bluuebunny/crossref_metadata_2025_split huggingface dataset:
Toxoplasma gondii ROP18 induces maternal–fetal dysfunction by downregulating CD73 expression on decidual macrophages
Authors: Guo Jingjing , Wang Xiaohui , Wei Lei , Li Shuai , Wang Junwei , Zhang Yan , Yang Ruohan , Zhang Han , Xu Aiqun , Jiang Yuzhu , Hu Xuemei 
Published: February 2025

Abstract

Background
Decidual macrophages (dMφ) are pivotal in maintaining maternal–fetal immune tolerance during normal pregnancy by expressing a range of immune-suppressive molecules, including CD73. It has been demonstrated that Toxoplasma gondii (T. gondii) infection during pregnancy can impair dMφ function, potentially leading to adverse pregnancy outcomes, through downregulation of these inhibitory molecules. T. gondii rhoptry protein 18 (TgROP18), a key virulence factor of T. gondii, is associated with the incapacitation of the host’s innate and adaptive immune responses to protect the parasite from elimination. However, the role of TgROP18 in modulating CD73 expression on dMφ and the underlying mechanisms remain to be elucidated.

Methods
Wild-type (WT) and CD73-deficient (CD73−/−) pregnant mice were subjected to intraperitoneal injection of T. gondii RH or RH-Δrop18 on gestational day (Gd) 8, and subsequently euthanized on Gd 14. Pregnancy outcomes were then evaluated, and the expression levels of CD73, arginase 1 (Arg-1), and interleukin 10 (IL-10) were quantified by flow cytometry. Mononuclear cells isolated from the human aborted decidual tissues were also infected with T. gondii RH or RH-Δrop18 for the analysis of CD73 expression with flow cytometry. Additionally, infected human dMφ were used to assess the expression levels of CD73, Arg-1, IL-10, and their associated signaling molecules by western blot analysis. Furthermore, chromatin immunoprecipitation (ChIP) assays were performed to validate the involved signaling pathways.

Results
Compared with the T. gondii RH-infected group, milder adverse pregnancy outcomes and attenuated expression levels of CD73 on dMφ were observed in T. gondii RH-Δrop18-infected pregnant mice and human decidual tissues. Lysine-specific histone demethylase1 (LSD1) and snail family transcriptional repressor 1 (SNAIL1) were found to be involved in the downregulation of CD73 expression on dMφ following T. gondii infection. Subsequently, reduced expression of CD73 contribute to the downregulation of Arg-1 and IL-10 expression through adenosine A2a receptor (A2AR) / protein kinase A (PKA) / phosphorylated cAMP-response element binding protein (p-CREB) / CCAAT enhancer binding protein B (C/EBPβ) pathway.

Conclusions

TgROP18 can significantly reduce CD73 expression on dMφ through LSD1/SNAIL1 pathway, subsequently leading to the decreased expression levels of Arg-1 and IL-10 via adenosine/A2AR/PKA/p-CREB/C/EBPβ pathway, which ultimately contributes to maternal–fetal tolerance dysfunction of dMφ.

Graphical Abstract
AI for the hemodynamic assessment of critically ill and surgical patients: focus on clinical applications
Authors: Michard Frederic , Mulder Marijn P. , Gonzalez Filipe , Sanfilippo Filippo 
Published: February 2025

