bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between penicillin allergy labels and serious adverse events: a systematic review and meta-analysis
Authors: Zhang Shipeng , Dong Tianyi , Xian Jiawen , Xiao Xinyue , Yuan Jiaqing , Zeng Tong , Deng Kuan , Fu Rui , Wang Hanyu , Jiang Yanjie , Li Xueying 
Published: November 2024

Abstract

Background
To date, several studies have demonstrated that erroneous labeling of Penicillin allergy (PAL) can significantly impact treatment options and result in adverse clinical outcomes, while other studies have reported no negative effects. Therefore, to systematically evaluate these effects and investigate the association between adverse clinical outcomes and the Penicillin label, we conducted this meta-analysis.
Method
Searches were conducted in the PubMed, Embase, Cochrane Library, and Web of Science databases from inception to July 13, 2024. The search strategy utilized terms ("antibiotic allergy label," "penicillin allergy label," and "allergy label") and ("death," "readmission," "adverse outcome," and "clinical adverse outcome"). In the study selection process, the PICOS framework and stringent inclusion/exclusion criteria were applied. The quality of the initially included studies was independently assessed using the Newcastle-Ottawa Scale (NOS). Data from the included studies, including relative risk (RR) and 95% confidence intervals (CI), were extracted and analyzed using Stata 16.0. Sensitivity analyses were conducted to validate the results. Heterogeneity was assessed using the I² and Cochrane Q tests, and publication bias was evaluated using Egger’s test and funnel plot analysis.
Results
A total of 497 relevant studies were identified through four databases. Following a thorough screening process, 11 studies encompassing 1,200,785 participants were ultimately included. The combined evidence suggests that penicillin allergy labeling is associated with increased mortality RR = 1.06 (95% CI 1.06–1.07, I²=0.00%), acute heart failure(RR = 1.19, 95% CI 1.09–1.30, τ²=0.00, I²=92.39%), ICU events (RR = 1.10, 95% CI 1.01–1.19, τ²=0.00, I²=57.09%), and mechanical ventilation events (RR = 1.16, 95% CI 1.09–1.24, τ²=0.00, I²=23.11%). Additionally, there was no significant association with readmissions (RR = 1.05, 95% CI 0.95–1.16, I²=0.00%).
Conclusion
Our findings indicate that penicillin allergy labels are associated with an increased risk of mortality in patients, as well as being linked to acute heart failure, heightened ICU requirements, and mechanical ventilation.
Analysis of Multi-Scale Latent Heat Flux Remote Sensing Data Based on the RST Method and Its Application in Pre-Earthquake Thermal Anomalies
Authors: Zhang yuan , Zhou Hui , Li Xiang 
Published: November 2024

Abstract

This study explores the application of Robust Satellite Techniques (RST) for analyzing latent heat flux anomalies in relation to seismic activity, utilizing three datasets—GLEAM, PML, and ETMonitor—across various spatial resolutions. Focusing on four significant earthquakes in mainland China (Wenchuan, Ludian, Lushan, and Yushu), the research highlights consistent anomalous trends observed prior to these events, with notable increases in latent heat flux anomalies in the days leading up to each earthquake. The results reveal that RST values frequently approached or exceeded 1.5, particularly evident near the epicenter and along the fault zones, validating the correlation between latent heat flux anomalies and seismic activity.
The findings indicate varying spatiotemporal characteristics of anomalies across different earthquakes: the Wenchuan earthquake displayed longer-lasting anomalies, while the Lushan and Ludian earthquakes exhibited more concentrated and intense anomalies; the anomalies associated with the Yushu earthquake were less pronounced. Additionally, the analysis shows that land use types such as forests, medium to high cover grasslands, shrublands, and plain cultivated land—characterized by higher soil moisture content—were more likely to exhibit anomalies, aligning with previous research indicating that latent heat flux precursors are more prevalent in environmentally humid areas.
Overall, this study emphasizes the potential of using RST to detect thermal anomalies as precursors to earthquakes and suggests the need for further investigation into the background fields and anomaly thresholds at different spatial scales, as well as the relationship between latent heat anomalies and seismic events across various land surface types.
Determining the Levels of Mental Resilience and Imagery in National Team Athletes with Hearing Impairments
Authors: ÖZCAN Buşra 
Published: November 2024

