•Fatty liver disease (FLD) is a common clinical complication, is associated with high morbidity and mortality.•A machine learning model has been using to predict liver disease that could assist ...physicians in classifying high-risk patients and make a novel diagnosis.•The random forest model shows higher performance than other classification models.
Fatty liver disease (FLD) is a common clinical complication; it is associated with high morbidity and mortality. However, an early prediction of FLD patients provides an opportunity to make an appropriate strategy for prevention, early diagnosis and treatment. We aimed to develop a machine learning model to predict FLD that could assist physicians in classifying high-risk patients and make a novel diagnosis, prevent and manage FLD.
We included all patients who had an initial fatty liver screening at the New Taipei City Hospital between 1st and 31st December 2009. Classification models such as random forest (RF), Naïve Bayes (NB), artificial neural networks (ANN), and logistic regression (LR) were developed to predict FLD. The area under the receiver operating characteristic curve (ROC) was used to evaluate performances among the four models.
A total of 577 patients were included in this study; of those 377 patients had fatty liver. The area under the receiver operating characteristic (AUROC) of RF, NB, ANN, and LR with 10 fold-cross validation was 0.925, 0.888, 0.895, and 0.854 respectively. Additionally, The accuracy of RF, NB, ANN, and LR 87.48, 82.65, 81.85, and 76.96%.
In this study, we developed and compared the four classification models to predict fatty liver disease accurately. However, the random forest model showed higher performance than other classification models. Implementation of a random forest model in the clinical setting could help physicians to stratify fatty liver patients for primary prevention, surveillance, early treatment, and management.
Levothyroxine is a widely prescribed medication for the treatment of an underactive thyroid. The relationship between levothyroxine use and cancer risk is largely underdetermined. To investigate the ...magnitude of the possible association between levothyroxine use and cancer risk, this retrospective case‐control study was conducted using Taiwan’s Health and Welfare Data Science Center database. Cases were defined as all patients who were aged ≥20 years and had a first‐time diagnosis for cancer at any site for the period between 2001 and 2011. Multivariable conditional logistic regression models were used to calculate an adjusted odds ratio (AOR) to reduce potential confounding factors. A total of 601 733 cases and 2 406 932 controls were included in the current study. Levothyroxine users showed a 50% higher risk of cancer at any site (AOR: 1.50, 95% CI: 1.46‐1.54; P < .0001) compared with non–users. Significant increased risks were also observed for brain cancer (AOR: 1.90, 95% CI: 1.48‐2.44; P < .0001), skin cancer (AOR: 1.42, 95% CI: 1.17‐1.72; P < .0001), pancreatic cancer (AOR: 1.27, 95% CI: 1.01‐1.60; P = .03), and female breast cancer (AOR: 1.24, 95% CI: 1.15‐1.33; P < .0001). Our study results showed that levothyroxine use was significantly associated with an increased risk of cancer, particularly brain, skin, pancreatic, and female breast cancers. Levothyroxine remains a highly effective therapy for hypothyroidism; therefore, physicians should carefully consider levothyroxine therapy and monitor patients’ condition to avoid negative outcomes. Additional studies are needed to confirm these findings and to evaluate the potential biological mechanisms.
This retrospective case‐control study analyzed 23 million patients from Taiwan’s claims data. Levothyroxine use was significantly associated with an increased risk of brain, skin, pancreatic, and female breast cancers.
Breast cancer arises from breast epithelial cells that acquire genetic alterations leading to subsequent loss of tissue homeostasis. Several distinct epithelial subpopulations have been proposed, but ...complete understanding of the spectrum of heterogeneity and differentiation hierarchy in the human breast remains elusive. Here, we use single-cell mRNA sequencing (scRNAseq) to profile the transcriptomes of 25,790 primary human breast epithelial cells isolated from reduction mammoplasties of seven individuals. Unbiased clustering analysis reveals the existence of three distinct epithelial cell populations, one basal and two luminal cell types, which we identify as secretory L1- and hormone-responsive L2-type cells. Pseudotemporal reconstruction of differentiation trajectories produces one continuous lineage hierarchy that closely connects the basal lineage to the two differentiated luminal branches. Our comprehensive cell atlas provides insights into the cellular blueprint of the human breast epithelium and will form the foundation to understand how the system goes awry during breast cancer.
