bluuebunny/crossref_metadata_2025_split huggingface dataset:
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.
One decade of “English as a medium of instruction” (EMI) in healthcare education
Authors: Alhamami Munassir 
Published: February 2024

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

BackgroundKnee 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.MethodsIn 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.ResultsThe 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.ConclusionBy 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

IntroductionThe 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.MethodsIn 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.ResultsMetagenomic 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.DiscussionTaken together, our data suggested that delivery mode was an important factor influencing the mother-infant strain transmission.
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

BackgroundMicroRNAs (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.ObjectiveThe 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.ResultsIn 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.ConclusionIn 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.
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

IntroductionManual 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.MethodsThis 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.ResultsIn 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.DiscussionOur 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

ObjectivesThe 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.MethodsAsianNeo 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.ResultsThe 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 &lt; 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 &lt;29 weeks gestation (p &lt; 0.001) and on day 14 after birth for infants born at &lt;29 weeks gestation (p &lt; 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 &lt; 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 (&lt;80% vs. 80%–89%, OR of 3.35, p = 0.012 and &lt;80% vs. 90%–100%, OR of 5.31, p &lt; 0.001).ConclusionsIn 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

BackgroundNeonatal 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.MethodsIn 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.ResultsThe 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.ConclusionWe 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.
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

AimsDuring 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.DesignThis qualitative study was performed using the phenomenological hermeneutics method.MethodThis 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.ResultsThe 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.DiscussionThis 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.
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

IntroductionChloral 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.MethodsThis 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.ResultsCH 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.DiscussionCH 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

BackgroundPrehospital 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.MethodsIn 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.ResultsThe 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.ConclusionAddressing 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.