A mixed-methods survey and focus group study to understand researcher and clinician preferences for a Journal transparency Tool.

Transparency within biomedical research is essential for research integrity, credibility, and reproducibility. To increase adherence to optimal scientific practices and enhance transparency, we propose the creation of a journal transparency tool (JTT) that will allow users to obtain information about a given scholarly journal's operations and transparency policies. This study is part of a program of research to obtain user preferences to inform the proposed JTT. Here, we report on our consultation with clinicians and researchers. This mixed-methods study was conducted in two parts. The first part involved a cross-sectional survey conducted on a random sample of authors from biomedical journals. The survey asked clinicians and researchers about the inclusion of a series of potential scholarly metrics and user features in the proposed JTT. Quantitative survey items were summarized with descriptive statistics. Thematic content analysis was employed to analyze text-based responses. Subsequent focus groups used survey responses to further explore the inclusion of items in the JTT. Items with less than 70% agreement were used to structure discussion points during these sessions. Participants voted on the use of user features and metrics to be considered within the journal tool after each discussion. Thematic content analysis was conducted on interview transcripts to identify the core themes discussed. A total of 632 participants (5.5% response rate) took part in the survey. A collective total of 74.7% of respondents found it either 'occasionally, 'often', or 'almost always' difficult to determine if health information online is based on reliable research evidence. Twenty-two participants took part in the focus groups. Three user features and five journal tool metrics were major discussion points during these sessions. Thematic analysis of interview transcripts resulted in six themes. The use of registration was the only item to not meet the 70% threshold after both the survey and focus groups. Participants demonstrated low scholarly communication literacy when discussing tool metric suggestions. Our findings suggest that the JTT would be valuable for both researchers and clinicians. The outcomes of this research will contribute to developing and refining the tool in accordance with researchers and clinicians.

A Fe3+-Doped TiO2 Superhydrophilic Coating with Transparent and Long-Lasting Antifogging Properties Constructed Based on Nanostructured Antireflective and Capillary Anchoring Effects.

Superhydrophilic surfaces have attracted great interest in antifogging applications. However, balancing long-lasting superhydrophilicity and high transparency on antifogging surfaces remains a serious problem to be solved. The objective of this work is to prepare superhydrophilic coatings with transparent and long-lasting antifogging properties. In the design, a three-step method was used to obtain the target coatings: (1) magnetron sputtering deposition of a TiN film to provide high intensity, (2) anodic oxidation of the TiN film to obtain TiO2 nanoparticles intended for nanostructured antireflective and capillary structures, and (3) the sol-gel method for the preparation of Fe3+-doped TiO2 coatings using spin-coating in order to achieve superhydrophilicity. The nanostructures, due to their subwavelength dimensions, not only provide high transparency but also recoverable superhydrophilicity owing to the presence of a capillary anchoring effect that prevents the coating from dissolving and peeling off after soaking. The doping of Fe3+ broadened the photoresponse range and maintained the long-lasting superhydrophilicity. Tests showed that the 2 mol % Fe3+-doped TiO2 coating with nanostructures exhibited the highest transparency, longest-lasting superhydrophilicity, and antifogging properties. Furthermore, the coating provided excellent self-cleaning properties, as well as mechanical and chemical stability.

Radiofrequency-transparent local B0 shimming coils using float traps.

PURPOSE: Static field (B0) inhomogeneities present a major challenge in high-field MRI. Multicoil shimming using independent, local, direct-current (DC) shim coils has emerged as a powerful and flexible technique to address this issue. However, many-turn DC coils can lead to significant mutual coupling with radiofrequency (RF) coils, causing transmit field (B1 +) distortions and signal-to-noise ratio degradation. METHODS: We introduce an innovative RF-transparent DC coil that performs B0 shimming while minimizing RF performance impact. The design incorporates float traps to maintain high RF impedance, allowing flexible placement relative to the RF coil without compromising signal-to-noise ratio or affecting B1 +. We fabricated square-shaped DC coils with float traps for 3T MRI and compared them with conventional DC coils. To demonstrate high ΔB0/Amp efficiency, we conducted a B0 shimming experiment around a metal hip implant. RESULTS: Bench tests and MRI experimental results demonstrated that the RF-transparent DC coil effectively minimized RF interference, preserved signal-to-noise ratio, and maintained B1 +, even when placed near the RF receive coil. Additionally, the DC coil significantly improved B0 homogeneity near metal implants and substantially reduced image distortion. CONCLUSION: The RF-transparent DC coil offers a flexible, effective solution for managing B0 inhomogeneities, paving the way for integrating multiturn DC coils in clinical MRI settings without extensive hardware modifications.