Statins have been shown to be a beneficial treatment as chemotherapy and target therapy for lung cancer. This study aimed to investigate the effectiveness of statins in combination with epidermal ...growth factor receptor‐tyrosine kinase inhibitor therapy for the resistance and mortality of lung cancer patients. A population‐based cohort study was conducted using the Taiwan Cancer Registry database. From January 1, 2007, to December 31, 2012, in total 792 non‐statins and 41 statins users who had undergone EGFR‐TKIs treatment were included in this study. All patients were monitored until the event of death or when changed to another therapy. Kaplan‐Meier estimators and Cox proportional hazards regression models were used to calculate overall survival. We found that the mortality was significantly lower in patients in the statins group compared with patients in the non‐statins group (4‐y cumulative mortality, 77.3%; 95% confidence interval (CI), 36.6%‐81.4% vs. 85.5%; 95% CI, 78.5%‐98%; P = .004). Statin use was associated with a reduced risk of death in patients the group who had tumor sizes <3 cm (hazard ratio HR, 0.51, 95% CI, 0.29‐0.89) and for patients in the group who had CCI scores <3 (HR, 0.6; 95% CI, 0.41‐0.88; P = .009). In our study, statins were found to be associated with prolonged survival time in patients with lung cancer who were treated with EGFR‐TKIs and played a synergistic anticancer role.
In this article, we describe the use of statins on the beneficial mortality of lung cancer patients with EGFR‐TKIs therapy. We found that statins were associated with prolonged survival time in patients with lung cancer who were taking EGFR‐TKIs. These could be playing a synergic anticancer role during the TKI treatment period, as well as improving quality of life worldwide and medical practice overall.
This paper describes the route, from simulations toward experiments, for optimizing the magnetoelectric (ME) geometries for vortex magnetic field sensors. The research is performed on the base of the ...Metglas/Piezoelectric (PZT) laminates in both open and closed magnetic circuit (OMC and CMC) geometries with different widths (
), lengths (
), and diameters (
). Among these geometries, the CMC laminates demonstrate advantages not only in their magnetic flux distribution, but also in their sensitivity and in their independence of the position of the vortex center. In addition, the ME voltage signal is found to be enhanced by increasing the magnetostrictive volume fraction. Optimal issues are incorporated to realize a CMC-based ME double sandwich current sensor in the ring shape with
×
= 6 mm × 1.5 mm and four layers of Metglas. At the resonant frequency of 174.4 kHz, this sensor exhibits the record sensitivity of 5.426 V/A as compared to variety of devices such as the CMC ME sensor family, fluxgate, magnetoresistive, and Hall-effect-based devices. It opens a potential to commercialize a new generation of ME-based current and (or) vortex magnetic sensors.
Innovation is a complex process and has been shown to be influential towards different types of stakeholders. From the viewpoint of stakeholder theory, shareholders and creditors are more likely to ...be concerned about corporate financial performance. However, in the new era an enterprise’s responsibilities have to extend to other stakeholders, including its employees, suppliers and communities. This study aims to extend the literature by examining the individual effects of product and process innovations, and then their interactions with external collaboration, on firm performance and corporate social responsibility (CSR) activities in terms of local contributions for a sample of Vietnamese manufacturing firms during 2011–2013. Research findings suggest that process and product innovations are beneficial to firm performance in terms of market share, but not return on total assets. This implies that investment in innovative activities requires time to make positive changes in profitability, but it may help with winning customer loyalty. We also find evidence suggesting that innovation could make firms more obscure, especially when there are external parties involved. This motivates firms to send signals about their sustainability and goodwill through corporate social responsibility (CSR) activities. With regard to CSR activities, we are the first to provide a breakdown of categories of corporate social contribution towards the local well-being, and elaborate evidence on the effect of innovation on each category, rather than just a composite index of CSR as in some extant studies.
•The incidence of non-ST segment elevation myocardial infarction (NSTEMI) has been increased worldwide.•We developed an artificial intelligence approach to predict stable NSTEMI that would give ...valuable insight to reduce misdiagnosis in the clinical setting.•ANN prediction model showed a higher accuracy to predict NSTEMI patients.
Hospital admission rate for the patients with chest pain has already been increased worldwide but no existing risk score has been designed to stratify non-ST-elevation myocardial infarction (NSTEMI) from non-cardiogenic chest pain. Clinical diagnosis of chest pain in the emergency department is always highly subjective and variable. We, therefore, aimed to develop an artificial intelligence approach to predict stable NSTEMI that would give valuable insight to reduce misdiagnosis in the real clinical setting.
A standard protocol was developed to collect data from chest pain patients who had visited the emergency department between December 2016 and February 2017. All the chest pain patients with aged <20 years were primarily included in this study. However, STEMI, previous history of ACS, and out-of-hospital cardiac arrest were excluded from our study. An artificial neural network (ANN) model was then developed to predict NSTEMI patients. The accuracy, sensitivity, specificity, and receiver operating characteristic curve was used to measure the performance of this model.
