Construction Of Model Research Articles

Prevalence, influencing factors, and prediction model construction of anemia in ankylosing spondylitis based on real-world data: An exploratory study.

This study aimed to explore the prevalence and influencing factors of anemia in patients with ankylosing spondylitis (AS) using real-world data and to construct a predictive model for anemia in AS. In November 2023, we accessed the database from China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN). Clinical data of AS collected from the CERTAIN between March 2022 and September 2023 were analyzed. Demographic information, clinical assessment scales, and laboratory test results of the patients were collected. According to the anemia diagnostic criteria established by the World Health Organization (WHO) in 2018, patients were divided into anemia group and non-anemia group. Statistical analyses were performed using SPSS 25.0 software, including χ2 tests, independent sample t-tests to compare differences between the two groups, and multivariate stepwise logistic regression analysis to explore the influencing factors of anemia in AS. The predictive efficacy of the model was evaluated by plotting receiver operating characteristic (ROC) curves. Calibration was assessed through the Hosmer-Lemeshow goodness-of-fit test, and a calibration curve was plotted to comprehensively evaluate the predictive capability of the model. A total of 251 patients were included in this study, among which 58 cases had anemia (23.1%). There were significant differences in gender, ossification, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) indicators, and clinical assessment scale results between the two groups (P < 0.05). The results of multivariate stepwise logistic regression analysis showed that female gender, underweight, ossification, abnormal CRP and ESR were independent risk factors for anemia in AS (P < 0.05). Based on the results of multivariate stepwise logistic regression analysis, a predictive model for anemia in AS was established as Logit(P) = -5.02 + 2.041 × gender -1.11 × BMI(body mass index) category + 1.103 × ossification category + 0.942 × CRP category + 1.476 × ESR category. The ROC curve analysis showed that the area under the curve of the model for predicting anemia in AS was 0.857 (95% CI: 0.808 ~ 0.906). The Omnibus test of model coefficients yielded χ2 = 85.265, P < 0.001. The Hosmer-Lemeshow test showed χ2 = 7.005, P = 0.536 (P > 0.05). Analysis of real-world AS diagnosis and treatment data showed that the prevalence of anemia in Chinese AS was 23.1%. The occurrence of anemia was closely related to female gender, underweight, ossification, and abnormal CRP and ESR. The logistic model constructed based on these indicators for predicting the risk of anemia in AS demonstrated good efficacy.

Construction and evaluation of a prognostic model for cervical cancer based on dynamic hematological and clinical features

ObjectivesThis study aims to investigate the prognostic significance of dynamic hematological and clinical characteristics and to construct nomograms to predict overall survival (OS) in patients with cervical carcinoma who underwent radical radiotherapy.MethodsThe study analyzed patients with cervical cancer who underwent radical radiotherapy at The University of Hong Kong-Shenzhen Hospital between January 2015 and June 2022 and were staged as IB1 to IVA according to the International Federation of Gynecology and Obstetrics (FIGO) 2018 staging system. We identified predictive factors through univariate and multivariate cox regression analyses. Two multivariate analyses integrating different groups of variables were conducted independently. Concordance index (C-index), receiver operating characteristic (ROC), and Kaplan-Meier curves were used to evaluate the nomograms. Bootstrap validation was performed to determine the accuracy of the nomogram using 1000 resamples. The performances of proposed nomograms and FIGO 2018 staging system were compared to assess the prognostic value of hematological and inflammatory markers.ResultsOne hundred fifty-nine patients were included in this retrospective analysis. The median follow-up time was 41.37 months, and the 3-year OS rate was 82.6%. The first multivariate analysis of pre-treatment clinical factors and all hematological variables showed that FIGO2018 staging, and pre-treatment albumin levels were associated with 3-year OS. The final multivariate analysis incorporating all clinical factors, hematological variables, and inflammatory markers identified the following prognostic factors: FIGO2018 staging, rate of tumor shrinkage before brachytherapy, pre-treatment albumin levels, treatment times, minimum neutrophils during treatment, concurrent chemotherapy cycles, and lymphopenia grade. Calibration plots showed agreement between the OS predicted by the nomograms and actual OS. Kaplan–Meier curves demonstrated that patients in the high-risk group had shorter OS than those in the low-risk group (P ≤ 0.001). The C-index for the two nomograms was superior to that of the current FIGO2018 staging system, with values of 0.709 (95% Confidence Interval [CI], 0.622–0.795) and 0.803 (95% CI, 0.729–0.877), compared to 0.593 (95% CI, 0.508–0.678) for the FIGO system.ConclusionWe developed and validated nomograms to predict OS in cervical cancer patients staged IB1 to IVA who underwent radical radiotherapy RT. The prognostic significance of dynamic changes in blood and inflammatory markers has been confirmed. The proposed nomogram exhibits robust predictive capabilities for estimating OS in these patients, facilitating risk stratification and individualized treatment.

