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Abstract Interferon Regulatory Factors (IRFs), a family of transcription factors, profoundly influence the immune system, impacting both physiological and pathological processes. This review explores the diverse functions of nine mammalian IRF members, each featuring conserved domains essential for interactions with other transcription factors and cofactors. These interactions allow IRFs to modulate a broad spectrum of physiological processes, encompassing host defense, immune response, and cell development. Conversely, their pivotal role in immune regulation implicates them in the pathophysiology of various diseases, such as infectious diseases, autoimmune disorders, metabolic diseases, and cancers. In this context, IRFs display a dichotomous nature, functioning as both tumor suppressors and promoters, contingent upon the specific disease milieu. Post-translational modifications of IRFs, including phosphorylation and ubiquitination, play a crucial role in modulating their function, stability, and activation. As prospective biomarkers and therapeutic targets, IRFs present promising opportunities for disease intervention. Further research is needed to elucidate the precise mechanisms governing IRF regulation, potentially pioneering innovative therapeutic strategies, particularly in cancer treatment, where the equilibrium of IRF activities is of paramount importance.

Abstract The Phosphatidylinositol-3-kinase (PI3K) family is well-known to comprise three classes of intracellular enzymes. Class I PI3Ks primarily function in signaling by responding to cell surface receptor stimulation, while class II and III are more involved in membrane transport. Under normal physiological conditions, the PI3K signaling network orchestrates cell growth, division, migration and survival. Aberrant activation of the PI3K signaling pathway disrupts cellular activity and metabolism, often marking the onset of cancer. Currently, the Food and Drug Administration (FDA) has approved the clinical use of five class I PI3K inhibitors. These small-molecule inhibitors, which exhibit varying selectivity for different class I PI3K family members, are primarily used in the treatment of breast cancer and hematologic malignancies. Therefore, the development of novel class I PI3K inhibitors has been a prominent research focus in the field of oncology, aiming to enhance potential therapeutic selectivity and effectiveness. In this review, we summarize the specific structures of PI3Ks and their functional roles in cancer progression. Additionally, we critically evaluate small molecule inhibitors that target class I PI3K, with a particular focus on their clinical applications in cancer treatment. Moreover, we aim to analyze therapeutic approaches for different types of cancers marked by aberrant PI3K activation and to identify potential molecular targets amenable to intervention with small-molecule inhibitors. Ultimately, we propose future directions for the development of therapeutic strategies that optimize cancer treatment outcomes by modulating the PI3K family.

[Introduction] The new generation of the Internet of Things (IoT) is being fostered in the era of Internet of Everything (IoE), realizing its diverse development by selecting and combining new information, functions, and applications. The Energy Internet of Things (Energy IoT) which is based on IoT, envisions a future where physical things are connected through a dynamic network that exchanges information and energy. The Energy IoT is giving rise to new service models and methods for organizing, exchanging, and managing energy; It covers not only new concepts such as Energy-as-a-Service and Prosumer, but also leads to innovative applications in smart buildings, intelligent metering, smart grids, distributed energy, virtual power plants and more. [Method] This paper analyzed the current status of the Energy IoT, including its key industry drivers, potential technologies and applications, challenges and related research areas. [Result] This paper discusses and compares the definitions of Energy Internet and Energy IoT from academic and industry perspectives. And it analyzes some major stages and issues of future research in the Energy IoT. [Conclusion] This paper provides a useful reference for further research and practical applications in the field of Energy IoT.

Abstract Previous studies have shown that antimicrobial photodynamic inactivation (aPDI) can be strongly potentiated by the addition of the non-toxic inorganic salt, potassium iodide (KI). This approach was shown to apply to many different photosensitizers, including the xanthene dye Rose Bengal (RB) excited by green light (540 nm). Rose Bengal diacetate (RBDA) is a lipophilic RB derivative that is easily taken up by cells and hydrolyzed to produce an active photosensitizer. Because KI is not taken up by microbial cells, it was of interest to see if aPDI mediated by RBDA could also be potentiated by KI. The addition of 100 mM KI strongly potentiated the killing of Gram-positive methicillin-resistant Staphylocccus aureus, Gram-negative Eschericia coli, and fungal yeast Candida albicans when treated with RBDA (up to 15 µM) for 2 hours followed by green light (540 nm, 10 J/cm2). Both RBDA aPDI regimens (400 µM RBDA with or without 400 mM KI followed by 20 J/cm2 green light) accelerated the healing of MRSA-infected excisional wounds in diabetic mice, without damaging the host tissue.

Chronic urticaria (CU) is a prevalent skin disorder greatly impacting the patients’ life quality, in which immune dysregulation mediated by gut microbiome plays a significant role. Several studies have found the gut dysbiosis exists in patients with CU. In addition, infection may also be one of the causes of CU. The primary treatment currently used for CU is the second-generation non-sedating H1-antihistamines (nsAH). However, there are some limitations in current therapies. Based on the latest evidence, this review provides an updated overview of how the gut dysbiosis influences CU development, explores potential therapeutic approaches based on the gut microbiota and summarizes the interaction between gut microbiota and current treatment.

