bluuebunny/crossref_metadata_2025 huggingface dataset:
Using natural language processing to link patients' narratives to visual capabilities and sentiments
Authors: He Dongcheng , Chung Susana T. L.
Published: July 2024

SIGNIFICANCE

Analyzing narratives in patients' medical records using a framework that combines natural language processing (NLP) and machine learning may help uncover the underlying patterns of patients' visual capabilities and challenges that they are facing and could be useful in analyzing big data in optometric research.

PURPOSE

The primary goal of this study was to demonstrate the feasibility of applying a framework that combines NLP and machine learning to analyze narratives in patients' medical records. To test and validate our framework, we applied it to analyze records of low vision patients and to address two questions: Was there association between patients' narratives related to activities of daily living and the quality of their vision? Was there association between patients' narratives related to activities of daily living and their sentiments toward certain “assistive items”?

METHODS

Our dataset consisted of 616 records of low vision patients. From patients' complaint history, we selected multiple keywords that were related to common activities of daily living. Sentences related to each keyword were converted to numerical data using NLP techniques. Machine learning was then applied to classify the narratives related to each keyword into two categories, labeled based on different “factors of interest” (acuity, contrast sensitivity, and sentiments of patients toward certain “assistive items”).

RESULTS

Using our proposed framework, when patients' narratives related to specific keywords were used as input, our model effectively predicted the categories of different factors of interest with promising performance. For example, we found strong associations between patients' narratives and their acuity or contrast sensitivity for certain activities of daily living (e.g., “drive” in association with acuity and contrast sensitivity).

CONCLUSIONS

Despite our limited dataset, our results show that the proposed framework was able to extract the semantic patterns stored in medical narratives and to predict patients' sentiments and quality of vision.
Digital clinics for diagnosing and treating migraine
Authors: Nyberg Johan , Rosenbacke Rikard , Ben-Menachem Elinor
Published: July 2024

Purpose of review

Several innovative digital technologies have begun to be applied to diagnosing and treating migraine. We reviewed the potential benefits and opportunities from delivering migraine care through comprehensive digital clinics.

Recent findings

There are increasing applications of digitization to migraine diagnosis and management, including e-diaries, and patient self-management, especially after the COVID-19 pandemic. Digital care delivery appears to better engage chronic migraine sufferers who may struggle to present to physical clinics.

Digital clinics appear to be a promising treatment modality for patients with chronic migraine. They potentially minimize travel time, shorten waiting periods, improve usability, and increase access to neurologists. Additionally, they have the potential to provide care at a much lower cost than traditional physical clinics. However, the current state of evidence mostly draws on case-reports, suggesting a need for future randomized trials comparing digital interventions with standard care pathways.
Fluorogenic Aptamer Optimizations on a Massively Parallel Sequencing Platform
Authors: Kuo Yu-An , Chen Yuan-I , Wang Yanxing , Korkmaz Zeynep , Yonas Siem , He Yujie , Nguyen Trung D. , Hong Soonwoo , Nguyen Anh-Thu , Kim Sohyun , Seifi Saeed , Fan Po-Hsun , Wu Yuting , Yang Zhenglin , Liu Hung-Wen , Lu Yi , Ren Pengyu , Yeh Hsin-Chih
Published: July 2024

Fluorogenicaptamers (FAPs) have become an increasingly important tool in cellular sensing and pathogen diagnostics. However, fine-tuning FAPs for enhanced performance remains challenging even with the structural details provided by X-ray crystallography. Here we present a novel approach to optimize a DNA-based FAP (D-FAP), Lettuce, on repurposed Illumina next-generation sequencing (NGS) chips. When substituting its cognate chromophore, DFHBI-1T, with TO1-biotin, Lettuce not only shows a red-shifted emission peak by 53 nm (from 505 to 558 nm), but also a 4-fold bulk fluorescence enhancement. After screening 8,821 Lettuce variants complexed with TO1-biotin, the C14T mutation is found to exhibit an improved apparent dissociated constant (vs. 0.82 µM), an increased quantum yield (QY: 0.62 vs. 0.59) and an elongated fluorescence lifetime (τ: 6.00 vs. 5.77 ns), giving 45% more ensemble fluorescence than the canonical Lettuce/TO1-biotin complex. Molecular dynamic simulations further indicate that the π-π stacking interaction is key to determining the coordination structure of TO1-biotin in Lettuce. Our screening-and-simulation pipeline can effectively optimize FAPs without any prior structural knowledge of the canonical FAP/chromophore complexes, providing not only improved molecular probes for fluorescence sensing but also insights into aptamer-chromophore interactions.
Psychotic experiences and disorders in adolescents and young adults with intellectual disabilities: Evidence from a population-based birth cohort in the United Kingdom
Authors: Dardani Christina , Underwood Jack F G , Jones Hannah J. , Rammos Alexandros , Sullivan Sarah , Hull Laura , Khandaker Golam M. , Zammit Stan , Rai Dheeraj , Madley-Dowd Paul
Published: July 2024

Background

Evidence suggests that individuals with intellectual disabilities may be at increased risk of affective and non-affective psychotic disorders. However, studies so far have been limited by small and selected samples. Moreover, the role of early life trauma, a key environmental risk factor for psychosis, in the potential associations is unknown.

