bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of the COVID-19 lockdown in the United Kingdom on adolescent’s time use (CONTRAST study)
Authors: Pokhilenko Irina , Frew Emma , Murphy Marie , Pallan Miranda
Published: January 2025

Background

The COVID-19 pandemic has led to major changes in everyone’s lives, including adolescents. Given that adolescence is a crucial developmental stage, designing strategies to alleviate the impact of the COVID-19 on adolescents is critical. Furthermore, there is a growing literature on the relationship between how adolescents spend their time and impact upon health, nutrition, educational attainment and overall well-being outcomes, and the existence of a socioeconomic gradient with how time is allocated. Therefore, this study explored changes in adolescents’ time use during the first COVID-19 lockdown in the UK and the relationship between these changes and individual-level socioeconomic indicators including family affluence, free school meal eligibility, and food insecurity.

Methods

The data were collected from 11-15-year-olds using an online survey, which contained questions on demographic characteristics, socioeconomic indicators, and time use across a range of activities before and during the first COVID-19 lockdown. Changes in time use in relation to socioeconomic indicators were explored using descriptive and regression analysis.

Results

687 adolescents completed the survey. There was an overall decrease in the amount of time spent on school work, an increase in screen time, and an increase in sleep duration during the week. Descriptive analysis showed evidence of inequalities with changes in time use. In adjusted regression analyses, family affluence was associated with a greater increase in time spent on socialising with household members and a decrease in time spent on exercise. Free school meal eligibility and experience of food insecurity were associated with an increase in sleep duration.

Discussion

This study contributes to the body of evidence on the impact of the COVID-19 pandemic on young people and suggests that some of the pre-lockdown inequalities in time allocation were attenuated as a result of the lockdown. Furthermore, the results underscore the need for longer term surveillance to monitor changes in time use in adolescents to mitigate the impact on outcomes over the life course.
The CASPAR study protocol. Can cervical stiffness predict successful vaginal delivery after induction of labour? a feasibility, cohort study
Authors: Medford Elizabeth , Lane Steven , Merriel Abi , Sharp Andrew , Care Angharad
Published: January 2025

Background

Induction of labour (IOL) is a common obstetric intervention in the UK, affecting up to 33% of deliveries. IOL aims to achieve a vaginal delivery prior to spontaneous onset of labour to prevent harm from ongoing pregnancy complications and is known to prevent stillbirths and reduce neonatal intensive care unit admissions. However, IOL doesn’t come without risk and overall, 20% of mothers having an induction will still require a caesarean section birth and in primiparous mothers this rate is even higher.

There is no reliable predictive bedside tool available in clinical practice to predict which patient’s undergoing the IOL process will result in a vaginal birth; the fundamental aim of the IOL process. The Bishop’s Score (BS) remains in routine clinical practice as the examination tool to assess the cervix prior to IOL, despite it being proven to be ineffective as a predictive tool and largely subjective. This study will assess the use of the Pregnolia System, a new objective antenatal test of cervical stiffness. This study will explore its’ potential for pre-induction cervical assessment and indication of delivery outcome following IOL.

Methods

CASPAR is a feasibility study of term, primiparous women with singleton pregnancies undergoing IOL. Cervical stiffness will be assessed using the Pregnolia System; a novel, non-invasive, licensed, CE-marked, aspiration-based device proven to provide objective, quantitative cervical stiffness measurements represented as the Cervical Stiffness Index (CSI, in mbar). A measurement is obtained by applying the sterile single-use Pregnolia Probe directly to the anterior lip of the cervix, visualised via placement of a speculum.

Following informed consent, CASPAR study participants will undergo the Pregnolia System cervical stiffness assessment prior to their IOL process commencing. Participant questionnaires will evaluate the acceptability of this assessment tool in this population. This study will directly compare this novel antenatal test to the current BS for both patient experience of the different cervical assessment tools and for IOL outcome prediction.

Discussion

This feasibility study will explore the use of this novel device in clinical practice for pre-induction cervical assessment and delivery outcome prediction. Our findings will provide novel data that could be instrumental in transforming clinical practice surrounding IOL. Determining recruitment rates and acceptability of this new assessment tool in this population will inform design of a further powered study using the Pregnolia System as the point-of-care, bedside cervical assessment tool within an IOL prediction model.

Study registration

This study is sponsored by The University of Liverpool and registered at ClinicalTrials.gov, identifier NCT05981469, date of registration 7th July 2023.
Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification
Authors: D. Jaipriya , K. C. Sriharipriya
Published: January 2025

