Valid Model Research Articles

Refinement of a meaning-centered counseling program for Chinese patients with advanced cancer: integrating cultural adaptation and implementation science approaches

Abstract Background This mixed methods study identified needed refinements to a telehealth-delivered cultural and linguistic adaptation of Meaning-Centered Psychotherapy for Chinese patients with advanced cancer (MCP-Ch) to enhance acceptability, comprehensibility, and implementation of the intervention in usual care settings, guided by the Ecological Validity Model (EVM) and the Practical, Robust Implementation and Sustainability Model (PRISM). Methods Fifteen purposively sampled mental health professionals who work with Chinese cancer patients completed surveys providing Likert-scale ratings on acceptability and comprehensibility of MCP-Ch content (guided by the EVM) and pre-implementation factors (guided by PRISM), followed by semi-structured interviews. Survey data were descriptively summarized and linked to qualitative interview data. Three analysts independently coded the transcripts according to EVM and PRISM domains; discrepancies were resolved through discussion and consensus. Results Quantitative findings showed high appropriateness and relevance of MCP-Ch across five EVM domains of Language, Metaphors/Stories, Goals, Content, and Concepts. Qualitative analysis yielded 23 inductive codes under the seven EVM domains: (1) Language (3 subcodes), (2) Persons (2 subcodes), (3) Metaphors/Stories (2 subcodes), (4) Methods (8 subcodes), (5) Content (2 subcodes), (6) Goals (4 subcodes), and (7) Concepts (2 subcodes). Themes based on PRISM included (1) Intervention characteristics (organizational perspective, 7 subcodes; and patient perspective, 6 subcodes) (2) External environment (2 subcodes), (3) Implementation and sustainability infrastructure (4 subcodes), and (4) Recipients (organizational characteristics, 5 subcodes; and patient characteristics, 4 subcodes). Conclusion Recommendations for next steps include increasing the MCP-Ch protocol’s flexibility and adaptability to allow interventionists to flexibly tailor MCP-Ch material to meet patients’ individual needs, simplifying content to improve comprehension and acceptability, providing additional training to Chinese-serving providers to increase adoption and sustainability, and considering interpreter-assisted delivery to increase access. Findings yielded important information to maximize cultural relevance as well as the implementation and sustainability potential of MCP-Ch in real-world settings.

Chemotherapy-induced diminished murine ovarian reserve model and impact of low-dose chemotherapy on fertility.

To establish a murine model of chemotherapy-induced diminished ovarian reserve (DOR) and investigate residual fertility after chemotherapy exposure. Two different chemotherapy protocols were tested to establish a valid DOR model by comparing follicle densities in mice given either protocol versus physiological solution. An ovarian stimulation protocol was then selected from among different gonadotropins by counting the number of day-2 embryos obtained from normal mice. Finally, DOR mice were stimulated 5 and 8 weeks after chemotherapy with the chosen gonadotropin protocols and day-2 embryos were recovered after mating, as was ovarian tissue for further immunohistological analyses. Seventy-two Naval Medical Research Institute (NMRI) mice. To compare day-2 embryo counts in both normal and chemotherapy-induced DOR mice. Ovarian histology and morphology were also investigated by follicle counting and classification, as was immunostaining for apoptosis (cleaved caspase-3), activation (phospho-Akt) and proliferation (Ki67). A dose of 12 mg/kg busulfan (Bu) + 120 mg/kg cyclophosphamide (Cy) was chosen to establish the DOR model, as it significantly reduced the ovarian reserve compared to both control mice (physiological solution) and the 1.2 mg/kg Bu + 12 mg/kg Cy protocol, without depleting it completely. When stimulated with 3.75 IU Menopur, normal mice produced significantly more embryos than DOR mice given 12 mg/kg Bu + 120 mg/kg Cy (41.40 ± 14.74 versus 23.67 ± 15.55 day-2 embryos). While the follicle count was statistically diminished after single-dose chemotherapy administration, the remaining follicles did not display any difference in terms of apoptosis, activation or proliferation rates. We successfully established a chemotherapy-induced DOR model using 12 mg/kg Bu + 120 mg/kg Cy, as evidenced by lower, but not completely depleted, follicle numbers and fewer retrieved embryos. Histological study of ovarian tissue exposed to DOR-inducing chemotherapy revealed that surviving follicles were of the same quality as tissue not exposed to chemotherapy.

