bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantum dark energy in a seven-dimensional universe
Authors: Kesanasetty Sasidhar
Published: February 2024

This paper explains the dark energy and acceleration of the universe by quantizing the space in hidden dimensions, which provides the basis and background for the gravitational force through the curvature of space-time. Space-time is considered to be made of a four-dimensional elastic grid in a seven-dimensional universe in which matter also expands along with the universe. Each cube of the grid is considered a quantum of hidden three-dimensional space of Planck volume containing Planck charge, which makes the universe seven-dimensional. The dark energy is explained by the electrostatic repulsion between Planck charges in each quantum of the hidden space. Mathematically, this electrostatic repulsion is related to the Hubble constant, which explains the accelerated expansion, dark energy, and increase in the cosmological potential energy of matter. Expansion of space-time is considered not due to the creation of the new space but due to the stretching of the existing space-time itself like an elastic ruler where the proper length and volume remain constant. The relative values of the Planck constant, gravitational constant, permittivity of free space, and Boltzmann constant are shown to vary owing to the expansion or contraction of space-time in the cosmological, gravitational, and relativistic frameworks. This theory also builds a framework for the relativistic Newtonian theory of gravity, the relativistic MONDian (Modified Newtonian dynamics) gravity, identifies a valid reason for the transition of Newtonian gravity to MOND at a0 and explains the dynamics of galaxy clusters without the dark matter.
Analisis Wacana pada Akun Twitter @DivHumas_Polri dalam Manajemen Krisis Tragedi Kanjuruhan
Authors: Putri Wahyu , Afrilia Ascharisa Mettasatya , Istiyanto Bekti , Hermawan Rizal Rizky
Published: February 2024

Polri sebagai Lembaga penegak hukum berperan sebagai lembaga pengayom masyarakat dan sudah semestinya memiliki reputasi positif dimasyarakat. Namun, faktanya reputasi Polri mengalami penurunan secara signifikan setelah adanya tragedi Kanjuruhan Tragedi tersebut pada akhirnya menyeret nama Polri yang dinilai memiliki andil berupa kelalaian dalam menembakkan gas air mata pada saat tragedi Kanjuruhan terjadi. Sejumlah opini masyarakat khususnya di media sosial pada akhirnya menyudutkan pihak Polri sehingga berdampak negatif kepada keberadaan reputasi Polri. Humas Polri memanfaatkan media sosial Twitter untuk mengembalikan reputasi pasca tragedi Kanjuruhan. Penelitian ini bertujuan untuk menganalisis wacana yang dibangun Humas Polri sebagai strategi manajemen krisis. Penelitian ini merupakan penelitian deskriptif dengan menggunakan metode kualitatif dan menggunakan pendekatan teoretis analisis wacana kritis yang diusulkan oleh Van Dijk. Hasil penelitian menunjukkan bahwa wacana humas Polri melalui media sosial twitter menunjukkan strategi image restoration menyangkal dengan menyalahkan pihak lain. Hal ini tercermin pada teks yang focus menjabarkan pihak-pihak yang terlibat sebagai pemicu tragedi Kanjurugan. Ketepatan dalam pemilihan strategi dapat mengukuhkan sikap organisasi serta mampu mengubah opini publik dari negatif dan antipati menjadi positif dan simpati.
STUDENTS AND FACULTY LEVEL OF KNOWLEDGE AND AWARENESS OF DIGITAL CITIZENSHIP IN DISTANCE LEARNING ENVIRONMENT
Authors: Hawamdeh Mahmoud
Published: February 2024

COVID-19 pandemic has changed the education landscape and increased the acceptance and adoption of distance educations by both individuals and academic institutions. The rise in e-learning and distance education and the increased access to the Internet and learning technology, it becomes critical that provide positive, safe, legal, and ethical behavioural online. While COVID 19 acerated the use and adoption of online learning, it also highlighted the importance of fostering effective digital citizenship given the level of misinformation and conspiracy theories generated spread on the Internet and social media about the pandemic. In this study, the researcher examined the students and faculty perception of digital citizenship practices in distance learning environments at Al-Quds Open University. The study used a mix method approach with 8 faculty participating in the qualitative interview and 559 students participating in the quantitative survey.  The study's findings revealed that while students and teachers were aware of digital citizenship principles, they lacked in-depth understanding and knowledge of topics like digital rights, digital security, and digital ethics. Faculty and students both believed that digital citizenship is vital and required for protecting students' privacy and cultivating ethical technology use. Students and faculty both agreed on the importance of incorporating digital citizenship skills like digital rights, digital security, and digital ethics within e-learning curricula.
Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort
Authors: Zhou Ren , Li Zhaolong , Liu Jian , Qian Dewei , Meng Xiangdong , Guan Lichun , Sun Xinxin , Li Haiqing , Yu Min
Published: February 2024

Background

Our study aimed to develop machine learning algorithms capable of predicting red blood cell (RBC) transfusion during valve replacement surgery based on a preoperative dataset of the non-anemic cohort.