In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, decoding EEG signals poses significant challenges due to their complexity, dynamic nature, and low signal-to-noise ratio (SNR). Traditional EEG pattern recognition algorithms typically involve two key steps: feature extraction and feature classification, both crucial for accurate operation. In this work, we propose a novel method that addresses these challenges by employing empirical mode decomposition (EMD) for feature extraction and a parallel convolutional neural network (PCNN) for feature classification. This approach aims to mitigate non-stationary issues, improve performance speed, and enhance classification accuracy. We validate the effectiveness of our proposed method using datasets from the BCI competition IV, specifically datasets 2a and 2b, which contain motor imagery EEG signals. Our method focuses on identifying two- and four-class motor imagery EEG signal classifications. Additionally, we introduce a transfer learning technique to fine-tune the model for individual subjects, leveraging important features extracted from a group dataset. Our results demonstrate that the proposed EMD-PCNN method outperforms existing approaches in terms of classification accuracy. We conduct both qualitative and quantitative analyses to evaluate our method. Qualitatively, we employ confusion matrices and various performance metrics such as specificity, sensitivity, precision, accuracy, recall, and f1-score. Quantitatively, we compare the classification accuracies of our method with those of existing approaches. Our findings highlight the superiority of the proposed EMD-PCNN method in accurately classifying motor imagery EEG signals. The enhanced performance and robustness of our method underscore its potential for broader applicability in real-world scenarios.
Advancing biomonitoring of eDNA studies with the Anaconda R package: Integrating soil and One Health perspectives in the face of evolving traditional agriculture practices
Authors: Stenger Pierre-Louis , Léopold Audrey , Dinh Kelly , Mournet Pierre , Robert Nadia , Drouin Julien , Wamejonengo Jacques , Russet Sylvie , Ibanez Thomas , Maggia Laurent , Carriconde Fabian
Published: January 2025

Soil health and One Health are global concerns, necessitating the development of refined indicators for effective monitoring. In response, we present the Anaconda R Package, a novel tool designed to enhance the analysis of eDNA data for biomonitoring purposes. Employing a combination of different approaches, this package allows for a comprehensive investigation of species abundance and community composition under diverse conditions. This study applied the Anaconda package to examine the impact of two types of duration fire-fallow cropping systems, using natural forests as a reference, on soil fungal and bacterial communities in Maré Island (New Caledonia). Condition-specific taxa were identified, particularly pathogenic fungi and bacteria, demonstrating the importance of long-term fallowing efforts. Notably, this package also revealed the potential contributions of beneficial soil microbes, including saprophytes and plant-endophyte fungi, in suppressing soil-borne pathogens. Over-represented microbial ASVs associated with both plant and animal pathogens, including those of potential concern for human health, were identified. This underscores the importance of maintaining intrinsic balance for effective disease suppression. Importantly, the advanced analytical and statistical methods offered by this package should be harnessed to comprehensively investigate the effects of agricultural practice changes on soil health within the One Health framework. Looking ahead, the application of this method extends beyond the realm of One Health, offering valuable insights into various ecological scenarios. Its versatility holds promise for elucidating complex interactions and dynamics within ecosystems. By leveraging this tool, researchers can explore the broader implications of agricultural practice modifications, facilitating informed decisions and sustainable environmental management.
Masking is good, but conforming is better: The consequences of masking non-conformity within the college classroom
Authors: Sullivan Jessica , Moss-Racusin Corinne , Louis Kengthsagn
Published: January 2025

In the years following the acute COVID-19 crisis, facemask mandates became increasingly rare, rendering masking a highly visible personal choice. Across three studies conducted in the U.S. in 2022 and 2023 (N = 2,973), the current work provided a novel exploration of the potential impacts of adhering to vs. deviating from group masking norms within college classrooms. Experiments 1 and 2 used causal methods to assess the impact of hypothetical target students’ masking behavior on participants’ beliefs about that student’s classroom fit (e.g., how well they fit in, how much their professor likes them, whether they are invited to study group). Maskers were expected to experience more classroom inclusion relative to non-maskers, but the largest effects were conformity effects: participants expected that students who deviated from a class’s dominant mask-wearing behavior would experience massively lower classroom fit. Study 3 used correlational and qualitative methods to establish the real-world impact of mask conformity in a diverse sample of college students. Students reported believing that masking–and mask conformity–impacted others’ perceptions of them, and reported avoiding deviating from masking norms. Students reported that their desire for mask-conformity impacted both their willingness to enroll in courses and their actual masking behavior, suggesting both academic and public health impacts. Across all three studies, we asked whether pressures to conform have disproportionate effects on particular groups, by exploring the effects of gender (Studies 1 and 3), immune-status (Studies 2 and 3) and race (Study 3). Our data raise important issues that should be considered when determining whether to e.g., enact mask mandates within college classrooms and beyond, and for understanding the cognitive and social consequences of mask wearing.
Compassion fatigue and associated factors among nurses working in Jimma Zone public hospitals, southwest Ethiopia: A facility based cross-sectional study
Authors: Ganeti Duguma Debela , Dereje Fufa Bikila , Bayana Kebede Ebissa , Shemsi Seid Sheka , Wogane Ilala Birhanu , Bacha Benti Nuritu , Belay Belachew Yeshitila
Published: January 2025

Background

Nurses are at risk of developing compassion fatigue, which has negative impacts on their well-being, quality care and leads to patient mortality and a financial burden on the healthcare system. However, data on compassion fatigue is scarce in Africa, particularly Ethiopia. Therefore, this study aimed to assess level compassion fatigue and associated factors among nurses in Jimma Zone public hospitals, Ethiopia.

