bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial intelligence on biomedical signals: technologies, applications, and future directions
Authors: Lee Yoon Jae , Park Cheoljeong , Kim Hodam , Cho Seong J. , Yeo Woon-Hong 
Published: December 2024

AbstractIntegrating artificial intelligence (AI) into biomedical signal analysis represents a significant breakthrough in enhanced precision and efficiency of disease diagnostics and therapeutics. From traditional computational models to advanced machine learning algorithms, AI technologies have improved signal processing by efficiently handling complexity and interpreting intricate datasets. Understanding physiological data, which requires highly trained professionals, is now more accessible; in regions with limited access, AI tools expand healthcare accessibility by providing high-level diagnostic insights, ultimately improving health outcomes. This review explores various AI methodologies, including supervised, unsupervised, and reinforcement learning, and examines their synergy for biomedical signal analysis and future directions in medical science. By capturing a comprehensive overview of the current state and prospects of AI-driven healthcare, this paper highlights the transformative potential of AI in analyzing biomedical signals.
Graphical Abstract
Mini-InternVL: a flexible-transfer pocket multi-modal model with 5% parameters and 90% performance
Authors: Gao Zhangwei , Chen Zhe , Cui Erfei , Ren Yiming , Wang Weiyun , Zhu Jinguo , Tian Hao , Ye Shenglong , He Junjun , Zhu Xizhou , Lu Lewei , Lu Tong , Qiao Yu , Dai Jifeng , Wang Wenhai 
Published: December 2024

AbstractMulti-modal large language models (MLLMs) have demonstrated impressive performance in vision-language tasks across a wide range of domains. However, the large model scale and associated high computational cost pose significant challenges for training and deploying MLLMs on consumer-grade GPUs or edge devices, thereby hindering their widespread application. In this work, we introduce Mini-InternVL, a series of MLLMs with parameters ranging from 1 billion to 4 billion, which achieves 90% of the performance with only 5% of the parameters. This significant improvement in efficiency and effectiveness makes our models more accessible and applicable in various real-world scenarios. To further promote the adoption of our models, we are developing a unified adaptation framework for Mini-InternVL, which enables our models to transfer and outperform specialized models in downstream tasks, including autonomous driving, medical image processing, and remote sensing. We believe that our models can provide valuable insights and resources to advance the development of efficient and effective MLLMs.
Anticancer potentials of bioactive compounds in some locally available spices in Nigeria
Authors: Saheed Emmanuel Saviour 
Published: December 2024

AbstractSpices have garnered widespread appreciation and extensive research in numerous nations for their potential in treating chronic and life-threatening illnesses, including cancer. Nigeria, located in the African area, is experiencing an annual increase in cancer mortality. Multiple scientific studies substantiate the cancer-preventive attributes of spices, including cloves, cinnamon, garlic, ginger, and turmeric, which are widely available in Nigeria. This review primarily examines the bioactive chemicals found in these spices. Some of these compounds have demonstrated noteworthy anticancer and cancer preventative effects in both experimental models and human clinical studies. This study offers a comprehensive update on the anticancer potential of some locally accessible spices in Nigeria. The purpose of this review is to show studies carried out in test tubes, living organisms, and humans on these selected spices in order to determine their potential as cancer treatments and management agents, as well as the bioactive components that are responsible for these effects.
Graphical Abstract
A brief review on methods and materials for electrode modification: electroanalytical applications towards biologically relevant compounds
Authors: Pimpilova Mariya 
Published: December 2024

