bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of obesity on outcomes in patients receiving extracorporeal membrane oxygenation: A systematic review and meta-analysis
Authors: Ng Wincy Wing-Sze , Leung Ka-Chun , Hui Rex Wan-Hin , Yeung Ng Pauline , Ngai Chun-Wai , Sin Simon Wai-Ching
Published: January 2025

Given the growing obesity pandemic, the impact of obesity on outcomes of Extracorporeal Membrane Oxygenation (ECMO) would be increasingly relevant to our daily practise. This meta-analysis aims to evaluate the impact of obesity on ECMO outcomes, integrating the latest evidence. Systematic literature search was conducted from inception until December 2024 on MEDLINE, Embase and the Cochrane Library using the terms ‘ECMO’, ‘obesity’, and their related terms. Twenty-eight studies were included from 2013 to 2024, including a total of 74,330 ECMO patients (Mean age 52.84 ± 13.55 years). Obese patients had a similar risk of in-hospital or 30-day mortality when compared to non-obese patients (Risk difference −2%, 95%CI −5% to −1%, I2 = 85%, p = 0.25). Subgroup analysis of patients on V-V-ECMO showed a trend towards lower mortality in obese patients which did not reach statistical significance (risk difference −6%, 95%CI −13% to 0%, I2 = 53%, p = 0.06). Subgroup analysis of patients on V-A-ECMO showed significantly higher mortality in obese patients (risk difference 5%, 95%CI 1% to 9%, I2 = 54%, p = 0.007). Regarding secondary outcomes, obesity had no significant association with major bleeding or thrombotic complications (Risk difference 0%, 95%CI −1% to 2%, I2 = 15%, p = 0.63). Obesity was associated with significantly shorter hospital length-of-stay (Mean difference −2.92 days, 95% CI −5.03 to −0.80, I2 = 74%, p = 0.007), but had no impact on ECMO duration (Mean difference 0.35 days, 95%CI −0.03 to 0.74, I2 = 41%, p = 0.07). In summary, our meta-analysis showed that obesity was a favourable prognostic factor in V-V-ECMO. However, obesity increased mortality in V-A-ECMO patients. The modality of ECMO support should be taken into consideration when evaluating ECMO candidacy in individual obese patients.
Conflicts of interest in the International Agency for Research on Cancer process of identifying carcinogenic hazards to humans
Authors: Elmore Susan A , Berry Colin , Bolon Brad , Boorman Gary A , Bradley Alys E , Cohen Samuel M , Klaunig James E , Kluxen Felix M , Maronpot Robert R , Nyska Abraham , Papenfuss Tracey L , Rehg Jerold E , Resnik David B , Rietjens Ivonne MCM , Rosol Thomas J , Suttie Andrew W , Schoeb Trenton R , Strupp Christian , Thoolen Bob , Weber Klaus
Published: January 2025

Managing conflicts of interest (COIs) in scientific decision-making is important for minimizing bias and fostering public trust in science. Proper management of COIs has added significance when scientists are making decisions that impact public policy, such as assessing substances for carcinogenicity. The International Agency for Research on Cancer (IARC) organizes expert working groups to identify putative carcinogens and determine whether or not the hazard is likely to present significant potential harm to humans. While IARC has policies for managing COIs, prior professional experience with the substance being assessed is not defined as a COI. Indeed, IARC working group members are chosen based on subject matter expertise, including prior publication on the substance under review. However, a person’s prior experience with a substance poses a significant potential COI by equipping them with strong pre-existing views about the substance’s toxicity and carcinogenicity. To minimize the risk of bias in IARC working groups, participants with voting powers should be independent scientific experts with sufficient professional experience to review carcinogenicity data but with no substantial prior experience with the substance under review. A related IARC practice restricting data review by working groups to selected publications is another significant COI. Instead, all accessible data should be available for consideration by working groups in assessing the carcinogenic hazard of substances. Another recommendation to reduce potential bias would be to reinstate the option of “probably not carcinogenic to humans”.
High-dimensional, outcome-dependent missing data problems: Models for the human KIR loci
Authors: van der Burg Lars Leonardus Joannes , Putter Hein , Baldauf Henning , Sauter Jürgen , Schetelig Johannes , de Wreede Liesbeth C , Böhringer Stefan
Published: January 2025