Methods

A total of 423 patients who underwent valvular replacement surgery from January 2015 to December 2020 were enrolled. A comprehensive database that incorporated demographic characteristics, clinical conditions, and results of preoperative biochemistry tests was used for establishing the models. A range of machine learning algorithms were employed, including decision tree, random forest, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), support vector classifier and logistic regression (LR). Subsequently, the area under the receiver operating characteristic curve (AUC), accuracy, recall, precision, and F1 score were used to determine the predictive capability of the algorithms. Furthermore, we utilized SHapley Additive exPlanation (SHAP) values to explain the optimal prediction model.

Results

The enrolled patients were randomly divided into training set and testing set according to the 8:2 ratio. There were 16 important features identified by Sequential Backward Selection for model establishment. The top 5 most influential features in the RF importance matrix plot were hematocrit, hemoglobin, ALT, fibrinogen, and ferritin. The optimal prediction model was CatBoost algorithm, exhibiting the highest AUC (0.752, 95% CI: 0.662–0.780), which also got relatively high F1 score (0.695). The CatBoost algorithm also showed superior performance over the LR model with the AUC (0.666, 95% CI: 0.534–0.697). The SHAP summary plot and the SHAP dependence plot were used to visually illustrate the positive or negative effects of the selected features attributed to the CatBoost model.

Conclusions

This study established a series of prediction models to enhance risk assessment of intraoperative RBC transfusion during valve replacement in no-anemic patients. The identified important predictors may provide effective preoperative interventions.
The effects of polyester microfibres on the development and seed yield of white mustard (Sinapis alba L.)
Authors: Harrison Eleanor Grace , Reiling Kevin , Halfpenny Richard Kenneth , Gwinnett Claire
Published: February 2024

Microplastics are found in agricultural soils worldwide; however, little is known about the impacts of this ubiquitous pollutant on the growth and development of crops. Microfibres are one of the dominant microplastic types found in agricultural soils. Sources of microfibres in the agricultural environment are multiple, including soil amendment applications, wastewater irrigation and atmospheric deposition, with agricultural soils subsequently acting as an accumulating sink for plastics. A key consideration in an agricultural setting is yield; the seed yield is critical, as this is the part with economic value. This study investigates the influence of polyester microfibres, a prevalent type of microplastic in agricultural soils, on the growth and seed yield of Sinapis alba (white mustard). Polyester microfibres were added to soil samples at concentrations of 0.1% and 1% w/w, simulating environmental exposure levels. The study evaluated flower production, seed yield, pod-to-seed ratio, and chlorophyll fluorescence as indicators of plant health and reproductive success. Results revealed significant changes in chlorophyll fluorescence values (Fv/Fm), reduced flower production control (74 ± 37 flowers in the control, 31 ± 27 in the 0.1 treatment and 44 ± 31 in the 1 % treatment), and changes in pod-to-seed ratio (3.5 ± 0.65 seeds per pod in the control, 2.76 ± 0.5 seeds per pod in the 0.1% treatment and 2.83 ± 0.81 seeds per pod in the 1% treatment) in response to polyester microfibre exposure. Polyester microfibres were demonstrated to act as a stressor to S. alba, changing the chlorophyll fluorescence values, reducing the flower number, and in turn, reducing the pod-to-seed ratio. This study provides evidence that microfibres could reduce seed yield but that further research is required to elucidate the mechanisms by which these changes are occurring.
Transporting results in an observational epidemiology setting: purposes, methods, and applied example
Authors: Scelo Ghislaine , Zugna Daniela , Popovic Maja , Strandberg-Larsen Katrine , Richiardi Lorenzo
Published: February 2024

In the medical domain, substantial effort has been invested in generating internally valid estimates in experimental as well as observational studies, but limited effort has been made in testing generalizability, or external validity. Testing the external validity of scientific findings is nevertheless crucial for the application of knowledge across populations. In particular, transporting estimates obtained from observational studies requires the combination of methods for causal inference and methods to transport the effect estimates in order to minimize biases inherent to observational studies and to account for differences between the study and target populations. In this paper, the conceptual framework and assumptions behind transporting results from a population-based study population to a target population is described in an observational setting. An applied example to life-course epidemiology, where internal validity was constructed for illustrative purposes, is shown by using the targeted maximum likelihood estimator.
ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization
Authors: Liu Mingyou , Wu Tao , Li Xue , Zhu Yingxue , Chen Sen , Huang Jian , Zhou Fengfeng , Liu Hongmei
Published: February 2024

Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments.

Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/.

Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.
Checkpoint CD24 function on tumor and immunotherapy
Authors: Huang Shiming , Zhang Xiaobo , Wei Yingtian , Xiao Yueyong
Published: February 2024

CD24 is a protein found on the surface of cells that plays a crucial role in the proliferation, invasion, and spread of cancer cells. It adheres to cell membranes through glycosylphosphatidylinositol (GPI) and is associated with the prognosis and survival rate of cancer patients. CD24 interacts with the inhibitory receptor Siglec-10 that is present on immune cells like natural killer cells and macrophages, leading to the inhibition of natural killer cell cytotoxicity and macrophage-mediated phagocytosis. This interaction helps tumor cells escape immune detection and attack. Although the use of CD24 as a immune checkpoint receptor target for cancer immunotherapy is still in its early stages, clinical trials have shown promising results. Monoclonal antibodies targeting CD24 have been found to be well-tolerated and safe. Other preclinical studies are exploring the use of chimeric antigen receptor (CAR) T cells, antibody-drug conjugates, and gene therapy to target CD24 and enhance the immune response against tumors. In summary, this review focuses on the role of CD24 in the immune system and provides evidence for CD24 as a promising immune checkpoint for cancer immunotherapy.
The cellular composition of the tumor microenvironment is an important marker for predicting therapeutic efficacy in breast cancer
Authors: Dou Tingyao , Li Jing , Zhang Yaochen , Pei Wanru , Zhang Binyue , Wang Bin , Wang Yanhong , Jia Hongyan
Published: February 2024

At present, the incidence rate of breast cancer ranks first among new-onset malignant tumors in women. The tumor microenvironment is a hot topic in tumor research. There are abundant cells in the tumor microenvironment that play a protumor or antitumor role in breast cancer. During the treatment of breast cancer, different cells have different influences on the therapeutic response. And after treatment, the cellular composition in the tumor microenvironment will change too. In this review, we summarize the interactions between different cell compositions (such as immune cells, fibroblasts, endothelial cells, and adipocytes) in the tumor microenvironment and the treatment mechanism of breast cancer. We believe that detecting the cellular composition of the tumor microenvironment is able to predict the therapeutic efficacy of treatments for breast cancer and benefit to combination administration of breast cancer.
One decade of “English as a medium of instruction” (EMI) in healthcare education
Authors: Alhamami Munassir
Published: February 2024

Introduction

This paper analyzes published healthcare studies about “English as a medium of instruction” (EMI), indexed in the Scopus database from 2013 to 2022.

Methods

The author used published criteria of systematic reviews and limited the findings to healthcare education using several key terms; this returned 137 articles. The author then downloaded and carefully read the articles. The majority of articles (102) were deleted because they did not meet the selection criteria discussed in the methods section, thus the final list comprised 35 research studies. Next, the author analyzed the articles’ bibliometric indexes, such as author, funding information, context, research instruments, years of publication, place of publication, and citations. In addition, the key findings and recommendations of these studies were presented.

Results and discussion

Most of the studies assessed were conducted in the last five years in Arabic speaking countries by non-language specialists, and the language of instruction was not the main focus of the studies. The studies were most often about attitudes of students, and used quantitative methods such as questionnaires. The results show diverse and conflicted results such as positive impacts and positive attitudes in some cases, negative impacts and attitudes in others, and preferences for either monolingual or bilingual approaches. The findings demonstrate the need for experimental and rigorous mixed methods studies that involve different stakeholders and are conducted by both applied linguists and healthcare education specialists. Future research should move beyond student attitudes and utilize rigorous mixed methods involving researchers from both linguistics and healthcare education to deepen our understanding of EMI’s complex impact in diverse contexts.
Automated measurement and grading of knee cartilage thickness: a deep learning-based approach
Authors: Guo JiangRong , Yan Pengfei , Qin Yong , Liu MeiNa , Ma Yingkai , Li JiangQi , Wang Ren , Luo Hao , Lv Songcen
Published: February 2024

Background

Knee cartilage is the most crucial structure in the knee, and the reduction of cartilage thickness is a significant factor in the occurrence and development of osteoarthritis. Measuring cartilage thickness allows for a more accurate assessment of cartilage wear, but this process is relatively time-consuming. Our objectives encompass using various DL methods to segment knee cartilage from MRIs taken with different equipment and parameters, building a DL-based model for measuring and grading knee cartilage, and establishing a standardized database of knee cartilage thickness.

Methods

In this retrospective study, we selected a mixed knee MRI dataset consisting of 700 cases from four datasets with varying cartilage thickness. We employed four convolutional neural networks—UNet, UNet++, ResUNet, and TransUNet—to train and segment the mixed dataset, leveraging an extensive array of labeled data for effective supervised learning. Subsequently, we measured and graded the thickness of knee cartilage in 12 regions. Finally, a standard knee cartilage thickness dataset was established using 291 cases with ages ranging from 20 to 45 years and a Kellgren–Lawrence grading of 0.

Results

The validation results of network segmentation showed that TransUNet performed the best in the mixed dataset, with an overall dice similarity coefficient of 0.813 and an Intersection over Union of 0.692. The model’s mean absolute percentage error for automatic measurement and grading after segmentation was 0.831. The experiment also yielded standard knee cartilage thickness, with an average thickness of 1.98 mm for the femoral cartilage and 2.14 mm for the tibial cartilage.

Conclusion

By selecting the best knee cartilage segmentation network, we built a model with a stronger generalization ability to automatically segment, measure, and grade cartilage thickness. This model can assist surgeons in more accurately and efficiently diagnosing changes in patients’ cartilage thickness.
Inspecting mother-to-infant microbiota transmission: disturbance of strain inheritance by cesarian section
Authors: Yang Ru , Wang Yinan , Ying Zhiye , Shi Zeyao , Song Yan , Yan Jing , Hou Shulin , Zhao Zicheng , Hu Yanling , Chen Qiong , Peng Wentao , Li Xiaowen
Published: February 2024

Introduction

The initial acquisition and subsequent development of the microbiota in early life is crucial to future health. Cesarean-section (CS) birth is considered to affect early microbial transmission from mother to infant.