Methods

Using data from Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort, we investigated the associations between ID, psychotic disorders, and psychotic experiences in adulthood, and assessed the potential mediating role of trauma in childhood. Individuals with intellectual disabilities were identified using a multisource measure utilising cognitive, functional, and diagnostic indicators from ALSPAC combined with health and administrative records. Psychotic disorder clinical diagnoses were extracted through linkage to primary care records. Psychotic experiences were assessed at ages 18 and 24 using the semi-structured Psychosis-Like Symptoms interview (PLIKSi). Traumatic experiences between ages 5 and 11 were assessed with questionnaires and interviews administered to children and parents at multiple ages. Multiple imputation was performed across all analyses to mitigate potential bias due to missing data.

Findings

The maximum sample after multiple imputation was 9,407. We found evidence of associations between intellectual disabilities and psychotic disorders (OR= 4.57; 95%CI: 1.56-13.39). Evidence was weaker in the case of psychotic experiences (OR=1.63; 95%CI: 0.93-2.84). There was some evidence suggesting a potential mediating role of traumatic experiences in the associations between ID and psychotic experiences (OR= 1.09; 95%CI: 1.03-1.15). Evidence was less consistent in the case of psychotic disorders. Complete records analyses yielded comparable estimates.

Interpretation

Intellectual disabilities are associated with psychotic disorders and experiences in young adulthood. Further research into the contribution of trauma could shape current intervention strategies for psychotic disorders in this population.

Funding

The Baily Thomas Charitable Fund
Healthcare visits of the population 50+ in Europe from 2004 to 2022
Authors: Nguyen Van Kinh , Reuter Anna , Abd El Aziz Mirna , Bärnighausen Till
Published: July 2024

Background

The COVID-19 pandemic brought major reductions in healthcare visits in Europe. The long-run consequences for healthcare systems depend on the paths to post-pandemic levels, especially among the groups in highest need of sustained care.

Methods

We used individual longitudinal data from 27 European countries from the Survey of Health, Ageing and Retirement in Europe, divided in three phases: pre-pandemic (2004-2019), pandemic (2021), and post-pandemic (2022). We analysed the number of healthcare visits by gender over time and their correlation with age and chronic conditions using a Bayesian spatio-temporal model.

Results

Before COVID-19, the nominal rate of healthcare visits varied among countries from two to six visits per year and increased over time. The COVID-19 pandemic severely affected the number of healthcare visits, with an estimated reduction from 65% to 95% across countries. During COVID-19, older individuals and those with chronic conditions had a more than proportional reduction in the number of healthcare visits. After COVID-19, the pattern of healthcare visits mostly returned to pre-pandemic levels, with an over-shooting for people with cancer and a lag for people with cardiovascular diseases.

Conclusion

The quick recovery in the number of healthcare visits for most European countries indicate that access to care stabilised again. Yet, the different trends for vulnerable groups show that these groups require timely action to prevent long-term consequences of missed care.
Large Language Models Diagnose Facial Deformity
Authors: Lee Jungwook , Xu Xuanang , Kim Daeseung , Deng Hannah H. , Kuang Tianshu , Lampen Nathan , Fang Xi , Gateno Jaime , Yan Pingkun
Published: July 2024

Purpose

This study examines the application of Large Language Models (LLMs) in diagnosing jaw deformities, aiming to overcome the limitations of various diagnostic methods by harnessing the advanced capabilities of LLMs for enhanced data interpretation. The goal is to provide tools that simplify complex data analysis and make diagnostic processes more accessible and intuitive for clinical practitioners.

Methods

An experiment involving patients with jaw deformities was conducted, where cephalometric measurements (SNB Angle, Facial Angle, Mandibular Unit Length) were converted into text for LLM analysis. Multiple LLMs, including LLAMA-2 variants, GPT models, and the Gemini-Pro model, were evaluated against various methods (Threshold-based, Machine Learning Models) using balanced accuracy and F1-score.

Results

Our research demonstrates that larger LLMs efficiently adapt to diagnostic tasks, showing rapid performance saturation with minimal training examples and reducing ambiguous classification, which highlights their robust in-context learning abilities. The conversion of complex cephalometric measurements into intuitive text formats not only broadens the accessibility of the information but also enhances the interpretability, providing clinicians with clear and actionable insights.

