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The relationship between interdisciplinarity and citation impact is affected by many factors, and the citation time window is a crucial factor. Our study examines the effect of the citation time window on the relationship between interdisciplinarity and scientific impact. All journal articles published in 2006 in Web of Science (WoS) are considered. The relationship between interdisciplinarity and scientific impact is explored by conducting a year-by-year negative binomial regression analysis with different interdisciplinarity indicators. Three diversity single-property indicators (namely variety, balance, and disparity) and three typical composite interdisciplinarity indicators (Rao-Stirling index (RS), Leinster–Cobbold diversity indices (LCDiv), and DIV) are used in this study. The results show that evaluating the scientific impact of interdisciplinarity requires a sufficiently long citation time window. However, the length of the citation time window is different for different interdisciplinarity indicators. A 4-year citation time window is necessary when the variety indicator is used, whereas balance and disparity require at least 11-year and 13-year citation time windows, respectively. The citation time window is the same (at least 5 years) for the three composite interdisciplinarity indicators (RS, LCDiv, and DIV). The recommended length of the citation time window is based only on this study and may be affected by the data set, regression model, and discipline classification system.

Reservoir X has high-conductivity fractures developed in the gas-water reservoir. During development, severe edge and bottom water invasion occurs, and after water appears in gas wells, water production rapidly increases and even quickly floods the wells. Pilot experiments such as gas injection to block water in the reservoir have shown no significant effects. The discussion indicates that reservoir drainage has become the main research direction for the next phase of production. Since the reservoir shows no obvious patterns when studied using equivalent models, an embedded discrete fracture model was used to characterize and represent the highconductivity fracture network in the eastern area of the reservoir based on the water production characteristics. Sensitivity analysis of the high-conductivity fracture network in the eastern area was conducted, and reasonable drainage indicators for the eastern area were optimized. The results show that the embedded discrete fracture model can effectively characterize the high-conductivity fracture network; The fracture length, aperture, and permeability of high-conductivity fractures significantly impact development effects. The greater the fracture length and aperture, the more severe the edge and bottom water invasion, and the more local residual gas remains; The optimal production-to-injection ratio in the eastern area of the reservoir is between 15 and 20 m3/104 m3. The conclusions suggest that the research results can provide certain reference value for field production.

The advent of precision therapy has revolutionized breast cancer treatment, driven by the development of innovative diagnostic techniques and targeted drugs. Identifying biomarkers related to therapy response is crucial for tailoring treatment strategies for breast cancer patients. Liquid biopsies have emerged as minimally invasive techniques for biomarker profiling, leveraging the increasing sensitivity for detecting oncogenic drivers. These liquid biopsy methods, involving the testing of circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) in biofluids, offer more opportunities for early cancer detection, monitoring treatment efficacy, and identifying resistance mechanisms. This review focuses on the technical methodologies employed for the detection of CTCs and ctDNA. Beyond the technical aspects, we discuss the clinical applications of these biomarkers in breast cancer, including their roles in early detection, monitoring treatment response, and guiding therapeutic decisions. We also address the challenges associated with CTC and ctDNA detection, such as low concentrations in biofluids and tumor heterogeneity, which can complicate analysis and interpretation. By discussing the current landscape of CTC and ctDNA methodologies and their clinical implications, this review highlights the potential of liquid biopsies to enhance personalized medicine approaches in breast cancer management.

Inflammation is considered as one of the most primary protective innate immunity responses, closely related to the body’s defense mechanism for responding to chemical, biological infections, or physical injuries. Furthermore, prolonged inflammation is undesirable, playing an important role in the development of various diseases, such as heart disease, diabetes, Alzheimer’s disease, atherosclerosis, rheumatoid arthritis, and even certain cancers. Marine-derived fungi represent promising sources of structurally novel bioactive natural products, and have been a focus of research for the development of anti-inflammatory drugs. This review covers secondary metabolites with anti-inflammatory activities from marine-derived fungi, over the period spanning August 2018 to July 2024. A total of 285 anti-inflammatory metabolites, including 156 novel compounds and 11 with novel skeleton structures, are described. Their structures are categorized into five categories: terpenoids, polyketides, nitrogen-containing compounds, steroids, and other classes. The biological targets, as well as the in vitro and in vivo screening models, were surveyed and statistically summarized. This paper aims to offer valuable insights to researchers in the exploration of natural products and the discovery of anti-inflammatory drugs.

