bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structural simulation of Ti–Ha–CaCO3 biocomposites using finite element analysis (FEA) for biomechanical stability of hip implant
Authors: Ibrahim Hassan Kobe , Abolarin Mathew Sunday , Abdulrahman Asipita Salawu , Omoniyi Peter Olorunleke , Mahamood Rasheedat Modupe , Jen Tien-Chien , Akinlabi Esther Titilayo 
Published: July 2024

AbstractThe structural integrity of new biocomposite implants is critical in ensuring the success of biomedical implants under physiological loading conditions. Studying the stress distribution, deformation, and potential failure modes under different loading scenarios is complex, expensive, and time-consuming, as it involves repeated surgery on clinical assessment. The present study aims to investigate the biomechanical stability of hip implants made of a Ti–Ha–CaCO3 biocomposite using finite element analysis. The Ti–Ha–CaCO3 biocomposite was modeled and simulated using Solidworks. The model mesh was generated to represent the implant’s geometry accurately, and normal human activities (standing and jumping) were considered the boundary conditions with the lower part of the femur fixed. The model was subjected to static loading following ISO 7206-4 with an equivalent load of 2300 N according to ASTM F2996-13 standard. The Ti–Ha–CaCO3 biocomposite demonstrated outstanding biomechanical stability under loading circumstances. The maximum von Mises stress (354.7 MPa) observed with the GSB-femur model in the implant was below the yield strength of the titanium implant, indicating that the implant can withstand applied loads without experiencing permanent deformation. However, 74.11 MPa was obtained as acceptable von Mises stress using GSB intramedullary rods for bone fixation. The most stable implant is DSB, with the lowest displacement value of 2.68 mm. Low equivalent strains were achieved for all the implants, as the highest strain (0.012) was obtained in the simulation of the stem DSB-femur model. Low-stress signals (SS) were obtained for the implant-femur models, indicating they are suitable for replacing bone for that loading. The DSB (7.19) is the most suitable among the studied stem-femur models, and GSB (0.87) remains the suitable intramedullary rod-femur model with the lowest SS.
Classifying Neuronal Cell Types Based on Shared Electrophysiological Information from Humans and Mice
Authors: Ophir Ofek , Shefi Orit , Lindenbaum Ofir 
Published: July 2024

AbstractThe brain is an intricate system that controls a variety of functions. It consists of a vast number of cells that exhibit diverse characteristics. To understand brain function in health and disease, it is crucial to classify neurons accurately. Recent advancements in machine learning have provided a way to classify neurons based on their electrophysiological activity. This paper presents a deep-learning framework that classifies neurons solely on this basis. The framework uses data from the Allen Cell Types database, which contains a survey of biological features derived from single-cell recordings from mice and humans. The shared information from both sources is used to classify neurons into their broad types with the help of a joint model. An accurate domain-adaptive model, integrating electrophysiological data from both mice and humans, is implemented. Furthermore, data from mouse neurons, which also includes labels of transgenic mouse lines, is further classified into subtypes using an interpretable neural network model. The framework provides state-of-the-art results in terms of accuracy and precision while also providing explanations for the predictions.
Possible protective effect of rosuvastatin in chemotherapy-induced cardiotoxicity in HER2 positive breast cancer patients: a randomized controlled trial
Authors: Kettana Khlood M. , El‑Haggar Sahar M. , Alm El-Din Mohamed A. , El‑Afify Dalia R. 
Published: July 2024