AbstractThis review provides an overview of the advancements in electrochemical sensors and biosensors, along with their applications. The review covers the methods and materials used for modifying the surface of electrodes, and also discusses the use of electrochemical sensors for quantitative analysis of biologically relevant compounds, such as hydrogen peroxide, dopamine, serotonin, glucose, and other markers of oxidative stress and neurotransmitters. Various electrochemical characterization methods have also been highlighted. Recently, there has been a growing interest in combining recognition elements with electronic elements to establish electrochemical sensors and biosensors. These devices have proven to be effective in detecting chemical and biological targets through changes in electrochemical activity at electrode interfaces. The use of nanomaterials has significantly improved the sensitivity and selectivity of electrochemical sensing platforms. Electrode materials are critical to the construction of high-performance sensors for detecting target molecules. The integration of functional nanomaterials can enhance catalytic activity, conductivity, and biocompatibility, leading to more accurate and sensitive biosensing. Overall, the development of functional electrode materials, along with various electrochemical methods, has greatly expanded the potential applications of electrochemical devices.
Graphical Abstract
INAHTA member agency stories of engaging, adaptable, and impactful HTA
Authors: Söderholm Werkö Sophie , Bouchard Sylvie , Romli Erni Z. , Li Chunmei , Huang Li-Ying , Pelekanou Charlotte , Elston Lauren , Schuller Tara 
Published: December 2024

Abstract
Health technology assessment (HTA) agencies assess evidence to support decision making about which technologies to provide and pay for in the health system. HTA impact is understood as the influence that HTA report findings can have in the health system, including impacts on reimbursement decisions, changes to health outcomes, or broader system or societal impacts. The International Network of Agencies for Health Technology Assessment (INAHTA) is a global network of publicly funded HTA agencies. INAHTA’s mission, in part, is to advance the impact of HTA to support reimbursement decisions and the optimal use of health system resources. Each year, INAHTA awards the David Hailey Award for Best Impact Story to the member agency that shares the best story, as voted by fellow members, about HTA impact. The impact story sharing program in INAHTA contributes to a deeper understanding of what works well (or not so well) in achieving HTA impact. This paper provides six impact stories from agencies that were finalists for the 2021 and 2022 David Hailey Impact Award for Best Impact Story: the Institut national d’excellence en santé et en services sociaux, the Malaysian Health Technology Assessment Section, Ontario Health, the Center for Drug Evaluation, the National Institute for Health and Care Excellence, and Health Technology Wales. These stories demonstrate that HTA agencies can, in differing ways, effectively support governments in their efforts to place evidence at the centre of decision making.
Genetic relationships among some old walnuts (Juglans regia L.) genotypes in Iran and selection of superior genotypes
Authors: Zarinkolah Maryam , Karimi Hamid Reza , Esmaeilzadeh Majid , Malekzadeh Khalil , Sahhafi Seyyed Rasoul 
Published: December 2024

Abstract
Walnut (Juglans regia L.) is one of the oldest and most economically important edible nut species due to its high nutritional value. Iran is known as one of the most important centres of the origin and diversity of walnuts worldwide. This research was carried out to determine the genetic relationships of some old walnut genotypes in Iran in order to select superior genotypes and better conserve them. The results of morphological evaluation of the fruits showed that the highest coefficients of variation were related to nut weight, kernel weight, percentage kernel, thickness and weight of the shell, and thickness and weight of the packing tissue in both years. The average morphological traits among the studied genotypes were different, such that the highest nut weights in the first and second years were 15.59 and 14.79 g and the corresponding lowest values were 7.37 and 7.79 g, respectively. The highest and lowest kernel weights were observed in the first years to be 8.09 and 3.80 g and in the second years were 6.94 and 2.65 g, respectively. The highest kernel percentages in the first and second years were 64.97 and 59.79% and the lowest percentages were 39.52 and 27.80% in the first and second years, respectively. Molecular evaluation results showed that the 16 start codon of target (SCoT) primers amplified a total of 166 bands with an average of 10.44 bands per primer. The number of polymorphic bands was changed from 2 to 19 in primers SCoT-19 and SCoT-15. The average polymorphism information content value was 0.30 and the highest amount of this index (0.36) was observed in SCoT-14 and SCoT-19 primers. The results of principal component analysis and analysis of molecular variance indicated the presence of relatively high diversity within the investigated genotypes. The amount of genetic diversity within the investigated regions was 84% and that between areas was 16%. Based on the obtained results, it was concluded that SCoT markers could provide helpful information on the genetic relationships among walnut genotypes, which can be used in future walnut breeding and conservation programmes.
Estimation of Small Failure Probability in High‐Dimensional Groundwater Contaminant Transport Modeling Using Subset Simulation Coupled With Preconditioned Crank‐Nicolson MCMC
Authors: Xu Teng , Zhang Shiqiang , Lu Chunhui , Zhang Jiangjiang , Ye Yu 
Published: December 2024

