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.

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.

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.

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.

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.

The Hidden Patient Connections: Predicting Hormonal Therapy Medication Discontinuation Using Hypergraph Neural Network on Clinical Communications.

Hormonal therapy is an important adjuvant treatment for breast cancer patients, but medication discontinuation of such therapy is not uncommon. The goal of this paper is to conduct research on the modeling of clinic communications, which have shown value in understanding medication discontinuation, to predict the discontinuation of hormonal therapy medications. Notably, we leveraged the Hypergraph Neural Network to capture the hidden connections of patients that were inferred from clinical communications. Combining the content of clinical communications as well as the demographics, insurance, and cancer stage information, our model achieved an AUC of 67.9%, which significantly outperformed other baselines such as Graph Convolutional Network (65.3%), Random Forest (62.7%), and Support Vector Machine (62.8%). Our study suggested that incorporating the hidden patient connections encoded in clinical communications into prediction models could boost their performance. Future research would consider combining structured medical records and clinical communications to better predict medication discontinuation.

Non-Metastatic Axillary Lymph Nodes Have Distinct Morphology and Immunophenotype in Obese Breast Cancer patients at Risk for Metastasis.

Obese patients have worse breast cancer outcomes than normal weight women including a 50% to 80% increased rate of axillary nodal metastasis. Recent studies have shown a potential 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 breast cancer patients. In this study, a deep learning framework was developed to identify morphological differences of non-metastatic axillary nodes between node-positive and node-negative obese breast cancer patients. Pathology review of the model-selected patches found an increase in the average size of adipocytes (p-value=0.004), an increased amount of white space between lymphocytes (p-value<0.0001), and an increased amount of red blood cells (p-value<0.001) in non-metastatic lymph nodes of node-positive breast cancer patients. Our downstream immunohistology (IHC) analysis showed a decrease of CD3 expression and increase of leptin expression in fat-replaced axillary lymph nodes in obese node-positive patients. In summary, our findings suggest a novel direction to further investigate the crosstalk between lymph node adiposity, lymphatic dysfunction, and breast cancer nodal metastases.

CT characterization of retractor related liver injuries after pancreaticoduodenectomy: Retrospective analysis of a single institution experience.

PURPOSE: Retractor related liver injuries (RRLI) are reported after upper gastrointestinal tract surgeries; most commonly laparoscopic cholecystectomy and gastric surgeries. The aim of this study was to characterize the incidence, identification, type, severity, clinical features and risk factors for RRLI after open and robotic pancreaticoduodenectomy. METHODS: A 6-year retrospective study of 230 patients was performed. Clinical data was extracted from the electronic medical record. Post-operative imaging was reviewed and graded using the American Association for the Surgery of Trauma (AAST) liver injury scale. RESULTS: 109 patients met eligibility criteria. RRLI occurred in 23/109 (21.1%), with a higher incidence in the robotic/combinedapproach (4/9) compared with open (19/100). Most common injury was an intraparenchymal hematoma (56.5%), grade II (78.3%), located in segments II/III (77%). 39.1% of injuries were not reported on the CT interpretation. There was a statistically significant elevation of postoperative AST/ALT in the RRLI group [median AST 219.5 vs. 72.0 (p < 0.001), ALT 203.0 vs. 69.0 (p < 0.001)]. Trends toward lower preoperative platelet counts and longer operations were observed in the RRLI group. No significant difference in hospital length of stay or post-operative pain scores were noted. CONCLUSION: RRLI occurred frequently after pancreaticoduodenectomy, however most injuries were low grade and the only clinical significance was a transient increase in transaminases. A trend toward higher injury rates was observed in robotic cases. In this population, RRLI was often unrecognized on postoperative imaging.

Development and Validation of the Veterans Affairs Eosinophilic Esophagitis Cohort.

BACKGROUND & AIMS: Gaps remain in understanding the epidemiology of eosinophilic esophagitis (EoE). Our aim was to identify and validate a national cohort of individuals with EoE using Veterans Health Administration (VHA) data. METHODS: We used 2 validation strategies to develop algorithms that identified adults with EoE between 1999 and 2020. The first validation strategy applied International Classification of Diseases (ICD) code algorithms to a base cohort of individuals who underwent esophagogastroduodenoscopy with esophageal biopsy specimens. The second applied ICD code algorithms to a base cohort of all individuals in the VHA. For each ICD algorithm applied, a random sample of candidate EoE cases and non-EoE controls were selected and the charts were reviewed manually by a blinded reviewer. Each algorithm was modified iteratively until the prespecified diagnostic accuracy end point (95% confidence lower bound for a positive predictive value [PPV], >88%) was achieved. We compiled individuals from each strategy's maximum performance algorithm to construct the Veterans Affairs Eosinophilic Esophagitis Cohort. RESULTS: The maximum performance algorithm from the first validation strategy included 2 or more ICD code encounters for EoE separated by more than 30 days and achieved a 93.3% PPV (lower bound, 88.1%) for identifying true EoE cases. The maximum performance algorithm from the second validation strategy included 4 or more ICD code encounters for EoE in which 2 codes were separated by at least 30 days, and similarly achieved a 93.3% PPV (lower bound, 88.1%). Combining both strategies yielded 6637 individuals, which comprised the Veterans Affairs Eosinophilic Esophagitis Cohort. CONCLUSIONS: We developed and validated 2 highly accurate coding algorithms for EoE and established a nationwide VHA cohort of adults with EoE for future studies.