AbstractCardiotoxicity is a side effect of chemotherapy in human epidermal growth factor receptor 2 (HER2) positive breast cancer patients receiving both anthracyclines and trastuzumab. We looked for a possible protective effect of rosuvastatin against chemotherapy-induced cardiotoxicity. Methods: 50 newly diagnosed HER2 positive breast cancer patients were randomly allocated into two groups: 25patients in each. Group 1(control group) received doxorubicin for 4 cycles (3 months) followed by trastuzumab adjuvant therapy. Group 2 (treatment group) received doxorubicin for 4 cycles (3 months) followed by trastuzumab adjuvant therapy and 20 mg of oral rosuvastatin 24 h before the first cycle of chemotherapy and once daily for the rest of the follow-up period (6 months). Transthoracic echocardiography was done, and blood samples were collected for patients 24 h before the initiation of therapy, after 3 months and after 6 months to assess serum levels of high sensitivity cardiac troponin I (hs-cTnI), Myeloperoxidase (MPO), Interleukin-6 (IL-6) and Alanine aminotransferase (ALT). The study was retrospectively registered in Clinical Trials.gov in April 2022. Its ID is NCT05338723. Compared to control group, Rosuvastatin-treated group had a significantly lower decline in LVEF after 3 months and after 6 months. They had significantly lower Hs-cTnI and IL-6 after 3 months and after 6 months, and significantly lower MPO after 6 months. Four patients in control group experienced cardiotoxicity while no one in rosuvastatin-treated group. Rosuvastatin attenuated cardiotoxicity, so it is a promising protective agent against chemotherapy-induced cardiotoxicity.
Resveratrol Modulates Diabetes-Induced Neuropathic Pain, Apoptosis, and Oxidative Neurotoxicity in Mice Through TRPV4 Channel Inhibition
Authors: Osmanlıoğlu Haci Ömer , Nazıroğlu Mustafa 
Published: July 2024

AbstractDiabetic peripheral neuropathy (DPN) is caused by several factors, including reactive free oxygen radicals (ROS)-induced excessive Ca2+ influx. Transient receptor potential (TRP) vanilloid 4 (TRPV4) is a member of the Ca2+-permeable TRP superfamily. Resveratrol (RESV) has been extensively utilized in TRP channel regulation due to its pharmacological properties, which include antioxidant and TRP inhibitory effects. The protective function of RESV and the contribution of TRPV4 to streptozotocin (STZ)-induced neuropathic pain in mice are still unclear. Here, we evaluated the effects of RESV through the modulation of TRPV4 on Ca2+ influx, ROS-mediated pain, apoptosis, and oxidative damage in the mouse dorsal root ganglion (DRGs). From the 32 mice, four groups were induced: control, RESV, STZ, and STZ + RESV. We found that the injection of RESV reduced the changes caused by the STZ-induced stimulation of TRPV4, which in turn increased mechanical/thermal neuropathic pain, cytosolic Ca2+ influx, TRPV4 current density, oxidants (lipid peroxidation, mitochondrial ROS, and cytosolic ROS), and apoptotic markers (caspase-3, -8, and -9). The RESV injection also increased the STZ-mediated reduction of viability of DRG and the amounts of glutathione, glutathione peroxidase, vitamin A, β-carotene, and vitamin E in the brain, erythrocytes, plasma, liver, and kidney. All of these findings suggest that TRPV4 stimulation generates oxidative neurotoxicity, neuropathic pain, and apoptosis in the STZ-induced diabetic mice. On the other hand, neurotoxicity and apoptosis were reduced due to the downregulation of TRPV4 carried out through the RESV injection.
Graphical Abstract
An overview of how resveratrol (RESV) inhibits TRPV4 in mice to modulate the effects of diabetes mellitus-induced diabetic peripheral neuropathy (DPN). Ruthenium red (RuR) inhibits TRPV4, while GSK1016790A (GSK) and reactive free oxygen radicals (ROS) activate it. In the mitochondria of DRGs, the glucose oxidation brought on by diabetes mellitus (STZ) causes an intracellular free Ca2+ and Zn2+ influx excess that is dependent on TRPV4. The administration of STZ leads to the DRG becoming more depolarized (ΔΨm), which in turn causes an increase in mitochondrial ROS, apoptosis, and caspases (caspase-3, caspase-8, and caspase-9) by downregulating enzymatic (glutathione peroxidase, GSH-Px) and non-enzymatic (glutathione (GSH), vitamin A, and vitamin E) antioxidants. The mice’s molecular pathways were diminished by the RESV injections. (Increase (↑); diminish (↓))
DNA Damage and Chromatin Rearrangement Work Together to Promote Neurodegeneration
Authors: Sharma Harman , Koirala Sushma , Chew Yee Lian , Konopka Anna 
Published: July 2024