AbstractThe accurate prediction of groundwater contamination is challenging due to uncertainties arising from the inherent heterogeneity of aquifers, inadequate site characterization, and limitations in conceptual mathematical models. These factors can result in an underestimation of contaminant concentrations. For effective contaminant prevention and control, it is important to estimate the probability of exceeding the allowed threshold for contaminant concentrations, known as the failure probability of groundwater contamination. Computing small failure probabilities using classical Monte Carlo simulation (MCS) requires computing a large number of samplers to converge to a stationary target value, which is time‐consuming. To address this, in this paper, we develop a novel approach for calculating small failure probabilities, known as subset simulation (SS) coupled with preconditioned Crank‐Nicolson Markov chain Monte Carlo (pCN‐SS), which combines subset simulation with preconditioned Crank‐Nicolson Markov chain Monte Carlo (pCN‐MCMC) to promote computational efficiency. We have tested the performance of the proposed algorithm in both a mathematical example and a numerical case study of groundwater contamination. The results demonstrate that pCN‐SS provides improved accuracy and efficiency for evaluating small failure probabilities for high‐dimensional groundwater contamination, specifically for hydraulic conductivity as a source of uncertainty. Compared to classical MCS and traditional SS, pCN‐SS requires fewer model evaluations but produces stable and accurate results.
Bypassing the lattice BCS–BEC crossover in strongly correlated superconductors through multiorbital physics
Authors: Witt Niklas , Nomura Yusuke , Brener Sergey , Arita Ryotaro , Lichtenstein Alexander I. , Wehling Tim O. 
Published: December 2024

AbstractSuperconductivity emerges from the spatial coherence of a macroscopic condensate of Cooper pairs. Increasingly strong binding and localization of electrons into these pairs compromises the condensate’s phase stiffness, thereby limiting critical temperatures – a phenomenon known as the BCS–BEC crossover in lattice systems. In this study, we demonstrate enhanced superconductivity in a multiorbital model of alkali-doped fullerides (A3C60) that goes beyond the limits of the lattice BCS–BEC crossover. We identify that the interplay of strong correlations and multiorbital effects results in a localized superconducting state characterized by a short coherence length but robust stiffness and a domeless rise in critical temperature with increasing pairing interaction. To derive these insights, we introduce a new theoretical framework allowing us to calculate the fundamental length scales of superconductors, namely the coherence length (ξ0) and the London penetration depth (λL), even in presence of strong electron correlations.
Winner–loser plant trait replacements in human-modified tropical forests
Authors: Pinho Bruno X. , Melo Felipe P. L. , ter Braak Cajo J. F. , Bauman David , Maréchaux Isabelle , Tabarelli Marcelo , Benchimol Maíra , Arroyo-Rodriguez Victor , Santos Bráulio A. , Hawes Joseph E. , Berenguer Erika , Ferreira Joice , Silveira Juliana M. , Peres Carlos A. , Rocha‐Santos Larissa , Souza Fernanda C. , Gonçalves-Souza Thiago , Mariano-Neto Eduardo , Faria Deborah , Barlow Jos 
Published: December 2024

Abstract
Anthropogenic landscape modification may lead to the proliferation of a few species and the loss of many. Here we investigate mechanisms and functional consequences of this winner–loser replacement in six human-modified Amazonian and Atlantic Forest regions in Brazil using a causal inference framework. Combining floristic and functional trait data for 1,207 tree species across 271 forest plots, we find that forest loss consistently caused an increased dominance of low-density woods and small seeds dispersed by endozoochory (winner traits) and the loss of distinctive traits, such as extremely dense woods and large seeds dispersed by synzoochory (loser traits). Effects on leaf traits and maximum tree height were rare or inconsistent. The independent causal effects of landscape configuration were rare, but local degradation remained important in multivariate trait-disturbance relationships and exceeded the effects of forest loss in one Amazonian region. Our findings highlight that tropical forest loss and local degradation drive predictable functional changes to remaining tree assemblages and that certain traits are consistently associated with winners and losers across different regional contexts.
Cooling efficacy of trees across cities is determined by background climate, urban morphology, and tree trait
Authors: Li Haiwei , Zhao Yongling , Wang Chenghao , Ürge-Vorsatz Diana , Carmeliet Jan , Bardhan Ronita 
Published: December 2024