Methods

In this study, we collected fecal samples from 34 CS infants and their mothers from West China Second Hospital, Sichuan University to assess the microbiota developmental trajectory of mothers and infants. We explored mother-infant gut microbiome transmission via comparison with corresponding Finnish data.

Results

Metagenomic analysis of gut microbiota profiles indicated that the communities of mothers and infants were distinct. The composition of the infant gut microbiome was highly variable but also followed predictable patterns in the early stages of life. Maternal communities were stable and mainly dominated by species from Bacteroidacea spp. We used PStrain to analyze and visualize strain transmission in each mother-infant pair. Excluding missing data, we included 32 mother-infant pairs for analysis of strain transmission. Most CS deliveries (65.6%, 21/32) did not demonstrate transmission of strains from mother to infant. To further explore the mother-infant strain transmission, we analyzed metagenomics data from Finnish mother-infant pairs. A total of 32 mother-infant pairs were included in the analysis, including 28 vaginal delivery (VD) infants and four CS infants. Strain transmission was observed in 30 infants, including 28 VD infants and two CS infants. All VD infants received transmitted stains from their mothers. Finally, a total of 193 strain transmission events were observed, comprising 131 strains and 45 species.

Discussion

Taken together, our data suggested that delivery mode was an important factor influencing the mother-infant strain transmission.
Phylogenomic discernments into Anaerolineaceae thermal adaptations and the proposal of a candidate genus Mesolinea
Authors: Bedoya-Urrego Katherine , Alzate Juan F.
Published: February 2024

This study delves into the evolutionary history of Anaerolineaceae, a diverse bacterial family within the Chloroflexota phylum. Employing a multi-faceted approach, including phylogenetic analyses, genomic comparisons, and exploration of adaptive features, the research unveils novel insights into the family’s taxonomy and evolutionary dynamics. The investigation employs metagenome-assembled genomes (MAGs), emphasizing their prevalence in anaerobic environments. Notably, a novel mesophilic lineage, tentatively named Mesolinea, emerges within Anaerolineaceae, showcasing a distinctive genomic profile and apparent adaptation to a mesophilic lifestyle. The comprehensive genomic analyses shed light on the family’s complex evolutionary patterns, including the conservation of key operons in thermophiles, providing a foundation for understanding the diverse ecological roles and adaptive strategies of Anaerolineaceae members.
Maternal hsa-miR-423-5p associated with the cognitive development of babies in pregnant women without mental disorders
Authors: do Amaral Cainá Corrêa , Nedel Fernanda , Ferrúa Camila Perelló , Garcia Tiago Fernandez , Corrêa Geovanna Peter , Giorgi Roberta , Longoni dos Santos Aline , de Assis Adriano Martimbianco , de Avila Quevedo Luciana , Ghisleni Gabriele Cordenonzi , de Matos Mariana Bonati , Pinheiro Karen Amaral Tavares , Trettim Jéssica Puchalski , Pinheiro Ricardo Tavares
Published: February 2024

Background

MicroRNAs (miRNAs) are small non-coding RNAs capable of regulating gene expression post-transcriptionally. MiRNAs are recognized as key regulators of diverse biological and developmental processes. During the pregnancy–puerperal cycle, numerous changes occur in the female body for the formation, growth, and development of the baby. After birth, there is a critical period in child development, as rapid gains in the physical, cognitive, and socio-emotional domains constitute the “building blocks” of children’s later growth.

Objective

The aim of this study was to investigate the association between maternal expression of hsa-miR-423-5p during the first and second trimesters of pregnancy and neurocognitive development at 90 days of life in infants. Methods: This is a longitudinal study included in a population-based cohort study, carried out in a city in southern Brazil. The Bayley III was used to assess the babies’ cognitive development. Blood samples from mothers were obtained for RNA extraction from serum and analysis of miRNA expression by qRT-PCR.

Results

In total, 87 dyads (mother–baby) were included. The average gestational age was 15.86 weeks (SD ± 5.55). An association of maternal miRNA with infant cognitive development was found; as maternal miR-423-5p increases, infants’ cognitive development increases by 2.40 (95% CI 0.37; 4.43, p = 0.021) points at 3 months of age.