Transparent, Flexible, Responsive Switching "Delayed" Amphiphilic Coatings Designed on the Basis of the Full-Cycle Antifouling Strategy.

Marine fouling on the surface of ships and equipment not only creates problems of enhanced resistance to navigation and increased energy consumption but also leads to unclear vision and inaccurate data collection. Antifouling coatings to resist fouling are effective, but it is difficult to achieve long-lasting fouling protection with a single interface state. Switching the status of the interface by intelligent response is a reasonable way to achieve full-cycle efficient antifouling. In this study, the hydrophobic and active antifouling interface in the initial state was achieved by adopting the fluorine-containing group and the natural extract (citronellol) as the antifouling active site. The switching of the interface relies on silanes, which respond to the generation of zwitterions in a seawater environment. Eventually, the interface switched from the hydrophobic state to the amphiphilic state with delayed formation, which achieved continued antifouling. Based on the full-cycle antifouling concept, the combination of low surface energy and antifouling active ingredients in the initial state sustainably switched surfaces in the midterm (free radicals generated during the hydrolysis process), and amphiphilic interfaces formed by "delays" produced an antifouling effect from the initial stage to the subsequent stage. The excellent antifouling activity (bacterial and diatom attachment inhibition by over 90% and significantly reduced mussel adhesion force), optical transparency, and flexibility of these coatings indicate the potential for the application of antifouling coatings prepared from hyperbranched silicone-based resins; they can also be used for data extraction sensors, underwater probes, marine photovoltaics, and other areas where transparency is required.

Ethical Principles of Clinical and Translational Research.

There are potential ethical pitfalls when performing clinical and translational research. The growing emphasis on evidence-based medicine and an understanding of basic ethical principles demands attention from the clinician scientist. Underlying principles such as respect for autonomy and considering the risk-benefit profile for each participant should remain the foundation when considering ethical issues in clinical research. Developing technology, the growth of large databases, and unanticipated health crises require ethical guidelines to be dynamic and continuously reviewed to adequately address potential ethical quandaries. Although there are various international guidelines in this area, there remains heterogeneity in their interpretation and application.

Overcoming the Tradeoff of Visible Transparency and Electrical Conductance via Dual Smoothing of Dielectric/Metal Interfaces in Cu-Thin-Layer-Based Transparent Electrodes.

The rapid advancement of flexible optoelectronic devices, such as light-emitting diodes, solar cells, and electrochromic devices, necessitates the development of high-performance flexible transparent electrodes (TEs). Dielectric/metal/dielectric (DMD)-type TEs are promising alternatives to conventional indium tin oxide (ITO) due to the high electrical conductance, excellent visible transparency, and sufficient mechanical flexibility. However, the tradeoff between electrical conductance and visible transparency poses a challenge to performance enhancement. This study introduces an Ar-ion-mediated interface modification method to address this tradeoff by dual smoothing of dielectric/metal interfaces in TiOx/Cu/ZnO TEs. Implementing this dual smoothing methodology significantly enhances both electrical conductance and visible light transmittance, achieving a Haacke figure of merit 200% higher than that of an unmodified otherwise identical structure. The highest figure of merit is 0.113 Ω-1, a record high for Cu-thin-layer-based DMD TEs, far surpassing ITO electrode values. Further, the enhanced optoelectronic performance remains highly durable under severe and simultaneous electrical, thermal, and mechanical stresses, showcasing the potential for significant advances in flexible optoelectronics.

Referential transparency of verbs in child-directed input by Japanese and American caregivers.

Children acquiring Japanese differ from those acquiring English with regard to the rate at which verbs are learned (Fernald & Morikawa, ). One possible explanation is that Japanese caregivers use verbs in referentially transparent contexts, which facilitate the form-meaning link. We examined this hypothesis by assessing differences in verb usage by Japanese and American caregivers during dyadic play with their infants (5-22 months). We annotated verb-containing utterances for elements associated with referential transparency and compared across groups. Contrary to our hypotheses, we found that Japanese caregivers used verbs in fewer referentially transparent contexts than American caregivers, or did not significantly differ from American caregivers, depending on the measure. These findings cast doubt on cross-cultural differences in referential transparency between Japanese and American child-directed input.

