bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring Pain Reduction through Physical Activity: A Case Study of Seven Fibromyalgia Patients
Authors: Jenssen Marit Dagny Kristine , Salvi Elisa , Fors Egil Andreas , Nilsen Ole Andreas , Ngo Phuong Dinh , Tejedor Miguel , Bellika Johan Gustav , Godtliebsen Fred
Published: July 2024

Fibromyalgia is a chronic disease that affects a considerable fraction of the global population, primarily women. Physical activity is often recommended as a tool to manage the symptoms. In this study, we tried to replicate a positive result of pain reduction through physical activity. After collecting pain and physical activity data from seven women with fibromyalgia, one patient experienced a considerable reduction in pain intensity. According to the patient, the improvement was related to physical activity. Our study was conducted to investigate the replicability of this result through personalized activity recommendations. Out of the other six patients, three experienced a reduction in pain. The remaining three patients did not experience any pain relief. Our results show that two of these were not able to follow the activity recommendations. These results indicate that physical activity may have a positive effect on chronic pain patients. To estimate how effective physical activity can be for this patient group, an intervention with longer follow-ups and larger sample sizes needs to be performed in the future.
Machine Learning Meets Meta-Heuristics: Bald Eagle Search Optimization and Red Deer Optimization for Feature Selection in Type II Diabetes Diagnosis
Authors: Chellappan Dinesh , Rajaguru Harikumar
Published: July 2024

This article investigates the effectiveness of feature extraction and selection techniques in enhancing the performance of classifier accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data, three feature extraction (FE) methods are used, namely Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson’s Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques, Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Models—GMMs, Expectation Maximization—EM, Logistic Regression—LoR, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF, are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data.
Enhancing Trauma Care: A Machine Learning Approach with XGBoost for Predicting Urgent Hemorrhage Interventions Using NTDB Data
Authors: Zhang Jin , Jin Zhichao , Tang Bihan , Huang Xiangtong , Wang Zongyu , Chen Qi , He Jia
Published: July 2024

Objective: Trauma is a leading cause of death worldwide, with many incidents resulting in hemorrhage before the patient reaches the hospital. Despite advances in trauma care, the majority of deaths occur within the first three hours of hospital admission, offering a very limited window for effective intervention. Unfortunately, a significant increase in mortality from hemorrhagic trauma is primarily due to delays in hemorrhage control. Therefore, we propose a machine learning model to predict the need for urgent hemorrhage intervention. Methods: This study developed and validated an XGBoost-based machine learning model using data from the National Trauma Data Bank (NTDB) from 2017 to 2019. It focuses on demographic and clinical data from the initial hours following trauma for model training and validation, aiming to predict whether trauma patients require urgent hemorrhage intervention. Results: The XGBoost model demonstrated superior performance across multiple datasets, achieving an AUROC of 0.872 on the training set, 0.869 on the internal validation set, and 0.875 on the external validation set. The model also showed high sensitivity (77.8% on the external validation set) and specificity (82.1% on the external validation set), with an accuracy exceeding 81% across all datasets, highlighting its high reliability for clinical applications. Conclusions: Our study shows that the XGBoost model effectively predicts urgent hemorrhage interventions using data from the National Trauma Data Bank (NTDB). It outperforms other machine learning algorithms in accuracy and robustness across various datasets. These results highlight machine learning’s potential to improve emergency responses and decision-making in trauma care.
Statistical Learning of Incidental Perceptual Regularities Induces Sensory Conditioned Cortical Responses
Authors: Greco Antonino , D’Alessandro Marco , Gallitto Giuseppe , Rastelli Clara , Braun Christoph , Caria Andrea
Published: July 2024

Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio–visual associations, while recording Electroencephalography (EEG). Our results showed that learning task-irrelevant associations between audio–visual stimuli resulted in anticipatory neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence. Moreover, we observed specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio–visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities could induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information about the predicted consecutive stimulus.
Dorsal Anterior Cingulate Cortex Coordinates Contextual Mental Imagery for Single-Beat Manipulation during Rhythmic Sensorimotor Synchronization
Authors: Uemura Maho , Katagiri Yoshitada , Imai Emiko , Kawahara Yasuhiro , Otani Yoshitaka , Ichinose Tomoko , Kondo Katsuhiko , Kowa Hisatomo
Published: July 2024

