Association of food insecurity with overall and disease-specific mortality among cancer survivors in the US.

PURPOSE: To investigate the association of food insecurity with overall and disease-specific mortality among US cancer survivors. METHODS: Data from the National Health and Nutrition Examination Survey (NHANES 1999-2018) were used to examine the impact of food insecurity on mortality risks among cancer survivors in the US. Study participants aged ≥ 20 years who had a history of cancer and completed the Adult Food Security Survey Module were included. Mortality data [all-cause, cancer, and cardiovascular (CVD) specific] through December 31, 2019 were obtained through linkage to the National Death Index. Using multivariable Cox proportional hazard regression, hazard ratios of mortality based on food security status were estimated. RESULTS: Among 5032 cancer survivors (mean age 62.5 years; 58.0% women; 86.2% non-Hispanic White), 596 (8.8%) reported food insecurity. Overall, 1913 deaths occurred (609 cancer deaths and 420 CVD deaths) during the median follow-up of 6.8 years. After adjusting for age, food insecurity was associated with a higher risk of overall (HR = 1.93; 95% CI = 1.56-2.39), CVD-specific (HR = 1.95; 95% CI = 1.24-3.05), and cancer-specific (HR = 1.70; 95% CI = 1.20-2.42) mortality (P < 0.001). However, after adjusting for socioeconomic characteristics and health-related factors (physical activity, diet quality measured by healthy eating index), the association between food insecurity and overall mortality was no longer statistically significant. CONCLUSIONS: Food insecurity was associated with a greater risk of overall mortality among cancer survivors. Further studies are needed to confirm these findings and evaluate whether the observed association represents a causal phenomenon and, if so, whether the effect is modifiable with food assistance programs.

Disparities in the prevalence of suicidal ideation according to oral contraceptive pill use among US women: A cross-sectional study.

Background and Aims: The relationship between oral contraceptive pill (OCP) and suicidal ideation remains unclear. This study aims to estimate the prevalence of suicidal ideation among US women and evaluate their associates overall and according to OCP use status. Methods: Data from the National Health and Nutrition Examination Survey (NHANES) 2005-2012 were used to calculate the prevalence and associates of suicidal ideation in women using OCP. Suicidal ideation was assessed using the Patient Health Questionnaire-9. Overall and OCP-specific weighted prevalence of suicidal ideation were estimated. Multivariable logistic regressions were used to investigate overall and OCP-specific associates. Results: The prevalence of suicidal ideation was 3.6% with no evident disparity between OCP groups, suggesting that OCP use is not associated with increased prevalence of suicidal ideation. Smoking was inversely associated with suicidal ideation in the former users of OCP. In the overall population, the prevalence of suicidal ideation was greater in those who were: Black or Hispanic, smoking, taking antidepressants, those with lower educational attainment, and women with low and middle income. Conclusion: Our findings suggest that OCP use was not associated with increased prevalence of suicidal ideation. Unique associates were identified among different OCP groups.

Drp1-dependent mitochondrial fragmentation mediates photoreceptor abnormalities in type 1 diabetic retina.

Recent studies have highlighted that retinal neurodegeneration precedes microvascular changes in diabetic retinopathy (DR), but the specific mechanisms remain unclear. Given the pivotal role of dysfunctional mitochondria and oxidative stress in early DR, our objective was to observe mitochondria-related alterations in the neural retina of type one diabetic mellitus mice with no evidence of DR (T1DM-NDR). We aimed to identify the key mitochondrial-related proteins contributing to mitochondrial injury. Our study revealed that T1DM-NDR mice exhibited outer retina thinning, including the ellipsoid zone, inner segment, and outer segment. Additionally, there was an impaired amplitude of the b-wave in electroretinogram (ERG) and a disorganized arrangement of the photoreceptor layer. In both the retina of DM mice and high glucose (HG)-treated 661w cells, mitochondria appeared swollen and fragmented, with disrupted cristae, disorganized or shortened branches in the mitochondrial network, and decreased mitochondrial membrane potential. Among the mitochondrial-related proteins, dynamin-related protein 1 (Drp1) was upregulated, and the ratio of phosphorylated Drp1 protein at serine 616 (S616) and serine 637 (S637) sites significantly increased in the retina of DM mice. The administration of Mdivi-1 ameliorated high-glucose-induced dysfunctional mitochondria, thereby protecting T1DM-NDR mice retina from morphological and functional injuries. Our findings suggest that hyperglycemia promotes Drp1-mediated mitochondrial dysfunction, which may be a significant factor in the development of DR. The inhibition of high-glucose-induced mitochondrial fission emerges as a potential and innovative intervention strategy for preventing DR.