Practice Report: On the development of medical English ESP learning model via VR space in collaboration with Finland

The paper outlines the construction process of a medical English Collaborative Online International Learning (COIL) model developed in collaboration with universities in Finland and the USA in a 3D virtual learning space (metaverse) mediated by Cross Reality (XR) technology. This technology mobilizes all aspects of Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). This paper will describe the development and construction of a medical English COIL model developed in a 3D virtual learning space (metaverse) mediated by Cross Reality (XR) technology in detail. It will then present a report on the actual lessons conducted using the model. Furthermore, using the model, it will examine the development of metacognitive ability and the L2 ideal self of the students in the classes. It will also investigate whether it is possible to learn medical English in a communicative way.

Frailty risk prediction models for patients undergoing maintenance hemodialysis in China: a systematic review

ObjectiveTo promote the application of high-quality frailty risk prediction models in the field of debilitation among Chinese patients undergoing MHD, and to provide a basis for optimisation and improvement of future studies.MethodsA literature search was conducted in Chinese and English databases (PubMed, Web of Science, Cochrane Library, CINAHL, Embase, CNKI, Wanfang, VIP, SinoMed) and the cutoff date for which was April 30, 2024. Literature characteristics, types of studies, predictors, model construction methods and results were analysed and compared.ResultsTen studies met the inclusion criteria, and seven were focused on model development and validation. A total of 12 predictive models were included across these 10 studies; three of these were solely model development studies, while seven were both model development and validation. The area under the curve (AUC) for the subjects’ operating characteristics was > 0.7 in all ten studies. The most frequently identified predictors in the models included age, nutritional status, the presence of multimorbidity, gender, and depression. While the overall applicability of the ten studies was deemed satisfactory, it is important to note that all studies exhibited a high risk of bias, particularly concerning the data analysis component.ConclusionThe frailty risk prediction models for patients undergoing maintenance hemodialysis have demonstrated satisfactory applicability; however, they are all associated with a significant risk of bias and lack comprehensive external validation. To develop more accurate and practical prediction models, future studies must rely on large-sample, multicenter prospective cohort studies and adhere to a rigorous study design.Clinical trial numberNot applicable.

Clustering of Urinary Biomarkers to Identify Interstitial Cystitis Subtypes and Different Clinical Characteristics and Treatment Outcomes