Missing data problems are common in biological, high-dimensional data, where data can be partially or completely missing. Algorithms have been developed to reconstruct the missing values by means of imputation or expectation-maximization algorithms. For missing data problems, it has been suggested that the regression model of interest should be incorporated into the imputation procedure to reduce bias of the regression coefficients. We here consider a challenging missing data problem, where diplotypes of the KIR loci are to be reconstructed. These loci are difficult to genotype, resulting in ambiguous genotype calls. We extend a previously proposed expectation-maximization algorithm by incorporating a potentially high-dimensional regression model to model the outcome. Three strategies are evaluated: (1) only allelic predictors, (2) allelic predictors and forward-backward selection on haplotype predictors, and (3) penalized regression on a saturated model. In a simulation study, we compared these strategies with a baseline expectation-maximization algorithm without outcome model. For extreme choices of effect sizes and missingness levels, the outcome-based expectation-maximization algorithms outperformed the no-outcome expectation-maximization algorithm. However, in all other cases, the no-outcome expectation-maximization algorithm performed either superior or comparable to the three strategies, suggesting the outcome model can have a harmful effect. In a data analysis concerning death after allogeneic hematopoietic stem cell transplantation as a function of donor KIR genes, expectation-maximization algorithms with and without outcome showed very similar results. In conclusion, outcome based missing data models in the high-dimensional setting have to be used with care and are likely to lead to biased results.
Weighting methods for truncation by death in cluster-randomized trials
Authors: Isenberg Dane , Harhay Michael O , Mitra Nandita , Li Fan
Published: January 2025

Patient-centered outcomes, such as quality of life and length of hospital stay, are the focus in a wide array of clinical studies. However, participants in randomized trials for elderly or critically and severely ill patient populations may have truncated or undefined non-mortality outcomes if they do not survive through the measurement time point. To address truncation by death, the survivor average causal effect has been proposed as a causally interpretable subgroup treatment effect defined under the principal stratification framework. However, the majority of methods for estimating the survivor average causal effect have been developed in the context of individually randomized trials. Only limited discussions have been centered around cluster-randomized trials, where methods typically involve strong distributional assumptions for outcome modeling. In this article, we propose two weighting methods to estimate the survivor average causal effect in cluster-randomized trials that obviate the need for potentially complicated outcome distribution modeling. We establish the requisite assumptions that address latent clustering effects to enable point identification of the survivor average causal effect, and we provide computationally efficient asymptotic variance estimators for each weighting estimator. In simulations, we evaluate our weighting estimators, demonstrating their finite-sample operating characteristics and robustness to certain departures from the identification assumptions. We illustrate our methods using data from a cluster-randomized trial to assess the impact of a sedation protocol on mechanical ventilation among children with acute respiratory failure.
The Relationship Between Trait Mindfulness and Depression Severity Among People With Schizophrenia: The Role of Perceived Public Stigma Toward Mental Illness
Authors: Rayan Ahmad
Published: January 2025

Background:

Studies have found that trait mindfulness is associated with lower levels of depressive symptoms among people diagnosed with schizophrenia. Still, the role of the perceived public stigma in this association has yet to be established.

Aims:

The purpose of this study was to assess the association between mindfulness and depressive symptoms experienced by people diagnosed with schizophrenia, controlling for the impact of their demographics and their perceived public stigma against mental illness.

Methods:

A quantitative descriptive correlational design was used. The sample included 184 Jordanian outpatients diagnosed with schizophrenia who completed self-administered measures of mindfulness, depressive symptoms, public stigma against mental illness, and demographic information. Multiple hierarchical regression analysis was performed to identify the unique variance in perceived depression explained by participants’ demographic and clinical variables, public stigma, and mindfulness.

Results:

Participants had moderate perceived discrimination and moderate to severe depression. Age, gender, perceived physical pain, perceived public stigma, and mindfulness were significantly correlated with depression among the study participants. After controlling for demographic and clinical variables, public stigma was significantly associated with depression and accounted for 14% additional variance above and beyond the 37% accounted for by demographic and clinical variables. Mindfulness accounted for a 15% additional variance above and beyond the variance accounted for by all other predictors.

Conclusion:

Anti-stigma programs could be combined with mindfulness-based interventions to reduce depression in people diagnosed with schizophrenia.
A detection of multi-level co-location patterns based on column calculation and HDBSCAN clustering
Authors: Yang Ting , Wang Lizhen , Zhou Lihua , Chen Hui
Published: January 2025

A spatial co-location pattern is a subset of a spatial feature set whose instances prevalently appear in nearby locations in space. The objective of spatial co-location pattern mining is to detect co-location patterns that are non-obvious, informative, or predictive. Due to the heterogeneity in the distribution of spatial instances, spatial co-location patterns are classified into global co-location patterns (GCPs) and local co-location patterns (LCPs). The technique that discovers both simultaneously is termed multi-level co-location pattern mining (MLCPM). However, existing MLCPM methods have room for improvement in efficiently identifying GCPs and perform poorly in discovering prevalent sub-areas of LCPs. To address these issues, we propose a novel MLCPM framework called ML-CCHDB. This framework enhances GCP mining efficiency by optimizing the column calculation method tailored for MLCPM. Furthermore, it utilizes the HDBSCAN clustering method to identify potential prevalent sub-areas of LCPs and develops an adaptive approach for generating input parameters to enhance detection efficiency and quality. Experimental results on both synthetic and real datasets demonstrate that the column calculation optimizations in ML-CCHDB effectively enhance efficiency. Moreover, HDBSCAN strikes a balance between efficiency and quality in prevalent sub-area mining. These results fully validate the proposed framework's effectiveness and efficiency.
TSDNet: A two-stage decomposition-based hybrid deep neural network for long-term time series forecasting
Authors: Cho Sukhyun , Kim Dokyun , Park Jonghun , Park In-Beom
Published: January 2025

