bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk factors and incidence of postoperative delirium after cardiac surgery in children: a systematic review and meta-analysis
Authors: Fu Maoling , Yuan Quan , Yang Qiaoyue , Song Wenshuai , Yu Yaqi , Luo Ying , Xiong Xiaoju , Yu Genzhen 
Published: February 2024

AbstractDelirium, a form of acute cerebral dysfunction, is a common complication of postoperative cardiac surgery in children. It is strongly associated with adverse outcomes, including prolonged hospitalization, increased mortality, and cognitive dysfunction. This study aimed to identify risk factors and incidence of delirium after cardiac surgery in children to facilitate early identification of delirium risk and provide a reference for the implementation of effective prevention and management. A systematic literature search was conducted in PubMed, Web of Science, Embase, Cochrane Library, Scopus, CNKI, Sinomed, and Wanfang for studies published in English or Chinese from the inception of each database to November 2023. The PRISMA guidelines were followed in all phases of this systematic review. The Risk of Bias Assessment for Nonrandomized Studies tool was used to assess methodological quality. A total of twelve studies were included in the analysis, with four studies classified as overall low risk of bias, seven studies as moderate risk of bias, and one study as high risk of bias. The studies reported 39 possible predictors of delirium, categorized into four broad groups: intrinsic and parent-related factors, disease-related factors, surgery and treatment-related factors, and clinical scores and laboratory parameters. By conducting qualitative synthesis and quantitative meta-analysis, we identified two definite factors, four possible factors, and 32 unclear factors related to delirium. Definite risk factors included age and mechanical ventilation duration. Possible factors included developmental delay, cyanotic heart disease, cardiopulmonary bypass time, and pain score. With only a few high-quality studies currently available, well-designed and more extensive prospective studies are still needed to investigate the risk factors affecting delirium and explore delirium prevention strategies in high-risk children.
Impact of an integrated health, nutrition, and early child stimulation and responsive care intervention package delivered to preterm or term small for gestational age babies during infancy on growth and neurodevelopment: study protocol of an individually randomized controlled trial in India (Small Babies Trial)
Authors: Chowdhury Ranadip , Manapurath Rukman , Sandøy Ingvild Fossgard , Upadhyay Ravi Prakash , Dhabhai Neeta , Shaikh Saijuddin , Chellani Harish , Choudhary Tarun Shankar , Jain Abhinav , Martines Jose , Bhandari Nita , Strand Tor A. , Taneja Sunita ,   on behalf of the Small Babies Trial Group
Published: February 2024

Abstract
Background
Preterm and term small for gestational age (SGA) babies are at high risk of experiencing malnutrition and impaired neurodevelopment. Standalone interventions have modest and sometimes inconsistent effects on growth and neurodevelopment in these babies. For greater impact, intervention may be needed in multiple domains—health, nutrition, and psychosocial care and support. Therefore, the combined effects of an integrated intervention package for preterm and term SGA on growth and neurodevelopment are worth investigating.

Methods
An individually randomized controlled trial is being conducted in urban and peri-urban low to middle-socioeconomic neighborhoods in South Delhi, India. Infants are randomized (1:1) into two strata of 1300 preterm and 1300 term SGA infants each to receive the intervention package or routine care. Infants will be followed until 12 months of age. Outcome data will be collected by an independent outcome ascertainment team at infant ages 1, 3, 6, 9, and 12 months and at 2, 6, and 12 months after delivery for mothers.

Discussion
The findings of this study will indicate whether providing an intervention that addresses factors known to limit growth and neurodevelopment can offer substantial benefits to preterm or term SGA infants. The results from this study will increase our understanding of growth and development and guide the design of public health programs in low- and middle-income settings for vulnerable infants.

Trial registration
The trial has been registered prospectively in Clinical Trial Registry – India # CTRI/2021/11/037881, Registered on 08 November 2021.
Investigation of inter-rater and test-retest reliability of Y balance test in college students with flexible flatfoot
Authors: Zheng Yalin , Feng Renzhi , Hu Weiyin , Huang Peng 
Published: February 2024

Abstract
Background
The Lower Quarter Y Balance Test (YBT-LQ) has been widely used to assess dynamic balance in various populations. Dynamic balance in flexible flatfoot populations is one of the risk factors for lower extremity injuries, especially in college populations in which more exercise is advocated. However, no study has demonstrated the reliability of the YBT-LQ in a college student flexible flatfoot population.