Development and External Validation of the Hidradenitis Suppurativa Cutaneous Abscess Prediction Score-2 (HSCAPS-2): A Clinical Decision Support Tool for Diagnosis of Hidradenitis Suppurativa over Cutaneous Abscess.

Patients with hidradenitis suppurativa (HS) experience a 10-year diagnosis delay, on average. Accordingly, time to diagnosis represents one of the greatest unmet needs in HS, which to date has not been adequately addressed. A general lack of awareness about HS in the medical community and a notable heterogeneity in clinical presentation, which is most often confused with cutaneous abscess (CA), forms the basis of poor disease recognition and diagnosis delay. Our objective was to develop and validate a prediction model for diagnosis of HS versus site-specific cutaneous abscess (CA). We performed a cross-sectional study in which the model was developed using a large clinical database and externally validated using a sample of clinical records at Penn State Health. Prediction model discrimination and calibration were evaluated using the c-statistic, calibration intercept/slope, and a flexible calibration curve. Variable selection identified 15 predictors, 7 of which remained after model simplification. The following characteristics were predictive of HS relative to site-specific CA: female sex, increasing age up to 44 years, African American race, race other than White or African American, increasing body mass index, polycystic ovarian syndrome, and acne. The c-statistics of the 7-variable model in validation was 0.77 (95% CI 0.73-0.80). Calibration intercept and slope were 0.29 (95% CI 0.14, 0.43) and 1.09 (95% CI 0.90, 1.28). Clinical characteristics can predict diagnosis of HS over CA in practice without reliance on a specialty-specific examination to identify disease and potentially reduce diagnosis delay.

Emergency department crowding: An assessment of the potential impact of the See-and-Treat protocol for patient flow management at an Italian hospital.

Crowding and patient flow management are among the most relevant issues for emergency departments (EDs). This results in delayed treatment, adverse outcomes and increased costs. For these reasons, nurse-independent treatment protocols were developed aimed at managing non-emergency patients outside EDs thus improving patient flow. Our objective was to assess the potential impact of the implementation of the "See-and-Treat" protocol on eligible patients and related healthcare costs at an Italian ED. We selected all minor access codes from 2022 at the ED of Sassuolo Hospital in Northern Italy. We only included subjects discharged to home, while we excluded those who required specialized medical care or had received "Fast-Track" treatment. We identify a list of medical conditions to identify subjects eligible for inclusion in the See-and-Treat protocol and calculate the related healthcare costs. Of 40,906 individual ED admissions, 2,607 (6.4%) qualified for See-and-Treat management. Limb injuries and pain were the leading conditions at presentation. Through cost analysis, we found that implementation of the See-and-Treat protocol may result in savings over €100,000/year at Sassuolo Hospital, and over €7 million if projected to the entire Emilia-Romagna Region. Despite some limitations affecting protocol implementation, especially availability of highly-specialized and specifically-trained nurses, the study provided a foundation for a more comprehensive understanding of the implementation of the See-and-Treat protocol as a possible valid model with a view to both human and economic healthcare resources.

Prediction Models for Post-Stroke Cognitive Impairment: A Systematic Review and Meta-Analysis.

