bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unmet Social Needs and Breast Cancer Screening Utilization and Stage at Presentation
Authors: Goel Neha , Lubarsky Maya , Hernandez Alexandra E. , Benck Kelley , Lee Emma , Kesmodel Susan , Knaul Felicia , Kobetz Erin , Anderson Benjamin O. 
Published: February 2024

ImportanceUnmet social needs in local populations may hinder the development of targeted cancer control interventions aimed at improving screening utilization and early-stage breast cancer diagnosis to ultimately improve breast cancer survival disparities.ObjectiveTo evaluate if (1) city-funded screening mammography is associated with utilization of screening mammography, (2) unmet social needs are associated with utilization of screening mammography, and (3) unmet social needs are associated with later-stage disease at diagnosis.Design, Setting, and ParticipantsThis cohort study included patients with stages I-IV invasive ductal or lobular carcinoma treated at an academic medical center (including both an underserved safety-net hospital [SNH] and a National Cancer Institute–designated academic cancer center [ACC]) from 2020 to 2023. Eligible patients were aged 18 years or older and able to consent. Data were analyzed between July 2023 and September 2023.ExposureThe Health Leads Social Needs Screening Toolkit, a screening tool that gathers information on the most common social need domains affecting patient health.Main Outcomes and MeasuresUnivariable and multivariable logistic regression was utilized to evaluate the following primary outcomes: (1) routine screening mammography and (2) American Joint Committee on Cancer 8th edition clinical stage at presentation.ResultsOf the 322 women who completed the Health Leads Social Needs Screening Toolkit, 201 (62%) self-identified as Hispanic, 63 (19%) as non-Hispanic Black, and 63 (19%) as non-Hispanic White. Two hundred fifty-five (76%) patients with access to city-funded screening mammography completed a screening mammogram. Patients who presented to the SNH were more likely to present with late-stage disease compared with early-stage disease (15 of 48 [31%] vs 50 of 274 [18%]; P = .04). On multivariable logistic regression, not completing a screening mammography was associated with having an increasing number of unmet social needs (OR, 0.74; 95% CI, 0.55-0.99; P = .047) and an increasing age at diagnosis (OR, 0.92; 95% CI, 0.89-0.96; P &amp;lt; .001). Moreover, increasing unmet social needs was significantly associated with late-stage diagnosis above and beyond screening mammography (OR, 1.38; 95% CI, 1.01-1.89; P = .04).Conclusions and RelevanceIn this cohort study, access to screening mammography did not translate to utilization of screening mammography, increasing unmet social needs were significantly associated with lower rates of screening mammography, and those with increasing unmet social needs were more likely to present with late-stage disease. This association transcended recruitment site (SNH vs ACC), indicating that patients in either hospital setting may benefit from unmet social needs screening to overcome access to care barriers associated with late-stage disease at diagnosis.
Effectiveness of Virtual Reality Training in Teaching Personal Protective Equipment Skills
Authors: Tsukada Keisuke , Yasui Youichi , Miyata Satoshi , Fuyumuro Junko , Kikuchi Tomomi , Mizuno Takuhiro , Nakayama Satoshi , Kawano Hirotaka , Miyamoto Wataru 
Published: February 2024