AbstractUrban planners and other stakeholders often view trees as the ultimate panacea for mitigating urban heat stress; however, their cooling efficacy varies globally and is influenced by three primary factors: tree traits, urban morphology, and climate conditions. This study analyzes 182 studies on the cooling effects of urban trees across 17 climates in 110 global cities or regions. Tree implementation reduces peak monthly temperatures to below 26 °C in 83% of the cities. Trees can lower pedestrian-level temperatures by up to 12 °C through large radiation blockage and transpiration. In tropical, temperate, and continental climates, a mixed-use of deciduous and evergreen trees in open urban morphology provides approximately 0.5 °C more cooling than a single species approach. In arid climates, evergreen species predominate and demonstrate more effective cooling within compact urban morphology. Our study offers context-specific greening guidelines for urban planners to harness tree cooling in the face of global warming.
Blood-based quantification of Aβ oligomers indicates impaired clearance from brain in ApoE ε4 positive subjects
Authors: Blömeke Lara , Rehn Fabian , Pils Marlene , Kraemer-Schulien Victoria , Cousin Anneliese , Kutzsche Janine , Bujnicki Tuyen , Freiesleben Silka D. , Schneider Luisa-Sophie , Preis Lukas , Priller Josef , Spruth Eike J. , Altenstein Slawek , Schneider Anja , Fliessbach Klaus , Wiltfang Jens , Hansen Niels , Rostamzadeh Ayda , Düzel Emrah , Glanz Wenzel , Incesoy Enise I. , Buerger Katharina , Janowitz Daniel , Ewers Michael , Perneczky Robert , Rauchmann Boris-Stephan , Teipel Stefan , Kilimann Ingo , Laske Christoph , Munk Matthias H. , Spottke Annika , Roy Nina , Heneka Michael T. , Brosseron Frederic , Wagner Michael , Roeske Sandra , Ramirez Alfredo , Schmid Matthias , Jessen Frank , Bannach Oliver , Peters Oliver , Willbold Dieter 
Published: December 2024

Abstract
Background
Quantification of Amyloid beta (Aβ) oligomers in plasma enables early diagnosis of Alzheimer’s Disease (AD) and improves our understanding of underlying pathologies. However, quantification necessitates an extremely sensitive and selective technology because of very low Aβ oligomer concentrations and possible interference from matrix components.

Methods
In this report, we developed and validated a surface-based fluorescence distribution analysis (sFIDA) assay for quantification of Aβ oligomers in plasma.

Results
The blood-based sFIDA assay delivers a sensitivity of 1.8 fM, an inter- and intra-assay variation below 20% for oligomer calibration standards and no interference with matrix components. Quantification of Aβ oligomers in 359 plasma samples from the DELCODE cohort reveals lower oligomer concentrations in subjective cognitive decline and AD patients than healthy Control participants.

Conclusions
Correlation analysis between CSF and plasma oligomer concentrations indicates an impaired clearance of Aβ oligomers that is dependent on the ApoE ε4 status.
Degenerative Schultererkrankungen und Schulterverletzungen: Epidemiologie, ICD10-Codierverhalten und Versorgungsrealität – Datenanalyse von 4,9 Mio. Versicherten der AOK Baden-Württemberg
Authors: Azarderakhsh Jasmin , Siebenlist Sebastian , Schneider Olaf , Beck Frauke , Flechtenmacher Johannes 
Published: December 2024

