bluuebunny/crossref_metadata_2025_split huggingface dataset:
Molecular underpinnings of aging contributing to systemic sclerosis pathogenesis
Authors: Yang Monica M. , Boin Francesco , Wolters Paul J. 
Published: October 2024

Purpose of review
Systemic sclerosis (SSc) is a systemic autoimmune disease characterized by diffuse organ fibrosis and vasculopathy. Aberrant aging has been increasingly implicated in fibrotic diseases of the lung and other organs. The aim of this review is to summarize the established mechanisms of aging and how they may contribute to the pathogenesis of SSc.

Recent findings
Shortened telomeres are present in SSc patients with interstitial lung disease (SSc-ILD) and associate with disease severity and mortality. Although the cause of telomere length shortening is unknown, immune mechanisms may be at play. Senescent cells accumulate in affected organs of SSc patients and contribute to a pathologic cellular phenotype that can be profibrotic and inflammatory. In addition to identifying patients with a more severe phenotype, biomarkers of aging may help identify patients who have worse outcomes with immunosuppression.

Summary
Aging mechanisms, including telomere dysfunction and cellular senescence, likely contribute to the progressive fibrosis, vasculopathy, and immune dysfunction of SSc. Further work is needed to understand whether aberrant aging is an initiator or perpetuator of disease, and whether this is cell or organ specific. A better understanding of the role aging mechanisms play in SSc will contribute to our understanding of the underlying pathobiology and may also influence management of patients exhibiting the aging phenotype.
Artificial Intelligence and Admissions to Health Professions Educational Programs
Authors: Lewis Lisa S. , Hartman Ann Michelle , Brennan-Cook Jill , Felsman Irene C. , Colbert Briana , Ledbetter Leila , Gedzyk-Nieman Stephanie A. 
Published: October 2024

Background:
The use of large language models (LLMs) and artificial intelligence (AI) tools to prepare health professions admissions applications is increasing. These tools can improve writing significantly but raise ethical concerns about application authenticity.

Purpose:
This scoping review explored the literature on use of AI by applicants applying to health professions programs and by admission reviewers.

Methods:
Following Joanna Briggs Institute and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, a search was conducted in multiple databases, which identified 1706 citations. After screening, 18 articles were included.

Results:
Articles included in the review focused on the (1) use of AI to screen applicants or predict ranking and interview invitations, (2) ethical implications of AI-generated personal statements, (3) potential to detect AI-generated applications, and (4) use of AI to write or analyze letters of reference.

Conclusions:
AI tools can enhance the efficiency of the admissions review process, but clear guidelines are required to address ethical issues. Further research is needed, particularly in nursing education.
Is Migraine Surgery Financially Viable for the Hospital and Surgeon? A Single-Surgeon, Ten-Year Study at an Academic Institution
Authors: Nicksic Peter J , Albano Nicholas , Edalatpour Armin , Afifi Ahmed M 
Published: October 2024

Background:
Data exists to demonstrate that migraine surgery is cost-effective for the long-term treatment of headaches, but no data exists regarding its financial viability for healthcare institutions. The goals of the current study are to demonstrate that (1) migraine surgery is financially viable for an academic institution with a net-positive profitability and (2) migraine surgery is a sought-after therapy that can draw patients into the healthcare system.

Methods:
A retrospective chart review of migraine and bilateral breast reduction (BBR) consults seen in clinic between 05/01/2011-05/30/2020 was conducted. Patient age, sex, payor information, and distance from the hospital was collected. Profit margin, percentage profit, collection percentage, and conversion rate to surgery from clinic was calculated.

Results:
407 new migraine consults and 119 new BBR consults were included from which, 150 underwent migraine surgeries and 117 underwent BBR respectively. Migraine surgery group came from a further distance for the initial consult (P-value<0.001) and for surgery (P-value<0.001). There were also more migraine surgery patients from out-of-state (P-value=0.012). Migraine clinic patients had a larger ratio of private insurers (P-value<0.001). Migraine surgery patients had a greater total charge (P-value<0.001), estimated reimbursement (P-value=0.001), and total cost (P-value<0.001).

Conclusions:
This study provides evidence that migraine surgery is financially viable for hospitals and while performing similarly to bilateral breast reduction in profit margin, it brought in more patients from outside the catchment area of our institution. These findings may encourage hospitals to recruit migraine surgeons to improve care for this underserved patient population.
Intraoperative Neurophysiologic Monitoring Improves Neurologic Outcomes in Eloquent Brain Areas and Aids in Increasing the Volume of Resected Glioma: Current Results Compared With Historical Controls
Authors: Ilgaz Aydinlar Elif , Sari Ramazan , Yalinay Dikmen Pinar , Elmaci İlhan 
Published: October 2024

Purpose:
This study aims to show the impact of multimodal intraoperative neurophysiologic monitoring (IOM) in glioma surgery in preventing severe neurologic injury and increasing tumor removal by comparing the historical cases where IOM was not used.