Analyst optimism, information disclosure, and stock price collapse risk: Empirical insights from China's A-share market.

This study selects stock data of listed companies in China's A-share stock market from 2011 to 2020 as research samples. Using a fixed-effects model, it examines the impact of analyst optimism on stock price collapses and the moderating effect of information disclosure quality. Simultaneously, it conducts additional research to explore the potential transmission mechanisms involved. The main findings are as follows: Firstly, a positive correlation exists between analyst optimism and the risk of stock price collapse. Secondly, improving information disclosure quality of listed companies can enhance the positive impact of analyst optimism on the risk of stock price collapses and expedite the market's adjustment of overly optimistic valuations of listed companies. Additionally, analyst optimism can increase the risk of stock price collapses by affecting institutional ownership. These findings provide theoretical support for regulatory authorities to revise and improve the "information disclosure evaluation" system, regulate the analyst industry, guide analyst behavior, and encourage listed companies to enhance internal governance and improve information disclosure practices.

The association of islet autoantibodies with the neural retinal thickness and microcirculation in type 1 diabetes mellitus with no clinical evidence of diabetic retinopathy.

OBJECTIVE: To examine the association between islet autoantibodies (IAbs) and the retinal neurovascular changes in type 1 diabetes mellitus (T1DM) with no diabetic retinopathy (NDR). METHODS: This cross-sectional study measured the neural retinal structure and microvascular density of 118 NDR eyes using spectral-domain optical coherence tomography angiography. Retinal structure parameters included retinal thickness (RT), inner retinal thickness (iRT), retina never fibral layer thickness (RNFL thickness), ganglion cell complex thickness (GCC thickness), and loss volume of GCC. Microvascular parameters included vessel density of superficial capillary plexus (sVD), vessel density of deep capillary plexus, and vessel density of choroid capillary plexus. Comparison and correlation analyses of these OCTA parameters were made with various IAbs, including glutamic acid decarboxylase antibody (GADA), tyrosine phosphatase-related islet antigen 2 antibody (IA2A), and zinc transporter 8 antibody (ZnT8A). A general linear model was used to understand the association of IAbs with the retina parameters. RESULTS: The IAb positive (IAbs +) group, which included 85 patients, had thinner RT (235.20 ± 18.10 mm vs. 244.40 ± 19.90 mm at fovea, P = 0.021) and thinner iRT (120.10 ± 9.00 mm vs. 124.70 ± 6.90 mm at parafovea, P = 0.015), compared with the IAb negative (IAbs-) group comprising 33 patients. Furthermore, a more severe reduction of RT was demonstrated in the presence of multiple IAbs. Among the three IAbs, GADA was the most significant independent risk factor of all-round RT decrease (ß = -0.20 vs. -0.27 at fovea and parafovea, respectively, P < 0.05), while titers of IA2A negatively affect sVD in the parafovea (ß = -0.316, P = 0.003). CONCLUSIONS: IAbs are associated with neural retinal thinning and microcirculation reduction in T1DM patients before the clinical onset of diabetic retinopathy.

Efficient Raman Lasing and Raman-Kerr Interaction in an Integrated Silicon Carbide Platform.

