Basis For Decisions Research Articles

University Contributions to Energy Saving and The SDG: A Case Study from Taiwan

Introduction: This study explores five energy-saving action plans promoted by the Faculty of Education of a university in Taiwan and evaluates their contribution to the five indicators of the Sustainable Development Goal (SDG). The results of quantitative analysis based on Analytic Hierarchy Process (AHP) found that: 1. "Updating power-consuming equipment" (weight value .433) is the highest priority implementation plan; 2. The five plans have a significant impact on the characteristic indicator "net zero carbon emissions" has the highest contribution (weight value .284); 3. Promoting energy-saving actions requires considering multi-faceted contributions to balance the development of various SDGs indicators promoted by the university. The significance of this study is to provide a specific basis for the priority order of energy-saving actions in the case college, and to provide multiple perspectives for the formulation of follow-up action plans. In addition, this study’s value lies in its development of an evaluative model that universities can apply to design or compare sustainability programs. Objective: This study explores five energy-saving action plans promoted by the Faculty of Education of a university in Taiwan and evaluates their contribution to the five indicators of the Sustainable Development Goal (SDG). Theoretical Framework: The theoretical framework of this study centers on the Analytic Hierarchy Process (AHP), utilizing a hierarchical structure and pairwise comparison to quantify the priority of energy-saving action plans and assess their contributions to the SDG. This framework provides a basis for decision-making, enhancing the precision and systematization of resource allocation and action plan development in the case study institution. Method: This study adopts a case study approach, with a workshop consisting of five participants. Evaluation data were collected through interview questionnaires, and AHP analysis was conducted using Expert Choice software to generate weight values and sensitivity analysis charts, which were used to assess the study's results and findings. Results and Discussion: The results indicate that: 1. "Upgrading power-consuming equipment" is the highest priority action plan; 2. The plan contributes the most to "net zero carbon emissions." These findings, derived from AHP analysis, highlight the need to consider multi-dimensional contributions of energy-saving actions to balance the development of various SDG indicators. Additionally, the results may be subject to case-specific variations and limitations in generalizability. Research Implications: This study found that "upgrading power-consuming equipment" contributes most to "net zero carbon emissions," suggesting that universities should prioritize technological upgrades and combine them with educational promotion to enhance multi-dimensional SDG contributions, providing a model for energy-saving program evaluation and decision-making for other universities. Originality/Value: The originality and value of this study lie in the application of the AHP method to assess the contribution of university energy-saving actions to the SDG, innovatively proposing specific action priorities and decision-making references. This model serves as a reference for universities to formulate energy-saving strategies and can be extended to other universities, facilitating inter-university comparisons of energy-saving effectiveness and the sharing of best practices.

Post-translational modifications orchestrate mTOR-driven cell death in cardiovascular disease

The mechanistic target of rapamycin (mTOR) signaling pathway is a central regulator of cellular physiology, modulating processes such as metabolism, protein synthesis, growth, and various forms of cell death. Increasing evidence has revealed that dysregulation of mTOR activity, often triggered or exacerbated by aberrant post-translational modifications (PTMs), contributes to the onset and progression of cardiovascular diseases (CVDs), including atherosclerosis, myocardial infarction, heart failure, and ischemia-reperfusion injury. PTMs such as phosphorylation, ubiquitination, SUMOylation, acetylation, and glycosylation alter mTOR's upstream regulators and downstream effectors, influencing the balance between apoptosis, autophagy, pyroptosis, and ferroptosis. These regulatory mechanisms provide a molecular basis for cell fate decisions during cardiovascular stress and injury. In this review, we systematically summarize recent advances in the understanding of PTM-mediated control of mTOR signaling, with a focus on cardiovascular pathophysiology. We also highlight emerging therapeutic strategies that target PTMs or the mTOR axis, including mTOR inhibitors, AMPK activators, proteasome blockers, and SUMOylation modulators, all of which show promise in preclinical or clinical settings. Understanding how PTMs fine-tune mTOR activity and cell death may pave the way for novel, targeted interventions in cardiovascular medicine and offer potential avenues for the development of precision therapies.