Nitrogen heterocycles have drawn considerable attention because of their structurally novel and significant biological activities. Marine-derived fungi, especially the Aspergillus species, possess unique metabolic pathways to produce secondary metabolites with novel structures and potent biological activities. This review prioritizes the structural diversity and biological activities of nitrogen heterocycles that are produced by marine-derived Aspergillus species from January 2019 to January 2024, and their relevant biological activities. A total of 306 new nitrogen heterocycles, including seven major categories—indole alkaloids, diketopiperazine alkaloids, quinazoline alkaloids, isoquinoline alkaloids pyrrolidine alkaloids, cyclopeptide alkaloids, and other heterocyclic alkaloids—are presented in this review. Among these nitrogen heterocycles, 52 compounds had novel skeleton structures. Remarkably, 103 compounds showed various biological activities, such as cytotoxic, antimicrobial, anti-inflammatory, antifungal, anti-virus, and enzyme-inhibitory activities, and 21 compounds showed potent activities. This paper will guide further investigations into the structural diversity and biological activities of nitrogen heterocycles derived from the Aspergillus species and their potential contributions to the future development of new natural drug products in the medicinal and agricultural fields.

Highly sensitive and selective monitoring of amino metabolites such as glutamine, arginine, tryptophan and related proteins played significant roles in early diagnosis and warning of lymphoma. But those limited abundance and lacked chromophore group in vivo were bottleneck of multivariate analysis. This work aims to develop a novel UHPLC-Triple-TOF-HRMS method for simultaneous quantitation of 20 kinds of amino metabolites and tracing different proteins based on a new mass spectrometry probe (3-bromopropyl) triphenylphosphonium (3-BMP) with ability of enhance ionization efficiency and targeted labeling amino functional groups. An excellent linearity with R 2  ≥ 0.9995 and inter- and intra-day RSD were 1.43-5.22% and 1.22-5.87%, respectively. Satisfactory recoveries were 87.09-95.82%. Limit of detection (S/ N  = 3) was 4.0–12.0 fmol. Further, up-regulated haptoglobin, coagulation factor VII and catalase could directly negatively regulate Ala, Lys and Phe, which caused Trp, His, Ser, Asp and Pro expression decreased significantly in lymphoma patients ( p  < 0.05). Ultimately, a machine learning model was established to predict lymphoma with accuracy rate of 93.68%. Above all, this study would provide multivariate analysis strategy for in-depth explore relationship aminos associated proteins and pathogenesis and helpful for early warning of lymphoma patients under free-disease state.

Background While dual-energy X-ray absorptiometry (DXA) remains the gold standard for osteoporosis diagnosis, its clinical utility is constrained by cost and accessibility challenges. This study aims to develop a predictive model for osteoporosis using routinely available clinical blood biomarkers, thereby providing an innovative and accessible approach for early detection. Methods We retrospectively analyzed 8,144 orthopedic inpatients who underwent DXA scans at Panyu Hospital of Guangzhou University of Chinese Medicine between January 2022 and December 2023. Demographic characteristics and first 24-hour admission blood parameters were collected. Potential predictors were identified through univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and Boruta algorithm. Ten supervised machine learning algorithms were employed to construct predictive models. Model performance was evaluated using area under the curve (AUC), calibration plots, decision curve analysis (DCA), accuracy, sensitivity, and specificity in the test cohort. SHapley Additive exPlanations (SHAP) analysis provided interpretable visualization of feature contributions. Results The cohort was randomly divided into training ( n  = 5,702) and testing sets ( n  = 2,442). Feature selection convergence across three methods identified 11 key predictors. The logistic regression model demonstrated superior performance in the testing set (AUC = 0.800), outperforming other algorithms in calibration and clinical utility assessments. SHAP analysis revealed age, gender, uric acid concentration, alkaline phosphatase levels, hemoglobin levels, and neutrophil count as the six most influential predictors. An accessible web-based risk calculator has been deployed at: https://op-lm.shinyapps.io/osteoporosis/ . Conclusion We developed an easy-to-use online calculator based on machine learning, which outperforms traditional models, enabling patients to preliminarily screen for osteoporosis using routine blood test results from their health check-ups. This interpretable machine learning model demonstrated promising performance and may assist in improving osteoporosis screening and risk stratification in clinical settings. Clinical trial number Not applicable.

