p, 0.037). Conclusions: : Intracranial arterial pulsatility was associated with presumed neuroimaging markers of the glymphatic system, but the results were not consistent across different markers. Further studies are warranted to confirm these findings.

Abstract With the development of organic germanium and nanotechnology, germanium serves multiple biological functions, and its potential value in biochemistry and medicine has increasingly captured the attention of researchers. In recent years, germanium has gradually gained significance as a material in the field of biomedicine and shows promising application prospects. However, there has been a limited amount of research conducted on the biological effects and mechanisms of germanium, and a systematic evaluation is still lacking. Therefore, the aim of this review is to systematically examine the application of germanium in the field of biomedicine and contribute new insights for future research on the functions and mechanisms of germanium in disease treatment. By conducting a comprehensive search on MEDLINE, EMBASE, and Web of Science databases, we systematically reviewed the relevant literature on the relationship between germanium and biomedicine. In this review, we will describe the biological activities of germanium in inflammation, immunity, and antioxidation. Furthermore, we will discuss its role in the treatment of neuroscience and oncology-related conditions. This comprehensive exploration of germanium provides a valuable foundation for the future application of this element in disease intervention, diagnosis, and prevention.

Myocardial ischemia-reperfusion (MI/R) injury is common in patients who undergo revascularization therapy for myocardial infarction, often leading to cardiac dysfunction. Carbon monoxide (CO) has emerged as a therapeutic molecule due to its beneficial properties such as anti-inflammatory, anti-apoptotic, and mitochondrial biogenesis-promoting properties. However, its clinical application is limited due to uncontrolled release, potential toxicity, and poor targeting efficiency. To address these limitations, a peroxynitrite (ONOO−)-triggered CO donor (PCOD585) is utilized to generate a poly (lactic-co-glycolic acid) (PLGA)-based, biomimetic CO nanogenerator (M/PCOD@PLGA) that is coated with the macrophage membrane, which could target to the ischemic area and neutralize proinflammatory cytokines. In the ischemic area, local produced ONOO− triggers the continuous release of CO from M/PCOD@PLGA, which efficiently ameliorates MI/R injury by clearing harmful ONOO−, attenuating the inflammatory response, inhibiting cardiomyocyte apoptosis, and promoting mitochondrial biogenesis. This study provides a novel insight into the safe therapeutic use of CO for MI/R injury by utilizing a novel CO donor combined with biomimetic technology. The M/PCOD@PLGA nanogenerator offers targeted delivery of CO to the ischemic area, minimizing potential toxicity and enhancing therapeutic efficacy.

This study aimed to investigate the impact of different curing regimes on the strength of magnesium silicate hydrate cement (MSHC). The study employed various microscopic tests, including XRD, TG, SEM, and MIP, to analyze the underlying mechanisms. The research findings demonstrated that the use of short-term high-temperature curing under the two-stage variable temperature curing regime (TTC) significantly enhanced the early strength of MSHC, with a 3d strength exceeding 30 MPa. In contrast, under standard curing (SC), the strength was only around 2 MPa. The samples subjected to TTC did not exhibit the strength retrogression phenomenon observed under high-temperature curing in previous research, and a high-temperature curing duration of 48 h was found to be more beneficial for the strength development of MSHC. However, from a long-term perspective, the late-stage strength of the TTC samples was lower than that of the SC samples. Microscopic tests revealed that increasing the curing temperature substantially improved the early hydration degree of MSHC, reduced its porosity, and resulted in a more compact cement matrix, thereby enhancing the early strength. Under TTC, hydration continued in the later stage, leading to a decrease in porosity and promoting further strength growth. However, in the long-term, the microstructure of the TTC samples became loose, and the interface connection between silica particles and gel weakened. Compared to the SC samples, the TTC samples exhibited higher porosity, resulting in lower long-term strength.