Modernizing authorship criteria and transparency practices to facilitate open and equitable team science.

Background: The rapid acceleration of authorship inflation-increasing numbers of authors per publication in collaborative research-has rendered the traditional "substantial contributions" criterion for authorship and the lack of transparency in author contributions increasingly problematic.Methods and results: To address these challenges, a revamped approach to authorship is proposed, replacing the rigid requirement of "substantial contributions" with a more flexible, project-specific criterion of "sufficient contributions," as determined and justified by the authors for each project. This change more accurately reflects and accommodates the proliferation of scientific collaboration ("team science" or "group science"). It broadens the scope and granularity of roles deserving of authorship by integrating the Contributor Roles Taxonomy (CRediT) and Method Reporting with Initials for Transparency (MeRIT) systems. It mandates in-text documentation of who did what (e.g., who collected what data) and moves beyond the typical binary (all-or-none) classification by assigning a gradated contribution level to each author for each role. Contributions can be denoted using an ordinal scale-either coarse (e.g., lead, equal, and supporting) or fine-grained (e.g., minimal, slight, moderate, substantial, extensive, and full). To support the implementation of the revamped approach, an authorship policy template is provided.Conclusions: Adopting proportional, role-specific credit allocation and explicit documentation of contributions fosters a more transparent, equitable, and trustworthy scientific environment.

The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons.

BACKGROUND: Despite continuous performance improvements, especially in clinical contexts, a major challenge of Artificial Intelligence based Decision Support Systems (AI-DSS) remains their degree of epistemic opacity. The conditions of and the solutions for the justified use of the occasionally unexplainable technology in healthcare are an active field of research. In March 2024, the European Union agreed upon the Artificial Intelligence Act (AIA), requiring medical AI-DSS to be ad-hoc explainable or to use post-hoc explainability methods. The ethical debate does not seem to settle on this requirement yet. This systematic review aims to outline and categorize the positions and arguments in the ethical debate. METHODS: We conducted a literature search on PubMed, BASE, and Scopus for English-speaking scientific peer-reviewed publications from 2016 to 2024. The inclusion criterion was to give explicit requirements of explainability for AI-DSS in healthcare and reason for it. Non-domain-specific documents, as well as surveys, reviews, and meta-analyses were excluded. The ethical requirements for explainability outlined in the documents were qualitatively analyzed with respect to arguments for the requirement of explainability and the required level of explainability. RESULTS: The literature search resulted in 1662 documents; 44 documents were included in the review after eligibility screening of the remaining full texts. Our analysis showed that 17 records argue in favor of the requirement of explainable AI methods (xAI) or ad-hoc explainable models, providing 9 categories of arguments. The other 27 records argued against a general requirement, providing 11 categories of arguments. Also, we found that 14 works advocate the need for context-dependent levels of explainability, as opposed to 30 documents, arguing for context-independent, absolute standards. CONCLUSIONS: The systematic review of reasons shows no clear agreement on the requirement of post-hoc explainability methods or ad-hoc explainable models for AI-DSS in healthcare. The arguments found in the debate were referenced and responded to from different perspectives, demonstrating an interactive discourse. Policymakers and researchers should watch the development of the debate closely. Conversely, ethicists should be well informed by empirical and technical research, given the frequency of advancements in the field.

OpenAI o1-Preview vs. ChatGPT in Healthcare: A New Frontier in Medical AI Reasoning.

