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Abstract Background Internal migrants in China have long been at a disadvantage in terms of access to publicly financed services, as well as the utilization of public health services. The aim of the study was to examine inequities in the use of basic public health services between internal migrants and the local population and estimate the factors that contributed to inequity in use. Methods The data for this study was derived from the 2017 wave of the China Migrants Dynamic Survey. Basic public health services utilization was measured by the establishment of health records, health education and chronic disease management. We performed multivariable logistic regressions to examine inequities in the utilization of basic public health services between locals and internal migrants, and Oaxaca-Blinder decomposition was used to explore possible explanations for such inequities between the two groups. Results A total of 27,998 cases were included in the analysis. We found that the utilization rates for establishment of health records, health education and chronic disease management among internal migrants were 71.3%, 49.2% and 65.7% lower than their local counterparts, respectively. The decomposition results indicated that the inequities in the establishment of health records between locals and internal migrants were mainly explained by whether people had heard of the National Basic Public Health Services Program (NBPHSP) (17.67%) and by health insurance (5.99%). The contributors to the inequities in health education between locals and internal migrants were community involvement (14.71%) and whether people had heard of the NBPHSP (13.89%). The main factors contributing to the difference in utilization of chronic disease management between the two groups were whether people had heard of the NBPHSP (14.49%) and community involvement (8.43%). Conclusions To reduce inequities in the utilization of basic public health services between locals and internal migrants, measures need to be taken to improve knowledge about the basic public health services and to help migrants integrate into the local community.

Abstract Background Maternal hemoglobin (Hb) is related to nutritional status, and it widely fluctuates during pregnancy. However, the relationship between Hb and blood pressure (BP) during pregnancy is unclear. This study aimed to estimate the associations between maternal Hb in various trimesters and its changes with BP during pregnancy. Methods We conducted this study by using data from the Electronic Medical Record System (EMRS) database of Zhoushan Maternal and Child Care Hospital in Zhejiang Province, China. The pregnancy duration was divided into seven periods: before 14th, 14 to 17th, 18 to 22nd, 23 to 27th, 28 to 31st, 32 to 35th, and 36 to delivery; generalized estimating equations (GEE) and multiple linear regressions were used for exploring their associations. Results 8168 pregnant women in the first trimester were included in this study. Hb level decreased significantly from the first to late-second trimester and then increased but did not return to the level of the first trimester. After adjustment for the potential confounders, compared with women with Hb 110 ~ 119 g/L, those with Hb = 140 g/L groups had significantly higher SBP and DBP than the other groups. A significant nonlinear relationship was found between hemoglobin changes with both SBP and DBP in different gestational weeks (P non-linear 120 g/L during pregnancy and increased maternal Hb from the first to third trimesters should especially be given more attention.

Abstract Here we presented the initial experience of clipping aneurysms using fully endoscopic techniques and aimed to evaluate the safety and feasibility of fully endoscopic techniques for aneurysms. This was a retrospective single-center study in which patients were scheduled to undergo aneurysm clipping using fully endoscopic techniques. We collected patients’ records, radiological neuroimaging, aneurysm-related variables and surgical procedures in detail, as well as postoperative outcomes. All patients were followed up for neurological examinations and computed tomography (CT) as well as computed tomography angiography (CTA) regularly after surgery. We reviewed the radiological and clinical data of 7patients who underwent aneurysm clipping via fully endoscopic techniques at our department from Jan. 2022 to Jul. 2024, including 2 middle cerebral artery aneurysms, 3 cerebral anterior communicating artery aneurysms, 1 anterior cerebral aneurysm and 1 ophthalmic aneurysm. No uncontrolled rupture of aneurysm occurred during operation. Postclipping endoscopic inspection as well as postoperative CTA demonstrated complete occlusion of the aneurysm and preservation of parent, branching, and perforating vessels. None postclipping cerebral infarction caused by branch or perforator compromise were observed after clipping. No mortality was recorded. Follow-up ranged from 1 to 10 months. Six patients (71.4%) showed excellent or good recoveries. The remaining patient recorded improved KPS. With the accumulation of experience and technological progress, the fully endoscopic technique could enable safe and effective clipping of an aneurysm, which provided valuable information for decision-making during surgery and shed a new light on aneurysms clipping.