Quantum-cutting luminescent solar concentrators (QC-LSCs) have great potential to serve as large-area solar windows. These QC nanocrystals can realize a photoluminescence quantum yield (PLQY) of as high as 200% with virtually zero self-absorption loss. Based on our previous work, we have constructed a Monte Carlo simulation model that is suitable to simulate the performance of the QC-LSCs, which can take into account the band-edge emissions and near-infrared emissions of the QC-materials. Under ideal PLQY conditions, CsPbClxBr3−x:Yb3+-based LSCs can reach 12% of the size-independent external quantum efficiency (ηext). Even if LSCs have a certain scattering factor, the CsPbClxBr3−x:Yb3+-based LSCs can still obtain an ηext exceeding 6% in the window size (>1 m2). The flux gain (FG) of the CsPbClxBr3−x:Yb3+-based LSC-PV system can reach 14 in the window size, which is a very encouraging result.

BackgroundCognitive impairment is prevalent in Chinese patients with hypertension; however, current evidence on prevalence and risk factors is required to be synthesized.ObjectivesThis systematic review and meta-analysis aimed to evaluate the prevalence and risk factors of cognitive impairment in Chinese patients with hypertension.MethodsTwo reviewers independently searched PubMed, Web of Science, Embase, The Cochrane Library, CNKI, CBM, the Wanfang database, and the VIP database from their inception to 7 June 2023. The gray literature and the reference lists of the included studies were also retrieved manually. Moreover, we also independently performed the eligibility screening, data extraction, and data synthesis. The primary outcome was the prevalence of cognitive impairment in Chinese patients with hypertension, and the secondary outcomes were the risk factors for cognitive impairment in patients with hypertension. R (version 4.0.3) was used for data synthesis.ResultsIn total, 82 studies involving 53,623 patients with hypertension were included in this meta-analysis. The pooled prevalence of cognitive impairment in patients with hypertension was 37.6% (95% CI: 33.2–42.2%). A total of 12 risk factors, including advanced age (r = −0.34, 95% CI: −0.45, −0.21), female sex (OR = 1.15, 95% CI: 1.01–1.32), BMI > 24 Kg/m2 (OR = 1.76, 95% CI: 1.04–3.00), lower educational level (OR = 2.01, 95% CI: 1.10–3.67), single status (OR = 1.63, 95% CI: 1.32–2.02), complications with diabetes (OR = 1.44, 95% CI: 1.14–1.80), coronary heart disease (OR = 1.49, 95% CI: 1.12–1.97), higher stage of hypertension [stage 3 vs. stage 1, OR = 3.08, 95% CI: 1.82–5.22; stage 2 vs. stage 1, OR = 1.83, 95% CI: 1.29–2.60], no regular physical activity (OR = 0.40, 95% CI: 0.21–0.77), higher levels of systolic blood pressure (r = −0.25, 95% CI: −0.42, −0.08), Hcy (r = −0.39, 95% CI: −0.63, −0.09), and IL-6 (r = −0.26, 95% CI: −0.48, −0.02) were detected.ConclusionCognitive impairment is prevalent in Chinese patients with hypertension, and the increased prevalence was associated with several demographic characteristics, complicated disease, no regular physical activity, worse hypertension status (higher stages and SBP), and high levels of biomarkers. Therefore, more attention should be paid to the early identification and treatment of patients with hypertension who are at high risk for cognitive impairment in clinical practice. In addition, relevant risk factors should be controlled to reduce the incidence of cognitive impairment.Systematic review registrationhttp://www.crd.york.ac.uk/PROSPERO, identifier [CRD42023410437].