Flexible pulse-by-pulse regulation of sensorimotor synchronization is crucial for voluntarily showing rhythmic behaviors synchronously with external cueing; however, the underpinning neurophysiological mechanisms remain unclear. We hypothesized that the dorsal anterior cingulate cortex (dACC) plays a key role by coordinating both proactive and reactive motor outcomes based on contextual mental imagery. To test our hypothesis, a missing-oddball task in finger-tapping paradigms was conducted in 33 healthy young volunteers. The dynamic properties of the dACC were evaluated by event-related deep-brain activity (ER-DBA), supported by event-related potential (ERP) analysis and behavioral evaluation based on signal detection theory. We found that ER-DBA activation/deactivation reflected a strategic choice of motor control modality in accordance with mental imagery. Reverse ERP traces, as omission responses, confirmed that the imagery was contextual. We found that mental imagery was updated only by environmental changes via perceptual evidence and response-based abductive reasoning. Moreover, stable on-pulse tapping was achievable by maintaining proactive control while creating an imagery of syncopated rhythms from simple beat trains, whereas accuracy was degraded with frequent erroneous tapping for missing pulses. We conclude that the dACC voluntarily regulates rhythmic sensorimotor synchronization by utilizing contextual mental imagery based on experience and by creating novel rhythms.
RETRACTED: Abou-Donia et al. Using Plasma Autoantibodies of Central Nervous System Proteins to Distinguish Veterans with Gulf War Illness from Healthy and Symptomatic Controls. Brain Sci. 2020, 10, 610
Authors: Abou-Donia Mohamed B. , Lapadula Elizabeth S. , Krengel Maxine H. , Quinn Emily , LeClair Jessica , Massaro Joseph , Conboy Lisa A. , Kokkotou Efi , Abreu Maria , Klimas Nancy G. , Nguyen Daniel D. , Sullivan Kimberly
Published: July 2024

The Brain Sciences Editorial Office retracts the article “Using Plasma Autoantibodies of Central Nervous System Proteins to Distinguish Veterans with Gulf War Illness from Healthy and Symptomatic Controls” [...]
Integrating Machine Learning with Robotic Rehabilitation May Support Prediction of Recovery of the Upper Limb Motor Function in Stroke Survivors
Authors: Quattrocelli Sara , Russo Emanuele , Gatta Maria , Filoni Serena , Pellegrino Raffaello , Cangelmi Leonardo , Cardone Daniela , Merla Arcangelo , Perpetuini David
Published: July 2024

Motor impairment is a common issue in stroke patients, often affecting the upper limbs. To this standpoint, robotic neurorehabilitation has shown to be highly effective for motor function recovery. Notably, Machine learning (ML) may be a powerful technique able to identify the optimal kind and intensity of rehabilitation treatments to maximize the outcomes. This retrospective observational research aims to assess the efficacy of robotic devices in facilitating the functional rehabilitation of upper limbs in stroke patients through ML models. Specifically, clinical scales, such as the Fugl-Meyer Assessment (A-D) (FMA), the Frenchay Arm Test (FAT), and the Barthel Index (BI), were used to assess the patients’ condition before and after robotic therapy. The values of these scales were predicted based on the patients’ clinical and demographic data obtained before the treatment. The findings showed that ML models have high accuracy in predicting the FMA, FAT, and BI, with R-squared (R2) values of 0.79, 0.57, and 0.74, respectively. The findings of this study suggest that integrating ML into robotic therapy may have the capacity to establish a personalized and streamlined clinical practice, leading to significant improvements in patients’ quality of life and the long-term sustainability of the healthcare system.
Unravelling the Acute, Chronic and Steroid-Refractory Management of High-Grade Neurological Immune-Related Adverse Events: A Call to Action
Authors: Malvaso Antonio , Giglio Pierpaolo , Diamanti Luca , Gastaldi Matteo , Vegezzi Elisa , Pace Andrea , Bini Paola , Marchioni Enrico
Published: July 2024

Rare side effects of immune-checkpoint inhibitors (ICIs) are known as neurological immune-related adverse events (n-irAEs). Typically, n-irAEs affect the peripheral nervous system, primarily presenting as myositis, polyradiculoneuropathy, or cranial neuropathy. Less commonly, they impact the central nervous system, resulting in encephalitis, meningitis, or myelitis. High-grade n-irAEs managing and recognizing remains challenging, considering the risk of mortality and long-term disability. To date, strong scientific data are lacking to support the management of high-grade clinical forms. We performed a systematic literature search, selecting all articles describing high-grade steroid-resistance n-irAEs. and we reported them in a practical review. Specifically, current recommendations advise stopping ICI use and beginning corticosteroid treatment. Our findings highlighted that in steroid-resistant n-irAEs, it should be recommended to quickly escalate to plasma exchange (PLEX) and/or intravenously immunoglobulins (IVIg), usually in association with other immunosuppressants. Furthermore, newer evidence supports the use of drugs that may specifically block inflammation without reducing the anti-tumour effect of ICIs. In this practical review, we provide new evidence regarding the therapeutic approach of high-grade n-irAEs, particularly in steroid-resistant cases. We would also stress the importance of informing the scientific community of the discrepancy between current guidelines and clinical evidence in these rare forms of pathology.
Prior Engagement in Physical Activity Correlates with Enhanced Quality of Life Perceptions among Older Adults during COVID-19 Lockdown
Authors: Migliaccio Gian Mario , Gonzales Cesar Ivan Aviles , Kalcev Goce , Cantone Elisa , Nonnis Marcello , Urban Antonio , Marchegiani Sonia , Pinna Samantha , Tusconi Massimo , Primavera Diego , Carta Mauro Giovanni
Published: July 2024