Conclusion

In this context, it is suggested to use this miRNA as a biomarker of child neurocognitive development detectable in the prenatal period, thus allowing the planning of early interventions.
3D-CAM: a novel context-aware feature extraction framework for neurological disease classification
Authors: Ying Yuhan , Huang Xin , Song Guoli , Zhao Yiwen , Zhao XinGang , Shi Lin , Gao Ziqi , Li Andi , Gao Tian , Lu Hua , Fan Guoguang
Published: February 2024

In clinical practice and research, the classification and diagnosis of neurological diseases such as Parkinson’s Disease (PD) and Multiple System Atrophy (MSA) have long posed a significant challenge. Currently, deep learning, as a cutting-edge technology, has demonstrated immense potential in computer-aided diagnosis of PD and MSA. However, existing methods rely heavily on manually selecting key feature slices and segmenting regions of interest. This not only increases subjectivity and complexity in the classification process but also limits the model’s comprehensive analysis of global data features. To address this issue, this paper proposes a novel 3D context-aware modeling framework, named 3D-CAM. It considers 3D contextual information based on an attention mechanism. The framework, utilizing a 2D slicing-based strategy, innovatively integrates a Contextual Information Module and a Location Filtering Module. The Contextual Information Module can be applied to feature maps at any layer, effectively combining features from adjacent slices and utilizing an attention mechanism to focus on crucial features. The Location Filtering Module, on the other hand, is employed in the post-processing phase to filter significant slice segments of classification features. By employing this method in the fully automated classification of PD and MSA, an accuracy of 85.71%, a recall rate of 86.36%, and a precision of 90.48% were achieved. These results not only demonstrates potential for clinical applications, but also provides a novel perspective for medical image diagnosis, thereby offering robust support for accurate diagnosis of neurological diseases.
Knowledge-based quality assurance of a comprehensive set of organ at risk contours for head and neck radiotherapy
Authors: Brooks Jamison , Tryggestad Erik , Anand Aman , Beltran Chris , Foote Robert , Lucido J. John , Laack Nadia N. , Routman David , Patel Samir H. , Seetamsetty Srinivas , Moseley Douglas
Published: February 2024

Introduction

Manual review of organ at risk (OAR) contours is crucial for creating safe radiotherapy plans but can be time-consuming and error prone. Statistical and deep learning models show the potential to automatically detect improper contours by identifying outliers using large sets of acceptable data (knowledge-based outlier detection) and may be able to assist human reviewers during review of OAR contours.

Methods

This study developed an automated knowledge-based outlier detection method and assessed its ability to detect erroneous contours for all common head and neck (HN) OAR types used clinically at our institution. We utilized 490 accurate CT-based HN structure sets from unique patients, each with forty-two HN OAR contours when anatomically present. The structure sets were distributed as 80% for training, 10% for validation, and 10% for testing. In addition, 190 and 37 simulated contours containing errors were added to the validation and test sets, respectively. Single-contour features, including location, shape, orientation, volume, and CT number, were used to train three single-contour feature models (z-score, Mahalanobis distance [MD], and autoencoder [AE]). Additionally, a novel contour-to-contour relationship (CCR) model was trained using the minimum distance and volumetric overlap between pairs of OAR contours to quantify overlap and separation. Inferences from single-contour feature models were combined with the CCR model inferences and inferences evaluating the number of disconnected parts in a single contour and then compared.

Results

In the test dataset, before combination with the CCR model, the area under the curve values were 0.922/0.939/0.939 for the z-score, MD, and AE models respectively for all contours. After combination with CCR model inferences, the z-score, MD, and AE had sensitivities of 0.838/0.892/0.865, specificities of 0.922/0.907/0.887, and balanced accuracies (BA) of 0.880/0.900/0.876 respectively. In the validation dataset, with similar overall performance and no signs of overfitting, model performance for individual OAR types was assessed. The combined AE model demonstrated minimum, median, and maximum BAs of 0.729, 0.908, and 0.980 across OAR types.

Discussion

Our novel knowledge-based method combines models utilizing single-contour and CCR features to effectively detect erroneous OAR contours across a comprehensive set of 42 clinically used OAR types for HN radiotherapy.
Contemporary fluid management, humidity, and patent ductus arteriosus management strategy for premature infants among 336 hospitals in Asia
Authors: Hsieh Yao-Chi , Jeng Mei-Jy , Lin Ming-Chih , Lin Yuh-Jyh , Rohsiswatmo Rinawati , Dewi Rizalya , Chee Seok Chiong , Neoh Siew Hong , Velasco Belen Amparo E. , Imperial Ma. Lourdes S. , Nuntnarumit Pracha , Ngerncham Sopapan , Chang Yun Sil , Kim Sae Yun , Quek Bin Huey , Amin Zubair , Kusuda Satoshi , Miyake Fuyu , Isayama Tetsuya
Published: February 2024

Objectives

The management of patent ductus arteriosus (PDA) is a critical concern in premature infants, and different hospitals may have varying treatment policies, fluid management strategies, and incubator humidity. The Asian Neonatal Network Collaboration (AsianNeo) collected data on prematurity care details from hospitals across Asian countries. The aim of this study was to provide a survey of the current practices in the management of PDA in premature infants in Asian countries.

Methods

AsianNeo performed a cross-sectional international questionnaire survey in 2022 to assess the human and physical resources of hospitals and clinical management of very preterm infants. The survey covered various aspects of hospitals resources and clinical management, and data were collected from 337 hospitals across Asia. The data collected were used to compare hospitals resources and clinical management of preterm infants between areas and economic status.

