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Abstract Subarachnoid hemorrhage (SAH), a stroke subtype associated with high mortality, is closely linked to neuroinflammation. Phloretin, a naturally occurring flavonoid abundant in fruits, possesses anti-inflammatory properties. However, its specific role in SAH remains unclear. Therefore, we aimed to investigate the potential role of phloretin in SAH. We established in vitro and in vivo SAH models to assess the effects of phloretin. Subsequently, utilizing SAH-related public datasets from the GEO database, we identified key genes associated with SAH and investigated the potential mechanism of action of phloretin. Our findings reveal that phloretin significantly improves prognostic outcomes and mitigates inflammation in SAH mice. Moreover, our results suggest that phloretin mitigates neuroinflammation by inhibiting the TLR2/MYD88/NF-κB pathway.

Abstract Severe psoriasis has a long course and poor outcome, and it has long been a problem for patients. Understanding the independent risk factors that contribute to patients with severe psoriasis is critical for the development of effective treatment strategies. This large, multicenter study recruited 2,109 plaque psoriasis patients from 12 hospitals across China (October 31, 2019 – May 31, 2022). The logistic regression model underwent internal validation and external validation using two independent cohorts over future time periods (June 1, 2022 – January 31, 2023). The discriminative power of our model was substantiated by a C-index of 0.863 (95% CI: 0.848–0.879) in internal validation, further affirmed through 1,000 bootstrap validation (C-index: 0.860, 95% CI: 0.836–0.885) and external validation cohorts, where the C-index reached up to 0.910 (95% CI: 0.868–0.953) and 0.951 (95% CI: 0.924–0.977) in 2 external validation cohorts. To enhance accessibility for clinicians, the model has been made available as a dynamic nomogram and QR code. Our study identified 10 risk factors (the “DELPHI” consensus dichotomy, the DLQI index, the extent of skin involvement as measured by body surface area, the age of the patient at the time of clinical visit, sex, body weight in kilograms, career, the presence of scalp involvement, facial involvement, and arthropathy) for the overall severity of psoriasis (PASI ≥ 10). “Nomogram-10” provides clinicians with a practical tool to develop personalized intervention strategies based on an individual’s risk profile. Trial registration: Chinese Clinical Trial Registry: ChiCTR1900024852.

Abstract The impact force of debris flow is not only an important indicator of the risk assessment of debris flow and the strength impact resistance of buildings against debris flow, but also an important parameter in the design of various debris flow prevention projects (such as the check dam and the drainage channel, etc.). The pressure sensors are arranged at different positions (monitoring points) on the upstream face of the check dam. By changing the slope of the drainage channel, the bulk density of debris flow and the slope gradient of the upstream face of the check dam, the time history curves of the impact force at the monitoring points under different experiment conditions are obtained. The characteristic value of the impact force of debris flow acting on the surface of the sand retaining dam is analyzed, and the evolution law of mean value and maximum value of impact force of debris flow at the same detection location with the above conditions is obtained. The mean value and maximum value of debris flow impact force at different detection locations under the same working condition are analyzed to obtain the evolution law of debris flow impact force at different locations, and then the distribution trend of debris flow impact force on the upstream face of the check dam is obtained. The research results provide scientific and reasonable theoretical basis and technical support for the stability analysis of the check dam, so as to better serve the disaster prevention and reduction of debris flow, which will improve the technical level of the debris flow prevention project to a certain extent.

Abstract Research exploring the link between immune cell profiles and the development of endometritis remains scant. This gap necessitates further study to decode the complex interrelations influencing this condition. In this analysis, we leveraged two-sample Mendelian randomization to examine the causal ties between the phenotypes of immune cells and the incidence of endometritis. Our evaluation hinged on data from 3757 participants hailing from Sardinia, focusing on a diverse array of 731 immune phenotypes, and cross-referenced with endometritis data sourced from the UK Biobank. To ensure rigor, we performed sensitivity analyses, utilized MR-Egger and MR-Presso to check for pleiotropy, and applied Cochran’s Q test for assessing the heterogeneity of our findings. Our investigation identified numerous immune characteristics associated with endometritis. For certain immune traits, a lower risk of endometritis was observed, including: Absolute Counts of CD39 + CD4 + T cells, CD25 + CD39 + CD4 regulatory T cells, and CD25 + + CD8 + T cells; Absolute Counts of Switched Memory B cells; CD19 expression on IgD + CD38dim and Switched Memory B cells; CD20 expression on IgD + CD38− Unswitched Memory B cells; percentage of Switched Memory B cells among lymphocytes; CD16-CD56 expression on HLA DR + Natural Killer cells; percentage of CD11c + CD62L− monocytes; CD86 expression on monocytes; CCR2 expression on CD14 + CD16 + monocytes; and CD14 expression on Monocytic Myeloid-Derived Suppressor Cells, with Odds Ratios (ORs) between 0.413 and 0.703. On the contrary, increased risks of endometritis were linked with: the percentage of Effector Memory CD4 + T cells within the CD4 + T cell population; percentages of HLA DR + T cells and HLA DR + CD8 + T cells among T cells; CD4 expression on CD28 + CD4 + T cells; CD20 expression on CD20- CD38- B cells; percentage of IgD + CD24 + B cells within the B cell population; CD62L expression on CD62L + myeloid Dendritic Cells; and Absolute Counts of Plasmacytoid Dendritic Cells, with ORs from 1.473 to 2.677, indicating these traits potentially elevate the risk of developing endometritis. Our research delineates distinct causal links between specific immune cell phenotypes and endometritis, offering new perspectives that could contribute to the pinpointing of new therapeutic avenues for this condition.

Abstract Accumulating evidence supports that glucocorticoid treatment for viral pneumonia (VPA) can shorten the disease course and improve survival. However, currently, the use of glucocorticoids in treating VPA remains controversial. Moreover, a unified standard for the dosage and duration of glucocorticoid therapy has not been presented in published articles. A retrospective analysis was conducted in patients who were hospitalized for severe influenza virus-associated pneumonia, and they received sequential treatment with high-dose glucocorticoids and short-course oral glucocorticoids. Patients were followed up for 3 months. A total of 11 patients were included in the study (average age 56 years). There was no gender difference, but age and underlying diseases could be risk factors for severe influenza virus-associated pneumonia. The types of viruses causing pneumonia included influenza A/B. The main clinical symptoms of patients were fever, cough, sputum production, and dyspnea. Chest computed tomography showed multiple ground-glass shadows in the lobes, and the presence of bacterial and fungal infections was accompanied by consolidation shadows. After glucocorticoid therapy, the symptoms improved. None of the patients underwent tracheal intubation, and all survived. After a 3-month follow-up, lung CT absorption in all patients had reached more than 80%, and lung imaging absorption in 20% patients was complete. No serious complications occurred in any of the patients. Sequential treatment with high-dose steroids and short-course oral glucocorticoids may be helpful for reducing the tracheal intubation rate and mortality rate in patients with severe influenza virus-associated pneumonia. Additionally, short-course oral glucocorticoids may reduce pulmonary fibrosis in patients with severe influenza virus-associated pneumonia without any serious complications.