Method

A facility-based cross-sectional study was employed from May 25 to June 25, 2023. A systematic sampling technique was employed to select among 422 respondents. Data were collected using pretested self-administered questionnaires. Professional Quality of Life Scale-5 was used for measuring compassion fatigues. Data were entered using Epi data version 4.6 and analyzed using SPSS version 25. Linear regression were done to identify factors associated with compassion fatigue. Statistically significant was declared at a p-value of ≤ 0.05 with 95% CI.

Result

From a total of 422 respondents, 412(97.6%) of them gave complete responses. 47% of respondents, had a moderate level of compassion fatigue. Total experience [β = -0.04; 95%CI (-0.06, -0.01); p = 0.005], perceived social support [β = -0.13; 95% CI (-0.17, -0.08); p<0.001], self-compassion [β = -0.09; 95% CI (-0.14, -0.03); p = 0.003], support seeking [β = -0.23; 95% CI (-0.42, -0.04 p = 0.017], emergency ward [β = 0.36; 95% CI (0.2, 0.51); p <0.001], ICU [β = 0.38; 95% CI (0.21, 0.54); p<0.001], pediatric ward [β = 0.23; 95% CI (0.10, 0.36); p < 0.001] and average sleep hours per day [β = 0.46; 95% CI (0.35, 0.57); p<0.001] were statistically signifantly factors.

Conclusion and recommendation

The study revealed that one in four nurses had high level of compassion fatigue. The factors associated were work experience, perceived social support, self-compassion, coping strategies, work unit, and sleep hours. Therefore, stakeholders including hospital managers should implement targeted strategies to prevent compassion fatigue including training on coping strategy and, self-compassion and creating culture of team work among nurses.
Discovering the allure of forests: Exploring adolescent queries in nature-rich environments
Authors: Arroz A. M. , Gabriel R. , Silva A. R. , Piasentin F. , Amorim I. R. , Picanço A. , Matos S.
Published: January 2025

This study explores adolescents’ inherent curiosity about nature through the production of self-generated questions during a field visit to a nature-rich environment, followed by descriptive-interpretative analysis using focus groups. Utilizing cultural probes and content-free question tokens, we collected 164 valid questions produced by 36 adolescents during the field session. Biotic elements, like species, turned out to be more intriguing than abiotic elements, originating 89.6% of the questions. The predominant topics were related to species adaptation, extinction, dispersion, and diversity, with younger adolescents showing a notable interest in nature conservation, while older adolescents highlighted biodiversity dynamics. These findings were corroborated by the ranking of the TOP-5 most interesting questions, where biodiversity dynamics, nature conservation and plant physiology occupied the same relative positions. Our results indicate that in a nature-rich environment and through an inquiry-based approach, adolescents were encouraged to express curiosity about nature. This approach could be a valuable educational strategy to enhance their connection to nature, promote conservation responsibility, and benefit the environment.
Signalling pathways involved in urotensin II induced ventricular myocyte hypertrophy
Authors: Al Ali Hadeel S. , Rodrigo Glenn C. , Lambert David G.
Published: January 2025

Sustained pathologic myocardial hypertrophy can result in heart failure(HF); a significant health issue affecting a large section of the population worldwide. In HF there is a marked elevation in circulating levels of the peptide urotensin II(UII) but it is unclear whether this is a result of hypertrophy or whether the high levels contribute to the development of hypertrophy. The aim of this study is to investigate a role of UII and its receptor UT in the development of cardiac hypertrophy and the signalling molecules involved. Ventricular myocytes isolated from adult rat hearts were treated with 200nM UII for 48hours and hypertrophy was quantified from measurements of length/width (L/W) ratio. UII resulted in a change in L/W ratio from 4.53±0.10 to 3.99±0.06; (p<0.0001) after 48hours. The response is reversed by the UT-antagonist SB657510 (1μM). UT receptor activation by UII resulted in the activation of ERK1/2, p38 and CaMKII signalling pathways measured by Western blotting; these are involved in the induction of hypertrophy. JNK was not involved. Moreover, ERK1/2, P38 and CaMKII inhibitors completely blocked UII-induced hypertrophy. Sarcoplasmic reticulum (SR) Ca2+-leak was investigated in isolated myocytes. There was no significant increase in SR Ca2+-leak. Our results suggest that activation of MAPK and CaMKII signalling pathways are involved in the hypertrophic response to UII. Collectively our data suggest that increased circulating UII may contribute to the development of left ventricular hypertrophy and pharmacological inhibition of the UII/UT receptor system may prove beneficial in reducing adverse remodeling and alleviating contractile dysfunction in heart disease.
Representation models and processing operators for quantum informational multi-media
Authors: Li Yajun
Published: January 2025