Methods:
Fifty-nine patients with glial tumors located nearby the eloquent area, operated by the same surgeon, were included in the study. Between 2008 and 2012, 21 patients were operated on without IOM (non-IOM); between 2018 and 2021, 38 patients were operated on with IOM.

Results:
The preoperative Karnofsky performance status scale (KPSS) scores were not statistically significant between non-IOM and IOM groups (P = 0.351). Postoperative KPSS (mean 97.9) scores were 15.7% higher than preoperative KPSS (mean 84.6) in the IOM group (P < 0.001). Conversely, there was no significant difference between preoperative and postoperative KPSS scores (mean 78.5 and 81.5, respectively) in the non-IOM group (P = 0.472). Moreover, postoperative KPSS scores were 20% higher in the IOM group than in the non-IOM group (P < 0.001). Preoperative tumor sizes were double the size in the non-IOM group compared with those in the IOM group (P = 0.007). Nevertheless, the postsurgery tumor residue volume was almost four times higher in the non-IOM group than that in the IOM group (P = 0.035). A median of 93.35% of the tumor volume was resected in the IOM group, but only 77.26% of the tumor was removed in the non-IOM group (P < 0.001).

Conclusions:
Intraoperative neurophysiologic monitoring helps in a more radical tumor resection in glial tumors located close to the eloquent area, improves postoperative neurologic outcomes, and maintains the patient's quality of life.
Application of Ensemble Machine Learning to Metabolomic Data Identifies Metabolites Associated with Macrophage Polarization
Authors: Gannavaram Aadi , Paul Rahul , Karaiskos Spyros , Howard Marissa , Liotta Lance , Seshaiyer Padmanabhan 
Published: October 2024

AbstractTowards developing quantitative models of anti-tumor activities of macrophages, we evaluated the effects of cytokines, tumor exosomes, and polarization states of macrophages in a tumor microenvironemnt using a system of differential equations. We modeled the non-linear dynamics of macrophage polarization states (M0/M1/M2), tumor cell killing by macrophages, and evasion of macrophage mediated killing by tumor originated extracellular vesicle decoys. Solving these coupled differential equations using numerical approaches, showed that the rate of macrophage polarization into the M1 state is the critical determinant of anti-tumor activity mediated by M1 polarized macrophages. To determine what metabolomic factors correlate with the polarization of naïve macrophage into anti-tumor M1 or pro-tumor M2 phenotypes, we performed LC/MS-based untargeted metabolomic analysis. Statistical analysis using Python-Scikit-learn was performed on the metabolomic data from naïve, M1 or M2 polarized murine macrophages followed by multiple feature selection methods. Application of ensemble machine learning methods to both secreted and cell associated metabolites revealed novel molecules of fatty acid metabolism to be the main mediators of polarization. Integration of ensemble machine learning feature-ranking tools into our analysis of metabolomic data identified new potential targets in macrophage metabolism for enhancing anti-tumor activities.
Business model reconfiguration in low-tech firms: a process-based case study
Authors: Rosa Souza Aruana , Faccin Kadigia , Kretschmer Caroline 
Published: October 2024

PurposeThis study aims to capture organizational transformation by delving into the process of business model (BM) reconfiguration in small firms operating within low-tech industries. It seeks to uncover the specific BM components that enabled a traditional less volatile and smaller organizations to adapt and innovate. We offer some recommendations in the form of insights for practitioners and other firms facing similar challenges.Design/methodology/approachWe analyzed the history of a small firm belonging to a low-tech industry. Applying a single case study allowed us to explore organizational transformation in depth over time and identify the essential elements in the process of changing the BM. The analysis was performed using the procedural approach, with narratives, visual maps and temporal staggering.FindingsThe small firms went through many changes that resulted in BM reconfiguration at the organizational and industrial levels. The role of partners was relevant in this process, as well as organizational flexibility and more adaptable planning to make the necessary changes over time. The findings demonstrate the dynamism of the BM, with the prioritization of changes in organizational resources and capabilities, in order to adapt to the external environment, over time to create and capture value in a low-technology-intensive sector.Originality/valueThis study contributes a novel perspective to the field of Business Model Innovation (BMI) by highlighting the dynamic nature of BM in the under-explored area of small-scale enterprises of non-technology-intensive sectors. It emphasizes the critical role of serendipity, ambidexterity, pivoting, partnerships, adaptive planning and financial process organization in achieving sustained value creation and capturing value in the evolving external environments.
Increasing customer service competencies of airports: virtual integration competence, warmth and intimacy of artificial intelligence services
Authors: Ku Edward C.S. 
Published: October 2024

