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Abstract Background Terpenes have been shown to have an effect on metabolic status. However, it is unclear how they are associated with the metabolic syndrome (MetS) and whether insulin resistance (IR) mechanisms are involved. Methods This cross-sectional study included data from the National Health and Nutrition Examination Survey (2013–2014). Serum terpenes (α- pinene, β- pinene, and limonene) were quantified by gas chromatography-tandem mass spectrometry. MetS was diagnosed by lipid, glucose, blood pressure, and waist circumference criteria. IR status was assessed using the homeostasis model assessment of insulin resistance (HOMA-IR) model and triglyceride-glucose index. Regression analysis models, mixed-effects models (weighted quantile sum regression models, quantile g-computation and Bayesian Kernel machine regression models), and mediation analysis were used for analysis. Results Among 1563 U.S. general population (mean age 39.0 years), 447 (28.6%) had a diagnosis of MetS, and the median concentration was 0.08 ng/mL for α- pinene, 0.08 ng/mL for β- pinene, and 1.18 ng/mL for limonene. The terpene mixture showed positive joint association with MetS (OR = 1.454, 95%CI = 1.153, 1.833) and HOMA-IR (β = 0.569, 95%CI = 0.196, 0.942), with limonene contributing relatively large weights in both MetS (41.1%) and HOMA-IR (68.9%). Furthermore, HOMA-IR was positively associated with MetS (OR = 2.299, 95%CI = 1.888, 2.817) and its diagnostic components. When identified as a mediator, HOMA-IR accounted for approximately 15.8%, 20.0%, and 14.3% of the indirect effects between terpenes (α- pinene, β- pinene, and limonene) and MetS, respectively. The results were consistent when the IR biomarker was replaced by triglyceride-glucose index. Conclusion Serum terpenes were positively associated with MetS, which might be partially mediated by the mechanism of IR.

Abstract Background Efficient triage in emergency departments (EDs) is critical for timely and appropriate care. Traditional triage systems primarily rely on structured data, but the increasing availability of unstructured data, such as clinical notes, presents an opportunity to enhance predictive models for assessing emergency severity and to explore associations between patient characteristics and severity outcomes. This study aimed to evaluate the effectiveness of combining structured and unstructured data to predict emergency severity more accurately. Methods Data from the 2021 National Hospital Ambulatory Medical Care Survey (NHAMCS) for adult ED patients were used. Emergency severity was categorized into urgent (scores 1–3) and non-urgent (scores 4–5) based on the Emergency Severity Index. Unstructured data, including chief complaints and reasons for visit, were processed using a Bidirectional Encoder Representations from Transformers (BERT) model. Structured data included patient demographics and clinical information. Four machine learning models—Logistic Regression, Random Forest, Gradient Boosting, and Extreme Gradient Boosting—were applied to three data configurations: structured data only, unstructured data only, and combined data. A mean probability model was also created by averaging the predicted probabilities from the structured and unstructured models. Results The study included 8,716 adult patients, of whom 74.6% were classified as urgent. Association analysis revealed significant predictors of emergency severity, including older age (OR = 2.13 for patients 65 +), higher heart rate (OR = 1.56 for heart rates > 90 bpm), and specific chronic conditions such as chronic kidney disease (OR = 2.28) and coronary artery disease (OR = 2.55). Gradient Boosting with combined data demonstrated the highest performance, achieving an area under the curve (AUC) of 0.789, an accuracy of 0.726, and a precision of 0.892. The mean probability model also showed improvements over structured-only models. Conclusions Combining structured and unstructured data improved the prediction of emergency severity in ED patients, highlighting the potential for enhanced triage systems. Integrating text data into predictive models can provide more accurate and nuanced severity assessments, improving resource allocation and patient outcomes. Further research should focus on real-time application and validation in diverse clinical settings.

Abstract This article investigates the axial compression performance of glass fiber reinforced plastic (GFRP) tube restrained transverse ring reinforced concrete composite columns. Six composite columns were designed for axial compression experiments and subjected to multi condition ABAQUS finite element simulations. Analyzed the effects of constraint width, circumferential spacing, GFRP tube thickness, and concrete strength on the bearing capacity of composite materials, mainly studying the effect of hoop section form on the mechanical properties of composite columns. The results show that the spacing between the stirrups and the width of the constraint on the bearing capacity show a trend of increasing first and then decreasing, with the optimal constraint width being 40–60 mm; Increasing the thickness of GFRP tubes can effectively improve the bearing capacity, but the gain effect weakens with the increase of concrete strength.