To enhance the efficacy of multimedia quantum processing and diminish processing overhead, an advanced multimedia quantum representation model and quantum video display framework are devised. A range of framework processing operators are also developed, including an image color compensation operator, a bit plane inversion operator, and a frame displacement operator. In addition, to address image security issues, two quantum image operations have been proposed: color transformation operation and pixel blending operation. The research results indicated that the grayscale cost of the framework designed in this study was 33.8, the color cost was 40.5, and the total cost was 574 δ. In terms of color, the distribution of image elements in the red, green, and blue (RGB) channels was more balanced. In summary, the quantum video display framework has significant advantages in processing efficiency and image security. Compared to previous studies, the model proposed in this study exhibits higher processing speed and better processing quality in the face of complex image and video data. It effectively addresses the limitations of existing processing techniques while addressing emerging image security issues.
Moroccan natural products for multitarget-based treatment of Alzheimer’s disease: A computational study
Authors: Guerguer Fatima Zahra , Bouribab Amal , Karim El Mehdi , Khedraoui Meriem , Amegrissi Fatiha , Raouf Yasir S. , Samadi Abdelouahid , Chtita Samir
Published: January 2025

Alzheimer’s disease is a neurodegenerative disorder that impairs neurocognitive functions. Acetylcholinesterase, Butyrylcholinesterase, Monoamine Oxidase B, Beta-Secretase, and Glycogen Synthase Kinase Beta play central roles in its pathogenesis. Current medications primarily inhibit AChE but fail to halt or reverse disease progression due to the multifactorial nature of Alzheimer’s. This underscores the necessity of developing multi-target ligands for effective treatment. This study investigates the potential of phytochemical compounds from Moroccan medicinal plants as multi-target agents against Alzheimer’s disease, employing computational approaches. A virtual screening of 386 phytochemical compounds, followed by an assessment of pharmacokinetic properties and ADMET profiles, led to the identification of two promising compounds, naringenin (C23) and hesperetin (C24), derived from Anabasis aretioides. These compounds exhibit favourable pharmacokinetic profiles and strong binding affinities for the five key targets associated with the disease. Density functional theory, molecular dynamics simulations, and MM-GBSA calculations further confirmed their structural stability, with a slight preference for C24, exhibiting superior intermolecular interactions and overall stability. These findings provide a strong basis for further experimental research, including in vitro and in vivo studies, to substantiate their potential efficacy in Alzheimer’s disease.
Assessment of antenatal care quality in Ethiopia: Facility-based study using service provision assessment data
Authors: Alemu Addisu Alehegn , Welsh Alec , Getachew Theodros , Khajehei Marjan
Published: January 2025

Background

Antenatal care (ANC) coverage in low- and middle-income countries has increased in the past few decades. However, merely increasing care coverage may not enhance maternal and newborn health unless the recommended service components are also provided. Our aim was to assess the quality of ANC and its associated factors in Ethiopia.

Methods

We used data from 2,042 pregnant women whose first ANC consultation was observed. Data were obtained from the 2021–2022 Ethiopian Service Provision Assessment survey conducted among a nationally representative sample of 1,158 healthcare facilities. Twenty-four components of ANC were assessed, and their values were summed to generate a total ANC quality score (range: 0–24). A higher score indicated a superior ANC quality. A multiple generalized Poisson regression model was fitted to identify factors influencing the quality of ANC. All statistical analyses were performed using STATA version 16.

Results

The mean ANC quality score was 11 (standard deviation [SD]: 3.8). Blood pressure measurement was the most commonly performed ANC component, at 79.5%, and breast examination for cancer screening was the most neglected component of ANC, at 3.3%. ANC quality was higher in the Amhara region (incidence rate ratio [IRR]: 1.088; 95% confidence interval [CI]: 1.0–1.171) and Southern Nations, Nationalities, and Peoples’ Region (IRR: 1.081; 95% CI: 1.002–1.166), and when the care was provided by a female healthcare worker (IRR: 1.054; 95% CI: 1.021–1.088). On the other hand, ANC quality decreased in rural healthcare facilities (IRR: 0.964; 95% CI: 0.932–0.998), clinics (IRR: 0.666; 95% CI: 0.581–0.764), and health posts (IRR: 0.817; 95% CI: 0.732–0.91). Similarly, ANC quality decreased when gestational age at the first antenatal visit increased (IRR: 0.994; 95% CI: 0.992–0.996) and when care was received from a non-nearby healthcare facility (IRR: 0.956; 95% CI: 0.923–0.990).

Conclusion

Overall, the quality of ANC in Ethiopia is suboptimal. Encouraging women to initiate ANC early and utilize nearby facilities, assisting providers in delivering standardized services through preservice training, supervision, and continuous education, and ensuring the availability and proper use of necessary resources at all facilities are important to improve ANC quality.
Trends of adverse pregnancy outcomes and its determinants in Arba Minch Zuria and Gacho Baba Woredas from 2018 to 2022: Analysis of health and demographic surveillance data
Authors: Gebru Zeleke , Berhe Fekadeselassie , Shibiru Shitaye , Honja Bereket , Kote Mesfin , Baharu Alazr , Awoke Tadesse
Published: January 2025

Introduction

Many family members and the expectant mother view pregnancy as a time of joyful anticipation. However, it can also bring about a range of issues that may pose serious and potentially life-threatening risks for both the mother and the unborn child. Adverse pregnancy outcomes are an alarming public issue in different parts of the world and have a seriously harmful influence on both their health and well-being. Nevertheless, less is known about trends and determinates of adverse pregnancy outcomes in the Arba Minch zuria and Gacho Baba districts in southern Ethiopia. Therefore, this study aimed to fill these gaps in the study setting.