Methods
A cross-sectional observational study. 30 college students with flexible flatfoot were recruited from Beijing Sports University. They have been thrice assessed for the maximal reach distance of YBT under the support of the lower limb on the flatfoot side. Test and retest were performed with an interval of 14 days. The outcome measures using the composite score and normalized maximal reach distances in three directions (anterior, posteromedial, and posterolateral). The relative reliability was reported as the Intraclass Correlation Coefficient (ICC). Minimal Detectable Change (MDC), Smallest worthwhile change (SWC), and Standard Error of Measurement (SEM) were used to report the absolute reliability.

Results
For inter-rater reliability, the ICC values for all directions ranged from 0.84 to 0.92, SEM values ranged from 2.01 to 3.10%, SWC values ranged from 3.67 to 5.12%, and MDC95% values ranged from 5.58 to 8.60%. For test-retest reliability, the ICC values for all directions ranged from 0.81 to 0.92, SEM values ranged from 1.80 to 2.97%, SWC values ranged from 3.75 to 5.61%, and MDC95% values ranged from 4.98 to 8.24%.

Conclusions
The YBT-LQ has “good” to “excellent” inter-rater and test-retest reliability. It appears to be a reliable assessment to use with college students with flexible flatfoot.

Trial registration
This trial was prospectively registered at the Chinese Clinical Trial Registry with the ID number ChiCTR2300075906 on 19/09/2023.
Web-based questionnaire survey for exploring engagement characteristics of advance care planning in Japan: a cross-sectional study
Authors: Nakanishi Yasuhiro , Tsugihashi Yukio , Hayasaka Akira , Nishioka Yuichi , Akahane Manabu 
Published: February 2024

Abstract
Objective
Definitive promotion of advance care planning (ACP) practices will require policy interventions tailored to the characteristics of the Japanese population and society. However, effective policies for promoting ACP are currently lacking in Japan. This study aimed to explore the characteristics of Japanese people who engaged in ACP activities through a web-based questionnaire survey, which was administered to individuals aged 25–64 years and classified into four occupational categories (non-medical/non-caregiving professionals [general population], physicians, nurses, and caregivers).

Results
The total sample size was 1,648, with equal occupational category and age group distributions. Respondents in the general population group were less likely to discuss or document ACP than those in the other groups. Stepwise logistic regression analysis showed a significant difference in the adjusted odds ratio (aOR) of the independent variables of “attended cardiopulmonary resuscitation (CPR) training session(s)” (aOR: 1.93; 95% confidence interval [CI]: 1.18–3.15) and “having experience in performing CPR” (aOR: 2.61; 95% CI: 1.51–4.54) for respondents who discussed ACP with their families. A significant difference was observed in the aOR of the independent variable of “having experience in performing CPR” (aOR: 4.58; 95% CI: 2.30–9.13) for respondents who documented a written record of ACP.
The interaction between DNA methylation and tumor immune microenvironment: from the laboratory to clinical applications
Authors: Zhu Daoqi , Zeng Siying , Su Chao , Li Jingjun , Xuan Yiwen , Lin Yongkai , Xu Enwu , Fan Qin 
Published: February 2024

AbstractDNA methylation is a pivotal epigenetic modification that affects gene expression. Tumor immune microenvironment (TIME) comprises diverse immune cells and stromal components, creating a complex landscape that can either promote or inhibit tumor progression. In the TIME, DNA methylation has been shown to play a critical role in influencing immune cell function and tumor immune evasion. DNA methylation regulates immune cell differentiation, immune responses, and TIME composition Targeting DNA methylation in TIME offers various potential avenues for enhancing immune cytotoxicity and reducing immunosuppression. Recent studies have demonstrated that modification of DNA methylation patterns can promote immune cell infiltration and function. However, challenges persist in understanding the precise mechanisms underlying DNA methylation in the TIME, developing selective epigenetic therapies, and effectively integrating these therapies with other antitumor strategies. In conclusion, DNA methylation of both tumor cells and immune cells interacts with the TIME, and thus affects clinical efficacy. The regulation of DNA methylation within the TIME holds significant promise for the advancement of tumor immunotherapy. Addressing these challenges is crucial for harnessing the full potential of epigenetic interventions to enhance antitumor immune responses and improve patient outcomes.
Impact of COVID-19 on Florida family dependency drug courts
Authors: Golan Olivia K. , Ahmed Fatema Z. , Andraka-Christou Barbara , Totaram Rachel , Asi Yara , Atkins Danielle 
Published: February 2024