Green and Low-Carbon Strategy of Logistics Enterprises Under “Dual Carbon”: A Tripartite Evolutionary Game Simulation

In the low-carbon era, there is a serious challenge of climate change, which urgently needs to promote low-carbon consumption behavior in order to build sustainable low-carbon consumption patterns. The establishment of this model not only requires in-depth theoretical research as support, but also requires tripartite cooperation between the government, enterprises and the public to jointly promote the popularization and practice of the low-carbon consumption concept. Therefore, by constructing a tripartite evolutionary game model and simulation analysis, this study deeply discusses the mechanism of government policy on the strategy choice of logistics enterprises. The stability strategy and satisfying conditions are deeply analyzed by constructing a tripartite evolutionary game model of the logistics industry, government, and consumers. With the help of MATLAB R2023b simulation analysis, the following key conclusions are drawn: (1) The strategic choice of logistics enterprises is affected by various government policies, including research and development intensity, construction intensity, and punishment intensity. These government policies and measures guide logistics enterprises toward low-carbon development. (2) The government’s research, development, and punishment intensity are vital in determining whether logistics enterprises adopt low-carbon strategies. R&D efforts incentivize logistics companies to adopt low-carbon technologies by driving technological innovation and reducing costs. The penalties include economic sanctions to restrain companies that do not comply with low-carbon standards. In contrast, construction intensity mainly affects the consumption behavior of consumers and then indirectly affects the strategic choice of logistics enterprises through market demand. (3) Although the government’s active supervision is a necessary guarantee for logistics enterprises to implement low-carbon strategies, more is needed. This means that in addition to the government’s policy support, it also needs the active efforts of the logistics enterprises themselves and the improvement of the market mechanism to promote the low-carbon development of the logistics industry jointly. This study quantifies the impact of different factors on the system’s evolution, providing a precise decision-making basis for policymakers and helping promote the logistics industry’s and consumers’ low-carbon transition. It also provides theoretical support for the logistics industry’s low-carbon development and green low-carbon consumption and essential guidance for sustainable development.

Effects of letrozole combined with clomiphene in the treatment of polycystic ovary syndrome: a meta-analysis.

PCOS is a common endocrine gynecological disorder, and its associated hormonal abnormalities and ovulatory dysfunction are leading causes of infertility. Ovulation induction is considered a fundamental treatment for PCOS patients. Clomiphene and letrozole are currently widely used ovulation-inducing agents. This study employs meta-analysis to comprehensively and systematically evaluate the clinical efficacy and adverse effects of letrozole combined with clomiphene in the treatment of PCOS. The aim of this study is to compare the clinical efficacy and adverse effects of letrozole combined with clomiphene in the treatment of PCOS, providing a theoretical basis for therapeutic decision-making. RCTs on letrozole combined with clomiphene for PCOS were retrieved from databases including PubMed, EMBASE, Cochrane Library, CNKI, Wanfang Medical Network, VIP Chinese Journal Service Platform, and CBM, with the search period spanning from database inception to March 2025. Data analysis was performed using Review Manager 5.3 and Stata software. Dichotomous variables were analyzed using RR with 95% CI, while continuous variables were assessed using SMD with 95% CI. The Cochrane Risk of Bias tool was used to evaluate the bias and risk of eligible studies. Two researchers independently screened the literature, extracted data, and assessed the risk of bias in included studies. A total of 10 studies involving 931 PCOS patients were included. Compared with the control group, letrozole combined with clomiphene significantly improved pregnancy rates (RR = 1.50, 95% CI: 1.228-1.77, P < 0.00001), ovulation rates (RR = 1.29, 95% CI: 1.15-1.444, P < 0.0001), and estradiol levels (SMD = -1.13, 95% CI: -2.02 to -0.24, P = 0.001). However, no significant effects were observed on luteinizing hormone levels (SMD = -0.48, 95% CI: -1.16 to 0.21, P = 0.17), follicle-stimulating hormone levels (SMD = -0.45, 95% CI: -1.08 to 0.17, P = 0.15), endometrial thickness (SMD = 0.69, 95% CI: -0.57 to 1.96, P = 0.28), number of mature follicles (SMD = 0.83, 95% CI: -0.26 to 1.93, P = 0.15), diameter of mature follicles (SMD = 0.77, 95% CI: -0.55 to 2.09, P = 0.25), miscarriage rates (RR = 0.38, 95% CI: 0.18 to 0.81, P < 0.0001), or adverse effects (RR = 0.77, 95% CI: 0.23 to 2.64, P = 0.68). Letrozole combined with clomiphene therapy helps improve ovulation rates, pregnancy rates, and hormonal levels in PCOS patients.