Dermatopathology training is a crucial aspect of the education and training of residents in dermatology and venereology, while interpretation of pathological sections is essential for this training. We employ the general pattern teaching method to enhance teaching quality, focusing on initial observation of sections with the naked eye and low-power lens to deduce biopsy sites and diagnose diseases based on image outline and color contrast. Diseases which can be diagnosed by general pattern analysis can be categorized into six groups: changes in the epidermis, mass-like hyperplasia and band-like changes in the dermis, changes in the fat layer, unique shape, and vascular proliferation. Some of these major categories can be further divided into subgroups. There are a total 18 common pathological patterns, including 53 prevalent skin diseases, 26 of which are required to be mastered according to the training guideline. The general pattern teaching method in combination with informative content and an engaging approach can effectively stimulate residents' enthusiasm for learning skin pathology and enhance their educational experience, leading to the improvement of outcome of dermatopathology training programs for residents in dermatology and venereology.

Photoactivatable carbon monoxide‐releasing molecules (CORMs), typically based on transition‐metal carbonyl complexes, have reliance on activation by UV or visible light that restricts their biomedical applications. To address this limitation, a near‐infrared (NIR)‐responsive nanoplatform is presented based on upconversion nanoparticles (UCNPs) loading with manganese carbonyl complex Mn2(CO)10 that concurrently releases CO and manganese ion (Mn2+). With the UCNPs, the more tissue‐penetrable NIR is used to locally generate UV light for photodecomposition of Mn2(CO)10 into CO and manganese oxide (MnOX), after which MnOX is reduced to Mn2+ by the overexpressed glutathione in cancer cells. Moreover, the released Mn2+ can serve as a magnetic resonance imaging contrast agent to monitor the NIR‐controlled corelease of CO and Mn2+ in real time. Therefore, this nanoplatform can provide a potential strategy for NIR‐enabled spatiotemporally release of CO and Mn2+, enhancing the controlled delivery and biomedical application of CORMs.

Background Impaired ventilatory efficiency during exercise is a predictor of mortality in chronic obstructive pulmonary disease. However, little is known about the clinical features and associated factors of impaired ventilatory efficiency in China.Methods We conducted a cross-sectional community-based study in China and collected demographic and clinical information, cardiopulmonary exercise testing, spirometry, and CT data. Impaired ventilatory efficiency was defined by a nadir ventilatory equivalent for CO2 production above the upper limit of normal. Multivariable linear and logistic regression models were used to explore the clinical features and associated factors of impaired ventilatory efficiency.Results The final analyses included 941 subjects, 702 (74.6%) of whom had normal ventilatory efficiency and 239 (25.4%) had impaired ventilatory efficiency. Participants with impaired ventilatory efficiency had more chronic respiratory symptoms, poorer lung function and exercise capacity, and more severe emphysema (natural logarithm transformation of the low-attenuation area of the lung with attenuation values below −950 Hounsfield units, logLAA−950: 0.19±0.65 vs −0.28±0.63, p

Background There are limited data on the clinical features and longitudinal prognosis of variable obstruction, particularly among never smokers and different variable obstruction types. Therefore, we aimed to evaluate the clinical characteristics of the participants with variable obstruction and determine the relationship between variable obstruction and the development of chronic obstructive pulmonary disease (COPD) and the decline of lung function in a community-dwelling study of Chinese, especially among never smokers and different variable obstruction subtypes.Methods Participants with preserved spirometry (postbronchodilator forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ≥0.70) at baseline from the Early COPD cohort were included in our analysis. Participants with variable obstruction (prebronchodilator FEV1/FVC

Neural Radiance Fields (NeRFs) are an emerging approach to 3D reconstruction that use neural networks to reconstruct scenes. However, its applications for multi-view satellite photogrammetry, which aim to reconstruct the Earth’s surface, struggle to acquire accurate digital surface models (DSMs). To address this issue, a novel framework, Geometric Constrained Neural Radiance Field (GC-NeRF) tailored for multi-view satellite photogrammetry, is proposed. GC-NeRF achieves higher DSM accuracy from multi-view satellite images. The key point of this approach is a geometric loss term, which constrains the scene geometry by making the scene surface thinner. The geometric loss term alongside z-axis scene stretching and multi-view DSM fusion strategies greatly improve the accuracy of generated DSMs. During training, bundle-adjustment-refined satellite camera models are used to cast rays through the scene. To avoid the additional input of altitude bounds described in previous works, the sparse point cloud resulting from the bundle adjustment is converted to an occupancy grid to guide the ray sampling. Experiments on WorldView-3 images indicate GC-NeRF’s superiority in accurate DSM generation from multi-view satellite images.

Chronic hepatitis B virus (HBV) infection remains one of the major global public health concerns, and it develop into liver fibrosis, cirrhosis, and hepatocellular carcinoma. Recent evidence suggests that endosomal and autophagic vesicles are beneficial for HBV replication. However, it has not been well elucidated how HBV exploits such intracellular vesicle systems for its replication. RAB5A, a member of small GTPase family, plays crucial roles in early endosome biogenesis and autophagy initiation. We observed that RAB5A mRNA and protein levels were significantly increased in HBV-expressing hepatoma cell lines as well as in liver tissue samples from chronic HBV-infected patients. Moreover, RAB5A silencing inhibited HBV replication and subviral particle (SVP) expression significantly in HBV-transfected and -infected hepatoma cells, whereas RAB5A overexpression increased them. Mechanistically, RAB5A increases HBV replication through enhancement of early endosome (EE) – late endosome (LE) activation by interacting with EEA1, as well as enhancing autophagy induction by interacting with VPS34. Additionally, HBV infection enhances RAB5A-mediated dual activation of EE-LE system and autophagy. Collectively, our findings highlight that HBV utilizes RAB5A-mediated dual activation of endosomal and autophagic vesicle pathways for its own replication and persistence. Therefore, RAB5A is a potential target for chronic HBV infection treatment.