This editorial explores the recent advancements in generative artificial intelligence with the newly-released OpenAI o1-Preview, comparing its capabilities to the traditional ChatGPT (GPT-4) model, particularly in the context of healthcare. While ChatGPT has shown many applications for general medical advice and patient interactions, OpenAI o1-Preview introduces new features with advanced reasoning skills using a chain of thought processes that could enable users to tackle more complex medical queries such as genetic disease discovery, multi-system or complex disease care, and medical research support. The article explores some of the new model's potential and other aspects that may affect its usage, like slower response times due to its extensive reasoning approach yet highlights its potential for reducing hallucinations and offering more accurate outputs for complex medical problems. Ethical challenges, data diversity, access equity, and transparency are also discussed, identifying key areas for future research, including optimizing the use of both models in tandem for healthcare applications. The editorial concludes by advocating for collaborative exploration of all large language models (LLMs), including the novel OpenAI o1-Preview, to fully utilize their transformative potential in medicine and healthcare delivery. This model, with its advanced reasoning capabilities, presents an opportunity to empower healthcare professionals, policymakers, and computer scientists to work together in transforming patient care, accelerating medical research, and enhancing healthcare outcomes. By optimizing the use of several LLM models in tandem, healthcare systems may enhance efficiency and precision, as well as mitigate previous LLM challenges, such as ethical concerns, access disparities, and technical limitations, steering to a new era of artificial intelligence (AI)-driven healthcare.

Exploring Angiotensin II and Oxidative Stress in Radiation-Induced Cataract Formation: Potential for Therapeutic Intervention.

Radiation-induced cataracts (RICs) represent a significant public health challenge, particularly impacting individuals exposed to ionizing radiation (IR) through medical treatments, occupational settings, and environmental factors. Effective therapeutic strategies require a deep understanding of the mechanisms underlying RIC formation (RICF). This study investigates the roles of angiotensin II (Ang II) and oxidative stress in RIC development, with a focus on their combined effects on lens transparency and cellular function. Key mechanisms include the generation of reactive oxygen species (ROS) and oxidative damage to lens proteins and lipids, as well as the impact of Ang II on inflammatory responses and cellular apoptosis. While the generation of ROS from water radiolysis is well established, the impact of Ang II on RICs is less understood. Ang II intensifies oxidative stress by activating type 1 receptors (AT1Rs) on lens epithelial cells, resulting in increased ROS production and inflammatory responses. This oxidative damage leads to protein aggregation, lipid peroxidation, and apoptosis, ultimately compromising lens transparency and contributing to cataract formation. Recent studies highlight Ang II's dual role in promoting both oxidative stress and inflammation, which accelerates cataract development. RICs pose a substantial public health concern due to their widespread prevalence and impact on quality of life. Targeting Ang II signaling and oxidative stress simultaneously could represent a promising therapeutic approach. Continued research is necessary to validate these strategies and explore their efficacy in preventing or reversing RIC development.

The Utilization of Blockchain for Data Security for the Chronic Pain Physician.

PURPOSE OF REVIEW: The COVID-19 pandemic accelerated the surge in medical data utilization, notably impacting chronic pain management given its enormous economist cost. While the collection and use of data enhances research and care quality, data exchange risks compromising integrity and privacy, exemplified by the Change Healthcare cyberattack. Here, we review the potential of blockchain for the utilization for cybersecurity in the healthcare system with an emphasis on the field of chronic pain. RECENT FINDINGS: Blockchain technology emerges as a potential solution, offering decentralized, secure, and immutable data management. Blockchain ensures transparency, integrity, and data privacy, which is crucial in healthcare. Smart contracts may offer automated, secure management of implantable neuromodulation devices such as spinal cord stimulators and intrathecal pumps. Blockchain's potential in pharmaceutical supply chain integrity is exemplified in preliminary efforts ensuring that the medication retrieved by the patient is indeed the intended medication. Despite limitations such as speed of transactions, blockchain presents innovative avenues for healthcare security and quality improvement, necessitating further development for widespread implementation. Blockchain's applicability is not only applicable to chronic pain management, but can be used in medicine as a whole.

Investing in green, sustaining the planet: The role of fintech in promoting corporate green investment in the Chinese energy industry.