Purpose: Interstitial cystitis/bladder pain syndrome (IC/BPS) is mysterious and difficult to diagnose without cystoscopic hydrodistention. This study aimed to explore non-invasive and highly reliable urine biomarkers to identify Hunner’s IC (HIC) and different non-Hunner’s IC (NHIC) subtypes. Methods: In total, 422 women with and without clinically diagnosed IC/BPS (n = 376 and 46, respectively) were retrospectively enrolled. Patients were diagnosed with HIC or NHIC by cystoscopic hydrodistention under anesthesia. Then, the maximal bladder capacity (MBC) and glomerulation grade were determined. Thirteen urine inflammatory cytokines, chemokines, and oxidative stress biomarkers based on the previously reported predictors of IC/BPS were assayed using commercial microsphere kits. The dataset was randomly divided into training (70%) and test (30%) sets for model construction and validation using logistic regression and stepwise variable selection techniques. To construct the predictive models, univariate analysis was performed to evaluate the discriminative power of each urinary biomarker, measured by the area under the curve (AUC). Biomarkers with AUC values &lt; 0.6 were excluded from further modeling. Multivariate logistic regression was then employed, with variables selected through stepwise forward selection based on log-likelihood criteria. For dichotomization, cutoff values were determined using quartile ranges from the control group. The final model’s performance was assessed using AUC, accuracy, sensitivity, and specificity in both training and test sets. Results: By setting the screening criterion to AUC ≥ 0.60, the potential urinary biomarkers for identifying IC/BPS cases were eotaxin, monocyte chemoattractant protein-1, tumor necrosis factor-alpha (TNF-α), 8-hydroxy-2′-deoxyguanosine (8-OHdG), and 8-isoprostane. Those for identifying HIC from the IC/BPS cohort were interleukin (IL)-6, IL-8, interferon γ-inducible protein 10 (IP-10), and regulated on activation, normal T-cell expressed and secreted (RANTES). A diagnostic algorithm using a cluster of urinary biomarkers included TNF-α ≥ 0.95 pg/mL or 8-OHDG ≥ 22.34 pg/mL and 8-isoprastane ≥ 22.34 pg/mL for identifying IC/BPS from the overall cohort; for identifying HIC from the IC/BPS cohort, the urinary IP-10 ≥ 3.74 pg/mL or IP-10 ≥ 19.94 pg/mL was added. Conclusions: Using a cluster of urinary biomarkers such as TNF-α or 8-OHdG and 8-isoprostane can identify IC/BPS from a study cohort, and adding the urinary IP-10 can distinguish HIC from IC/BPS cases.

Mortality Risk Prediction in Patients With Antimelanoma Differentiation-Associated, Gene 5 Antibody-Positive, Dermatomyositis-Associated Interstitial Lung Disease: Algorithm Development and Validation.

Patients with antimelanoma differentiation-associated gene 5 antibody-positive dermatomyositis-associated interstitial lung disease (anti-MDA5+DM-ILD) are susceptible to rapidly progressive interstitial lung disease (RP-ILD) and have a high risk of mortality. There is an urgent need for a reliable prediction model, accessible via an easy-to-use web-based tool, to evaluate the risk of death. This study aimed to develop and validate a risk prediction model of 3-month mortality using machine learning (ML) in a large multicenter cohort of patients with anti-MDA5+DM-ILD in China. In total, 609 consecutive patients with anti-MDA5+DM-ILD were retrospectively enrolled from 6 hospitals across China. Patient demographics and laboratory and clinical parameters were collected on admission. The primary endpoint was 3-month mortality due to all causes. Six ML algorithms (Extreme Gradient Boosting [XGBoost], logistic regression (LR), Light Gradient Boosting Machine [LightGBM], random forest [RF], support vector machine [SVM], and k-nearest neighbor [KNN]) were applied to construct and evaluate the model. After applying inclusion and exclusion criteria, 509 (83.6%) of the 609 patients were included in our study, divided into a training cohort (n=203, 39.9%), an internal validation cohort (n=51, 10%), and 2 external validation cohorts (n=92, 18.1%, and n=163, 32%). ML identified 8 important variables as critical for model construction: RP-ILD, erythrocyte sedimentation rate (ESR), serum albumin (ALB) level, age, C-reactive protein (CRP) level, aspartate aminotransferase (AST) level, lactate dehydrogenase (LDH) level, and the neutrophil-to-lymphocyte ratio (NLR). LR was chosen as the best algorithm for model construction, and the model demonstrated excellent performance, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.866, a sensitivity of 84.8%, and a specificity of 84.4% on the validation data set and an AUC of 0.90, a sensitivity of 85.0%, and a specificity of 83.9% on the training data set. Calibration curves and decision curve analysis (DCA) confirmed the model's accuracy and clinical applicability. Moreover, the model showed strong predictive performance in the external validation cohorts (cohort 1: AUC=0.836, 95% CI 0.754-0.916; cohort 2: AUC=0.915, 95% CI 0.871-0.959), indicating good generalizability. This model was integrated into a web-based tool to predict the 3-month mortality for patients with anti-MDA5+DM-ILD. We successfully developed a robust clinical prediction model and an accompanying web tool to estimate the 3-month mortality risk for patients with anti-MDA5+DM-ILD.