ImportanceTraining on the proper use of personal protective equipment (PPE) is critical for infection prevention among health care workers. Traditional methods, such as face-to-face and video-based training, can strain resources and present challenges.ObjectiveTo determine the effectiveness of 360° virtual reality (VR) training for PPE donning and doffing compared with face-to-face and video training in enhancing the PPE use skills of prospective health care practitioners.Design, Setting, and ParticipantsA blinded, prospective, and randomized noninferiority clinical trial was conducted from August to December 2021 at Teikyo University School of Medicine in Tokyo, Japan, with a mixed population of medical students. Participants were second- to fourth-year medicine, medical technology, or pharmacy students aged 20 years or older with no prior PPE training. Participants were randomized into 1 of 3 training groups (VR, face-to-face, or video) based on their enrollment order. An intention-to-treat analysis was conducted.InterventionA 30-minute lecture on PPE procedures was delivered to all participants before the training. After the lecture, the VR group trained with an immersive 360° VR tool, the face-to-face group trained with actual PPE, and the video group trained by watching video footage on a computer and a projector. After 3 days, a standardized practical skills test was administered.Main Outcomes and MeasuresThe primary outcome was the mean score on a 20-point practical skills test, and the secondary outcome was the percentage of correct execution.ResultsA total of 91 participants were recruited and randomized into 3 groups: VR (n = 30), face-to-face (n = 30), and video (n = 31) training. After excluding 1 participant due to illness, 90 participants (mean [SD] age, 24.2 [3.15] years; 54 males [60.0%]) completed the assessment. The mean (SD) scores were 17.70 (2.10) points for the VR group, 17.57 (2.45) points for the face-to-face group, and 15.87 (2.90) points for the video group. The VR group demonstrated no significant difference in performance from the face-to-face group. However, the VR group had significantly higher effectiveness than the video group (17.70 vs 15.87 points; P = .02).Conclusions and RelevanceResults of this trial indicate that VR training was as effective as face-to-face training in enhancing PPE donning and doffing skills and was superior to video training. The findings suggest that VR training is a viable resource-conserving training option.Trial RegistrationJapan Registry of Clinical Trials Identifier: jRCT103021029
Variations in Suicide Risk and Risk Factors After Hospitalization for Depression in Finland, 1996-2017
Authors: Aaltonen Kari , Sund Reijo , Hakulinen Christian , Pirkola Sami , Isometsä Erkki 
Published: February 2024

ImportanceAlthough incidence of suicide in depression varies remarkably temporally, risk factors have been modeled as constant and remain uncharted in the short term. How effectively factors measured at one point in time predict risk at different time points is unknown.ObjectiveTo examine the absolute risk and risk factors for suicide in hospitalized patients with depression starting from the first days after discharge up to 2 years and to evaluate whether the size of relative risk by factor displays temporal patterns over consecutive phases of follow-up.Design, Setting, and ParticipantsThis population-based study using Finnish registers (hospital discharge, population, and cause of death registers) included all hospitalizations for depression as the principal diagnosis in Finland from 1996 to 2017, with a maximum follow-up of 2 years. Data were analyzed from January 2022 to November 2023.Main Outcomes and MeasuresIncidence rate (IR), IR ratios, hazard functions, and hazard ratios for suicide by consecutive time periods (0 to 3 days, 4 to 7 days, 7 to 30 days, 31 to 90 days, 91 to 365 days, and 1 to 2 years) since discharge.ResultsThis study included 193 197 hospitalizations among 91 161 individuals, of whom 51 197 (56.2%) were female, and the mean (SD) age was 44.0 (17.3) years. Altogether, patients were followed up to 226 615 person-years. A total of 1219 men and 757 women died of suicide. Incidence of suicide was extremely high during the first days after discharge (IR of 6062 [95% CI, 4963-7404] per 100 000 on days 0 to 3; IR of 3884 [95% CI, 3119-4835] per 100 000 on days 4 to 7) and declined thereafter. Several factors were associated with risk of suicide over the first days after discharge. Current suicide attempt by hanging or firearms increased the risk of suicide most on days 0 to 3 (IR ratio, 18.9; 95% CI, 3.1-59.8) and on days 0 to 7 (IR ratio, 10.1; 95% CI, 1.7-31.5). Temporal patterns of the size of the relative risk diverged over time, being constant, declining, or increasing. Clinical factors had the strongest association immediately. Relative risk remained constant among men and even increased among those with alcohol or substance use disorder.Conclusions and RelevanceIn this study, patients hospitalized for depression had extremely high risk of suicide during the first days after discharge. Thereafter, incidence declined steeply but remained high. Within the periods of the highest risk of suicide, several factors increased overall risk manyfold. Risk factors’ observed potencies varied over time and had characteristic temporal patterns.
Academic Surgery in the Era of Large Language Models
Authors: Rengers Timothy A. , Thiels Cornelius A. , Salehinejad Hojjat 
Published: February 2024