Abstract Growing research has suggested an association between chronic inflammation and Intervertebral disc degeneration (IVDD), but whether there is a causal effect remains unknown. This study adopted two-sample Mendelian randomization (MR) approach to explore the etiological role of chronic inflammation in IVDD risk. Here, summary statistics for C-reactive protein (CRP), interleukin (IL)-1 $$\mathcal {\alpha }$$ α , IL-1 $$\mathcal {\beta }$$ β , IL-6 expression and IVDD were obtained from genome-wide association studies (GWAS) of European ancestry. MR analyses were conducted by using inverse variance weighted (IVW), Wald Ratio, weighted median, and MR-Egger method. Sensitivity analyses were conducted to assess the robustness of the results. The MR analyses suggested a lack of causal association of CRP, IL-6 , and IL-1 $$\mathcal {\alpha }$$ α levels on IVDD (CRP-IVDD: odds ratio [OR] = 0.97, 95% confidence interval [CI] 0.86–1.09, P = 0.583; IL-6-IVDD: OR = 1.04, 95% CI 0.86–1.27, P = 0.679; IL-1 $$\mathcal {\alpha }$$ α -IVDD: OR = 1.09, 95%CI 1.00–1.18, P = 0.058). However, there was a sign of a connection between genetically elevated IL-1 $$\mathcal {\beta }$$ β levels and a decreased IVDD incidence (OR = 0.87, 95%CI 0.77–0.99, P = 0.03). Our findings suggest a connection between IL-1 $$\mathcal {\beta }$$ β levels and the risk of IVDD. However, due to the support of only one SNP, heterogeneity and pleiotropy tests cannot be performed, the specific underlying mechanisms warrant further investigation.

Abstract Four distinct species of human coronaviruses (HCoVs) circulate in humans. Despite the recent attention due to SARS-CoV-2, a comprehensive understanding of the molecular epidemiology and genomic evolution of HCoVs remains unclear. Here, we employed primary differentiated human nasal epithelial cells for the successful isolation and genome sequencing of HCoVs derived from two retrospective cohorts in Singapore and Tanzania. Phylodynamic inference shows that HCoV-229E and HCoV-OC43 were subject to stronger genetic drift and reduced purifying selection from the early 2000s onwards, primarily targeting spike Domain A and B. This resulted in increased lineage diversification, coinciding with a higher effective reproductive number (Re>1.0). However, HCoV-NL63 and HCoV-HKU1 experienced weaker genetic drift and selective pressure with prolonged regional persistence. Our findings suggest that HCoV-229E and HCoV-OC43 viruses are adept at generating new variants and achieving widespread intercontinental dissemination driven by continuous genetic drift, recombination, and complex migration patterns.

Objective: Colorectal cancer (CRC) is a prevalent malignant tumor with a high fatality rate. CircPDIA4 has been shown to have a vital role in cancer development by acting as a facilitator. Nevertheless, the impact of the circPDIA4/miR-9-5p/SP1 axis on development of CRC has not been studied. Methods: Western blot, immunohistochemistry, and reverse transcription-quantitative polymerase chain reaction assays were used to analyze gene expression. The CCK-8 assay was used to assess cell growth. The Transwell assay was used to detect invasion and migration of cells. The luciferase reporter and RNA immunoprecipitation tests were used to determine if miR-9-5p and circPDIA4 (or SP1) bind to one another. An in vivo assay was used to measure tumor growth. Results: It was shown that circPDIA4 expression was greater in CRC cell lines and tissues than healthy cell lines and tissues. CircPDIA4 knockdown prevented the invasion, migration, and proliferation of cells in CRC. Additionally, the combination of circPDIA4 and miR-9-5p was confirmed, as well as miR-9-5p binding to SP1. Rescue experiments also showed that the circPDIA4/miR-9-5p/SP1 axis accelerated the development of CRC. In addition, SP1 combined with the promoter region of circPDIA4 and induced circPDIA4 transcription. CircPDIA4 was shown to facilitate tumor growth in an in vivo assay. Conclusions: The circPDIA4/miR-9-5p/SP1 feedback loop was shown to aggravate CRC progression. This finding suggests that the ceRNA axis may be a promising biomarker for CRC patient treatment.