Implementing stimulated Raman scattering in a low-loss microresonator could lead to Raman lasing. Here, we report the demonstration of an efficient Raman laser with >50% power efficiency in an integrated silicon carbide platform for the first time. By fine-tuning the free spectral range (FSR) of 43 µm-radius silicon carbide microresonators, the Stokes resonance corresponding to the dominant Raman shift of 777 cm-1 (23.3 THz) is aligned to the center of the Raman gain spectrum, resulting in a low power threshold of 2.5 mW. The peak Raman gain coefficient is estimated to be (0.75 ± 0.15) cm/GW in the 1550 nm band, with an approximate full width at half-maximum of (120 ± 30) GHz. In addition, the microresonator is designed to exhibit normal dispersion at the pump wavelength near 1550 nm while possessing anomalous dispersion at the first Stokes near 1760 nm. At high enough input powers, a Kerr microcomb is generated by the Stokes signal acting as the secondary pump, which then mixes with the pump laser through four-wave mixing to attain a wider spectral coverage. Furthermore, cascaded Raman lasing and the occurrence of multiple Raman shifts, including 204 cm-1 (6.1 THz) and 266 cm-1 (8.0 THz) transitions, are also observed. Finally, we show that the Stokes Raman could also help broaden the spectrum in a Kerr microcomb which has anomalous dispersion at the pump wavelength. Our example of a 100 GHz-FSR microcomb has a wavelength span from 1200 to 1900 nm with 300 mW on-chip power.

An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer.

Accurate tumor diagnosis by pathologists relies on identifying specific morphological characteristics. However, summarizing these unique morphological features in tumor classifications can be challenging. Although deep learning models have been extensively studied for tumor classification, their indirect and subjective interpretation obstructs pathologists from comprehending the model and discerning the morphological features accountable for classifications. In this study, we introduce a new approach utilizing Style Generative Adversarial Networks, which enables a direct interpretation of deep learning models to detect significant morphological characteristics within datasets representing patients with deficient mismatch repair/microsatellite instability-high gastric cancer. Our approach effectively identifies distinct morphological features crucial for tumor classification, offering valuable insights for pathologists to enhance diagnostic accuracy and foster professional growth.

Machine learning for differentiating between pancreatobiliary-type and intestinal-type periampullary carcinomas based on CT imaging and clinical findings.

PURPOSE: To develop a diagnostic model for distinguishing pancreatobiliary-type and intestinal-type periampullary adenocarcinomas using preoperative contrast-enhanced computed tomography (CT) findings combined with clinical characteristics. METHODS: This retrospective study included 140 patients with periampullary adenocarcinoma who underwent preoperative enhanced CT, including pancreaticobiliary (N = 100) and intestinal (N = 40) types. They were randomly assigned to the training or internal validation set in an 8:2 ratio. Additionally, an independent external cohort of 28 patients was enrolled. Various CT features of the periampullary region were evaluated and data from clinical and laboratory tests were collected. Five machine learning classifiers were developed to identify the histologic type of periampullary adenocarcinoma, including logistic regression, random forest, multi-layer perceptron, light gradient boosting, and eXtreme gradient boosting (XGBoost). RESULTS: All machine learning classifiers except multi-layer perceptron used achieved good performance in distinguishing pancreatobiliary-type and intestinal-type adenocarcinomas, with the area under the curve (AUC) ranging from 0.75 to 0.98. The AUC values of the XGBoost classifier in the training set, internal validation set and external validation set are 0.98, 0.89 and 0.84 respectively. The enhancement degree of tumor, the growth pattern of tumor, and carbohydrate antigen 19-9 were the most important factors in the model. CONCLUSION: Machine learning models combining CT with clinical features can serve as a noninvasive tool to differentiate the histological subtypes of periampullary adenocarcinoma, in particular using the XGBoost classifier.

Triptolide attenuates cardiac remodeling by inhibiting pyroptosis and EndMT via modulating USP14/Keap1/Nrf2 pathway.