Abstract
Several artificial intelligence (AI)-driven tools have emerged for the hemodynamic evaluation of critically ill and surgical patients. This article provides an overview of current developments and potential clinical applications of machine learning (ML) for blood pressure measurements, hypotension prediction, hemodynamic profiling, and echocardiography. ML algorithms have shown promise in enabling continuous, non-invasive blood pressure monitoring by analyzing pulse oximetry waveforms, though these methods require periodic calibration with traditional oscillometric brachial cuffs. Additionally, a variety of ML models have been trained to forecast impending hypotension. However, clinical research indicates that these algorithms often primarily rely on mean arterial pressure, leading to questions about their added predictive value. The issue of false-positive alerts is also significant and can result in unwarranted clinical interventions. In terms of hemodynamic profiling, ML algorithms have been proposed to automatically classify patients into specific hemodynamic endotypes. However, current evidence suggests these models tend to replicate conventional hemodynamic profiles found in medical textbooks or depicted on advanced hemodynamic monitors. This raises questions about their practical clinical utility, especially given occasional discrepancies that could impact treatment decisions. Point-of-care ultrasound (POCUS) has gained traction for evaluating cardiac function in patients experiencing circulatory shock. ML algorithms now embedded in some POCUS systems can assist by recognizing ultrasound images, guiding users for optimal imaging, automating and reducing the variability of key echocardiographic measurements. These capabilities are especially beneficial for novice operators, potentially enhancing accuracy and confidence in clinical decision-making. In conclusion, while several AI-based technologies show promise for refining hemodynamic assessment in both critically ill and surgical patients, their clinical value varies. Comprehensive validation studies and real-world testing are essential to identify which innovations will genuinely contribute to improving the quality of care.
Safety and effectiveness of triple-drug therapy with ivermectin, diethylcarbamazine, and albendazole in reducing lymphatic filariasis prevalence and clearing circulating filarial antigens in Mombasa, Kenya
Authors: Khaemba Christabel , Njenga Sammy M. , Omondi Wyckliff P. , Kirui Elvis , Oluka Margaret , Guantai Anastacia , Aklillu Eleni 
Published: February 2025

Abstract

Background
In 2018, Kenya introduced triple-drug therapy with ivermectin, diethylcarbamazine, albendazole (IDA) through mass drug administration (MDA) to accelerate the elimination of lymphatic filariasis (LF). This community-based surveillance study assessed the safety and effectiveness of IDA-MDA in reducing LF-antigenemia prevalence and circulating filarial antigens (CFA) clearance among LF infected individuals.

Methods
A total of 8928 residents in Mombasa, Kenya, were screened for CFA using the Filarial Test Strip: 3464 were screened in 2018 and 5464 in 2021 after two annual IDA-MDA rounds. CFA-positive individuals in 2021 were re-tested at two and four months of post-MDA for CFA-clearance rates. Adverse events (AEs) associated with IDA-MDA were monitored via door-to-door visits on days 1, 2, and 7 post-MDA to document the incidence, type and risk factors. Efficacy outcomes included post-MDA LF-antigenemia prevalence reduction after two rounds of annual MDA and CFA clearance rate. Chi-square test compared proportions, and logistic regression analysis identified AE predictors.

Results
LF antigenemia prevalence significantly decreased from 1.39% (n = 48) in 2018 to 0.66% (n = 36) in 2021 [P < 0.001; 95% confidence interval (CI) for difference in proportions: 0.003–0.012]. CFA clearance rates were 63.2% (12/19, 95% CI: 41.0–80.1%) at 2 months and 68.4% (13/19, 95% CI: 46.0–86.6%) at 4 months post-MDA. Among 53 CFA-positive individuals monitored, the cumulative 7-day AE incidence was 37.7% (95% CI: 25.6–51.7), higher than the general population’s 27.3% (95% CI: 26.4–28.2). Common AEs included nausea (11.3%), diarrhea (11.3%), abdominal pain (7.6%), and headache (5.7%). Risk factors for AEs included age, overweight status, concomitant medication use, chronic illness, and fasting before MDA.

Conclusions
Triple therapy with IDA is safe and well-tolerated, with some mild-to-moderate and transient adverse events among LF-infected individuals. The high incidence of AEs highlights the need for safety monitoring during MDA. The significant reductions in LF antigenemia prevalence and high CFA clearance rates underscore IDA's effectiveness in reducing LF transmission, positioning it as a key strategy for eliminating LF as a public health problem by 2030.

Graphical abstract
Blood group diversity in Borneo
Authors: Assyuhada Mat Ghani Siti Nor , Hajar Che Ghazali Norul , ‘Adani Sanusi Nurul , Hassan Mohd Nazri , Mohammad Isa Nilie Syamila , Ali Nur Saiyidah Hanisah , Mohamad Siti Aisyah , Aziz Mohd Yusmaidie , Furqoni Abdul Hadi , Chambers Geoffrey Keith , Zulkafli Zefarina , Edinur Hisham Atan 
Published: February 2025

Abstract

Background
Protein and glycoprotein antigens on the surface of red blood cells are encoded by highly polymorphic genes in human nuclear genomes. Allelic variants of the blood group genes are known to be distributed differently across populations. Consequently, they are widely used for ancestral analyses and for designing donor recruitment strategies. In Malaysia, blood group allele frequency population data are available for many population groups in Peninsular Malaysia, but not for those from Sabah and Sarawak of Borneo. Thus, this study contributes to a wider systematic collection of blood group data and expands the range of analyses.