Abstract In intensive care unit (ICU) patients undergoing mechanical ventilation (MV), the occurrence of difficult weaning contributes to increased ventilator-related complications, prolonged hospitalization duration, and a significant rise in healthcare costs. Therefore, early identification of influencing factors and prediction of patients at risk of difficult weaning can facilitate early intervention and preventive measures. This study aimed to strengthen airway management for ICU patients by constructing a risk prediction model with comprehensive and individualized offline programs based on machine learning techniques. This study involved the collection of data from 487 patients undergoing MV in the ICU, with a total of 36 variables recorded. The dataset was divided into a training set (70% of the data) and a test set (30% of the data). Five machine learning models, namely logistic regression, random forest, support vector machine, light gradient boosting machine, and extreme gradient boosting, were compared to predict the risk of difficult weaning in ICU patients with MV. Significant influencing factors were identified based on the results of these models, and a risk prediction model for ICU patients with MV was established. When evaluating the models using AUC (Area under the Curve of ROC) and Accuracy as performance metrics, the Random Forest algorithm exhibited the best performance among the five machine learning algorithms. The area under the operating characteristic curve for the subjects was 0.805, with an accuracy of 0.748, recall (0.888), specificity (0.767) and F1 score (0.825). This study successfully developed a risk prediction model for ICU patients with MV using a machine learning algorithm. The Random Forest algorithm demonstrated the highest prediction performance. These findings can assist clinicians in accurately assessing the risk of difficult weaning in patients and formulating effective individualized treatment plans. Ultimately, this can help reduce the risk of difficult weaning and improve the quality of life for patients.

In recent years, lifelong learning has gained prominence, necessitating a continuous commitment from learners to enhance their skills and knowledge. During the lifelong learning process, it is essential to precisely assess the cognitive states of lifelong learners, as this will provide a learning report and further support subsequent learning activities. In the literature, researchers have proposed various cognitive diagnosis models (CDMs) to estimate the cognitive states based on learners' responses. However, learners' responses are noisy for different reasons, including guessing, slipping, accidentally clicking, and network issues. Rashly fitting the CDMs with noisy responses would yield imprecise cognitive state estimation. To tackle this problem, we first unify all types of noise underlying learners' responses. Then, we propose a novel diffusion-based cognitive diagnosis framework (DiffCog) to extend existing CDMs and enhance their effectiveness and robustness. DiffCog does so by addressing the following two technical challenges in denoising: 1) the hard-to-track problem and high computational cost in discrete and sparse responses and 2) the unknown extent of noise underlying responses. Specifically, DiffCog tackles these technical challenges by: 1) introducing responses encoders to project responses to continuous cognitive states for case of adding easy-to-track noise and reducing computation cost and 2) incorporating a time extractor and a denoise module to trace the noisy cognitive states back to the noise-free ones in a personalized way. We conduct extensive and sufficient experiments on three real-world datasets, and the results demonstrate that our proposed DiffCog not only elevates the performance ceiling of existing CDMs but also enhances their robustness to noise.

BackgroundMachine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate. We developed a fully automated pipeline based on the Key–bidirectional encoder representations from transformers (BERT) approach and large-scale medical records for continued pretraining, which effectively converts long free text into standard ICD codes. By adjusting parameter settings, such as mixed templates and soft verbalizers, the model can adapt flexibly to different requirements, enabling task-specific prompt learning. ObjectiveThis study aims to propose a prompt learning real-time framework based on pretrained language models that can automatically label long free-text data with ICD-10 codes for cardiovascular diseases without the need for semiautomatic preprocessing. MethodsWe integrated 4 components into our framework: a medical pretrained BERT, a keyword filtration BERT in a functional order, a fine-tuning phase, and task-specific prompt learning utilizing mixed templates and soft verbalizers. This framework was validated on a multicenter medical dataset for the automated ICD coding of 13 common cardiovascular diseases (584,969 records). Its performance was compared against robustly optimized BERT pretraining approach, extreme language network, and various BERT-based fine-tuning pipelines. Additionally, we evaluated the framework’s performance under different prompt learning and fine-tuning settings. Furthermore, few-shot learning experiments were conducted to assess the feasibility and efficacy of our framework in scenarios involving small- to mid-sized datasets. ResultsCompared with traditional pretraining and fine-tuning pipelines, our approach achieved a higher micro–F1-score of 0.838 and a macro–area under the receiver operating characteristic curve (macro-AUC) of 0.958, which is 10% higher than other methods. Among different prompt learning setups, the combination of mixed templates and soft verbalizers yielded the best performance. Few-shot experiments showed that performance stabilized and the AUC peaked at 500 shots. ConclusionsThese findings underscore the effectiveness and superior performance of prompt learning and fine-tuning for subtasks within pretrained language models in medical practice. Our real-time ICD coding pipeline efficiently converts detailed medical free text into standardized labels, offering promising applications in clinical decision-making. It can assist doctors unfamiliar with the ICD coding system in organizing medical record information, thereby accelerating the medical process and enhancing the efficiency of diagnosis and treatment.