Objective This study aims to explore the predictive value of endplate morphology and pedicle screw bone quality score on screw loosening after single-level lumbar spinal fusion surgery. Methods A retrospective analysis was conducted on the clinical data of 207 patients who underwent single-level lumbar spinal fusion (34 in the screw loosening group and 173 in the non-screw loosening group). Univariate analysis and binary logistic regression model analysis were performed using SPSS 27.0. MedCalc 23 was used to plot the receiver operating characteristic (ROC) curve to evaluate diagnostic efficacy. Results Through comparative analysis of clinical data, we found statistically significant differences between the two groups in terms of endplate morphology, lumbar CT values, and PBQ scores( P <0.05). The results of the binary logistic regression analysis indicated that endplate morphology (OR = 17.088, 95% CI: 3.886–75.142; p  < 0.001) and PBQ score (OR = 3.347, 95% CI: 1.473–7.603; p  = 0.004) are independent risk factors for screw loosening after single-level lumbar spinal fusion surgery. The ROC analysis showed that the area under the curve (AUC) for endplate morphology was 0.731 (95% confidence interval [CI]: 0.665–0.790), with the optimal threshold representing irregular endplate morphology (sensitivity: 94.1%, specificity: 52.0%). The AUC for the PBQ score was 0.791 (95% CI: 0.729–0.844), with an optimal threshold of 3.198 (sensitivity: 91.2%, specificity: 61.8%). Furthermore, the predictive model constructed using both endplate morphology and PBQ score had an AUC of 0.870 (95% confidence interval: 0.817–0.913), with a maximum Youden index of 0.668, yielding a diagnostic sensitivity of 88.2% and specificity of 78.6%. Conclusion Endplate morphology and pedicle screw bone quality score have significant reference value for diagnosing screw loosening after single-level lumbar spinal fusion surgery.

Objective The purpose of this study is to explore and analyze the risk factors for interbody cage subsidence in patients undergoing single-segment transforaminal lumbar interbody fusion (TLIF) and to construct and validate a visual nomogram risk prediction model. Methods A retrospective analysis was conducted on the clinical data of 159 patients who underwent single-segment TLIF at the Spine Surgery Department of Panyu District Traditional Chinese Medicine Hospital from January 2021 to June 2023. Using the caret package in R, patients were randomly divided into a training set ( n  = 111) and a validation set ( n  = 48) in a 7:3 ratio. Multivariable logistic regression was employed for variable selection and the construction of the nomogram model. The predictive model’s discrimination, calibration, and clinical utility were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results There were no statistically significant differences in various indicators between the training set ( n  = 111) and the validation set ( n  = 48) ( P  > 0.05). Univariate analysis in the training set revealed that age, bone density, endplate morphology, anterior vertebral bone spurs, lumbar CT values, and VBQ were statistically significant. Multivariable logistic regression analysis indicated that bone density, anterior vertebral bone spurs, and lumbar CT values were independent predictors of interbody cage subsidence ( P  < 0.05), and a nomogram model was constructed based on these indicators. The area under the ROC curve (AUC) for the training set and validation set was 0.93 (95% CI 0.89–0.98) and 0.93 (95% CI 0.86–1.00), respectively. The calibration curves showed good fit (training set P  = 0.616; validation set P  = 0.904). DCA analysis demonstrated that the model has high clinical utility. Conclusion Bone density, anterior vertebral bone spurs, and lumbar CT values are risk factors for interbody cage subsidence in patients after single-segment transforaminal lumbar interbody fusion. The constructed nomogram model exhibits good predictive value and clinical utility.

The fungus genus Xylaria is an important source of drug discoveries in scientific fields and in the pharmaceutical industry due to its potential to produce a variety of structured novel and bioactive secondary metabolites. This review prioritizes the structures of the secondary metabolites of Xylaria spp. from 1994 to January 2024 and their relevant biological activities. A total of 445 new compounds, including terpenoids, nitrogen-containing compounds, polyketides, lactones, and other classes, are presented in this review. Remarkably, among these compounds, 177 compounds show various biological activities, including cytotoxic, antimicrobial, anti-inflammatory, antifungal, immunosuppressive, and enzyme-inhibitory activities. This paper will guide further investigations into the structures of novel and potent active natural products derived from Xylaria and their potential contributions to the future development of new natural drug products in the agricultural and medicinal fields.