Background: This longitudinal study aimed to evaluate whether prior engagement in a physical exercise program correlated with enhanced perceptions of quality-of-life components among older adults during the COVID-19 lockdown period. Methods: The cohort comprised elderly individuals (aged ≥ 65 years) who had previously partaken in a 12-week randomized controlled trial investigating the effects of a mixed aerobic–anaerobic, moderate-intensity exercise program. Participants’ health-related quality of life was assessed using the Short Form Health Survey-12 item (SF-12) at the beginning of the initial trial and, again, one year later during the COVID-19 lockdown. In the exercise group, 44 participants were included, while the control group consisted of 49 participants, with computer-based, double-blind randomization conducted in Cagliari, Italy. The differences in scores for each SF-12 item between the two groups from T0 to T1 were compared using one-way ANOVA with Bonferroni corrections. Data were analyzed using the Statistical Package for Social Sciences (SPSS) version 27. Results: No statistically significant differences were observed on average by age (exercise group vs. control group 72.20 ± 4.78 vs. 72.91 ± 4.77; F = 0.513, p = 0.476). A decrease from T0 to T1 towards a better score on the SF-12 was observed in the exercise group compared to the control group in item 1 (F = 67.463, p < 0.0001); in item 5 (F = 4.319, p = 0.041); item 8 (F = 4.269, p = 0.041); item 9 (F = 10.761, p = 0.001); item 10 (F = 170.433, p < 0.001); and item 11 (F = 4.075, p = 0.046). Conclusions: The results suggest that participation in a moderate physical exercise program one year prior may have equipped older adults with better coping mechanisms to navigate the stress and isolation imposed by the COVID-19 lockdown, as reflected by their enhanced scores on quality-of-life components pertaining to mental well-being. Exercise may confer a protective effect against the adverse psychological impacts of stressful events like the pandemic, even among older adults with chronic conditions. This study underscores the potential benefits of exercise interventions for promoting quality of life and preventing mood disorders in the elderly population.
Substance-Induced Psychiatric Disorders, Epigenetic and Microbiome Alterations, and Potential for Therapeutic Interventions
Authors: Nohesara Shabnam , Mostafavi Abdolmaleky Hamid , Thiagalingam Sam
Published: July 2024

Substance use disorders (SUDs) are complex biopsychosocial diseases that cause neurocognitive deficits and neurological impairments by altering the gene expression in reward-related brain areas. Repeated drug use gives rise to alterations in DNA methylation, histone modifications, and the expression of microRNAs in several brain areas that may be associated with the development of psychotic symptoms. The first section of this review discusses how substance use contributes to the development of psychotic symptoms via epigenetic alterations. Then, we present more evidence about the link between SUDs and brain epigenetic alterations. The next section presents associations between paternal and maternal exposure to substances and epigenetic alterations in the brains of offspring and the role of maternal diet in preventing substance-induced neurological impairments. Then, we introduce potential therapeutic agents/approaches such as methyl-rich diets to modify epigenetic alterations for alleviating psychotic symptoms or depression in SUDs. Next, we discuss how substance use–gut microbiome interactions contribute to the development of neurological impairments through epigenetic alterations and how gut microbiome-derived metabolites may become new therapeutics for normalizing epigenetic aberrations. Finally, we address possible challenges and future perspectives for alleviating psychotic symptoms and depression in patients with SUDs by modulating diets, the epigenome, and gut microbiome.
Real-Time Analysis of Neuronal Cell Cultures for CNS Drug Discovery
Authors: Akere Millicent T. , Zajac Kelsee K. , Bretz James D. , Madhavaram Anvitha R. , Horton Austin C. , Schiefer Isaac T.
Published: July 2024

The ability to screen for agents that can promote the development and/or maintenance of neuronal networks creates opportunities for the discovery of novel agents for the treatment of central nervous system (CNS) disorders. Over the past 10 years, advances in robotics, artificial intelligence, and machine learning have paved the way for the improved implementation of live-cell imaging systems for drug discovery. These instruments have revolutionized our ability to quickly and accurately acquire large standardized datasets when studying complex cellular phenomena in real-time. This is particularly useful in the field of neuroscience because real-time analysis can allow efficient monitoring of the development, maturation, and conservation of neuronal networks by measuring neurite length. Unfortunately, due to the relative infancy of this type of analysis, standard practices for data acquisition and processing are lacking, and there is no standardized format for reporting the vast quantities of data generated by live-cell imaging systems. This paper reviews the current state of live-cell imaging instruments, with a focus on the most commonly used equipment (IncuCyte systems). We provide an in-depth analysis of the experimental conditions reported in publications utilizing these systems, particularly with regard to studying neurite outgrowth. This analysis sheds light on trends and patterns that will enhance the use of live-cell imaging instruments in CNS drug discovery.
Effect of Aerobic Exercise versus Non-Invasive Brain Stimulation on Cognitive Function in Multiple Sclerosis: A Systematic Review and Meta-Analysis
Authors: Elkhooly Mahmoud , Di Stadio Arianna , Bernitsas Evanthia
Published: July 2024