Abstract In the past decades, substantial progress has been made in human action recognition. However, most existing studies and datasets for human action recognition utilise still images or videos as the primary modality. Image‐based approaches can be easily impacted by adverse environmental conditions. In this paper, the authors propose combining RGB images and point clouds from LiDAR sensors for human action recognition. A dynamic lateral convolutional network (DLCN) is proposed to fuse features from multi‐modalities. The RGB features and the geometric information from the point clouds closely interact with each other in the DLCN, which is complementary in action recognition. The experimental results on the JRDB‐Act dataset demonstrate that the proposed DLCN outperforms the state‐of‐the‐art approaches of human action recognition. The authors show the potential of the proposed DLCN in various complex scenarios, which is highly valuable in real‐world applications.

BackgroundMental health issues pose a significant challenge for medical providers and the general public. The World Health Organization predicts that by 2030, mental health problems will become the leading cause of global disease burden, highlighting the urgent need for effective mental health interventions. Virtual reality-cognitive behavioral therapy (VR-CBT) has emerged as a promising treatment for neuropsychiatric disorders, offering immersive and engaging therapeutic experiences.ObjectiveThis study aims to assess the advancements in applying virtual reality (VR) technology for diagnosing and treating mental illnesses such as anxiety, depression, and attention deficit hyperactivity disorder (ADHD). It also explores the integration of artificial intelligence (AI) with VR technology in mental health treatment and introduces the CBT-CHAT Treatment Framework to enhance cognitive behavioral therapy.MethodA systematic search was conducted across the databases of Google Scholar, PubMed, and Web of Science, followed by a scoping review. Duplicates were removed using the EndNote literature management software. Each article was then carefully read and evaluated for its research content, methodology, results, and contribution to the application of VR technology in mental health domains.ResultsThe search retrieved 686 articles, and after applying inclusion and exclusion criteria, 32 articles were ultimately selected. These articles covered the application of VR in anxiety, depression, and ADHD. The research results indicate that VR shows promising outcomes in the diagnosis, treatment, and rehabilitation of anxiety, depression, and ADHD, particularly in the diagnosis, treatment, and rehabilitation of ADHD.ConclusionThe previously published studies consistently demonstrate that VR is an effective tool for supporting the treatment of mental illnesses across various settings and recommend its incorporation into clinical practice.

BackgroundSurgery for tumors in the cerebellopontine angle is always a significant challenge due to the densely packed neurovascular structures, the narrow deep location, and the complex relationship between the lesions and surrounding neurovascular structures. Recently, great attention has been given to the neuroendoscope for its exclusive advantages, which have added a new dimension to many classical microscopic surgeries. However, the feasibility and advisability of fully endoscopic neurosurgery for cerebellopontine angle tumors remain to be further evaluated.MethodsWe retrospectively collected the clinical outcomes and endoscopic surgical experience of 12 patients with tumors in the cerebellopontine angle (CPA) from January 2022 to April 2024 in our department. We analyzed patients’ records, radiological neuroimaging, tumor-related variables, surgical procedures, and postoperative outcomes in detail. All patients were regularly followed up with neurological examinations and magnetic resonance imaging (MRI)/computed tomography (CT).ResultsThe pathology of the series included five cases of acoustic neuroma, six cases of meningioma, and one case of teratoma. The mean largest diameter of the lesion was 29.5 mm ±8.5 mm. Headache, hearing loss, and dizziness were the top three most common symptoms. All tumors were resected using the hand technique. No hemorrhage, cerebrospinal fluid leaks, or intracranial infections occurred. All patients with meningioma achieved Simpson grade II resection, and the remaining tumors underwent gross total resection, confirmed by both intraoperative and postoperative imaging. Overall, 91.7% of patients maintained normal facial nerve function postoperatively (HB1). One patient with acoustic neuroma experienced transient facial paralysis after surgery (HB2), which resolved during follow-up at 3 months postoperation. Clinical symptoms of all the other patients were resolved or ameliorated after surgery, with no new neurological deficits. The Karnofsky Performance Scale (KPS) scores remained unchanged or improved for all patients postoperatively.ConclusionsWith the accumulation of experience and technological progress, the fully endoscopic retrosigmoid approach could enable safe and effective resection of cerebellopontine angle tumors, providing a panoramic view and illumination of deep-seated structures.