Discrete coordination nanochains based on photoluminescent dyes reveal intrachain exciton migration dynamics

Elucidating exciton migration in polymer chains has been one of the major research goals in photophysics for over half a century. While great efforts have been made to understand picosecond phenomena by ultrafast spectroscopy, ambiguous molecular conformations and/or random polymer sequences have hindered the construction of an ideal exciton migration model. Here we present the creation of unique end-capped coordination nanochains and quantitative description of intrachain exciton migration therein. The nanochain features unique molecular architectures in discrete polynuclear complexes, with a linear and rigid structure, the defined number of metal nuclei, and charge neutrality. These features allow well-defined arrangement of emissive dye moieties, making the nanochain a sound platform for studying exciton dynamics. Readily accessible absorption spectroscopy, and photoluminescence lifetime and quantum yield measurements allow the construction of continuous-time Markov chains model, thereby estimating non-trivial exciton migration across the metal center.

Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis.

Renal replacement therapy (RRT) plays a crucial role in managing acute pancreatitis (AP). This study aimed to develop and evaluate predictive models for determining the need for RRT among patients with AP in the intensive care unit (ICU). A retrospective selection of patients with AP was made from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version V2.0). The cohort was randomly divided into a training set (447 patients) and a validation set (150 patients). The least absolute shrinkage and selection operator (LASSO) regression cross-validation method was utilized to identify key features for model construction. Using these features, four machine learning (ML) algorithms were developed. The optimal model was visualized and clarified using SHapley Additive exPlanations (SHAP) and presented as a nomogram. The mean age of the cohort was 59.17years, with an average Acute Physiology and Chronic Health Evaluation II (APACHE II) score of 17.55. Acute kidney injury (AKI) was observed in 52.43% of patients with AP, and 9.05% required RRT. After feature selection, four of 41 clinical factors were ultimately chosen for use in model construction. The Lasso-Logistic Regression (Lasso-LR) model showed a high discriminative ability to predict RRT risk in patients with AP, with an area under the receiver operating characteristic (AUROC) of 0.955 (95% CI 0.924-0.987) in the training set. In the validation set, it maintained its discriminative performance, achieving an AUROC of 0.985 (95% CI 0.970-1.000). Calibration curves indicated an excellent fit in both sets (Brier scores: 0.039 and 0.032, respectively), suggesting high consistency. Decision curve analysis (DCA) highlighted the Lasso-LR model's significant clinical utility in predicting RRT likelihood in patients with AP. Developed via the LASSO regression cross-validation method, the Lasso-LR model significantly excels in predicting the requirement for RRT in patients with AP, demonstrating its potential for clinical application.

Model construction for an integrated approach to child abuse cases that treats both the internal and external worlds

Associate Professor Norifumi Senga, Nihon Fukushi University, previously worked at child guidance centres that support children and their families. His research on child abuse support takes an integrated approach. This is necessary due to the complex interplay of issues associated with child abuse. Support needs to take a multi-agency, multi-professional collaboration and approach from a variety of perspectives. There is no established integrated approach to offering support for child abuse cases and Senga believes this inconsistency further exacerbates the confusion and anxiety experienced by children and their families. He is working to develop an integrated approach that takes from more than one theory, technique or perspective. Above all else, support in child abuse cases must offer a guarantee of a safe place that encourages a child’s development. Given that external and internal worlds are interlinked, a cohesive approach is crucial, by which the dangers in the external world are eliminated and then the psychological (internal) issues are tackled. Senga is developing a model construction that takes an integrated approach to child abuse cases, treating internal and external worlds. In doing so, he is considering the essence of diverse practices and adapting them to fit Japanese culture and the cases he sees daily. He has analysed several theories and techniques, such as the holonical approach and considered how they can be most efficiently applied to fit the needs of those he treats.