Abstract
Background
To promote parent-child reunification, family dependency drug courts (FDDCs) facilitate substance use disorder treatment for people whose children have been removed due to parental substance use. The COVID-19 pandemic disrupted FDDC operations, forcing FDDCs to quickly adapt to new circumstances. Although existing research has examined COVID-19 impacts on adult drug courts and civil dependency courts, studies have yet to examine the impact of COVID-19 on FDDCs specifically.

Methods
To explore the impact of COVID-19 on FDDCs, we conducted 20 focus groups and 5 individual interviews with court team members from five Florida FDDCs between 2020 and 2022. Data were analyzed using iterative categorization.

Results
Five overarching themes emerged. First, FDDCs adopted virtual technology during the pandemic and more flexible drug screening policies. Second, virtual technology was perceived as improving hearing attendance but decreasing client engagement. FDDC team members discussed a potential hybrid in-person/virtual hearing model after the pandemic. Third, COVID-19 negatively impacted parent-child visitation opportunities, limiting development of bonds between parents and children, and parent-child bonding is a key consideration during judicial reunification decisions. Fourth, COVID-19 negatively impacted the mental health of court team members and clients. Court team members adopted new informal roles, such as providing technical support and emotional counseling to clients, in addition to regular responsibilities, resulting in feeling overwhelmed and overworked. Court team members described clients as feeling more depressed and anxious, in part due to limited visitation opportunities with children, which decreased clients’ motivation for substance use recovery. Fifth, COVID-19 decreased recruitment of potential clients into FDDCs.

Conclusions
If FDDCs continue to rely on virtual hearings beyond the pandemic, they must develop practices for improving client engagement during virtual hearings. FDDCs should preemptively develop procedures for improving parent-child visitation during future public health crises, because limited visitation opportunities could weaken parent-child bonding and, ultimately, the likelihood of reunification.
Loss of Sarm1 reduces retinal ganglion cell loss in chronic glaucoma
Authors: Zeng Huilan , Mayberry Jordan E. , Wadkins David , Chen Nathan , Summers Daniel W. , Kuehn Markus H. 
Published: February 2024

AbstractGlaucoma is one of the leading causes of irreversible blindness worldwide and vision loss in the disease results from the deterioration of retinal ganglion cells (RGC) and their axons. Metabolic dysfunction of RGC plays a significant role in the onset and progression of the disease in both human patients and rodent models, highlighting the need to better define the mechanisms regulating cellular energy metabolism in glaucoma. This study sought to determine if Sarm1, a gene involved in axonal degeneration and NAD+ metabolism, contributes to glaucomatous RGC loss in a mouse model with chronic elevated intraocular pressure (IOP). Our data demonstrate that after 16 weeks of elevated IOP, Sarm1 knockout (KO) mice retain significantly more RGC than control animals. Sarm1 KO mice also performed significantly better when compared to control mice during optomotor testing, indicating that visual function is preserved in this group. Our findings also indicate that Sarm1 KO mice display mild ocular developmental abnormalities, including reduced optic nerve axon diameter and lower visual acuity than controls. Finally, we present data to indicate that SARM1 expression in the optic nerve is most prominently associated with oligodendrocytes. Taken together, these data suggest that attenuating Sarm1 activity through gene therapy, pharmacologic inhibition, or NAD+ supplementation, may be a novel therapeutic approach for patients with glaucoma.
Clinical application of machine learning models in patients with prostate cancer before prostatectomy
Authors: Guerra Adalgisa , Orton Matthew R. , Wang Helen , Konidari Marianna , Maes Kris , Papanikolaou Nickolas K. , Koh Dow Mu 
Published: February 2024

Abstract
Background
To build machine learning predictive models for surgical risk assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical prostatectomy; and to compare the use of decision curve analysis (DCA) and receiver operating characteristic (ROC) metrics for selecting input feature combinations in models.