Objective: In this study, we investigated the effects of noninvasive brain stimulation (NIBS) and exercise on cognition in patients with multiple sclerosis (pwMS). Methods: A literature search was performed using the Cochrane Library, Scopus, PubMed and Web of Science. The time interval used for database construction was up to February 2024; the collected trials were subsequently screened, and the data were extracted. Results: We identified 12 studies with 208 pwMS treated with noninvasive brain stimulation. Seven of the twelve studies concluded that NIBS was effective in improving reaction time, attention and processing speed. Additionally, 26 articles investigated the effect of various types of exercise on cognition among 708 pwMS. Twelve studies used aerobic exercise only, three studies used resistance only, one used yoga, and ten studies used mixed forms of exercise, such as Pilates, resistance and Frenkel coordination. Aerobic exercise was effective in improving at least one cognitive domain in ten studies. Resistance exercise was found to improve cognition in three studies. Yoga failed to show any improvement in one study. Conclusions: NIBS might be an effective intervention for cognition improvement among pwMS. Aerobic exercise and combined forms of exercise are the most frequently investigated and applied and found to be effective. Further studies are needed, especially for resistance, balance and stretching exercises.
Model Test and Numerical Simulation of Two Typical Close-Fitting Pile–Wall Integrated Structures in Deep Excavation
Authors: Wu Changjiang , Shen Wuqin , Xu Ying , Wei Guowei
Published: July 2024

Compared to conventional support methods, the close-fitting pile–wall integration technique features a minimized construction spacing between the retaining pile and the basement retaining wall. This approach leverages the pile stiffness to minimize the wall thickness and enhance underground space utilization. However, it currently lacks significant discussions and measured data about the interaction laws between the pile and the wall. The model test and finite element method (FEM) are employed to study the deformation and internal force interaction laws of two typical close-fitting pile–wall integrated structures, and a comparison with conventional design is conducted. Furthermore, this study separately investigates the impact of sensitivity factors, specifically the pile–wall stiffness ratio and floor plate stiffness, on both structures during the basement construction and serviceability stages. The test results can closely match the numerical simulation. The study results reveal that the wall impacts the bending moment of the pile to some extent. The internal force in the wall is significantly influenced by the lateral deformation of the pile and the floor plate. Compared to conventional designs, this structure significantly reduces the bending moment of the wall, particularly in the composite structure. Additionally, the analysis of sensitivity factors reveals their considerable influence on the pile–wall interaction.
Opportunities and Threats of Adopting Digital Twin in Construction Projects: A Review
Authors: Wang Maoying , Ashour Mojtaba , Mahdiyar Amir , Sabri Soheil
Published: July 2024

Digital twin (DT) is recognized as a pillar in the transition from traditional to digital construction, yet the risks (opportunities and threats) associated with its implementation have not been thoroughly determined in the literature. In addition, there is a scarcity of research relating the risks of DT implementation to DT maturity levels, which has hindered the optimum consideration of such risks when DT is adopted at different maturity levels. To address these gaps, this study conducted a literature review of 1889 documents from Scopus and Web of Science databases. After rigorous filtration, 72 documents were selected and comprehensively reviewed. A total of 47 risk factors (RFs) were identified and categorized into opportunities (economic, technical, environmental and sustainability, monitoring and safety, and management) and threats (economic, technical, and policy and management). Subsequently, these RFs were mapped onto the five-level DT maturity model, providing users with insights into opportunities and threats on each level. The exhaustive list of RFs and proposed integration of a DT maturity model with corresponding RFs enables stakeholders to identify the risks in their specific use cases and facilitate the decision-making and success in transition across various levels of DT in real-life construction projects.
A Dynamic Simulation Model for Near-Zero Rebar-Cutting Waste through Special-Length-Priority Optimization
Authors: Oh Jinhyuk , Kim Sunkuk , Widjaja Daniel Darma
Published: July 2024

Global economic fluctuations as exemplified by the recent COVID-19 financial crisis significantly impact the construction industry, particularly steel rebar supply chain and procurement. This impedes engineers’ efforts toward achieving near-zero rebar-cutting waste due to dynamic rebar minimum order quantities and maximum lengths imposed by steel mills. This study addresses the challenge of achieving near-zero rebar-cutting waste by proposing a model that simulates the level of optimization in minimizing rebar-cutting waste amidst such dynamics. The model was implemented in a case study involving reinforced concrete columns in a high-rise building. While achieving near-zero waste consistently proved challenging, particularly for greater than 50 tons of minimum quantity, the study identified a maximum 12 m rebar variant that attained this target regardless of minimum order quantity. Nonetheless, this study introduces a real-time decision-support system for rebar procurement, empowering engineers to optimize usage and minimize waste. This system facilitates near-zero rebar-cutting waste levels in response to rebar procurement requirement dynamics.
Load Capacity Prediction of Corroded Steel Plates Reinforced with Adhesive and High-Strength Bolts Using a Particle Swarm Optimization Machine Learning Model
Authors: Zhou Xianling , Li Ming , Li Qicai , Sun Guohua , Liu Wenyuan
Published: July 2024