Non-isothermal mathematical model of blocking tecnogenic fractures

Nowadays, large oil fields have moved to the stage of declining production, to maintain reservoir pressure, it is necessary to apply flooding technologies. To maintain the previous rates of oil production, it is necessary to force selections by increasing the value of downhole pressure on the injection wells. However, the risks of exceeding the fracturing pressure are increasing, which can lead to the formation of technogenic fractures. An intensive increase in the fracture can lead to an increase in the risks of premature water reaching through it into the drainage zone of the producing wells, which will lead to an increase in the value of the water oil ratio. The analysis of current numerical mathematical models of colmatation of technogenic fracture has shown the status of determining the volume of leaks of the colmatation agent beyond the fracture, considering changes in the temperature field at the bottom of the injection well. This problem is relevant, since special research complexes have been conducted at several oil and gas fields to determine the growth of technogenic fractures that arose because of excess fracturing pressure and fell into the drainage zone of producing wells. A change in the temperature field of the reservoir will allow direct changes in the viscosity of the injected colmatation agent, as well as determine the amount of leakage of the agent beyond the limits of the technogenic fractures. The article describes the construction of a non-isothermal physico-mathematical model of injection of a suspension system (water-reagent) into the reservoir, considering changes in the temperature field of the reservoir, the volume of reagent leaks beyond the limits of the technogenic fracture, considered for the first time. The aim of the work is to establish the dependences of the leakage volume of the colmatation agent, the critical time of filling the fracture from changes in the temperature field at the bottom of the injection well. A nonisothermal reservoir simulation model has been constructed showing the stages of initiation of a technogenic fracture with its subsequent colmation. The distribution of the concentration of the colmatation reagent both in the fracture and outside it, depending on the change in the temperature field at the bottom of the well, is obtained. It is determined that the volume of reagent leaks decreases if changes in the temperature field at the bottom of the injection well are considered with identical well operation parameters and geological and physical characteristics of the formation.

Vertical Slowness-Constrained Joint Anisotropic Parameters and Event-Location Inversion for Downhole Microseismic Monitoring

The construction of accurate anisotropic velocity models is essential for effective microseismic monitoring in hydraulic fracturing. Ignoring anisotropy can result in significant distortions in microseismic event locations and their interpretation. Although methods exist to simultaneously invert anisotropic parameters and event locations using microseismic arrival times, the results heavily depend on accurate initial models and sufficient ray coverage due to strong trade-offs among multiple parameters. Microseismic waveform inversion for anisotropic parameters remains challenging due to the low signal-to-noise ratio of the data and the high computational cost. To address these challenges, we propose a method for jointly inverting event locations and velocity updates based on arrival times and vertical slowness estimates, under the assumption of small horizontal velocity variations. Vertical slowness estimates, which are independent of source information and easily obtainable, provide an additional constraint that enhances inversion stability. We test the proposed method in four synthetic examples under various conditions. The results demonstrate that incorporating vertical slowness effectively constrains and stabilizes conventional travel-time inversion, especially in scenarios with poor raypath coverage. Additionally, we apply this method to a field case and find that it produces more reasonable event locations compared to inversions using arrival times alone. This joint inversion method can enhance the accuracy of anisotropic structures and event locations, which thus help with fracture characterization in tight and low-permeability reservoirs. It may serve as an effective downhole monitoring approach for hydrocarbon and geothermal energy production.

Computer Simulation and Speedup of Solving Heat Transfer Problems of Heating and Melting Metal Particles with Laser Radiation

The study of the process of laser action on powder materials requires the construction of mathematical models of the interaction of laser radiation with powder particles that take into account the features of energy supply and are applicable in a wide range of beam parameters and properties of the particle material. A model of the interaction of pulsed or pulse-periodic laser radiation with a spherical metal particle is developed. To find the temperature distribution in the particle volume, the non-stationary three-dimensional heat conductivity equation with a source term that takes into account the action of laser radiation is solved. In the plane normal to the direction of propagation of laser radiation, the change in the radiation intensity obeys the Gaussian law. It is possible to take into account changes in the intensity of laser radiation in space due to its absorption by the environment. To accelerate numerical calculations, a computational algorithm is used based on the use of vectorized data structures and parallel implementation of operations on general-purpose graphics accelerators. The features of the software implementation of the method for solving a system of difference equations that arises as a result of finite-volume discretization of the heat conductivity equation with implicit scheme by the iterative method are presented. The model developed describes the heating and melting of a spherical metal particle exposed by multi-pulsed laser radiation. The implementation of the computational algorithm developed is based on the use of vectorized data structures and GPU resources. The model and calculation results are of interest for constructing a two-phase flow model describing the interaction of test particles with laser radiation on the scale of the entire calculation domain. Such a model is implemented using a discrete-trajectory approach to modeling the motion and heat exchange of a dispersed admixture.