Objective: Routine surveillance with duplex ultrasound (DUS) examination is recommended after femoral-popliteal and femoral-tibial-pedal vein bypass grafts with various intervals postoperatively. The presently used methodology to analyze bypass graft DUS examination does not use all the available data and has been shown to have a significant rate for missing impending bypass graft failure. The objective of this research is to investigate recurrent neural networks (RNNs) to predict future bypass graft occlusion or stenosis. Methods: This study includes DUS examinations of 663 patients who had bypass graft operations done between January 2009 and June 2022. Only examinations without missing values were included. We developed two RNNs (a bidirectional long short-term memory unit and a bidirectional gated recurrent unit) to predict bypass graft occlusion and stenosis based on peak systolic velocities collected in the 2 to 5 previous DUS examinations. We excluded the examinations with missing values and split our data into training and test sets. Then, we applied 10-fold cross-validation on training to optimize the hyperparameters and compared models using the test data. Results: The bidirectional long short-term memory unit model can gain an overall sensitivity of 0.939, specificity of 0.963, and area under the curve of 0.950 on the prediction of bypass graft occlusion, and an overall sensitivity of 0.915, specificity of 0.909, and area under the curve of 0.912 predicting the development of a future critical stenosis. The results on different bypass types show that the system performs differently on different types. The results on subcohorts based on gender, smoking status, and comorbidities show that the performance on current smokers is lower than the never smoker. Conclusions: We found that RNNs can gain good sensitivity, specificity, and accuracy for the detection of impending bypass graft occlusion or the future development of a critical bypass graft stenosis using all the available peak systolic velocity data in the present and previous bypass graft DUS examinations. Integrating clinical data, including demographics, social determinants, medication, and other risk factors, together with the DUS examination may result in further improvements. Clinical Relevance: Detecting bypass graft failure before it occurs is important clinically to prevent amputations, salvage limbs, and save lives. Current methods evaluating screening duplex ultrasound examinations have a significant failure rate for detecting a bypass graft at risk for failure. Artificial intelligence using recurrent neural networks has the potential to improve the detection of at-risk bypass graft before they fail. Additionally, artificial intelligence is in the news and is being applied to many fields. Vascular surgeons need to know its potential to improve vascular outcomes.

Plasmonic metasurfaces with adjustable optical responses can be achieved through phase change materials (PCMs) with high optical contrast. However, the on–off behavior of the phase change process results in the binary response of photonic devices, limiting the applications to the two-stage modulation. In this work, we propose a reconfigurable metasurface emitter based on a gold nanorod array on a VO2 thin film for achieving continuously tunable narrowband thermal emission. The electrode line connecting the center of each nanorod not only enables emission excitation electrically but also activates the phase transition of VO2 beneath the array layer due to Joule heating. The change in the dielectric environment due to the VO2 phase transition results in the modulation of emissivity from the plasmonic metasurfaces. The device performances regarding critical geometrical parameters are analyzed based on a fully coupled electro-thermo-optical finite element model. This new metasurface structure extends the binary nature of PCM based modulations to continuous reconfigurability and provides new possibilities toward smart metasurface emitters, reflectors, and other nanophotonic devices.

To calculate the scattering impact of scratches on space-based telescope systems accurately, this study proposes fitting scratch scattering using an anisotropic scattering model based on actual test results. The stray light nonuniformity and total stray light of each mirror in a Cook-type off-axis three-mirror system was calculated. A scratch illuminated with unit radiation power can generate a maximum stray light level of 7 × 10-5 and can obscure dark targets with a brightness ratio of 1 × 10-5 to 8 × 10-5. Furthermore, as the wavelength decreases, the total stray light generated by this scratch can reach 5.7 × 10-4 when illuminated with unit radiation power in the 200 nm ultraviolet band. Such scratches on the mirrors of an off-axis three-mirror telescope cause 7–8 times higher nonuniformity of stray light than roughness scattering, decreasing signal-to-noise ratio and affecting target recognition and image processing.

Background: Cases with the limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy neuropathologic change (LATE-NC), Alzheimer's disease (AD), and mixed AD+TDP-43 pathology (AD+LATE-NC) share similar symptoms, which makes it a challenge for accurate diagnosis. Exploring the patterns of gray matter structural covariance networks (SCNs) in these three types may help to clarify the underlying mechanism and provide a basis for clinical interventions. Methods: We included ante-mortem MRI data of 10 LATE-NC, 39 AD, and 25 AD+LATE-NC from the ADNI autopsy sample. We used four regions of interest (left posterior cingulate cortex, right entorhinal cortex, frontoinsular and dorsolateral prefrontal cortex) to anchor the default mode network (DMN), salience network (SN), and executive control network (ECN). Finally, we assessed the SCN alternations using a multi-regression model-based linear-interaction analysis. Results: Cases with autopsy-confirmed LATE-NC and AD showed increased structural associations involving DMN, ECN, and SN. Cases with AD+LATE-NC showed increased structural association within DMN while decreased structural association between DMN and ECN. The volume of peak clusters showed significant associations with cognition and AD pathology. Conclusions: This study showed different SCN patterns in the cases with LATE-NC, AD, and AD+LATE-NC, and indicated the network disconnection mechanism underlying these three neuropathological progressions. Further, SCN may serve as an effective biomarker to distinguish between different types of dementia.

