Methane C(sp3)-H bond activation by water microbubbles.

Microbubble-induced oxidation offers an effective approach for activating the C(sp3)-H bond of methane under mild conditions, achieving a methane activation rate of up to 6.7% per hour under optimized parameters. In this study, microbubbles provided an extensive gas-liquid interface that promoted the formation of hydroxyl (OH˙) and hydrogen radicals (H˙), which facilitated the activation of methane, leading to the generation of methyl radicals (CH3˙). These species further participated in free-radical reactions at the interface, resulting in the production of ethane and formic acid. The microbubble system was optimized by adjusting gas-liquid interaction time, water temperature, and bubble size, with the optimal conditions (150 s of water-gas interaction, 15 °C, 50 µm bubble size) yielding a methane conversion rate of 171.5 ppm h-1, an ethane production rate of 23.5 ppm h-1, and a formic acid production rate of 2.3 nM h-1 during 8 h of continuous operation. The stability and efficiency of this process, confirmed through electron spin resonance, high-resolution mass spectrometry, and gas chromatography, suggest that microbubble-based methane activation offers a scalable and energy-efficient pathway for methane utilization.

Theanine enhances resistance to Botrytis cinerea in postharvest strawberry fruit via modulating cell-wall and phenylpropanoid metabolisms.

Theanine (N-ethyl-γ-glutamine), as a unique non-protein amino acid, plays vital roles in abiotic stress resistance, while its roles in biotic stress resistance are still unclear. Gray mold caused by Botrytis cinerea is a major disease in strawberries. Effects of theanine on the development of gray mold, cell-wall and phenylpropanoid metabolisms in strawberries were investigated in this study. Results showed that 5 mmol L-1 theanine treatment reduced disease incidence and severity of gray mold in strawberries with antifungal activity in vitro. Meanwhile, theanine treatment enhanced the accumulation of phenolic compounds and lignin, especially ellagic acid, cyanidin, and quercetin, which was associated with increased phenylpropanoid pathway related enzyme activities. Moreover, theanine induced callose deposition and suppressed cell- wall disassembling enzymes, accompanied by higher levels of water insoluble pectin, hemicellulose and cellulose. Therefore, theanine treatment could alleviate decay of B. cinerea-inoculated strawberries by regulating phenylpropanoid and cell-wall metabolisms, maintaining higher levels of phenolic compounds and cell-wall components, thereby contributing to disease resistance and cell-wall structure integrity.

Impact of the curriculum system on the quality of stomatology graduates: a multivariable analysis.

BACKGROUND: The curriculum system is a central component in achieving the goals and specifications of talent training schemes. However, problems and difficulties exist in curriculum provision due to a lack of curriculum system design logic. This study aimed to investigate the correlation between the university curriculum system and graduate quality and to reveal the design logic of the curriculum system. METHODS: A total of 699 stomatology graduates from a university in Zhejiang were selected as research subjects from 2015 to 2022. The students' curriculum system and graduate quality data were collected and classified. The graduate quality information contained (1) the National Board Dental Examination (NBDE) pass rate and score, (2) the postgraduate entrance rate and destination, and (3) the employment rate and work institution. Regression analysis was also conducted to assess the correlation between the curriculum system and graduate quality. RESULTS: Regression analysis revealed significant associations between general education, specialization, and stomatology X courses and NBDE score, postgraduate entrance rate and destination, and work institution. All courses except public elective courses had significant impacts on NBDE score. General basic, medical English, and employment guidance courses significantly influenced the postgraduate entrance rate and destination. Restricted elective and public elective courses had significant effects on employment rate and work institution. CONCLUSIONS: Increasing the quality of specialized and stomatology X courses in the curriculum system is beneficial for deepening graduates' expertise and enhancing their education. Moreover, English courses are suggested to be offered in the early stage to lay a better language foundation.

Fact Check: Assessing the Response of ChatGPT to Alzheimer's Disease Myths.