Comparison of LNR- and LODDS-based predictive models for prognosis in non-elderly patients with locally advanced rectal cancer undergoing neoadjuvant therapy

PurposeThe purpose of this study was to comprehensively assess the efficacy of lymph node ratio (LNR) and log odds of positive lymph nodes (LODDS) in predicting survival outcomes in non-elderly locally advanced rectal cancer (LARC) patients treated with neoadjuvant chemoradiotherapy (NCRT).MethodsThe 1643 non-elderly LARC patients undergoing NCRT between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and were randomly assigned at a ratio of 7:3. We used Cox regression models to identify independent prognostic factors, then constructed nomogram models to predict cancer-specific survival (CSS) and overall survival (OS). The relative weight in nomogram models, receiver operating characteristic (ROC), area under the curve (AUC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) were performed to evaluate and compare the predictive performance between LNR and LODDS.ResultsThe distribution of LNR and LODDS showed that LODDS exhibited a more detailed stratification when LNR was equal to the extreme value. In predicting CSS, the LNR (hazard ratio (HR), 1.987; 95% confidence interval (CI), 1.375–2.872) and LODDS (HR, 1.568; 95% CI, 1.154–2.131) were independent risk factors in corresponding nomogram models. Regarding OS, the LODDS (HR, 1.387; 95% CI, 1.060–1.816) showed an independent predictive value. All evaluation methods confirmed the reliability of both models. Although the LNR-based model showed better performance for short-term CSS and the LODDS-based model demonstrated slightly better prediction for long-term CSS and OS, these two models showed largely comparable predictive ability.ConclusionsThe LNR and LODDS can provide complementary prognostic value in survival prediction, offering clinicians a comprehensive basis for clinical decision-making.

Optimization of enterprise financial health assessment model based on genetic algorithm

This article studied an optimization method for enterprise financial health assessment (FHA) model based on genetic algorithm (GA). In response to the problems of strong subjectivity, insufficient dynamism, and limited data processing capabilities in traditional evaluation models, this article optimized the weights of financial indicators through GAs to improve the scientific and accurate evaluation results. It used 11 financial indicators as the main data to collect financial data from multiple enterprises and form a dataset. GA can be used to optimize the weights of financial indicators. This article constructed a FHA model, evaluated the constructed model, conducted corresponding analysis based on experimental results, and drew conclusions. The results showed that the model optimized by GA had significant improvements in evaluation accuracy and adaptability. The maximum mean squared error (MSE) of the GA in all comparative experiments was 19.962, which was lower than the minimum MSE of 20.043 obtained by other comparative methods. The maximum Mean Absolute Error (MAE) of the GA in all comparisons was 4.988, which was lower than the minimum MAE of 5.031 obtained by other comparison methods. This indicates that the enterprise financial health evaluation model based on GA has sufficient accuracy, is more objective in determining the weights of financial indicators, and has higher scientific validity in the evaluation results. The optimized enterprise FHA model provides more effective services for enterprise financial management. By optimizing the enterprise FHA model based on GA, it provides scientific basis for enterprise decision-making, improves enterprise management level, and provides new ideas and methods for the application of GA in enterprise finance. Future research can further explore the combined application of GAs and other intelligent algorithms, explore more efficient optimization methods, and promote the development of FHA research for enterprises.

A Study on Tourists' Low-Carbon Tourism Behavioral Intention Based on the Theory of Planned Behavior

Based on the Theory of Planned Behavior (TPB), this study takes the Qingming Shanghe Garden as an example to explore the influencing mechanism of tourists' intention for low-carbon tourism. Through a mixed research method, including questionnaire surveys and field research, the relationships among tourists' attitudes towards low-carbon tourism were analyzed. Through a mixed research method, including questionnaire surveys and field research, the relationships among tourists' attitudes towards low-carbon tourism, subjective norms, perceived behavioral control, and their behavioral intentions were investigated. The findings reveal that behavioral attitude, subjective norms, and perceived behavioral control all have significant positive impacts on tourists' intention for low-carbon tourism. The findings reveal that behavioral attitude, subjective norms, and perceived behavioral control all have significant positive impacts on tourists' intention for low-carbon tourism, with behavioral attitude having the most prominent influence. The research conclusions provide decision-making basis for low-carbon management in scenic spots and offer references for the low-carbon transformation of the tourism industry. The conclusions provide decision-making basis for low-carbon management in scenic spots and offer references for the low-carbon transformation of the tourism industry.