AbstractNeurodegenerative diseases have a complex origin and are composed of genetic and environmental factors. Both DNA damage and chromatin rearrangement are important processes that occur under pathological conditions and in neurons functioning properly. While numerous studies have demonstrated the inseparable relationship between DNA damage and chromatin organization, understanding of this relationship, especially in neurodegenerative diseases, requires further study. Interestingly, recent studies revealed that known hallmark proteins involved in neurodegenerative diseases function in both DNA damage and chromatin reorganization, and this review discusses the current knowledge of this relationship. This review focused on hallmark proteins involved in various neurodegenerative diseases, such as the microtubule-associated protein tau, TAR DNA/RNA binding protein 43 (TDP-43), superoxide dismutase 1 (SOD1), fused in sarcoma (FUS), huntingtin (HTT), α-synuclein, and β-amyloid precursor protein (APP). Hence, DNA damage and chromatin rearrangement are associated with disease mechanisms in distinct neurodegenerative diseases. Targeting common modulators of DNA repair and chromatin reorganization may lead to promising therapies for treating neurodegeneration.
Spanish Society of Medical Oncology recommendations for comprehensive assessment and care of cancer survivors’ needs
Authors: Gallego Alejandro , Beato Carmen , Brozos Elena , De La Cruz Susana , García Ruth Vera 
Published: July 2024

Abstract
This article reviews the contemporary and inclusive definition of cancer survivorship, including patients with and without disease who have completed or continue to undergo treatment. The Spanish Society of Medical Oncology (SEOM) describes in this article the needs of these patients and outlines a care model based on an estimation of cancer incidence and identification of patient needs, to enable the provision of practical actions to achieve effective care. The objectives of this review are to identify the main effects of cancer on survivors and to establish appropriate ways of measuring these effects, as well as discussing the management of physical, psychological and social, occupational, financial, and other health-related needs. We suggest a multidisciplinary care model and training programs for the different professionals involved in care, and highlight challenges and the future role of the SEOM and health-care policy in ensuring optimum care of cancer survivors.
How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responses
Authors: Lin Jionghao , Han Zifei , Thomas Danielle R. , Gurung Ashish , Gupta Shivang , Aleven Vincent , Koedinger Kenneth R. 
Published: July 2024

AbstractOne-on-one tutoring is widely acknowledged as an effective instructional method, conditioned on qualified tutors. However, the high demand for qualified tutors remains a challenge, often necessitating the training of novice tutors (i.e., trainees) to ensure effective tutoring. Research suggests that providing timely explanatory feedback can facilitate the training process for trainees. However, it presents challenges due to the time-consuming nature of assessing trainee performance by human experts. Inspired by the recent advancements of large language models (LLMs), our study employed the GPT-4 model to build an explanatory feedback system. This system identifies trainees’ responses in binary form (i.e., correct/incorrect) and automatically provides template-based feedback with responses appropriately rephrased by the GPT-4 model. We conducted our study using the responses of 383 trainees from three training lessons (Giving Effective Praise, Reacting to Errors, and Determining What Students Know). Our findings indicate that: 1) using a few-shot approach, the GPT-4 model effectively identifies correct/incorrect trainees’ responses from three training lessons with an average F1 score of 0.84 and AUC score of 0.85; and 2) using the few-shot approach, the GPT-4 model adeptly rephrases incorrect trainees’ responses into desired responses, achieving performance comparable to that of human experts.
Comparison of Demographics: National Amyotrophic Lateral Sclerosis Registry and Clinical Trials Data
Authors: Han Moon , Raymond Jaime , Larson Theodore C. , Mehta Paul , Horton D. Kevin 
Published: July 2024

Abstract
Objective
To characterize the participant demographics in the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database compared with the web-portal National Amyotrophic Lateral Sclerosis (ALS) Registry (the Registry).

Methods
Demographics and ALS symptom information were compared between the self-reported registrant data in the Registry web portal (2010–2021) and the latest available PRO-ACT data (updated August 2022), which is a collection of clinical trials data.