Abstract

Background
National athletes with hearing impairments often face unique challenges that may impact their mental resilience and performance. Understanding the relationship between the levels of imagery and mental resilience in this population is critical for developing effective support strategies.
Aims
The purpose of this study is to examine the relationship between the levels of imagery and mental resilience in national athletes with hearing impairments.
Methods
A total of 54 professional athletes participated voluntarily in the research. After identifying outlier data through Box plot analysis, data from 2 participants were excluded, resulting in a final sample of 52 participants. Sociodemographic information was collected using a personal information form developed by the researcher. Measurement tools included the “Sport Imagery Questionnaire” and the “Sport Mental Resilience Inventory.” Normality of the score distribution was confirmed through skewness and kurtosis analysis. Descriptive statistics (frequency and percentage) were employed in accordance with these findings. For data analysis, the “Independent Sample T-Test” was utilized for comparisons between two independent groups, while the “One-Way ANOVA” was used for comparisons involving more than two groups. The “Pearson Correlation Test” examined the relationship between two numerical variables.
Conclusions
The study found that as the average scores from the mental resilience scale increased, levels of imagery in sports, scores from the sports imagery inventory, levels of motivational specific imagery, and levels of motivational general mastery also increased. This suggests a positive interaction between studies on imagery and mental resilience.
JEL Codes
C153, B150, C230, C310
Predicting the Impact of Extreme Weather on Agricultural Losses in the Delmarva Peninsula using Multi-Step Machine Learning and Financial Crop Loss Data
Authors: Nourali Zahra , Shortridge Julie E 
Published: November 2024

Abstract

The increasing occurrence of extreme weather events due to climate change presents significant challenges for agricultural production. Existing research on climatic impacts to agriculture has predominantly focused on changes in yield for major crops, providing limited insights into overall losses and impacts on diverse regional agricultural systems. This study addresses this gap by using financial crop loss data and crop insurance payouts to gain a more comprehensive understanding of agricultural impacts in diverse agricultural regions. To address the irregular data structure of financial loss data, we developed multi-step machine learning models to quantify the relationship between weather-related financial crop loss and contributing climatic factors. The Delmarva Peninsula in the Eastern United States is used as a case study location to demonstrate this methodology over the period from 1980 to 2018. Multi-step configurations of linear regression, random forest, and support vector machine approaches are compared in terms of their classification and estimation accuracy using a repeated hold-out cross-validation analysis. Results indicate that machine learning methods, particularly random forest, outperform both statistical approaches and our null baseline model, demonstrating superior generalizability in agricultural damage estimation. Multistep configurations that address irregular data distributions are shown to have a significant influence on models' capacity to detect and estimate damage occurrence. The study reveals a preference for simpler modeling approaches that minimize variance in handling unseen data, as well as the importance of accounting for seasonal patterns, spatial groupings, and persistent weather phenomena in accurately estimating agricultural losses.
Comparative Network Analysis under ABA and Chitosan Treatments: Unveiling Key Transcriptional Regulators of Valerenic Acid Biosynthesis in Valerian Hairy Roots
Authors: Mokhtari Arash , Azadi Pejman , Ebrahimi Morteza , Sobhani Ahmad , Zarghami Reza , Amirian Rasoul , Arezi Iman , Shafaei mozhdeh 
Published: November 2024