Abstract Objective To systematically review the clinical efficacy and safety of high tibial osteotomy (HTO) combined with platelet-rich plasma (PRP) for treating knee osteoarthritis (KOA) and to provide evidence of the effectiveness of evidence-based medicine for treating this disease. Methods Clinical studies on the use of HTO combined with PRP for the treatment of KOA before September 2024 were identified. The literature that met the inclusion criteria was strictly screened out, the literature information was extracted, and the data were input into RevMan5.4 for analysis. Results Ten studies (12 controls) with 625 patients were included. Overall, the visual analog scale (VAS) score (mean difference (MD) = -0.53, 95% confidence interval (CI) (-0.76, -0.31), P

Abstract Background Gliomas are the highly aggressive brain tumor and also the most devastating human tumors. The latent TGF binding proteins (LTBP) had been found to be involved in malignant biological process and could be used as potent biomarkers in several solid tumors. While the role of LTBP family in human glioma remain to be elucidated. Methods Normalized gene expression and corresponding clinical data of 2407 gliomas samples in public datasets were downloaded from Gliovis. Kaplan–Meier methods and Cox regression analysis was used for survival analyses.Western blot (WB) and Immunohistochemical (IHC) testing were employed to test LTBPs protein level in 154 gliomas samples. Correlation between LTBP2 expression and immune infiltration was evaluated by immunofluorescence (IF) and IHC in glioma tissues. CCK8 and flow cytometric analysis were used to detect the effect of LTBP2 on glioma cells. Orthotopic glioma- mouse models were utilized to evaluate effects in vivo. Results LTBP2 mRNA level was dramatically higher in glioma samples compared with non-tumor brain tissues in XENA-TCGA_GTEx, Gill and Gravendeel datasets (all P

Abstract Bilirubin is widely recognized to possess antioxidant and anti-inflammatory characteristics. However, the relationship between bilirubin and coronary artery disease (CAD) remains controversial, particularly in individuals receiving Percutaneous Coronary Intervention (PCI). Given that statins may enhance the production of heme oxygenase-1 (HO-1) and bilirubin, we investigated the long-term cardiovascular prognostic role of bilirubin levels elevated by statin use in patients undergoing PCI. Data of 6945 subjects undergoing PCI were enrolled in this study. We divided the patients into two groups based on serum total bilirubin (TB) levels detected prior to PCI. The high TB group consisted of patients with serum TB values > 8.4 μmmol/L, while the low TB group consisted of patients with serum TB values ≤ 8.4 μmmol/L. The median follow-up time was 836 days. Cox proportional hazards models were performed to evaluate the hazard ratios (HRs) and 95% confidence interval (CI) for the incidence of major adverse cardiovascular event (MACE) associated with bilirubin levels. The association between TB levels and risk of MACE was significant [adjusted HR = 0.557, 95% CI (0.59–0.96), p = 0.020). Linear analysis was performed to determine the association between preadmission usage of statin and bilirubin level. The preadmission usage of statin independently linearly increases TB [adjusted-β = 0.371, 95% CI (0.134–0.608), p = 0.002] and direct bilirubin (DB) [adjusted-β = 0.411, 95% CI (0.300–0.522), p

Abstract This paper proposes a new impulse excitation technique using a square plate. First, the functional relationship between the modal frequency of the specimen and the geometrical dimensions and mechanical parameters was established by using the finite element method. Then, the continuous functional relationship derived by a homotopy method allowed the frequency ratios to be related to the thickness-to-length ratio and Poisson’s ratio. By measuring the frequency ratios and thickness-to-length ratio, Poisson’s ratio could be calculated using this functional relationship. When the density and Poisson’s ratio were known, Young’s modulus could be identified inversely in conjunction with the finite element analysis. Finally, a comparison test between this method and the traditional impulse excitation technique was designed and implemented, and the results showed that this method has advantages in both testing efficiency and accuracy. The study provides a new idea for system identification, which has important application value and promotion significance.