Since the testing of convex mirrors often requires a compensator with a larger aperture than the mirror under test, this is undoubtedly a trade-off not worth pursuing. Moreover, for surfaces with higher levels of asphericity, it further escalates the challenges in testing. In this study, high-accuracy testing of large convex aspheric surfaces with rotary symmetry was achieved via a method that combines sub-aperture stitching and Computer-Generated Holograms (CGH) compensation testing. Applying the methodology outlined in this study, practical testing was conducted on a large-aperture convex aspheric mirror with a diameter of 538 mm. The outcomes demonstrated that the center testing data and the outer ring testing data were stitched by the stitching algorithm to obtain the surface error of the full-aperture. The results were compared to those of Luphoscan and compensation testing, with a residual Root Mean Square (RMS) less than λ/20 (λ = 632.8 nm). Finally, the error analysis of the entire testing process showed that the accuracy was better than λ/50. The results demonstrate that the method can achieve highly accurate test of large convex aspheric surfaces.

ABSTRACTThe gut metabolome acts as an intermediary between the gut microbiota and host, and has tremendous diagnostic and therapeutic potential. Several studies have utilized bioinformatic tools to predict metabolites based on the different aspects of the gut microbiome. Although these tools have contributed to a better understanding of the relationship between the gut microbiota and various diseases, most of them have focused on the impact of microbial genes on the metabolites and the relationship between microbial genes. In contrast, relatively little is known regarding the effect of metabolites on the microbial genes or the relationship between these metabolites. In this study, we constructed a computational framework of Microbe-Metabolite INteractions-based metabolic profiles Predictor (MMINP), based on the Two-Way Orthogonal Partial Least Squares (O2-PLS) algorithm to predict the metabolic profiles associated with gut microbiota. We demonstrated the predictive value of MMINP relative to that of similar methods. Additionally, we identified the features that would profoundly impact the prediction performance of data-driven methods (O2-PLS, MMINP, MelonnPan, and ENVIM), including the training sample size, host disease state, and the upstream data processing methods of the different technical platforms. We suggest that when using data-driven methods, similar host disease states and preprocessing methods, and a sufficient number of training samples are necessary to achieve accurate prediction.

Abstract Objective This study investigated cerebral small vessel disease (CSVD) damage patterns in early‐onset and late‐onset Alzheimer's disease (EOAD and LOAD) and their effects on cognitive function. Methods This study included 93 participants, 45 AD patients (14 EOAD and 31 LOAD), and 48 normal controls (13 YNC and 35 ONC) from the ADNI database. All participants had diffusion tensor imaging data; some had amyloid PET and plasma p‐tau181 data. The study used peak width of skeletonized mean diffusivity (PSMD) to measure CSVD severity and compared PSMD between patients and age‐matched controls. The effect of age on the relationship between PSMD and cognition was also examined. The study also repeated the analysis in amyloid‐positive AD patients and amyloid‐negative controls in another independent database (31 EOAD and 38 LOAD), and the merged database. Results EOAD and LOAD showed similar cognitive function and disease severity. PSMD was validated as a reliable correlate of cognitive function. In the ADNI database, PSMD was significantly higher for LOAD and showed a tendency to increase for EOAD; in the independent and merged databases, PSMD was significantly higher for both LOAD and EOAD. The impact of PSMD on cognitive function was notably greater in the younger group (YNC and EOAD) than in the older group (ONC and LOAD), as supported by the ADNI and merged databases. Interpretation EOAD has less CSVD burden than LOAD, but has a greater impact on cognition. Proactive cerebrovascular prevention strategies may have potential clinical value for younger older adults with cognitive decline.

Abstract Livestreaming commerce has become a shopping option following the outbreak of COVID-19, and many sellers have adopted livestreaming marketing to increase their sales and market share. Although livestreaming marketing offers many opportunities, sellers face the challenge of identifying an effective product demonstration format to attract more viewers and increase engagement behaviors during livestreaming sessions. Based on social capital and signaling theories, this study evaluates the relationships among social capital acquisition, social endorsement, and consumer engagement constructs across three different livestreaming marketing product demonstration formats. Partial least squares structural equation modeling (PLS-SEM) multivariate analysis shows that social capital acquisition and endorsement positively affect consumer engagement across all livestreaming formats. A cross-case assessment based on Henseler’s bootstrap-based multigroup analysis reveals that although the preference for the interview livestreaming marketing format is lower, it is more efficient in attracting consumer engagement than the tutorial and behind-the-scenes livestreaming marketing formats. This study is thus the first in the scientific literature to examine consumer engagement’s antecedents across different livestreaming marketing formats.