Results
Greater percentages of younger (≤ 59 years old) but smaller percentages of older (60 + years old) participants were represented in PRO-ACT compared to Registry. Enrollment for minority race groups was greater in the Registry portal data, but race information was largely missing/unknown in PRO-ACT database. Median age at the time of diagnosis and age at the time of symptom onset were significantly higher for Registry enrollees compared to the participants of PRO-ACT. Symptom onset sites were similarly reported, but duration between self-noted symptom onset and diagnosis was slight, but significantly longer for the Registry enrollees (11 vs. 9 months). Hispanic were as likely as non-Hispanic to participate in research studies, based on the Registry data.

Conclusion
There was a notable difference in the age distribution and minority representation of enrollees between the PRO-ACT and Registry study populations. Age distribution in the PRO-ACT database skewed to a younger and less diverse cohort. Despite the clinical heterogeneity and complex disease mechanism of ALS, identifying the underrepresented demographic niche in the PRO-ACT and Registry study populations can help improve patient participation and criteria for patient selection to enhance generalizability.
Tourniquets
Authors: Johnston Luke R. , Parker William J. , Walker Patrick , Bradley Matthew J. 
Published: July 2024

Abstract
Purpose of Review
Tourniquets have a longstanding history in combat casualty care, but only became widely accepted in the last 20 years as effective and safe. This review seeks to examine the history of tourniquets in combat casualty care and analyze their translation from military to civilian trauma care.

Recent Findings
The most recent research focusing on prehospital tourniquet use has focused on the expansion of tourniquets to the civilian trauma patient population. Research has shown that tourniquet use can be effectively taught to civilians without medical training, and that placement of tourniquets in the prehospital environment can reduce blood loss and shock at admission. Other recent research has established the effectiveness of tourniquets in unique military environments such as in cold weather gear and when chemical, biological, radiological, and nuclear (CBRN) gear is worn.

Summary
Tourniquets have become a standard piece of equipment for deployed servicemembers for their lifesaving potential. Evidence is building that tourniquets can play a similar role in civilian trauma, but more work is needed to demonstrate the cost effectiveness and mortality benefit of widespread civilian tourniquet training and application.
Multi-view subspace clustering based on multi-order neighbor diffusion
Authors: Long Yin , Xu Hongbin , Xiang Yang , Du Xiyu , Yang Yanying , Zhao Xujian 
Published: July 2024

AbstractMulti-view subspace clustering (MVC) intends to separate out samples via integrating the complementary information from diverse views. In MVC, since the structural information in the graph is crucial to the graph learning, most of the existing algorithms construct the superficial graph from the original data by directly measuring the similarity between the first-order complementary nearest neighbors. However, the information provided by the superficial graph structure would be influenced by contaminated or absent samples. To address this problem, in the proposed method, the higher-order complementary neighbor graphs are exploited to discover the latent structural information between the samples, and fusing the latent structural information across different orders to achieve the MVC. Specifically, the higher-order neighbor graphs under different views are leveraged to estimate the missing samples. Then, to integrate the neighbor graphs of different orders, the multi-order neighbor diffusion fusion is proposed. Nevertheless, the above problem of diffusion fusion is an intractable non-convex issue. Thus, to address it, the multi-order neighbor diffusion fusion is considered as a combination problem of the solution under different order, and the heuristic algorithm is leveraged to solve it. In this way, not only the data representation under different view and also the neighbor structure under different order can be diffused under a joint optimization framework, thus the consistency and integral information among various perspectives and orders can be utilized effectively and simultaneously. Experiments on both incomplete and complete multi-view dataset demonstrate the convincingness of the high-order neighborhood structure based subspace clustering scheme by comparing with the existing approaches.
Improving the reliability of nanosatellite swarms by adopting blockchain technology
Authors: Ibrahim Hussein A. , Shouman Marwa A. , El-Fishawy Nawal A. , Ahmed Ayman 
Published: July 2024