A total of 268 chest pain patients were included in this study; of those, 47 (17.5%) was stable NSTEMI, and 221 (82.5%) was unstable angina patients. Serval risk factors such as cardiac risk factor, systolic blood pressure, hemoglobin, corrected QT interval (QTc), PR interval, glutamic-oxaloacetic transaminase, glutamic pyruvic transaminase and troponin were independently associated with stable NSTEMI. The area under the receiver operating characteristic (AUROC) and accuracy of ANN were 98.4, and 92.86. Additionally, the sensitivity, specificity, positive predictive value, and negative predictive value of the ANN model was 90.91, 93.33, 76.92, and 97.67 respectively.
Our prediction model showed a higher accuracy to predict NSTEMI patients. This model has a potential application in disease detection, monitoring, and prognosis of chest pain at risk of AMI.
Benzodiazepines are a widely used medication in developed countries, particularly among elderly patients. However, benzodiazepines are known to affect memory and cognition and might thus enhance the ...risk of dementia. The objective of this review is to synthesize evidence from observational studies that evaluated the association between benzodiazepines use and dementia risk.
We performed a systematic review and meta-analysis of controlled observational studies to evaluate the risk of benzodiazepines use on dementia outcome. All control observational studies that compared dementia outcome in patients with benzodiazepine use with a control group were included. We calculated pooled ORs using a random-effects model. Ten studies (of 3,696 studies identified) were included in the systematic review, of which 8 studies were included in random-effects meta-analysis and sensitivity analyses. Odds of dementia were 78% higher in those who used benzodiazepines compared with those who did not use benzodiazepines (OR 1.78; 95% CI 1.33-2.38). In subgroup analysis, the higher association was still found in the studies from Asia (OR 2.40; 95% CI 1.66-3.47) whereas a moderate association was observed in the studies from North America and Europe (OR 1.49; 95% CI 1.34-1.65 and OR 1.43; 95% CI 1.16-1.75). Also, diabetics, hypertension, cardiac disease, and statin drugs were associated with increased risk of dementia but negative association was observed in the case of body mass index. There was significant statistical and clinical heterogeneity among studies for the main analysis and most of the sensitivity analyses. There was significant statistical and clinical heterogeneity among the studies for the main analysis and most of the sensitivity analyses. Key Messages: Our results suggest that benzodiazepine use is significantly associated with dementia risk. However, observational studies cannot clarify whether the observed epidemiologic association is a causal effect or the result of some unmeasured confounding variable. Therefore, more research is needed.
The study used clinical data to develop a prediction model for breast cancer survival. Breast cancer prognostic factors were explored using machine learning techniques. We conducted a retrospective ...study using data from the Taipei Medical University Clinical Research Database, which contains electronic medical records from three affiliated hospitals in Taiwan. The study included female patients aged over 20 years who were diagnosed with primary breast cancer and had medical records in hospitals between January 1, 2009 and December 31, 2020. The data were divided into training and external testing datasets. Nine different machine learning algorithms were applied to develop the models. The performances of the algorithms were measured using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. A total of 3914 patients were included in the study. The highest AUC of 0.95 was observed with the artificial neural network model (accuracy, 0.90; sensitivity, 0.71; specificity, 0.73; PPV, 0.28; NPV, 0.94; and F1-score, 0.37). Other models showed relatively high AUC, ranging from 0.75 to 0.83. According to the optimal model results, cancer stage, tumor size, diagnosis age, surgery, and body mass index were the most critical factors for predicting breast cancer survival. The study successfully established accurate 5-year survival predictive models for breast cancer. Furthermore, the study found key factors that could affect breast cancer survival in Taiwanese women. Its results might be used as a reference for the clinical practice of breast cancer treatment.
Abstract
Objective
Birth month and climate impact lifetime disease risk, while the underlying exposures remain largely elusive. We seek to uncover distal risk factors underlying these relationships ...by probing the relationship between global exposure variance and disease risk variance by birth season.
Material and Methods
This study utilizes electronic health record data from 6 sites representing 10.5 million individuals in 3 countries (United States, South Korea, and Taiwan). We obtained birth month–disease risk curves from each site in a case-control manner. Next, we correlated each birth month–disease risk curve with each exposure. A meta-analysis was then performed of correlations across sites. This allowed us to identify the most significant birth month–exposure relationships supported by all 6 sites while adjusting for multiplicity. We also successfully distinguish relative age effects (a cultural effect) from environmental exposures.
Results
Attention deficit hyperactivity disorder was the only identified relative age association. Our methods identified several culprit exposures that correspond well with the literature in the field. These include a link between first-trimester exposure to carbon monoxide and increased risk of depressive disorder (R = 0.725, confidence interval 95% CI, 0.529-0.847), first-trimester exposure to fine air particulates and increased risk of atrial fibrillation (R = 0.564, 95% CI, 0.363-0.715), and decreased exposure to sunlight during the third trimester and increased risk of type 2 diabetes mellitus (R = −0.816, 95% CI, −0.5767, −0.929).
Conclusion
A global study of birth month–disease relationships reveals distal risk factors involved in causal biological pathways that underlie them.
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