Due to recent developments in deep learning models, the field of time series forecasting has undergone significant advancements related to forecasting accuracy and reliability across various industrial sectors. Unfortunately, traditional deep learning models often encounter long-term and multivariate forecasting challenges due to complex temporal patterns. To overcome this, decomposition-based approaches have been proposed. However, there have been few attempts to utilize an appropriate network type for each decomposed component. In this paper, we propose the two-stage decomposition-based hybrid deep neural network (TSDNet) for enhancing the accuracy of long-term time series forecasting. To effectively manage complicated time series data with varying periodicities, TSDNet accommodates a single linear layer for forecasting smooth trend components and a convolutional module for complex seasonal components. Extensive experiments on various benchmark and real-world financial datasets show that TSDNet mostly improves the forecasting accuracy compared to the existing methods considered, particularly in long-term forecasting scenarios. Furthermore, ablation studies were conducted to examine the impact of the number of decomposition stages and the implementation of different modules on the decomposed elements, suggesting the effectiveness of the proposed approach.
What’s on the agenda? Examining public health communication about opioids
Authors: Willis Erin , Wang Ye , Goudarzvand Somaieh , Lee Yugyung
Published: January 2025

The way media portray public health problems influences the public’s perception of problems and related solutions. Social media allows users to engage with news and to collectively construct meaning. This paper examined news in comparison to user-generated content related to opioids to understand the role of second-level agenda-setting in public health. We analyzed 162,760 tweets about the opioid crisis, and compared the main topics and their sentiments with 2998 opioid stories from The New York Times online. Evidence from this study suggests that second-level agenda setting on social media is different from the news; public communication about opioids on X/Twitter highlights attributes that are different from the ones highlighted in news. The findings suggest that public health communication should strategically utilize social media data, including obtaining consumer insight from personal tweets, listening to diverse views and warning signs from issue tweets, and tuning to the media for policy trends.
Evaluating the development level of cultural tourism industry in Hunan Province using dynamic network DEA-Malmquist model
Authors: Yao Xianlin , Lu Hui
Published: January 2025

The cultural tourism industry plays an important role in improving local infrastructure on a large scale, and enhancing public services. It is an important industry that continuously improves people’s quality of life and happiness index. This study utilizes a two-stage network DEA (data envelopment analysis) model, combined with the Malmquist index, to evaluate the development status of Hunan Province's cultural and tourism industry from both static and dynamic perspectives. The research results indicate that the development of the cultural and tourism industry in Hunan Province is generally at a high level and shows a continuous upward trend. Except for 2018 and 2019, the MI index for the development of cultural tourism industry in Hunan Province is greater than 1, and the overall development trend is good. However, from the point of view of the average value of the province’s cultural tourism industry development performance, the number of regions lower than the provincial average is more than the number of regions higher than the provincial average, indicating that the regional differences in the development of Hunan Province’s cultural tourism industry are more obvious.
Research on inspiratory muscle training action recognition based on passive RFID tags and hybrid deep learning
Authors: An Xiaofeng , Raga Rodolfo C
Published: January 2025

Chronic obstructive pulmonary disease (COPD) significantly impacts the quality of life for affected individuals. Inspiratory muscle training emerges as a crucial component in the rehabilitative care of COPD patients. A newly developed respiratory status sensing system is unveiled in an article, utilizing passive radio frequency identification (RFID) positioning technology for non-invasive and remote monitoring of COPD patient activities, achieving recognition accuracy 95.6% in classification tasks. A smart sensing suit was designed, integrating liquid metal tags and passive RFID technology for real-time data collection. At the core of this approach lies the utilization of a CNN-LSTM-AM hybrid deep learning model, precisely identifying respiratory movement patterns and stages. Results from experiments underscore the exceptional effectiveness of the model, particularly in terms of its design and learning capabilities, all substantiated by rigorous performance evaluations. The model’s outstanding performance is attributed to its use of convolutional neural networks (CNNs) for feature extraction, long short-term memory (LSTM) networks for time-series data analysis, and attention mechanisms (AMs) for focusing on essential features. These components collectively enhance the accuracy of identifying respiratory patterns. This study introduces an innovative remote sensing method for COPD patient rehabilitation, aiming to establish a new clinical standard for home-based applications.
Improved 3D face reconstruction and expression driving based on ResNest
Authors: Dong Xue
Published: January 2025