Stroke is one of the most serious illnesses worldwide and is the primary cause of acquired disability among adults. Post-stroke cognitive impairment (PSCI) is a complication of stroke that significantly impacts patients' daily activities and social functions. Therefore, developing a risk prediction model for PSCI is essential for identifying and preventing disease progression. This study systematically reviewed and analyzed PSCI prediction models, identifying the associated risk factors. We systematically retrieved literature from PubMed, Cochrane Library, Embase, and other sources. Two researchers independently extracted the literature and assessed the risk of bias using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and The Prediction Model Risk of Bias Assessment Tool (PROBAST). A total of 20 articles describe the PSCI prediction model, with an incidence rate ranging from 8% to 75%. The area under the receiver operating characteristic curve (AUC) value for the development models ranged from 0.66 to 0.969, while the validation models ranged from 0.763 to 0.893. Age, diabetes, hypersensitive C-reactive protein (hs-CRP), hypertension, and homocysteine (hcy) were identified as the strongest predictors. In this systematic review, several PSCI prediction models demonstrate promising prediction performance, although they often lack external validation and exhibit high heterogeneity in some predictive factors. Therefore, we recommend that medical practitioners utilize a comprehensive set of predictive factors to screen for high-risk PSCI patients. Furthermore, future research should prioritize refining and validating existing models by incorporating novel variables and methodologies.

Plural molecular and cellular mechanisms of pore domain KCNQ2 encephalopathy

KCNQ2 variants in children with neurodevelopmental impairment are difficult to assess due to their heterogeneity and unclear pathogenic mechanisms. We describe a child with neonatal-onset epilepsy, developmental impairment of intermediate severity, and KCNQ2 G256W heterozygosity. Analyzing prior KCNQ2 channel cryoelectron microscopy models revealed G256 as a node of an arch-shaped non-covalent bond network linking S5, the pore turret, and the ion path. Co-expression with G256W dominantly suppressed conduction by wild-type subunits in heterologous cells. Ezogabine partly reversed this suppression. Kcnq2G256W/+ mice have epilepsy leading to premature deaths. Hippocampal CA1 pyramidal cells from G256W/+ brain slices showed hyperexcitability. G256W/+ pyramidal cell KCNQ2 and KCNQ3 immunolabeling was significantly shifted from axon initial segments to neuronal somata. Despite normal mRNA levels, G256W/+ mouse KCNQ2 protein levels were reduced by about 50%. Our findings indicate that G256W pathogenicity results from multiplicative effects, including reductions in intrinsic conduction, subcellular targeting, and protein stability. These studies provide evidence for an unexpected and novel role for the KCNQ2 pore turret and introduce a valid animal model of KCNQ2 encephalopathy. Our results, spanning structure to behavior, may be broadly applicable because the majority of KCNQ2 encephalopathy patients share variants near the selectivity filter.

Predicting Resilient Modulus of Fine‐Grained Soils Using Support Vector Machine‐RBF and Random Forest

The resilient modulus (Mr) of subgrade soils is an important element in road pavement structural design procedures. However, because of regional differences in soil parameters and the nature of test procedures, determining Mr in the laboratory has become cumbersome. The purpose of this study is to develop a reliable soft computing approach for forecasting Mr of subgrade soils using support vector machine‐based linear kernel (SVM‐LR), polynomial kernel (SVM‐Poly), radial basic function (SVM‐RBF), and random forest (RF) techniques. For this purpose, a dataset of 138 soil samples from diverse geographical locations in Ethiopia was gathered, and the models were trained and tested using an 85% training and 15% test data split. All of the recommended models used confining pressure (CP), notional maximum axial stress (NmaxAS), specific gravity, P#200, silt%, clay%, liquid limit (LL), plasticity index (PI), maximum dry density, Wopt, and swell as input variables. The correctness of the proposed models was assessed by comparing predicted and observed Mr values with various statistical approaches such as root means square error (RMSE), mean squared error (MSE), mean absolute error (MAE), and determination coefficient (R2). The support vector machine RBF model outperformed all other models in validation, with R2 values of 0.99 and 0.91, MAE of 0.1 and 1.76, MSE of 0.01 and 29.54, and RMSE of 0.1 and 5.43. The Taylor diagrams of the test and training datasets show that the SVM‐RBF model outperforms the other models. Sensitivity analysis was performed to explore the impact of input variable quantity on the output of Mr for the MPL model, and it was determined that the top five essential parameters are, in decreasing order, LL, P#200, silt%, PI, and clay%. In contrast, the nominal maximum axial stress and Gs have the least impact.

BEMTrace: Visualization-driven approach for deriving Building Energy Models from BIM.