Abstract Community detection is a critical component of network analysis and a hot topic in social computing. Detecting community structure in dynamic networks has important theoretical and practical implications for understanding the intrinsic function of networks and predicting network behavior. However, the majority of existing dynamic community detection methods adopt shallow models, which have limited ability to excavate complex non-linear structures and tend to generate undesirable community structures. In order to obtain an accurate and robust community structure in dynamic networks, we are inspired by network representation learning and utilize the deep learning to detect evolving communities in dynamic networks. In this paper, we propose a novel dynamic community detection method by fusing Deep Learning and Evolutionary Clustering (DLEC). This work attempts to combine deep learning and evolutionary clustering into a unified framework. First, we propose a matrix construction strategy to fully reveal the inherent community structures via the underlying community memberships. Then, we develop a novel multi-layer deep autoencoder framework that consists of multiple non-linear functions to extract the latent deep representation of the dynamic network. Based on the evolutionary clustering framework, a graph regularization term is introduced to ensure the smoothness of the community evolution. Finally, we employ the K-means clustering algorithm on the low-dimensional network space to obtain the community structure. Extensive experimental results on synthetic and real-world networks show that the proposed DLEC algorithm can effectively detect high-quality communities in dynamic networks.

Abstract Leaf senescence represents the final stage of leaf development, involving transcription factors (TFs)-mediated genetic reprogramming events. The timing of crop leaf senescence has a major influence on the yield and quality of crop in agricultural production. As important regulator of plant growth, the significance of TFs in the regulation of leaf senescence have been highlighted in various plant species by recent advances in genetics. However, studies on underlying molecular mechanisms are still not adequately explained. In this study, for analyzing the regulation of TFs on senescence of tobacco leaves, we combined gene differential expression analysis with weighted gene co-expression network analysis (WGCNA) to analyze the time-series gene expression profiles in senescing tobacco leaf. Among 3517 TF genes expressed in tobacco leaves, we identified 21, 35, and 183 TFs that were associated with early, middle, and late stages of tobacco leaf senescence, respectively. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation results reveal that these senescence response TFs are correlated with several biological pathways such as plant hormone signal transduction, ubiquitin mediated proteolysis and MAPK signaling pathway, indicating the roles of TFs in regulating leaf senescence. Our results provide implications for future studies of the potential regulatory mechanisms of TFs involved in senescence of tobacco leaves.

Abstract Exposure to mercury has been associated with adverse effects on pregnancy outcomes. However, there is limited literature on mercury exposure and pregnancy outcomes in Chinese pregnant women. Our study was to investigate the possible association between maternal mercury exposure and spontaneous preterm birth and birth weight. This study was a nested case-control study. The association between blood mercury concentration and both spontaneous preterm birth and birth weight was analyzed using conditional logistic regression and linear regression adjusted for the potential confounding factors, respectively. The dose-response relationship between mercury concentration and birth outcomes was estimated using restricted cubic spline regression. The mean concentration of mercury was 2.8 ± 2.2 µg/L. A positive relationship was observed between maternal blood mercury concentration and SPB when analyzed as a continuous variable. However, it was not found to be statistically significant (adjusted OR = 1.10, 95% CI = 0.95–1.26, P = 0.202). Moderate mercury exposure was associated with a higher risk of SPB (Q3 vs. Q1: crude OR = 2.50, 95% CI = 1.16–5.41, P = 0.02; adjusted OR = 3.49, 95% CI = 1.33–9.11, P = 0.011). After considering the combined effects of chemicals other than mercury exposure (including lead, selenium, and cadmium), the results remained consistent. There was no statistically significant association between blood mercury levels and birth weight (adjusted coefficient = 18.64, P-value = 0.075). There were no statistically significant dose-response associations between mercury concentration and birth outcomes (SPB: P = 0.076; birth weight: P = 0.885). Public health policies should focus on reducing environmental releases of mercury, improving food safety standards, and providing education to pregnant women about the risks of mercury exposure and preventive measures.

Abstract Vitamin C (VC), due to its chemical properties, can provide more oxygen-containing functional groups such as hydroxyl groups for biochar (BC), which promotes the adsorption of tetracycline on biochar. Therefore, in this study, cow dung biochar (CDBC) was modified with VC and VC-modified CDBC (CDBC-VC) was synthesized. The modified biochar was characterized and related factors, adsorption kinetics, isotherms and adsorption mechanisms were investigated. Adsorption kinetics indicate a fast rate of adsorption. The adsorption isotherms showed that the maximum adsorption capacity was 31.72 mg/g (CDBC) and 50.90 mg/g (CDBC-VC), respectively, and the adsorption process was inhomogeneous with multiple molecular layers and the adsorbent has a higher affinity. Mechanistic studies showed that hydrogen bonding interactions, π-π electron donor-acceptor interactions, hydrophobic interactions, and electrostatic interactions were the key to the adsorption process. The analysis of adsorbent regeneration showed that CDBC-VC had good adsorption performance. CDBC and CDBC-VC showed the best performance in simulated industrial wastewater with removal rates of 78.81% and 93.69%. The adsorption mechanism was comprehensively analyzed using six machine learning models. The extreme gradient boosting model gave the best fit. Analysis of the weights of the input variables for predicting adsorption efficiency showed that the ratio of initial TC concentration to BC dosage (29.8%), specific surface area (23%), isoelectric point (8.8%), and ash content (7.7%) had a significant effect on the predicted results.

Abstract The peripheral nervous system consists of ganglia, nerve trunks, plexuses, and nerve endings, that transmit afferent and efferent information. Regeneration after a peripheral nerve damage is sluggish and imperfect. Peripheral nerve injury frequently causes partial or complete loss of motor and sensory function, physical impairment, and neuropathic pain, all of which have a negative impact on patients’ quality of life. Because the mechanism of peripheral nerve injury and healing is still unclear, the therapeutic efficacy is limited. As peripheral nerve injury research has processed, an increasing number of studies have revealed that biological scaffolds work in tandem with progenitor cells to repair peripheral nerve injury. Here, we fabricated collagen chitosan nerve conduit bioscaffolds together with collagen and then filled neuroepithelial stem cells (NESCs). Scanning electron microscopy showed that the NESCs grew well on the scaffold surface. Compared to the control group, the NESCs group contained more cells with bigger diameters and myelinated structures around the axons. Our findings indicated that a combination of chitosan-collagen bioscaffold and neural stem cell transplantation can facilitate the functional restoration of peripheral nerve tissue, with promising future applications and research implications.