Background The relationship between airway inflammation in chronic obstructive pulmonary disease (COPD) and clinical characteristics remains unclear. This study aimed to investigate the airway inflammatory phenotypes in COPD and their association with clinical characteristics.Methods 895 patients with COPD were recruited from Guangdong Province, China in this study. Each patient underwent questionnaire interviews, spirometry testing, CT scans and induced sputum examination. Classification of airway inflammation phenotypes was based on sputum inflammatory cell counts. Covariance analysis was applied to assess associations with airway inflammation phenotypes.Results In this study, we found that neutrophilic phenotype (NP, 58.0%) was the most common airway inflammation phenotype in patients with COPD, followed by mixed granulocytic phenotype (MGP, 32.6%), eosinophilic phenotype (EP, 5.4%) and paucigranulocytic phenotype (PP, 4.0%). Compared with NP patients, those with MGP exhibited more frequent chronic respiratory symptoms, and a higher proportion of individuals classified under Global Initiative for Chronic Obstructive Lung Disease stages 3 and 4. After adjusting for confounding factors, MGP patients had lower lung function, and more severe emphysema and air trapping. On the contrary, patients with PP had the best pulmonary function and less emphysema and air trapping.Conclusions NP was the most common airway inflammation phenotype in patients with COPD. Patients with MGP had more respiratory symptoms, greater loss of lung function, and more severe emphysema and gas trapping compared with those with NP. Meanwhile, PP may be a phenotype of mild damage to lung structure in patients with COPD.

Antimicrobial photodynamic therapy (aPDT) has become a potent contender in the fight against microbial infections, especially in the context of the rising antibiotic resistance crisis. Recently, there has been significant interest in polyphenolic natural products as potential photosensitizers (PSs) in aPDT, given their unique chemical structures and inherent antimicrobial properties. Polyphenolic natural products, abundant and readily obtainable from natural sources, are generally regarded as safe and highly compatible with the human body. This comprehensive review focuses on the latest developments and future implications of using natural polyphenols as PSs in aPDT. Paramount polyphenolic compounds, including curcumin, hypericin, quercetin, hypocrellin, celastrol, riboflavin, resveratrol, gallic acid, and aloe emodin, are elaborated upon with respect to their structural characteristics, absorption properties, and antimicrobial effects. Furthermore, the aPDT mechanism, specifically its targeted action on microbial cells and biofilms, is also discussed. Polyphenolic natural products demonstrate immense potential as PSs in aPDT, representing a promising alternate approach to counteract antibiotic-resistant bacteria and biofilm-related infections.

Background: Alzheimer’s disease (AD) is a complex disorder, and its risk is influenced by multiple genetic and environmental factors. In this study, an AD risk gene prediction framework based on spatial and temporal features of gene expression data (STGE) was proposed.Methods: We proposed an AD risk gene prediction framework based on spatial and temporal features of gene expression data. The gene expression data of providers of different tissues and ages were used as model features. Human genes were classified as AD risk or non-risk sets based on information extracted from relevant databases. Support vector machine (SVM) models were constructed to capture the expression patterns of genes believed to contribute to the risk of AD.Results: The recursive feature elimination (RFE) method was utilized for feature selection. Data for 64 tissue-age features were obtained before feature selection, and this number was reduced to 19 after RFE was performed. The SVM models were built and evaluated using 19 selected and full features. The area under curve (AUC) values for the SVM model based on 19 selected features (0.740 [0.690–0.790]) and full feature sets (0.730 [0.678–0.769]) were very similar. Fifteen genes predicted to be risk genes for AD with a probability greater than 90% were obtained.Conclusion: The newly proposed framework performed comparably to previous prediction methods based on protein-protein interaction (PPI) network properties. A list of 15 candidate genes for AD risk was also generated to provide data support for further studies on the genetic etiology of AD.

Abstract Acute myeloid leukemia (AML) is a deadly hematological malignancy. Cellular morphology detection of bone marrow smears based on the French–American–British (FAB) classification system remains an essential criterion in the diagnosis of hematological malignancies. However, the diagnosis and discrimination of distinct FAB subtypes of AML obtained from bone marrow smear images are tedious and time-consuming. In addition, there is considerable variation within and among pathologists, particularly in rural areas, where pathologists may not have relevant expertise. Here, we established a comprehensive database encompassing 8245 bone marrow smear images from 651 patients based on a retrospective dual-center study between 2010 and 2021 for the purpose of training and testing. Furthermore, we developed AMLnet, a deep-learning pipeline based on bone marrow smear images, that can discriminate not only between AML patients and healthy individuals but also accurately identify various AML subtypes. AMLnet achieved an AUC of 0.885 at the image level and 0.921 at the patient level in distinguishing nine AML subtypes on the test dataset. Furthermore, AMLnet outperformed junior human experts and was comparable to senior experts on the test dataset at the patient level. Finally, we provided an interactive demo website to visualize the saliency maps and the results of AMLnet for aiding pathologists’ diagnosis. Collectively, AMLnet has the potential to serve as a fast prescreening and decision support tool for cytomorphological pathologists, especially in areas where pathologists are overburdened by medical demands as well as in rural areas where medical resources are scarce.