Building Information Modeling (BIM) describes a central data pool covering the entire life cycle of a construction project. Similarly, Building Energy Modeling (BEM) describes the process of using a 3D representation of a building as a basis for thermal simulations to assess the building's energy performance. This paper explores the intersection of BIM and BEM, focusing on the challenges and methodologies in converting BIM data into BEM representations for energy performance analysis. BEMTrace integrates 3D data wrangling techniques with visualization methodologies to enhance the accuracy and traceability of the BIM-to-BEM conversion process. Through parsing, error detection, and algorithmic correction of BIM data, our methods generate valid BEM models suitable for energy simulation. Visualization techniques provide transparent insights into the conversion process, aiding error identifcation, validation, and user comprehension. We introduce context-adaptive selections to facilitate user interaction and to show that the BEMTrace workfow helps users understand complex 3D data wrangling processes.

Prediction of Snatch and Clean and Jerk Performance From Physical Performance Measures in Elite Male Weightlifters.

Sandau, I and Kipp, K. Prediction of snatch and clean and jerk performance from physical performance measures in elite male weightlifters. J Strength Cond Res 39(1): 33-40, 2025-This study aimed to build a valid model to predict maximal weightlifting competition performance using ordinary least squares linear regression (OLR) and penalized (Ridge) linear regression (penLR) in 29 elite male weightlifters. One repetition maximum (1RM) or 3RM test results of assistant exercises were used as predictors. Maximal performance data of competition and assistant exercises were collected during a macrocycle in preparation for a competition. One repetition maximum snatch pull, 3RM back squat, 1RM overhead press, and body mass were used to predict the 1RM snatch; and 1RM clean pull, 3RM front squat, 1RM overhead press, and body mass were used to predict the 1RM clean and jerk. Model validation was performed using cross-validation (CV) and external validation (EV; random unknown dataset) for the coefficient of determination and root mean square error (RMSE). Results revealed that penLR models present more plausible output in the relative importance of highly correlated predictors. Of note, the 1RM snatch pull is the most relevant predictor for the 1RM snatch, whereas the 1RM clean pull and 3RM front squat are the most relevant predictors for the 1RM clean and jerk. Validation-based absolute predictive error (RMSE) ranged between ≈ 3-9 kg for the 1RM snatch and ≈ 3-7 kg for the 1RM clean and jerk, depending on the model (OLR vs. penLR) and validation procedure (CV vs. EV). In conclusion, penLR models should be used over OLR models to analyze highly correlated predictors because of more plausible model coefficients and smaller predictive errors.

The Results of the Fiscal Control Activity in the Member States of the European Union in the field of VAT

The article presents an analysis of the influence that the results of the control activity obtained by the Member States had on the value-added tax. An important part of the work is dedicated to the activity of international fiscal control due to international cooperation resulting from cross-border economic activities. Analyzing the budgetary control activity carried out by the 28 member states of the European Union during 6 consecutive years, a series of similarities and differences can be noted both in terms of the implementation method and the results obtained. Among the financial obligations taxpayers owe, the proposed econometric analysis focuses on the value-added tax. Thus, to carry out the econometric analysis, we used the dependent variable: VAT related to fiscal revenues, and as an independent variable: additional VAT established, as a result of fiscal control, against the total of additional budgetary obligations resulting from fiscal control activity. The result indicates a statistically valid linear regression model, whereby a percentage increase in the value-added tax established by the tax control activity leads to a 0.14% increase in the total tax revenues collected.

The Burr distribution as a model for the delay between key events in an individual's infection history.

Understanding the temporal relationship between key events in an individual's infection history is crucial for disease control. Delay data between events, such as infection and symptom onset times, is doubly censored because the exact time at which these key events occur is generally unknown. Current mathematical models for delay distributions are derived from heuristic justifications. Here, we derive a new model for delay distributions, specifically for incubation periods, motivated by bacterial-growth dynamics that lead to the Burr family of distributions being a valid modelling choice. We also incorporate methods within these models to account for the doubly censored data. Our approach provides biological justification in the derivation of our delay distribution model, the results of fitting to data highlighting the superiority of the Burr model compared to currently used models when the mode of the distribution is clearly defined or when the distribution tapers off. Under these conditions, our results indicate that the derived Burr distribution is a better-performing model for incubation-period data than currently used methods, with the derived Burr distribution being 13 times more likely to be a better-performing model than the gamma distribution for Legionnaires' disease based on data from a known outbreak.