Methods

An open, dynamic cohort study design was employed among 8885 pregnancies from 2018 to 2022, health and demographic surveillance data were involved. Data to be collected at Arba Minch Health and Demographic Surveillance Site (HDSS) using a structured questionnaire. A log-binomial regression model was used to identify determinates. A P-value of less than 0.05 was considered to declare a statistically significant association.

Results

In this study, the overall adverse pregnancy outcomes were 8.89 per 1000 live births [95%CI: 6.93, 10.84]. The stillbirth and abortion rates were 5.74 per 1000 live births (95%CI: 4.36, 7.54) and 3.15 per 1000 live births [95%CI: 1.97, 4.02], respectively. The trends of adverse pregnancy outcomes showed 11.1/1000 in 2018 and 14.1/1000 in 2022. Age > 34 years old (aPPR = 2.93, 95%CI: 1.67, 5.17), antenatal care (aPPR = 0.52, 95%CI: 0.33, 0.83), and history of pregnancy loss (aPPR = 2.68, 95%CI: 1.36, 5.29) were identified as determinates for adverse pregnancy outcomes.

Conclusion

The prevalence of adverse pregnancy outcomes is still high, and trends vary from time to time. As such, attention is needed for the women who had a previous history of pregnancy loss, and addressing those determinants could potentially reduce the rates of stillbirths and abortions, ultimately promoting healthier pregnancies and better pregnancy outcomes.
Physical and biological properties of a novel anti-adhesive punctate uneven gelatin film
Authors: Ozamoto Yuki , Horii Tsunehito , Morita Shinichiro , Tsujimoto Hiroyuki , Oe Yasumitsu , Minato Hiroshi , Ueda Joe , Ichikawa Hiroshi , Kawauchi Akihiro , Hagiwara Akeo , Kageyama Susumu , Tani Masaji
Published: January 2025

Since abdominal adhesion are quite problematic in abdominal and pelvic surgery, the conventional HA/CMC film are commonly used as an anti-adhesive material. However, such types are difficult to be rolled and delivered through the port of laparoscopic surgical devices due to adherence to the laparoscopic port or other parts of the films. To create an anti-adhesion film with more favorable handling properties and anti-adhesive effect, we developed a novel punctate uneven gelatin film (PU GF). In this study, we examined the physical strength, flexibilities and adhesiveness between film to tissues or film each other, compared to the conventional film and the flat gelatin film (Flat GF). In addition, we investigated the cell proliferation on each film and the anti-adhesive effect of the films and those reattachment possibility using a rat cecum abrasion model. The PU GF showed excellent tensile strength, ductility, and adherence to tissue compared to Flat GF and the conventional film. Moreover, the adherence of PU GF to the other film and to a silicon sheet were much lower than those of the Flat GF and conventional film. The proliferation of cells in PU GF and Flat GF were suppressed compared with control, though increased with time. The anti-adhesive scores of the PU GF after one time and re-attachment were significantly higher than that of non-covered control, although there was no significant difference between that of the conventional film and control. Our findings suggest that PU GF improve handling properties of laparoscopic surgery as it has excellent physical strength, ductility, and adherence to tissue, and low adherence to trocar. In addition, the punctate film may be more useful with the re-attachability without tearing and to retained sufficient anti-adhesion effect.
Evaluating the impact of COVID-19 protection measures and staff absence on radiotherapy practice: A simulation study
Authors: Jambor Elisabeth , Viana Joe , Reuter-Oppermann Melanie , Müller-Polyzou Ralf
Published: January 2025

Background

Radiotherapy practice for cancer treatment is resource-intensive and demands optimised processes for patient throughput while guaranteeing the quality and safety of the therapy. With the COVID-19 pandemic, ad-hoc changes in the operation of radiotherapy centres became necessary to protect patients and staff. This simulation study aimed to quantify the impact of designated COVID-19 protection measures and pandemic-related staff absence on patient waiting times and throughput. The approach also enables analysis of protective measures and process adjustments for future business disruptions.

Methods

A discrete event simulation model of a stand-alone radiotherapy centre was developed and used to analyse changes in patient flow when implementing COVID-19 protection measures and experiencing staff absence. The simulation results support business continuity planning and decision-making in radiotherapy. In total, twenty-one scenarios in three categories were analysed. Category 1 scenarios investigated the effect of healthcare staff and equipment shortfalls. Category 2 scenarios simulated the impact of additional COVID-19 protection measures at low COVID-19 incidence rates, while category 3 scenarios evaluated the changes at high incidence rates.

Results

The simulation results suggested increased patient waiting times when staff is absent. Most scenarios of the three categories behave similarly despite increased patient waiting times due to COVID-19 protection measures in categories 2 and 3. The most significant increase in patient waiting times occurs when only two radiation therapists are available. The absence of a linear accelerator for cancer treatment also leads to increased waiting times. Scenarios where one administrator is absent show the longest average and maximum waiting times for low COVID-19 incidence rates. COVID-19 protection measures reduce patient throughput. In all scenarios, with reduced patient throughput, follow-up radiation appointments were affected.