Abstract The optimal discrete element model and bonding parameters that match the structural features of cornstalks during harvest were obtained. Based on the differences in mechanical properties of the stem bark and stem pith in the inter-nodal cornstalk, the biomechanical-specific parameters were measured using the compression, shear, and bending tests. The bonded particle models of stem bark and stem pith were constructed using fraction particles with radii of 1 mm and 1.47 mm, which were further bound to form a bilayer-bonded particle model of the cornstalk. The Plackett–Burman, steepest ascent, and response surface tests were conducted to identify the factors and their optimal values that significantly impacted the stem bark-stem bark, stem pith-stem pith, and stem bark-stem pith bonding parameters. The cornstalk’s shear and bending mechanical properties were assessed to verify the overall characteristic parameters. The findings revealed that the cornstalk model created, and the calibrated bonding parameters, were highly accurate and capable of simulating the shearing and bending behaviors of the real cornstalk. The inter-nodal cornstalk’s bonded particle model created and the identified bonding parameters for the cornstalk could contribute to a theoretical and research basis for the next stage in cornstalk modeling with nodes and other applications.

Abstract The current study aimed to evaluate the effect of lymph node ratio (LNR) on the short-term and long-term outcomes of colorectal cancer (CRC) patients who underwent radical CRC surgery. We retrospectively collected CRC patients who underwent radical surgery from Jan 2011 to Jan 2020 in a single-center hospital. The patients were divided into the high LNR group and the low group according to the median. The baseline information and the short-term outcomes were compared between the high group and the low group. Univariate and multivariate logistic regression was performed to analyze the independent predictors for overall survival (OS) and disease-free survival (DFS). A 1:1 proportional propensity score matching (PSM) was used to reduce the selection bias between the two groups. Kaplan–Meier method was used to estimate the OS and DFS between the two groups in different T stages. A total of 1434 CRC patients undergoing radical surgery were enrolled in this study, and there were 730 (50.9%) patients in the low LNR group and 704 (49.1%) patients in the high LNR group. After the PSM, there were 618 patients in both groups, the baseline characteristics between the two groups had no significant difference (p > 0.05). After comparing the Surgery-related information and The Short-term outcomes, the high LNR group had a longer hospital stay (after PSM, p

Abstract Convenient and effective biomarkers are essential for the early diagnosis and treatment of Alzheimer’s disease (AD). In the cross-sectional study, 103 patients with AD, 82 patients with aMCI and 508 normal controls (NC) were enrolled. The single‐molecule array (Simoa) technique was used to assess the levels of plasma proteins, including NfL, T-tau, P-tau-181, Aβ40, Aβ42. Montreal Cognitive Assessment (MoCA) was used to assess the overall cognitive function of all subjects. Moreover, Amyloid PET and structural head MRI were also performed in a subset of the population. In the follow-up, the previous 508 normal older adults were followed up for two years, then COX regression analysis was used to investigate the association between baseline plasma proteins and future cognitive outcomes. NfL, T-tau, P-tau-181, Aβ40, Aβ42 and Aβ42/40 were altered in AD dementia, and NfL, Aβ42 and Aβ42/40 significantly outperformed all plasma proteins in differentiating AD dementia from NC, while NfL and Aβ42/40 could effectively distinguish between aMCI and NC. However, only plasma NfL was associated with future cognitive decline, and it was negatively correlated with MoCA (r = − 0.298, p

Abstract Volatile organic compounds (VOCs) represent a significant component of air pollution. However, studies evaluating the impact of VOC exposure on chronic obstructive pulmonary disease (COPD) have predominantly focused on single pollutant models. This study aims to comprehensively assess the relationship between multiple VOC exposures and COPD. A large cross-sectional study was conducted on 4983 participants from the National Health and Nutrition Examination Survey. Four models, including weighted logistic regression, restricted cubic splines (RCS), weighted quantile sum regression (WQS), and the dual-pollution model, were used to explore the association between blood VOC levels and the prevalence of COPD in the U.S. general population. Additionally, six machine learning algorithms were employed to develop a predictive model for COPD risk, with the model’s predictive capacity assessed using the area under the curve (AUC) indices. Elevated blood concentrations of benzene, toluene, ortho-xylene, and para-xylene were significantly associated with the incidence of COPD. RCS analysis further revealed a non-linear and non-monotonic relationship between blood levels of toluene and m-p-xylene with COPD prevalence. WQS regression indicated that different VOCs had varying effects on COPD, with benzene and ortho-xylene having the greatest weights. Among the six models, the Extreme Gradient Boosting (XGBoost) model demonstrated the strongest predictive power, with an AUC value of 0.781. Increased blood concentrations of benzene and toluene are significantly correlated with a higher prevalence of COPD in the U.S. population, demonstrating a non-linear relationship. Exposure to environmental VOCs may represent a new risk factor in the etiology of COPD.

Abstract Maritime objects frequently exhibit low-quality and insufficient feature information, particularly in complex maritime environments characterized by challenges such as small objects, waves, and reflections. This situation poses significant challenges to the development of reliable object detection including the strategies of loss function and the feature understanding capabilities in common YOLOv8 (You Only Look Once) detectors. Furthermore, the widespread adoption and unmanned operation of intelligent ships have generated increasing demands on the computational efficiency and cost of object detection hardware, necessitating the development of more lightweight network architectures. This study proposes the EL-YOLO (Efficient Lightweight You Only Look Once) algorithm based on YOLOv8, designed specifically for intelligent ship object detection. EL-YOLO incorporates novel features, including adequate wise IoU (AWIoU) for improved bounding box regression, shortcut multi-fuse neck (SMFN) for a comprehensive analysis of features, and greedy-driven filter pruning (GDFP) to achieve a streamlined and lightweight network design. The findings of this study demonstrate notable advancements in both detection accuracy and lightweight characteristics across diverse maritime scenarios. EL-YOLO exhibits superior performance in intelligent ship object detection using RGB cameras, showcasing a significant improvement compared to standard YOLOv8 models.