In this study, a zebra-crossing detection method based on cascaded Hough transform (CHT) and vanishing point (VP) characteristics is proposed. In this method, the principle of detecting straight lines in the parallel coordinate system is applied to zebra-crossing detection. Each edge point of the image obtained by edge detection is represented in the parallel coordinate system to find the VP. Using the VP coordinate as the judgment condition, those straight lines that do not pass through the VP but meet the straight-line condition are excluded to obtain the straight lines passing through both sides of the zebra crossing, and finally fit the edge points on the straight line, and get the zebra-crossing fitting line segment. Experiments show that CHT has obvious advantages in detection accuracy and speed compared with the Hough transform. At the same time, VPs can be used to eliminate interference segments, which provide support for the accuracy of zebra-crossing detection. This method can get zebra-crossing location information without using region of interest extraction, which also provides a reference method for road detection in some specific cases.

Abstract Background The role of airway impairment assessed by impulse oscillometry (IOS) in patients with chronic obstructive pulmonary disease (COPD) remains unclear. Therefore, this study aimed to analyze the proportion and clinical characteristics of airway impairment assessed by IOS across COPD severities, and explore whether airway impairment is a subtype of COPD. Methods This study was based on cross-sectional data from the ECOPD cohort in Guangdong, China. Subjects were consecutively recruited from July 2019 to August 2021. They filled out questionnaires and underwent lung function tests, IOS and computed tomography (CT). COPD was defined as post-bronchodilator forced expiratory volume in one second/forced vital capacity upper limit of normal or

The automatic vectorization of building shape from very high resolution remote sensing imagery is fundamental in many fields, such as urban management and geodatabase updating. Recently, deep convolutional neural networks (DCNNs) have been successfully used for building edge detection, but the results are raster images rather than vectorized maps and do not meet the requirements of many applications. Although there are some algorithms for converting raster images into vector maps, such vector maps often have too many vector points and irregular shapes. This article proposed a building shape vectorization hierarchy, which combined DCNNs-based building edge detection and a corner extraction algorithm based on principle component analysis for rapidly extracting building corners from the building edges. Experiments on the Jiangbei New Area Buildings and Massachusetts Buildings datasets showed that compared with the state-of-the-art corner detectors, the building vector corners extracted using our proposed algorithm had fewer breakpoints and isolated points, and our building vector boundaries were more complete and regular. In addition, the building shapes extracted using our hierarchy were 7.94% higher than the nonmaximum suppression method in terms of relaxed overall accuracy on the Massachusetts dataset. Overall, our proposed hierarchy is effective for building shape vectorization.

Abstract Improvements in reproductive techniques have resulted in the live birth rates from IVF procedures increasing from 5% to approximately 30% in recent decades but has plateaued since. Emerging preclinical and clinical data implicates endometrial receptivity deficiencies in patients with recurrent implantation failure (RIF) as the predominant factor hindering successful implantation. Mechanisms on how local endometrial injury (LEI) improves implantation rates in patients with RIF are currently unknown. We hypothesized that LEI may influence perivascular endometrial mesenchymal stem/progenitor cells (eMSCs) which are thought to regenerate the stromal vascular component of the functional layer every month. Here, we assessed the effect of LEI on the proportion and function of eMSCs present in consecutive LEI biopsies. Consecutive paired mid-luteal phase endometrial biopsies obtained from patients with RIF were digested to single cells and the proportion of SUSD2-expressing cells determined. Growth kinetics and decidualization were compared between the consecutive LEI samples. A mid-luteal LEI altered the decidualization capacity of SUSD2+ eMSCs in women with RIF, but not their proportion or clonogenicity. With the potential of LEI to improve IVF outcomes in women with RIF, additional investigations are needed to understand the impact of the altered decidualization response in eMSCs.

Abstract Load forecast provides effective and reliable guidance for power construction and grid operation. It is essential for the power utility to forecast the exact in-future coming energy demand. Advanced machine learning methods can support competently for load forecasting, and extreme gradient boosting is an algorithm with great research potential. But there is less research about the energy time series itself as only an internal variable, especially for feature engineering of time univariate. And the machine learning tuning is another issue to applicate boosting method in energy demand, which has more significant effects than improving the core of the model. We take the extreme gradient boosting algorithm as the original model and combine the Tree-structured Parzen Estimator method to design the TPE-XGBoost model for completing the high-performance single-lag power load forecasting task. We resample the power load data of the Île-de-France Region Grid provided by Réseau de Transport d’Électricité in the day, train and optimise the TPE-XGBoost model by samples from 2016 to 2018, and test and evaluate in samples of 2019. The optimal window width of the time series data is determined in this study through Discrete Fourier Transform and Pearson Correlation Coefficient Methods, and five additional date features are introduced to complete feature engineering. By 500 iterations, TPE optimisation ensures nine hyperparameters’ values of XGBoost and improves the models obviously. In the dataset of 2019, the TPE-XGBoost model we designed has an excellent performance of MAE = 166.020 and MAPE = 2.61%. Compared with the original model, the two metrics are respectively improved by 14.23 and 14.14%; compared with the other eight machine learning algorithms, the model performs with the best metrics as well.