This paper examines the response characteristics of a warship’s double-layer plates under a secondary near-field explosion after the ship’s outer plate has been perforated by shaped metal jets. First, the effectiveness of the Coupled Eulerian–Lagrangian (CEL) method was validated, showing numerical simulations to be well aligned with experimental results. Subsequently, the damage inflicted on the outer plate by metal jets was simplified to a prefabricated orifice, further studying the explosive impact response of double-layer plates under different inter-compartmental water levels and charge distances. Our findings indicated the following: (1) shockwave and bubble pulsation loads are the main causes of deformation in the outer plate; (2) the driving of the outer plate and the flooding water between compartments are the main causes of deformation in the inner plate; and (3) deformation in the outer plate will decrease as the water level in the compartment increases, while deformation in the inner plate will increase with the increasing water level. Consequently, under certain specific damage, the ingress of water into a compartment effectively enhances the explosion resistance of the double-layer plates.

Aim: C1q/TNF-related protein 6 (CTRP6) is a novel adipokine involved in insulin resistance. Thus, we aim to investigate the expression profile of CTRP6 in the plasma, adipose tissue and placenta of GDM patients and mice. Methods: Chinese Han pregnant women (GDM n = 9, control n = 10) with a scheduled caesarean section delivery were recruited. A number of high-fat diet (HFD) induced-pregnancy C57BL/6 mice were chosen as an animal model of GDM. Circulating levels of CTRP6 and adiponectin were examined by ELISA. CTRP6 expression in adipose tissue and placenta were detected by real-time qPCR and WB. Result: Plasma CTRP6 levels were decreased during the first and second trimesters in mice, as well as the second and third trimesters in patients, while they were increased at delivery in GDM patients and mice. Plasma CTRP6 levels were significantly correlated with WBC, systolic pressure, diastolic pressure and fasting blood glucose. Moreover, CTRP6 mRNA expression in the subcutaneous (sWAT) and omental white adipose tissue (oWAT), as well as in the placenta, was significantly higher in GDM human patients at cesarean delivery. Furthermore, the mRNA expression of Ctrp6 was increased in the sWAT and visceral WAT (vWAT), whilst decreased in the interscapular brown adipose tissue (iBAT), of GDM mice at cesarean delivery. Conclusion: Dynamically expressed CTRP6 may be served as a candidate target for treatment of GDM.

In recent years, the proliferation of wearable healthcare devices has marked a revolutionary shift in the personal health monitoring and management paradigm. These devices, ranging from fitness trackers to advanced biosensors, have not only made healthcare more accessible, but have also transformed the way individuals engage with their health data. By continuously monitoring health signs, from physical-based to biochemical-based such as heart rate and blood glucose levels, wearable technology offers insights into human health, enabling a proactive rather than a reactive approach to healthcare. This shift towards personalized health monitoring empowers individuals with the knowledge and tools to make informed decisions about their lifestyle and medical care, potentially leading to the earlier detection of health issues and more tailored treatment plans. This review presents the fabrication methods of flexible wearable healthcare devices and their applications in medical care. The potential challenges and future prospectives are also discussed.

In diverse realms of research, such as holographic optical tweezer mechanical measurements, colloidal particle motion state examinations, cell tracking, and drug delivery, the localization and analysis of particle motion command paramount significance. Algorithms ranging from conventional numerical methods to advanced deep-learning networks mark substantial strides in the sphere of particle orientation analysis. However, the need for datasets has hindered the application of deep learning in particle tracking. In this work, we elucidated an efficacious methodology pivoted toward generating synthetic datasets conducive to this domain that resonates with robustness and precision when applied to real-world data of tracking 3D particles. We developed a 3D real-time particle positioning network based on the CenterNet network. After conducting experiments, our network has achieved a horizontal positioning error of 0.0478 μm and a z-axis positioning error of 0.1990 μm. It shows the capability to handle real-time tracking of particles, diverse in dimensions, near the focal plane with high precision. In addition, we have rendered all datasets cultivated during this investigation accessible.