ImportanceThis review aims to assess the benefits and risks of implementing large language model (LLM) solutions in an academic surgical setting.ObservationsThe integration of LLMs and artificial intelligence (AI) into surgical practice has generated international attention with the emergence of OpenAI’s ChatGPT and Google’s Bard. From an administrative standpoint, LLMs have the potential to revolutionize academic practices by reducing administrative burdens and improving efficiency. LLMs have the potential to facilitate surgical research by increasing writing efficiency, building predictive models, and aiding in large dataset analysis. From a clinical standpoint, LLMs can enhance efficiency by triaging patient concerns and generating automated responses. However, challenges exist, such as the need for improved LLM generalization performance, validating content, and addressing ethical concerns. In addition, patient privacy, potential bias in training, and legal responsibility are important considerations that require attention. Research and precautionary measures are necessary to ensure safe and unbiased use of LLMs in surgery.Conclusions and RelevanceAlthough limitations exist, LLMs hold promise for enhancing surgical efficiency while still prioritizing patient care. The authors recommend that the academic surgical community further investigate the potential applications of LLMs while being cautious about potential harms.
Building trustworthy NeuroSymbolic AI Systems: Consistency, reliability, explainability, and safety
Authors: Gaur Manas , Sheth Amit 
Published: February 2024

AbstractExplainability and Safety engender trust. These require a model to exhibit consistency and reliability. To achieve these, it is necessary to use and analyzedataandknowledgewith statistical and symbolic AI methods relevant to the AI application––neither alone will do. Consequently, we argue and seek to demonstrate that the NeuroSymbolic AI approach is better suited for making AI a trusted AI system. We present the CREST framework that shows howConsistency,Reliability, user‐levelExplainability, andSafety are built on NeuroSymbolic methods that use data and knowledge to support requirements for critical applications such as health and well‐being. This article focuses on Large Language Models (LLMs) as the chosen AI system within the CREST framework. LLMs have garnered substantial attention from researchers due to their versatility in handling a broad array of natural language processing (NLP) scenarios. As examples, ChatGPT and Google's MedPaLM have emerged as highly promising platforms for providing information in general and health‐related queries, respectively. Nevertheless, these models remain black boxes despite incorporating human feedback and instruction‐guided tuning. For instance, ChatGPT can generateunsafe responsesdespite instituting safety guardrails. CREST presents a plausible approach harnessing procedural and graph‐based knowledge within a NeuroSymbolic framework to shed light on the challenges associated with LLMs.
The AI Institute for Engaged Learning
Authors: Lester James , Bansal Mohit , Biswas Gautam , Hmelo‐Silver Cindy , Roschelle Jeremy , Rowe Jonathan 
Published: February 2024

AbstractThe EngageAI Institute focuses on AI‐driven narrative‐centered learning environments that create engaging story‐based problem‐solving experiences to support collaborative learning. The institute's research has three complementary strands. First, the institute creates narrative‐centered learning environments that generate interactive story‐based problem scenarios to elicit rich communication, encourage coordination, and spark collaborative creativity. Second, the institute creates virtual embodied conversational agent technologies with multiple modalities for communication (speech, facial expression, gesture, gaze, and posture) to support student learning. Embodied conversational agents are driven by advances in natural language understanding, natural language generation, and computer vision. Third, the institute is creating an innovative multimodal learning analytics framework that analyzes parallel streams of multimodal data derived from students’ conversations, gaze, facial expressions, gesture, and posture as they interact with each other, with teachers, and with embodied conversational agents. Woven throughout the institute's activities is a strong focus on ethics, with an emphasis on creating AI‐augmented learning that is deeply informed by considerations of fairness, accountability, transparency, trust, and privacy. The institute emphasizes broad participation and diverse perspectives to ensure that advances in AI‐augmented learning address inequities in STEM. The institute brings together a multistate network of universities, diverse K‐12 school systems, science museums, and nonprofit partners. Key to all of these endeavors is an emphasis on diversity, equity, and inclusion.
The efficacy of the Self‐Administered Interview in minimising the misinformation effect
Authors: Bird Emily , Wiener Jan , Attard‐Johnson Janice 
Published: February 2024