A machine learning (ML) model, optimized by the Particle Swarm Optimization (PSO) algorithm, was developed in this study to predict the shear slip load of adhesive/bolt-reinforced corroded steel plates. An extensive database comprising 490 experimental or numerical specimens was initially employed to train the ML models. Eight ML algorithms (RF, AdaBoost, XGBoost, GBT, SVR, kNN, LightGBM, and CatBoost) were utilized for shear slip load prediction, with their hyperparameters set to default values. Subsequently, the PSO algorithm was employed to optimize the hyperparameters of the above ML algorithms. Finally, performance metrics, error analysis, and score analysis were employed to evaluate the prediction capabilities of the optimized ML models, identifying PSO-GBT as the optimal predictive model. A user-friendly graphical user interface (GUI) was also developed to facilitate engineers using the PSO-GBT model developed in this study to predict the shear slip load of adhesive/bolt-reinforced corroded steel plates.
Enhancing Sustainable Thermal Comfort of Tropical Urban Buildings with Indoor Plants
Authors: Priya Udayasoorian Kaaviya , Senthil Ramalingam
Published: July 2024

Rapid urbanization exacerbates the urban heat island effect, raising local temperatures and endangering residents’ health and well-being. The decreasing green spaces resulting from urbanization necessitate global action focused on reducing heat island intensity and addressing heat stress. Urban green infrastructure (UGI) offers solutions for enhanced comfort and reduced pollution through passive methods. Various large-scale UGI projects have been implemented to regulate temperature and improve air quality in urban areas. More research on smaller green spaces is essential to improve the microclimate in space-constrained urban cities. This experimental study examines the thermal effectiveness of potted plants located on balconies of a mid-rise residential building in Chennai, India. The study aims to enlighten balcony greening’s role in reducing heat stress by monitoring temperature and humidity indoors and outdoors, with and without potted plants at similar solar radiation. Potted plants significantly lowered indoor air and surface temperatures by about 3 °C. Thus, balconies offer untapped potential for green interventions that are often unnoticed in tropical climates like India. The challenges in the installation and maintenance of UGI hinder the widespread adoption of UGI even though UGI positively influences residential well-being. The significant findings benefit urban planners and architects, enlightening strategies to enhance urban thermal comfort and mitigate heat stress through small-scale and cost-effective green interventions. This research contributes to sustainable urban development in tropical climates, aligning with several UN Sustainable Development Goals (SDGs), including SDGs 3, 7, 11, 13, and 15.
Impact of the Built Environment on Ageing in Place: A Systematic Overview of Reviews
Authors: E Jiaxuan , Xia Bo , Chen Qing , Buys Laurie , Susilawati Connie , Drogemuller Robin
Published: July 2024

Background: As the global demographic shifts towards an ageing population, the significance of the built environment in facilitating ageing in place gains prominence. This study addresses the critical question of how a built environment can support older adults’ independence and enhance their quality of life. Method: Utilising a systematic review of review papers, this research scrutinises existing literature to uncover the influence of urban planning, physical accessibility, and residential design on the older people’s physical and mental well-being. More specially, the study methodically examines existing review articles that span various aspects of the built environment related to ageing in place. Results: The analysis reveals that specific elements of the built environment, notably rational urban planning and accessible urban spaces, as well as adaptable housing designs, significantly impact ageing individuals’ health, social engagement, and overall satisfaction with life. It also identifies contradictions in the effects of environmental features on cognitive health and well-being, underscoring areas that warrant further exploration. Conclusions: The study concludes that thoughtful urban and housing designs are pivotal in creating age-friendly environments that support ageing in place. It emphasises the need for future research and policy development aimed at enhancing the living conditions of older adults within their communities.
A Nitrogen/Oxygen Dual-Doped Porous Carbon with High Catalytic Conversion Ability toward Polysulfides for Advanced Lithium–Sulfur Batteries
Authors: Shu Xiaoyan , Yang Yuanjiang , Yang Zhongtang , Wang Honghui , Yu Nengfei
Published: July 2024

Lithium–sulfur batteries (LSBs) have attracted widespread attention due to their high theoretical energy density and low cost. However, their development has been constrained by the shuttle effect of lithium polysulfides and their slow reaction kinetics. In this work, a nitrogen/oxygen dual-doped porous carbon (N/O-PC) was synthesized by annealing the precursor of zeolitic imidazolate framework-8 grown in situ on MWCNTs (ZIF-8/MWCNTs). Then, the N/O-PC composite served as an efficient host for LSBs through chemical adsorption and providing catalytic conversion sites of polysulfides. Moreover, the interconnected porous carbon-based structure facilitates electron and ion transfer. Thus, the S/N/O-PC cathode exhibits high cycling stability (a stable capacity of 685.9 mA h g−1 at 0.2 C after 100 cycles). It also demonstrates excellent rate performance with discharge capacities of 1018.2, 890.2, 775.1, 722.7, 640.4, and 579.6 mAh g−1 at 0.2, 0.5, 1.0, 2.0, 3.0, and 5.0 C, respectively. This work provides an effective strategy for designing and developing high energy density, long cycle life LSBs.
Neuro-Oncologic Veterinary Trial for the Clinical Transfer of Microbeam Radiation Therapy: Acute to Subacute Radiotolerance after Brain Tumor Irradiation in Pet Dogs
Authors: Eling Laura , Kefs Samy , Keshmiri Sarvenaz , Balosso Jacques , Calvet Susan , Chamel Gabriel , Drevon-Gaud Renaud , Flandin Isabelle , Gaudin Maxime , Giraud Lucile , Laissue Jean Albert , Pellicioli Paolo , Verry Camille , Adam Jean-François , Serduc Raphaël
Published: July 2024