Experimental murine non-alcoholic steatohepatitis is associated with behavioural, cognitive, and peripheral neuronal dysfunction

Introduction. Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH), the leading causes of chronic liver disease worldwide, are associated with a wide range of psychoneurological complications and conditions. However, the causal relationship between liver and nervous system disease remains poorly understood, which warrants the development of clinically relevant and valid animal models thereof.Aim. The objective of this work was to characterize the short- and long-term psychoneurological and peripheral neuronal deficits that complicate different stages of NAFLD/NASH in mice.Materials and methods. 68 adult male C57Bl/6 mice were randomized into Control or NASH groups. NASH was induced over 3 (Experiment 1) or 6 (Experiment 2) mo using a combined model including a high-fat diet and low doses of carbon tetrachloride. Control group received standard chow, drinking water, and equivolume normal saline. Animal behaviour was assessed by the Open field (OF), Elevated plus maze (EPM), and Light/dark box (LDB) tests at 1, 2, 3, and 6 mo of NASH induction. Visuospatial memory was assessed by the Spontaneous alternation in the T-maze and Novel object recognition tests at 1, 2, and 3 mo of NASH modelling, and using the Barnes maze at 6 mo of NASH induction. Following 3 mo of NASH induction, needle electroneuromyography (ENMG) was performed on the gastrocnemius and biceps muscles with the electrical stimulation of the sciatic and musculocutaneous nerves, respectively. Liver pathology was confirmed by histomorphology. Statistical analysis was performed using Prism 10.2.3 and R 4.2.3 with RStudio 2024.09.0.Results and discussion. Experimental modelling was associated with poorer overall survival (p < 0.05, p < 0.01) and substantial evidence of liver injury, i.e. cholestatic hepatitis, medio- and macrovesicular steatosis, focal necrosis and fibrosis of varying severity (p < 0.05, p < 0.01). Mice with NASH exhibited markers of elevated anxiety in the OF, EPM, and LDB tests (p < 0.05, p < 0.01), which were mostly specific to the very onset of liver disease (1 mo) as well as its later stages (6 mo). NASH was also associated with a significant decrease in spontaneous alternation at 3 mo (p < 0.01), negative object disrimination at 2 mo (p < 0.05), and poorer memory retention in the Barnes maze (p < 0.05, p < 0.01) compared with Control. ENMG data analysis revealed significantly lower peak M-wave amplitudes (p < 0.01) and threshold currents (p < 0.05) in the gastrocnemius, and increased peak latency in the biceps in the NASH group (p < 0.05).Сonclusion. Experimental alimentary/toxic NASH in male C57Bl/6 mice is associated with increased anxiety-like behaviour, visuospatial memory acquisition and retention impairment, and evidence of axonal and demyelinating peripheral motor neuropathy.

Development and validation of a novel model to predict post-stroke cognitive impairment within 6 months after acute ischemic stroke.

Cognitive decline following acute ischemic stroke (AIS), termed post-stroke cognitive impairment (PSCI), is a prevalent phenomenon that significantly elevates disability and mortality rates among affected patients. The objective of this investigation was to develop a robust clinical prediction model capable of forecasting PSCI within six months post-AIS and subsequently validate its effectiveness. A cohort of 573 AIS patients was stratified into two groups: those with PSCI (260 cases) and those who remained cognitively normal (CN) (313 cases). These patients were further subdivided into three distinct cohorts: a development cohort comprising 193 AIS patients, an internal validation cohort with 193 AIS patients, and an external validation cohort encompassing 187 AIS patients. A thorough multifactor logistic regression analysis was conducted to identify independent predictors of PSCI, which were subsequently incorporated into the prediction model for comprehensive analysis and validation. The discriminatory power, calibration accuracy, and clinical net benefits of the prediction model were rigorously evaluated using the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and decision curve analyses, respectively. Utilizing a meticulously selected panel of variables, including smoking status, alcohol consumption, female gender, low educational attainment, NIHSS score at admission, stroke progression, diabetes mellitus, atrial fibrillation, stroke localization, HCY levels, and Lp-PLA2 levels, a clinical prediction model was formulated to predict the occurrence of PSCI within six months of AIS. The model demonstrated AUC-ROC values of 0.898 (95%CI, 0.853-0.942), 0.847 (95%CI, 0.794-0.901), and 0.849 (95%CI, 0.7946-0.9031) in the development, internal validation, and external validation cohorts, respectively. Further validation through calibration curve analyses, Hosmer-Lemeshow goodness-of-fit tests, and additional metrics confirmed the model's impressive predictive performance. The proposed model exhibits strong discriminative ability for predicting PSCI and holds considerable promise for guiding clinical decision-making. However, ongoing optimization with multicenter data is necessary to bolster its robustness and broaden its applicability.