Abstract Disulfidptosis is a recently identified mode of regulated cell death. Regulating disulfidptosis in carcinoma is a promising therapeutic approach. Long non-coding RNAs (lncRNAs) have been reported to be related to the occurrence and development of many cancers. Disulfidptosis-related lncRNAs (DRLs) in HPV-negative oral squamous cell carcinoma (OSCC) have not been studied. Based on The Cancer Genome Atlas (TCGA) database, least absolute shrinkage selection operator (LASSO) analysis and Cox regression analysis were used to identify overall survival related DRLs and construct the signature. Kaplan–Meier, time-dependent receiver operating characteristics (ROC) and principal component analyses (PCA) were explored to demonstrate the prediction potential of the signature. Subgroup analysis stratified by different clinicopathological characteristics were conducted. Nomogram was established by DRLs signature and independent clinicopathological characteristics. The calibration plots were performed to reveal the accuracy of nomogram. Immune cell subset infiltration, immunotherapy response, drug sensitivity analysis, and tumor mutation burden (TMB) were conducted. Underlying functions and pathways were explored by Gene Set Enrichment Analysis (GSEA) analysis. Previous lncRNA signatures of OSCC were retrieved from PubMed for further validation. Gene expression omnibus (GEO) datasets (GSE41613 and GSE85446) were merged as an external validation for DRLs signature. Consensus clustering analysis of DRLs signature and experimental validation of DRLs were also explored. This research sheds light on the robust performance of DRLs signature in survival prediction, immune cell infiltration, immune escape, and immunotherapy of HPV-negative OSCC.

Abstract Identification of an early biomarker and effective testing device to differentiate dry eye disease secondary to autoimmune disease (Sjögren’s syndrome dry eye disease) from non-Sjögren’s dry eye disease are prerequisites for appropriate treatment. We aimed to demonstrate the capacity of a new photo-detection device to evaluate tear lactoferrin levels as a tool for differentiating systemic conditions associated with dry eye disease. Patients with non-Sjögren’s and Sjögren’s syndrome dry eye disease (n = 54 and n = 52, respectively) and controls (n = 11) were enrolled. All participants completed the Ocular Surface Disease Index questionnaire. Tear collection was performed with Schirmer test, and tear break-up time was examined using a slit lamp. Tear lactoferrin was evaluated using our newly developed photo-detection device. The average lactoferrin concentration was significantly lower in samples from patients with non-Sjögren’s dry eye disease (0.337 ± 0.227 mg/mL, n = 54) and Sjögren’s syndrome dry eye disease (0.087 ± 0.010 mg/mL, n = 52) than in control samples (1.272 ± 0.54 mg/mL, n = 11) (p

Abstract Artificial intelligence (AI) holds immense promise for K-12 education, yet understanding the factors influencing students’ engagement with AI courses remains a challenge. This study addresses this gap by extending the technology acceptance model (TAM) to incorporate cognitive factors such as AI intrinsic motivation (AIIM), AI readiness (AIRD), AI confidence (AICF), and AI anxiety (AIAX), alongside human–computer interaction (HCI) elements like user interface (UI), content (C), and learner-interface interactivity (LINT) in the context of using generative AI (GenAI) tools. By including these factors, an expanded model is presented to capture the complexity of student engagement with AI education. To validate the model, 210 Chinese students spanning grades K7 to K9 participated in a 1 month artificial intelligence course. Survey data and structural equation modeling reveal significant relationships between cognitive and HCI factors and perceived usefulness (PU) and ease of use (PEOU). Specifically, AIIM, AIRD, AICF, UI, C, and LINT positively influence PU and PEOU, while AIAX negatively affects both. Furthermore, PU and PEOU significantly predict students’ attitudes toward AI curriculum learning. These findings underscore the importance of considering cognitive and HCI factors in the design and implementation of AI education initiatives. By providing a theoretical foundation and practical insights, this study informs curriculum development and aids educational institutions and businesses in evaluating and optimizing AI4K12 curriculum design and implementation strategies.

Abstract Tumor-to-normal ratio (T/N) measurement of 18F-FBPA is crucial for patient eligibility to receive boron neutron capture therapy. This study aims to compare the difference in standard uptake value ratios on brain tumors and normal brains using PET/MR ZTE and atlas-based attenuation correction with the current standard PET/CT attenuation correction. Regarding the normal brain uptake, the difference was not significant between PET/CT and PET/MR attenuation correction methods. The T/N ratio of PET/CT-AC, PET/MR ZTE-AC and PET/MR AB-AC were 2.34 ± 0.95, 2.29 ± 0.88, and 2.19 ± 0.80, respectively. The T/N ratio comparison showed no significance using PET/CT-AC and PET/MR ZTE-AC. As for the PET/MRI AB-AC, significantly lower T/N ratio was observed (− 5.18 ± 9.52%; p

Abstract Lifestyles maybe associated with the immune and inflammatory state of human body. We aimed to comprehensively explore the relationship between lifestyles and circulating immune-inflammatory markers in the general population. Data from NHANES 1999–2014 was used. Lifestyle factors included leisure-time physical activity (LTPA), diet quality (Healthy Eating Index-2015, HEI-2015), alcohol consumption, cigarettes smoking, sleep hour and sedentary time. Immune makers included C-reactive protein (CRP), neutrophil–lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet–lymphocyte ratio (PLR) and monocyte–lymphocyte ratio (MLR). Generalized linear regression models were used to adjust confounders. Regressions of restricted cubic splines were utilized to evaluate the potentially non-linear relationships between exposures and outcomes. As results, HEI was negatively associated with CRP (P

Abstract The role of skeletal muscle and adipose tissue in the progression of cancer has been gradually discussed, but it needs further exploration. The objective of this study was to provide an in-depth analysis of skeletal muscle and fat in digestive malignancies and to construct novel predictors for clinical management. This is a retrospective study that includes data from Cancer Center, the First Hospital of Jilin University. Basic characteristic information was analyzed by T tests. Correlation matrices were drawn to explore the relationship between CT-related indicators and other indicators. Cox risk regression analyses were performed to analyze the association between the overall survivals (OS) and various types of indicators. A new indicator body composition score (BCS) was then created and a time-dependent receiver operating characteristic curve was plotted to analyze the efficacy of the BCS. Finally, a nomogram was produced to develop a scored-CT system based on BCS and other indicators. C-index and calibration curve analyses were performed to validate the predictive accuracy of the scored-CT system. A total of 575 participants were enrolled in the study. Cox risk regression model revealed that VFD, L3 SMI and VFA/SFA were associated with prognosis of cancer patients. After adjustment, BCS index based on CT was significantly associated with prognosis, both in all study population and in subgroup analysis according to tumor types (all study population: HR 2.036, P

Abstract Diabetic patients are at high risk of developing lacrimal gland dysfunction, and the antimalarial drug artesunate (ART) was recently used to induce experimental-induced diabetes mellitus. This study’s objective is to investigate the lacrimal gland alteration and the effect of ART on experimentally induced diabetes rat models and its related mechanisms. Forty rats were divided into five groups (8 rats/group): healthy control group (HC), diabetic group (DM), 50 mg/kg ART intervention diabetic group [DM + ART (50 mg/kg)], 100 mg/kg ART intervention diabetic group [DM + ART (100 mg/kg)] and 6 U/kg Insulin intervention diabetic group (DM + INS). The morphology of the eyeball and lacrimal gland tissues was determined using hematoxylin and eosin staining. In addition, external lacrimal glands were harvested for electronic microscopic examination, NFκB1, and TNF-α protein expression evaluation by immunohistochemistry and mRNA expression analysis by RT-PCR. Histopathological and ultrastructural changes suggest ART intervention has an improved structural effect. Protein expression of NFκB1 in the DM + ART (100 mg/kg) group was decreased. TNF-α significantly decreased in the DM + ART (50 mg/kg) and insulin groups. We concluded that ART improves structural changes in a lacrimal gland in diabetic rats. The present study provides further evidence of the therapeutic effect of ART on the lacrimal gland of diabetic rats by decreasing the expression of NFκB1 and TNF-α.