INTRODUCTION: There are many myths regarding Alzheimer's disease (AD) that have been circulated on the internet, each exhibiting varying degrees of accuracy, inaccuracy, and misinformation. Large language models, such as ChatGPT, may be a valuable tool to help assess these myths for veracity and inaccuracy; however, they can induce misinformation as well. OBJECTIVE: This study assesses ChatGPT's ability to identify and address AD myths with reliable information. METHODS: We conducted a cross-sectional study of attending geriatric medicine clinicians' evaluation of ChatGPT (GPT 4.0) responses to 16 selected AD myths. We prompted ChatGPT to express its opinion on each myth and implemented a survey using REDCap to determine the degree to which clinicians agreed with the accuracy of each of ChatGPT's explanations. We also collected their explanations of any disagreements with ChatGPT's responses. We used a 5-category Likert-type scale with a score ranging from -2 to 2 to quantify clinicians' agreement in each aspect of the evaluation. RESULTS: The clinicians (n = 10) were generally satisfied with ChatGPT's explanations. Among the 16 myths, the clinicians were generally satisfied with these explanations, with [mean (SD) score of 1.1(±0.3)]. Most clinicians selected "Agree" or "Strongly Agree" for each statement. Some statements obtained a small number of "Disagree" responses. There were no "Strongly Disagree" responses. CONCLUSION: Most surveyed health care professionals acknowledged the potential value of ChatGPT in mitigating AD misinformation; however, the need for more refined and detailed explanations of the disease's mechanisms and treatments was highlighted.

Optimizing word embeddings for small dataset: a case study on patient portal messages from breast cancer patients.

Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. We introduce a novel adaptation of the word2vec model, PK-word2vec (where PK stands for prior knowledge), for small-scale messages. PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec in a case study of patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. The dataset was composed of 1389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7981 non-medical and 1116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p = 0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers ( p = 0.774 under a paired t-test). PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention.

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

Perceptions and Utilization of Online Peer Support Among Informal Dementia Caregivers: Survey Study.

Background: Informal dementia caregivers are those who care for a person living with dementia and do not receive payment (eg, family members, friends, or other unpaid caregivers). These informal caregivers are subject to substantial mental, physical, and financial burdens. Online communities enable these caregivers to exchange caregiving strategies and communicate experiences with other caregivers whom they generally do not know in real life. Research has demonstrated the benefits of peer support in online communities, but this research is limited, focusing merely on caregivers who are already online community users. Objective: We aimed to investigate the perceptions and utilization of online peer support through a survey. Methods: Following the Andersen and Newman Framework of Health Services Utilization and using REDCap (Research Electronic Data Capture), we designed and administered a survey to investigate the perceptions and utilization of online peer support among informal dementia caregivers. Specifically, we collected types of information that influence whether an informal dementia caregiver accesses online peer support: predisposing factors, which refer to the sociocultural characteristics of caregivers, relationships between caregivers and people living with dementia, and belief in the value of online peer support; enabling factors, which refer to the logistic aspects of accessing online peer support (eg, eHealth literacy and access to high-speed internet); and need factors, which are the most immediate causes of seeking online peer support. We also collected data on caregivers' experiences with accessing online communities. We distributed the survey link on November 14, 2022, within two online locations: the Alzheimer's Association website (as an advertisement) and ALZConnected (an online community organized by the Alzheimer's Association). We collected all responses on February 23, 2023, and conducted a regression analysis to identifyn factors that were associated with accessing online peer support. Results: We collected responses from 172 dementia caregivers. Of these participants, 140 (81.4%) completed the entire survey. These caregivers were aged 19 to 87 (mean 54, SD 13.5) years, and a majority were female (123/140, 87.9%) and White (126/140, 90%). Our findings show that the behavior of accessing any online community was significantly associated with participants' belief in the value of online peer support (P=.006). Moreover, of the 40 non-online community caregivers, 33 (83%) had a belief score above 24-the score that was assigned when a neutral option was selected for each belief question. The most common reasons for not accessing any online community were having no time to do so (14/140, 10%) and having insufficient online information-searching skills (9/140, 6.4%). Conclusions: Our findings suggest that online peer support is valuable, but practical strategies are needed to assist informal dementia caregivers who have limited time or online information-searching skills.

Novel use of structural equation modeling to examine the development of a framework of patient-centered two-way referral systems for building digital subjective well-being healthcare: A cross-sectional survey in Central China.