Development and Validation of a Preclinical Canine Model for Delayed Onset Fracture-Related Infections

Background: Fracture-Related Infections (FRIs) are a challenging complication in orthopaedics. FRI incidence is considerably high, particularly in open fractures. FRI management typically involves multiple surgical interventions and prolonged antibiotic therapies. This regimen is often ineffective at infection eradication, resulting in poor outcomes and inefficient use of healthcare resources such that improved preventative and therapeutic interventions are needed. To effectively address these gaps, valid preclinical animal models for FRIs are needed. The purpose of this study was to develop and validate a canine ulna model for delayed FRIs that accurately mimics the clinical course noted in patients. Methods: In this model, a distal ulnar ostectomy was established, then internally stabilized with plates and screws that were pre-incubated with methicillin-resistant Staphylococcus aureus. After a 3-week period, all animals underwent irrigation and debridement of the fracture site followed by clinical, radiographic, bacteriologic, and histologic assessments over the subsequent 8 weeks. Results: This preclinical canine model established a valid representation of delayed FRI in patients based on clinical, radiographic, bacteriologic, and histologic features. Bilateral distal ulnar ostectomies stabilized with MRSA-incubated implants were consistently associated with clinical signs of local infection, radiographic evidence for delayed union with osteomyelitis and implant failure, and implant-associated biofilm formation 3 weeks after “fracture” creation. Conclusion: The translational rigor of the model allows for efficient and effective testing of novel preventive and therapeutic interventions aimed at improving outcomes for FRI patients.

Availability of Critical Benchmark Experiments for the Pebble Tanker Transportation Model for Nuclear Criticality Safety Validation of TRISO Pebbles

This study addresses the need for comprehensive investigations into TRi-structural ISOtropic (TRISO) fuel pebble transportation validation. In this work, an exploratory model, the pebble tanker(PT), was developed with the aim of facilitating the validation of nuclear criticality safety calculations in the context of industrial-scale transportation of TRISO fuel. The PT model was designed to investigate the availability and applicability of critical benchmark experiments crucial for assessing the transportation of these pebbles. This work incorporated sensitivity/uncertainty (S/U) similarity studies to quantify the applicability of critical benchmark experiments and to address nuclear data uncertainties in the context of TRISO transportation. Two container models were investigated: one for the Hermes-type pebble and one for the Pebble Bed Modular Reactor (PBMR)–type pebble. The models were simplified, considering fuel, containment, and either water or air, to enable a focus on the underlying physics of applications involving TRISO fuel pebbles using the PT model. A crucial aspect under consideration was the capacity of the transport package to hold pebbles while ensuring subcriticality in the flooded state. An approach in the criticality validation process involves assessing the similarity between systems through an integral index parameter evaluation. This involves calculating a correlation coefficient (referred to as ck) based on shared nuclear data–induced uncertainty between a benchmark experiment and the application of the PT model. To facilitate this analysis, the SCALE tools, particularly the CSAS6-Shift, TSUNAMI-3D-Shift, and TSUNAMI-IP sequences, were employed for comprehensive studies in neutronics and S/U analysis. Our findings showed that there are sufficient critical experimental benchmarks to perform this validation of the PT model in the most reactive state, i.e. when the tanker is flooded. This paper provides valuable insights into validating a transport package for Generation IV TRISO fuel pebbles.