Currently, 3D face modeling has been more and more widely used in medical, animation, games, and other fields. With the application of deep neural network, 3D face modeling method based on deep learning is the main research direction at present. However, the accuracy of the 3D face reconstruction model is generally insufficient. To solve this problem, an improved algorithm model based on deep residual network is proposed in this paper. A new convolutional module of Squeeze-and-Excitation is introduced to increase the channel dependence of the entire model, and then Blendshape is combined to drive the entire face data. The normalization error of the new improved neural network algorithm is only 3.12% compared with other traditional algorithm models. The loss function was reduced by 56.72%, 48.95%, and 41.86%, respectively. Compared with the traditional algorithm, the improved algorithm has higher precision and more perfect expression driving ability. This study contributes to improving the accuracy of the whole 3D face reconstruction technology.
A prediction model for expressway traffic accidents under extreme weather conditions based on support vector machine
Authors: Li Tengfei , Han Lei
Published: January 2025

Road traffic safety has always been a topic of special concern for countries around the world. With changes in weather conditions and a significant increase in the number of highways and vehicles, traffic accidents occur frequently. To effectively predict traffic accidents and reduce the amount of accidents, a support vector machine based traffic accident prediction model under extreme weather conditions is proposed. Firstly, the relationship between different extreme weather conditions and road traffic events is analyzed. Then, genetic algorithms are used to optimize accident data, and support vector machines are fused for accident prediction. The results show that on the AVOID dataset, when the iteration reaches the 32nd time, the loss function value of the research method is the smallest, 10−5; the loss function of other algorithms is significantly larger. When the accuracy of the research method is 80% and 90%, the corresponding recall rates are 87.89% and 79.98%, respectively. Among the prediction errors of different algorithms, the max relative error of the research method is 1.214%, and the mini relative error is 0.0213%. Compared to other models, the overall error of the research method decreased by 2.021%. In the prediction application of non-serious accidents, the research method has the highest prediction accuracy of 94.53%. The above results indicate that the proposed accident prediction model can accurately predict different types of accidents under extreme weather conditions, providing certain technical support for the prevention and development of traffic accidents.
Spark-based Internet map sensitive geographic information detection under the maintenance of national geographic information security
Authors: Wei Xinling , Lv Haicheng
Published: January 2025

There are various types of geographic information to be tested, single detection methods for sensitive geographic information, and low detection accuracy in the Internet. In this regard, research will focus on the detection of classified geographic information in Internet maps. To detect sensitive geographic information on Internet maps, a single and combined sensitive word library is first constructed. Words in sensitive word library and feature words’ similarity, as well as the sensitivity of map files, are calculated. Based on geographic information sensitivity, the classified geographic information presence in map files is detected, and the classified geographic information detection on Internet maps is implemented under Spark computing framework. After adding the combination sensitive vocabulary, the detection accuracy and recall rate were significantly improved, and F-measure value increased by 0.07. The optimal accuracy of the research method, the sensitive text detection model based on conditional random field, and the sensitive word detection algorithm for map sensitive information detection are 0.74, 0.65, and 0.59, respectively. The optimal recall rates are 0.84, 0.76, and 0.68, respectively, and the optimal F-measure values are 0.78, 0.70, and 0.64, respectively. The detection accuracy, recall rate, and F-metric of Spark cluster mode are better than those of single machine modes, indicating that parallelized sensitive geographic information detection has good comprehensive performance. This is beneficial for detecting sensitive geographic information on Internet maps.
Design and analysis of energy efficient urban buildings based on BIM model
Authors: Wu Yang
Published: January 2025

Traditional buildings face problems such as low construction efficiency and high costs during the construction process, which cannot meet the requirements of green and sustainable development of buildings. To address this, a design model is constructed using BIM technology as a framework, which optimizes the energy efficiency of multiple building indicators and constructs a building energy efficiency optimization model based on decomposition of multiple objectives. Considering the cost and convergence issues of the goal solving model, an agent-assisted multi-objective particle swarm optimization model is introduced to construct an energy-saving design optimization model. The fitness of the particle swarm is optimized through an agent optimization strategy, thereby adjusting the speed and position of particles and improving the model optimization effect. Performed performance testing on the proposed model, and the proposed model has the best optimization performance under the Ackley function. After 100 iterations, it tends to converge. At this point, the best fitness value of the model is 0.452. Applying the proposed technology to specific cases for target optimization of urban single-room office buildings, the proposed optimization model has a shorter search time, a minimum of 2408 uncomfortable hours, and a minimum total energy consumption of 47.5, which are superior to the other three models. Finally, by comparing the comprehensive application effects of the model, the proposed model has the best performance in terms of super volume index, reaching 26,473 in urban office buildings. Compared to the comparison model, the proposed model takes 1.3 h, and the overall optimization time is the shortest. In traditional residential building testing, the proposed model has a super volume index of 50,132, which is also the best and has the shortest training time. It can be seen that the proposed optimization model has excellent optimization ability, and compared to other technologies in energy-saving optimization design, the proposed technology has the best optimization effect. Research technology will provide important technical support for the energy-saving optimization design and construction management of modern buildings.
Lip pattern recognition algorithm based on attention mechanism and multi-scale features
Authors: Zhou Hongcheng , Yan Hao
Published: January 2025