Background: Digital health technologies are progressively assuming significant roles in aspects encompassing in-hospital management, patient-centered design, and tiered referral systems. Nevertheless, current studies do not involve exploration into the potential value and mechanisms of digital health in a patient-centered context. This study aimed to explore the development of a framework of comprehensive, evidence-based digital health technologies for the construction of welfare-oriented healthcare. Methods: From March to June 2023, a cross-sectional online study was performed, involving 335 respondents with prior referral experiences hailing from the Central China region. Data on welfare-oriented healthcare factors (clinical pathway management, medical structure configuration, healthcare service accessibility, two-way referrals) underwent factor analysis in advance, and correlation between these factors and their association with two-way referrals was evaluated by testing for direct and indirect (mediating) effects. Results: Firstly, there existed a significant positive correlation between integrative medical indicators and welfare-centered healthcare (ß = 0.02-0.16, p < 0.05). Furthermore, two-way referral had an direct association with integrative medical parameters and the welfare healthcare service system (ß = 0.15-0.31, p < 0.05), but exerted a partial mediatory function in the welfare healthcare service system (ß = 0.005-0.021, α < 0.05). Two-way referrals partially mediate the integrated medical indicators, mainly through direct effects, while also providing complementary support. Clinical pathways, medical structure, and accessibility are closely linked to welfare healthcare and significantly influence healthcare quality. Thus, improving these factors should be prioritized. Conclusion: This study proposes a method combining integrated evaluation indicators with pathway mechanism design. This pathway mechanism design includes key steps such as patient registration, information extraction, hospital allocation or referral, diagnosis and treatment, rehabilitation plan monitoring, service feedback, and demand resolution. This design aims to change patients' intentions in seeking healthcare, thereby increasing their acceptance of bidirectional referrals, and ultimately enhancing the effectiveness and realization of welfare healthcare.

A Systematic Review of ChatGPT and Other Conversational Large Language Models in Healthcare.

Background: The launch of the Chat Generative Pre-trained Transformer (ChatGPT) in November 2022 has attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including healthcare. Numerous studies have since been conducted regarding how to employ state-of-the-art LLMs in health-related scenarios to assist patients, doctors, and public health administrators. Objective: This review aims to summarize the applications and concerns of applying conversational LLMs in healthcare and provide an agenda for future research on LLMs in healthcare. Methods: We utilized PubMed, ACM, and IEEE digital libraries as primary sources for this review. We followed the guidance of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRIMSA) to screen and select peer-reviewed research articles that (1) were related to both healthcare applications and conversational LLMs and (2) were published before September 1st, 2023, the date when we started paper collection and screening. We investigated these papers and classified them according to their applications and concerns. Results: Our search initially identified 820 papers according to targeted keywords, out of which 65 papers met our criteria and were included in the review. The most popular conversational LLM was ChatGPT from OpenAI (60), followed by Bard from Google (1), Large Language Model Meta AI (LLaMA) from Meta (1), and other LLMs (5). These papers were classified into four categories in terms of their applications: 1) summarization, 2) medical knowledge inquiry, 3) prediction, and 4) administration, and four categories of concerns: 1) reliability, 2) bias, 3) privacy, and 4) public acceptability. There are 49 (75%) research papers using LLMs for summarization and/or medical knowledge inquiry, and 58 (89%) research papers expressing concerns about reliability and/or bias. We found that conversational LLMs exhibit promising results in summarization and providing medical knowledge to patients with a relatively high accuracy. However, conversational LLMs like ChatGPT are not able to provide reliable answers to complex health-related tasks that require specialized domain expertise. Additionally, no experiments in our reviewed papers have been conducted to thoughtfully examine how conversational LLMs lead to bias or privacy issues in healthcare research. Conclusions: Future studies should focus on improving the reliability of LLM applications in complex health-related tasks, as well as investigating the mechanisms of how LLM applications brought bias and privacy issues. Considering the vast accessibility of LLMs, legal, social, and technical efforts are all needed to address concerns about LLMs to promote, improve, and regularize the application of LLMs in healthcare.

Optimizing Word Embeddings for Patient Portal Message Datasets with a Small Number of Samples.

Background: Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. Objective: We introduce a novel adaptation of the word2vec model, PK-word2vec, for small-scale messages. Methods: PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec on patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. Results: The dataset was composed of 1,389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7,981 non-medical and 1,116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p=0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers (p = 0.774 under a paired t-test). Conclusions: PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

Carboxyl graphene modified PEDOT:PSS organic electrochemical transistor for in situ detection of cancer cell morphology.

Circulating tumor cells in human peripheral blood play an important role in cancer metastasis. In addition to the size-based and antibody-based capture and separation of cancer cells, their electrical characterization is important for rare cell detection, which can prove fatal in point-of-care testing. Herein, an organic electrochemical transistor (OECT) biosensor made of solution-gated carboxyl graphene mixed with PEDOT:PSS for the detection of cancer cells in situ is reported. Carboxyl graphene was used in this work to modulate cancer cell morphology, which differs significantly from normal blood cells, to achieve rare cancer cell detection. When the concentration of carboxyl graphene mixed in PEDOT:PSS was increased from 0 to 5 mg mL-1, the cancer cell surface area increased from 218 µm2 to 530 µm2, respectively. A change in cell morphology was also detected by the OECT. Negative charges in the cancer cells induced a positive shift in gate voltage, which was approximately 40 mV for spherical-shaped cells. When the cell surface area increased, transfer curves of transistor revealed a negative shift in gate voltage. Therefore, the sensor can be used for in situ detection of cancer cell morphology during the cell capture process, which can be used to identify whether the captured cells are deformable.