BackgroundsMany pregnant women suffer from more than one pregnancy complication. However, whether those women experienced a higher risk of adverse birth outcomes is unclear. This study aims to assess the association between the comorbidity of gestational diabetes mellitus (GDM) and hypertension disorders of pregnancy (HDP) and adverse birth outcomes.MethodsThe data was from the Zhoushan Maternal and Child Health Hospital electronic medical recorder system (EMRS) between 2015 and 2022. Multivariate linear regression model was used to analyze the association of GDM, HDP, and comorbidity with birth weight and gestational age, respectively. Multiple logistic regression model was used to analyze the association of GDM, HDP, and comorbidity with adverse birth outcomes.Results13645 pregnant women were included. GDM+HDP was significantly associated with a higher risk of composite adverse neonatal outcomes (OR=1.82, 95%CI: 1.02-3.04), including preterm birth, placenta previa, and/or neonatal jaundice, a higher risk of small for gestational age (SGA) (OR=2.2, 95% CI: 1.24 3.92) and large for gestational age (LGA) (OR=2.33, 95% CI: 1.64 3.31) compared with the normal group. Further analysis showed that HDP diagnosed in the 21-27th week comorbid with GDM had the lowest gestational age at delivery (β= -1.57, P=0.0002) and birth weight (β= -189.57, P=0.0138). Moreover, combined hyperglycemia (CH) comorbid with HDP had the strongest association with reduced gestational age (β= -0.83, P=0.0021).ConclusionPregnant women suffering from both GDM and HDP had a higher risk of adverse neonatal outcomes; hence, the prevent and treatment of GDM and HDP, especially their comorbidity, are very important for pregnant women.

Objective To assess the fibrinogen function in patients with hepatitis B-related cirrhosis and explore the relationship between dysfibrinogenemia and bleeding and thrombotic events.Methods Medical records and laboratory data of the patients with hepatitis B-related cirrhosis were collected. Patients were categorized into three groups based on the Child-Pugh score. Fibrinogen activity and antigen, fibrinogen-bound sialic acid (FSA), fibrinogen polymerization and fibrinolysis kinetic analysis, thrombin-antithrombin complex (TAT) and plasmin-α2-antiplasmin complex (PAP) were detected.Results Eighty patients with seventeen, thirty-eight and twenty-five in Child-Pugh A, B and C, respectively, were included. Seventeen patients experienced bleeding events and eight patients had thrombotic events. Fibrinogen activity and antigen levels were reduced with the severity of cirrhosis. Twenty-two patients exhibited dysfibrinogenemia. The FSA levels in patients with non-dysfibrinogenemia and those with dysfibrinogenemia were increased to 1.25 and 1.37 times of healthy controls, negatively correlated with fibrinogen activity (ρ = −0.393, p = 0.006). Compared to healthy controls, the amount of clot formation was reduced (p

Abstract Background Although the relationship between oxidative stress and insulin resistance (IR) has been established, the associations of the composite dietary antioxidant index (CDAI) and its components with the surrogate index of insulin resistance (IR), triglyceride-glucose index (TyG), is still not clear. Methods This study analyzed the cross-sectional data of the National Health and Nutrition Examination Survey (NHANES) from 2003 to 2018. Multivariate linear regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were used to analyze the associations of the CDAI and its components with the TyG. In addition, subgroup analysis and several sensitivity analyses were conducted. Results A total of 14,673 participants with complete data were included, with a median age of 50 years and 7,257 women (49%). Multivariate linear regression showed that after full adjustment, the CDAI was significantly negatively associated with the TyG [β: -0.005, 95% CI: (-0.008, -0.002), p = 0.002]. The model in which six nutrients were mutually corrected showed that vitamin E (per-SD increase) was most strongly associated with the TyG [β: -0.062, 95% CI: (-0.074, -0.050), p