Hexose transporters (HXT) play a crucial role in the pathogenicity of Magnaporthe oryzae, serving not only as key facilitators for acquiring and transporting sugar nutrients to support pathogen development, but also as sugar sensors which receive transduction signals. The objective of this study is to investigate the impact of MoHXT1-3 on rice pathogenicity and hexose affinity. MoHXT1-3 deletion mutants were generated using CRISPR/Cas9 technology, and their affinity for hexose was evaluated through yeast complementation assays and electrophysiological experiments in Xenopus oocytes. The results suggest that MoHXT1 does not contribute to melanin formation or hexose transportation processes. Conversely, MoHXT2, despite displaying lower affinity towards the hexoses tested in comparison to MoHXT3, is likely to have a more substantial impact on pathogenicity. The analysis of the transcription profiles demonstrated that the deletion of MoHXT2 caused a decrease in the expression of MoHXT3, whereas the knockout of MoHXT3 resulted in an upregulation of MoHXT2 transcription. It is noteworthy that the MoHXT2M145K variant displayed an incapacity to transport hexoses. This investigation into the functional differences in hexose transporters in Magnaporthe oryzae provides insights into potential advances in new strategies to target hexose transporters to combat rice blast by blocking carbon nutrient supply.

The combination of an absorbing structure and a road is a promising strategy for road deicing using microwaves. In this study, cement mortar (CM) specimens containing a carbon fiber screen (CFS) were prepared to concentrate electromagnetic losses on a road surface. The effect of the size and depth of the CFS on the surface heating efficiency of the microwave was studied and optimized, and a microwave deicing experiment was conducted. The results indicated that the destructive interference produced by the CFS led to the effective surface heating of the CM/CFS specimens. The optimal surface heating rate was 0.83 °C/s when the spacing, depth, and width of the CFS were 5.22, 13.31, and 2.80 mm, respectively. The deicing time was shortened by 21.68% from 83 to 65 s, and the heating rate increased by 17.14% from 0.70 to 0.82 °C/s for the specimen with CFS-1, which was 15 mm depth. Our results demonstrate that CM/CFS composite structures can be effectively applied to increase the capacity and accelerate the development of the microwave deicing of roads.

In the pedestrian navigation system, researchers have reduced measurement errors and improved system navigation performance by fusing measurements from multiple low-cost inertial measurement unit (IMU) arrays. Unfortunately, the current data fusion methods for inertial sensor arrays ignore the system error compensation of individual IMUs and the correction of position information in the zero-velocity interval. Therefore, these methods cannot effectively reduce errors and improve accuracy. An error compensation method for pedestrian navigation systems based on a low-cost array of IMUs is proposed in this paper. The calibration method for multiple location-free IMUs is improved by using a sliding variance detector to segment the angular velocity magnitude into stationary and motion intervals, and each IMU is calibrated independently. Compensation is then applied to the velocity residuals in the zero-velocity interval after zero-velocity update (ZUPT). The experimental results show a significant improvement in the average noise performance of the calibrated IMU array, with a 3.01-fold increase in static noise performance. In the closed-loop walking experiment, the average horizontal position error of a single calibrated IMU is reduced by 27.52% compared to the uncalibrated IMU, while the calibrated IMU array shows a 2.98-fold reduction in average horizontal position error compared to a single calibrated IMU. After compensating for residual velocity, the average horizontal position error of a single IMU is reduced by 0.73 m, while that of the IMU array is reduced by 64.52%.

This paper presents a magnetic resonance image deblurring and denoising model named the isotropic total variation regularized least absolute deviations measure (LADTV). More specifically, the least absolute deviations term is first adopted to measure the violation of the relation between the desired magnetic resonance image and the observed image, and to simultaneously suppress the noise that may corrupt the desired image. Then, in order to preserve the smoothness of the desired image, we introduce an isotropic total variation constraint, yielding the proposed restoration model LADTV. Finally, an alternating optimization algorithm is developed to solve the associated minimization problem. Comparative experiments on clinical data demonstrate the effectiveness of our approach to synchronously deblur and denoise magnetic resonance image.