Results

The policy of PDA management for preterm infants varied across Asian countries in AsianNeo. Hospitals in Northeast Asia were more likely to perform PDA ligation (p < 0.001) than hospitals in Southeast Asia. Hospitals in Northeast Asia had stricter fluid restrictions in the first 24 h after birth for infants born at <29 weeks gestation (p < 0.001) and on day 14 after birth for infants born at <29 weeks gestation (p < 0.001) compared to hospitals in Southeast Asia. Hospitals in Northeast Asia also had a more humidified environment for infants born between 24 weeks gestation and 25 weeks gestation in the first 72 h after birth (p < 0.001). A logistic regression model predicted that hospitals were more likely to perform PDA ligation for PDA when the hospitals had a stricter fluid planning on day 14 after birth [Odds ratio (OR) of 1.70, p = 0.048], more incubator humidity settings (<80% vs. 80%–89%, OR of 3.35, p = 0.012 and <80% vs. 90%–100%, OR of 5.31, p < 0.001).

Conclusions

In advanced economies and Northeast Asia, neonatologists tend to adopt a more conservative approach towards fluid management, maintain higher incubator humidity settings and inclined to perform surgical ligation for PDA.
Developing and evaluating a predictive model for neonatal hyperbilirubinemia based on UGT1A1 gene polymorphism and clinical risk factors
Authors: Cui Zhaoyang , Shen Wensheng , Sun Xuetong , Li Yan , Liu Ying , Sun Zhiyong
Published: February 2024

Background

Neonatal hyperbilirubinemia (NHB) is one of the most common diseases in the neonatal period. Without timely diagnosis and treatment, it can lead to long-term complications. In severe cases, it may even result in fatality. The UGT1A1 gene and clinical risk factors play important roles in the development and progression of NHB.

Methods

In this study, we conducted a cohort study and analyzed 3258 newborns from the Jilin Women And Children Health Hospital in northern China, including 372 children with hyperbilirubinemia. We established a predictive model using a logistic regression model based on clinical risk factors and the polymorphism of the G211A locus in the UGT1A1 gene of newborns. Furthermore, the performance of the prediction model was evaluated using the ROC curve.

Results

The logistic regression model indicates that the following factors are associated with an increased risk of NHB: the time when stool turns yellow [P ≤ 0.001, OR 1.266 (95% CI: 1.125-1.425)]; neonatal cephalohematoma [P ≤ 0.001, OR 33.642 (95% CI: 21.823-51.861)]; hemolytic disease of newborn [P ≤ 0.001, OR 33.849 (95% CI: 18.589-61.636)]; neonatal weight loss [P ≤ 0.001, OR 11.275 (95% CI: 7.842-16.209)]; neonatal premature rupture of membranes (PROM) history [P = 0.021, OR 1.422 (95% CI: 1.056-1.917)]; genetic polymorphism at the UGT1A1 gene G211A locus. Gestational age is a protective factor [P ≤ 0.001, OR 0.766 (95% CI: 0.686-0.855)]. Compared to natural labor, cesarean section is a protective factor [P = 0.011, OR 0.711 (95% CI: 0.546-0.926)], while assisted delivery is a risk factor [P = 0.022, OR 2.207 (95% CI: 1.121-4.346)]. The area under the curve (AUC) of this prediction model is 0.804 (95% CI: 0.777-0.831), indicating good discrimination ability and value for predicting the risk of NHB after birth.

Conclusion

We have developed and evaluated a predictive model that combines UGT1A1 gene polymorphism and clinical risk factors for the first time. By using this nomogram and taking into account the results of serum total bilirubin measurement or transcutaneous bilirubin measurement, early prediction of the risk of neonatal hyperbilirubinemia can be achieved.
Exploring the potential of nutraceutical to combat gliomas: focus on mIDH2 protein
Authors: Murali Poornimaa , Karuppasamy Ramanathan
Published: February 2024

Somatic transformations in the key catalytic residues of the Isocitrate Dehydrogenase (IDH) enzyme assist in the onset of distinct malignancies including glioma. Currently, enasidenib is the FDA-approved drug used to target IDH2 protein. However, the use of enasidenib as a plausible mIDH2 inhibitor is constrained by poor brain penetrating capability and dose-limiting toxicity. Thus, the present study aimed to explore the potential of nutraceuticals to synergistically elevate the efficacy of the existing drugs available for glioma management. The binding affinity and free energy of the nutraceuticals were evaluated using molecular docking and MM-GBSA analysis. The resultant 14 compounds were subjected to machine learning-based rescoring strategies to distinguish binders from nonbinders. The pharmacokinetic and toxicity analysis was also implemented alongside virtual cell line assay. The results of our study identified DB14002 (D-alpha-Tocopherol acetate, analog of Vitamin E) as the potential hit compound with appreciable binding affinity, brain penetrating capability and antineoplastic activity against glioma cell lines. In the end, the conformational stability and dynamic characteristics of DB14002 were examined for a stipulated time frame of 250ns. Indeed, the outcomes of our study culminate the use of DB14002 as a synergistic drug-like candidate which could be translated as a plausible inhibitor of mIDH2 in the forthcoming years.
Role cognition of assigned nurses supporting Hubei Province in the fight against COVID-19 in China: a hermeneutic phenomenological study
Authors: Zhang Xu , Wang Yaqian , Chen Yuanyuan , Yang Hailing , Luan Xiaorong
Published: February 2024

Aims

During the COVID-19 epidemic, nurses played a crucial role in clinical treatment. As a special group, front-line nurses, especially those assigned to support Hubei Province in the fight against COVID-19 between February and April 2020, brought diverse experiences from different provinces in China in taking care of COVID-19 patients and role cognition. Therefore, our purpose is to explore the real coping experience and role cognition of front-line nurses during the novel coronavirus outbreak to provide relevant experience references for society and managers in the face of such major public health emergencies in the future.