Abstract Background Lung cancer is a leading public health concern worldwide. Previous evidence suggests that chronic obstructive pulmonary disease (COPD) and asthma may contribute to its development. However, whether these common chronic pulmonary diseases are causal factors of lung cancer remained unclear. Methods Summary statistics from genome-wide association studies (GWAS) were used for Mendelian randomization (MR) analysis. Genetic data for COPD were obtained from the Global Biobank Meta-Analysis Initiative, and asthma data were retrieved from the UK Biobank cohort. Suitable instrumental variables were selected based on quality control measures. GWAS summary data for lung cancer were obtained from a large study involved 85,716 participants. MR analysis was performed using various methods, and sensitivity analyses were conducted. Multivariable MR (MVMR) analysis was employed to account for potential confounding factors. Results Our MR analysis revealed a significant causal association between COPD and lung cancer, including its subtypes such as lung squamous cell carcinoma, lung adenocarcinoma, and small cell lung carcinoma. Genetically predicted COPD was associated with a 64% increased risk of lung cancer and a 2.3 to 2.8-fold increased risk of the different subtypes. However, in the MVMR analysis adjusting for smoking, alcohol drinking, and body mass index, the association between COPD and lung cancer became non-significant. No significant association was observed between asthma (childhood-onset and adult-onset) and lung cancer and its histological subtypes. Conclusions Our study suggests a potential causal association between COPD and lung cancer. However, this association became non-significant after adjusting for smoking in the multivariable analysis.

AimsResearch on the triglyceride-glucose (TyG) index in patients with stable coronary artery disease (SCAD) remains relatively limited. Therefore, this study aims to investigate the association of the TyG index and its derived indicators, including the baseline TyG index, the baseline triglyceride glucose-body mass index (TyG-BMI), the mean TyG index, and the triglyceride glucose index-standard deviation (TyG-SD), with the prognosis of SCAD patients undergoing percutaneous coronary intervention (PCI).MethodsThis retrospective study enrolled 2,306 patients. The Cox proportional hazards model was utilized to evaluate the association between the four TyG indicators and major adverse cardiovascular and cerebrovascular events (MACCE). The predictive ability of the four TyG indicators for MACCE was assessed using the time-dependent receiver operating characteristic (ROC) curve. Kaplan-Meier survival analysis was employed to assess the prognostic differences among groups.ResultsAfter a median follow-up of 26.1 months, a total of 352 patients (15.3%) experienced MACCE. The Cox regression analysis revealed that under a fully adjusted model, when the four TyG indicators were stratified by tertiles, patients in the highest tertile of each TyG indicator had a significantly increased risk of MACCE compared to those in the lowest tertile. Specifically, the hazard ratio for baseline TyG index was 1.653 (95% confidence intervals (CI): 1.234-2.214), for baseline TyG-BMI was 2.467 (95%CI: 1.849-3.293), for mean TyG index was 2.451 (95%CI: 1.794-3.349), and for TyG-SD was 1.896 (95%CI: 1.430-2.513). Time-dependent ROC curve demonstrated that the mean TyG index had the strongest predictive ability for MACCE at each follow-up time point. The Kaplan-Meier analysis results showed that when the four TyG indicators were grouped by tertiles, there were significant differences in the cumulative incidence of MACCE among the three groups for each indicator (P < 0.05).ConclusionHigher levels of the TyG index and its derived indicators were each independently and positively associated with the risk of MACCE in SCAD patients undergoing PCI. Among these indicators, the mean TyG index demonstrated the greatest predictive value for the risk of MACCE at each follow-up time point. Consequently, tracking the long-term trends of the TyG index may be prioritized in clinical practice.