Conclusion

Integrating LLMs into the diagnosis of jaw deformities marks a significant advancement in making diagnostic processes more accessible and reducing reliance on specialized training. These models serve as valuable auxiliary tools, offering clear, understandable outputs that facilitate easier decision-making for clinicians, particularly those with less experience or in settings with limited access to specialized expertise. Future refinements and adaptations to include more comprehensive and medically specific datasets are expected to enhance the precision and utility of LLMs, potentially transforming the landscape of medical diagnostics.
Influence of nitrogen stoichiometry and the role of Sm
5d
states in SmN thin films
Authors: Meléndez-Sans A. , Pereira V. M. , Chang C. F. , Kuo C.-Y. , Chen C. T. , Tjeng L. H. , Altendorf S. G.
Published: July 2024

We report a comprehensive study of the synthesis of epitaxial SmN thin films on LaAlO3 (100) by molecular beam epitaxy under slow-growth conditions. By carefully tuning the substrate temperature and the molecular nitrogen partial pressure, we are able to control the nitrogen content of our samples and produce near-stoichiometric, well-ordered SmN thin films. The influence of nitrogen defects on the samarium ion valence and the electronic structure is investigated by x-ray absorption and photoelectron spectroscopy, suggesting that the empty Sm 5d states may act as a charge reservoir stabilizing the 4f5 configuration in the nitrogen-deficient compound. The spectra also reveal a significant hybridization of the Sm 4f and N 2p states.

Published by the American Physical Society
2024
Multiferroicity and Topology in Twisted Transition Metal Dichalcogenides
Authors: Abouelkomsan Ahmed , Bergholtz Emil J. , Chatterjee Shubhayu
Published: July 2024

Van der Waals heterostructures have recently emerged as an exciting platform for investigating the effects of strong electronic correlations, including various forms of magnetic or electrical orders. Here, we perform an unbiased exact diagonalization study of the effects of interactions on topological flat bands of twisted transition metal dichalcogenides (TMDs) at odd integer fillings. For hole-filling νh=1, we find that the Chern insulator phase, expected from interaction-induced spin-valley polarization of the bare bands, is quite fragile, and gives way to spontaneous multiferroic order—coexisting ferroelectricity and ferromagnetism, in the presence of long-range Coulomb repulsion. We provide a simple real-space picture to understand the phase diagram as a function of interaction range and strength. Our findings establish twisted TMDs as a novel and highly tunable platform for multiferroicity, and we outline a potential route towards electrical control of magnetism in the multiferroic phase.

Published by the American Physical Society
2024
Curiosity-driven search for novel nonequilibrium behaviors
Authors: Falk Martin J. , Roach Finnegan D. , Gilpin William , Murugan Arvind
Published: July 2024

Exploring the full spectrum of novel behaviors that a system can produce can be an intensive task. Sampling techniques developed in response to this exploration challenge often require a predefined metric, such as distance in a space of known order parameters. However, order parameters are rarely known for nonequilibrium systems, especially in the absence of a diverse set of example behaviors, creating a chicken-and-egg problem. Here, we combine active and unsupervised learning for automated exploration of nonequilibrium systems with unknown order parameters. We iteratively use active learning based on current order parameters to expand the library of behaviors and relearn order parameters based on this expanded library. We demonstrate the utility of this approach in Kuramoto models of increasing complexity. In addition to reproducing known phases, we reveal previously unknown behavior and related order parameters, and we demonstrate how to align search with human intuition.

Published by the American Physical Society
2024
Life cycle cost analysis at scale: a reference architecture-based approach
Authors: Shaw Conor , de Andrade Pereira Flávia , Farghaly Karim , Hoare Cathal , Hartmann Timo , O'Donnell James
Published: July 2024

Purpose

This research demonstrates the theoretical merit of a reference architecture-based approach to life cycle cost (LCC) analysis system provision in the built environment. LCC insight is considered fundamental to sustainable decision making by asset managers; however, the current capabilities in practice do not align with the political ambition and the scale of competencies required to realise sectoral emissions–reduction targets.

Design/methodology/approach

In pursuing practical outcomes, the study employs a custom design science research-inspired methodology. Domain requirements are gathered via literature research as an initial top-down software reference architecture which is refined, bottom-up, through testing and implementation in a representative case study. A prototype IT system and reference architecture artefact are developed and used to evaluate the concept qualitatively through broad practitioner focus groups.

Findings

Sentiment analysis of the expert opinions is broadly positive and helps to substantiate the proposal’s theoretical suitability in addressing the scalability challenge. Additionally, constructive feedback provides guidance towards this trajectory, highlighting the importance of aligning with existing communities and standards, broadening future research scope to consider further scenarios and prioritisation of efforts to build trust around contracts and data quality.

Originality/value

The novelty of the work is the provision of the reusable LCC reference architecture development methodology.

Practical implications

The concept has the potential to provide LCC capabilities to industry at scale while the artefacts developed herein can be appended to existing LCC standards as implementation guidance to support IT system developers. Furthermore, the developed methodology can be employed in harmonisation efforts between policy and practice.
Non-Fourier computations of heat and mass transport in nanoscale solid-fluid interactions using the Galerkin finite element method
Authors: Alsenafi Abdulaziz , Alazemi Fares , Nawaz M.
Published: July 2024

Purpose

To improve the thermal performance of base fluid, nanoparticles of three types are dispersed in the base fluid. A novel theory of non-Fourier heat transfer is used for design and development of models. The thermal performance of sample fluids is compared to determine which types of combination of nanoparticles are the best for an optimized enhancement in thermal performance of fluids. This article aims to: (i) investigate the impact of nanoparticles on thermal performance; and (ii) implement the Galerkin finite element method (GFEM) to thermal problems.