Nonmetastatic Axillary Lymph Nodes Have Distinct Morphology and Immunophenotype in Obese Patients with Breast Cancer at Risk for Metastasis.

Obese patients with breast cancer have worse outcomes than their normal weight counterparts, with a 50% to 80% increased rate of axillary nodal metastasis. Recent studies suggest a link between increased lymph node adipose tissue and breast cancer nodal metastasis. Further investigation into potential mechanisms underlying this link may reveal potential prognostic utility of fat-enlarged lymph nodes in patients with breast cancer. This study used a deep learning model to identify morphologic differences in nonmetastatic axillary nodes between obese, node-positive, and node-negative patients with breast cancer. The model was developed using nested cross-validation on 180 cases and achieved an area under the receiver operator characteristic curve of 0.67 in differentiating patients using hematoxylin and eosin-stained whole slide images. The morphologic analysis of the predictive regions showed an increased average adipocyte size (P = 0.004), increased white space between lymphocytes (P < 0.0001), and increased red blood cells (P < 0.001) in nonmetastatic lymph nodes of node-positive patients. Preliminary immunohistochemistry analysis on a subset of 30 patients showed a trend of decreased CD3 expression and increased leptin expression in fat-replaced axillary lymph nodes of obese, node-positive patients. These findings suggest a novel direction to further investigate the interaction between lymph node adiposity, lymphatic dysfunction, and breast cancer nodal metastases, highlighting a possible prognostic tool for obese patients with breast cancer.

Fabrication of PEDOT:PSS-based solution gated organic electrochemical transistor array for cancer cells detection.

Organic electrochemical transistor (OECT) was applied in chemical and biological sensing. In this work, we developed a simple and repeatable method to fabricate OECT array, which had been successfully used to detect cancer cells. PEDPT:PSS conductive film between source and drain electrodes were patterned through photolithography, which can achieve uniform devices with same electrical characterization. When MCF-7 cancer cells are captured on the PEDOT:PSS surface via specifical antibody, the transfer characteristic of OECT shifts to higher gate electrode voltage due to the electrostatic interaction between cancer cells and device. The effective gate voltage shift can reach about 63 mV when the concentration of cancer cells increased to 5000. The shift of effective gate voltage is related to the cancer cell morphology, which is increased in the first 1 h and decreased when the capture time was larger than 1 h. The device of OECT array can increase the sample flux and make the detection result more accurate. It is expected that OECT array will have promising practical applications in single cancer cell detection in the future.

A molecularly imprinted polypyrrole electrochemiluminescence sensor based on a novel zinc-based metal-organic framework and chitosan for determination of enrofloxacin.

Nowadays, bacterial resistance caused by the abuse of antibiotics has become a worldwide problem. In this work, a quinolone antibiotic, enrofloxacin (ENR), was rapidly monitored by combining a selective molecular imprinting polymer (MIP) with the electrochemiluminescence (ECL) method. Zn-PTC, a novel zinc-based metal-organic framework (MOF) that has a large specific surface area and ultra-high luminous efficiency, was used as the ECL luminophore. Chitosan (CHIT) was used to contact the specific surface area of molecularly imprinted polymer films and further improved the detection sensitivity. Subsequently, the molecularly imprinted polypyrrole was electropolymerized on the surface of the Zn-PTC and CHIT modified glassy carbon electrode (GCE). The specific sites that could target recombining ENR were shaped on the surface of MIP after extracting the ENR templates. The specific concentrations of ENR could be detected according to the difference in ECL intensity (ΔECL) between the eluting and rebinding of ENR. After optimization, a good linear response of ΔECL and a logarithm of specific ENR concentrations could be obtained in the range of 1.0 × 10-12-1.0 × 10-4 mol L-1, with a detection limit of 9.3 × 10-13 mol L-1 (S/N = 3). Notably, this study provided a rapid, convenient, and cheap method for the detection of ENR in actual samples.

Examining Online Behaviors of Adult-Child and Spousal Caregivers for People Living With Alzheimer Disease or Related Dementias: Comparative Study in an Open Online Community.