Scientific, inquiry, and animation integration: The IPSIA learning model in chemistry

This study addresses the issue of low active participation among students in chemistry learning, as well as their difficulties in understanding macroscopic, microscopic, and symbolic concepts, which contribute to unsatisfactory learning outcomes. To tackle these challenges and enhance students' performance in chemistry, a valid, practical, and effective IPSIA learning model was developed. The research employed a development approach based on the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) model, beginning with a thorough analysis of the curriculum. This was followed by the design of the IPSIA model, which was validated using a questionnaire instrument. The results of the validity test scored 98, categorizing it as "very good." Additionally, the practical test of the IPSIA model also received a score of 98, indicating that it is user-friendly. During the implementation phase, the effectiveness of the model was assessed through test results. The improvement in students' chemistry learning was evidenced by normalized gain scores: 0.57 for the control class and 0.72 for the experimental class at Senior High School (SMA) 2 Padangsidimpuan, as well as 0.52 for the control class and 0.64 for the experimental class at SMA 5 Padangsidimpuan. These findings demonstrate that the IPSIA learning model is valid, practical, and effective in improving students' chemistry learning outcomes.

Reversal of the motivational effects of tetrabenazine by NMDA receptor blockade.

Apathy is a syndrome of decreased goal-directed activity, one of the main features of different brain disorders. Despite its high prevalence and life-threatening potential, there are currently very few options for its pharmacological treatment, which may be related to the lack of valid animal models. The vesicular monoamine transporter 2 inhibitor tetrabenazine (TBZ) was used in this study to model apathy-related behavior in pathologies linked to a depletion of dopamine. The atypical dopamine transporter inhibitor CE-123 and the NMDA receptor antagonist MK-801 were evaluated for their effects on goal-directed activity in intact and TBZ-treated rats to compare dopamine and non-dopamine approaches. To assess goal-directed behavior, the progressive ratio 3 (PR3) operant schedule of food reinforcement was conducted in adult male rats. To assess the motivational changes underlying the schedule, a model analysis based on the mathematical principles of reinforcement was applied. Treatment with TBZ (0.3 mg/kg) induced a decrease in response rate as the number of required responses increased. This effect was not accompanied by a decrease in the incentive value of the reinforcer or locomotor disturbances, suggesting that decreased tolerance to high effort demands was the underlying mechanism of the decrease in goal-directed activity. Treatment with MK-801 increased operant activity in both TBZ-treated and pharmacologically naïve rats. Our results support the previously proposed view that the TBZ-treated rats can be a model of apathy-related behavior in pathologies linked to a depletion of dopamine and suggest that NMDA receptors are a potential therapeutic target for the development of novel approaches to the treatment of apathy in both dopamine-depleted and dopamine-intact states.

Using Conceptual Blending Theory to help college students design online communication models

Objective: This study examines modal social media visuals using Conceptual Blending Theory (CBT) and its theoretical foundation, Conceptual Blending Network, to improve college students' visual communication on social media. Methods: The study explores social media visuals and CBN's mechanism. It goes beyond meme design to explain the key factors and reinforcement strategies needed to use CBN to create social media visuals. This study creates an empirical experimental model for validation, a novel approach. The experimental model teaches college students how to create engaging social media visuals. Three university teachers in relevant fields collaborated on the research project with 232 diverse undergraduates who completed surveys and interviews. Results: The study found that undergraduates recognise the importance of visual language organisation for accurately conveying complex information on social media. Collaboration with experts and the introduction of CBT and CBN helped university students accept this advanced visual language organisation method for social media visuals. Conclusion: Participants' understanding, comprehension, and practical exposure to the proposed methodology significantly reduced their difficulties in designing compelling social media visuals. This research helps us understand social media visual dynamics and informs educators and practitioners who want to improve digital visual communication.