AbstractTwo experiments investigated the efficacy of the Self‐Administered Interview (SAI) in immediate recall and minimising misinformation into later recall. In Experiment 1, participants (N = 114) viewed a reconstructed crime video and completed the SAI, Written Free Recall (WFR), or no recall for immediate recall, followed by a Delayed Recall questionnaire 1‐to‐2 weeks later. Experiment 2 replicated the procedure, except after the delay, participants (N = 141) were exposed to misinformation before Delayed Recall. Across both experiments, the SAI enhanced immediate memory, particularly for person details, compared to WFR. However, this initial enhancement did not lead to more correct information after the delay, although it reduced incorrect responses compared to no immediate recall. Surprisingly, neither the SAI nor the WFR reduced susceptibility to misinformation. The findings suggest completing an SAI immediately or soon after witnessing an event could enhance memory recall, reinforcing its potential utility for witnesses.
Biodegradable Near‐Infrared‐IIb Rare‐Earth Nanoprobe Enables Time‐Programmable Neuroimaging
Authors: Zheng Xue , Du Yijing , Dang Zetao , Lan Ke , Liu Guantong , Ren Jingyan , Wang Xin , Zhu Shoujun 
Published: February 2024

AbstractNeural imaging plays a crucial role in understanding the structure and function of the living body. While near‐infrared‐II (NIR‐II) imaging techniques have emerged as potential tools for non‐invasive and high‐resolution imaging of neural activity, there have been rare relevant reports on in vivo neural imaging. This study presents the development of biodegradable NIR‐II rare‐earth nanoprobes as a novel contrast agent for enhanced neural imaging. The biodegradable NIR‐II rare‐earth nanoprobes exhibit excellent long wavelength emission, high quantum yield, and tunable biodegradation time in vivo. This work demonstrates the effectiveness of these nanoprobes in enhancing the imaging depth and resolution in a mouse model of neural activity, thereby facilitating a better understanding of the structure and function of the nervous system. Furthermore, their biodegradability ensures minimal long‐term toxicity and allows for repeatedly monitoring of damaged nerves during the recovery or deterioration process. The results highlight the potential of biodegradable NIR‐II rare‐earth nanoprobes for advancing neural imaging techniques and facilitating deeper insights into neuroscience research and clinical diagnostics.
Monolithic Conjugated Microporous Polymer Aerogel for Triboelectric Nanogenerator
Authors: Meng Nan , Zhang Yu , Liu Wei , Chen Qiaochu , Soykeabkaew Nattakan , Liao Yaozu 
Published: February 2024

AbstractTriboelectric nanogenerator (TENG) has emerged as a novel nanotechnology for energy harvesting and self‐powered sensing via utilizing various low‐frequency mechanical energies throughout the surrounding environment. However, designing TENG with high physical robustness, flexibility, and output performance is rather challenging. This study develops monolithic conjugated microporous polymer aerogel (CMPA) with delocalized π‐conjugation to construct TENG with a robust porous structure. The as‐produced CMPA is composed of hollow intertwined nanotubes with a high degree of flexibility and high specific surface area (up to 528 m2 g−1). Due to its intrinsic nature of conjugated electron transfer and architectural characteristics, the CMPA‐based TENG (effective area: 4.9 cm2) shows a high sensitivity to mechanical force and excellent electric output (open‐circuit voltage: 72 V, short‐circuit peak current: 3.2 µA and transferred charge: 48 nC). Moreover, the CMPA‐based TENG can charge micro‐electronic devices and sense human motions. This work will inspire the design of aerogel‐based electronic devices and the management of renewable energy resources.
Encapsulation of Highly Dispersed Au NPs by Strong Metal–Support Interactions in Porous Titania Nanoplates for Efficient Electrosynthesis of H2O2
Authors: He Yilei , Wei Yanze , Wang Zumin , Xia Tian , Rao Fu , Song Zhifan , Yu Ranbo 
Published: February 2024