Real-Time Modeling of Static, Dynamic and Mixed Eccentricity in Permanent Magnet Synchronous Machines

Eccentricity faults are one of the main causes that significantly affect the performance of permanent magnet synchronous machines (PMSMs). Monitoring eccentricity in real time could prevent failures by adapting operation conditions and maintenance schedule when early signs of deterioration are detected. This article proposes making a circuit-type model of a permanent magnet machine with an easily configurable eccentricity for simulations and real-time analysis of signals under different operating conditions. The basis for the construction of the circuit model will be the simulation of the PMSM with 49 different coordinates of the rotor center, using the finite element analysis (FEA). The presence of eccentricity causes a variation in the inductances, the no-load flux and the expansion torque depending on the position of the rotor. The model proposes the use of bilinear interpolation (BI) to estimate the inductance matrix, the no-load flux vector captured by the stator winding and the cogging torque due to the presence of the magnets in the rotor, all of them for each rotor position. The validation is done by comparing the precision in the results of the machine’s self-inductances, the torque and the voltage waveform at the PMSM terminals and the static torque of the PMSM. The circuit model results are validated in two ways: (1) through experimental simulation, comparing the same results obtained using FEA and (2) through practical experimentation, producing a dynamic eccentricity in the machine of 0.3 mm. The results show that the proposed model is capable of accurately reproducing the behavior of the PMSM against eccentricity faults and presents computational time savings close to 99% compared to the response time obtained using FEA. This rapid PMSM model, parameterizable according to the degree of eccentricity, is the basis for the real-time simulation of the main machine waveforms, such as voltage, current and torque.

COVID-19 vaccine uptake in Zimbabwe and Sierra Leone: an application of Health Belief Model constructs.

While African countries, in general, experienced a milder COVID-19 impact compared to Western nations, they faced challenges with vaccine uptake. Specifically, Zimbabwe and Sierra Leone saw vaccine acceptance rates below global averages. This research delves into the underlying factors that influenced these disparities in vaccine acceptance in these two countries, using the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB) as guiding frameworks. This study utilized data from a cross-sectional survey encompassing 2,312 participants from areas where the Africa Christian Health Associations Platform (ACHAP) operates in Zimbabwe and Sierra Leone. The survey assessed respondents' views in line with core HBM and TPB constructs, in addition to their levels of vaccine acceptance. We then employed adjusted logistic regression models to investigate the correlation between health behavior change theory constructs and vaccine uptake, taking into account variables like gender, age, education, and country of residence. Several associations were identified, including high vaccine uptake correlated with a heightened perceived threat of COVID-19 (OR = 2.674; p < .001), recognized benefits of vaccination (OR = 1.482; p < .001), stronger perceived behavior control (OR = 2.189; p < .001), and fewer perceived barriers to vaccination (OR = 0.173; p < .001). Conversely, low vaccine uptake was linked to diminished perceived threats (OR = 0.540; p < .001), fewer perceived benefits (OR = 0.762; p < .001), weaker perceived behavior control (OR = 0.429; p < .001), and heightened perceptions of barriers (OR = 2.001; p < .001). Results underscore the significance and utility of theoretical constructs in understanding variations in vaccine uptake levels. They highlight the importance of relying on well-established theories to grasp decision-making mechanisms and to shape suggestions for behavior modification. Consequently, to boost vaccine acceptance, public health campaigns should focus on reshaping risk perceptions, addressing obstacles, emphasizing the advantages of getting vaccinated, and fostering a sense of self-efficacy within target communities.