Abstract Dietary antioxidants may have beneficial effects on bone health, but it remains uncertain in children and adolescents. This study investigates the association of composite dietary antioxidant index (CDAI) with bone mineral density (BMD) in children and adolescents aged 8–19 years from the National Health and Nutrition Examination Survey (NHANES) 2007–2010. The study assessed the relationship between CDAI and BMD in 2994 individuals aged 8–19 years (average age 13.48 ± 3.32 years) from the NHANES 2007–2010. Multivariate linear regression analyses were utilized to detect the association between CDAI and total spine, femur neck, and total femur BMD, adjusting for confounders including age, race/ethnicity, sex, poverty income ratio (PIR), body mass index (BMI), serum phosphorus and calcium. Stratified analyses and interaction tests were performed to examine the stability of the results. The weighted characteristics showed that subjects in the fourth CDAI quartile were more likely to be older, men, and Non-Hispanic White. They have higher values of serum total calcium and phosphorus. After adjusting all confounders, CDAI was positively associated with the total spine (β = 0.0031 95% CI 0.0021–0.0040), total femur (β = 0.0039 95% CI 0.0028–0.0049), and femur neck BMD (β = 0.0031 95% CI 0.0021–0.0040) in children and adolescents. Furthermore, we found no interaction effects between different race/ethnicity, age, and sex groups. Our findings suggest that dietary intake of multiple antioxidants was positively associated with BMD in children and adolescents. These findings provide valuable evidence for improving bone health in the early stages of life. However, more prospective studies are required to validate our findings and their causal relationship.

Abstract Objective To evaluate the safety and efficacy of the granisetron transdermal delivery system (GTDS) combined with Dexamethasone for preventing chemotherapy-induced nausea and vomiting (CINV) in patients receiving Capecitabine plus Oxaliplatin (CapeOX) therapy. Design Open-label, prospective, multi-center phase II trial. Setting Three institutions. Participants Fifty-four patients scheduled to receive CapeOX chemotherapy. Interventions Participants received GTDS (3.1 mg applied to the upper arm 48 h before chemotherapy, replaced on day 5, and discarded on day 12) and Dexamethasone. Main outcome measures The primary endpoint was the complete control rate of CINV. Secondary endpoints included the duration of delayed complete control, complete control rate in the acute phase, safety, and quality of life. Results The complete control rate for delayed CINV over the entire period (25–480 h) was 72.7% (95% CI 0.57–0.88). The duration of delayed complete control was 17.2 ± 4.5 days, with 51.5% of patients experiencing no nausea during the delayed phase. The complete control rate in the acute phase was 81.8% (95% CI 0.69–0.95). No serious adverse events related to the antiemetic regimen were reported. Conclusion Prolonged administration of GTDS is safe and effective for preventing CINV in patients with gastrointestinal malignancies treated with CapeOX. Trial Registration ClinicalTrials.gov registry (NCT05325190); registered on October 10, 2021.

Abstract The water-shale interaction affect the shale structure, leading to wellbore instability and increasing drilling costs. The extent of structural changes within the shale can be determined non-destructively by analyzing its acoustic characteristics. Experiments were conducted to investigate the acoustic properties of shale from the Yanchang Formation in the Ordos Basin before and after exposure to brines of varying types, soaking times, and salinities. The study investigated the effects of brine type, soaking time, and salinity on shale’s acoustic properties, including changes in acoustic wave propagation speed, P/S wave velocity ratio, and both time-domain and frequency-domain amplitudes. The results indicate that although the type of brine has a limited impact on the water-shale interaction, KCl exhibits a significant inhibitory effect. However, the soaking time and the brine salinity have a significant impact on the acoustic properties of shale. As the soaking time increases, the decrease in wave velocity increases, the P/S wave velocity ratio increases, and the decrease in time-domain amplitude increases. The amplitude of the main frequency in the frequency domain signal also decreases with the increase of reaction time, which is consistent with the analysis results of the time domain signal. As the salinity of brine increases, the decrease in wave velocity decreases, the P/S wave velocity ratio decreases, and the decrease in time-domain amplitude decreases. The amplitude of the main frequency in the frequency domain signal also decreases with the increase of brine salinity, which is consistent with the analysis results of the time domain signal. This work establishes the relationship between water-shale interaction and acoustic characteristics, which can quantitatively evaluate the degree of interaction between water and shale without damaging shale. Furthermore, this research provides new insights and guidance for predicting drilling collapse cycles and optimizing drilling fluid compositions.