AbstractTuning the electrocatalytic selectivity and long‐term stability for industrially significant, yet reactively unfavorable products, remains a challenge in oxygen reduction. Herein, the Au/TiO2 catalysts with strong metal–support interactions (SMSI) are designed and synthesized through an in situ auto‐reduction of Au‐modified Ti‐MOF. Highly dispersed ultra‐low loading of Au NPs strongly capsulated in porous TiO2 substrate, together with conductive carbon derivated from MOFs, enables Au/TiO2 electrocatalysts to achieve excellent H2O2 electrosynthesis through the two‐electron oxygen reduction reaction (2e ORR). Au(1.0)/TiO2 (0.52 wt% Au loading) exhibited a high selectivity of 90%, a remarkable Faradaic efficiency of 98%, and 72.2 mg L−1 h−1 H2O2 production. Highly dispersed Au NPs promote active site exposure, while the conductive carbon and the porous superstructure enhance mass diffusion. Notably, the SMSI between Au NPs and TiO2 substrate leads to superb stability (over 168 h) of the electrocatalysts, as it ensures continuous electronic interactions. Experimental characterizations and density functional theory (DFT) simulations further revealed that the SMSI effect medicates the activation of the *OOH intermediate formation and the d‐band center, which are conductive to 2e ORR. Moreover, this strategy provides a potential way toward high‐performance electrocatalysts in flow system devices for continuously purifying sewage and chemical bleaching in extreme environments.
Colossal Magnetoresistive Switching Induced by d0 Ferromagnetism of MgO in a Semiconductor Nanochannel Device with Ferromagnetic Fe/MgO Electrodes
Authors: Ohya Shinobu , Tsuruoka Shun , Kaneda Masaya , Shinya Hikari , Fukushima Tetsuya , Takeda Takahito , Tadano Yuriko , Endo Tatsuro , Anh Le Duc , Masago Akira , Katayama‐Yoshida Hiroshi , Tanaka Masaaki 
Published: February 2024

AbstractExploring potential spintronic functionalities in resistive switching (RS) devices is of great interest for creating new applications, such as multifunctional resistive random‐access memory and novel neuromorphic computing devices. In particular, the importance of the spin‐triplet state of cation vacancies in oxide materials, which is induced by localized and strong O–2p on‐site Coulomb interactions, in RS devices has been overlooked. d0 ferromagnetism sometimes appears due to the spin‐triplet state and ferromagnetic Zener's double exchange interactions between cation vacancies, which are occasionally strong enough to make nonmagnetic oxides ferromagnetic. Here, for the first time, anomalous and colossal magneto‐RS (CMRS) with very high magnetic field dependence is demonstrated by utilizing an unconventional RS device composed of a Ge nanochannel with all‐epitaxial single‐crystalline Fe/MgO electrodes. The device shows colossal and unusual behavior as the threshold voltage and ON/OFF ratio strongly depend on a magnetic field, which is controllable with an applied voltage. This new phenomenon is attributed to the formation of d0‐ferromagnetic filaments by attractive Mg vacancies due to the spin‐triplet states with ferromagnetic double exchange interactions and the ferromagnetic proximity effect of Fe on MgO. The findings will allow the development of energy‐efficient CMRS devices with multifield susceptibility.
Layered High‐Entropy Metallic Glasses for Photothermal CO2 Methanation
Authors: Yu Xiwen , Ding Xue , Yao Yingfang , Gao Wanguo , Wang Cheng , Wu Chengyang , Wu Congping , Wang Bing , Wang Lu , Zou Zhigang 
Published: February 2024

AbstractHigh entropy alloys and metallic glasses, as two typical metastable nanomaterials, have attracted tremendous interest in energy conversion catalysis due to their high reactivity in nonequilibrium states. Herein, a novel nanomaterial, layered high entropy metallic glass (HEMG), in a higher energy state than low‐entropy alloys and its crystalline counterpart due to both the disordered elemental and structural arrangements, is synthesized. Specifically, the MnNiZrRuCe HEMG exhibits highly enhanced photothermal catalytic activity and long‐term stability. An unprecedented CO2 methanation rate of 489 mmol g−1 h−1 at 330 °C is achieved, which is, to the authors’ knowledge, the highest photothermal CO2 methanation rate in flow reactors. The remarkable activity originates from the abundant free volume and high internal energy state of HEMG, which lead to the extraordinary heterolytic H2 dissociation capacity. The high‐entropy effect also ensures the excellent stability of HEMG for up to 450 h. This work not only provides a new perspective on the catalytic mechanism of HEMG, but also sheds light on the great catalytic potential in future carbon‐negative industry.