Design

This qualitative study was performed using the phenomenological hermeneutics method.

Method

This is a qualitative phenomenological study. Semi-structured in-depth interviews were used to collect data. The interviewees were 53 front-line nurses who assisted and supported the fight against COVID-19 in Hubei Province during the COVID-19 epidemic. Data were collected through individual online and telephone interviews using a semi-structured interview during March 2020. The COREQ guidance was used to report this study.

Results

The findings revealed that front-line nurses assisting in the fight against COVID-19 developed a context-specific role cognition of their work and contribution to society. The qualitative analysis of the data revealed 15 sub-categories and 5 main categories. These five themes represented the different roles identified by nurses. The roles included expectations, conflicts, adaptation, emotions, and flow of blessing. Belief in getting better, a sense of honor, and training could help them to reduce feelings of conflict in this role and adapt more quickly.

Discussion

This article discusses the real coping experience and role cognition of front-line nurses during the novel coronavirus epidemic. It provides relevant experience references for society and managers to face similar major public health emergencies in the future. This study makes a significant contribution to the literature because it demonstrates how non-local nurses sent to Hubei to work perceived their roles as part of a larger narrative of patriotism, duty, solidarity, and hope.
Climate change anxiety, fear, and intention to act
Authors: von Gal Alessandro , Fabiani Greta , Piccardi Laura
Published: February 2024

Climate change anxiety (CCA) is an emerging yet not clearly defined construct. Here, we examine the relationship between CCA and climate change-related fear in response to messages differently framing uncertainty and anticipation concerning climate change, exploring how the former differs from general anxiety measures. To this purpose, young and healthy volunteers were assigned to three different framing conditions. Their emotional responses as well as eco-emotions and beliefs about climate change were collected. By employing a Bayesian approach, we found that framing the consequences of climate change effectively induces heightened fear and that CCA strongly predicted fear levels, while general anxiety measures did not. Overall, these results reflect CCA’s unique and specific nature in influencing climate change-related fear. Interestingly, we found fear to predict intention scores only following the framings that did not effectively induce action intentions, consistent with prior findings on fear without efficacy framing. Instead, reading about the negative consequences motivated action the most. Following this framing, we found that eco-anger, instead of fear, consistently predicted intentions to engage in climate action. These results emphasize the complex interplay between CCA, eco-emotions, efficacy, and behavioral engagement.
Meaning-making while staying connected matters in psychological adaptation during pandemic: a longitudinal moderated mediation study
Authors: Kim Bin-Na , Kang Hyo Shin , Park Jungkyu
Published: February 2024

Adversity may bring about both negative and positive changes in psychological adaptation. Although there is mounting evidence regarding the psychological distress during the pandemic, the other side of posttraumatic change, posttraumatic growth (PTG) and its predictors are relatively underexamined. Moreover, there is a paucity of longitudinal investigations that examined intra- and interpersonal predictors responsible for both sides of psychological adaptation. Therefore, this study comprehensively examined the longitudinal relationship among cognitive processing, social support, and adaptation during the pandemic using a moderated mediation model. Specifically, it was tested whether two types of event-related rumination mediated the link between perceived stress and ambilateral adaptational outcomes, and whether social support moderated the mediating pathways of ruminations on adaptation. After informed consent, a representative sample of adults was followed up for over a year, and answered a package of online questionnaires. The results showed that intrusive rumination prospectively predicted greater psychological distress and less PTG in response to stress, whereas deliberate rumination led to less psychological distress and more PTG over time. As predicted, the indirect protective effect of deliberate rumination was stronger when perceived social support was higher. This longitudinal study highlighted the core factors responsible for continued suffering and personal growth during the pandemic. These results have both practical and clinical implications for mental healthcare in the post-COVID era, when the heterogeneity of psychological adaptation increases and preparation for the next pandemic is warranted.
Short-term chloral hydrate as an add-on treatment may improve sleep and alleviate agitation in inpatients with treatment resistant schizophrenia: a retrospective case series study
Authors: Shelef Assaf , Alaa Habashi , Bloemhof-Bris Esther , Halperin Dania , Weizman Shira , Stryjer Rafael
Published: February 2024

Introduction

Chloral hydrate (CH), a medication dating back to 1832, is tranquilizer and sleep promoter still used today. It remains an option for short-term insomnia therapy and sedation before medical procedures, despite its controversial safety profile.