Fluorescent materials have garnered significant interest in optical display and information storage fields for their vibrant and distinct color presentation. Extensive research has been conducted on carbon-based fluorescent materials owing to their environmental sustainability, versatility, and adjustable optical properties. However, it is difficult to integrate high-resolution fluorescent structure on various wafer-level substrate for abundant optical display and information storage. This study successfully fabricated wafer-level carbon-based fluorescent flexible film with ∼100 μm thick using a standardized “lithography + annealing” process. The sample demonstrates high fluorescence intensity with a peak at 603 nm and fluorescence lifetime of 0.3517 ns. When excited by light of 365 nm, 485 nm, and 525 nm wavelengths, the film displays yellow, green, and red, showcasing exceptional color tuning capability. This flexible film is portable and can be readily integrated with various substrates, offering significant technical and theoretical support for advancing carbon-based optical display and information storage technologies.

ObjectiveConflicting associations have been noted between thyroid function and gestational diabetes mellitus (GDM), with indications that pre-pregnancy BMI might influence these relationships. This study aims to examine the effect of thyroid hormone indices and their mediation role on the risk of GDM.MethodsPregnant women in our study were recruited from the Zhoushan Pregnant Women Cohort, Zhejiang Province, China. Participants who had their first prenatal follow-up and measured thyroid biomarkers in the first trimester, and oral glucose tolerance test (OGTT) records in the second trimester were eligible for inclusion in this study. The data were extracted from the Electronic Medical Record System database, at Zhoushan Maternal and Child Care Hospital. Maternal information about sociodemographic and health-related characteristics was extracted from the dataset. A unique personal identification number was provided to link both datasets. Multivariate logistic regression models were applied to investigate the correlations between thyroid hormone indices with GDM. The interaction effects of first-trimester thyroid hormone indices with pre-pregnancy BMI on GDM risk using a generalized linear regression model. Furthermore, the mediation analysis was used to explore the potential mediating effects of thyroid hormone indices on the relationship between pre-pregnancy BMI and GDM.ResultsOverall, 5895 pregnant women were included in this study. The first-trimester FT4, thyroid feedback quantile-based index (TFQI), thyrotropin index (TSHI) and thyrotroph thyroxine resistance index (TT4RI) levels were negatively associated with fasting blood glucose (FBG) and postprandial blood glucose (PBG2H) in the second trimester (all P

Abstract The causal association between vitamin E status and osteoarthritis (OA) remains controversial in previous epidemiological studies. We employed a Mendelian randomization (MR) analysis to explore the causal relationship between circulating alpha-tocopherol levels (main forms of vitamin E in our body) and OA. The instrumental variables (IVs) of circulating alpha-tocopherol levels were obtained from a Genome-wide association study (GWAS) dataset of 7781 individuals of European descent. The outcome of OA was derived from the UK biobank. Two-sample MR analysis was used to estimate the causal relationship between circulating alpha-tocopherol levels and OA. The inverse-variance weighted (IVW) method was the primary analysis in this analysis. We used the MR-Egger method to determine horizontal pleiotropic in this work. The heterogeneity effect of instrumental IVs was detected by MR-Egger and IVW analyses. Sensitivity analysis was performed by removing single nucleotide polymorphism (SNP) one by one. Three SNPs (rs964184, rs2108622, and rs11057830) (P

Abstract Background Rectal temperature (RT) is an important index of core temperature, which has guiding significance for the diagnosis and treatment of pet diseases. Objectives Development and evaluation of an alternative method based on machine learning to determine the core temperatures of cats and dogs using surface temperatures. Animals 200 cats and 200 dogs treated between March 2022 and May 2022. Methods A group of cats and dogs were included in this study. The core temperatures and surface body temperatures were measured. Multiple machine learning methods were trained using a cross-validation approach and evaluated in one retrospective testing set and one prospective testing set. Results The machine learning models could achieve promising performance in predicting the core temperatures of cats and dogs using surface temperatures. The root mean square errors (RMSE) were 0.25 and 0.15 for cats and dogs in the retrospective testing set, and 0.15 and 0.14 in the prospective testing set. Conclusion The machine learning model could accurately predict core temperatures for companion animals of cats and dogs using easily obtained body surface temperatures.