Abstract Background: Preserved ratio impaired spirometry (PRISm) refers to decreased forced expiratory volume in 1 s (FEV1) in the setting of preserved ratio. Little is known about the role of PRISm and its complex relation with small airway dysfunction (SAD) and lung volume. Therefore, we aimed to investigate the associations between PRISm and SAD and lung volume. Methods: We conducted a cross-sectional community-dwelling study in China. Demographic data, standard respiratory epidemiology questionnaire, spirometry, impulse oscillometry (IOS) and computed tomography (CT) data were collected. PRISm was defined as post-bronchodilator FEV1/FVC ≥ 0.70 and FEV1 0.07 kPa/L/s and LAA− 856>20%, respectively. Analysis of covariance and logistic regression were used to determine associations between PRISm and SAD and lung volume. We then repeated the analysis with a lower limit of normal definition of spirometry criteria and FVC definition of PRISm. Moreover, we also performed subgroup analyses in ever smoker, never smoker, subjects without airway reversibility or self-reported diagnosed asthma, and subjects with CT-measured total lung capacity ≥70% of predicted. Results: The final analysis included 1439 subjects. PRISm had higher odds and more severity in spirometry-defined SAD (pre-bronchodilator: odds ratio [OR]: 5.99, 95% confidence interval [95%CI]: 3.87–9.27, P

Abstract Peripheral myelin protein 22 (PMP22) and epithelial membrane proteins (EMP-1, -2, and -3) belong to a small hydrophobic membrane protein subfamily, with four transmembrane structures. PMP22 and EMPs are widely expressed in various tissues and play important roles in cell growth, differentiation, programmed cell death, and metastasis. PMP22 presents its highest expression in the peripheral nerve and participates in normal physiological and pathological processes of the peripheral nervous system. The progress of molecular genetics has shown that the genetic changes of the PMP22 gene, including duplication, deletion, and point mutation, are behind various hereditary peripheral neuropathies. EMPs have different expression patterns in diverse tissues and are closely related to the risk of malignant tumor progression. In this review, we focus on the four members in this protein family which are related to disease pathogenesis and discuss gene mutations and post-translational modification of them. Further research into the interactions between structural alterations and function of PMP22 and EMPs will help understand their normal physiological function and role in diseases and might contribute to developing novel therapeutic tools.

Understanding the mechanism of detoxification initiation in arthropods after pesticide exposure is crucial. Although the identity of transcription factors that induce and regulate the expression of detoxification genes in response to pesticides is beginning to emerge, whether transcription factors directly interact with xenobiotics is unclear. The findings of this study revealed that a nuclear hormone receptor, Tetranychus cinnabarinus hormone receptor (HR) TcHR96h, regulates the overexpression of the detoxification gene TcGSTm02, which is involved in cyflumetofen resistance. The nuclear translocation of TcHR96h increased after cyflumetofen exposure, suggesting direct binding with cyflumetofen. The direct binding of TcHR96h and cyflumetofen was supported by several independent proteomic assays that quantify interactions with small molecules. Together, this study proposes a model for the initiation of xenobiotic detoxification in a polyphagous agricultural pest. These insights not only provide a better understanding of the mechanisms of xenobiotic detoxification and metabolism in arthropods, but also are crucial in understanding adaptation in polyphagous herbivores.

IntroductionDuring the outbreak of Coronavirus disease 2019 (COVID-19), health care workers wore personal protective equipment including masks, gloves and goggles for a long time. In order to reduce the transmission routes of the virus, public places were sprayed with disinfectant. Moreover, the body, hands and clothing were frequently disinfected and washed for hygiene purposes. Studies have shown that these practices could easily irritate the skin and damage the skin barrier. Long-term irritation or exposure to allergens may lead to the occurrence of contact dermatitis (CD).MethodsSubject headings were searched via the National Library of Medicine (PubMed) and web of science databases: COVID-19; contact dermatitis; adverse skin reaction; PPE; dermatitis; mask; glory; hand hygiene, disinfection; face shield; goggle; protect cloth. A total of 246 and 646 articles were retrieved from the two databases, respectively. 402 articles remained after removing duplicates. Reviews, non-English articles, articles that could not be accessed to read or did not conform to our topic were excluded. Finally, a total of 32 cross-sectional studies, 9 case reports and 2 randomized controlled trials were included.DiscussionThis article reviews reports of CD caused by various prevention and hygiene measures during the COVID-19 pandemic. The amount of skin damage caused by COVID-19 prevention measures could be decreased by improved education about skin management.