Conclusions

The simulated scenario results suggest that appropriate staffing of the radiotherapy centre during a pandemic crisis is essential and that staff absence can lead to prolonged patient waiting times and reduced throughput with severe continuity of care consequences. The simulation model demonstrated that centre administrators are a bottleneck if they must perform COVID-19 protection measures in addition to their administrative duties. The effect could be mitigated by outsourcing COVID-19 protection tasks to external service providers or other centre staff.
Scalable and energy-efficient task allocation in industry 4.0: Leveraging distributed auction and IBPSO
Authors: Li Qingwen , Fan Tang Wai , Kei Lam Sui , Li Zhaobin
Published: January 2025

Industry 4.0 has transformed manufacturing with the integration of cutting-edge technology, posing crucial issues in the efficient task assignment to multi-tasking robots within smart factories. The paper outlines a unique method of decentralizing auctions to handle basic tasks. It also introduces an improved variant of the improved Binary Particle Swarm Optimization (IBPSO) algorithm to manage complicated tasks that require multi-robot collaboration. The main contributions we make are: the design of an auction decentralization algorithm (AOCTA) which allows for an efficient and flexible task distribution in dynamic contexts, the optimization of coalition formation in complex jobs by using IBPSO and improves the efficiency of energy and decreases the cost of computation as well as thorough simulations that show that our proposed method significantly surpasses conventional methods for efficiency, task completion rates in terms of energy usage, task completion rate, and scaling of the system. This research contributes to the development of smart manufacturing through providing an effective solution that aligns with the sustainability objectives and addresses operational efficiency as well as environmental impacts. Addressing the challenges posed by dynamic task allocation in distributed multi-robot systems, these advanced technologies provide a comprehensive solution, facilitating the evolution of innovative manufacturing systems.
Association of the abdominal aortic calcification with all-cause and cardiovascular disease-specific mortality: Prospective cohort study
Authors: Sheng Chang , Cai Zhou , Yang Pu
Published: January 2025

Background

Abdominal aortic calcification (AAC) is a prevalent form of vascular calcification associated with adverse cardiovascular outcomes. While previous studies on AAC and cardiovascular risk exist, many have limitations such as small sample sizes and limited clinical significance outcomes. This study aims to prospectively investigate the association between AAC and all-cause and cardiovascular disease (CVD)-specific mortality rates in a nationally representative sample of adults in the United States, using data from the National Health and Nutrition Examination Survey (NHANES).

Methods

The study, conducted on NHANES participants aged 40 years or older during the 2013–2014 cycle, assessed AAC using the Kauppila scoring system. Demographic characteristics, mortality data, and comorbid factors such as age, gender, diabetes, and hypertension were considered. Statistical analyses, including weighted percentages, Kaplan-Meier survival curves, and multivariable Cox proportional hazards regression models, were employed to evaluate the associations between AAC and mortality risks.

Results

After analyzing a final sample of 2717 participants, the study found a significant association between severe AAC (SAAC) and higher all-cause mortality risk (HR 1.70, 95% CI 1.17–2.48). The dose-response relationship indicated an increased risk with higher AAC scores. However, no independent association was observed between AAC and cardiovascular mortality. Stratified analysis revealed variations in the AAC-all-cause mortality association based on gender and hypertension.

Conclusion

This population-based study provides valuable insights into the prospective association between AAC and all-cause mortality, emphasizing the potential role of AAC assessment in identifying individuals at higher risk.
Novel methotrexate long-acting system using ambroxol coating and hydroxypropyl methylcellulose encapsulation for preferential and enhanced lung cancer efficiency
Authors: Abdullah Samaa , Altwaijry Najla , Alnakhli Meaad , ALenezi Ghezlan , Al-Masud Alaa A. , Henidi Hanan , Jamous Yahya F.
Published: January 2025

Methotrexate (MTX) is classified as an antimetabolite. It’s commonly used to treat lung cancer. MTX is an immunosuppressant following the above-mentioned mechanism of action due to its poor selectivity. The tricky move is to investigate the solid dispersions and coating using a co-delivery system of MTX and Ambroxol (ABL). ABL is known for its, anticancer and preferential pulmonary distribution after oral administration. The goals was development were the MTX physiochemical modulation for pulmonary enhanced distribution, MTX resistance modulation and long-acting system development using ABL middle coating and HPMC outer coating. The selection of the optimum MTX-ABL dispersion was done based on the FT-IR characterization. The MTX-release analysis results for the optimized MTX-ABL solid dispersion and the HPMC-coated MTX-ABL gel product were tested for release in the gastrointestinal simulated media to select the most optimum HPMC amounts to coat the MTX-ABL optimum solid dispersion. Moreover, different characterizations of FT-IR, X-ray diffraction and Scanning electron microscopy investigations were completed for the MTX, ABL, the ABL-MTX optimized solid dispersion and the optimum MTX-ABL-HPMC gel. The cytotoxicity assay and the ELISA to assess the levels of BAX, BCL-2, TGF-β and FR-ɑ after the MTX, ABL and the optimized MTX-ABL solid dispersion groups were tested against lung cancer cells, A549 cells, for 24 h. The sustained release character and HPMC-ABL encapsulation of MTX were confirmed. The MTX-ABL solid dispersion showed less MTX resistance without the need to use the high MTX concentrations in comparison to the MTX alone. The apoptotic, anti-metastatic, and MTX preferential lung cancer uptake profiles were higher using the MTX-ABL solid dispersion than in the MTX or ABL. The MTX-ABL-HPMC gel could serve as an alternative to the MTX-oral tablets available in the markets with enhanced efficacy and safety profile.
A recurrence model for non-puerperal mastitis patients based on machine learning
Authors: Li Gaosha , Yu Qian , Dong Feng , Wu Zhaoxia , Fan Xijing , Zhang Lingling , Yu Ying
Published: January 2025

