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Automatic and robust object detection in remote sensing images is of vital significance in real-world applications such as land resource management and disaster rescue. However, poor performance arises when the state-of-the-art natural image detection algorithms are directly applied to remote sensing images, which largely results from the variations in object scale, aspect ratio, indistinguishable object appearances, and complex background scenario. In this paper, we propose a novel Feature Enhancement Network (FENet) for object detection in optical remote sensing images, which consists of a Dual Attention Feature Enhancement (DAFE) module and a Context Feature Enhancement (CFE) module. Specifically, the DAFE module is introduced to highlight the network to focus on the distinctive features of the objects of interest and suppress useless ones by jointly recalibrating the spatial and channel feature responses. The CFE module is designed to capture global context cues and selectively strengthen class-aware features by leveraging image-level contextual information that indicates the presence or absence of the object classes. To this end, we employ a context encoding loss to regularize the model training which promotes the object detector to understand the scene better and narrows the probable object categories in prediction. We achieve our proposed FENet by unifying DAFE and CFE into the framework of Faster R-CNN. In the experiments, we evaluate our proposed method on two large-scale remote sensing image object detection datasets including DIOR and DOTA and demonstrate its effectiveness compared with the baseline methods.

Objective The triglyceride–glucose (TyG) index has been shown to be a new alternative measure for insulin resistance. However, no study has attempted to investigate the association of the TyG index with incident atrial fibrillation (AF) in the general population without known cardiovascular diseases. Methods Individuals without known cardiovascular diseases (heart failure, coronary heart disease, or stroke) from the Atherosclerosis Risk in Communities (ARIC) cohort were recruited. The baseline TyG index was calculated as the Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. The association between the baseline TyG index and incident AF was examined using Cox regression. Results Of 11,851 participants, the mean age was 54.0 years; 6586 (55.6%) were female. During a median follow-up of 24.26 years, 1925 incidents of AF cases (0.78/per 100 person-years) occurred. An increased AF incidence with a graded TyG index was found by Kaplan‒Meier curves ( P  < 0.001). In multivariable-adjusted analysis, both < 8.80 (adjusted hazard ratio [aHR] = 1.15, 95% confidence interval [CI] 1.02, 1.29) and > 9.20 levels (aHR 1.18, 95% CI 1.03, 1.37) of the TyG index were associated with an increased risk of AF compared with the middle TyG index category (8.80–9.20). The exposure-effect analysis confirmed the U-shaped association between the TyG index and AF incidence ( P  = 0.041). Further sex-specific analysis showed that a U-shaped association between the TyG index and incident AF still existed in females but not in males. Conclusions A U-shaped association between the TyG index and AF incidence is observed in Americans without known cardiovascular diseases. Female sex may be a modifier in the association between the TyG index and AF incidence. Graphical Abstract

Background Commencing work at an early age has been linked to various risk factors for coronary heart disease (CHD), such as shift work and intensive job strain. However, the relationship between starting work too early and CHD risk remains largely unclear. We examined the association between age at job initiation and the risk of CHD. Methods UK Biobank participants aged 38 to 70 years without cardiovascular disease who provided data on their age at job initiation were included. The primary outcome was CHD, which was ascertained using hospital and death records. The hazard ratios (HRs) and 95% confidence interval (CIs) for the association between age at job initiation and CHD were calculated using multivariable Cox regression. Results Of the 501,971 participants, 114,418 eligible participants were included in the final analysis. The median age at job initiation was 19.0 years. During the mean follow-up of 12.6 years, 6,130 (5.4%) first CHD events occurred. We observed that age at job initiation was inversely associated with CHD (HR 0.98, 95% CI 0.97–0.99), and the association was potentially J-shaped. The HRs for the < 17-year, 17–18-year, and 19–21-year age groups were 1.29 (95%CI 1.18–1.41), 1.12 (95% CI 1.03–1.22) and 1.05 (95% CI 0.97–1.14), respectively, compared with those of the ≥ 22-year group. Conclusions Age at job initiation was associated with incident CHD, which was independent of socioeconomic status. Participants who commenced employment before the age of 19 years exhibited a higher risk of developing CHD later in adulthood.