Objective: The enigmatic nature of Endometriosis (EMS) pathogenesis necessitates investigating alterations in signaling pathway activity to enhance our comprehension of the disease's characteristics. Methods: Three published gene expression profiles (GSE11691, GSE25628, and GSE7305 datasets) were downloaded, and the “combat” algorithm was employed for batch correction, gene expression difference analysis, and pathway enrichment difference analysis. The protein-protein interaction (PPI) network was constructed to identify core genes, and the relative enrichment degree of gene sets was evaluated. The Lasso regression model identified candidate gene sets with diagnostic value, and a risk scoring diagnostic model was constructed for further validation on the GSE86534 and GSE5108 datasets. CIBERSORT was used to assess the composition of immune cells in EMS, and the correlation between EMS diagnostic value gene sets and immune cells was evaluated. Results: A total of 568 differentially expressed genes were identified between eutopic and ectopic endometrium, with 10 core genes in the PPI network associated with cell cycle regulation. Inflammation-related pathways, including cytokine-receptor signaling and chemokine signaling pathways, were significantly more active in ectopic endometrium compared to eutopic endometrium. Diagnostic gene sets for EMS, such as homologous recombination, base excision repair, DNA replication, P53 signaling pathway, adherens junction, and SNARE interactions in vesicular transport, were identified. The risk score's area under the curve (AUC) was 0.854, as indicated by the receiver operating characteristic (ROC) curve, and the risk score's diagnostic value was validated by the validation cohort. Immune cell infiltration analysis revealed correlations between the risk score and Macrophages M2, Plasma cells, resting NK cells, activated NK cells, and regulatory T cells. Conclusion: The risk scoring diagnostic model, based on pathway activity, demonstrates high diagnostic value and offers novel insights and strategies for the clinical diagnosis and treatment of Endometriosis.

The nutritional components of Spirulina residue were determined and evaluated. The insoluble dietary fiber was prepared by alkaline acid treatment from algae residue. The swelling capacity, water holding capacity and grease holding capacity were determined. Insoluble dietary fiber was obtained from Spirulina residue after extraction of phycobiliprotein by alkali acid treatment. Its swelling capacity, water retention capacity and oil retention capacity were measured. The adsorption capacity of dietary fiber for sodium cholate and NO2– was studied. The adsorption capacity of sodium cholic acid and NO2- by dietary fiber was researched. The results indicated that algae residue was rich in nutrients, mainly including protein, carbohydrate and water. The content of phosphorus, magnesium and calcium in minerals was the highest. Algae residue was rich in essential amino acids (lysine, valine, leucine and isoleucine) and non essential amino acids (arginine, glutamine and glutamate). The insoluble dietary fiber had a certain water holding capacity, grease holding capacity and swelling capacity, and the adsorption capacity of sodium cholic acid was relatively obvious. In the simulated human gastric fluid environment of pH=2, it had better adsorption on NO2–. This study results will provide a basis for the high value utilization of Spirulina residue.

Abstract Background Aging has been evidenced to bring about some structural and functional lung changes, especially in COPD. However, whether aging affects SAD, a possible precursor of COPD, has not been well characterized. Objective We aimed to comprehensively assess the relationship between aging and SAD from computed tomography, impulse oscillometry, and spirometry perspectives in Chinese. Methods We included 1859 participants from ECOPD, and used a linear-by-linear association test for evaluating the prevalence of SAD across various age subgroups, and multivariate regression models for determining the impact of age on the risk and severity of SAD. We then repeated the analyses in these subjects stratified by airflow limitation. Results The prevalence of SAD increases over aging regardless of definitional methods. After adjustment for other confounding factors, per 10-yrs increase in age was significantly associated with the risk of CT-defined SAD (OR 2.57, 95% CI 2.13 to 3.10) and the increase in the severity of air trapping (β 2.09, 95% CI − 0.06 to 4.25 for LAA-856), airway reactance (β − 0.02, 95% CI − 0.04 to − 0.01 for X5; β 0.30, 95% CI 0.13 to 0.47 for AX; β 1.75, 95% CI 0.85 to 2.66 for Fres), as well as the decrease in expiratory flow rates (β − 3.95, 95% CI − 6.19 to − 1.71 for MMEF%predicted; β − 5.42, 95% CI − 7.88 to − 2.95 for FEF50%predicted) for SAD. All these associations were generally maintained in SAD defined by IOS or spirometry. After stratification of airflow limitation, we further found that the effect of age on LAA-856 was the most significant among almost all subgroups. Conclusions Aging is significantly associated with the prevalence, increased risk, as well as worse severity of SAD. CT may be a more optimal measure to assess aging-related SAD. The molecular mechanisms for the role of aging in SAD need to be explored in the future. Trial registration Chinese Clinical Trial Registry ChiCTR1900024643. Registered on 19 July 2019

Abstract Background The increase of low-density lipoprotein cholesterol (LDL-C) is widely accepted as an important factor in the occurrence of atherosclerosis. In recent years, the guidelines have recommended non-high density lipoprotein cholesterol (non-HDL-C) as a secondary target for lipid-lowering therapy. But even as research on the relationship between LDL-C/HDL-C and atherosclerosis increases, it is still undetermined which index is most closely related to the severity of acute ST-segment elevation myocardial infarction (STEMI). Methods 901 patients who received coronary angiography due to chest pain were selected. Among them, 772 patients with STEMI represented the test group, and 129 patients with basically normal coronary angiography represented the control group. Researchers measured fasting blood lipids and other indicators after admission, and determined the severity of coronary artery disease using the Gensini score. Results LDL-C/HDL-C and non-HDL-C indexes were statistically different between the two patient groups. In the test group, total cholesterol (TC), triglycerides (TG), LDL-C, high density lipoprotein cholesterol (HDL-C), non-HDL-C, arteriosclerosis index (AI), and LDL-C/HDL-C all correlated with the patients' Gensini score. After applying the stepwise method of multiple linear regression analysis (R 2 = 0.423, β = 0.518, p