Objective

Non-puerperal mastitis (NPM) is an inflammatory breast disease affecting women during non-lactation periods, and it is prone to relapse after being cured. Accurate prediction of its recurrence is crucial for personalized adjuvant therapy, and pathological examination is the primary basis for the classification, diagnosis, and confirmation of non-puerperal mastitis. Currently, there is a lack of recurrence models for non-puerperal mastitis. The aim of this research is to create and validate a recurrence model using machine learning for patients with non-puerperal mastitis.

Methods

We retrospectively collected laboratory data from 120 NPM patients, dividing them into a non-recurrence group (n = 59) and a recurrence group (n = 61). Through random allocation, these individuals were split into a training cohort and a testing cohort in a 90%:10% ratio for the purpose of building the model. Additionally, data from 25 NPM patients from another center were collected to serve as an external validation cohort for the model. Univariate analysis was used to examine differential indicators, and variable selection was conducted through LASSO regression. A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. The finally selected model was interpreted and evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, Decision curve analysis (DCA), and Shapley Additive Explanations (SHAP) plots.

Results

The logistic regression model emerged as the optimal model for predicting recurrence of NPM with machine learning, primarily utilizing three variables: FIB, bacterial infection, and CD4+ T cell count. The model showed an AUC of 0.846 in the training cohort and 0.833 in the testing cohort. The calibration curve indicated excellent calibration of the model. DCA revealed that the model possessed favorable clinical utility. Furthermore, the model effectively achieved in the external validation group, with an AUC of 0.825.

Conclusion

The machine learning model developed in this study, serving as an effective tool for predicting NPM recurrence, aids doctors in making more individualized treatment decisions, thereby enhancing therapeutic efficacy and reducing the risk of recurrence.
A machine learning based variable selection algorithm for binary classification of perinatal mortality
Authors: Sadiq Maryam , Shah Ramla
Published: January 2025

The identification of significant predictors with higher model performance is the key objective in classification domain. A machine learning-based variable selection technique termed as CARS-Logistic model is proposed by coupling competitive adaptive re-weighted sampling(CARS) and logistic regression for binary classification. Based on five assessment criteria, the proposed method is found to be more efficient than Forward selection logistic regression model. The CARS-Logistic model is executed to determine the significant factors of perinatal mortality in Pakistan. The identified hazards communicated social, cultural, financial, and health-related characteristics which contain key information about perinatal mortality in Pakistan for policymakers.
Export competitiveness network of Chinese provincial agricultural products: E volution, performance, and influencing factors
Authors: Chen Ye , Liu Zhuoxi , Yin Zhongming
Published: January 2025

The development of top-tier agricultural product bases and demonstration zones aims to propel Chinese agriculture into the global market, form international competitiveness, and advance to the high end of the global industrial and value chain. This study contributes to the existing literature by investigating the performance and factors influencing the competitiveness of Chinese agricultural product exports at the provincial level, using a complex network perspective, and offers recommendations for improvement. In this regard, the article uses agricultural product export data from 2015 to 2022 to create export competitiveness networks of Chinese provincial agricultural products. Our analysis revealed four primary observations. Firstly, significant opportunities exist to improve the competitive edge of agricultural product exports across various Chinese provinces. Secondly, with the exception of Guangdong Province, the types of competitively robust agricultural products exported by each province are inconsistent. Thirdly, international sister-city relationships can foster the development of an export competitiveness network of Chinese provincial agricultural products, while geographic distance may hinder this network. These findings are consistent. Lastly, the development of this competition-focused export network is also influenced by province-level and national characteristics, and these influences have unique differences. All provinces should strive to produce distinctive and globally competitive agricultural goods to build their competitive advantage in international trade. They should also implement strategic regional planning to support the global expansion of their agricultural products.
Distinct immunological features of oropharyngeal cancer peritumoral tonsillar tissues from inflammatory tonsils and regional lymph nodes: A pilot study
Authors: Wakisaka Naohiro , Moriyama-Kita Makiko , Kondo Satoru , Kobayashi Eiji , Ueno Takayoshi , Nakanishi Yosuke , Endo Kazuhira , Sugimoto Hisashi , Yoshizaki Tomokazu
Published: January 2025

Background

Cancer immune responses are generated in secondary lymphoid organs, such as the lymph nodes and tonsils. In the current study, transcriptional profiles of peritumoral tonsillar tissues (PTTs) from oropharyngeal cancers (OPCs) were assessed and compared with those of inflammatory tonsils and regional lymph nodes (rLNs).