Design/methodology/approach

The mathematical models are developed using novel non-Fourier heat flux theory, conservation laws of computational fluid dynamics (CFD) and no-slip thermal boundary conditions. The models are approximated using thermal boundary layer approximations, and transformed models are solved numerically using GFEM. A grid-sensitivity test is performed. The accuracy, correction and stability of solutions is ensured. The numerical method adopted for the calculations is validated with published data. Quantities of engineering interest, i.e. wall shear stress, wall mass flow rate and wall heat flux, are calculated and examined versus emerging rheological parameters and thermal relaxation time.

Findings

The thermal relaxation time measures the ability of a fluid to restore its original thermal state, called thermal equilibrium and therefore, simulations have shown that the thermal relaxation time associated with a mono nanofluid has the most substantial effect on the temperature of fluid, whereas a ternary nanofluid has the smallest thermal relaxation time. A ternary nanofluid has a wider thermal boundary thickness in comparison with base and di- and mono nanofluids. The wall heat flux (in the case of the ternary nanofluids) has the most significant value compared with the wall shear stresses for the mono and hybrid nanofluids. The wall heat and mass fluxes have the highest values for the case of non-Fourier heat and mass diffusion compared to the case of Fourier heat and mass transfer.

Originality/value

An extensive literature review reveals that no study has considered thermal and concentration memory effects on transport mechanisms in fluids of cross-rheological liquid using novel theory of heat and mass [presented by Cattaneo (Cattaneo, 1958) and Christov (Christov, 2009)] so far. Moreover, the finite element method for coupled and nonlinear CFD problems has not been implemented so far. To the best of the authors’ knowledge for the first time, the dynamics of wall heat flow rate and mass flow rate under simultaneous effects of thermal and solute relaxation times, Ohmic dissipation and first-order chemical reactions are studied.
Power to the she: early-stage female entrepreneurs and innovation
Authors: Brownell Katrina , Kickul Jill , Hechavarria Diana
Published: July 2024

Purpose

Our study draws on gender role congruity theory to theorize and test an integrative conceptual model outlining how motives for entrepreneurial action explain the relationship between female entrepreneurship and innovation in nascent ventures.

Design/methodology/approach

To test our hypotheses, we employed structural equation modeling (SEM) in a sample of early-stage entrepreneurs (N = 533).

Findings

We found a positive relationship between nascent female entrepreneurs and innovation, and that this relationship is mediated by motives for recognition, respect, and autonomy.

Originality/value

By revealing a positive relationship between female entrepreneurship and innovation, our work provides a complementary perspective to the literature, which suggests that there are limits to innovation potential for female entrepreneurs. Further, we find that this relationship is explained by motives for recognition, respect, and autonomy, but not financial security. Finally, most empirical research focuses on the innovative outputs of established new ventures, while our work leverages a sample of early-stage entrepreneurs.
Web-aided data set expansion in deep learning: evaluating trainable activation functions in ResNet for improved image classification
Authors: Zhang Zhiqiang , Li Xiaoming , Xu Xinyi , Lu Chengjie , Yang Yihe , Shi Zhiyong
Published: July 2024

Purpose

The purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in the task of image classification. By introducing activation functions that adapt during training, the authors aim to determine whether such flexibility can lead to improved learning outcomes and generalization capabilities compared to static activation functions like ReLU. This research seeks to provide insights into how dynamic nonlinearities might influence deep learning models' efficiency and accuracy in handling complex image data sets.

Design/methodology/approach

This research integrates three novel trainable activation functions – CosLU, DELU and ReLUN – into various ResNet-n architectures, where “n” denotes the number of convolutional layers. Using CIFAR-10 and CIFAR-100 data sets, the authors conducted a comparative study to assess the impact of these functions on image classification accuracy. The approach included modifying the traditional ResNet models by replacing their static activation functions with the trainable variants, allowing for dynamic adaptation during training. The performance was evaluated based on accuracy metrics and loss profiles across different network depths.

Findings

The findings indicate that trainable activation functions, particularly CosLU, can significantly enhance the performance of deep learning models, outperforming the traditional ReLU in deeper network configurations on the CIFAR-10 data set. CosLU showed the highest improvement in accuracy, whereas DELU and ReLUN offered varying levels of performance enhancements. These functions also demonstrated potential in reducing overfitting and improving model generalization across more complex data sets like CIFAR-100, suggesting that the adaptability of activation functions plays a crucial role in the training dynamics of deep neural networks.