Abstract

Valerenic acid is a sesquiterpene compound found in Valeriana officinalis (valerian) and has gained attention for its potential as a natural remedy for anxiety, insomnia, and stress-related disorders due to its calming and sedative properties. The low compound content and slow plant growth have limited its production, so a biotechnological approach such as hairy root culture is needed to scale up valerenic acid without using natural resources. This study aims to elucidate the structural and dynamic features of the gene and transcription factor network underlying valerenic acid production in the hairy root cultures of valerian under ABA and chitosan elicitation. ABA treatment induced significant changes in the expression of BHLH and ERBP transcription factors. Their relative expression levels peaked on the second day. Among the genes encoding terpene synthase enzymes, TPS5 shows the strongest induction. When induced with chitosan, the transcription factors BHLH and ERBP play a critical role in enhancing valerenic acid accumulation by upregulating the expression of HMGR, TPS2, and TPS5. In addition, WRKY, ERBP, and MYB transcription factors act as critical regulators in the activation of terpene synthases. Chitosan treatment leads to the highest valerenic acid level (0.68 mg/g DW) on day 2. In comparison, ABA triggers the highest valerenic acid level (0.65 mg/g DW) on day 4. Our results have significant implications for the development of efficient and sustainable strategies for the large-scale production of valerenic acid, a valuable compound widely used in the pharmaceutical industry.
Nomophobia And Other Psycho-social Factors Affecting Sleep Quality Among Undergraduate Health Sciences Students: A Cross-Sectional Study
Authors: Khan Muhammad Anas , Lakhdir Maryam Pyar Ali , Azam Syed Iqbal , Asad Nargis 
Published: November 2024

Abstract

Background
Good sleep quality is crucial for overall health and wellbeing. Poor sleep quality has been linked with various factors including nomophobia, i.e., fear of not having a mobile phone. However, limited evidence is available in Pakistani setting therefore this study aims to determine the burden of nomophobia and poor sleep quality along with association between them among Pakistani undergraduate health sciences students.
Methods
We conducted a cross-sectional study at a private institute in Karachi, Pakistan. 551 undergraduate health sciences students were recruited via purposive sampling. The outcome variable is sleep quality status (Poor and good) and was measured using the Pittsburgh Sleep Quality Index. The primary exposure variable is degree of nomophobia and was assessed by the Nomophobia Questionnaire. Other psychosocial and demographic variables were also adjusted. Crude and adjusted prevalence ratios were computed with their 95% confidence intervals using Cox proportional algorithm.
Results
Nearly two-thirds of study participants, (n = 385, 69.1%) were females. among which 94.4%(520) were youth aged 15–24 years. 47.2%(260) students were enrolled from medical college as compared to 52.8%(291) from nursing. The prevalence of poor sleep quality was reported to be 45%. Moderate nomophobia was most prevalent at 56.8% followed by severe nomophobia (38.5%). The prevalence of poor sleep quality among students with severe nomophobia was 1.71 times (95% CI 0.9, 2.9) as compared to those with no or mild nomophobia. Prevalence of poor sleep quality among students with moderate nomophobia was 1.4 times as compared to those with no or mild nomophobia when all other variables are held constant.
Conclusion
Nomophobia and poor sleep quality were alarmingly prevalent among undergraduate health sciences indicating on the importance of drafting health policies concerning adolescents and young adults, with particular emphasis on medical students. Self-awareness and help-seeking can only be ensured to students if encouraged to share their feelings with unbiased discussion on the topic and are provided with a healthy support system. The curriculum also needs to be revised to address this issue, including providing regular counselling services and no smartphone campus days to raise awareness.
Deep Learning-Assisted Screening and Diagnosis of Scoliosis: Segmentation of Bare Back-Images via an Attention-Enhanced Convolutional Neural Network
Authors: Duan Xingyu , Ma Xiaojun , Zhu Mengqi , Wang Linan , You Dingqi , Deng Lili , Niu Ningkui 
Published: November 2024