BACKGROUND: Alzheimer disease or related dementias (ADRD) are severe neurological disorders that impair the thinking and memory skills of older adults. Most persons living with dementia receive care at home from their family members or other unpaid informal caregivers; this results in significant mental, physical, and financial challenges for these caregivers. To combat these challenges, many informal ADRD caregivers seek social support in online environments. Although research examining online caregiving discussions is growing, few investigations have distinguished caregivers according to their kin relationships with persons living with dementias. Various studies have suggested that caregivers in different relationships experience distinct caregiving challenges and support needs. OBJECTIVE: This study aims to examine and compare the online behaviors of adult-child and spousal caregivers, the 2 largest groups of informal ADRD caregivers, in an open online community. METHODS: We collected posts from ALZConnected, an online community managed by the Alzheimer's Association. To gain insights into online behaviors, we first applied structural topic modeling to identify topics and topic prevalence between adult-child and spousal caregivers. Next, we applied VADER (Valence Aware Dictionary for Sentiment Reasoning) and LIWC (Linguistic Inquiry and Word Count) to evaluate sentiment changes in the online posts over time for both types of caregivers. We further built machine learning models to distinguish the posts of each caregiver type and evaluated them in terms of precision, recall, F1-score, and area under the precision-recall curve. Finally, we applied the best prediction model to compare the temporal trend of relationship-predicting capacities in posts between the 2 types of caregivers. RESULTS: Our analysis showed that the number of posts from both types of caregivers followed a long-tailed distribution, indicating that most caregivers in this online community were infrequent users. In comparison with adult-child caregivers, spousal caregivers tended to be more active in the community, publishing more posts and engaging in discussions on a wider range of caregiving topics. Spousal caregivers also exhibited slower growth in positive emotional communication over time. The best machine learning model for predicting adult-child, spousal, or other caregivers achieved an area under the precision-recall curve of 81.3%. The subsequent trend analysis showed that it became more difficult to predict adult-child caregiver posts than spousal caregiver posts over time. This suggests that adult-child and spousal caregivers might gradually shift their discussions from questions that are more directly related to their own experiences and needs to questions that are more general and applicable to other types of caregivers. CONCLUSIONS: Our findings suggest that it is important for researchers and community organizers to consider the heterogeneity of caregiving experiences and subsequent online behaviors among different types of caregivers when tailoring online peer support to meet the specific needs of each caregiver group.

Automated classification of fat-infiltrated axillary lymph nodes on screening mammograms.

OBJECTIVE: Fat-infiltrated axillary lymph nodes (LNs) are unique sites for ectopic fat deposition. Early studies showed a strong correlation between fatty LNs and obesity-related diseases. Confirming this correlation requires large-scale studies, hindered by scarce labeled data. With the long-term goal of developing a rapid and generalizable tool to aid data labeling, we developed an automated deep learning (DL)-based pipeline to classify the status of fatty LNs on screening mammograms. METHODS: Our internal data set included 886 mammograms from a tertiary academic medical institution, with a binary status of the fat-infiltrated LNs based on the size and morphology of the largest visible axillary LN. A two-stage DL model training and fine-tuning pipeline was developed to classify the fat-infiltrated LN status using the internal training and development data set. The model was evaluated on a held-out internal test set and a subset of the Digital Database for Screening Mammography. RESULTS: Our model achieved 0.97 (95% CI: 0.94-0.99) accuracy and 1.00 (95% CI: 1.00-1.00) area under the receiver operator characteristic curve on 264 internal testing mammograms, and 0.82 (95% CI: 0.77-0.86) accuracy and 0.87 (95% CI: 0.82-0.91) area under the receiver operator characteristic curve on 70 external testing mammograms. CONCLUSION: This study confirmed the feasibility of using a DL model for fat-infiltrated LN classification. The model provides a practical tool to identify fatty LNs on mammograms and to allow for future large-scale studies to evaluate the role of fatty LNs as an imaging biomarker of obesity-associated pathologies. ADVANCES IN KNOWLEDGE: Our study is the first to classify fatty LNs using an automated DL approach.

Fact Check: Assessing the Response of ChatGPT to Alzheimer's Disease Statements with Varying Degrees of Misinformation.