Methods

RNA samples of PTTs and rLNs from 13 OPCs, and 4 inflammatory tonsils were subjected to microarray analysis, and differentially expressed genes (DEGs) identified from 730 nCounter Panel immune-related genes. Gene Set enrichment Analysis (GSEA) was used for DEG profiling of PTTs and rLNs between lymph node metastasis-negative and metastasis-positive cases. The top 20 genes, as ranked by GSEA metric scores, were extracted and subjected to principal component analysis (PCA). The correlation of each patient’s PCA score with lymph node status was assessed by Receiver Operating Characteristics (ROC) analysis.

Results

Comparing DEG analyses of PTTs with those of inflammatory tonsils and rLNs revealed 144 and 45 upregulated genes, respectively. ClueGO, a widely used Cytoscape plug-in, revealed activated pathways in PTTs, including lymphocyte proliferation (followed by T cell activation involved in the immune response) and positive regulation of leukocyte migration (followed by antimicrobial humoral immune response mediated by antimicrobial peptides) as the most significantly enriched immune system process functions in the gene ontology when comparing inflammatory tonsils and rLNs. The area under the ROC curves of PTTs and rLNs were 0.806 and 0.389, and were significant by DeLong’s test (p = 0.025).

Conclusion

PTTs exhibit unique immunological features distinguishing them from inflammatory tonsils and rLNs. Gene expression analysis of PTTs is useful for investigating the mechanism of OPC lymphatic spread, even compared with analysis of rLNs.
Edge intelligence for poultry welfare: Utilizing tiny machine learning neural network processors for vocalization analysis
Authors: Srinivasagan Ramasamy , El Sayed Mohammed Shawky , Al-Rasheed Mohammed Ibrahim , Alzahrani Ali Saeed
Published: January 2025

The health of poultry flock is crucial in sustainable farming. Recent advances in machine learning and speech analysis have opened up opportunities for real-time monitoring of the behavior and health of flock. However, there has been little research on using Tiny Machine Learning (Tiny ML) for continuous vocalization monitoring in poultry. This study addresses this gap by developing and deploying Tiny ML models on low-power edge devices to monitor chicken vocalizations. The focus is on overcoming challenges such as memory limitations, processing power, and battery life to ensure practical implementation in agricultural settings. In collaboration with avian researchers, a diverse dataset of poultry vocalizations representing a range of health and environmental conditions was created to train and validate the algorithms. Digital Signal Processing (DSP) blocks of the Edge Impulse platform were used to generate spectral features for studying fowl vocalization. A one-dimensional Convolutional Neural Network (CNN) model was employed for classification. The study emphasizes accurately identifying and categorizing different chicken noises associated with emotional states such as discomfort, hunger, and satisfaction. To improve accuracy and reduce background noise, noise-robust Tiny ML algorithms were developed. Before the removal of background noise, our average accuracy and F1 scores were 91.6% and 0.92, respectively. After the removal, they improved to 96.6% and 0.95.
Predicting marine habitat for marbled murrelets during breeding and nonbreeding seasons in the Salish Sea, British Columbia, Canada
Authors: Pastran Sonya A. , O’Hara Patrick D. , Fox Caroline H. , Drever Mark C. , Vennesland Ross , Bertram Douglas F.
Published: January 2025

The marbled murrelet (Brachyramphus marmoratus) is a small seabird inhabiting coastal regions along the Pacific coast of North America, and nests in old-growth forests usually within 80 km from shore. The Canadian population of marbled murrelets is listed as Threatened under the federal Species at Risk Act. To investigate the species’ marine distribution, we conducted analyses of the occurrence of marbled murrelets at-sea between 2000 and 2022, utilizing at-sea and marine shoreline surveys in the Canadian portion of the Salish Sea. The data were divided into breeding season (April to August) and non-breeding season (September to March) to examine the relationship between environmental covariates and the species’ distribution. We considered terrestrial covariates related to potential nesting habitat, as well as marine covariates related to Pacific sand lance (Ammodytes personatus) quality habitat, slope, depth, streams, tidal currents, shorelines and climate indices (NPGO). We compared marine distribution between breeding and nonbreeding seasons and predicted variations in covariate relationships. Our study focuses on identifying averaged relationships and key spatial areas to gauge habitat quality at a landscape scale. Using a Generalized Additive Modelling approach, we found that both marine and terrestrial covariates contributed to predicting murrelet distribution during both seasons. Notably, Pacific sand lance habitat played a significant role in both the breeding and nonbreeding season, while the overall amount of nesting habitat within an 80 km radius influenced occurrence probability in the nonbreeding season. Our analysis accurately predicted distribution patterns at a resolution of 3 x 3 km with an AUC of 0.89 and AUPRC of 0.52 for the breeding season, and AUC of 0.88 and a AUPRC of 0.28 for non-breeding season. Overall, our study highlights both terrestrial and marine drivers that influence the marine distribution of this threatened species and informs Canadian conservation efforts.