For the preliminary exploration of the deep neural network in the lip pattern recognition, a CNN-based lip pattern recognition algorithm is proposed. The lightweight network MobileNetV2 is more suitable for lip print recognition, but the recognition accuracy of this model needs to be improved compared with the existing machine learning recognition algorithms. The attention mechanism module and the grouped multi-scale feature fusion module are designed to not only increase the model’s ability to finely represent texture features, extract features, and adapt to different input scales but also to further compress the number of parameters in the network model. The ablation experimental results show that the improved recognition model achieves 98.56% recognition accuracy. The effectiveness of the improved algorithm is finally verified by the experiments.
An evolutionary game analysis of the prefabricated building supply side under dynamic government reward and punishment mechanisms
Authors: Luo Jiawei
Published: January 2025

From the standpoint of the supply side of prefabricated buildings, an evolutionary game model between the government and developers is constructed under the static and dynamic reward and punishment mechanisms in order to promote the development of prefabricated buildings and realize the transformation and upgrading of the construction industry. The simulation study of the example is conducted, and the impact of policy cost, subsidy upper limit, and tax upper limit on the system’s evolutionary stability strategy is examined. The findings demonstrate that when the government chooses static subsidy and static tax policies, there is no evolutionary stable strategy in the game system. The flaws of the static reward and punishment system can be adequately compensated for by the dynamic subsidy and dynamic tax policy, leading to an evolutionary stable state. The likelihood of developers producing prefabricated structures is negatively connected with policy cost and subsidy upper limits and favorably correlated with tax upper limits, according to the dynamic reward and punishment mechanism. The findings of the study have significant implications for government policy that will encourage the widespread use of prefabricated structures in China.
Research progress on natural products for improving insomnia
Authors: Yang Yi , Wang Xiaomei , Zheng Min , Bai Yu , Wu Qiqi , Guo Yuyue , Qu Hanlin , Sun Rui , Li Wujuan , Yue Changwu
Published: January 2025

Insomnia is a common problem when trying to sleep. The most common treatments for insomnia are sedative hypnotic drugs and non-drug therapies. The drugs that are most commonly used on the market are benzodiazepines and non-benzodiazepines. The use of insomnia drugs is limited due to side effects such as drug dependence, memory or movement disorders, and residual effects. The rodent model of insomnia, the diagnosis, monitoring, and treatment of insomnia, and some natural products for insomnia are discussed in this review, which provides reference for clinical application and research.
Study of EWM-based project schedule management in tobacco redrying plants
Authors: Zhang Donghua , You Youyang , Liu Xin
Published: January 2025

In order to solve the problem of schedule management in the technical transformation project of tobacco redrying plant, this study summarizes the basic principles and limitations of traditional theories and methods of project schedule management, and concludes that the critical chain project management method has a higher dimension and is safer. Time configuration is more reasonable, controllable, and accurate. At the same time, a key chain project buffer control model based on the entropy weight method is proposed to make up for the shortage of the key chain buffer setting in the actual application process, and the entropy weight method is introduced on the basis of the existing key chain buffer setting method, and it is a more optimized new idea, and the buffer is set by the uncertainty calculation of the process, which expands the research scope for the exploration of critical chain project management. The results of this study show that variance method and the actual construction period, which makes the results more reasonable and accurate. In addition, the total project duration determined by the entropy weight method is improved by 24.27%, 7.41%, and 2.65% compared with the cut-paste method, the root variance method, and the actual construction period. Moreover, the entropy weight method is more objective and effective than the Gantt chart method commonly used in the tobacco redrying industry and is more operable in the project schedule management.
Data acquisition and processing of body features of 24-style Taijiquan practitioners in mobile edge computing
Authors: Liu Cencen , Sun Yuqin , Chen Li
Published: January 2025

This research delves into a detailed examination of the physical data from practitioners of 24-style Taijiquan, utilizing mobile edge computing (MEC). The aim is to aid the spread and growth of 24-style Taijiquan while broadening the use of MEC algorithms. Initially, the study focuses on understanding the architecture and progression of MEC, examining its strengths in gathering and processing data. Then, using MEC to analyze physical feature data, the study discusses the impact of 24-style Taijiquan on the human body, as well as the influence of MEC analysis results on the future promotion and development of 24-style Taijiquan. The research findings demonstrate that (1) mature MEC has significant advantages in data collection and processing; (2) a distributed computing system using edge devices can efficiently, low-energy, and quickly respond to body data, demonstrating the advantages of MEC in collecting and processing physical data of 24-style Taijiquan practitioners. These results underscore the efficacy of employing mobile edge computing (MEC) for the analysis of physical data from 24-style Taijiquan practitioners, thereby aiding in its preservation and advancement.
Research on spread of panic emotional based on the epidemic mechanism under emergencies
Authors: Liu Bangquan , Wang Dingyuan , Zhu Shaojun , Zhang Yubin , Sun Dechao , Liu Dongquan , Zhou Guangyu
Published: January 2025