Originality/value

This study contributes to the field of deep learning by introducing and evaluating the impact of three novel trainable activation functions within widely used ResNet architectures. Unlike previous works that primarily focused on static activation functions, this research demonstrates that incorporating trainable nonlinearities can lead to significant improvements in model performance and adaptability. The introduction of CosLU, DELU and ReLUN provides a new pathway for enhancing the flexibility and efficiency of neural networks, potentially setting a new standard for future deep learning applications in image classification and beyond.
Community-engaged co-design of a quality improvement capacity building program within an integrated health system in Ontario, Canada
Authors: Rotteau Leahora , Magaz Mercedes , Wong Brian M. , Shearkhani Sara , Shabani Mohammad , Pradhan Rishma , Auguste Bourne , Bourne Laurie , Powis Jeff , Smith Kelly Michelle
Published: July 2024

Purpose

An integrated care system identified quality improvement (QI) capacity as a gap in advancing their integrated quality care priorities and improvement efforts. Here we describe the design and implementation of a QI capacity building program that aimed to (1) build QI capacity amongst diverse integrated care system members and (2) apply QI principles to advance integrated quality care priorities.

Design/methodology/approach

The integrated care system leaders, including community members, partnered with the University of Toronto Centre for Quality Improvement and Patient Safety to co-design and deliver the QI capacity building program focused on improving cancer screening rates. An existing acute care capacity building program was adapted. Content included QI tools, data to identify and monitor QI priorities, equity considerations, and empowering participants as change agents.

Findings

Participants were satisfied with the content and delivery of the program. Some described using QI tools and strategies in practice following the workshop. Challenges to using the tools included the current pressures facing primary care and the health system, resources, and data availability.

Practical implications

This QI capacity building program was challenging but feasible. Clarifying the target audience, being attentive to co-design, acknowledging post-pandemic system challenges and proactively addressing variable knowledge and barriers to QI work in practice will inform future iterations of this program.

Originality/value

While many examples of QI education programs exist, the majority target a single healthcare sector. We describe a novel QI capacity building model that bridges healthcare sectors and includes patient partners and community members as teachers and participants.
Exploring the inter-sectoral and inter-regional effect of tourism industry in Indonesia based on input-output framework
Authors: Li Fanglin , Sastri Ray , Edziah Bless Kofi , Setiyawan Arbi
Published: July 2024

Purpose

Tourism is an essential industry in Indonesia, and understanding its inter-sectoral and inter-regional connections is critical for policy development. This study examines the economic impact of regional tourism in Indonesia and the connections between different tourism-related regions and industries.

Design/methodology/approach

This study uses a non-survey method to estimate the inter-regional input-output table (IRIOT) in 2019, backward and forward linkage to identify the role of tourism in the economy, and the structural path analysis (SPA) to identify the inter-sectoral and inter-regional flow of tourism effect. The benchmark IRIOT 2016 published by Badan Pusat Statistik (BPS) serves as the primary data source.

Findings

The findings indicate that tourism has a relatively high impact on the overall national economy and plays an essential role in nine provinces. However, this study uses four provinces to represent Indonesian tourism: Jakarta, Jawa Timur, Bali, and Kepulauan Riau. The SPA result captures that Kepulauan Riau Province has the highest tourism multiplier effect and Jawa Timur has the highest coverage value. Moreover, the manufacturing sector receives the most benefit from the tourism effect, followed by trade, construction, agriculture, transportation, and electricity-gas. From a spatial perspective, tourism connections are not solely based on geographical proximity. Instead, they are established through an intricate supply chain network of manufactured goods. This emphasizes the significance of considering supply chain dynamics when investigating inter-regional relationships in the tourism sector.

Originality/value

This research contributes to the literature by estimating the IRIOT in 2019, disaggregating tourism activities from related economic sectors, constructing tourism-extended IRIOT, and identifying the critical path of tourism effect in numerous provinces with different economic structures. This novel approach offers valuable insights into the full spectrum of tourism’s economic impact, which has not been previously explored in this depth. This study is useful for policymaking, investment insight, and disaster mitigation.
Study of liquid photopolymer 3D printing resins exposed to accelerated aging for cultural heritage purposes
Authors: Higueras María , Carrasco-Huertas Ana , Calero-Castillo Ana Isabel , Moreno Alcaide Manuel , Collado Montero Francisco José
Published: July 2024

Purpose

This paper aims to study the suitability of a selection of 3D printing liquid photopolymer resins for their application in the cultural heritage context.

Design/methodology/approach

The main concerns regarding the conservation and restoration of cultural assets are the chemical composition and long-term behavior of the new materials that will be in contact with the original object. Because of this, four different LED curing resins were exposed to an accelerated aging procedure and tested to identify the materials which demonstrated a better result. Some specific properties of the material (color, glossiness, pH and volatile organic compound emissions) were measured before and after the exposure.

Findings

Some of the properties measured reported good results demonstrating a decent stability against the selected aging conditions. The main changes were produced in the colorimetric aspect, probably indicating other chemical reactions in the material. Likewise, a case study could be also executed to demonstrate the usefulness of these materials in the cultural field.

Research limitations/implications

It is necessary to study in more detail the long-lasting behavior of the materials employed with these technologies. Further analysis should be carried out highlighting the chemical composition and degradation process of the materials proposed.