BackgroundMultifaceted factors play a crucial role in the prevention and treatment of metabolic dysfunction-associated steatotic liver disease (MASLD). This study aimed to utilize multifaceted indicators to construct MASLD risk prediction machine learning models and explore the core factors within these models.MethodsMASLD risk prediction models were constructed based on seven machine learning algorithms using all variables, insulin-related variables, demographic characteristics variables, and other indicators, respectively. Subsequently, the partial dependence plot(PDP) method and SHapley Additive exPlanations (SHAP) were utilized to explain the roles of important variables in the model to filter out the optimal indicators for constructing the MASLD risk model.ResultsRanking the feature importance of the Random Forest (RF) model and eXtreme Gradient Boosting (XGBoost) model constructed using all variables found that both homeostasis model assessment of insulin resistance (HOMA-IR) and triglyceride glucose-waist circumference (TyG-WC) were the first and second most important variables. The MASLD risk prediction model constructed using the variables with top 10 importance was superior to the previous model. The PDP and SHAP methods were further utilized to screen the best indicators (including HOMA-IR, TyG-WC, age, aspartate aminotransferase (AST), and ethnicity) for constructing the model, and the mean area under the curve value of the models was 0.960.ConclusionsHOMA-IR and TyG-WC are core factors in predicting MASLD risk. Ultimately, our study constructed the optimal MASLD risk prediction model using HOMA-IR, TyG-WC, age, AST, and ethnicity.

AimsIn light of the escalating global incidence of Parkinson’s disease and the dearth of therapeutic interventions that can alter the disease’s course, there exists an urgent necessity to comprehensively elucidate and quantify the disease’s global burden.MethodsThis study analyzed the incidence, prevalence, and disability-adjusted life years (DALYs) of Parkinson’s disease at global, regional, and national levels based on the Global Burden of Disease Study 2021. Bayesian age-period cohort (BAPC) analysis was used to predict the burden in Parkinson’s disease from 2022 to 2035.ResultsIn 2021, 11.77 million people worldwide had Parkinson’s disease. Age-standardized rates of incidence, prevalence, and DALYs increased to 15.63/100,000, 138.63/100,000, and 89.59/100,000. The burden of Parkinson’s disease were higher in males than in females, and showed an increase and then a slight decrease with age. The disease burden was highest in East Asia. BAPC projection showed an increase in all metrics by 2035 except for a slight decrease in the age-standardized DALYs rates.ConclusionThe global burden of Parkinson’s disease has risen over the past 32 years, and there is a need to focus on key populations, as well as to improve health policies to prevent and treat Parkinson’s disease.

Background The motor imagery (MI) paradigm is widely used in active brain-computer interfaces (BCIs), but its effectiveness is hindered by accuracy limitations and individual variability. Tactile stimulation has been proposed as a potential method to enhance MI performance; however, integrating such stimulation in real-time MI tasks remains challenging due to the spontaneous nature of MI.Objective This study aimed to investigate the real-time effects of bilateral vibration tactile stimulation on the performance of left and right hand MI paradigms using a controlled trial with healthy subjects.Methods Fourteen healthy subjects participated in MI tasks under two conditions: without vibration stimulation and with bilateral vibration stimulation. Brain activation patterns and decoding accuracy of MI-BCI were compared and analyzed between these two conditions.Results The experimental findings demonstrated that vibration stimulation effectively activated the sensorimotor cortex and enhanced the event-related desynchronization (ERD) phenomenon induced by MI. Although bilateral vibration stimulation did not directly improve decoding accuracy of left and right hand MI, it notably peaked approximately 2 seconds post-stimulation, resulting in an approximate 7% increase in accuracy compared to baseline MI.Conclusion This study systematically explored the phased impact of continuous vibration stimulation on MI performance. The findings discuss potential optimization strategies for integrating tactile feedback into MI-based BCIs, thus laying groundwork for future research in tactile-enhanced MI brain-controlled systems.

Introduction Chronic kidney disease is a growing health issue, and the options of prevention and therapy remain limited. Although a number of observational studies have linked higher Lp(a) [lipoprotein(a)] levels to the kidney impairment, the causal relationship remains to be determined. The purpose of this study was to assess the causal association between Lp(a) levels and CKD.Methods We selected eight single-nucleotide polymorphisms (SNPs) significantly associated with Lp(a) levels as instrumental variables. Genome-wide association study (GWAS) from CKDGen consortium yielded the summary data information for CKD. We designed the bidirectional two-sample Mendelian randomization (MR) analyses. The estimates were computed using inverse-variance weighted (IVW), simple median, weighted median, and maximum likelihood. MR-Egger regression was used to detect pleiotropy.Results Fixed-effect IVW analysis indicated that genetically predicted Lp(a) levels were associated with CKD significantly (odds ratio, 1.039; 95% CI, 1.009–1.069; p = 0.010). The SNPs showed no pleiotropy according to result of MR-Egger test. Results from sensitivity analyses were consistent. In the inverse MR analysis, random-effect IVW method showed CKD had no causal effect on the elevated Lp(a) (odds ratio, 1.154; 95% CI, 0.845–1.576; p = 0.367).Conclusion In this bidirectional two-sample MR analysis, the causal deteriorating effects of genetically predicted plasma Lp(a) levels on the risk of CKD were identified. On the contrary, there is no evidence to support a causal effect of CKD on Lp(a) levels.