Synchrotron Microbeam Radiation Therapy (MRT) has repeatedly proven its superiority compared with conventional radiotherapy for glioma control in preclinical research. The clinical transfer phase of MRT has recently gained momentum; seven dogs with suspected glioma were treated under clinical conditions to determine the feasibility and safety of MRT. We administered a single fraction of 3D-conformal, image-guided MRT. Ultra-high-dose rate synchrotron X-ray microbeams (50 µm-wide, 400 µm-spaced) were delivered through five conformal irradiation ports. The PTV received ~25 Gy peak dose (within microbeams) per port, corresponding to a minimal cumulated valley dose (diffusing between microbeams) of 2.8 Gy. The dogs underwent clinical and MRI follow-up, and owner evaluations. One dog was lost to follow-up. Clinical exams of the remaining six dogs during the first 3 months did not indicate radiotoxicity induced by MRT. Quality of life improved from 7.3/10 [±0.7] to 8.9/10 [±0.3]. Tumor-induced seizure activity decreased significantly. A significant tumor volume reduction of 69% [±6%] was reached 3 months after MRT. Our study is the first neuro-oncologic veterinary trial of 3D-conformal Synchrotron MRT and reveals that MRT does not induce acute to subacute radiotoxicity in normal brain tissues. MRT improves quality of life and leads to remarkable tumor volume reduction despite low valley dose delivery. This trial is an essential step towards the forthcoming clinical application of MRT against deep-seated human brain tumors.
H-1 Parvovirus-Induced Oncolysis and Tumor Microenvironment Immune Modulation in a Novel Heterotypic Spheroid Model of Cutaneous T-Cell Lymphoma
Authors: Angelova Assia , Barf Milena , Just Alexandra , Leuchs Barbara , Rommelaere Jean , Ungerechts Guy
Published: July 2024

The rat protoparvovirus H-1 (H-1PV) is an oncolytic virus known for its anticancer properties in laboratory models of various human tumors, including non-Hodgkin lymphomas (NHL) of B-cell origin. However, H-1PV therapeutic potential against hematological malignancies of T-cell origin remains underexplored. The aim of the present study was to conduct a pilot preclinical investigation of H-1PV-mediated oncolytic effects in cutaneous T-cell lymphoma (CTCL), a type of NHL that is urgently calling for innovative therapies. We demonstrated H-1PV productive infection and induction of oncolysis in both classically grown CTCL suspension cultures and in a novel, in vivo-relevant, heterotypic spheroid model, but not in healthy donor controls, including peripheral blood mononuclear cells (PBMCs). H-1PV-mediated oncolysis of CTCL cells was not prevented by Bcl-2 overexpression and was accompanied by increased extracellular ATP release. In CTCL spheroid co-cultures with PBMCs, increased spheroid infiltration with immune cells was detected upon co-culture treatment with the virus. In conclusion, our preclinical data show that H-1PV may hold significant potential as an ingenious viroimmunotherapeutic drug candidate against CTCL.
The Health Impacts of Better Access to Axicabtagene Ciloleucel: The Case of Spain
Authors: Córdoba Raúl , López-Corral Lucía , Presa María , Martín-Escudero Victoria , Vadgama Sachin , Casado Miguel Ángel , Pardo Carlos
Published: July 2024

In this study, the health impacts of improving access to treatment with axicabtagene ciloleucel (axi-cel) was assessed in patients with relapsed/refractory diffuse large B-cell lymphoma after ≥2 lines of therapy in Spain. A partitioned survival mixture cure model was used to estimate the lifetime accumulated life years gained (LYG) and quality-adjusted life years (QALYs) per patient treated with axi-cel versus chemotherapy. Efficacy data were extracted from the ZUMA-1 trial for axi-cel and from the SCHOLAR-1 study for chemotherapy. In the base case, the incremental outcomes of axi-cel versus chemotherapy were evaluated in a cohort of 187 patients treated with CAR T-cell therapies, as reported by the “Spanish National Health System Plan for Advanced Therapies”, and in the alternative scenario in the full eligible population based on epidemiological estimates (n = 490). Taking those currently treated with axi-cel, compared with chemotherapy, axi-cel provided an additional 1341 LYGs and 1053 QALYs. However, when all eligible patients (n = 490) were treated, axi-cel provided an additional 3515 LYs and 2759 QALYs. Therefore, if all eligible patients were treated with axi-cel rather than those currently treated as per the registry (n = 187), there would have been an additional 303 patients treated, resulting in an additional 2173 LYGs and 1706 QALYs in total. The lack of access in Spain has led to a loss of a substantial number of LYGs and QALYs, and efforts should be made to improve access for all eligible patients.
Evaluation of the Mammalian Aquaporin Inhibitors Auphen and Z433927330 in Treating Breast Cancer
Authors: Charlestin Verodia , Tan Elijah , Arias-Matus Carlos Eduardo , Wu Junmin , Miranda-Vergara Maria Cristina , Lee Mijoon , Wang Man , Nannapaneni Dharma T. , Tennakoon Parinda , Blagg Brian S. J. , Ashfeld Brandon L. , Kaliney William , Li Jun , Littlepage Laurie E.
Published: July 2024