In recent years, fintech has rapidly developed on a global scale, bringing about significant transformations across various industries. Particularly in the energy sector, the potential of fintech to promote corporate green investment has increasingly become a focus. As the energy industry accounts for a significant proportion of global greenhouse gas emissions, guiding companies in implementing green investment has become an important issue for environmental protection and sustainable development. However, previous studies have rarely systematically investigated the influence of fintech on corporate green investment in the energy industry and the mechanisms involved. Hence, this study aims to bridge this research gap within the context of China. By utilizing valid survey data from 466 corporations in the Chinese energy industry and the method of structural equation modeling, the results indicate that: (1) Fintech can promote corporate green investment in the energy industry. (2) Fintech can promote corporate data-driven decision-making capability, information transparency, and sustainable development strategy. (3) Data-driven decision-making capability, information transparency, and sustainable development strategy can also promote corporate green investment in the energy industry. (4) Mediation analysis reveals that data-driven decision-making capability, information transparency, and sustainable development strategy each play a mediating role in the impact of fintech on corporate green investment. (5) Comparative analysis of the mediation effects indicates that there is a certain degree of difference among these three variables' mediating effects, but it did not reach a statistically significant level. This study provides rich insights for business practices, encouraging companies to strengthen their focus on green investment in the fintech era and offer guidance and recommendations to relevant stakeholders, thereby providing practical guidance for corporate green transformation and sustainable development. Finally, this research also contributes to policy development and optimization.

Semitransparent Cu2O films based on CuO Back Layer for Photoelelctrochemical Water Splitting and Photovoltaic Applications.

Cuprous oxide (Cu2O) as an intrinsic p-type semiconductor is promising for solar energy conversion. The major challenge in fabricating Cu2O lies in achieving both high transparency and high performance in a tandem device. The Cu2O photocathodes often employ gold as the back contact layer. However, it is not an optimal choice in tandem device due to its poor transmission, scarcity, and electron-hole recombination at the interface of Au and Cu2O. Here, we presented a facile method that utilizes the earth-abundant material copper oxide (CuO) to fabricate highly transparent Cu2O devices. The maximum transmittance of the Cu2O film on CuO (FTO/CuO/Cu2O) increased from 42% to 58% compared with Cu2O film on Au (FTO/Au/Cu2O) in 550-800 nm. After coating atomic layer deposition (ALD) layers and hydrogen evolution reaction (HER) catalyst, the photocurrent density at 0 V (versus RHE) of the semitransparent Cu2O photocathode with CuO as the back layer for photoelectrochemical (PEC) water splitting reached -4.9 mA·cm-2, which showed a 24.5% improvement compared with FTO/Au/Cu2O photocathode. Moreover, expanding the CuO layer strategy to the field of solar cells enables Cu2O solar cells to achieve a PCE of 2.37%.

The state of individual participant data sharing for the highest-revenue medicines.

BACKGROUND: Amid growing emphasis from pharmaceutical companies, advocacy groups, and regulatory bodies for sharing of individual participant data, recent audits reveal limited sharing, particularly for high-revenue medicines. Therefore, this study aimed to assess the individual participant data-sharing eligibility of clinical trials supporting the Food and Drug Administration approval of the top 30 highest-revenue medicines for 2021. METHODS: A cross-sectional analysis was conducted on 316 clinical trials supporting approval of the top 30 revenue-generating medicines of 2021. The study assessed whether these trials were eligible for individual participant data sharing, defined as being publicly listed on a data-sharing platform or confirmed by the trial sponsors as in scope for independent researcher individual participant data investigations. Information was gathered from various sources including ClinicalTrials.gov, the European Union Clinical Trials Register, and PubMed. Key factors such as the trial phase, completion dates, and the nature of the data-sharing process were also examined. RESULTS: Of the 316 trials, 201 (64%) were confirmed eligible for sharing, meaning they were either publicly listed on a data-sharing platform or confirmed by the trial sponsors as in scope for independent researcher individual participant data investigations. A total of 102 (32%) were confirmed ineligible, and for 13 (4%), the sponsor indicated that a full research proposal would be required to determine eligibility. The analysis also revealed a higher rate of individual participant data sharing among companies that utilized independent platforms, such as Vivli, for managing their individual participant data-sharing process. Trials not marked as completed had significantly lower eligibility for individual participant data sharing. CONCLUSION: This study highlights that a substantial portion of trials for top revenue-generating medicines are eligible for individual participant data sharing. However, challenges persist, particularly for trials that are marked as ongoing and for trials where the sharing processes are managed internally by pharmaceutical companies. Data-sharing rates could be improved by adopting open-access individual participant data-sharing models or using independent platforms. Standardizing policies to facilitate immediate individual participant data availability for approved medicines is necessary.

Spatial variability and uncertainty associated with soil moisture content using INLA-SPDE combined with PyMC3 probability programming.