Research on the network structure evolution characteristics of outdoor sports tourism flow in the Yangtze River Economic belt based on online travel notes

Taking the Yangtze River Economic Belt as the study area, this research comprehensively applies spatial analysis methods and social network analysis methods to investigate the evolutionary characteristics of outdoor sports tourism flow network structure, utilizing online travelogue data collected through Python-based techniques. By integrating spatial analysis and social network analysis, we study the evolution characteristics of the network structure of outdoor sports tourism flow in the Yangtze River Economic Belt from 2012 to 2018. The results show that (1) The Yangtze River Economic Belt has shown steady growth in the number of outdoor sports tourism nodes, with significant spatial disparities in their distribution. These nodes are concentrated primarily in the lower reaches (eastern China) and upper reaches (western China) of the Yangtze River, demonstrating an overall development pattern characterized by “higher density in eastern and western regions compared to the central area.“; (2) the overall network density of outdoor sports tourism flow is not high, but during the study period, the overall network density value of outdoor sports tourism flow continues to rise, the connections between nodes are increasingly close, and the internal structure of the network is becoming clearer with time; and (3) the imbalance of the outdoor sports tourism flow network has gradually increased, which strengthens the role of the urban agglomeration or central city in the outdoor sports tourism flow network, aggravates the unbalanced state of the entire network structure, and the polarization of the network has become increasingly significant. The outdoor sports tourism flow essentially conforms to the “Pareto principle”. This study provides a scientific decision-making basis for rationally planning outdoor sports tourism routes and scientifically developing outdoor sports tourism destinations, while also offering theoretical support for optimizing the spatial layout and spatial structure of the outdoor sports tourism industry in the Yangtze River Economic Belt.

Rolul înregistrarii unitare a datelor de cost în luarea deciziilor din sistemul sanitar din România

At present, in Romania there is no unitary system for collecting cost data at the hospital level, so the policies in the field are not based entirely on valid and solid evidence. One of the current trends in health management is the use effectiveness and efficiency indicators in the evidence-based decision-making process. The variability of medical practice is one of the elements that lead to difficulties in the analysis of cost and outcome data. In order to stay at the basis for decisions, cost data must be uniformly collected and must use a unitary methodology and tools, so that their processing and aggregation and analysis can be done without compromise and avoiding the induction of systematic errors and thus invalidity of data and results obtained from these data. A detailed knowledge of resources consumption patterns in hospitals is needed and this should be complemented with an in-depth cost analysis in order to be able to calculate standard costs and relative values that allow a real and informed assessment of the level of consumption in hospitals.

Exploring the threshold of human activity impact on urban ecosystem service value: a case study of Hefei, China

Investigating the intricate relationship between human activities and ecosystem service value (ESV) amid rapid urbanization is essential for advancing urban ecological sustainability and development. While previous research has extensively documented the broad impacts of human activities on ESV, several critical gaps remain in our understanding. Specifically, the mechanisms through which human activities affect ecosystem services with varying functions remain unclear. Additionally, the identification of local anomalies in the spatiotemporal association between human activities and ESV is still lacking. These gaps hinder our ability to develop targeted strategies for maintaining ESV equilibrium in the face of ongoing urbanization. To address these gaps, this research employed Land Use and Land Cover (LULC) data from Hefei spanning 2000 to 2020. An equivalent factor corrected ESV assessment model, an ESV flow matrix, a human activity intensity (HAI) measurement model, and a GeoDetector model were utilized to conduct a comprehensive analysis. The objective was to elucidate the driving mechanisms of HAI on ESV, establish clear thresholds for HAI’s impact on various functional components of ESV, and accurately identify localized ranges of anomalous impact. The results indicate that: (1) The ESV of Hefei declined continuously from 47.012 billion yuan in 2000 to 45.534 billion yuan in 2020. (2) This decline was attributed to increased losses and decreased gains. Losses primarily stemmed from the conversion of cropland to built-up land and the transformation of water area into cropland, while gains mainly originated from the reversion of built-up land to cropland and the conversion of cropland to water area. (3) HAI demonstrated a negative correlation with overall ESV, as well as with regulating and cultural service value, and displayed a transition from positive to negative effects on supporting and provisioning service, with the transition threshold range locked between 0.176 and 0.262. HAI exhibited three abnormal impact ranges on ESV, suggesting that strategic land management could sustain ESV equilibrium under both low and high intensities of human activity. This study fills a significant gap in our understanding of the threshold effects of HAI on different functional components of ESV and the identification of local anomalous information. These findings provide a scientific basis for urban ecological environment management and decision-making in the process of rapid urbanization.