AQPs contribute to breast cancer progression and metastasis. We previously found that genetic inhibition of Aqp7 reduces primary tumor burden and metastasis in breast cancer. In this study, we utilized two AQP inhibitors, Auphen and Z433927330, to evaluate the efficacy of therapeutic inhibition of AQPs in breast cancer treatment. The inhibitors were evaluated in breast cancer for both cytotoxicity and metabolic stability assays across both murine and human breast cancer cell lines. Both AQP inhibitors also affected the expression of other AQP transcripts and proteins, which demonstrates compensatory regulation between AQP family members. As a single agent, Auphen treatment in vivo extended overall survival but did not impact primary or metastatic tumor burden. However, Auphen treatment made cells more responsive to chemotherapy (doxorubicin) or endocrine treatment (tamoxifen, fulvestrant). In fact, treatment with Tamoxifen reduced overall AQP7 protein expression. RNA-seq of breast cancer cells treated with Auphen identified mitochondrial metabolism genes as impacted by Auphen and may contribute to reducing mammary tumor progression, lung metastasis, and increased therapeutic efficacy of endocrine therapy in breast cancer. Interestingly, we found that Auphen and tamoxifen cooperate to reduce breast cancer cell viability, which suggests that Auphen treatment makes the cells more susceptible to Tamoxifen. Together, this study highlights AQPs as therapeutic vulnerabilities of breast cancer metastasis that are promising and should be exploited. However, the pharmacologic results suggest additional chemical refinements and optimization of AQP inhibition are needed to make these AQP inhibitors appropriate to use for therapeutic benefit in overcoming endocrine therapy resistance.
Dynamics of Endothelial Cell Diversity and Plasticity in Health and Disease
Authors: Larionov Alexey , Hammer Christian Manfred , Fiedler Klaus , Filgueira Luis
Published: July 2024

Endothelial cells (ECs) are vital structural units of the cardiovascular system possessing two principal distinctive properties: heterogeneity and plasticity. Endothelial heterogeneity is defined by differences in tissue-specific endothelial phenotypes and their high predisposition to modification along the length of the vascular bed. This aspect of heterogeneity is closely associated with plasticity, the ability of ECs to adapt to environmental cues through the mobilization of genetic, molecular, and structural alterations. The specific endothelial cytoarchitectonics facilitate a quick structural cell reorganization and, furthermore, easy adaptation to the extrinsic and intrinsic environmental stimuli, known as the epigenetic landscape. ECs, as universally distributed and ubiquitous cells of the human body, play a role that extends far beyond their structural function in the cardiovascular system. They play a crucial role in terms of barrier function, cell-to-cell communication, and a myriad of physiological and pathologic processes. These include development, ontogenesis, disease initiation, and progression, as well as growth, regeneration, and repair. Despite substantial progress in the understanding of endothelial cell biology, the role of ECs in healthy conditions and pathologies remains a fascinating area of exploration. This review aims to summarize knowledge and concepts in endothelial biology. It focuses on the development and functional characteristics of endothelial cells in health and pathological conditions, with a particular emphasis on endothelial phenotypic and functional heterogeneity.
Mitochondrial NME6: A Paradigm Change within the NME/NDP Kinase Protein Family?
Authors: Proust Bastien , Herak Bosnar Maja , Ćetković Helena , Tokarska-Schlattner Malgorzata , Schlattner Uwe
Published: July 2024

Eukaryotic NMEs/NDP kinases are a family of 10 multifunctional proteins that occur in different cellular compartments and interact with various cellular components (proteins, membranes, and DNA). In contrast to the well-studied Group I NMEs (NME1–4), little is known about the more divergent Group II NMEs (NME5–9). Three recent publications now shed new light on NME6. First, NME6 is a third mitochondrial NME, largely localized in the matrix space, associated with the mitochondrial inner membrane. Second, while its monomeric form is inactive, NME6 gains NDP kinase activity through interaction with mitochondrial RCC1L. This challenges the current notion that mammalian NMEs require the formation of hexamers to become active. The formation of complexes between NME6 and RCC1L, likely heterodimers, seemingly obviates the necessity for hexamer formation, stabilizing a NDP kinase-competent conformation. Third, NME6 is involved in mitochondrial gene maintenance and expression by providing (d)NTPs for replication and transcription (in particular the pyrimidine nucleotides) and by a less characterized mechanism that supports mitoribosome function. This review offers an overview of NME evolution and structure and highlights the new insight into NME6. The new findings position NME6 as the most comprehensively studied protein in NME Group II and may even suggest it as a new paradigm for related family members.
On Using GeoGebra and ChatGPT for Geometric Discovery
Authors: Botana Francisco , Recio Tomas , Vélez María Pilar
Published: July 2024