ObjectivesWe aimed to develop and validate a prognostic nomogram and evaluate the discrimination of the nomogram model in order to improve the prediction of 30-day survival of critically ill myocardial infarction (MI) patients.DesignA retrospective cohort study.SettingData were collected from the Medical Information Mart for Intensive Care (MIMIC)-III database, consisting of critically ill participants between 2001 and 2012 in the USA.ParticipantsA total of 2031 adult critically ill patients with MI were enrolled from the MIMIC-III database.Primary and secondary outcomeThirty-day survival.ResultsIndependent prognostic factors, including age, heart rate, white blood cell count, blood urea nitrogen and bicarbonate, were identified by Cox regression model and used in the nomogram. Good agreement between the prediction and observation was indicated by the calibration curve for 30-day survival. The nomogram exhibited reasonably accurate discrimination (area under the receiver operating characteristic curve, 0.765, 95% CI, 0.716 to 0.814) and calibration (C-index, 0.758, 95% CI, 0.712 to 0.804) in the validation cohort. Decision curve analysis demonstrated that the nomogram was clinically beneficial. Additionally, participants could be classified into two risk groups by the nomogram, and the 30-day survival probability was significantly different between them (p<0.001).ConclusionThis five-factor nomogram can achieve a reasonable degree of accuracy to predict 30-day survival in critically ill MI patients and might be helpful for risk stratification and decision-making for MI patients.

ChatGPT, an artificial intelligence (AI) based on large-scale language models, has sparked interest in the field of health care. Nonetheless, the capabilities of AI in text comprehension and generation are constrained by the quality and volume of available training data for a specific language, and the performance of AI across different languages requires further investigation. While AI harbors substantial potential in medicine, it is imperative to tackle challenges such as the formulation of clinical care standards; facilitating cultural transitions in medical education and practice; and managing ethical issues including data privacy, consent, and bias. The study aimed to evaluate ChatGPT's performance in processing Chinese Postgraduate Examination for Clinical Medicine questions, assess its clinical reasoning ability, investigate potential limitations with the Chinese language, and explore its potential as a valuable tool for medical professionals in the Chinese context. A data set of Chinese Postgraduate Examination for Clinical Medicine questions was used to assess the effectiveness of ChatGPT's (version 3.5) medical knowledge in the Chinese language, which has a data set of 165 medical questions that were divided into three categories: (1) common questions (n=90) assessing basic medical knowledge, (2) case analysis questions (n=45) focusing on clinical decision-making through patient case evaluations, and (3) multichoice questions (n=30) requiring the selection of multiple correct answers. First of all, we assessed whether ChatGPT could meet the stringent cutoff score defined by the government agency, which requires a performance within the top 20% of candidates. Additionally, in our evaluation of ChatGPT's performance on both original and encoded medical questions, 3 primary indicators were used: accuracy, concordance (which validates the answer), and the frequency of insights. Our evaluation revealed that ChatGPT scored 153.5 out of 300 for original questions in Chinese, which signifies the minimum score set to ensure that at least 20% more candidates pass than the enrollment quota. However, ChatGPT had low accuracy in answering open-ended medical questions, with only 31.5% total accuracy. The accuracy for common questions, multichoice questions, and case analysis questions was 42%, 37%, and 17%, respectively. ChatGPT achieved a 90% concordance across all questions. Among correct responses, the concordance was 100%, significantly exceeding that of incorrect responses (n=57, 50%; P<.001). ChatGPT provided innovative insights for 80% (n=132) of all questions, with an average of 2.95 insights per accurate response. Although ChatGPT surpassed the passing threshold for the Chinese Postgraduate Examination for Clinical Medicine, its performance in answering open-ended medical questions was suboptimal. Nonetheless, ChatGPT exhibited high internal concordance and the ability to generate multiple insights in the Chinese language. Future research should investigate the language-based discrepancies in ChatGPT's performance within the health care context.