Abstract To predict the effect of chemical-freezing coupling erosion on the properties of four kinds of FRP-reinforced concrete. Rapid freeze–thaw tests were conducted. The mass loss rate, relative dynamic elastic modulus, compressive strength, and flexural capacity were tested to investigate the Mechanical Performance of specimens. The compression specimens are cylindrical specimens wrapped with FRP, and the flexural specimens are pasted with FRP prismatic specimens on the pre-cracked side. A database was built based on 45 groups of experimental test results, and the prediction effect of the BP neural network and CNN model on compressive strength and flexural capacity was compared, respectively. The results showed that CNN did a better job. Finally, the maximum number of freeze–thaw cycles of different FRP-reinforced specimens was predicted based on the CNN model with mass loss rate and relative dynamic modulus as the evaluation criteria. This method can provide a new perspective for predicting the durability of FRP-reinforced concrete.

Abstract As the main gel material of concrete, cement is used in an astonishing amount every year in the construction industry. However, a large amount of CO2 is emitted into the atmosphere while producing cement. Therefore, it is the general trend to look for substitutes for cement and develop new green concrete. Lithium slag (LS) is the industrial waste discharged from lithium salt plants. Through testing, it is found that the chemical composition of LS has a high degree of coincidence with ordinary Portland cement (OPC) Therefore, LS can be incorporated into concrete as supplementary cementations material (SCM) to prepare lithium slag concrete (LSC). The pollution of the natural environment caused by a large number of piled-up and landfilled LS is immeasurable. Consuming and using LS in large quantities and with high efficiency not only eliminates the pollution of lithium slag to the natural environment, but also helps to reduce the amount of cement used in green concrete and truly reuse waste resources. In order to study the mechanical properties of post-heated LSC, the test were carried out for LSC specimens after high-temperature. The main influence factors were considered, including the temperatures of 20℃, 100 ℃, 300 ℃, 500 ℃ and 700 ℃, the contents of lithium slag in LSC of 0%, 10%, 20% and 30%, cooling method of LSC after exposure high temperature. The results showed that the mechanical properties of LS concrete specimens were slightly improved at 100 ℃, and when the temperature was 300 ℃ or higher, the damage to the specimens was huge and irreversible. An appropriate amount of LS (20% lithium slag content) could improve the strength of LSC. This paper also studied the relationship between lithium slag content and strengths of LS concrete. The research results show that adding an appropriate amount of LS to concrete improves the mechanical properties of concrete. When the LS replacement rate is 20%, the mass loss rate of LSC after different high temperature treatments was the minimum. The cubic compressive strength, axial compressive strength, and flexural strength of specimens with 20% LS substitution can be increased by 8.16%, 8.33%, and 13.46% after high temperature. The cubic compressive strength, axial compressive strength, and flexural strength of specimens with 20% LS substitution can be increased by 8.16%, 8.33%, and 13.46% after high temperature.

Abstract The objective of this study is to comprehensively assess the behavior of partially encased concrete (PEC) columns with web openings under axial compression. The primary objectives of this study are to analyze damage patterns and investigate the influence of key parameters, such as concrete strength, opening rate, and opening shape, on the ductility index and ultimate load-carrying capacity. The study employs experimental testing to examine the response of the PEC columns, with a particular focus on the mechanisms of concrete fracture and flange flexing. Notably, the study reveals a significant impact of the opening rate on the bearing capacity, while the effect of opening shape is comparatively minor. Furthermore, computational analyses are conducted to deepen the understanding of structural behavior. The study builds upon existing research to propose a novel method for calculating the bearing capacity of PEC columns with web openings. This method introduces two discount factors to enhance predictive accuracy.

Abstract Due to the lack of sufficient labeled data for the prostate and the extensive and complex semantic information in ultrasound images, accurately and quickly segmenting the prostate in transrectal ultrasound (TRUS) images remains a challenging task. In this context, this paper proposes a solution for TRUS image segmentation using an end-to-end bidirectional semantic constraint method, namely the BiSeC model. The experimental results show that compared with classic or popular deep learning methods, this method has better segmentation performance, with the Dice Similarity Coefficient (DSC) of 96.74% and the Intersection over Union (IoU) of 93.71%. Our model achieves a good balance between actual boundaries and noise areas, reducing costs while ensuring the accuracy and speed of segmentation.

Abstract To examine the joint association of electronic screen time (EST), moderate-to-vigorous physical activity time (MVPA) and overweight/obesity with early pubertal development (EPD) in girls. A case–control study of 177 EPD girls and 354 girls with normal pubertal development was conducted between October 2019 and August 2022. Overweight/obesity was defined as body mass index ≥ 85th percentiles for age and sex. We found a non-significant increase of EPD risk among girls with high EST alone [OR: 2.75 (0.65–11.58)] or low MVPA alone [OR: 2.54 (0.74–8.69)], but a significant increase of EPD risk among girls with overweight/obesity alone [OR: 4.91 (1.01–23.92)], compared to girls without any of the three risk factors (low MVPA, high EST and overweight/obesity). Girls with any two of the three risk factors faced increased risk of EPD, and girls with all three risk factors faced the highest risk of EPD [OR and 95% CI: 26.10 (6.40–106.45)]. Being overweight/obesity might be more important than having low MVPA or high EST as a correlate of EPD compared to girls without any of the three risk factors, but the co-presence of low MVPA, high EST and overweight/obesity would largely increase the risk of EPD in girls.