Aim
This study determined the allele frequencies of nine clinically significant blood group systems (Kell, Kidd, Duffy, MNS, Dombrock, Colton, Lutheran, Cartwright and Vel) in the Bajau, Kadazan-Dusun, Murut and Melanau populations of Borneo.

Results
Many of the blood group systems examined here are polymorphic in the Bajau, Kadazan-Dusun, Murut and Melanau except for Kell, Colton, Lutheran, Cartwright and Vel. Population data mapping shows that these four populations are more closely related to other Austronesian populations, than other linguistically and historically unrelated population groups in Malaysia and elsewhere.

Conclusions
In this study, we have successfully typed variants that determine Kell, Kidd, Duffy, MNS, Dombrock, Colton, Lutheran, Cartwright and Vel blood group systems in the samples collected from unrelated Bajau, Kadazan-Dusun, Murut and Melanau individuals of Borneo. Many of these blood group systems were observed to be polymorphic in these populations. These findings are similar to those reported for other population groups in Peninsular Malaysia. Clinical complications that are more likely to arise in these people (gestation and transfusion alloimmunizations as well as risk to diseases) should be considered by health practitioners, healthcare providers and healthcare policy makers based on our observations.

Graphical abstract
Comparison of prognosis between epidermal growth factor mutation positive and negative groups in lung adenocarcinoma patients with brain metastases
Authors: Unat Damla Serçe , Arikan Şener , Kirbiyik Özgür , Balci Günseli , Ermin Sinem , Mertoğlu Aydan 
Published: February 2025

Abstract

Introduction
Brain metastasis (BM) in non-small cell lung cancer (NSCLC) is still an important reason for morbidity and mortality despite the advances in cancer treatment. Using tyrosine kinase inhibitors against epidermal growth factor receptors (EGFR) mutations revolutionized NSCLC treatment. We investigated whether the presence of EGFR mutation influences survival in patients with lung adenocarcinoma with BM.

Material and methods
The data of the patients with pathological diagnoses of NSCLC and BM at tertiary hospitals were analyzed retrospectively in terms of survival. A total of 2554 patients diagnosed with NSCLC pathologically between 01 January 2010 and 01 January 2021 were identified. After the exclusion of patients with a lack of data, unknown EGFR mutation status, no brain metastasis, and additional malignancy 336 patients were included in the study.

Results
It was found that EGFR ( +) patients were more female dominant (48.6% vs 13.3% p < 0.0001) and were have less history of smoking (47.2% vs 87.1%, p < 0.0001) and were better survival (79.2% vs 92.8%). We found negativity of EGFR increased death risk by 1.700 times (95% CI 1.323–2.183, p < 0.0001) in univariate analysis and by 1.724 times (95% CI 1.251–2.377, p = 0.0001) in multivariate analysis. When overall survivals were compared estimated overall survival time of EGFR ( −) patients was 10.088 (95% CI 8.571–11.606) months and of EGFR ( +) patients was 11.829 months (95% CI 10.336–13.323) (p < 0.001).

Conclusion
EGFR positivity was associated with survival. Also, survival was significantly longer in EGFR-positive patients with brain metastases diagnosed with NSCLC.
Clinical features and plasma metabolites analysis in Chinese obese children with nonalcoholic fatty liver disease
Authors: Liu Xiaoxiao , Ma Shifeng , Li Jing , Song Mingkun , Li Yun , Qi Yingyi , Liu Fei , Fang Zhongze , Zheng Rongxiu 
Published: February 2025

Abstract

Objective
This study aimed to investigate the clinical characteristics and plasma metabolites of nonalcoholic fatty liver disease (NAFLD) in Chinese obese children, and to develop machine learning-based NAFLD diagnostic models.