PurposeAirports are an essential part of the airline value chains. Artificial intelligence (AI) technologies are widely used at airports; the study aims to explore how the virtual integration competence and the perceived warmth of AI in airports increase customer service competencies and satisfy their passengers.Design/methodology/approachBased on the perspectives of digital competencies and hybrid intelligence, a continued usage intention model was analyzed using the partial least squares approach; this study used purposed sampling to collect data from those airports; participants who adopted the AI service in airports in Beijing, Taipei and Singapore who have the potential to use AI service usage experience more than three times and 384 completed questionnaires were analyzed.FindingsAI innovations serve human tasks at airports and analytics applications as change drivers and can replace legacy procedures. The research findings help point out the perceived warmth of AI and the virtual integration competence of airports utilizing the intimacy of AI services.Originality/valueAI innovations provide a service change to replace human tasks and intelligence and analytics applications at airports. AI services are a powerful tool for airports to serve their passengers efficiently; airports will collaborate with airlines to offer AI services to passengers.
What do we know about material handling in library? An empirical assessment in the Nordic region
Authors: Jefroy Niloofar , Sgarbossa Fabio 
Published: October 2024

PurposeLibraries, as a peaceful arena for promoting knowledge and culture, are often challenged by various means, which are mostly linked to the material handling. However, literature shorts fall in either recognizing how material handling comes under threat or evaluating the influence of promising solutions on the library’s performance. Thus, this study aims to present a comprehensive understanding of material handling in the library through the identification of characteristics, key performance indicators (KPIs) and challenges of this driving factor.Design/methodology/approachThis research benefits from a triangulation of qualitative methods, including systematic literature review, case study, in-depth semi-structured interview (SSI) and focus group meetings, to present a comprehensive understanding of material handling in the library.FindingsThe initial findings highlight the characteristics, i.e. incremental inventory, no packaging, open access order, etc., and the KPIs of material handling in the library: flexibility, productivity, service quality and cost. In addition, the challenges that limit the libraries’ performance are discovered based on field observation through five Nordic libraries (Norway and Finland), which are further studied for discovering the potential remedies. Corollaries advise that material handling challenges in the library are potentially tackled through three pillars, known as Triple E’s: employee, equipment and environment.Originality/valueThe performance of libraries is notably linked to the material handling processes and this agenda has received limited attention by academia. This study is among the initial research efforts in this context seeking to develop a comprehensive understanding of the characteristics, KPIs and challenges of material handling in the library and facilitate the improvement of library’s performance.
Analyzing the differential impact of variables on the success of solicited and unsolicited private participation in infrastructure projects using machine learning techniques
Authors: Ayat Muhammad , Ullah Mehran , Pervez Zeeshan , Lawrence Jonathan , Kang Chang Wook , Ullah Azmat 
Published: October 2024

PurposeThe study aims to examine the impact of key variables on the success of solicited and unsolicited private participation in infrastructure (PPI) projects using machine learning techniques.Design/methodology/approachThe data has information on 8,674 PPI projects primarily derived from the World Bank database. In the study, a machine learning framework has been used to highlight the variables important for solicited and unsolicited projects. The framework addresses the data-related challenges using imputation, oversampling and standardization techniques. Further, it uses Random forest, Artificial neural network and Logistics regression for classification and a group of diverse metrics for assessing the performances of these classifiers.FindingsThe results show that around half of the variables similarly impact both solicited and unsolicited projects. However, some other important variables, particularly, institutional factors, have different levels of impact on both projects, which have been previously ignored. This may explain the reason for higher failure rates of unsolicited projects.Practical implicationsThis study provides specific inputs to investors, policymakers and practitioners related to the impacts of several variables on solicited and unsolicited projects separately, which will help them in project planning and implementation.Originality/valueThe study highlights the differential impact of variables for solicited and unsolicited projects, challenging the previously assumed uniformity of impact of the given set of variables including institutional factors.
Research on the decision-making of neighborhood renewal projects based on low-carbon technology
Authors: Zhao Guochao , Wang Meixue , Zhang Juanfeng 
Published: October 2024