Background: Cardiac remodeling is a common pathological feature in many cardiac diseases, characterized by cardiac hypertrophy and fibrosis. Triptolide (TP) is a natural compound derived from Tripterygium wilfordii Hook F. However, the related mechanism of it in cardiac remodeling has not been fully understood. Methods and results: Transverse aortic constriction (TAC)-induced cardiac hypertrophic mouse model and angiotensin II (Ang II)-induced cardiomyocytes hypertrophic model were performed. Firstly, the results indicate that TP can improve cardiac function, decreased cardiomyocyte surface area and fibrosis area, as well as lowered the protein expressions of brain natriuretic peptide (BNP), ß-major histocompatibility complex (ß-MHC), type I and III collagen (Col I and III). Secondly, TP suppressed cardiac pyroptosis, and decreased the levels of Interleukin-1ß (IL-1ß), Interleukin-18 (IL-18) by Enzyme-linked immunosorbent assay (ELISA), and pyroptosis-associated proteins. Furthermore, TP enhanced the expressions of Nuclear factor erythroid 2-related factor 2 (Nrf2) and Heme oxygenase 1 (HO-1). Interestingly, when Nrf2 was silenced by siRNA, TP lost its properties of reducing pyroptosis and cardiac hypertrophy. In addition, in the Transforming Growth Factor ß1 (TGF-ß1)-induced primary human coronary artery endothelial cells (HCAEC) model, TP was found to inhibit the process of endothelial-to-mesenchymal transition (EndMT), characterized by the loss of endothelial-specific markers and the gain of mesenchymal markers. This was accompanied by a suppression of Slug, Snail, and Twist expression. Meanwhile, the inhibitory effect of TP on EndMT was weakened when Nrf2 was silenced by siRNA. Lastly, potential targets of TP were identified through network pharmacology analysis, and found that Ubiquitin-Specific Protease 14 (USP14) was one of them. Simultaneously, the data indicated that decrease the upregulation of USP14 and Kelch-like ECH-Associated Protein 1 (Keap1) caused by cardiac remodeling. However, Keap1 was decreased and Nrf2 was increased when USP14 was silenced. Furthermore, CoIP analysis showed that USP14 directly interacts with Keap1. Conclusion: TP can observably reduce pyroptosis and EndMT by targeting the USP14/Keap1/Nrf2 pathway, thereby significantly attenuating cardiac remodeling.

Comprehensive Proteomics Analysis Reveals Dynamic Phenotypes of Tumor-Associated Macrophages and Their Precursor Cells in Tumor Progression.

Tumor-associated macrophages (TAMs) are key regulators in tumor progression, but the precise role of bone marrow-derived monocytes (Mons) as TAM precursors and their dynamic phenotypes regulated by the tumor microenvironment (TME) remain unclear. Here, we developed an optimized microproteomics workflow to analyze low-cell-number mouse myeloid cells. We sorted TAMs and their corresponding Mons (1 × 105 per sample) from individual melanoma mouse models at both the early and late stages. We established the protein expression profiles for these cells by mass spectrometry. Subsequently, we analyzed the dynamics phenotypes of TAMs and identified a characteristic protein expression profile characterized by upregulated cholesterol metabolism and downregulated immune responses during tumor progression. Moreover, we found the downregulation of both STAT5 and PYCARD expression not only in late-stage TAMs but also in late-stage Mons, indicating a loss of the ability to induce inflammatory responses prior to Mons infiltration into TME. Taken together, our study provides valuable insights into the progression-dependent transitions between TAMs and their precursor cells, as well as the cross-organ communications of tumor and bone marrow.

Exploring gut-lung axis crosstalk in SARS-CoV-2 infection: Insights from a hACE2 mouse model.

Based on the forefront of clinical research, there is a growing recognition that the gut microbiota, which plays a pivotal role in shaping both the innate and adaptive immune systems, may significantly contribute to the pathogenesis of coronavirus disease 2019 (COVID-19). Although an association between altered gut microbiota and COVID-19 pathogenesis has been established, the causative mechanisms remain incompletely understood. Additionally, the validation of the precise functional alterations within the gut microbiota relevant to COVID-19 pathogenesis has been limited by a scarcity of suitable animal experimental models. In the present investigation, we employed a newly developed humanized ACE2 knock-in (hACE2-KI) mouse model, capable of recapitulating critical aspects of pulmonary and intestinal infection, to explore the modifications in the gut microbiota following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Examination of fecal samples using 16S rRNA gene profiling unveiled a notable reduction in species richness and conspicuous alterations in microbiota composition at 6 days postinfection (dpi). These alterations were primarily characterized by a decline in beneficial bacterial species and an escalation in certain opportunistic pathogens. Moreover, our analysis entailed a correlation study between the gut microbiota and plasma cytokine concentrations, revealing the potential involvement of the Lachnospiraceae_NK4A136_group and unclassified_f_Lachnospiraceae genera in attenuating hyperinflammatory responses triggered by the infection. Furthermore, integration of gut microbiota data with RNA-seq analysis results suggested that the increased presence of Staphylococcus in fecal samples may signify the potential for bacterial coinfection in lung tissues via gut translocation. In summary, our hACE2-KI mouse model effectively recapitulated the observed alterations in the gut microbiota during SARS-CoV-2 infection. This model presents a valuable tool for elucidating gut microbiota-targeted strategies aimed at mitigating COVID-19.