The vulnerable plaques in atherosclerosis can cause severe outcome with great danger of acute cardiovascular events. Thus, timely diagnosis and treatment of vulnerable plaques in early stage can effectively benefit the clinical management of atherosclerosis. In this work, a targeting theranostic strategy on early-stage vulnerable plaques in atherosclerosis is realized by a LAID nanoplatform with X-CT and fluorescent dual-mode imaging and lipid-inflammation integrated regulation abilities. The iodinated contrast agents (ICA), phenylboronic acid modified astaxanthin and oxidized-dextran (oxDEX) jointly construct the nanoparticles loaded with the lipid-specific probe LFP. LAID indicates an active targeting to plaques along with the dual-responsive disassembly in oxidative stress and acidic microenvironment of atherosclerosis. The X-CT signals of ICA execute the location of early-stage plaques, while the LFP combines with lipid cores and realizes the recognition of vulnerable plaques. Meanwhile, the treatment based on astaxanthin is performed for restraining the progression of plaques. Transcriptome sequencing suggests that LAID can inhibit the lipid uptake and block NF-κB pathway, which synergistically demonstrates a lipid-inflammation integrated regulation to suppression the plaques growing. The in vivo investigations suggest that LAID delivers a favorable theranostics to the early-stage vulnerable plaques, which provides an impressive prospect for reducing the adverse prognosis of atherosclerosis.

Abstract Background The calmodulin (CaM) and calmodulin-like (CML) proteins play regulatory roles in plant growth and development, responses to biotic and abiotic stresses, and other biological processes. As a popular fruit and ornamental crop, it is important to explore the regulatory mechanism of flower and fruit development of passion fruit. Results In this study, 32 PeCaM/PeCML genes were identified from passion fruit genome and were divided into 9 groups based on phylogenetic analysis. The structural analysis, including conserved motifs, gene structure and homologous modeling, illustrates that the PeCaM/PeCML in the same subgroup have relative conserved structural features. Collinearity analysis suggested that the expansion of the CaM/CML gene family likely took place mainly by segmental duplication, and the whole genome replication events were closely related with the rapid expansion of the gene group. PeCaM/PeCMLs were potentially required for different floral tissues development. Significantly, PeCML26 had extremely high expression levels during ovule and fruit development compared with other PeCML genes, suggesting that PeCML26 had potential functions involved in the development of passion fruit flowers and fruits. The co-presence of various cis-elements associated with growth and development, hormone responsiveness, and stress responsiveness in the promoter regions of these PeCaM/PeCMLs might contribute to their diverse regulatory roles. Furthermore, PeCaM/PeCMLs were also induced by various abiotic stresses. This work provides a comprehensive understanding of the CaM/CML gene family and valuable clues for future studies on the function and evolution of CaM/CML genes in passion fruit. Conclusion A total of 32 PeCaM/PeCML genes were divided into 9 groups. The PeCaM/PeCML genes showed differential expression patterns in floral tissues at different development stages. It is worth noting that PeCML26, which is highly homologous to AtCaM2, not only interacts with multiple BBR-BPC TFs, but also has high expression levels during ovule and fruit development, suggesting that PeCML26 had potential functions involved in the development of passion fruit flowers and fruits. This research lays the foundation for future investigations and validation of the potential function of PeCaM/PeCML genes in the growth and development of passion fruit.