Applications and Ethical Challenges of AI and Virtual Reality (VR/AR) Technologies in Preschool Education

With the rapid development of artificial intelligence and virtual reality technologies, AI educational robots and VR immersive games are gradually revolutionizing the model of early childhood education. This study focuses on the personalized social training achieved by AI educational robots through natural language processing and emotion recognition technologies, as well as the simulation of real social scenarios by immersive virtual environments constructed with VR technology, and deeply explores its dual promoting effect on the cognitive and social ability development of young children. To accurately evaluate the development level of children's social ability, this paper innovatively proposes a deep neural network prediction model (Our model) optimized based on particle swarm optimization algorithm. By comparing typical machine learning models such as decision tree, AdaBoost, XGBoost and LightGBM, The system verified the performance differences of different algorithms in social ability prediction. The experimental results show that the basic model decision tree performs poorly in core indicators such as accuracy rate (0.632), recall rate (0.614), precision rate (0.621), and F1 score (0.617), but its AUC value (0.868) is significantly better than that of XGBoost (0.845) and LightGBM (0.853). The AdaBoost model shows strong classification stability, with an AUC value reaching 0.899, second only to the Our model. XGBoost shows obvious performance abnormalities, with the accuracy rate dropping sharply to 0.633. It is speculated that there is a problem of insufficient parameter optimization. The optimization model proposed in this study performed outstandingly in all indicators. Its accuracy rate (0.789), AUC value (0.916), and F1 score (0.773) all ranked first. Among them, the F1 score increased by 9.6% compared with the suboptimal model, and the AUC value increased by 1.9%, fully verifying the effectiveness of the particle swarm optimization algorithm in the optimization of deep neural networks. The research results not only provide intelligent solutions for the assessment of children's social skills, but also open up new paths for the deep integration and precise application of AI+VR educational technologies, which have significant practical value for promoting the digital transformation of preschool education.

The cost-utility and budget impact analyses of Tirzepatide versus once-weekly Semaglutide as add-on therapy to metformin in patients with type 2 diabetes mellitus in China.

To evaluate the long-term economic implications and potential budgetary consequences for the national health insurance system (NHIS) of tirzepatide versus semaglutide as add-on therapy to metformin in patients with type 2 diabetes mellitus (T2DM) in China. The demographic and clinical efficacy data were derived from the SURPASS-2 trial. The cost-utility analysis was performed to estimate the cost-utility by UK Prospective Diabetes Study Outcomes Model Version 2.2 from the Chinese health care providers' perspective, incorporating costs and quality-adjusted life years (QALYs). Sensitivity analyses were employed to investigate the presence of uncertainty. The financial implications for the relevant budgetary authority over 5 years were estimated. In the long-term treatment simulation, tirzepatide 5 and 10 mg yielded incremental QALYs of 0.05 and 0.09, in comparison with semaglutide 1 mg, respectively. The associated incremental costs for these treatments were $23 563.42 and $47 583.74, respectively. Consequently, the incremental cost-utility ratios were calculated to be $445 125.24/QALY and $543 829.19/QALY, both of which exceeded the established threshold of $29 599.90/QALY. These base-case results were validated through robust sensitivity analyses. The budget impact analysis showed that such an expansion would incur an additional budgetary burden of approximately $80 million to $490 million for the NHIS over a simulated 5-year period. This dual analysis provides policymakers and payers with a comprehensive basis for decision-making. Tirzepatide offered substantial long-term health benefits, but they were not cost-effective compared with semaglutide 1 mg in T2DM patients inadequately controlled with metformin under China's health care providers' perspective. The entry of tirzepatide 5 mg into the NHIS may increase the financial burden of national medical insurance, but overall health care expenditures are expected to decrease, driven primarily by significantly reduced out-of-pocket expenses for patients.