Valproic acid regulates the miR-155/Jarid2 axis by affecting miR-155 promoter methylation in glioma.

The most frequent primary brain tumor in adults is glioma, yet no effective curative treatments are currently available. Our previous study demonstrated the enhancing effects of JARID2 on glioma sensitivity to TMZ treatment. In this study, miR-155 is predicted to target JARID2. miR-155 is overexpressed in clinical glioma specimens and cell lines. miR-155 overexpression in glioma cells enhances cell viability and represses cell apoptosis. Through targeting, miR-155 inhibits JARID2 expression. miR-155 inhibition inhibits glioma cell viability and enhances cell apoptosis, whereas JARID2 knockdown enhances cell viability and inhibits cell apoptosis; JARID2 knockdown partially reverses miR-155 inhibition effects on glioma phenotypes. miR-155 inhibition reduces but knockdown of JARID2 promotes the tumor formation ability of glioma cells in vivo. Valproic acid (VPA) upregulates JARID2 expression, inhibits glioma cell viability and enhances cell apoptosis. VPA downregulates the expression level of miR-155 by increasing the methylation level of the miR-155 promoter, suggesting that the miR-155/JARID2 axis is implicated in VPA inhibition of glioma cell viability and enhancement of glioma cell apoptosis. This study demonstrates a new mechanism of VPA treatment of gliomas by affecting the miR-155/JARID2 axis, which could be regarded as a new strategy for the prevention and treatment of glioma.

Interpretable artificial intelligence-based app assists inexperienced radiologists in diagnosing biliary atresia from sonographic gallbladder images.

BACKGROUND: A previously trained deep learning-based smartphone app provides an artificial intelligence solution to help diagnose biliary atresia from sonographic gallbladder images, but it might be impractical to launch it in real clinical settings. This study aimed to redevelop a new model using original sonographic images and their derived smartphone photos and then test the new model's performance in assisting radiologists with different experiences to detect biliary atresia in real-world mimic settings. METHODS: A new model was first trained retrospectively using 3659 original sonographic gallbladder images and their derived 51,226 smartphone photos and tested on 11,410 external validation smartphone photos. Afterward, the new model was tested in 333 prospectively collected sonographic gallbladder videos from 207 infants by 14 inexperienced radiologists (9 juniors and 5 seniors) and 4 experienced pediatric radiologists in real-world mimic settings. Diagnostic performance was expressed as the area under the receiver operating characteristic curve (AUC). RESULTS: The new model outperformed the previously published model in diagnosing BA on the external validation set (AUC 0.924 vs 0.908, P = 0.004) with higher consistency (kappa value 0.708 vs 0.609). When tested in real-world mimic settings using 333 sonographic gallbladder videos, the new model performed comparable to experienced pediatric radiologists (average AUC 0.860 vs 0.876) and outperformed junior radiologists (average AUC 0.838 vs 0.773) and senior radiologists (average AUC 0.829 vs 0.749). Furthermore, the new model could aid both junior and senior radiologists to improve their diagnostic performances, with the average AUC increasing from 0.773 to 0.835 for junior radiologists and from 0.749 to 0.805 for senior radiologists. CONCLUSIONS: The interpretable app-based model showed robust and satisfactory performance in diagnosing biliary atresia, and it could aid radiologists with limited experiences to improve their diagnostic performances in real-world mimic settings.

Patient-Provider Discussion About Lung Cancer Screening by Race and Ethnicity: Implications for Equitable Uptake of Lung Cancer Screening.