This paper explores the performance of ChatGPT and GeoGebra Discovery when dealing with automatic geometric reasoning and discovery. The emergence of Large Language Models has attracted considerable attention in mathematics, among other fields where intelligence should be present. We revisit a couple of elementary Euclidean geometry theorems discussed in the birth of Artificial Intelligence and a non-trivial inequality concerning triangles. GeoGebra succeeds in proving all these selected examples, while ChatGPT fails in one case. Our thesis is that both GeoGebra and ChatGPT could be used as complementary systems, where the natural language abilities of ChatGPT and the certified computer algebra methods in GeoGebra Discovery can cooperate in order to obtain sound and—more relevant—interesting results.
Investigating the Formation of Different (NH4)2[M(H2O)5(NH3CH2CH2COO)]2[V10O28]·nH2O (M = CoII, NiII, ZnII, n = 4; M = CdII, MnII, n = 2) Crystallohydrates
Authors: Chrappová Jana , Pateda Yogeswara , Bartošová Lenka , Rakovský Erik
Published: July 2024

Three hybrid compounds based on decavanadates, i.e., (NH4)2[Co(H2O)5(β-HAla)]2[V10O28]·4H2O (1), (NH4)2[Ni(H2O)5(β-HAla)]2[V10O28]·4H2O (2), and (NH4)2[Cd(H2O)5(β-HAla)]2[V10O28]·2H2O (3), (where β-Hala = zwitterionic form of β-alanine) were prepared by reactions in mildly acidic conditions (pH ~ 4) at room temperature. These compounds crystallise in two structure types, both crystallising in monoclinic P21/n space group but with dissimilar cell packing, i.e., as tetrahydrates (1 and 2) and as a dihydrate (3). An influence of crystal radii and spin state of the central atom in [M(H2O)5(β-HAla)]2+ complex cations on the crystal packing leading to the formation of different crystallohydrate forms was investigated together with previously prepared (NH4)2[Zn(H2O)5(β-HAla)]2[V10O28]·4H2O (4) and (NH4)2[Mn(H2O)5(β-HAla)]2[V10O28]·2H2O (5) and spin states of [M(H2O)5(β-HAla)]2+ (M = Co2+, Ni2+, and Mn2+) cations in solution were confirmed by 1H-NMR paramagnetic effects. FT-IR and FT-Raman spectra for 1–5 are in agreement with the X-ray structure analysis results.
Analysis of the Anisotropic Cyclic Material Behavior of EN AW-1050A H24 Derived from Strain-Controlled Testing Using a Clip-On Extensometer and an Optical System
Authors: Korschinsky Tim , Möller Benjamin , Kiel Marvin , Hecht Matthias
Published: July 2024

Due to its good conductive properties, unalloyed (pure) aluminum, such as EN AW-1050A H24, finds new fields of application in electromobility. To optimize components, the cyclic material behavior must be understood and described precisely as a foundation of a proper fatigue life estimation. Various cyclic tests were performed to not only derive the cyclic parameters to describe the material but also to find the most suitable procedure to deal with the challenges faced during the experiments. The main point of interest is the comparison between a surface-mounted clip-on extensometer and an optical system both used for strain control in cyclic tests. For the analysis of the anisotropic behavior of EN AW-1050A H24, un-notched flat specimens were extracted from sheet metal lengthways and crossways in respect to the rolling direction. While the cyclic material behavior for specimens of both directions of extraction is characterized by cyclic softening in general, the specimens extracted crossways show a strain-amplitude-dependent cyclic softening with strong strain localization especially at the contact points of the knives of the clip-on extensometer leading to an increased quantity of invalid experiments as well as sudden fractures. In the study, it was possible to show the benefits of a contactless optical strain control system when dealing with very soft metallic materials such as EN AW-1050A H24.
Phase-Field Simulation of Grain Growth in Uranium Silicide Nuclear Fuel
Authors: Pan Xiaoqiang , La Yongxiao , Liao Yuxuan , Wang Yifan , Lu Yonghong , Liu Wenbo
Published: July 2024

Uranium silicide (U3Si2) is regarded as a viable fuel option for improving the safety of nuclear power plants. In the present work, phase-field simulations were employed to investigate grain growth phenomena, encompassing both isotropic and anisotropic grain growth. In simulations of isotropic grain growth, it is commonly assumed that the energy and mobility of the grain boundaries (GBs) remain constant, represented by average values. The calculated grain growth kinetic rate constant, K, exhibits a close correspondence with the experimental measurements, indicating a strong agreement between the two. In simulations of anisotropic grain growth, the values of GB energy and mobility are correlated with the angular disparity between GBs. The simulation results demonstrated that the growth rate of U3Si2 can be influenced by both the energy anisotropy and mobility anisotropy of GBs. Furthermore, the anisotropy in mobility results in a greater prevalence of low-angle GB distribution in comparison to high-angle GBs. However, the energy anisotropy of GBs does not impact the frequency distribution of the angle difference between GBs.