Frequent emergencies always induce panic in the public and cause crowd chaos and casualties. To study the spread laws of panic in the crowd, an NEMSC (Normal-Expose- Mild Panicky-Severe Panicky-Cool) model for the spread of panic emotions was proposed based on the infectious disease model. An improved panic emotion propagation model considering intervention factors was established and the number of trends of crowd emotion contagion was observed by using the NEMSC model and its improved model, and the simulation results were compared and analyzed. Then, take the COVID-19 epidemic and the stampede on the Bund in Shanghai as examples for application simulation. The results show that the NEMSC model can describe the spread of panic emotions in crowds in emergencies, and the intervention factors can effectively inhibit the spread of different panic emotions, which is helpful for emergency decision-making management.
Reading between the lines: Democratic citizenship in the history curriculum in lower secondary education in Finland
Authors: Löfström Jan
Published: January 2025

The paper discusses the Finnish core curriculum (curriculum framework) for basic education, with focus on how lower secondary education in general, and history teaching specifically, is meant to develop the student's democratic citizenship. Developing the student's democratic citizenship is not an explicit aim of history teaching in the current core curriculum although the aims of history teaching there are very much about civic skills that support deliberative, participatory and liberal democracy. Support to the student's democratic citizenship and the liberal-democratic society is more visible as a goal in the sections of the core curriculum that describe the general goals and principles of basic education. In the paper it is suggested that the emphasis on the skills to construct, analyse and evaluate historical interpretations in the history curriculum overshadows more directly societal aims and political aspects of history teaching that are relevant in developing the student's democratic citizenship. It is suggested also that bringing third-order concepts in the history curriculum could valuably support the student's democratic citizenship and democratic consciousness by focusing on the personal meanings of history to the student – a perspective too often neglected in the curriculum.
MR-guided percutaneous interventional surgery robot system: A review
Authors: Zhang Weixi , Yu Jiaxing , Lu Xikun , Xu Yanjie
Published: January 2025

Background

The combination of MR and robotic technology can improve surgical accuracy, shorten surgical time, and even improve the location of lesions that cannot be reached by traditional percutaneous intervention. However, owing to the limitations of the MR technology, MR-guided interventional robots face many difficulties and challenges in sensor, material selection, driving mode, and mechanical structure design.

Aims

The purpose of this paper is to summarize and analyze the previous MR-guided interventional robots, provide reference for future research on MR-guided interventional robots, and propose a concept suitable for MR-guided bionic breast interventional robots.

Methods

Starting from the field of MR-guided interventional robots, a systematic classification of MR-guided interventional robots for different body parts was conducted by reviewing the literature of the past 20 years. The structural design, surgical environment requirements, and experimental results of these robots are analyzed in detail. The advantages and disadvantages of each type of robot are summarized, providing a comprehensive overview of the current state of the field.

Conclusion

MR-guided interventional robots have demonstrated significant efficacy in percutaneous procedures. When designing MR-guided interventional robots, it is crucial to consider structural compactness, the selection of the drive system, and the compatibility of sensor modules. Based on an extensive review of relevant literature, this paper ultimately proposes a structural design concept of MR-breast interventional robot, which imitates the bloodsucking process of leeches.
Hydronic floor heating: Analysis of energy consumption, temperature, and velocity fields
Authors: Azemati Ali Akbar , Rahimian Koloor Seyed Saeid , Khorasanizadeh Hossein , Petrů Michal , Shirkavand Hadavand Behzad , Sheikhzadeh Ghanbar Ali
Published: January 2025

Hydronic floor heating has been recognized as one of the important systems utilized for warming residential buildings, therefore, the flow field and temperature distribution studies of the floor and locally heated residential spaces are essential. In this work, such a study is performed on a room with different horizontal aspect ratios through three-dimensional numerical simulations using FLUENT software. The governing equations accounting for the continuity, momentum, and energy together with the radiation have been solved using the Boussinesq approximation, the radiation DO model, and the modified k − ε turbulence model, while the SIMPLE method was utilized. Results show that at least 65% of the total heat transfer from the heated floor is due to radiation, while in the locally heated room, the radiation from the radiator was at most 55% of the total. When floor heating was utilized, the uniform temperature is obtained for all of the room aspect ratios, whereas in the case of local heating, thermal stratification occurs along the room height, as the temperature was considerably higher at upper heights. In the floor heating, the air velocity throughout the room was less than 0.2 m/s, whereas, for the local heating, the air velocity was higher than 0.2 m/s close to the radiator. In comparison with the local heating, the floor heating caused at least 20% energy saving and optimum thermal and velocity comfort conditions.
Association of CVAI, LAP and SMI with risk of haematological toxicity after immunochemotherapy in patients with DLBCL: a retrospective study
Authors: Liao Caifeng , Lai Hurong , Tu Yansong , He Ling , Lin Chuyang , Tu Huaijun , Li Jian
Published: January 2025

Background:

The occurrence of adverse events after immunochemotherapy in patients with diffuse large B-cell lymphoma (DLBCL) frequently affects the course of chemotherapy, leading to a further decline in quality of life and survival.