Abstract Rapid mixing and precise timing are key for accurate biomedical assay measurement, particularly when the result is determined as the rate of a reaction: for example rapid immunoassay in which the amount of captured target is kinetically determined; determination of the concentration of an enzyme or enzyme substrate; or as the final stage in any procedure that involves a capture reagent when an enzyme reaction is used as the indicator. Rapid mixing and precise timing are however difficult to achieve in point-of-care devices designed for small sample volumes and fast time to result. By using centrifugal microfluidics and transposing the reaction surface from a chamber to a single mm-scale bead we demonstrate an elegant and easily manufacturable solution. Reagents (which may be, for example, an enzyme, enzyme substrate, antibody or antigen) are immobilised on the surface of a single small bead (typically 1–2 mm in diameter) contained in a cylindrical reaction chamber subjected to periodically changing rotational accelerations which promote both mixing and uniform mass-transfer to the bead surface. The gradient of Euler force across the chamber resulting from rotational acceleration of the disc, dΩ disc /dt, drives circulation of fluid in the chamber. Oscillation of Euler force by oscillation of rotational acceleration with period, T, less than that of the hydrodynamic relaxation time of the fluid, folds the fluid streamlines. Movement of the bead in response to the fluid and the changing rotational acceleration provides a dynamically changing chamber shape, further folding and expanding the fluid. Bead rotation and translation driven by fluid flow and disc motion give uniformity of reaction over the surface. Critical parameters for mixing and reaction uniformity are the ratio of chamber radius to bead radius, r chamber /r bead , and the product Tr chamber (dΩ disc /dt), of oscillation period and Euler force gradient across the fluid. We illustrate application of the concept using the reaction of horse radish peroxidase (HRP) immobilised on the bead surface with its substrate tetramethylbenzidine (TMB) in solution. Acceleration from rest to break a hydrophobic valve provided precise timing for TMB contact with the bead. Solution uniformity from reaction on the surface of the bead in volumes 20–50 uL was obtained in times of 2.5 s or less. Accurate measurement of the amount of surface-bound HRP by model fitting to the measured kinetics of colour development at 10 s intervals is demonstrated.

Abstract To investigate the association between serum neurofilament light chain (NfL) levels, inflammatory cytokines, and cognitive function to assess their utility in the early detection of mild cognitive impairment (MCI). We conducted a cross-sectional study involving 157 community-dwelling individuals aged 55 years and above, categorized into healthy controls, MCI, and probable Alzheimer's disease (AD). Serum levels of NfL, inflammatory cytokines, and AD pathology markers were measured using enzyme-linked immunosorbent assay (ELISA). Correlations between these biomarkers and cognitive function were analyzed, and the diagnostic performance of the cognitive assessment scales and serum biomarker concentrations was evaluated using receiver operating characteristic (ROC) curve analysis. Serum NfL levels were significantly elevated in MCI and probable AD groups compared to healthy controls. Positive correlations were found between serum NfL and inflammatory cytokines IL-1β, IL-6, and Aβ40. Combining serum NfL with p-tau217 and the Boston Naming Test significantly enhanced the predictive accuracy for MCI. However, combining serum NfL with inflammatory markers did not improve MCI prediction accuracy. Elevated serum NfL is associated with cognitive impairment and inflammatory markers, suggesting its potential as a peripheral serum biomarker for MCI detection. The combination of serum NfL with p-tau217 and cognitive tests could offer a more accurate prediction of MCI, providing new insights for AD treatment strategies.

Abstract Rate of penetration (ROP) is a key factor in drilling optimization, cost reduction and drilling cycle shortening. Due to the systematicity, complexity and uncertainty of drilling operations, however, it has always been a problem to establish a highly accurate and interpretable ROP prediction model to guide and optimize drilling operations. To solve this problem in the Tarim Basin, this study proposes four categories of hybrid physics-machine learning (ML) methods for modeling. One of which is residual modeling, in which an ML model learns to predict errors or residuals, via a physical model; the second is integrated coupling, in which the output of the physical model is used as an input to the ML model; the third is simple average, in which predictions from both the physical model and the ML model are combined; and the last is bootstrap aggregating (bagging), which follows the idea of ensemble learning to combine different physical models’ advantages. A total of 5655 real data points from the Halahatang oil field were used to test the performance of the various models. The results showed that the residual modeling model, with an R2 of 0.9936, had the best performance, followed by the simple average model and bagging with R2 values of 0.9394 and 0.5998, respectively. From the view of prediction accuracy, and model interpretability, the hybrid physics-ML model with residual modeling is the optimal method for ROP prediction.

Abstract The aim of this study was to evaluate the association between lifestyle profile and disease incidence/mortality in patients with non-alcoholic fatty liver disease (NAFLD). Lifestyle profiles ascertainment was based on the latent profile analysis. The associations of lifestyle profile and outcomes were analyzed by multivariate logistic or Cox regressions. Four lifestyle profiles (profile 1 and 2 for male, profile 3 and 4 for female) were established for all participants. Compared to profile 1, profile 2 (P = 0.042) and profile 3 (P = 0.013) had lower incidence for NAFLD. In contrast, profile 4 showed similar NAFLD prevalence compared to profile 1 (P = 0.756). Individuals with NAFLD within profile 3 had the best long-term survival, and the HR was 0.55 (95% CI 0.40–0.76) for all-cause mortality (compared to profile 1). Profile 4 (P = 0.098) and profile 2 (P = 0.546) had similar all-cause survival compared to profile 1. We explored the associations of healthy lifestyle score with mortality and incidence of NAFLD stratified by lifestyle profiles. We observed that with the increase of healthy lifestyle score, participants within profile 2 did not display lower NAFLD incidence and better long-term survival in NAFLD cases. In this study, lifestyle profiles were constructed in NHANES participants. The distinct lifestyle profiles may help optimize decision-making regarding lifestyle management in preventing NAFLD development, as well as selection of a more personalized approach for improving NAFLD survival.

Abstract A large number of burnt rocks in some open-pit mines in Xinjiang, Inner Mongolia and Ningxia have a great influence on the blasting effect. For this kind of rock, through the analysis of physical and chemical changes, combined with ANSYS/LS-DYNA and PFC 2D numerical simulation software, a burnt rock model with multiple joint cracks and irregular distribution is constructed to simulate the blasting process of burnt rock under the combined action of stress wave and detonation gas. The results show that the fracture of rock mass affects the propagation of blasting cracks in the fracture area, resulting in stress concentration and stress hindrance. The action time of stress wave is reduced, and the energy of blasting gas is partially absorbed by the fracture, resulting in uneven stress on the burnt rock bench and seriously affecting the bench blasting effect.

Abstract The paper proposes a SOC (State of Charge) estimation method for lead–carbon batteries based on the GA-MIUKF algorithm. The GA-MIUKF algorithm combines GA (Genetic Algorithm) for global search and optimization with the MI-UKF (Multi-innovation Unscented Kalman Filter) algorithm for estimating the SOC of lead–carbon batteries. By establishing an equivalent circuit model for the battery, the GA is employed to globally search and optimize the battery model parameters and the noise variance parameters in the MI-UKF algorithm. Comparative analyses with the UKF (Unscented Kalman Filter) algorithms and MI-UKF algorithms reveal that the SOC estimation method based on the GA-MIUKF algorithm yields more accurate results for lead–carbon battery SOC estimation, with an average estimation error of 2.0%. This highlights the efficacy of the proposed approach in enhancing SOC estimation precision.