ESG ratings empower high-quality development of logistics enterprises through digital transformation and green innovation

In the context of economic globalization, the logistics industry, as a key link connecting production and consumption, is facing many challenges, and at the same time, ESG scores are gradually becoming a key indicator of the ability to measure the sustainable development of enterprises. Most of the existing literature focuses on a single dimension to explore the impact of ESG on logistics companies, and fewer analyze how ESG scores can empower listed logistics companies to achieve high-quality development from the perspectives of digital transformation and green innovation at the same time. In addition, in terms of research methodology, existing studies in the literature are deficient in systematic data analysis and scientific construction of models, and lack comprehensive robustness tests. This article focuses on how ESG ratings can empower listed logistics companies to achieve high-quality development and explores this issue from two perspectives: digital transformation and green innovation. This paper conducts an empirical analysis based on the data of listed companies in the logistics industry, and the results of the study show that there is a significant positive correlation between ESG scores and both the size and operational efficiency of logistics companies. Further mechanistic tests show that ESG scores can promote innovation in logistics enterprises, and when disaggregated, they are found to be effective in promoting digital transformation and upgrading as well as green innovation. Meanwhile, the robustness and reliability of the study’s conclusions are fully demonstrated through a comprehensive robustness test, including various aspects of variable measurement, sample selection, and endogeneity issue treatment. This study not only enriches the theoretical framework about high-quality development and ESG scoring of logistics enterprises at the theoretical level, but also provides a scientific decision-making basis for managers of logistics enterprises in practice. In addition, the research results also provide important reference value for government regulators and investors and help promote sustainable development and standardized management of the logistics industry.

Human, Animal, or Mineral? Ethical Considerations for Studies of Fossilized Hominin Remains.

Ethical considerations around research and education with modern human subjects, use of human tissue, and studies involving humans are ubiquitous within the global scientific community. This commentary explores considerations around how and whether these types of ethical considerations may apply to hominin fossils. While some might propose that the age of a fossil could be the basis for decisions around their ethical handling, it may be that characteristics or behaviors of the hominins are a more appropriate measure to determine their ethical status. These types of ethical considerations have recently been examined in other historical human tissues, such as mummies and ancient DNA, with these ethical explorations being driven by a recognition of evolving ethical perspectives around the world. Thus, there is both the precedent and stimulus for the paleoanthropology community to revisit current ethical practices within the profession. Doing so would help identify and establish standards that are reflective of global perspectives around the contemporary ethical management of hominin fossils, and potentially assist in aligning these practices with other disciplines that work with both hominin fossils and human tissues.

Electrical Impedance Tomography-Guided Airway Clearance in Elderly Patients With Severe Pneumonia: A Prospective Study.

Elderly patients are prone to secretion retention and exacerbated lung infections due to weakened respiratory muscle strength and reduced ability to cough and expectorate. Airway clearance techniques (ACTs) can help to clear airway secretions, but objective bedside assessment of secretion clearance efficacy is lacking. Electrical impedance tomography (EIT) can dynamically monitor lung ventilation and provide a basis for clinical decision-making. This study was a prospective randomized controlled trial that included 50 elderly patients with severe pneumonia, who were randomized into EIT and non-EIT groups. The EIT group received personalized ACTs guided by real-time EIT imaging with dynamic adjustment of posture, percussion intensity, and active circulatory breathing technique (ACBT) frequency, whereas the non-EIT group received fixed-schedule ACTs (postural drainage every 2 h + percussion/vibration twice daily) without EIT feedback. The main observation indices included Clinical Pulmonary Infection Score (CPIS), respiratory mechanics indices, blood gas analysis indices, and extubation success rate. The EIT group showed significantly lower CPIS scores (p = 0.0137 on Day 7), higher dynamic compliance (p = 0.0193), lower airway resistance (p = 0.0039), lower peak airway pressure (p = 0.0288), and higher oxygenation index (p = 0.0143 on Day 5 and p = 0.0005 on Day 7) than the non-EIT group. The extubation success rate was significantly higher in the EIT group (88% vs. 56%, p = 0.0255). Additionally, the EIT group demonstrated progressive improvements in ventilation in specific regions (D7 vs. D1: p = 0.0004 for region of interest [ROI]3; p = 0.0059 for ROI4) and a significant decrease in the global inhomogeneity index at D7 (D7 vs. D1: p = 0.0025). EIT-guided ACT is safe and enhances treatment efficacy by significantly improving respiratory function and extubation success rate in elderly patients with severe pneumonia.