Objectives:

The primary objective of this study was to investigate the association between Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP) index and skeletal muscle mass index (SMI) at initial diagnosis and the risk of haematological toxicity following immunochemotherapy in patients with DLBCL.

Design:

Retrospective, single-centre study.

Methods:

CVAI, LAP and SMI were calculated by combining clinical data of the patients. This study included 213 patients, of whom 117 (55%) patients experienced grades 3–4 haematological toxicity after immunochemotherapy. Participants were divided into four groups (Q1, Q2, Q3, Q4) based on the quartiles of CVAI, LAP and SMI.

Results:

In the fully adjusted model, the risk of grades 3–4 haematological toxicity in group with the highest CVAI and LAP was reduced by 75.1% (OR: 0.249, 95% CI: 0.102–0.606, p = 0.002) and by 77.3% (OR: 0.227, 95% CI: 0.095–0.542, p = 0.001) compared to the group with the lowest CVAI and LAP. For SMI, the risk of grades 3–4 haematological toxicities in the group with the highest SMI was reduced by 62.9% compared with the lowest SMI group in the unadjusted model. The multivariable-adjusted restricted cubic spline curves and subgroup interaction analyses further confirmed the robustness of these findings.

Conclusion:

The results indicate that DLBCL patients with relatively high CVAI, LAP and SMI at initial diagnosis have a lower risk of severe haematological toxicity following chemotherapy. Therefore, CVAI, LAP and SMI at initial diagnosis are reliable and effective biomarkers for predicting severe haematological toxicity after immunochemotherapy in DLBCL patients.

Trial registration:

This is a retrospective study, and no registration on ClinicalTrials.gov.
Spacetime modeling of mortality by infectious and parasitic diseases in Brazil: a 20-year ecological and population-based study
Authors: Andrade Lucas Almeida , de Souza Carlos Dornels Freire , Paz Wandklebson Silva da , Gois Souza Danilo de , Góes José Augusto Passos , Camargo Emerson Lucas Silva , de Sousa Álvaro Francisco Lopes , Moretti Carneiro Liliane , Mendes Isabel Amélia Costa , Machado Araújo Karina , Santos Allan Dantas dos , Bezerra-Santos Márcio
Published: January 2025

Background:

Infectious and parasitic diseases (IPDs) encompass a broad range of illnesses predominantly associated with poverty. They are more prevalent in low- and middle-income countries, including Brazil, where they continue to be among the leading causes of mortality.

Objective:

This study aims to analyze the spatiotemporal dynamics of mortality due to IPDs in Brazil from 2000 to 2019.

Methods:

We conducted an ecological study using data on mortality by IPDs from the Brazilian Mortality Information System. We applied the segmented log-linear regression model to assess temporal trends. For spatial analysis, we used the local empirical Bayesian estimator and Moran indices. Retrospective spatiotemporal scan statistics were performed using the Poisson Probability Distribution Model.

Results:

Between 2000 and 2019, there were 2,155,513 deaths related to IPDs in Brazil. The leading causes of death included acute respiratory infections ( n = 1,130,069; 52.49%), septicemia ( n = 289,817; 13.46%), human immunodeficiency virus/acquired immunodeficiency syndrome ( n = 232,892; 10.82%), tuberculosis ( n = 104,121; 4.84%), and neglected tropical diseases such as Chagas disease ( n = 94,788; 4.40%) and schistosomiasis ( n = 10,272; 0.48%). An increasing temporal trend in the mortality rate from IPDs was observed in Brazil and across all its regions. Additionally, our spatiotemporal scan identified high-risk clusters of death in the Southeast and Northeast regions.

Conclusion:

Mortality from IPDs remains a significant public health concern in Brazil, with an increasing trend observed in all regions. Our findings underscore the urgent need for comprehensive intersectoral public policies. These policies should focus on a greater allocation of resources and investments in the most critical areas, aiming to significantly reduce the number of deaths, particularly in the most vulnerable regions.
Characteristics of Emerging Adulthood and Emotion Dysregulation for Predicting Well-Being: Contributions of Qualitative Comparative Analysis
Authors: Velert-Jiménez Saray , De-la-Barrera Usue , Gil-Gómez José-Antonio , Montoya-Castilla Inmaculada
Published: January 2025

Emerging adulthood is a developmental stage where individuals may face challenges in emotion regulation and well-being. While there has been progress in well-being research in recent years, studies analyzing the combination of emerging adulthood characteristics and dysregulation are limited. This research analyzed the contribution of emerging adulthood characteristics, in combination with emotion dysregulation, in predicting well-being using Qualitative Comparative Analysis (QCA) and regression models. The study assessed the characteristics of emerging adulthood, dysregulation, and psychological well-being in a sample of 209 Spanish emerging adults. The results highlighted the role of the identity exploration, experimentation, feeling in between, self-focused and dysregulation in relation to levels of well-being. Low experimentation and identity exploration may contribute to high levels of well-being when combined with other factors. The findings demonstrate the usefulness of Qualitative Comparative Analysis in understanding the combination of variables that determine high and low levels of psychological well-being in emerging adulthood.
Taking on the Titin: Muscle imaging as a diagnostic marker of biallelic TTN-related myopathy
Authors: Hayes LH , Neuhaus SB , Donkervoort S , Mohassel P , Foley AR , Dastgir J , Bharucha-Goebel D , Leach ME , Vuillerot C , Iannaccone ST , Grosmann CM , Beggs AH , Bönnemann CG
Published: January 2025