Methods
We recruited 222 obese children aged 4 to 17 years and divided them into an obese control group (OC) and an obese NAFLD group (ON) based on liver ultrasonography. Mass spectrometry metabolomic analysis was used to measure 106 metabolites in plasma. Binary logistic regression was used to identify NAFLD-related clinical variables. NAFLD-specific metabolites were illustrated via volcano plots, cluster heatmaps and metabolic network diagram. Additionally, we applied eight machine learning methods to construct three diagnostic models based on clinical variables, metabolites, and clinical variables combined with metabolites.

Results
By evaluating clinical variables and plasma metabolites, we identified 16 clinical variables and 14 plasma metabolites closely associated with NAFLD. We discovered that the level of 18:0-22:6 phosphatidylethanolamines was positively correlated with the levels of total cholesterol, triglyceride-glucose index, and triglyceride to high-density lipoprotein cholesterol ratio, whereas the level of glycocholic acid was positively correlated with with the levels of alanine aminotransferase, gamma-glutamyl transferase, insulin, and the homeostasis model assessment of insulin resistance. Additionally, we successfully developed three NAFLD diagnostic models that showed excellent diagnostic performance (areas under the receiver operating characteristic curves of 0.917, 0.954 and 0.957, respectively).

Conclusion
We identified 16 clinical variables and 14 plasma metabolites associated with NAFLD in obese Chinese children. Diagnostic models using these features showed excellent performance, indicating their potential for diagnosis.
Marrow adiposity content and composition are not associated with incident fragility fractures in post-menopausal women
Authors: Philippoteaux Cécile , Badr Sammy , Lombardo Daniela , Cailliau Emeline , Ruschke Stefan , Karampinos Dimitrios C , Cotten Anne , Paccou Julien 
Published: February 2025

Abstract

Introduction
Noninvasive measurement of bone marrow adipose tissue using MRI and proton density fat fraction (PDFF) may enhance clinical fractures prediction in postmenopausal women. This study aimed to assess the association between PDFF measurements and clinical fracture incidence.
Material and methods: A longitudinal study was conducted. Postmenopausal women with recent osteoporotic fractures (&lt;12 months) and with osteoarthritis without fractures were included. Lumbar spine and proximal femur PDFFs were measured at baseline using water-fat imaging (WFI) and DXA scans. Clinical fractures were recorded during follow-up.

Results
Among 195 participants (mean age 67.4 ± 10.0 years, BMI 27.2 ± 5.9 kg/m²), the PDFF (WFI-based) was higher at the proximal femur (57.8 ± 9.6%), particularly at the femoral head (90.0 ± 4.9%), compared to the lumbar spine. Over a mean follow-up of 37.2 ± 11.6 months, seven participants died, 29 (14.9%) experienced incident clinical fractures, and one was lost to follow-up. The lack of an association between WFI-based PDFFs and the incidence of clinical fractures was demonstrated regardless of the region of measurement (HR= 0.95 [95% CI 0.67–1.35], p=0.77 at the lumbar spine, HR= 1.07 [95% CI 0.71–1.63], p=0.74 at the femoral neck). Stepwise regression analysis did not alter these findings, and the variable ‘recent osteoporotic fractures’ was found to be significantly associated with incident clinical fractures.

Conclusions
This study found no evidence of a relationship between PDFF and clinical fracture incidence in postmenopausal women. Further studies are necessary in larger cohorts and longer follow-up periods.
Lung Volume-Dependent Decrease in Projected Frontal Area Contributes to Reducing Active Drag in Front Crawl
Authors: Washino Sohei , Murai Akihiko , Mankyu Hirotoshi , Kanehisa Hiroaki , Yoshitake Yasuhide 
Published: February 2025

ABSTRACT

Purpose
This study aimed to investigate whether a lung volume-dependent decrease in the projected frontal area (PFA) contributes to reducing active drag in the front crawl.

Methods
Twelve competitive male swimmers performed a 15 m front crawl at 1.20 m·s−1 while sustaining one of three lung volume levels: maximal inspiration (INSP), maximal expiration (EXP), or intermediate (MID). The three-dimensional positions of the reflective markers attached to the swimmers’ bodies were measured using an underwater motion-capture system. Based on the body shape obtained from the photogenic body scanner, an individual digital human model was created using model vertices color-coded into eight body segments. The time series of the volumetric swimming motion was reconstructed using the individual digital human model and motion capture data. The PFA of each body segment was calculated using image processing. The pressure drag index (PDI), defined as the value excluding the drag coefficient while simultaneously considering the PFA and horizontal velocity, was calculated for each body segment.