ZusammenfassungTrotz steigender Erkrankungszahlen in den letzten Jahren liegen bisher keine Daten zum
Krankheits- und Versorgungsgeschehen für degenerative Schultererkrankungen und
Schulterverletzungen sowie zum ICD10-Codierverhalten der versorgenden Ärztinnen und Ärzte vor.
Die vorliegende Arbeit stellt erstmals eine deskriptive, auf Abrechnungsdaten aus dem Jahr
2022 basierende Auswertung von codierten Schultererkrankungen von 4,9 Mio. Versicherten einer
gesetzlichen Krankenversicherung in Baden-Württemberg vor. In der Untersuchung wird
unterschieden zwischen unfallbedingten Verletzungen an der Schulter und Krankheiten,
hervorgerufen durch degenerative Veränderungen des Schulterapparats. Bei der ICD10-Codierung
wird zwischen der Angabe spezifischer (Verwendung von Schlüsselnummern der zugrunde liegenden
Erkrankung), und unspezifischer Codes, die lediglich das Symptom verschlüsseln, differenziert.
Laut Abrechnungsdaten waren Frauen etwas häufiger von Schultererkrankungen betroffen als
Männer (7,3% vs. 6,9%), wobei Frauen zum Zeitpunkt der Diagnosestellung im Mittel deutlich
älter waren. Bei Frakturen lag der Geschlechterunterschied durchschnittlich bei 20 Jahren. Die
Auswertung zum Codierverhalten machte deutlich, dass Hausärztinnen und Hausärzte im Vergleich
zu anderen Facharztgruppen häufiger unspezifische Schultererkrankungen wie Gelenkschmerz oder
Impingement-Syndrom codierten. Die Analyse der Leistungsinanspruchnahme zeigte, dass nur ein
Drittel der evaluierten Patientinnen und Patienten eine Bildgebung und nur 40% eine Verordnung
für Physiotherapie aufgrund einer Schulterdiagnose erhielten. Die Untersuchung der
Komorbiditäten ergab, dass Patientinnen und Patienten mit degenerativen Schultererkrankungen
häufiger von stoffwechselbedingten Krankheiten und Hypertonie betroffen waren als solche ohne
Schultererkrankungen. Diese Ergebnisse zur Häufigkeit codierter Schultererkrankungen in den
verschiedenen Gesundheitssektoren zeigt die Relevanz in der Bundesrepublik Deutschland für
Männer und Frauen gleichermaßen. Zusammenfassend lassen die Auswertungen – trotz methodischer
Einschränkungen – vermuten, dass bei der Diagnosestellung und der Verordnung von
therapeutischen Maßnahmen Potenzial für eine spezifischere Codierung gegeben sein könnte. Das
präzisere Wissen um die tatsächliche Ursache der Inanspruchnahme von Gesundheitsleistungen
kann einerseits für den Behandler hilfreich sein, um spezifische Diagnostik- und
Therapiemaßnahmen einzuleiten und andererseits einen u. U. erhöhten Versorgungsbedarf
innerhalb des Gesundheitssystems in der Bundesrepublik Deutschland zu identifizieren.
Artificial Intelligence in Medicine: Are We Ready?
Authors: Nagler Michael 
Published: December 2024

AbstractIn spite of my personal belief in the benefits of artificial intelligence (AI), reading Cathy O'Neil's book “Weapons of Math Destruction” left me feeling unsettled.1 She describes how flawed and unchecked algorithms are widely applied in areas that affect us all: hiring, credit scoring, access to education, and insurance pricing. In one example, a fixed percentage of teachers in a U.S. region was dismissed every year based on biased and opaque algorithms. The authors concluded that such algorithms act as “weapons of math destruction,” perpetuate and amplify societal biases, act unethically, and harm vulnerable populations. The question arises as to what happens when we apply these algorithms to medicine? How do we know whether we are giving our patients the correct diagnosis or prognosis? Are we still sure that patients are receiving the appropriate treatment? Would we notice if the algorithms were geared more toward the needs of companies (make a lot of money) or health insurance companies (spend as little as possible)? In fact, evidence of bias and inequality of algorithms in medicine is already available.2 Due to these risks, some of my colleagues suggest that AI should be completely banned from medicine.