Abstract Background Migraine is one of the world’s most prevalent and disabling diseases. Despite huge advances in neuroimaging research, more valuable neuroimaging markers are still urgently needed to provide important insights into the brain mechanisms that underlie migraine symptoms. We therefore aim to investigate the regional iron deposition in subcortical nuclei of migraineurs as compared to controls and its association with migraine-related pathophysiological assessments. Methods A total of 200 migraineurs (56 chronic migraine [CM], 144 episodic migraine [EM]) and 41 matched controls were recruited. All subjects underwent MRI and clinical variables including frequency/duration of migraine, intensity of migraine, 6-item Headache Impact Test (HIT-6), Migraine Disability Assessment (MIDAS), and Pittsburgh Sleep Quality Index (PSQI) were recorded. Quantitative susceptibility mapping was employed to quantify the regional iron content in subcortical regions. Associations between clinical variables and regional iron deposition were studied as well. Results Increased iron deposition in the putamen, caudate, and nucleus accumbens (NAC) was observed in migraineurs more than controls. Meanwhile, patients with CM had a significantly higher volume of iron deposits compared to EM in multiple subcortical nuclei, especially in NAC. Volume of iron in NAC can be used to distinguish patients with CM from EM with a sensitivity of 85.45% and specificity of 71.53%. As the most valuable neuroimaging markers in all of the subcortical nuclei, higher iron deposition in NAC was significantly associated with disease progression, and higher HIT-6, MIDAS, and PSQI. Conclusions These findings provide evidence that iron deposition in NAC may be a biomarker for migraine chronicity and migraine-related dysfunctions, thus may help to understand the underlying vascular and neural mechanisms of migraine. Trial registration ClinicalTrials.gov, number NCT04939922.

Abstract A high‐density Raman photometry based on a dual‐recognition strategy is created for accurately quantifying acetylcholinesterase (AchE) activity in 24 brain regions of free‐moving animals with network. A series of 5‐ethynyl‐1,2,3,3‐tetramethyl‐based molecules with different conjugated structures and substitute groups are designed and synthesized for specific recognition of AchE by Raman spectroscopy. After systematically evaluating the recognition ability toward AchE, 2‐(4‐((4‐(dimethylamino)benzoyl)oxy)styryl)‐5‐ethynyl‐1,3,3‐trimethyl‐3H‐indol‐1‐ium (ET‐5) is finally optimized for AchE determination, which shows the highest selectivity, the greatest sensitivity, and the fastest response time among the investigated seven molecules. More interestingly, using the developed probe for AchE with high accuracy and sensitivity, the optimized AchE regulated by nitric oxide (NO) is discovered for promoting the neurogenesis of neural stem cells (NSCs). Benefiting from the high‐density photometry, it is found that the activity and distribution of AchE varied in 24 brain regions, and the levels of AchE activity in 24 brain regions of Alzheimer's mice (AD) are lower than those of normal mice. It is the first time that a functional network of AchE in 24 brain regions is established. It is also found that the loss of AchE functional network in AD mice is restored and reconstructed by the controlled release of AchE regulated by NO.

Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively and effectively employed in IoT intrusion detection. However, the limited computing power and storage space of IoT devices restrict the feasibility of deploying resource-intensive intrusion detection systems on them. This article introduces the DL-BiLSTM lightweight IoT intrusion detection model. By combining deep neural networks (DNNs) and bidirectional long short-term memory networks (BiLSTMs), the model enables nonlinear and bidirectional long-distance feature extraction of complex network information. This capability allows the system to capture complex patterns and behaviors related to cyber-attacks, thus enhancing detection performance. To address the resource constraints of IoT devices, the model utilizes the incremental principal component analysis (IPCA) algorithm for feature dimensionality reduction. Additionally, dynamic quantization is employed to trim the specified cell structure of the model, thereby reducing the computational burden on IoT devices while preserving accurate detection capability. The experimental results on the benchmark datasets CIC IDS2017, N-BaIoT, and CICIoT2023 demonstrate that DL-BiLSTM surpasses traditional deep learning models and cutting-edge detection techniques in terms of detection performance, while maintaining a lower model complexity.