Methods
This retrospective observational study included two independent data sets: 139 participants from a single institution (training), and 55 from 15 other institutions (external validation), both treated with Robotic Assisted Radical Prostatectomy (RARP). Five ML models, based on different combinations of clinical, semantic (interpreted by a radiologist) and radiomics features computed from T2W-MRI images, were built to predict extracapsular extension in the prostatectomy specimen (pECE+). DCA plots were used to rank the models’ net benefit when assigning patients to prostatectomy with non-nerve-sparing surgery (NNSS) or nerve-sparing surgery (NSS), depending on the predicted ECE status. DCA model rankings were compared with those drived from ROC area under the curve (AUC).

Results
In the training data, the model using clinical, semantic, and radiomics features gave the highest net benefit values across relevant threshold probabilities, and similar decision curve was observed in the external validation data. The model ranking using the AUC was different in the discovery group and favoured the model using clinical + semantic features only.

Conclusions
The combined model based on clinical, semantic and radiomic features may be used to predict pECE + in patients with PCa and results in a positive net benefit when used to choose between prostatectomy with NNS or NNSS.
Multiomics and blood-based biomarkers of moyamoya disease: protocol of Moyamoya Omics Atlas (MOYAOMICS)
Authors: Ge Peicong , Yin Zihan , Tao Chuming , Zeng Chaofan , Yu Xiaofan , Lei Shixiong , Li Junsheng , Zhai Yuanren , Ma Long , He Qiheng , Liu Chenglong , Liu Wei , Zhang Bojian , Zheng Zhiyao , Mou Siqi , Zhao Zhikang , Wang Shuang , Sun Wei , Guo Min , Zheng Shuai , Zhang Jia , Deng Xiaofeng , Liu Xingju , Ye Xun , Zhang Qian , Wang Rong , Zhang Yan , Zhang Shaosen , Wang Chengjun , Yang Ziwen , Zhang Nijia , Wu Mingxing , Sun Jian , Zhou Yujia , Shi Zhiyong , Ma Yonggang , Zhou Jianpo , Yu Shaochen , Li Jiaxi , Lu Junli , Gao Faliang , Wang Wenjing , Chen Yanming , Zhu Xingen , Zhang Dong , Zhao Jizong 
Published: February 2024

Abstract
Background
Moyamoya disease (MMD) is a rare and complex cerebrovascular disorder characterized by the progressive narrowing of the internal carotid arteries and the formation of compensatory collateral vessels. The etiology of MMD remains enigmatic, making diagnosis and management challenging. The MOYAOMICS project was initiated to investigate the molecular underpinnings of MMD and explore potential diagnostic and therapeutic strategies.

Methods
The MOYAOMICS project employs a multidisciplinary approach, integrating various omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, to comprehensively examine the molecular signatures associated with MMD pathogenesis. Additionally, we will investigate the potential influence of gut microbiota and brain-gut peptides on MMD development, assessing their suitability as targets for therapeutic strategies and dietary interventions. Radiomics, a specialized field in medical imaging, is utilized to analyze neuroimaging data for early detection and characterization of MMD-related brain changes. Deep learning algorithms are employed to differentiate MMD from other conditions, automating the diagnostic process. We also employ single-cellomics and mass cytometry to precisely study cellular heterogeneity in peripheral blood samples from MMD patients.

Conclusions
The MOYAOMICS project represents a significant step toward comprehending MMD’s molecular underpinnings. This multidisciplinary approach has the potential to revolutionize early diagnosis, patient stratification, and the development of targeted therapies for MMD. The identification of blood-based biomarkers and the integration of multiple omics data are critical for improving the clinical management of MMD and enhancing patient outcomes for this complex disease.
Enhancing diagnostic deep learning via self-supervised pretraining on large-scale, unlabeled non-medical images
Authors: Tayebi Arasteh Soroosh , Misera Leo , Kather Jakob Nikolas , Truhn Daniel , Nebelung Sven 
Published: February 2024

Abstract
Background
Pretraining labeled datasets, like ImageNet, have become a technical standard in advanced medical image analysis. However, the emergence of self-supervised learning (SSL), which leverages unlabeled data to learn robust features, presents an opportunity to bypass the intensive labeling process. In this study, we explored if SSL for pretraining on non-medical images can be applied to chest radiographs and how it compares to supervised pretraining on non-medical images and on medical images.