AbstractSatellite swarm networks have occupied a prominent position in many modern applications due to their low cost, simplicity of design, and flexibility. Reliability is an influential factor in the design of satellite networks with different structures. Usually, small satellites are based on COST components, which may reduce continues operability due to the lack of using backup system on board the sagecraft. Any failure in one subsystem means a complete loss of the function and data stored in this subsystem; hence the need for a reliable and applicable solution for this matter is a crucial topic. Using the redundancy strategy in satellite swarm networks increases reliability and availability. Blockchain is characterized by using a distributed ledger which enables the database to be replicated across nodes in the network and results in increasing transparency, security, and trust. This paper suggests adoption of blockchain technology in distributed multi-satellite mission swarm networks to provide a high level of reliability and availability of the entire system; the blockchain is usually used to secure system transactions in multilayer approach by storage of the key parameters in more than one node; here we suggest the adoption of this approach not only to secure satellite network transaction, but also to increase system reliability so that failure of one node can be recovered by other nodes. We compared this approach with similar traditional networks that do not use blockchain. The results show a higher reliability efficiency of 95.3% for applying blockchain technology compared to 64.3% without the use of blockchain, as well as a higher availability of 99% compared to 91%.
A granularity data method for power frequency electric and electromagnetic fields forecasting based on T–S fuzzy model
Authors: Nie Peng , Yu Qiang , Li Zhenkun , Yuan Xiguo 
Published: July 2024

AbstractThe impact of electromagnetic radiation generated by signal transmission base stations and power stations to meet the needs of communication equipment and energy consumption on the environment has caused people concerns. Monitoring and prediction of electric and magnetic fields have become critical tasks for researchers. In this paper, we propose a granularity data method based on T–S (Takagi–Sugeno) fuzzy model, named fuzzy rule-based model, which utilizing finite rules that are determined by the deviations between the predicted values and the true values after the data goes through a granulation-degranulation mechanism, to predict the intensity of power frequency electric field and electromagnetic field. A series of experiments show that fuzzy rule-based models have better robustness and higher prediction accuracy in comparison with several existing prediction models. The improvement of the performance of the fuzzy rule-based model quantified in terms of Root Mean Squared Error is 20.86%, 51.91%, 62.28%, 65.10%, and 71.92% in comparison with that of the Ridge model, Lasso model, and a family of support vector machine model with different kernel functions, including linear kernel (SVM-linear), radial basis function (SVM-BRF), polynomial kernel (SVM-polynomial) respectively, on the electromagnetic field testing data, and 37.42%, 55.16%, 58.79%, 59.28%, 64.27% lower than that of the Ridge model, Lasso model, SVM-linear model, SVM-BRF model and SVM-polynomial model on the power frequency electric field testing data.
A multi-period intuitionistic fuzzy consensus reaching model for group decision making problem in social network
Authors: Yang Wei , Zhang Luxiang 
Published: July 2024

AbstractA new intuitionistic fuzzy consensus reaching model is developed with multi-period public opinions and expert evaluation values in social network environment. First, the public opinions are obtained by using the crawler software and sentiment analysis technology is used to transform public opinions into intuitionistic fuzzy decision matrix in each period. Attribute weights are calculated by using the time attenuation factor and changes in public opinion. Second, the social trust relationship is modeled and incomplete social trust relationships are completed by using Archimedean t-norm. The expert weights are calculated by using the dynamic trust degree and similarity degree. Third, a consensus framework is proposed for multiple-period decision making problem, which coordinates conflicts between experts through dual feedback paths. The collective opinion scores are calculated by using weights of periods and attribute weights obtained from the word frequency of public opinions. The tourism attraction recommendation method is used to illustrate the proposed method.
In Situ Synchrotron Diffraction Assessment of Reversibility of the Martensitic Transformation in Single-Crystalline Co–Ni–Ga Shape Memory Alloy Under Torsion
Authors: Lauhoff C. , Degener S. , Bolender A. , Liehr A. , Hübner L. , Frenck J.-M. , Molotnikov A. , Niendorf T. 
Published: July 2024

AbstractHeusler-type Co–Ni–Ga shape memory alloys attracted significant attention due to their excellent functional properties in single-crystalline state under both compressive and tensile loading. The present study investigates the superelastic deformation behavior under torsion. Using a newly installed torsion testing setup, in situ synchrotron diffraction was carried out on single-crystalline material in order to investigate the martensitic phase transformation. Incremental deformation experiments reveal a fully reversible martensitic transformation under torsional loading at room temperature, leading to excellent strain recovery after deformation to 6.5% shear strain. Furthermore, relevant aspects towards the analysis of powder diffraction data obtained for single-crystalline material in transmission mode under torsional loading are presented and critically discussed.