Abstract Background The lack of simple and affordable spirometry has led to the missed and delayed diagnoses of chronic respiratory diseases in communities. The PUS201P is a portable spirometry developed to solve this problem. Objective We aimed to verify the consistency of the PUS201P spirometer with conventional Jaeger spirometer. Methods In this cross-sectional study, we randomly recruited 202 subjects aged > 40 years. Testing with the portable spirometry and conventional spirometry were performed on all participants. We compared forced expiratory volume in one second (FEV1), forced vital capacity (FVC), FEV1/FVC measured by the PUS201P device with the conventional spirometer. Pearson correlation coefficient and Interclass Correlation Coefficient (ICC) were assessed to confirm the consistency of the measures from two instruments. Bland–Altman graph was created to assess the agreement of the measures from two devices. Results 202 participants were included in this study. The ICC on FEV1, FVC, FEV1/FVC measured by the portable spirometer and the conventional spirometer were 0.95 (95% confidence interval [CI]: 0.94–0.96), 0.92 (95% CI: 0.90–0.94], 0.93 (95% CI: 0.91–0.95), respectively. The Bland–Altman plots showed that the mean difference between the measures from two spirometers are always located in the 95% limits of agreement. Conclusions Our results support that the measures from the portable spirometer and the conventional spirometer have a good agreement and reproducibility. And the portable spirometer is a reliable tool to screen and diagnose chronic airway diseases in the primary care settings.

Abstract The time period before, during and after pregnancy represents a unique opportunity for interventions to cultivate sustained healthy lifestyle behaviors to improve the metabolic health of mothers and their offspring. However, the success of a lifestyle intervention is dependent on uptake and continued compliance. To identify enablers and barriers towards engagement with a lifestyle intervention, thematic analysis of 15 in-depth interviews with overweight or obese women in the preconception, pregnancy or postpartum periods was undertaken, using the integrated-Promoting Action on Research Implementation in Health Services framework as a guide to systematically chart factors influencing adoption of a novel lifestyle intervention. Barrier factors include time constraints, poor baseline knowledge, family culture, food accessibility, and lack of relevant data sources. Enabling factors were motivation to be healthy for themselves and their offspring, family and social support, a holistic delivery platform providing desired information delivered at appropriate times, regular feedback, goal setting, and nudges. From the findings of this study, we propose components of an idealized lifestyle intervention including (i) taking a holistic life-course approach to education, (ii) using mobile health platforms to reduce barriers, provide personalized feedback and promote goal-setting, and (iii) health nudges to cultivate sustained lifestyle habits.

Rosacea is a chronic inflammatory cutaneous disorder of uncertain etiology that mainly affects the centrofacial region, including cheeks, nose, chin, forehead, and eyes. The pathogenesis of rosacea remains unclear because it involves several complex factors. Additionally, the potential treatment methods need to be explored. We reviewed the common bacterial species in the skin microbiota and gut microbiota of rosacea patients such as Demodex folliculorum, Staphylococcus epidermidis, Bacillus oleronius, Cutibacterium acnes, and Helicobacter pylori and identified their role in the pathogenesis. Besides, we summarized the influence factors such as temperature and age on rosacea patients. We also systematically reviewed the commonly used clinical treatment methods, including antibiotics, probiotics. as well as their treatment mechanism and application precautions.

Interleukin (IL)-18, an interferon-γ inducer, belongs to the IL-1 family of pleiotropic pro-inflammatory factors, and IL-18 binding protein (IL-18BP) is a native antagonist of IL-18 in vivo, regulating its activity. Moreover, IL-18 exerts an influential function in host innate and adaptive immunity, and IL-18BP has elevated levels of interferon-γ in diverse cells, suggesting that IL-18BP is a negative feedback inhibitor of IL-18-mediated immunity. Similar to IL-1β, the IL-18 cytokine is produced as an indolent precursor that requires further processing into an active cytokine by caspase-1 and mediating downstream signaling pathways through MyD88. IL-18 has been implicated to play a role in psoriasis, atopic dermatitis, rosacea, and bullous pemphigoid in human inflammatory skin diseases. Currently, IL-18BP is less explored in treating inflammatory skin diseases, while IL-18BP is being tested in clinical trials for other diseases. Thereby, IL-18BP is a prospective therapeutic target.

Background and objective: Spirometry is commonly used to assess small airway dysfunction (SAD). Impulse oscillometry (IOS) can complement spirometry. However, discordant spirometry and IOS in the diagnosis of SAD were not uncommon. We examined the association between spirometry and IOS within a large cohort of subjects to identify variables that may explain discordant spirometry and IOS findings.Methods: 1,836 subjects from the ECOPD cohort underwent questionnaires, symptom scores, spirometry, and IOS, and 1,318 subjects were examined by CT. We assessed SAD with R5-R20 > the upper limit of normal (ULN) by IOS and two of the three spirometry indexes (maximal mid-expiratory flow (MMEF), forced expiratory flow (FEF)50%, and FEF75%) < 65% predicted. Multivariate regression analysis was used to analyze factors associated with SAD diagnosed by only spirometry but not IOS (spirometry-only SAD) and only IOS but not spirometry (IOS-only SAD), and line regression was used to assess CT imaging differences.Results: There was a slight agreement between spirometry and IOS in the diagnosis of SAD (kappa 0.322, p < 0.001). Smoking status, phlegm, drug treatment, and family history of respiratory disease were factors leading to spirometry-only SAD. Spirometry-only SAD had more severe emphysema and gas-trapping than IOS-only SAD in abnormal lung function. However, in normal lung function subjects, there was no statistical difference in emphysema and gas-trapping between discordant groups. The number of IOS-only SAD was nearly twice than that of spirometry.Conclusion: IOS may be more sensitive than spirometry in the diagnosis of SAD in normal lung function subjects. But in patients with abnormal lung function, spirometry may be more sensitive than IOS to detect SAD patients with clinical symptoms and CT lesions.