Originality/value

This paper contributes to the introduction of curing 3D printing resins in the restoration methodologies of cultural assets. For this, the study of a selection of properties represents the first stage to suggest or reject their use.
Interviewing students with special needs: Developing ethical considerations and interviewing protocols
Authors: Amjad Amjad Islam , Malik Muhammad Abid
Published: July 2024

Interviewing students with special needs (SSNs) comes with a unique set of ethical and practical challenges that require highly specialised skills and interviewing protocols. Our objectives were to identify the most appropriate tools for collecting data from SSNs, key differences in interviewing students with and without special needs, and ethical and practical challenges during it. The ultimate goal was to develop ethical considerations and interviewing protocols for SSNs (ECIP4SSN). It was done through a two‐step Delphi method, collecting data from 11 special education experts from 11 different countries. The experts believed the tools should be selected based on the study's nature; however, many of them preferred the interviews. They believed that interviewing special students is challenging due to their special needs. Challenges pertaining to consent, hyperactivity, emotional distress, and wrong and irrelevant answers were among the top challenges mentioned by them. Final ECIP4SSN consisted of the key ethical considerations and interviewing protocols i.e. obtaining the background information and support, training and preparations, consent, data collection, building rapport, emotional support, confidentiality, and anonymity, using supporting tools, language, methods of asking questions and validating responses. The ECIP4SSN will guide the university teachers, students and researchers in interviewing special students.
Uptake and transport of antibiotic kasugamycin from leaves to roots in rice (Oryza sativa L.) seedlings
Authors: Xu Huayang , Zhang Hongzhen , Wei Guoyu , Jiang Yuling , Charles Meto Yao , Hu Anlong
Published: July 2024

Kasugamycin (KSM), an aminoglycoside antibiotic, has been widely used as a natural fungicide to control plant diseases, particularly for managing rice blast. However, its uptake mechanism and transport in rice remain to be explored. In this article, rice seedlings were treated by the foliar spraying method, and the content of KSM in rice leaves, stems and roots under different treatments was detected by high‐performance liquid chromatography tandem mass spectrometry (HPLC‐MS/MS), respectively. Results showed that leaf‐applied KSM could be transported to the phloem and migrate to roots and stems after uptake by leaves. Concentration, temperature and pH had significant effects on the uptake of KSM. Compared with the control, the competitive inhibitors d‐glucose and phlorizin both inhibited the uptake of KSM, demonstrating that sugar transporter proteins were involved in the uptake process. The energy inhibitors dinitrophenol (DNP) and carbonyl cyanide chlorophenylhydrazone (CCCP) also significantly inhibited the uptake of KSM, indicating that the uptake of KSM required energy consumption. Thus, the uptake of KSM by rice was an active process involving sugar transporter proteins, and it could migrate downward through the phloem. This study contributes to the promotion of the scientific application of antibiotics and the biological control of crop diseases. It will also provide a theoretical basis for the development of root‐targeted pesticides and transport pesticides with phloem mobility.
Effect of a peer‐led emergency department behavioral intervention on non‐fatal opioid overdose: 18‐month outcome in the Navigator randomized controlled trial
Authors: Chambers Laura C. , Li Yu , Hallowell Benjamin D. , Langdon Kirsten J. , Samuels Elizabeth A. , Mahoney Linda A. , Beaudoin Francesca L. , Marshall Brandon D. L.
Published: July 2024

Background and aims

Emergency departments (EDs) provide an opportunity to identify people at risk of overdose and reduce the risk. We evaluated the effect of an ED behavioral intervention delivered by peer recovery support specialists (PRSSs) on non‐fatal opioid overdose.

Design

Two‐arm, randomized trial.

Setting

Two EDs in Rhode Island, USA.

Participants

ED patients presenting with an opioid overdose, complications of opioid use disorder or a recent history of opioid overdose (November 2018–May 2021). Among 648 participants, the mean age was 36.9 years, 68.2% were male and 68.5% were White.

Intervention and comparator

Participants were randomized to receive a behavioral intervention from a PRSS (n = 323) or a licensed clinical social worker (LICSW) (n = 325). PRSS and LICSW used evidence‐based interviewing and intervention techniques, informed by their lived experience (PRSS) or clinical theory and practice (LICSW).

Measurements

We identified non‐fatal opioid overdoses in the 18 months following the ED visit through linkage to statewide emergency medical services data using a validated case definition. The primary outcome was any non‐fatal opioid overdose during the 18‐month follow‐up period.

Findings

Among 323 participants randomized to the PRSS arm, 81 (25.1%) had a non‐fatal opioid overdose during follow‐up, compared with 95 (29.2%) of 325 participants randomized to the LICSW arm (P = 0.24). There was no statistically significant difference in the effectiveness of randomization to the PRSS arm versus the LICSW arm on the risk of non‐fatal opioid overdose, adjusting for the history of previous overdose (relative risk = 0.86, 95% confidence interval = 0.67–1.11).