UGDH promotes 5-fluorouracil resistance in colorectal cancer via the ROS-activated PI3K/AKT-EEF1A2-PRDX1 pathway.

UGDH promotes 5-fluorouracil resistance in colorectal cancer via the ROS-activated PI3K/AKT-EEF1A2-PRDX1 pathway.

Developing novel dynamic prediction methods for survival time to analyze short-term and long-term progression of Alzheimer's disease.

Developing novel dynamic prediction methods for survival time to analyze short-term and long-term progression of Alzheimer's disease.

E3 ligase recruitment by UBQLN2 protects substrates from proteasomal degradation.

Ubiquilins are a family of proteins critical to cellular proteostasis that are also linked to several neurodegenerative diseases, with specific mutations in UBQLN2 causing dominant, X-linked ALS. Despite an initial characterization as proteasomal shuttle factors, Ubiquilins have paradoxically been reported to stabilize numerous substrates. The basis of this triage decision remains enigmatic. Many other fundamental aspects of Ubiquilin function are unclear at the mechanistic level, such as the physiological significance of Ubiquilin phase separation, the unique role of each Ubiquilin paralog, and the mechanistic defects of ALS mutants. To address these questions, we utilized a library of triple knockout (TKO) rescue cell lines with physiological expression of single Ubiquilin paralogs or disease mutants in an isogenic background. Our findings reveal that UBQLN2 has a unique ability to protect substrates from degradation and that substrate stabilization correlates with the recruitment of multiple E3 ligases, including SCF bxo7 . We propose that E3 ligase recruitment promotes UBQLN2 phase separation, which protects substrates from proteasomal degradation. Consistent with this model, we demonstrate that ALS mutants, which were previously shown to have altered phase separation properties, also show a defect in substrate stabilization. Finally, we show that substrate stabilization appears to be a general feature of proteins that interact with the UBQLN2 Sti1 domains as amyloid precursor protein (APP) is also protected from proteasomal degradation by the formation of biomolecular condensates. This proposal unifies many existing observations in the field and presents a new paradigm for understanding Ubiquilin function in neurodegenerative disease.

The Influence of Credit Risk, Operational Efficiency, Liquidity, and Profitability on Company Value (Empirical Study on Banking Subsector Companies on the Indonesia Stock Exchange for the 2021-2024 Period)

The stock price, a gauge of firm value, reflects the market's assessment of the company's performance and prospects for the future. The company's high value indicates that investors have faith in management's capacity to allocate resources and promote sustained expansion. This study aims to investigate the relationship between credit risk, profitability, liquidity, and operational efficiency and corporate value. The study's population consists of the 40 banking subsector companies that were listed on the Indonesia Stock Exchange between 2021 and 2024. The sample strategy used in this study was non-probability sampling utilizing saturation sampling methodology. Data is taken from the company's financial filings for 2021–2024. The data analysis approach used in this study was multiple linear regression analysis using SPSS software. The results showed that while profitability has a positive effect on firm value, operational effectiveness and liquidity have a negative effect, and credit risk has no effect at all. The study's conclusions can lead the development of investment plans that provide the highest rate of return and provide a basis for strategic decisions relating to initiatives to increase business value.

ANALISIS KELAYAKAN INVESTASI PADA PENINGKATAN STATUS KLINIK UTAMA CAHAYA IMANI MENJADI RUMAH SAKIT TIPE D DI KABUPATEN HULU SUNGAI SELATAN PROVINSI KALIMANTAN SELATAN

This study aims to analyze the investment feasibility of upgrading Klinik Utama Cahaya Imani to a Type D Hospital in Hulu Sungai Selatan Regency, South Kalimantan Province. The analysis was conducted using methods such as Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PP), Profitability Index (PI), and Average Rate of Return (ARR). The research method applied was a qualitative descriptive method with a case study approach, through the collection of primary and secondary data from Klinik Cahaya Imani and related agencies. The analysis showed that the investment requirement amounts to Rp 110,060,402,638 with an estimated hospital revenue of Rp 32,339,793,340 per year. The financial analysis results indicated a positive NPV, IRR higher than the discount rate, PP faster than the economic life, PI greater than one, and ARR meeting the investment feasibility criteria. Thus, upgrading Klinik Utama Cahaya Imani to a Type D Hospital is financially feasible. The results of this study are expected to serve as a basis for managerial decision- making in developing health services in Hulu Sungai Selatan Regency