BACKGROUND: Physician-patient discussions regarding lung cancer screening (LCS) are uncommon and its racial and ethnic disparities are under-investigated. We examined the racial and ethnic disparities in the trends and frequency of LCS discussion among the LCS-eligible United States (US) population. METHODS: We analyzed data from the Health Information National Trends Survey from 2014 to 2020. LCS-eligible individuals were defined as adults aged 55 to 80 years old who have a current or former smoking history. We estimated the trends and frequency of LCS discussions and adjusted the probability of having an LCS discussion by racial and ethnic groups. RESULTS: Among 2136 LCS-eligible participants (representing 22.7 million US adults), 12.9% (95% CI, 10.9%-15%) reported discussing LCS with their providers in the past year. The frequency of LCS discussion was lowest among non-Hispanic White participants (12.3%, 95% CI, 9.9%-14.7%) compared to other racial and ethnic groups (14.1% in Hispanic to 15.3% in non-Hispanic Black). A significant increase over time was only observed among non-Hispanic Black participants (10.1% in 2014 to 22.1% in 2020; P = .05) and non-Hispanic Whites (8.5% in 2014 to 14% in 2020; P = .02). In adjusted analyses, non-Hispanic Black participants (14.6%, 95% CI, 12.3%-16.7%) had a significantly higher probability of LCS discussion than non-Hispanic Whites (12.1%, 95% CI, 11.4%-12.7%). CONCLUSION: Patient-provider LCS discussion was uncommon in the LCS-eligible US population. Non-Hispanic Black individuals were more likely to have LCS discussions than other racial and ethnic groups. There is a need for more research to clarify the discordance between LCS discussions and the actual screening uptake in this population.

Prevalence of Underreported Nicotine Exposure Among US Nonsmoking Adults: A Comparison of Self-Reported Exposure and Serum Cotinine Levels From NHANES 2013-2020.

INTRODUCTION: Secondhand smoke (SHS) poses a significant health risk. However, individuals who do not smoke may be unaware of their exposure, thereby failing to take protective actions promptly. AIMS AND METHODS: We assessed the prevalence of underreported nicotine exposure in a nationally representative sample of US nonsmoking adults using data from the US National Health and Examination Survey. Individuals with underreported nicotine exposure were defined as those who reported no exposure to all tobacco products (traditional tobacco, nicotine replacements, and e-cigarettes) or SHS, yet had detectable levels of serum cotinine (>0.015 ng/mL). We fitted logistic regression models to determine sociodemographic and chronic condition factors associated with underreported nicotine exposure. RESULTS: Our analysis included 13 503 adults aged 18 years and older. Between 2013 and 2020, the prevalence of self-reported SHS exposure, serum cotinine-assessed nicotine exposure, and underreported nicotine exposure among US nonsmokers were 22.0%, 51.2%, and 34.6%, respectively. Remarkably, 67.6% with detectable serum cotinine reported no SHS exposure. Males, non-Hispanic blacks, individuals of other races (including Asian Americans, Native Americans, and Pacific Islanders), and those without cardiovascular diseases were more likely to underreport nicotine exposure than their counterparts. The median serum cotinine value was higher in respondents who reported SHS exposure (0.107 ng/mL) than in those who reported no exposure (0.035 ng/mL). We estimate that approximately 56 million US residents had underreported nicotine exposure. CONCLUSIONS: Over a third of US nonsmokers underreport their nicotine exposure, underlining the urgent need for comprehensive public awareness campaigns and interventions. Further research into sociodemographic determinants influencing this underreporting is needed. IMPLICATIONS: Understanding the extent of underreported nicotine exposure is crucial for developing effective public health strategies and interventions. It is imperative to bolster public consciousness about the risks associated with SHS. Additionally, surveillance tools should also incorporate measures of exposure to outdoor SHS and e-cigarette vapor to enhance the quality of data monitoring. Findings from this study can guide tobacco control initiatives and inform smoke-free air legislation.

Genetic Testing for Cancer Risk and Perceived Importance of Genetic Information Among US Population by Race and Ethnicity: a Cross-sectional Study.