On-Demand Catalytic Platform for Glycerol Upgrade and Utilization.

Surplus byproducts generated during biomass exploitation, such as glycerol from biodiesel manufacturing, seriously undermine the credibility of renewable energy policies. Here, we establish an on-demand catalytic platform for the upgrade and utilization of glycerol via photoelectro-bioelectro-heterogeneous coupling catalysis. Combining theoretical descriptors, specifically the highest occupied molecular orbital energy levels and dual local softness values, along with systematic experimental validation, we demonstrated the reaction activity of glycerol and its upgraded products on BiVO4 photoelectrodes. Glyceric acid was identified as the optimal biofuel candidate through monohydroxyl oxidation of glycerol. Coupling the preferential upgrading of glycerol to glyceric acid by night and its reuse as biofuel by day, a hybrid biophotoelectrochemical system delivered an open-circuit voltage of 0.89 ± 0.02 V and a maximum power density of 0.41 ± 0.03 mW cm-2 with stable diurnal operation for over 10 days. This successful model construction provides valuable insights into the strategic integration of multiple energy sources and the exploration of coupling-catalytic platforms, charting new territory for the next-generation sustainable energy systems.

Radiomics in glioma: emerging trends and challenges.

Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma features. This review discusses the application, emerging trends, and challenges associated with using radiomics in glioma. Integrating deep learning algorithms enhances various radiomics components, including image normalization, region of interest segmentation, feature extraction, feature selection, and model construction and can potentially improve model accuracy and performance. Moreover, investigating specific tumor habitats of glioblastomas aids in a better understanding of glioblastoma aggressiveness and the development of effective treatment strategies. Additionally, advanced imaging techniques, such as diffusion-weighted imaging, perfusion-weighted imaging, magnetic resonance spectroscopy, magnetic resonance fingerprinting, functional MRI, and positron emission tomography, can provide supplementary information for tumor characterization and classification. Furthermore, radiomics analysis helps understand the glioma immune microenvironment by predicting immune-related biomarkers and characterizing immune responses within tumors. Integrating multi-omics data, such as genomics, transcriptomics, proteomics, and pathomics, with radiomics, aids the understanding of the biological significance of the underlying radiomics features and improves the prediction of genetic mutations, prognosis, and treatment response in patients with glioma. Addressing challenges, such as model reproducibility, model generalizability, model interpretability, and multi-omics data integration, is crucial for the clinical translation of radiomics in glioma.

Research on cutting mechanism and process optimization method of gear skiving

The cutting force and cutting temperature have a significant impact on the service life and durability of gear skiving cutters. Due to unreasonable design, the existing process parameters lead to dramatically nonuniform cutting force and cutting temperature, which aggravates the rapid wear of gear skiving cutters. To address this issue, this paper first establishes a finite element model of skiving the internal circular arc tooth in pin wheel housing, and the simulation model is simplified to improve computation efficiency. Next, the impact of single process parameter on cutting force and cutting temperature is analyzed by controlling variable. Then, an orthogonal experiment is designed and the method of range analysis is employed to evaluate the significance of each process parameter. Furthermore, a prediction model of cutting force and cutting temperature is established using a neural network optimized by genetic algorithm. This prediction model allows for the construction of a multi-objective optimization model for the process parameters. By solving this model, the optimal combination of process parameters within the given ranges can be obtained to achieve reasonable and balanced cutting force and cutting temperature.

Current progress of in vitro vascular models on microfluidic chips.