Abstract

Background: Traditional scoliosis screening necessitates a substantial number of specialized personnel and equipment, leading to inconvenience that can result in missed opportunities for early diagnosis and optimal treatment. We have developed a deep learning-based image segmentation model to enhance the efficiency of scoliosis screening.
Methods: A total of 350 patients with scoliosis and 108 healthy subjects were included in this study. The dataset comprised bare back images and full-length anteroposterior and lateral X-ray images from 458 participants. An attention mechanism was incorporated into the original U-Net architecture to build an attention U-Net model for image segmentation. The entire dataset was divided into training (321 cases), validation (46 cases), and test (91 cases) sets at a 7:1:2 ratio. The training set was used to train the attention U-Net model, whereas the validation set was used to fine-tune hyperparameters and prevent overfitting during training. The performance of the model was evaluated via the test set. After automatic segmentation of the back contour, a back asymmetry index was calculated viacomputer vision algorithms. The severity of scoliosis was classified on the basis of this index, and the classification results were statistically compared to those of three clinical experts.
Results: Following the segmentation of bare-back images and the application of computer vision algorithms, the U-Net model achieved an accuracy, precision, and recall rate of over 90% in predicting severe scoliosis. Notably, the AUC values for diagnosing scoliosis were 0.93 for the U-Net model and 0.92 for the associate chief physician, while for identifying severe scoliosis, the AUC values were 0.95 and 0.96, respectively.
Conclusion: The attentionU-Net model developed in this study achieved accuracy and precision in determining scoliosis severity comparable to that of clinical physicians by analyzing bare-back images. The model's ability to diagnose scoliosis was also similar to that ofclinical professionals. The use of this model for scoliosis screening and diagnosis offers advantages such as being radiation-free and improving efficiency. This provides a novel, noninvasive, and effective approach, as well as a theoretical foundation, for large-scale scoliosis screening.
Smartphone-based monitoring of heart rate variability and resting heart rate predicts variability in symptom exacerbations in people with complex chronic illness
Authors: Aitken Annie , Sawyer Abbey , Iwasaki Akiko , Krumholz Harlan M. , Preston Rory , Leeming Harry , Tosto-Mancuso Jenna , Proal Amy , Osborne Michael A. , Putrino David 
Published: November 2024

Abstract

Background: Complex chronic conditions like Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome involve energy limitations and changes in heart rate variability (HRV) and resting heart rate (HR). Mobile health technologies now offer real-time, valid measurements of HRV and HR, advancing symptom monitoring and management. Using a high-density dataset from an observational longitudinal study, we aimed to describe, quantify, and predict within-person co-variations in daily biometric data and subsequent crash, fatigue, and brain fog symptom occurrences.
Methods: Leveraging data collected through a mobile health app (n=4,244), we developed predictive models using mixed-effects linear regression and logistic regression to explore how within-person fluctuations in biometrics (HR, HRV, and respiratory rate) predict dynamic change in symptomology (crash, fatigue, and brain fog). Predictive performance was assessed using 5-fold stratified cross-validation and compared to a 20% holdout set to evaluate model generalizability to new observations and individuals.
Results: Across all symptom domains, within-person changes in HRV and HR consistently emerged as key predictors of symptom change across all models, with higher HR and lower HRV conferring risk for crashes, fatigue, and brain fog. Moreover, 7-day biometric stability (or variable dispersion) was a robust predictor of symptom occurrence and severity. Models trained solely on biometric features achieved moderate predictive performance in the stratified cross-validation set; however, incorporating random effects to capture individual-specific variations and prior-day symptom reports substantially enhanced model accuracy, with AUC values reaching .91.
Discussion and Conclusion: This study is the first to use data-driven models to predict everyday symptom experiences in individuals with complex chronic illnesses based on biometric fluctuations. Findings demonstrate the potential utility of mobile health tools for real-time monitoring of symptoms and highlight the need for further research to refine these predictive models and integrate them into clinical decision-making processes.
Knowledge, Attitude, and Behavior Towards Oncofertility Among Female Breast Cancer Patients in China
Authors: McClam Maria , Yan Ruike , Su Yujing , Xiao Shuo , Zhang Xiaoxiao 
Published: November 2024

Abstract

Background
Increased cancer survival rates and advances in cancer treatments have allowed many adolescent and young adult-aged cancer patients to live long lives after having cancer. Cancer treatment-induced reproductive toxicities and infertility is important for cancer patients to understand so they can make informed decisions about their reproductive health. This study aimed to assess the knowledge, attitude, and behavior towards oncofertility and fertility preservation among female breast cancer patients in Sichuan, China.
Methods
We created an online questionnaire survey to examine 113 Chinese breast cancer patients’ demographics, knowledge, attitude, experience, and behavior regarding their cancer and fertility preservation.
Results
Results showed that there is an inadequate oncofertility knowledge among surveyed breast cancer patients. On average, patients answered only half (49%) of the questions assessing their knowledge of oncofertility correctly. Although most breast cancer patients reported not proceeding with fertility preservation, they view oncofertility as important and said there remains a lack of communication about oncofertility from providers to patients. Patients expressed a need for more information on how cancer treatment impacts fertility and what options there are for fertility preservation.
Conclusions
Our study demonstrates that there is a need to improve patients' oncofertility knowledge in China as well as increase communication between oncologists, fertility specialists, and their patients.
A JPEG Compressed Image Encryption Method Based on Shamir Secret Sharing Scheme
Authors: Guan Yulong , Wu Zhen , Yang Hui , Cheng Hongyuan 
Published: November 2024