Background: There are many myths regarding Alzheimer's disease (AD) that have been circulated on the Internet, each exhibiting varying degrees of accuracy, inaccuracy, and misinformation. Large language models such as ChatGPT, may be a useful tool to help assess these myths for veracity and inaccuracy. However, they can induce misinformation as well. The objective of this study is to assess ChatGPT's ability to identify and address AD myths with reliable information. Methods: We conducted a cross-sectional study of clinicians' evaluation of ChatGPT (GPT 4.0)'s responses to 20 selected AD myths. We prompted ChatGPT to express its opinion on each myth and then requested it to rephrase its explanation using a simplified language that could be more readily understood by individuals with a middle school education. We implemented a survey using Redcap to determine the degree to which clinicians agreed with the accuracy of each ChatGPT's explanation and the degree to which the simplified rewriting was readable and retained the message of the original. We also collected their explanation on any disagreement with ChatGPT's responses. We used five Likert-type scale with a score ranging from -2 to 2 to quantify clinicians' agreement in each aspect of the evaluation. Results: The clinicians (n=11) were generally satisfied with ChatGPT's explanations, with a mean (SD) score of 1.0(±0.3) across the 20 myths. While ChatGPT correctly identified that all the 20 myths were inaccurate, some clinicians disagreed with its explanations on 7 of the myths.Overall, 9 of the 11 professionals either agreed or strongly agreed that ChatGPT has the potential to provide meaningful explanations of certain myths. Conclusions: The majority of surveyed healthcare professionals acknowledged the potential value of ChatGPT in mitigating AD misinformation. However, the need for more refined and detailed explanations of the disease's mechanisms and treatments was highlighted.

Novel inner filter effect-based near-infrared electrochemiluminescence sensor mediated by well-matched AgBr-nitrogen-doped Ti3C2 MXene and nonmetallic plasmon WO3•H2O.

The development of innovative and efficient strategy is of paramount importance for near-infrared (NIR) electrochemiluminescence (ECL) sensing, which can substantially promote ECL detection in a wide range of situations. Herein, the inner filter effect (IFE) strategy was designed to construct an ultrasensitive NIR ECL biosensor based on the well-matched AgBr nanocrystals (NCs) decorated nitrogen-doped Ti3C2 MXene nanocomposites (AgBr-N-Ti3C2) and hydrated defective tungsten oxide nanosheets (dWO3•H2O). Specifically, the AgBr-N-Ti3C2 nanocomposites displayed extremely effective NIR ECL emission because N-doping could accelerate electron transfer and boost the red-shift of the ECL spectrum. The nonmetallic plasmon dWO3•H2O was used as an absorber due to its facile tuning of the spectra overlap and higher molar extinction coefficients. Time-resolved emission decay curves proved that the decreased ECL intensity was ascribed to the IFE-based steady quenching mechanism. With the support of tetracycline (TC) aptamer and the complementary DNA chain, the fabricated NIR ECL-IFE biosensor performed a wide linear range of 100 nM âˆ¼ 10 fM with a low detection limit of 2.2 fM (S/N = 3), and it exhibited excellent stability, sensitivity, and reproducibility, so as to be applied to real samples. This strategy opens a new avenue to constructing an efficient NIR ECL-IFE system and shows excellent practical potential in actual sample analysis.

A novel molecularly imprinting polypyrrole electrochemiluminescence sensor based on MIL-101-g-C3N4 for supersensitive determination of ciprofloxacin.

Ciprofloxacin (CIP), a quinolone antibiotic, was rapidly and sensitively detected by integrating the molecularly imprinted polymer (MIP) with an ultra-sensitive electrochemiluminescence (ECL) method. g-C3N4, a typical polymer semiconductor, exhibited outstanding ECL efficiency and excellent ECL stability after combining with an iron-based metal-organic framework (MIL-101). Subsequently, the molecularly imprinted polypyrrole was electropolymerized on the composites of MIL-101-g-C3N4 modified glassy carbon electrode (GCE). The specific sites that could target rebinding the CIP molecules were formed on the surface of MIP after extracting the CIP templates. The determination of specific concentrations of CIP could be realized according to the difference in ECL intensity (△ECL) between the eluting and rebinding of the CIP. Under optimal conditions, a good linear response of △ECL and the logarithm of CIP concentrations was obtained in the range 1.0 × 10-9 ~ 1.0 × 10-5 mol/L, with a detection limit of 4.5 × 10-10 mol/L (S/N = 3) (the working potential was -1.8 ~ 0 V). The RSD of all points in the calibration plot was less than 5.0% and the real samples recovery was between 98.0 and 104%. This paper displays satisfactory selectivity and sensitivity, providing a rapid, convenient, and cheap method for the determination of CIP in real samples.