Long noncoding RNAs (lncRNAs) are RNA transcripts longer than 200 nucleotides that do not code for proteins but have been linked to cancer development and metastasis. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT-1) influences crucial cancer hallmarks through intricate molecular mechanisms, including proliferation, invasion, angiogenesis, apoptosis, and the epithelial-mesenchymal transition (EMT). The current article highlights the involvement of MALAT-1 in drug resistance, making it a potential target to overcome chemotherapy refractoriness. It discusses the impact of MALAT-1 on immunomodulatory molecules, such as major histocompatibility complex (MHC) proteins and PD-L1, leading to immune evasion and hindering anti-tumor immune responses. MALAT-1 also plays a significant role in cancer immunology by regulating diverse immune cell populations. In summary, MALAT-1 is a versatile cancer regulator, influencing tumorigenesis, chemoresistance, and immunotherapy responses. Understanding its precise molecular mechanisms is crucial for developing targeted therapies, and therapeutic strategies targeting MALAT-1 show promise for improving cancer treatment outcomes. However, further research is needed to fully uncover the role of MALAT-1 in cancer biology and translate these findings into clinical applications.

Abstract Cardiovascular disease (CVD) and depression are common diseases that lead to adverse health outcomes. Depressive Symptoms may be a risk factor for CVD. But few studies focused on the impact of socioeconomic factors, common medical history and dietary intake about this association. This study analyzed National Health and Nutrition Examination Survey (NHANES) 2007–2016. Complex sampling-weighted logistic regression models were used to compare the odds ratios (ORs) of CVD in participants with different depressive symptoms. 11,516 NHANES participants aged ≥ 40 years were included in the final analysis, of whom 1842 had CVD. Compared with participants with no/minimal depression, participants with mild, moderate, and moderately severe/severe depression had OR values of 1.25 (95% CI 1.01–1.54), 1.98 (95% CI 1.32–2.96), and 2.41 (95% CI 1.63–3.57). The association of depressive symptoms with CVD follow a dose-dependent pattern. The interactions of depressive symptoms with gender (Interaction P = 0.009), diabetes (Interaction P = 0.010), household income level (Interaction P = 0.002), dietary cholesterol intake (Interaction P = 0.017) on CVD were observed. More severe depressive symptoms are associated with increased risk of CVD in US population. The association may be more pronounced in the female population, population with diabetes, low family income level, or high dietary cholesterol intake.

Aim: Parkinson's disease (PD) tremor is associated with dysfunction in the basal ganglia (BG), cerebellum (CB), and sensorimotor networks (SMN). We investigated tremor-related static functional network connectivity (SFNC) and dynamic functional network connectivity (DFNC) in PD patients. Methods: We analyzed the resting-state functional MRI data of 21 tremor-dominant Parkinson's disease (TDPD) patients and 29 healthy controls. We compared DFNC and SFNC between the three networks and assessed their associations with tremor severity. Results: TDPD patients exhibited increased SFNC between the SMN and BG networks. In addition, they spent more mean dwell time (MDT) in state 2, characterized by sparse connections, and less MDT in state 4, indicating stronger connections. Furthermore, enhanced DFNC between the CB and SMN was observed in state 2. Notably, the MDT of state 2 was positively associated with tremor scores. Conclusion: The enhanced dynamic connectivity between the CB and SMN in TDPD patients suggests a potential compensatory mechanism. However, the tendency to remain in a state of sparse connectivity may contribute to the severity of tremor symptoms.

Background: Cold environments pose serious threats on human health, with increased risk for myocardial infarction, stroke, frostbite, and hypothermia. Acquired cold acclimation is required to minimize cold-induced injures and to improve metabolic health. However, the underlying mechanisms remain to be fully elucidated. Objective: We aimed to identify critical amino acids involved in cold acclimation and unmask the regulatory mechanisms. Methods: A total of twenty male participants were recruited and followed up after 3 months’ natural cold exposure. Cold-induced vasodilation (CIVD) tests and clinical biochemical analysis were performed at baseline and after 3-months cold exposure, whilst blood samples were collected, and plasma amino acids were analyzed by targeted metabolomics. To further confirm the effect of lysine on cold tolerance and explain the latent mechanism, mice were challenged with chronic cold exposure for 7 days with lysine supplement, then core and local surface temperature as well as thermogenesis activity were detected. Results: Continuous cold exposure shortened the CIVD onset time and increased the average finger temperature. Levels of the plasma lysine and glycine were decreased in both humans and mice. Venn analysis from three datasets revealed that lysine was the only significantly changed plasma amino acid, which strongly correlated with the altered CIVD. Moreover, mice sustained a relatively higher core temperature and surface temperature in the back, tail and paws upon lysine supplementation. Furthermore, lysine supplementation increased the level of histone H3K18cr and promoted the gene and protein expression of Cpt1a, Cpt2 and Cyp27a1 in liver. Conclusion: Our work identified lysine as a critical amino acid for the remodeling of hepatic histone crotonylation that facilitates cold acclimation.