Spatial variability and uncertainty associated with soil volumetric moisture content (SVMC) is crucial in moisture prediction accuracy, this paper sets out to address this point of SVMC by developing data-driven model. Grid samples of SVMC covered approximately a 3-ha field during the jointing growth stage of winter wheat, and SVMC were measured by Time Domain Reflectometry (TDR), located in North China Plain, China. Bayesian inference was performed to explore spatial heterogeneity, robustness, transparency, interpretability and uncertainty related to SVMC using python-based PyMC3 combined with Integrated Nested Laplace Approximation with the Stochastic Partial Differential Equation (INLA-SPDE) model. The results showed that the prediction surface of SVMC, the lower and upper limits of 95% credible intervals quantified uncertainty associated with SVMC, cauchy prior of the flexibility and adaptability to obtain state-of-the-art predictive performance is more robust than gaussian prior for SVMC prediction, the transparency and interpretability of SVMC prediction model were revealed by MCMC (Markov-Chain Monte-Carlo) trace plots, KDE (Kernel density estimates), and rank plots. The uncertainty associated with SVMC can explicitly be described using the highest-posterior density interval, the prediction lower and upper limits.

Negative performance feedback from algorithms or humans? effect of medical researchers' algorithm aversion on scientific misconduct.

Institutions are increasingly employing algorithms to provide performance feedback to individuals by tracking productivity, conducting performance appraisals, and developing improvement plans, compared to traditional human managers. However, this shift has provoked considerable debate over the effectiveness and fairness of algorithmic feedback. This study investigates the effects of negative performance feedback (NPF) on the attitudes, cognition and behavior of medical researchers, comparing NPF from algorithms versus humans. Two scenario-based experimental studies were conducted with a total sample of 660 medical researchers (algorithm group: N1 = 411; human group: N2 = 249). Study 1 analyzes the differences in scientific misconduct, moral disengagement, and algorithmic attitudes between the two sources of NPF. The findings reveal that NPF from algorithms shows higher levels of moral disengagement, scientific misconduct, and negative attitudes towards algorithms compared to NPF from humans. Study 2, grounded in trait activation theory, investigates how NPF from algorithms triggers individual's egoism and algorithm aversion, potentially leading to moral disengagement and scientific misconduct. Results indicate that algorithm aversion triggers individuals' egoism, and their interaction enhances moral disengagement, which in turn leads to increased scientific misconduct among researchers. This relationship is also moderated by algorithmic transparency. The study concludes that while algorithms can streamline performance evaluations, they pose significant risks to scientific misconduct of researchers if not properly designed. These findings extend our understanding of NPF by highlighting the emotional and cognitive challenges algorithms face in decision-making processes, while also underscoring the importance of balancing technological efficiency with moral considerations to promote a healthy research environment. Moreover, managerial implications include integrating human oversight in algorithmic NPF processes and enhancing transparency and fairness to mitigate negative impacts on medical researchers' attitudes and behaviors.

Transparency improves the accuracy of automation use, but automation confidence information does not.

Increased automation transparency can improve the accuracy of automation use but can lead to increased bias towards agreeing with advice. Information about the automation's confidence in its advice may also increase the predictability of automation errors. We examined the effects of providing automation transparency, automation confidence information, and their potential interacting effect on the accuracy of automation use and other outcomes. An uninhabited vehicle (UV) management task was completed where participants selected the optimal UV to complete missions. Low or high automation transparency was provided, and participants agreed/disagreed with automated advice on each mission. We manipulated between participants whether automated advice was accompanied by confidence information. This information indicated on each trial whether automation was "somewhat" or "highly" confident in its advice. Higher transparency improved the accuracy of automation use, led to faster decisions, lower perceived workload, and increased trust and perceived usability. Providing participant automation confidence information, as compared with not, did not have an overall impact on any outcome variable and did not interact with transparency. Despite no benefit, participants who were provided confidence information did use it. For trials where lower compared to higher confidence information was presented, hit rates decreased, correct rejection rates increased, decision times slowed, and perceived workload increased, all suggestive of decreased reliance on automated advice. Such trial-by-trial shifts in automation use bias and other outcomes were not moderated by transparency. These findings can potentially inform the design of automated decision-support systems that are more understandable by humans in order to optimise human-automation interaction.