Conclusions

In Rhode Island, USA, over one‐in‐four emergency department patients at high risk of overdose experience a non‐fatal opioid overdose in the 18 months post‐discharge. We found no evidence that the risk of non‐fatal opioid overdose differs for emergency department patients receiving a behavioral intervention from a peer recovery support specialist versus a licensed clinical social worker.
Association between cholelithiasis, cholecystectomy, and risk of breast and gynecological cancers: Evidence from meta‐analysis and Mendelian randomization study
Authors: Peng Jing , Li Lianghua , Ning Huai , Li Xiaocheng
Published: July 2024

Background

Observational studies have shown that cholelithiasis and cholecystectomy are associated with the risk of breast cancer (BC) and gynecological cancers, but whether these relationships are causal has not been established and remains controversial.

Methods

Our study began with a meta‐analysis that synthesized data from prior observational studies to examine the association between cholelithiasis, cholecystectomy, and the risk of BC and gynecological cancers. Subsequently, a two‐sample Mendelian randomization (MR) analysis was conducted utilizing genetic variant data to investigate the potential causal relationship between cholelithiasis, cholecystectomy, and the aforementioned cancers.

Results

The results of the meta‐analysis demonstrated a significant association between cholecystectomy and the risk of BC (risk ratio [RR] = 1.04, 95% confidence interval [CI]: 1.01–1.06, p = 0.002) and endometrial cancer (EC) (RR = 1.26, 95% CI: 1.02–1.56, p = 0.031). Conversely, no significant association was observed between cholelithiasis and the risk of BC, EC, and ovarian cancer. The MR analysis revealed no discernible causal connection between cholelithiasis and overall BC (p = 0.053), as well as BC subtypes (including estrogen receptor‐positive/negative). Similarly, there was no causal effect of cholecystectomy on BC risk (p = 0.399) and its subtypes. Furthermore, no causal associations were identified between cholelithiasis, cholecystectomy, and the risk of gynecological cancers (ovarian, endometrial, and cervical cancer [CC]) (all p > 0.05).

Conclusion

This study does not support a causal link between cholelithiasis and cholecystectomy and an increased risk of female cancers such as breast, endometrial, ovarian, and CC.
Clinical characteristics and HLA associations of azithromycin‐induced liver injury
Authors: Conlon Caroline , Li Yi‐Ju , Ahmad Jawad , Barnhart Huiman , Fontana Robert J. , Ghabril Marwan , Hayashi Paul H. , Kleiner David E. , Lee William M. , Navarro Victor , Odin Joseph A. , Phillips Elizabeth J. , Stolz Andrew , Vuppalanchi Raj , Halegoua‐DeMarzio Dina ,   for the Drug‐Induced Liver Injury Network (DILIN)
Published: July 2024

Background

Azithromycin (AZ) is a widely used antibiotic. The aim of this study was to characterise the clinical features, outcomes, and HLA association in patients with drug‐induced liver injury (DILI) due to AZ.

Methods

The clinical characteristics of individuals with definite, highly likely, or probable AZ‐DILI enrolled in the US Drug‐Induced Liver Injury Network (DILIN) were reviewed. HLA typing was performed using an Illumina MiSeq platform. The allele frequency (AF) of AZ‐DILI cases was compared to population controls, other DILI cases, and other antibiotic‐associated DILI cases.

Results

Thirty cases (4 definite, 14 highly likely, 12 probable) of AZ‐DILI were enrolled between 2004 and 2022 with a median age of 46 years, 83% white, and 60% female. Median duration of AZ treatment was 5 days. Latency was 18.5 days. 73% were jaundiced at presentation. The injury pattern was hepatocellular in 60%, cholestatic in 27%, and mixed in 3%. Ten cases (33%) were severe or fatal; 90% of these were hepatocellular. Two patients required liver transplantation. One patient with chronic liver disease died of hepatic failure. Chronic liver injury developed in 17%, of which 80% had hepatocellular injury at onset. HLA‐DQA1*03:01 was significantly more common in AZ‐DILI versus population controls and amoxicillin‐clavulanate DILI cases (AF: 0.29 vs. 0.11, p = 0.001 and 0.002, respectively).

Conclusion

Azithromycin therapy can lead to rapid onset of severe hepatic morbidity and mortality in adult and paediatric populations. Hepatocellular injury and younger age were associated with worse outcomes. HLA‐DQA1*03:01 was significantly more common in AZ cases compared to controls.
High peritoneal incision approach in endoscopic transabdominal preperitoneal patch plasty (TAPP) for inguinal hernia after radical prostatectomy
Authors: Yamamoto Tetsu , Hyakudomi Ryoji , Takai Kiyoe , Uchida Yuki , Ishitobi Kazunari , Hirahara Noriyuki , Tajima Yoshitsugu
Published: July 2024

Background

Inguinal hernia develops as one of the common complications after robotic or laparoscopic radical prostatectomy (RP). Transabdominal preperitoneal patch plasty (TAPP) for an inguinal hernia after RP is difficult to perform due to postoperative severe adhesions in the preperitoneal cavity. We have introduced a high peritoneal incision approach (HPIA) in TAPP for inguinal hernia patients in whom peritoneal dissection is difficult due to severe adhesions after RP. We evaluate the safety and efficacy of TAPP with a HPIA for patients with an inguinal hernia after robot‐assisted RP (RARP).