BACKGROUND: Genetic testing can help determine the risk of many cancers and guide cancer prevention and treatment plans. Despite increasing concern about disparities in precision cancer medicine, public knowledge and cancer genetic testing by race and ethnicity have not been well investigated. METHODS: We analyzed data from the 2020 Health Information National Trends Survey in 2022. Self-reported cancer genetic testing (e.g., Lynch syndrome, BRCA1/2) knowledge and utilization were compared by race and ethnicity. Perceived importance of genetic information for cancer care (prevention, detection, and treatment) was also examined in relation to the uptake of cancer genetic testing. Multivariable logistic regression models were employed to examine factors associated with knowledge and genetic testing to calculate predicted probability of undergoing genetic testing by race and ethnicity. RESULTS: Of 3551 study participants, 37.8% reported having heard of genetic testing for cancer risk and 3.9% stated that they underwent cancer genetic testing. Being non-Hispanic Black (OR=0.47, 95% CI=0.30-0.75) or Hispanic (OR=0.56, CI=0.35-0.90) was associated with lower odds of genetic testing knowledge. Although Hispanic or non-Hispanic Black respondents were more likely to perceive higher importance of genetic information versus non-Hispanic Whites, they had a lower predicted probability of cancer genetic testing. CONCLUSION: Non-Hispanic Black and Hispanic adults had lower knowledge and were less likely to undergo cancer genetic testing than non-Hispanic Whites. Further research is needed on sources of genetic testing information for racial and ethnic minorities and the barriers to accessing genetic testing to inform the development of effective cancer risk genetic testing promotion.

Intensive vision-guided network for radiology report generation.

Objective.Automatic radiology report generation is booming due to its huge application potential for the healthcare industry. However, existing computer vision and natural language processing approaches to tackle this problem are limited in two aspects. First, when extracting image features, most of them neglect multi-view reasoning in vision and model single-view structure of medical images, such as space-view or channel-view. However, clinicians rely on multi-view imaging information for comprehensive judgment in daily clinical diagnosis. Second, when generating reports, they overlook context reasoning with multi-modal information and focus on pure textual optimization utilizing retrieval-based methods. We aim to address these two issues by proposing a model that better simulates clinicians perspectives and generates more accurate reports.Approach.Given the above limitation in feature extraction, we propose a globally-intensive attention (GIA) module in the medical image encoder to simulate and integrate multi-view vision perception. GIA aims to learn three types of vision perception: depth view, space view, and pixel view. On the other hand, to address the above problem in report generation, we explore how to involve multi-modal signals to generate precisely matched reports, i.e. how to integrate previously predicted words with region-aware visual content in next word prediction. Specifically, we design a visual knowledge-guided decoder (VKGD), which can adaptively consider how much the model needs to rely on visual information and previously predicted text to assist next word prediction. Hence, our final intensive vision-guided network framework includes a GIA-guided visual encoder and the VKGD.Main results.Experiments on two commonly-used datasets IU X-RAY and MIMIC-CXR demonstrate the superior ability of our method compared with other state-of-the-art approaches.Significance.Our model explores the potential of simulating clinicians perspectives and automatically generates more accurate reports, which promotes the exploration of medical automation and intelligence.

An Uncertainty-Guided Deep Learning Method Facilitates Rapid Screening of CYP3A4 Inhibitors.

Cytochrome P450 3A4 (CYP3A4), a prominent member of the P450 enzyme superfamily, plays a crucial role in metabolizing various xenobiotics, including over 50% of clinically significant drugs. Evaluating CYP3A4 inhibition before drug approval is essential to avoiding potentially harmful pharmacokinetic drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Despite the development of several CYP inhibitor prediction models, the primary approach for screening CYP inhibitors still relies on experimental methods. This might stem from the limitations of existing models, which only provide deterministic classification outcomes instead of precise inhibition intensity (e.g., IC50) and often suffer from inadequate prediction reliability. To address this challenge, we propose an uncertainty-guided regression model to accurately predict the IC50 values of anti-CYP3A4 activities. First, a comprehensive data set of CYP3A4 inhibitors was compiled, consisting of 27,045 compounds with classification labels, including 4395 compounds with explicit IC50 values. Second, by integrating the predictions of the classification model trained on a larger data set and introducing an evidential uncertainty method to rank prediction confidence, we obtained a high-precision and reliable regression model. Finally, we use the evidential uncertainty values as a trustworthy indicator to perform a virtual screening of an in-house compound set. The in vitro experiment results revealed that this new indicator significantly improved the hit ratio and reduced false positives among the top-ranked compounds. Specifically, among the top 20 compounds ranked with uncertainty, 15 compounds were identified as novel CYP3A4 inhibitors, and three of them exhibited activities less than 1 µM. In summary, our findings highlight the effectiveness of incorporating uncertainty in compound screening, providing a promising strategy for drug discovery and development.

Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network.

Background: The thickness of the buccal bone of the anterior maxilla is an important aesthetic-determining factor for dental implant, which is divided into the thick (≥1 mm) and thin type (<1 mm). However, as a micro-scale structure that is evaluated through low-resolution cone-beam computed tomography (CBCT), its thickness measurement is error-prone under the circumstance of enormous patients and relatively inexperienced primary dentists. Further, the challenges of deep learning-based analysis of the binary thickness of buccal bone include the substantial real-world variance caused by pixel error, the extraction of fine-grained features, and burdensome annotations. Methods: This study built bilinear convolutional neural network (BCNN) with 2 convolutional neural network (CNN) backbones and a bilinear pooling module to predict the binary thickness of buccal bone (thick or thin) of the anterior maxilla in an end-to-end manner. The methods of 5-fold cross-validation and model ensemble were adopted at the training and testing stages. The visualization methods of Gradient Weighted Class Activation Mapping (Grad-CAM), Guided Grad-CAM, and layer-wise relevance propagation (LRP) were used for revealing the important features on which the model focused. The performance metrics and efficacy were compared between BCNN, dentists of different clinical experience (i.e., dental student, junior dentist, and senior dentist), and the fusion of BCNN and dentists to investigate the clinical feasibility of BCNN. Results: Based on the dataset of 4,000 CBCT images from 1,000 patients (aged 36.15±13.09 years), the BCNN with visual geometry group (VGG)16 backbone achieved an accuracy of 0.870 [95% confidence interval (CI): 0.838-0.902] and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.924 (95% CI: 0.896-0.948). Compared with the conventional CNNs, BCNN precisely located the buccal bone wall over irrelevant regions. The BCNN generally outperformed the expert-level dentists. The clinical diagnostic performance of the dentists was improved with the assistance of BCNN. Conclusions: The application of BCNN to the quantitative analysis of binary buccal bone thickness validated the model's excellent ability of subtle feature extraction and achieved expert-level performance. This work signals the potential of fine-grained image recognition networks to the precise quantitative analysis of micro-scale structures.

Cerebrospinal Fluid Proteomic Profiles in Patients with Postherpetic Neuralgia.

The intrinsic mechanism of postherpetic neuralgia (PHN) remains unclear. Herein, we aimed to seek the hub proteins in the cerebrospinal fluid (CSF), which display significant changes between the PHN and nonpainful patients (Control). First, the proteomic results showed that compared with the Control-CSF, there were 100 upregulated and 50 downregulated differentially expressed proteins (DEPs) in the PHN-CSF. Besides, functional analyses including gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) revealed that biological processes and pathways including complement activation, infection, coagulation, and lipid metabolism were activated, while synaptic organization was suppressed. Next, the protein-protein interaction (PPI) analysis indicated that increased PLG, F2, APOA1, APOA2, SERPINC1, and KNG1 and reduced APOE, which were all enriched in the top pathways according to the KEGG analysis, were defined as hub proteins. Finally, three of the hub proteins, such as PLG, APOA1, and APOE, were reconfirmed in a larger cohort using both enzyme-linked immunosorbent assay (ELISA) and Western blotting methods. Above all, the results indicated that PLG, APOA1, and APOE and their involved processes such as infection, inflammation, cholesterol metabolism, and coagulation shall be potential therapeutic approaches. (The raw mass spectrometry proteome data and search results have been deposited to the iProx-integrated Proteome Resources (http://www.iprox.cn) with the data set identifier IPX0007372000.).