In a point cloud semantic segmentation task, misclassification usually appears on the semantic boundary. A few studies have taken the boundary into consideration, but they relied on complex modules for explicit boundary prediction, which greatly increased model complexity. It is challenging to improve the segmentation accuracy of points on the boundary without dependence on additional modules. For every boundary point, this paper divides its neighboring points into different collections, and then measures its entanglement with each collection. A comparison of the measurement results before and after utilizing boundary information in the semantic segmentation network showed that the boundary could enhance the disentanglement between the boundary point and its neighboring points in inner areas, thereby greatly improving the overall accuracy. Therefore, to improve the semantic segmentation accuracy of boundary points, a Boundary–Inner Disentanglement Enhanced Learning (BIDEL) framework with no need for additional modules and learning parameters is proposed, which can maximize feature distinction between the boundary point and its neighboring points in inner areas through a newly defined boundary loss function. Experiments with two classic baselines across three challenging datasets demonstrate the benefits of BIDEL for the semantic boundary. As a general framework, BIDEL can be easily adopted in many existing semantic segmentation networks.

It is essential to estimate the indoor pesticides/insecticides exposure risk since reports show that 80% of human exposure to pesticides occurs indoors. As one of the three major contamination sources, surface collected pesticides contributed significantly to this risk. Here, a highly sensitive liquid freestanding membrane (FSM) SERS method based on iodide modified silver nanoparticles (Ag NPs) was developed for quantitative detection of insecticide deltamethrin (DM) residues in solution phase samples and on surfaces with good accuracy and high sensitivity. The DM SERS spectrum from 500 to 2500 cm−1 resembled the normal Raman counterpart of solid DM. Similar bands at 563, 1000, 1165, 1207, 1735, and 2253 cm−1 were observed as in the literature. For the quantitative analysis, the strongest peak at 1000 cm−1 that was assigned to the stretching mode of the benzene ring and the deformation mode of C-C was selected. The peak intensity at 1000 cm−1 and the concentration of DM showed excellent linearity from 39 to 5000 ppb with a regression equation I = 649.428 + 1.327 C (correlation coefficient R2 = 0.991). The limit of detection (LOD) of the DM was found to be as low as 11 ppb. Statistical comparison between the proposed and the HPLC methods for the analysis of insecticide deltamethrin (DM) residues in solution phase samples showed no significant difference. DM residue analysis on the surface was mimicked by dropping DM pesticide on the glass surface. It is found that DM exhibited high residue levels up to one week after exposure. This proposed SERS method could find application in the household pesticide residues analysis.

With a high thermal efficiency, high reliability and good fuel economy, diesel engines have been widely used. However, with the increasingly stringent standards regarding non-road diesel engine emissions, diesel engines can hardly satisfy the particle emission requirements through internal purification alone. To reduce the particle emission and noise levels of the non-road diesel engine R180, this paper optimized the original muffler, and endowed the muffler with a particulate matter (PM) filtering function to improve the muffling. This study first proposed stainless steel fiber as the filtering medium as it is inexpensive and accessible; a bench experiment was conducted to verify the particle filtration performance and its effect on the overall engine performance. Then, the structure of the existing muffler in non-road diesel engines R180 was optimized, and the stainless steel fiber filtering was integrated. The internal flow field of the optimized muffler was obtained in the computational fluid dynamics software FLUENT, and the acoustic and filtration performance was studied. The experimental and simulation results indicated that the optimized muffler could achieve both particle filtration and noise reduction.

Due to the periodic and dynamic changes of traffic flow and the spatial–temporal coupling interaction of complex road networks, traffic flow forecasting is highly challenging and rarely yields satisfactory prediction results. In this paper, we propose a novel methodology named the Augmented Multi-component Recurrent Graph Convolutional Network (AM-RGCN) for traffic flow forecasting by addressing the problems above. We first introduce the augmented multi-component module to the traffic forecasting model to tackle the problem of periodic temporal shift emerging in traffic series. Then, we propose an encoder–decoder architecture for spatial–temporal prediction. Specifically, we propose the Temporal Correlation Learner (TCL) which incorporates one-dimensional convolution into LSTM to utilize the intrinsic temporal characteristics of traffic flow. Moreover, we combine TCL with the graph convolutional network to handle the spatial–temporal coupling interaction of the road network. Similarly, the decoder consists of TCL and convolutional neural networks to obtain high-dimensional representations from multi-step predictions based on spatial–temporal sequences. Extensive experiments on two real-world road traffic datasets, PEMSD4 and PEMSD8, demonstrate that our AM-RGCN achieves the best results.