Identification of a pyroptosis-related prognostic model for colorectal cancer and validation of the core gene SPTBN5

BackgroundPyroptosis, an emerging type of programmed cell death. The mechanisms of pyroptosis mainly include inflammasome-activated pyroptosis and non-inflammasome-activated pyroptosis. Multiple prognostic scoring systems that utilize pyroptosis-related gene expression have been validated as effective predictors of patient outcomes. But the relationship between pyroptosis and colorectal cancer remains unclear. This study has established a gene signature associated with pyroptosis to forecast the prognosis of CRC patients. MethodsAn analysis of 52 pyroptosis genes was conducted in both CRC and normal colorectal tissues, leading to the discovery of differentially expressed genes (DEGs). Core pyroptosis-related genes were identified using least absolute shrinkage and selection operator (LASSO) Cox regression to establish a prognostic risk score (PRS) for predicting CRC patient outcomes. The TCGA cohort was split into high-risk and low-risk groups based on the PRS, followed by Gene Ontology (GO) and KEGG pathway analyses. Additionally, differences in the enrichment scores of 16 immune cell types and the activity of 13 immune-related pathways were compared. The role of SPTBN5, a core pyroptosis-related gene, was validated through functional experiments on human colorectal adenocarcinoma cells (SW480).Results40 differentially expressed genes were identified from 52 pyroptosis genes. A risk model was subsequently developed using 25 core pyroptosis-related genes identified through LASSO Cox regression analysis, and this model was validated in GEO cohorts. GO and KEGG pathway analyses showed that the DEGs are predominantly associated with mineral absorption, thyroid hormone synthesis, and pancreatic secretion. Functional experiments demonstrated that down-regulation of SPTBN5 expression through transfection led to significant decreases in the proliferation, migration, and clonogenicity of SW480 cells.ConclusionThe PRS can identify high-risk CRC patient groups and predict patient prognosis. SPTBN5 may present a potential therapeutic target for CRC.

PD-1 interactome in osteosarcoma: identification of a novel PD-1/AXL interaction conserved between humans and dogs

The PD-1/PDL-1 immune checkpoint inhibitors revolutionized cancer treatment, yet osteosarcoma remains a therapeutic challenge. In some types of cancer, PD-1 receptor is not solely expressed by immune cells but also by cancer cells, acting either as a tumor suppressor or promoter. While well-characterized in immune cells, little is known about the role and interactome of the PD-1 pathway in cancer. We investigated PD-1 expression in human osteosarcoma cells and studied PD-1 protein–protein interactions in cancer. Using U2OS cells as a model, we confirmed PD-1 expression by western blotting and characterized its intracellular as well as surface localization through flow cytometry and immunofluorescence. High-throughput analysis of PD-1 interacting proteins was performed using a pull-down assay and quantitative mass spectrometry proteomic analysis. For validation and molecular modeling, we selected tyrosine kinase receptor AXL—a recently reported cancer therapeutic target. We confirmed the PD-1/AXL interaction by immunoblotting and proximity ligation assay (PLA). Molecular dynamics (MD) simulations uncovered binding affinities and domain-specific interactions between extracellular (ECD) and intracellular (ICD) domains of PD-1 and AXL. ECD complexes exhibited strong binding affinity, further increasing for the ICD complexes, emphasizing the role of ICDs in the interaction. PD-1 phosphorylation mutant variants (Y223F and Y248F) did not disrupt the interaction but displayed varying strengths and binding affinities. Using bemcentinib, a selective AXL inhibitor, we observed reduced binding affinity in the PD-1/AXL interaction, although it was not abrogated. To facilitate the future translation of this finding into clinical application, we sought to validate the interaction in canine osteosarcoma. Osteosarcoma spontaneously occurs at significantly higher frequency in dogs and shares close genetic and pathological similarities with humans. We confirmed endogenous expression of PD-1 and AXL in canine osteosarcoma cells, with PD-1/AXL interaction preserved in the dog cells. Also, the interacting residues remain conserved in both species, indicating an important biological function of the interaction. Our study shed light on the molecular basis of the PD-1/AXL interaction with the implication for its conservation across species, providing a foundation for future research aimed at improving immunotherapy strategies and developing novel therapeutic approaches.