Methods

Patients characteristics and surgical outcome were evaluated by a retrospective analysis.

Results

From January 2014 to December 2017, 21 consecutive patients underwent TAPP for an inguinal hernia after RARP. Twenty‐four lesions were the type 3b and three were type 3a according to the Nyhus classification. A circular incision TAPP was performed for 10 hernia lesions in eight patients and TAPP with HPIA was utilized for 17 lesions in 13 patients. The mean operation time for the unilateral hernia in the HPIA (137.8 ± 20.7 min) was significantly shorter than that (182.2 ± 42.0 min) in the circular incision TAPP (p = .038). The HPIA was complete in all patients, while the circular incision TAPP was converted to intraperitoneal onlay mesh (IPOM)intraperitoneal onlay mesh in five patients (55.6%, p = .008) due to dense adhesions with difficult dissection. No recurrent was observed after follow‐up period of 48 months in both groups.

Conclusions

The TAPP with HPIA is feasible and a safe and reliable treatment of choice in patients with an inguinal hernia after RARP.
Advancing equity and inclusion in educational practices with AI‐powered educational decision support systems (AI‐EDSS)
Authors: Viberg Olga , Kizilcec René F. , Wise Alyssa Friend , Jivet Ioana , Nixon Nia
Published: July 2024

A key goal of educational institutions around the world is to provide inclusive, equitable quality education and lifelong learning opportunities for all learners. Achieving this requires contextualized approaches to accommodate diverse global values and promote learning opportunities that best meet the needs and goals of all learners as individuals and members of different communities. Advances in learning analytics (LA), natural language processes (NLP), and artificial intelligence (AI), especially generative AI technologies, offer potential to aid educational decision making by supporting analytic insights and personalized recommendations. However, these technologies also raise serious risks for reinforcing or exacerbating existing inequalities; these dangers arise from multiple factors including biases represented in training datasets, the technologies' abilities to take autonomous decisions, and processes for tool development that do not centre the needs and concerns of historically marginalized groups. To ensure that Educational Decision Support Systems (EDSS), particularly AI‐powered ones, are equipped to promote equity, they must be created and evaluated holistically, considering their potential for both targeted and systemic impacts on all learners, especially members of historically marginalized groups. Adopting a socio‐technical and cultural perspective is crucial for designing, deploying, and evaluating AI‐EDSS that truly advance educational equity and inclusion. This editorial introduces the contributions of five papers for the special section on advancing equity and inclusion in educational practices with AI‐EDSS. These papers focus on (i) a review of biases in large language models (LLMs) applications offers practical guidelines for their evaluation to promote educational equity, (ii) techniques to mitigate disparities across countries and languages in LLMs representation of educationally relevant knowledge, (iii) implementing equitable and intersectionality‐aware machine learning applications in education, (iv) introducing a LA dashboard that aims to promote institutional equality, diversity, and inclusion, and (v) vulnerable student digital well‐being in AI‐EDSS. Together, these contributions underscore the importance of an interdisciplinary approach in developing and utilizing AI‐EDSS to not only foster a more inclusive and equitable educational landscape worldwide but also reveal a critical need for a broader contextualization of equity that incorporates the socio‐technical questions of what kinds of decisions AI is being used to support, for what purposes, and whose goals are prioritized in this process.
Continued cancer drug approvals in Japan and Europe after market withdrawal in the United States: A comparative study of accelerated approvals
Authors: Hakariya Hayase , Moriarty Frank , Ozaki Akihiko , Mulinari Shai , Saito Hiroaki , Tanimoto Tetsuya
Published: July 2024

Regulatory authorities must balance ensuring evidence of efficacy and safety of new drugs. Various regulatory pathways, such as the accelerated approval program in the United States (US), allow authorities to quickly approve drugs for severely ill patients by granting market authorization based on surrogate end points and pending confirmatory trials. In this cross‐sectional study, we considered 23 indications of cancer drugs that received accelerated approval by the US Food and Drug Administration (FDA) but were subsequently withdrawn as of April 2023. Our investigation extended to assessing the regulatory status of these accelerated approvals in the European Union (EU) and Japan, examining relevant regulatory documents and identifying factors contributing to the withdrawal in the United States. Comparing regions, we found that for 52% (12/23) and 30% (7/23) of withdrawn accelerated approvals in the United States, sponsors had also sought marketing authorization from the European Medicines Agency (EMA) and Japan's Pharmaceuticals and Medical Devices Agency (PMDA), respectively. As of the April 30, 2023 study cutoff date, 83% (10/12) of drug–indication pairs remained approved by the EMA, while the PMDA retained 100% (7/7). For these indications, the time from FDA withdrawal until the study cutoff date ranged from 0.23 years to 11.45 years for EMA approvals (median: 1.28 years) and 1.10 years to 11.45 years for PMDA approvals (median: 3.22 years). These findings highlight substantial regulatory discrepancies concerning cancer drugs with unconfirmed benefits. Addressing these discrepancies may involve requiring pharmaceutical companies to confirm clinical benefits using more robust end points and fostering international harmonization in regulators' assessment.