Aim Non‐alcoholic fatty liver disease (NAFLD)‐related advanced liver fibrosis (Stage 3 or 4) was reported to be linked to worse prognosis in patients with heart failure with preserved ejection fraction (HFpEF). This study aims to assess the relationship between liver fibrosis scores and new‐onset atrial fibrillation (AF) incidence in patients with HFpEF in the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial. Methods and results Baseline liver fibrosis levels, assessed by NAFLD fibrosis score (NFS) or Fibrosis‐4 index (FIB‐4), with AF incidence were expressed as hazard ratios (HRs) using the Cox proportional hazard model. The risk for advanced fibrosis was estimated to be 21.5% (447/2072) as assessed by FIB‐4 (>3.25) and 4.2% (88/2072) as assessed by NFS (>0.676) in HFpEF patients without baseline AF. After a median follow‐up of 3.11 years, 106 new‐onset AF cases occurred. In multivariate analysis, elevated NFS [NFS = −1.455–0.676: HR 2.44, 95% confidence interval (CI) 1.27–4.68; NFS > 0.676: HR 3.36, 95% CI 1.27–6.80; per 1 unit increase: HR 1.15, 95% CI 1.01–1.32], not FIB‐4 (FIB‐4 = 1.45–3.25: HR 1.02, 95% CI 0.67–1.55; FIB‐4 > 3.25: HR 1.69, 95% CI 0.76–3.79; per 1 unit increase: HR 1.13, 95% CI 0.93–1.37), was associated with increased AF incidence. The NFS (C‐index 0.662), not FIB‐4 (C‐index 0.531), had a moderate predictive ability in predicting incident AF. Conclusions Among patients with HFpEF, the risk of advanced liver fibrosis is associated with an increased incidence of new‐onset AF and may be a novel predictor for new‐onset AF. Additional studies are needed to confirm our results.

Background Several kinds of motor dysfunction have been studied for predicting future fall risk in community-dwelling older individuals. However, no study has tested the ability of the fine motor index (FINEA) and gross motor index (GROSSA) to predict the risk of falling, as well as the specific fall type. Objective We investigated the associations of FINEA/GROSSA scores with fall risk, explained falls, and unexplained falls. Methods A total of 6267 community-dwelling adults aged ≥ 50 years from the Irish Longitudinal Study on Aging (TILDA) cohort were included. First, the associations of FINEA and GROSSA scores with the history of total falls, explained falls and unexplained falls were assessed in a cross-sectional study and further verified in a prospective cohort after 2 years of follow-up by Poisson regression analysis. Results We found that high FINEA and GROSSA scores were positively associated with almost all fall histories (FINEA scores: total falls: adjusted prevalence ratio [aPR] = 1.28, P  = 0.009; explained falls: aPR = 1.15, P  = 0.231; unexplained falls: aPR = 1.88, P  < 0.001; GROSSA scores: total falls: aPR = 1.39, P  < 0.001; explained falls: aPR = 1.28, P  = 0.012; unexplained falls: aPR = 2.18, P  < 0.001) in a cross-sectional study. After 2 years of follow-up, high FINEA scores were associated with an increased incidence of total falls (adjusted rate ratio [aRR] = 1.42, P  = 0.016) and explained falls (aRR = 1.51, P  = 0.020) but not with unexplained falls (aRR = 1.41, P  = 0.209). High GROSSA scores were associated with an increased incidence of unexplained falls (aRR = 1.57, P  = 0.041) and were not associated with either total falls (aRR = 1.21, P  = 0.129) or explained falls (aRR = 1.07, P  = 0.656). Compared with individuals without limitations in either the FINEA or GROSSA, individuals with limitations in both indices had a higher risk of falls, including total falls (aRR = 1.35, P  = 0.002), explained falls (aRR = 1.31, P  = 0.033) and unexplained falls (aRR = 1.62, P  = 0.004). Conclusion FINEA scores were positively associated with accidental falls, while GROSSA scores were positively associated with unexplained falls. The group for whom both measures were impaired showed a significantly higher risk of both explained and unexplained falls. FINEA or GROSSA scores should be investigated further as possible tools to screen for and identify community-dwelling adults at high risk of falling.