PurposeThis study proposes low-carbon technology (LCT) solutions from the perspective of incremental cost-effectiveness and public satisfaction based on calculating carbon emissions and economic costs.Design/methodology/approachAccording to the citation frequency, 11 indicators of low-carbon neighborhood (LCN) were selected so as to construct the low-carbon renewal potential evaluation model. Five neighborhoods were selected to evaluate low-carbon renewal potential based on the driving-pressure-state-impact-response (DPSIR). Moreover, the neighborhoods with the highest renewal potential were selected for further analysis. Then, the feasibility decision was carried out among seven typical LCTs based on the value engineering (VE) method. Finally, the TOPSIS method was applied to calculate the public satisfaction and demand so as to get the priorities of these LCTs. Through comprehensive analysis, the final LCT solutions could be carried out.FindingsOur practice proves that the evaluation model combined with the decision-making methods can provide scientific decision-making support for the LCT solutions. Some LCTs perform consistently across different neighborhoods by comparing VE results and TOPSIS rankings. The solar photovoltaic (PV) (T3) has high value and significant attention which gives it a top priority for development, while the energy-efficient windows and doors (T2) have relatively low value.Originality/valueThere is a lack of research that considers the economic cost, low-carbon efficiency and public satisfaction when proposing LCT solutions for neighborhood renewal projects. Faced with the problem, we practice the decision-making from two dimensions, that is, the “feasibility decision with VE” and the “priorities decision with TOPSIS.” In this way, a balance between incremental cost-effectiveness and public satisfaction is achieved, and LCT solutions are proposed.
Incentives of the digital platform of experimental resource-sharing for megaprojects
Authors: Wang Yuying , Zhou Guohua 
Published: October 2024

PurposeThe suppliers of experimental resources required in megaprojects are driven by short-term interests, presuming that participation in the digital platform would only increase their inputs and fail to rapidly expand their revenue, resulting in their insufficient motivation to participate. This paper aims to design effective incentives for these suppliers exhibiting the aforementioned behaviour to drive them to participate and actively share their resources on the platform.Design/methodology/approachThis paper develops incentives for applying the digital platform for experimental resource sharing by using a reverse induction approach to model and solve an incomplete information game. It compares the traditional experiment management mode and the new mode of applying the digital platform, taking the degree of sharing experimental resources on the platform as the variable and constructing three incentive models. By analysing these different degrees of sharing and the different experimental and informatisation capabilities of the suppliers, it could obtain the optimal incentive scheme for changes in sharing behaviour.FindingsThe results show that the designed incentives could increase the participation of suppliers in the platform and the number of their shared resources and make the benefits of both the supplier and the demand side reach the optimal state of a win-win situation. However, a higher degree of sharing by suppliers does not yield better results. In addition, the incentive coefficients for this degree should be set based on the suppliers’ different experimental and informatisation capabilities and the ratio of input cost-sharing, so as to avoid blind inputs from both supply and demand.Originality/valueThis study fills the research gap regarding incentives of the digital platform of experimental resource-sharing for megaprojects; it contributes to the body of knowledge by providing a quantitative perspective of understanding the experimental resource-sharing behaviour that motivates the usage of the digital platform. Furthermore, it reveals the incentive mechanism for application in different scenarios, and quantitative analysis is conducted to provide practical insights into promoting the new experiment management mode in megaprojects for more effective incentivisation.
Online portfolio selection with parameterized characteristics
Authors: Tu Xueyong , Li Bin 
Published: October 2024

PurposeOnline portfolio selection sequentially allocates wealth among a set of assets and aims to maximize the investor’s cumulative return in the long run. Various existing algorithms in the finance and accounting area adopt an indirect approach to exploit one asset characteristic through the channel of assets’ expected return and thus cannot fully leverage the power of various asset characteristics found in the literature. This study aims to propose new algorithms to overcome this issue to enhance investment performance.Design/methodology/approachWe propose a parameterized portfolio selection (PPS) framework, which directly incorporates multiple asset characteristics into portfolio weights. This framework can update parameters timely based on final performance without intermediate steps and produce efficient portfolios. We further append L1 regularization to constrain the number of active asset characteristics. Solving the PPS formulation numerically, we design two online portfolio selection (OLPS) algorithms via gradient descent and alternating direction method of multipliers.FindingsEmpirical results on five real market datasets show that the proposed algorithms outperform the state of the arts in cumulative returns, Sharpe ratios, winning ratios, etc. Besides, short-term characteristics are more important than long-term characteristics, and the highest return category is the most important characteristic to improve portfolio performance.Originality/valueThe proposed PPS algorithms are new end-to-end online learning approaches, which directly optimize portfolios by asset characteristics. Such approaches thus differ from existing studies, which first predict returns and then optimize portfolios. This paper provides a new algorithmic framework for investors’ OLPS.