Hydrogen Sulfide Enhances Browning Repression and Quality Maintenance in Fresh-Cut Peaches via Modulating Phenolic and Amino Acids Metabolisms.

Effects of hydrogen sulfide (H2S) on the browning and quality maintenance of fresh-cut peach fruit were studied. The results showed that H2S treatment repressed the development of surface browning, suppressed the increase in respiration rate and weight loss, and delayed the decline of firmness while soluble solids content (SSC) and microbial growth were unaffected during storage. H2S treatment maintained higher contents of phenolic compounds, especially neo-chlorogenic acid, catechin, and quercetin, and delayed the degradation of phenolic compounds by enhancing the activities of phenolic biosynthesis-related enzymes and inhibiting the oxidative activities of polyphenol oxidase (PPO) in comparison with control. Moreover, H2S stimulated the accumulation of amino acids and their derivatives including proline, γ-aminobutyric acid (GABA), and polyamines (PAs) via enhancing biosynthesis and repressing degradation compared to control. These results suggested that H2S treatment enhanced the accumulation of phenolic, amino acids, and their derivatives by modulating phenolic and amino acids metabolisms, which contributed to the higher antioxidant activity and membrane integrity maintenance, ultimately repressing browning development and maintaining the quality. Therefore, the current study speculated that H2S might be a promising approach for browning inhibition and quality maintenance in fresh-cut peach fruit.

Fatigue, Pain, and Medication: Mining Online Posts Regarding Rheumatoid Arthritis From Reddit.

Rheumatoid arthritis (RA), a chronic and systemic autoimmune disease that primarily attacks the joints around the body, is affecting a large number of people worldwide through severe symptoms and complications. Therefore, it is crucial to understand these patients' problems and support needs such that effective strategies or solutions can be made to improve their long-term treatment experience. In this paper, we present an in-depth study that is based on the structural topic model to uncover the themes and concerns in online RA posts from Reddit, an American social news aggregation, content rating, and discussion website. In addition, we compared the topic prevalence differences before and after the COVID-19 pandemic to understand the impact of the pandemic on these online users. This study demonstrates the potential of using text-mining techniques on social media data to learn the treatment experiments of RA patients.

Association between fat-infiltrated axillary lymph nodes on screening mammography and cardiometabolic disease.

Objective: Ectopic fat deposition within and around organs is a stronger predictor of cardiometabolic disease status than body mass index (BMI). Fat deposition within the lymphatic system is poorly understood. This study examined the association between the prevalence of cardiometabolic disease and ectopic fat deposition within axillary lymph nodes (LNs) visualized on screening mammograms. Methods: A cross-sectional study was conducted on 834 women presenting for full-field digital screening mammography. The status of fat-infiltrated LNs was assessed based on the size and morphology of axillary LNs from screening mammograms. The prevalence of cardiometabolic disease was retrieved from the electronic medical records, including type 2 diabetes mellitus (T2DM), hypertension, dyslipidemia, high blood glucose, cardiovascular disease, stroke, and non-alcoholic fatty liver disease. Results: Fat-infiltrated axillary LNs were associated with a high prevalence of T2DM among all women (adjusted odds ratio: 3.92, 95% CI: [2.40, 6.60], p-value < 0.001) and in subgroups of women with and without obesity. Utilizing the status of fatty LNs improved the classification of T2DM status in addition to age and BMI (1.4% improvement in the area under the receiver operating characteristic curve). Conclusion: Fat-infiltrated axillary LNs visualized on screening mammograms were associated with the prevalence of T2DM. If further validated, fat-infiltrated axillary LNs may represent a novel imaging biomarker of T2DM in women undergoing screening mammography.

Bladder cancer prognosis using deep neural networks and histopathology images.

Background: Recent studies indicate that bladder cancer is among the top 10 most common cancers in the world (Saginala et al. 2022). Bladder cancer frequently reoccurs, and prognostic judgments may vary among clinicians. As a favorable prognosis may help to inform less aggressive treatment plans, classification of histopathology slides is essential for the accurate prognosis and effective treatment of bladder cancer patients. Developing automated and accurate histopathology image analysis methods can help pathologists determine the prognosis of patients with bladder cancer. Materials and methods: In this study, we introduced Bladder4Net, a deep learning pipeline, to classify whole-slide histopathology images of bladder cancer into two classes: low-risk (combination of PUNLMP and low-grade tumors) and high-risk (combination of high-grade and invasive tumors). This pipeline consists of four convolutional neural network (CNN)-based classifiers to address the difficulties of identifying PUNLMP and invasive classes. We evaluated our pipeline on 182 independent whole-slide images from the New Hampshire Bladder Cancer Study (NHBCS) (Karagas et al., 1998; Sverrisson et al., 2014; Sverrisson et al., 2014) collected from 1994 to 2004 and 378 external digitized slides from The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/tcga). Results: The weighted average F1-score of our approach was 0.91 (95% confidence interval (CI): 0.86-0.94) on the NHBCS dataset and 0.99 (95% CI: 0.97-1.00) on the TCGA dataset. Additionally, we computed Kaplan-Meier survival curves for patients who were predicted as high risk versus those predicted as low risk. For the NHBCS test set, patients predicted as high risk had worse overall survival than those predicted as low risk, with a log-rank p-value of 0.004. Conclusions: If validated through prospective trials, our model could be used in clinical settings to improve patient care.