Results
There were significant interactions between lung volume and body segment on PFA and PDI (both p < 0.001). Specifically, the PFA and PDI of the head segment were smaller in INSP than in EXP (p ≤ 0.046); in addition, the PFA and PDI of the trunk and femur segments were smaller in INSP and MID than in EXP (p ≤ 0.003).

Conclusions
These findings suggest that the decrease in PFA caused by the increase in lung volume directly contributes to reducing active drag.
Infrastructure Decision Preferences and the Influence of Social Justice Education
Authors: Van-Hein Sackey Charles , Cao Christine , Nock Destenie , Armanios Daniel , Davis Alex 
Published: February 2025

Social justice considerations are critical in today’s engineering education. As an example, access to electricity will be crucial to the development of sub-Saharan Africa (SSA) by powering essential sectors and services that would yield economic growth and an improved quality of life. Because infrastructure investments are often allocated to urban areas, it is important for stakeholders to equally prioritize rural electrification in their efforts to achieve universal electrification. These stakeholders make decisions on allocating infrastructure investment based on their underlying values and preferences. It is therefore important to introduce and integrate ideas of fairness and social justice into the engineering curriculum. Our paper investigates the influence of social justice education on decision preferences among students tasked with planning electricity systems. We engaged graduate and undergraduate students in a discrete choice experiment to determine their preferences for equality. In our study, we find that an interactive social justice education module can be effective in increasing equality preferences among non–U.S.-citizen students by 22%. Among students with relatively low preferences for equality, we find that the education module was effective in increasing preference by 18% on average and up to 191%, which translated into more equitable resource allocation. As such, we show that an education intervention for stakeholders may be effective in improving equitable electrification in SSA. The preference elicitation and education module presented in this paper could be repurposed by instructors to include other contexts in their course content (e.g., money, water, housing, etc.) or to people wanting to educate decision makers about social justice. History: This paper has been accepted for the INFORMS Transactions on Education Special Issue on Diversity, Equity and Inclusion in OR/MS Classrooms. Funding: This work was supported by the National Science Foundation [Grant 2121730].
Estimating Erratic Measurement Errors in Network-Wide Traffic Flow via Virtual Balance Sensors
Authors: Zheng Zhenjie , Wang Zhengli , Fu Hao , Ma Wei 
Published: February 2025

Large-scale traffic flow data are collected by numerous sensors for managing and operating transport systems. However, various measurement errors exist in the sensor data and their distributions or structures are usually not known in the real world, which diminishes the reliability of the collected data and impairs the performance of smart mobility applications. Such irregular error is referred to as the erratic measurement error and has not been well investigated in existing studies. In this research, we propose to estimate the erratic measurement errors in networked traffic flow data. Different from existing studies that mainly focus on measurement errors with known distributions or structures, we allow the distributions and structures of measurement errors to be unknown except that measurement errors occur based on a Poisson process. By exploiting the flow balance law, we first introduce the concept of virtual balance sensors and develop a mixed integer nonlinear programming model to simultaneously estimate sensor error probabilities and recover traffic flow. Under suitable assumptions, the complex integrated problem can be equivalently viewed as an estimate-then-optimize problem: first, estimation using machine learning (ML) methods, and then optimization with mathematical programming. When the assumptions fail in more realistic scenarios, we further develop a smart estimate-then-optimize (SEO) framework that embeds the optimization model into ML training loops to solve the problem. Compared with the two-stage method, the SEO framework ensures that the optimization process can recognize and compensate for inaccurate estimations caused by ML methods, which can produce more reliable results. Finally, we conduct numerical experiments using both synthetic and real-world examples under various scenarios. Results demonstrate the effectiveness of our decomposition approach and the superiority of the SEO framework. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods for Urban Mobility. Funding: The work described in this paper was supported by the National Natural Science Foundation of China [Grant Project No. 72288101, 72101012, 72301023] and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Grant Project No. PolyU/15206322]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0493 .