Abstract

With the rise of cloud computing and social networks, images, as a significant representative of the multimedia big data era, hold undeniable value. Among them, JPEG images play a critical role in global image transmission and storage. To address security vulnerabilities and internet threats, both the academic world and the industrial world have proposed various security protection schemes for JPEG images. However, traditional JPEG encryption compression methods are overly reliant on keys, and once a key is lost, the integrity of the original image cannot be restored. To solve this issue, Secret Image Sharing (SIS) technology has demonstrated tremendous potential in modern data sharing, offering high security and fault tolerance, which effectively avoids excessive concentration of key dependency. In this paper, we propose a novel secret JPEG image sharing scheme that introduces Galois field-based SIS technology into the Huffman coding step of the JPEG compression process. This not only enhances the visual security of the compressed image but also resolves the problem of poor key loss tolerance. Furthermore, we improved the original scheme, making the encrypted ciphertext image harder to recognize while ensuring both the compression sharing and the restoration of the original image. Experimental results show that our scheme fully complies with the format requirements and exhibits high security.
Real-Time Smoke Detection Algorithm with Transformer
Authors: Wang Zhong , Lei Lanfang , Li Tong , Zu Xian , Shi Peibei 
Published: November 2024

Abstract

Smoke detection technology plays a crucial role in fire prevention and is vital for ensuring the safety of people's lives and property. However, existing smoke detection models face challenges such as false positives in complex environments and missed edge detections of smoke, especially for small smoke targets. Additionally, high computational and inference costs limit their application. To address these challenges, this paper proposes a Real-Time Smoke Detection Algorithm with Transformer, named RT-DETR-Smoke. First, to reduce computational costs, we introduce a real-time, end-to-end object detector, which includes a high-efficiency hybrid encoder and uncertainty minimization in query selection. Next, to address the issue of missed smoke edge detection, we employ Coordinate Attention to facilitate the fusion of smoke features across different spatial locations. Finally, to accommodate the multi-scale characteristics of fire and the complexity of various environments, we propose the WShapeIoU loss function to accelerate model convergence and improve smoke detection accuracy. Experimental results on a custom smoke dataset show that RT-DETR-Smoke achieves an accuracy of 87.75% mAP@0.5 and excels in real-time performance, with a processing speed of 445.50 FPS, demonstrating its potential for practical applications. Compared to existing models, the proposed method significantly improves both accuracy and speed.
Design, synthesis and antiviral activity of coumarin derivatives containing 1,3,4- oxadiazole/thidiazole
Authors: Hu Yuzhi , Sun Zhiling , Zeng Wei , Qiu Yujiao , Xu Zhiyuan , Zhang Jing , Feng Gang 
Published: November 2024

Abstract

In this paper, a series of oxadiazole/thidiazole containing coumarin derivative derivatives were designed, synthesized and characterized using NMR and HRMS. The evaluation of antiviral activity revealed that some of the synthesized compounds exhibited good in vivo antiviral efficacy against tobacco mosaic virus (TMV). Notably, compounds H6 and Y5 demonstrated exceptional therapeutic and protective effects against TMV, with EC50 values of 180.7, 190.3 and 215.8, 218.6 µg/mL, respectively, surpassing the efficacy of NingNanmycin, which exhibited EC50 values of 284.1 and 247.1 µg/mL. The preliminary mechanistic studies indicated that H6 and Y5 had ahigh binding affinity for the tobacco mosaic virus capsid protein (TMV-CP), potentially obstructing the self-assembly and replication processes of TMV particles. Furthermore, the chlorophyll content and superoxide dismutase (SOD) activity in tobacco leaves increased, while the malondialdehyde (MDA) content decreased. H6 has the potential to be developed as a novel antiviral.