Abstract The early development of the gut microbiome is governed by multiple factors and has significantly long-term effects on later-in-life health. To minimize inter-individual variations in the environment, we determined developmental trajectories of the gut microbiome in 28 healthy neonates during their stay at a postpartum center. Stool samples were collected at three time points: the first-pass meconium within 24 h of life, and at 7 and 28 days of age. Illumina sequencing of the V3–V4 region of 16S rRNA was used to investigate microbiota profiles. We found that there was a distinct microbiota structure at each time point, with a significant shift during the first week. Proteobacteria was most abundant in the first-pass meconium; Firmicutes and Actinobacteria increased with age and were substituted as the major components. Except for a short-term influence of different delivery modes on the microbiota composition, early microbiome development was not remarkably affected by gravidity, maternal intrapartum antibiotic treatment, premature rupture of membranes, or postnatal phototherapy. Hence, our data showed a similar developmental trajectory of the gut microbiome during the first month in healthy neonates when limited in environmental variations. Environmental factors external to the host were crucial in the early microbiome development.

Abstract The epidermal growth factor receptor (EGFR) Thr790 Met (T790M) mutation is responsible for approximately half of the acquired resistance to EGFR-tyrosine kinase inhibitor (TKI) in non-small-cell lung cancer (NSCLC) patients. Identifying patients at diagnosis who are likely to develop this mutation after first- or second-generation EGFR-TKI treatment is crucial for better treatment outcomes. This study aims to develop and validate a radiomics-based machine learning (ML) approach to predict the T790M mutation in NSCLC patients at diagnosis. We collected retrospective data from 210 positive EGFR mutation NSCLC patients, extracting 1316 radiomics features from CT images. Using the LASSO algorithm, we selected 10 radiomics features and 2 clinical features most relevant to the mutations. We built models with 7 ML approaches and assessed their performance through the receiver operating characteristic (ROC) curve. The radiomics model and combined model, which integrated radiomics features and relevant clinical factors, achieved an area under the curve (AUC) of 0.80 (95% confidence interval [CI] 0.79–0.81) and 0.86 (0.87–0.88), respectively, in predicting the T790M mutation. Our study presents a convenient and noninvasive radiomics-based ML model for predicting this mutation at the time of diagnosis, aiding in targeted treatment planning for NSCLC patients with EGFR mutations.

Abstract miR-374a-5p expression and localization in intracranial aneurysm (IA) tissues were detected, and its correlation with vascular smooth muscle cells (VSMCs) and macrophage markers was analyzed. Using platelet-derived growth factor-BB (PDGF-BB) induced VSMC model, elastase-induced IA rat model. Subsequently, miR-374a-5p was knocked down or overexpressed. We investigated the effects of miR-374a-5p on phenotypic conversion, and in vivo experiments were also carried out to verify the findings. The targeted relationship between miR-374a-5p and WNTA5 was analyzed. The effect of WNT5A inhibition on VSMC phenotypic transformation and THP-1-derived macrophage polarization was explored. Clinical studies have shown that miR-374a-5p was upregulated in IA patients. miR-374a-5p was negatively correlated with SM22α, α-SMA, CD206, and positively correlated with CD86. In vitro experiments showed that knocking down miR-374a-5p reversed the promotion of SM22α and α-SMA expression by PDGF-BB, while overexpression of miR-374a-5p had the opposite effect. In addition, knocking down miR-374a-5p also reversed the decrease in Calponin, TIMP3, TIMP4, and IL-10 levels caused by PDGF-BB, and further reduced the levels of MMP1, MMP3, MMP9, IL-1β, IL-6, and TNF-α. These findings were further validated in vivo. In IA rats, there were notable increases in both systolic and diastolic blood pressure, along with an elevated M1/M2 ratio and the occurrence of vascular lesions. However, these symptoms were improved after knocking down miR-374a-5p. Furthermore, miR-374a-5p could target the WNT signals (WNT2B, WNT3, and WNT5A). miR-374a-5p regulated the VSMC phenotypic conversion and M1 macrophage polarization by targeting WNT5A, thereby impacting the progression of IA.

Abstract In plants, B3 transcription factors play important roles in a variety of aspects of their growth and development. While the B3 transcription factor has been extensively identified and studied in numerous species, there is limited knowledge regarding its B3 superfamily in pepper. Through the utilization of genome-wide sequence analysis, we identified a total of 106 B3 genes from pepper (Capsicum annuum), they are categorized into four subfamilies: RAV, ARF, LAV, and REM. Chromosome distribution, genetic structure, motif, and cis-acting element of the pepper B3 protein were analyzed. Conserved gene structure and motifs outside the B3 domain provided strong evidence for phylogenetic relationships, allowing potential functions to be deduced by comparison with homologous genes from Arabidopsis. According to the high-throughput transcriptome sequencing analysis, expression patterns differ during different phases of fruit development in the majority of the 106 B3 pepper genes. By using qRT-PCR analysis, similar expression patterns in fruits from various time periods were discovered. In addition, further analysis of the CaRAV4 gene showed that its expression level decreased with fruit ripening and located in the nucleus. B3 transcription factors have been genome-wide characterized in a variety of crops, but the present study is the first genome-wide analysis of the B3 superfamily in pepper. More importantly, although B3 transcription factors play key regulatory roles in fruit development, it is uncertain whether B3 transcription factors are involved in the regulation of the fruit development and ripening process in pepper and their specific regulatory mechanisms because the molecular mechanisms of the process have not been fully explained. The results of the study provide a foundation and new insights into the potential regulatory functions and molecular mechanisms of B3 genes in the development and ripening process of pepper fruits, and provide a solid theoretical foundation for the enhancement of the quality of peppers and their selection and breeding of high-yield varieties.

Abstract To evaluate the independent association of seasonal variation with GDM incidence in Tianjin, China, and to test whether there is an additive interaction between seasonal variation and pre-pregnancy body mass index (BMI) on GDM incidence. A population-based observational cohort study was conducted using the healthcare records data from Tianjin, China. Logistic regression was used to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Additive interaction between pre-pregnancy BMI groups and seasons was estimated by using relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (S). Among the 112,639 pregnant women, 20.8% developed GDM at 24–28 weeks of gestation. The multivariable adjusted ORs and 95% CIs were 1.00, 1.00 (0.96–1.05), 1.15 (1.09–1.20) and 1.22 (1.16–1.29) respectively based on seasons (spring, summer, autumn and winter). Compared with the spring/summer and pre-pregnant BMI

Abstract Monitoring extent and severity is vital in the ulcerative colitis (UC) follow-up, however, current assessment is complex and low cost-effectiveness. We aimed to develop a routine blood-based clinical decision support tool, Jin’s model, to investigate the extent and severity of UC. The multicentre retrospective cohort study recruited 975 adult UC inpatients and sub-grouped into training, internal validation and external validation set. Model was developed by logistics regression for the extent via Montreal classification and for the severity via Mayo score, Truelove and Witts score (TWS), Mayo endoscopic score (MES) and Degree of Ulcerative colitis Burden of Luminal Inflammation (DUBLIN) score. In Montreal classification, left-sided and extensive versus proctitis model achieved area under the receiver operating characteristic curve (AUROC) of 0.78 and 0.81 retrospectively. For severity, Mayo score model, TWS model, MES model and DUBLIN score model achieved an AUROC of 0.81, 0.70, 0.74 and 0.70 retrospectively. The models also were evaluated with satisfactory calibration and clinical unity. Jin’s model was free with open access at http://jinmodel.com:3000/ . Jin’s model is a noninvasive, convenient, and efficient approach to assess the extent and severity of UC.