Abstract Online rumors are widespread and difficult to identify, which bring serious harm to society and individuals. To effectively detect and govern online rumors, it is necessary to conduct in-depth semantic analysis and understand the content features of rumors. This paper proposes a TFI domain ontology construction method, which aims to achieve semantic parsing and reasoning of the rumor text content. This paper starts from the term layer, the frame layer, and the instance layer, and based on the reuse of the top-level ontology, the extraction of core literature content features, and the discovery of new concepts in the real corpus, obtains the core classes (five parent classes and 88 subclasses) of the rumor domain ontology and defines their concept hierarchy. Object properties and data properties are designed to describe relationships between entities or their features, and the instance layer is created according to the real rumor datasets. OWL language is used to encode the ontology, Protégé is used to visualize it, and SWRL rules and pellet reasoner are used to mine and verify implicit knowledge of the ontology, and judge the category of rumor text. This paper constructs a rumor domain ontology with high consistency and reliability.

Abstract Background Ultrasound three-dimensional visualization, a cutting-edge technology in medical imaging, enhances diagnostic accuracy by providing a more comprehensive and readable portrayal of anatomical structures compared to traditional two-dimensional ultrasound. Crucial to this visualization is the segmentation of multiple targets. However, challenges like noise interference, inaccurate boundaries, and difficulties in segmenting small structures exist in the multi-target segmentation of ultrasound images. This study, using neck ultrasound images, concentrates on researching multi-target segmentation methods for the thyroid and surrounding tissues. Method We improved the Unet++ to propose PA-Unet++ to enhance the multi-target segmentation accuracy of the thyroid and its surrounding tissues by addressing ultrasound noise interference. This involves integrating multi-scale feature information using a pyramid pooling module to facilitate segmentation of structures of various sizes. Additionally, an attention gate mechanism is applied to each decoding layer to progressively highlight target tissues and suppress the impact of background pixels. Results Video data obtained from 2D ultrasound thyroid serial scans served as the dataset for this paper.4600 images containing 23,000 annotated regions were divided into training and test sets at a ratio of 9:1, the results showed that: compared with the results of U-net++, the Dice of our model increased from 78.78% to 81.88% (+ 3.10%), the mIOU increased from 73.44% to 80.35% (+ 6.91%), and the PA index increased from 92.95% to 94.79% (+ 1.84%). Conclusions Accurate segmentation is fundamental for various clinical applications, including disease diagnosis, treatment planning, and monitoring. This study will have a positive impact on the improvement of 3D visualization capabilities and clinical decision-making and research in the context of ultrasound image.

ABSTRACTMegaregion has emerged as a global urban form, typically based on the polycentric strategy to enhance regional development. How to measure megaregional spatial structure and discriminate different roles of cities has become increasingly important to enrich the knowledge of the formation of a megaregion. Meanwhile, various indices have been used to identify vital nodes in the field of complex network. Which indices, however, are suitable for megaregion analysis remain unsolved. To address this requirement, this study first reviewed the typical indices for identifying vital nodes in the complex network theory, and pointed out that in a weighted city network scenario, weighted degree centrality, hub & authority score, and S-core decomposition (which represent network centrality, connectivity, and structures, respectively) are suitable for analyzing megaregional spatial structures. Then, we explored the city hierarchies and spatial structure in Guangdong Province, China, using the three indices. The hierarchical structure of the weighted city network in Guangdong Province had been identified using S-core decomposition. From the perspective of polycentric structure, Guangzhou and Shenzhen have the strongest node degrees and strength of mobility flows, while the Guangzhou-Dongguan-Shenzhen corridor has been identified via the hub & authority score which is designed to evaluate the connectivity in a weighted network. Moreover, we conducted a comparison analysis of three indices. The findings of this study not only enrich the understanding of city hierarchies and the structure of a megaregion, but also highlight that although various indices are available, they should be applied selectively in accordance with the study context.

In this paper, we propose statistical inference methodologies for estimating the shape parameter α of inverse generalized Weibull (IGW) distribution. Specifically, we develop two approaches: (1) a bounded-risk point estimation strategy for α and (2) a fixed-accuracy confidence interval estimation method for α. For (1), we introduce a purely sequential estimation strategy, which is theoretically shown to possess desirable first-order efficiency properties. For (2), we present a method that allows for the precise determination of sample size without requiring prior knowledge of the other two parameters of the IGW distribution. To validate the proposed methods, we conduct extensive simulation studies that demonstrate their effectiveness and consistency with the theoretical results. Additionally, real-world data applications are provided to further illustrate the practical applicability of the proposed procedures.