The vascular tissue, as an integral component of the human circulatory system, plays a crucial role in retaining normal physiological functions within the body. Pathologies associated with the vasculature, whether direct or indirect, also constitute significant public health concerns that afflict humanity, leading to the wide studies on vascular physiology and pathophysiology. Given the precious nature of human derived vascular tissue, substantial efforts have been dedicated to the construction of vascular models. Due to the high cost associated with animal experimentation and the inability to directly translate results to human, there is an increasing emphasis on the use of primary human cells for the development of in vitro vascular models. For instance, obtaining an ApoE-/- mouse model for atherosclerosis research typically requires feeding a high-fat diet for over 10 weeks, whereas in vitro vascular models can usually be formed within 2 weeks. With advancements in microfluidic technology, in vitro vascular models capable of precisely emulating the hemodynamic environment within human vessels are becoming increasingly sophisticated. Microfluidic vascular models are primarily constructed through two approaches: 1) directly constructing the vascular models based on the three-layer structure of the vascular wall; 2) co-culture of endothelial cells and supporting cells within hydrogels. The former is effective to replicate vascular tissue structure mimicking vascular wall, while the latter has the capacity to establish microvascular networks. This review predominantly presents and discusses recent advancements in template design, construction methods, and potential applications of microfluidic vascular models based on polydimethylsiloxane (PDMS) soft lithography. Additionally, some refined methodologies addressing the limitations of conventional PDMS-based soft lithography techniques are also elaborated, which might hold profound importance in the field of vascular tissue engineering on microfluidic chips.

Analysis of factors associated with intercostal neuralgia after osteoporotic thoracic spine fracture and construction of a prediction model

ObjectiveThis study aims to investigate the associated factors of intercostal neuralgia in patients with osteoporotic vertebral compression fractures (OVCF) of the thoracic spine and to develop a predictive model to assess the likelihood of patients developing intercostal neuralgia following thoracic vertebral fractures.MethodsThe retrospective study involved 518 patients with thoracic OVCF treated at our hospital, among whom those with and without intercostal neuralgia were matched at a 1:1 ratio.Relevant basic clinical data and imaging parameters of the patients were recorded, t-test was used for continuous variables and chi-square test for categorical variables to determine the factors associated with intercostal neuralgia. Subsequently, the above associated variables were screened using univariate and multivariate logistic regression models to obtain associated factors. Finally, a prediction model for osteoporotic thoracic spine fracture was developed and validated.ResultsThis study included a total of 104 patients based on the presence or absence of intercostal neuralgia.The results of multifactorial logistic regression analysis showed that injured vertebral intervertebral foraminal area (P = 0.0008, regression coefficient estimate 0.0490, 95% confidence interval 0.0219–0.0798, OR = 1.0503), injured vertebral intervertebral foraminal volume (P = 0.0001, regression coefficient value − 0.0028, 95% confidence interval − 0.0044 to -0.0015, OR = 0.9972), and nerve root area (P = 0.0038, regression coefficient estimate=-0.0876, 95% confidence interval − 0.1506 to -0.0309, OR = 0.9161) were independent associated factors.The fatty degeneration ratio have a positive promotional effect on the probability of developing intercostal pain.The area under the ROC curve (AUC) of the prediction model was 0.851, which indicated that the line graph model had a certain degree of predictive validity.ConclusionThoracic osteoporotic fractures are a common geriatric disease, and changes in the morphological parameters of the intervertebral foramina, such as a reduction in the area and volume of the injured vertebral intervertebral foramina, as well as fatty degeneration of the thoracic back muscles, suggest an increased probability of developing intercostal neuralgia.

EEG analysis of speaking and quiet states during different emotional music stimuli

IntroductionMusic has a profound impact on human emotions, capable of eliciting a wide range of emotional responses, a phenomenon that has been effectively harnessed in the field of music therapy. Given the close relationship between music and language, researchers have begun to explore how music influences brain activity and cognitive processes by integrating artificial intelligence with advancements in neuroscience.MethodsIn this study, a total of 120 subjects were recruited, all of whom were students aged between 19 and 26 years. Each subject is required to listen to six 1-minute music segments expressing different emotions and speak at the 40-second mark. In terms of constructing the classification model, this study compares the classification performance of deep neural networks with other machine learning algorithms.ResultsThe differences in EEG signals between different emotions during speech are more pronounced compared to those in a quiet state. In the classification of EEG signals for speaking and quiet states, using deep neural network algorithms can achieve accuracies of 95.84% and 96.55%, respectively.DiscussionUnder the stimulation of music with different emotions, there are certain differences in EEG between speaking and resting states. In the construction of EEG classification models, the classification performance of deep neural network algorithms is superior to other machine learning algorithms.