Abstract The purpose of this study was to investigate whether patients with colorectal cancer (CRC) combined with hyperuricemia remitted 1 year after CRC surgery. CRC patients combined with hyperuricemia who underwent radical surgery were included from a single clinical center from Jan 2016 to Dec 2021. Baseline characteristics was compared between the remission group and the non-remission group. Multivariate logistic regression was used to find the possible predictive factors of hyperuricemia remission. A total of 91 patients were included for data analysis, retrospectively. There were 34 (37.4%) patients in the remission group and 57 (62.6%) patients in the non-remission group. The mean preoperative weight and body mass index (BMI) were 61.2 ± 10.7 (kg) and 24.1 ± 3.3 (kg/m2). 21 (23.1%) patients had a history of drinking. We found that the weight and BMI were not significantly different before and 1 year after CRC surgery (P > 0.05). In contrast, uric acid values were significantly decreased (P 0.05). According to multivariate logistic regression, we found that the history of drinking was a predictive factor of hyperuricemia remission (OR = 0.046, 95% CI 0.005–0.475, P = 0.010). CRC patients with hyperuricemia had a 37.4% remission from hyperuricemia 1 year after CRC surgery. Tumor location, tumor stage, and tumor size did not predict the remission of hyperuricemia. Notably, the history of drinking was a predictive factor of hyperuricemia remission.

Abstract Enhancing energy-environmental efficiency (EEE) is crucial for achieving energy conservation and emission reduction goals. Investigating the mechanism through which green technology innovation (GTI) affects EEE and understanding the role of environmental regulation (ER) in this process provides a theoretical basis for efficient utilization of GTI and ER. This study employs a Dynamic Spatial Durbin Model and utilizes panel data from 2003 to 2017 for 30 Chinese provinces to examine the impact of GTI on EEE in the presence of ER. The empirical results reveal: (1) GTI has a U-shaped impact on EEE, primarily driven by SubGI. (2) GTI’s influence on EEE is predominantly reflected in PTE, also stemming from SubGI. (3) The interaction term between ER and GTI is 0.0022, while the GTI coefficient is − 0.0741, and the GTI quadratic term coefficient is 0.0007, all statistically significant. This implies that ER mitigates the negative impact of GTI on EEE while strengthening its positive effect. These findings provide empirical evidence and policy insights for more effectively utilizing GTI and ER to enhance EEE and achieve energy conservation and emissions reduction goals.

Abstract Malignant brain tumors consist of malignancies originated primarily within the brain and the metastatic lesions disseminated from other organs. In spite of intensive studies, malignant brain tumors remain to be a medical challenge. Patient-derived organoid (PDO) can recapitulate the biological features of the primary tumor it was derived from and has emerged as a promising drug-screening model for precision therapy. Here we show a proof-of-concept based on early clinical study entailing the organoids derived from the surgically resected tumors of 26 patients with advanced malignant brain tumors enrolled during December 2020 to October 2021. The tumors included nine glioma patients, one malignant meningioma, one primary lymphoma patient, and 15 brain metastases. The primary tumor sites of the metastases included five from the lungs, three from the breasts, two from the ovaries, two from the colon, one from the testis, one of melanoma origin, and one of chondrosarcoma. Out of the 26 tissues, 13 (50%) organoids were successfully generated with a culture time of about 2 weeks. Among these patients, three were further pursued to have the organoids derived from their tumor tissues tested for the sensitivity to different therapeutic drugs in parallel to their clinical care. Our results showed that the therapeutic effects observed by the organoid models were consistent to the responses of these patients to their treatments. Our study suggests that PDO can recapitulate patient responses in the clinic with high potential of implementation in personalized medicine of malignant brain tumors.

Abstract In recent years, air pollution has become a factor that cannot be ignored, affecting human lives and health. The distribution of high-density populations and high-intensity development and construction have accentuated the problem of air pollution in China. To accelerate air pollution control and effectively improve environmental air quality, the target of our research was cities with serious air pollution problems to establish a model for air pollution prediction. We used the daily monitoring data of air pollution from January 2016 to December 2020 for the respective cities. We used the long short term memory networks (LSTM) algorithm model to solve the problem of gradient explosion in recurrent neural networks, then used the particle swarm optimization algorithm to determine the parameters of the CNN-LSTM model, and finally introduced the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) decomposition to decompose air pollution and improve the accuracy of model prediction. The experimental results show that compared with a single LSTM model, the CEEMDAN-CNN-LSTM model has higher accuracy and lower prediction errors. The CEEMDAN-CNN-LSTM model enables a more precise prediction of air pollution, and may thus be useful for sustainable management and the control of air pollution.

Abstract Tumor cells face constant stress of ischemic (nutrient paucity and hypoxia) environment when they migrate and invade too fast to outgrow the nearest blood vessels. During the temporary loss of support from circulation, the tumor cells must act self-sufficient to survive and then to migrate to re-connect with the nearest blood supply or die. We have previously reported that ablation of the low-density lipoprotein receptor-related protein 1 (LRP-1) completely nullified the ability of tumour cells to migrate and invade under serum-free conditions in vitro and to form tumours in vivo. The mechanism behind the important function by cell surface LRP-1 was not fully understood. Herein we show that LRP-1 orchestrates two parallel cell surface signalling pathways to support the full constitutive tumour cell migration. First, LRP-1 stabilizes activated epidermal growth factor receptor (EGFR) to contribute half of the pro-motility signalling. Second, LRP-1 mediates secreted Hsp90α autocrine signalling to bring the other half of pro-motility signalling. Only combined inhibitions of the EGFR signalling and the eHsp90α autocrine signalling led to the full blockade of the tumour cell migration as the LRP-1 depletion did. This finding uncovers a novel mechanism by which certain breast cancer cells use LRP-1 to engage parallel signalling pathways to move when they lose contact with blood support.