Methods
We utilized a vision transformer and initialized its weights based on the following: (i) SSL pretraining on non-medical images (DINOv2), (ii) supervised learning (SL) pretraining on non-medical images (ImageNet dataset), and (iii) SL pretraining on chest radiographs from the MIMIC-CXR database, the largest labeled public dataset of chest radiographs to date. We tested our approach on over 800,000 chest radiographs from 6 large global datasets, diagnosing more than 20 different imaging findings. Performance was quantified using the area under the receiver operating characteristic curve and evaluated for statistical significance using bootstrapping.

Results
SSL pretraining on non-medical images not only outperformed ImageNet-based pretraining (p < 0.001 for all datasets) but, in certain cases, also exceeded SL on the MIMIC-CXR dataset. Our findings suggest that selecting the right pretraining strategy, especially with SSL, can be pivotal for improving diagnostic accuracy of artificial intelligence in medical imaging.

Conclusions
By demonstrating the promise of SSL in chest radiograph analysis, we underline a transformative shift towards more efficient and accurate AI models in medical imaging.

Relevance statement
Self-supervised learning highlights a paradigm shift towards the enhancement of AI-driven accuracy and efficiency in medical imaging. Given its promise, the broader application of self-supervised learning in medical imaging calls for deeper exploration, particularly in contexts where comprehensive annotated datasets are limited.

Graphical Abstract
Community-based active-case finding for tuberculosis: navigating a complex minefield
Authors: MacPherson Peter , Shanaube Kwame , Phiri Mphatso D. , Rickman Hannah M. , Horton Katherine C. , Feasey Helena R. A. , Corbett Elizabeth L. , Burke Rachael M. , Rangaka Molebogeng X. 
Published: February 2024

AbstractCommunity-based active case finding (ACF) for tuberculosis (TB) involves an offer of screening to populations at risk of TB, oftentimes with additional health promotion, community engagement and health service strengthening. Recently updated World Health Organization TB screening guidelines conditionally recommend expanded offer of ACF for communities where the prevalence of undiagnosed pulmonary TB is greater than 0.5% among adults, or with other structural risk factors for TB. Subclinical TB is thought to be a major contributor to TB transmission, and ACF, particularly with chest X-ray screening, could lead to earlier diagnosis. However, the evidence base for the population-level impact of ACF is mixed, with effectiveness likely highly dependent on the screening approach used, the intensity with which ACF is delivered, and the success of community- and health-system participation. With recent changes in TB epidemiology due to the effective scale-up of treatment for HIV in Africa, the impacts of the COVID-19 pandemic, and the importance of subclinical TB, researchers and public health practitioners planning to implement ACF programmes must carefully and repeatedly consider the potential population and individual benefits and harms from these programmes. Here we synthesise evidence and experience from implementing ACF programmes to provide practical guidance, focusing on the selection of populations, screening algorithms, selecting outcomes, and monitoring and evaluation. With careful planning and substantial investment, community-based ACF for TB can be an impactful approach to accelerating progress towards elimination of TB in high-burden countries. However, ACF cannot and should not be a substitute for equitable access to responsive, affordable, accessible primary care services for all.
Email in healthcare: pros, cons and efficient use
Authors: Ginn Stephen 
Published: February 2024

SUMMARYEmail is a major means of communication in healthcare and it facilitates the fast delivery of messages and information. But email's ubiquity has brought challenges. It has changed the way we get things done, and working days can be dictated by the receipt and reply of multiple email messages, which drown out other priorities. This article examines email's advantages and disadvantages and, with a focus on healthcare professionals, examines what individuals and organisations can do to ensure email works for us, rather than – as can seem the case – the other way around.