BackgroundDue to the scarcity and high cost of MRI in resource-constrained regions, prompt diagnosis and treatment of rotator cuff tears remain problematic in these areas. Therefore, extensive research has been conducted to predict rotator cuff tears using simple and affordable anteroposterior radiographs. It remains unclear whether non-standard anteroposterior radiographs would have a notable impact on the preciseness of the diagnosis.MethodWe analyzed patients treated for shoulder pain at hospitals. These patients underwent shoulder joint MRI and standard anteroposterior radiographs, were categorized into those with rotator cuff tears and a control group. We assessed whether the radiographs were standard anteroposterior radiographs using classification criteria from previous studies. Three assessors independently measured the acromiohumeral interval, upwards migration index, acromion index, critical shoulder angle, and double-circle radius ratio in radiographic images. The intraclass correlation coefficient and receiver operating characteristic curves were used to assess measurement reliability and predictive capabilities of each predictive method for rotator cuff tears.ResultsThis study included 102 non-standard radiographs that met the research criteria for the measurement and analysis. The intragroup correlation coefficients for the acromiohumeral interval, upwards migration index, and double-circle radius ratio were above 0.7 (0.77, 0.71, 0.76), while those for the acromion index and critical shoulder angle exceeded 0.8 (0.86 and 0.87). In non-standard radiographs, the double-circle radius ratio reliably predicted rotator cuff tears (p 0.05). The areas under the receiver operating characteristic curves of the double-circle radius ratio, estimated by the three researchers for rotator cuff tears.ConclusionThis study found that non-standard radiographs significantly impaired the diagnostic performance of the acromiohumeral interval, upwards migration index, acromion index, and critical shoulder angle. Only the double-circle radius ratio maintained its predictive power (although this diminished capability may fall short of clinical relevance) and demonstrated high applicability. These findings indicate the need for researchers to prioritize the quality of radiographs and focus on reducing the sensitivity of the prediction method in relation to radiograph quality. The capability exhibited by the double-circle radius ratio warrants further investigation, to facilitate a simplified diagnosis of rotator cuff tears.

Abstract The formation of carbon-hydrogen (C-H) bonds via the reaction of small inorganic molecules is of great significance for understanding the fundamental transition from inorganic to organic matter, and thus the origin of life. Yet, the detailed mechanism of the C-H bond formation, particularly the time scale and molecular-level control of the dynamics, remain elusive. Here, we investigate the light-induced bimolecular reaction starting from a van der Waals molecular dimer composed of two small inorganic molecules, H2 and CO. Employing reaction microscopy driven by a tailored two-color light field, we identify the pathways leading to C-H photobonding thereby producing HCO+ ions, and achieve coherent control over the reaction dynamics. Using a femtosecond pump-probe scheme, we capture the ultrafast formation time, i.e., 198 ± 16 femtoseconds. The real-time visualization and coherent control of the dynamics contribute to a deeper understanding of the most fundamental bimolecular reactions responsible for C–H bond formation, thus contributing to elucidate the emergence of organic components in the universe.

Abstract Rough set theory places great importance on approximation accuracy, which is used to gauge how well a rough set model describes a target concept. However, traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set model. To overcome this, two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is proposed. The first is the relative average approximation accuracy, which is based on all sets in the universe and has several basic properties. The second is the absolute average approximation accuracy, which is based on undefinable sets and has yielded significant conclusions. We also explore the relationship between these two types of average approximation accuracy. Finally, the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables.

Abstract Media convergence works by processing information from different modalities and applying them to different domains. It is difficult for the conventional knowledge graph to utilise multi‐media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective. To address the issue, an inference method based on Media Convergence and Rule‐guided Joint Inference model (MCRJI) has been proposed. The authors not only converge multi‐media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction. First, a multi‐headed self‐attention approach is used to obtain the attention of different media features of entities during semantic synthesis. Second, logic rules of different lengths are mined from knowledge graph to learn new entity representations. Finally, knowledge graph inference is performed based on representing entities that converge multi‐media features. Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi‐media features and knowledge graph inference, demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi‐media features.