Background:

The accurate diagnosis of titin-related myopathies ( TTN-RM) is challenging due to the “gigantism” of the coding gene TTN with an incompletely understood landscape of normal genetic variation, an increasing number of pathogenic variants, and wide phenotypic variability of both cardiac and muscle involvement. Particularly in situations of potentially incomplete genotypes, clinicians need more phenotyping tools to help confidently determine the pathogenicity of variants in TTN and accurately diagnose titinopathies.

Objective:

To illustrate the pattern of muscle involvement found by muscle imaging in patients with TTN-RM.

Methods:

We reviewed the clinical and imaging data of patients with TTN-RM. Cross secitonal MR images of the lower extremity muscles were scored for degree of abnormality using the Mercuri scoring system and patterns were identified with comparison across muscle groups. Ultrasound images were also reviewed and described.

Results:

Eleven patients with TTN-RM had clinical and imaging data available for review. The relatively more severe involvement of the semitendinosus muscle in the hamstring group (“semitendinosus sign”) emerged as a consistent feature in patients with recessive TTN-RM despite clinical heterogeneity.

Conclusions:

Here we find that despite considerable complexity, the pattern of muscle involvement on MRI and ultrasound may aid in the confirmation of TTN-RM by establishing compatibility with the diagnosis.
Newborn screening programs for spinal muscular atrophy worldwide in 2023
Authors: Vrščaj Eva , Dangouloff Tamara , Osredkar Damjan , Servais Laurent ,   the SMA NBS World Study Group
Published: January 2025

Background

Spinal muscular atrophy is a rare, genetic neuromuscular disorder. Disease-modifying therapies, when administered early, have shown improved outcomes, leading to the implementation of numerous newborn screening programs for spinal muscular atrophy.

Objective

The aim of this study was to evaluate the progress in implementing newborn screening for spinal muscular atrophy and therapy accessibility worldwide, after the first paper published in 2021.

Methods

We conducted a survey, contacted experts from 143 countries worldwide, gathered responses from 86 experts from 80 countries.

Results

By 2023, 31 countries reported established programs, 33 in the beginning of the year 2024; identifying approximately 1176 cases of spinal muscular atrophy. Additionally, the availability of disease-modifying therapies has expanded. At least one therapy is now accessible in 62 countries. Challenges, such as lack of governmental support, resource constraints, and varying healthcare priorities continue to impede implementation in some countries.

Conclusions

The data shows a significant increase in the implementation of newborn screening programs since 2021. The experts are still expressing a strong need for equitable access to standard of care for all the patients globally. Despite all setbacks, collaborative efforts have played a crucial role in newborn screening for spinal muscular atrophy implementation and currently 7% of world newborns are being screened, projections indicate an estimated 18% screening rate by 2028.
MICU1 related myopathy – a rare report from India
Authors: Baskar Dipti , Ganji Suma Reddy , Thomas Aneesha , Polavarapu Kiran , Nandeesh Bevinahalli N. , Sanka Sai Bhargava , Srivastava Kosha , Kotambail Ananthapadmanabha , Arunachal Gautham , Boddu Vijay Kumar , Nashi Saraswati , Nalini Atchayaram , Vengalil Seena
Published: January 2025

Mitochondrial Calcium Uptake 1 ( MICU1) is an important component of mitochondrial calcium channel regulator. Mutations in MICU1 result in a rare syndrome of myopathy with extrapyramidal features. Here we report a rare case of MICU1 related myopathy from India. A 23 years old male presented with 10 years history of proximal muscle weakness with exertional myalgia and fatigue. Examination showed facial dysmorphism with facial weakness and mildly reduced visual acuity. Limb girdle pattern of weakness with hypoactive tendon reflexes were noted without extrapyramidal signs. He had elevated serum creatine level of 1542 IU/L. Muscle MRI had novel findings of selective fatty infiltration of hamstrings, medial gastrocnemius and soleus. Muscle biopsy showed myopathic with secondary neurogenic changes along with few COX deficient fibres. Genetic analysis showed compound heterozygous pathogenic variants in MICU1 gene at intron 9 (c.1072-1 G > C) – splice site variant and exon 5 (c.513T > A) – stop gained variant, both resulting in loss of function of the protein. The variants were segregating in unaffected parents in heterozygous state with variant c.1072-1 G > C in the unaffected father and variant c.513T > A (p. Tyr171*) in the unaffected mother confirming the diagnosis. This report highlights the phenotype of limb girdle weakness with facial dysmorphism and optic atrophy expanding the spectrum of MICU1 related syndrome with novel MRI muscle and histopathological findings.