Aims The prognostic significance of N‐terminal pro B‐type natriuretic peptide (NT‐proBNP) in heart failure with preserved ejection fraction (HFpEF) has been well established. HFpEF and atrial fibrillation (AF) commonly coexist, and each contributes to poor outcomes independently. Nevertheless, the ability of NT‐proBNP to predict AF in HFpEF patients remains uncertain. Methods and results A total of 367 HFpEF patients without baseline AF from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial were included. The Cox proportional hazard model was used to assess the association of NT‐proBNP with the risk of AF. The C‐statistic, categorical net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate the ability of NT‐proBNP in new‐onset AF prediction. During a median follow‐up of 2.91 years, 17 (4.63%) new‐onset AF cases occurred. Every 1000 pg/mL increase in NT‐proBNP was associated with a 16% increase in the risk of AF occurrence after adjustments (hazard ratio, 1.16 [95% CI, 1.02–1.32]). NT‐proBNP showed a moderate performance for new‐onset AF at 3 years (C‐statistic, 0.67). Adding NT‐proBNP to CHADS2/R2CHADS2/CHA2DS2‐VASc/C2HSET scores improved their predictive performance for AF risk (CHADS2: C‐statistic, 0.63, CHADS2+NT: C‐statistic, 0.69, NRI, 47.46%, IDI, 1.18%; R2CHADS2: C‐statistic, 0.65, R2CHADS2+NT: C‐statistic, 0.70, NRI, 48.03%, IDI, 0.51%; CHA2DS2‐VASc: C‐statistic, 0.67, CHA2DS2‐VASc+NT: C‐statistic, 0.72, NRI, 49.41%, IDI, 0.86%; C2HSET: C‐statistic, 0.77, C2HSET+NT: C‐statistic, 0.80, NRI, 50.32%, IDI, 1.58%). Conclusions Among patients with HFpEF, the NT‐proBNP level was positively associated with the incidence of new‐onset AF and may be a promising predictor.

Background: Whether heart rate (HR) fluctuation after admission has an impact on the outcomes of critically ill myocardial infarction (MI) patients in intensive care unit remains unknown. Methods: A total of 2,031 MI patients were enrolled from the Medical Information Mart for Intensive Care (MIMIC-III) database. HR fluctuation was calculated as the maximum HR minus the minimum HR in the initial 24 h after admission. Participants were divided into 3 groups, namely, low HR fluctuation [<30 beats per minute (bpm)], medium HR fluctuation (30–49 bpm), and high HR fluctuation (≥ 50 bpm). The main outcomes were 30–day and 1-year mortality. Cox regression and restricted cubic spline model were used. Results: Each 10-bpm increase in HR fluctuation was associated with a higher risk of 30-day mortality and 1-year mortality, with adjusted hazard ratios of 1.122 (95% CI, 1.083–1.162) and 1.107 (95% CI, 1.074–1.140), respectively. Compared with the low HR fluctuation group, the high HR fluctuation group suffered a significantly higher risk of mortality after adjustment, with hazard ratios of 2.156 (95% CI, 1.483–3.134) for 30-day mortality and 1.796 (95% CI, 1.354–2.381) for 1-year mortality. A typical J-type curve was observed in restricted cubic splines for the association between HR fluctuation and 30-day or 1-year mortality of MI patients, with the lowest risk on the HR fluctuation of 30 bpm. Sensitivity analyses emphasized the robustness of our results. Conclusions: This retrospective cohort study revealed an independent positive association between HR fluctuation and 30-day and 1-year mortality in critically ill MI patients, which warrants further investigation.

Non-invasive detection of unstable angina (UA) patients with different severity of coronary lesions remains challenging. This study aimed to identify plasma lipoproteins (LPs) that can be used as potential biomarkers for assessing the severity of coronary lesions, determined by the Gensini score (GS), in UA patients. We collected blood plasma from 67 inpatients with angiographically normal coronary arteries (NCA) and 230 UA patients, 155 of them with lowGS (GS ≤ 25.4) and 75 with highGS (GS > 25.4), and analyzed it using proton nuclear magnetic resonance spectroscopy to quantify 112 lipoprotein variables. In a logistic regression model adjusted for four well-known risk factors (age, sex, body mass index and use of lipid-lowering drugs), we tested the association between each lipoprotein and the risk of UA. Combined with the result of LASSO and PLS-DA models, ten of them were identified as important LPs. The discrimination with the addition of selected LPs was evaluated. Compared with the basic logistic model that includes four risk factors, the addition of these ten LPs concentrations did not significantly improve UA versus NCA discrimination. However, thirty-two selected LPs showed notable discrimination power in logistic regression modeling distinguishing highGS UA patients from NCA with a 14.9% increase of the area under the receiver operating characteristics curve. Among these LPs, plasma from highGS patients was enriched with LDL and VLDL subfractions, but lacked HDL subfractions. In summary, we conclude that blood plasma lipoproteins can be used as biomarkers to distinguish UA patients with severe coronary lesions from NCA patients.