Methods

This study investigated the potential benefits of chloral hydrate addition for increasing sleep duration and reducing agitation and violence in inpatients with treatment-resistant schizophrenia (TRS). A retrospective, observational case series design was utilized, analyzing data from fourteen patients diagnosed with TRS disorders.

Results

CH addition increased the rate of full night sleep and decreased the rates of agitation and verbal and physical violence events. Notably, no adverse events including falls were reported during CH addition.

Discussion

CH shows some short-term benefits in improving sleep disorders and reducing violent and agitated behavior in patients with TRS. Our study has limitations due to its small sample size, retrospective design and lack of a control group. A large-scale, double-blind, randomized trial is needed to further explore the efficacy and safety of CH in psychiatric populations with TRS accompanied by agitation, violence and disturbed sleep.
Comprehensive analysis of vulnerability status and associated affect factors among prehospital emergency patients: a single-center descriptive cross-sectional study
Authors: Zhang Jiange , Ding Ning , Cao Xue , Zang Shuting , Ren Ying , Qin Lijie , Xu Lijun , Cheng Yanwei , Li Hongyan
Published: February 2024

Background

Prehospital emergency care is a critical but often understudied aspect of healthcare. Patient vulnerability in this setting can significantly impact outcomes. The aim of this study was to investigate the vulnerability status and to determine associated affect factors among prehospital emergency patients in China.

Methods

In this cross-sectional study conducted in China, from April 2023 to July 2023, we assessed the vulnerability of prehospital emergency patients using the Safety in Prehospital Emergency Care Index (SPECI) scale. We conducted a detailed questionnaire-based survey to gather demographic and disease-related information. We employed the SPECI scale, consisting of two subscales, to evaluate patient vulnerability. Statistical analyses, including t-tests, ANOVA, and multiple linear regression, were used to identify factors associated with vulnerability.

Results

The study included a total of 973 prehospital emergency patients, with a response rate of 81.9%. These patients exhibited a low-to-moderate level of vulnerability, with an average SPECI score of 14.46 out of 40. Vulnerability was significantly associated with age (particularly those aged 60 and above), disease severity (severe conditions increased vulnerability), disease type (circulatory diseases correlated with higher vulnerability), alterations in consciousness, and chronic diseases. Unexpectedly, digestive system diseases were negatively correlated with vulnerability.

Conclusion

Addressing patient vulnerability in prehospital care is essential. Tailored interventions, EMS provider training, and interdisciplinary collaboration can mitigate vulnerability, especially in older patients and those with severe conditions.
Predictive factors of coronavirus disease (COVID-19) vaccination series completion: a one-year longitudinal web-based observational study in Japan
Authors: Kobayashi Takaomi , Tokiya Mikiko , Matsumoto Akiko , Nakano Takashi , Hirota Yoshio , Hara Megumi
Published: February 2024

Introduction

Addresing vaccine hesitancy is considered an important goal in management of the COVID-19 pandemic. We sought to understand what factors influenced people, especially those initially hesitant, to receive two or more vaccine doses within a year of the vaccine’s release.

Methods

We conducted longitudinal Web-based observational studies of 3,870 individuals. The surveys were conducted at four different time points: January 2021, June 2021, September 2021, and December 2021. In the baseline survey (January 2021), we assessed vaccination intention (i.e., “strongly agree” or “agree” [acceptance], “neutral” [not sure], and “disagree” or “strongly disagree” [hesitance]), and assumptions about coronavirus disease (COVID-19), COVID-19 vaccine, COVID-19-related health preventive behavior, and COVID-19 vaccine reliability. In subsequent surveys (December 2021), we assessed vaccination completion (i.e., ≥2 vaccinations). To investigate the relationship between predictors of COVID-19 vaccination completion, a multivariable logistic regression model was applied. Adjusted odds ratios (AORs) and 95% confidence intervals (CIs) were calculated while adjusting for gender, age, marital status, presence of children, household income category, and presence of diseases under treatment. In a stratified analysis, predictors were determined based on vaccination intention.

Results

Approximately 96, 87, and 72% of those who demonstrated acceptance, were not sure, or hesitated had been vaccinated after 1 year, respectively. Overall, significant factors associated with COVID-19 vaccine compliance included the influence of others close to the index participant (social norms) (AOR, 1.80; 95% CI, 1.56–2.08; p < 0.001), vaccine confidence (AOR, 1.39; 95% CI, 1.18–1.64; p < 0.001) and structural constraints (no time, inconvenient location of medical institutions, and other related factors) (AOR, 0.80; 95% CI, 0.70–0.91; p = 0.001). In the group of individuals classified as hesitant, significant factors associated with COVID-19 vaccine compliance included social norms (AOR, 2.43; 95% CI, 1.83–3.22; p < 0.001), confidence (AOR, 1.44; 95% CI, 1.10–1.88; p = 0.008), and knowledge (AOR, 0.69; 95% CI, 0.53–0.88; p = 0.003).

Discussion

We found that dissemination of accurate information about vaccines and a reduction in structural barriers to the extent possible enhanced vaccination rates. Once the need for vaccination becomes widespread, it becomes a social norm, and further improvements in these rates can then be anticipated. Our findings may help enhance vaccine uptake in the future.