An International Standard RWD Database Designed - Taiwan Experience.

The use of Real-World Data (RWD) in medical data analysis is nowadays required. RWD may come from electronic health records (EHRs), insurance claims, medical products, Internet of Things sensors, health screenings, etc. The goal of RWD is that the data used for analysis should not be affected by environmental variables, experimental control, research context settings, etc. RWD can effectively reduce the cost and improve the accuracy of medical research. The clinical data and patient self-report are integrated, cleaned and pre-processed, and the data format is unified and standardized by international standard formats to provide a structured database for clinical research by this study. An international standard real-world research database was established through the breast cancer database of a medical center in Taipei.

Artificial Intelligence Approach for Severe Dengue Early Warning System.

Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complications. Dengue fever is one of the global burden diseases which annually have 50-100 million cases with 500,000 cases of severe dengue fever, of which 22,000 deaths occur mostly in children. Despite the discovery of vaccines, vector control is still the main approach for prevention efforts. Early detection and accessibility to medical care can reduce severe Dengue mortality rate from 50% to 2%. In the previous study, both statistical and machine learning methods have the potential for predicting a Dengue outbreak, but the study is still fragmented and limited on implementing the generated model into an early warning system application. In this study, we developed an artificial intelligence model with spatiotemporal to predict Dengue outbreak and Dengue incidence case which is ready to be implemented into an early warning system application. Indonesia, especially Semarang City, has experienced an endemic Dengue. We used Semarang City spatiotemporal, meteorological, climatological, and Dengue surveillance epidemiology data from January 2014 to December 2021 in 16 districts of Semarang City. We reviewed 7208 samples from 16 districts and 1 city per week during 8 years. The entire dataset was divided into training (80%) and testing (20%) to develop a prediction model. We used machine learning and Long Short Term Memory (LSTM) to predict Dengue outbreak 1 week before the event for each district. and machine learning to predict Dengue incident cases 1 week before the event for each district. Accuracy, area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score were considered to evaluate the Dengue outbreak prediction model. The Dengue incidence cases prediction model will evaluate using Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R2). Extra Trees Classifier model shown outperform in Dengue outbreak prediction, with accuracy 0.8925, AUROC 0. 9529, Recall 0.6117, precision 0.8880, and F1 score 0.7238. CatBoost Regressor model is shown to outperform in Dengue incidence cases prediction, with R2 0.5621, MAE 0.6304, MSE 1.1997, and RMSE 1.0891. The study proves that Artificial Intelligence (AI) with a spatiotemporal approach can give higher performance in Dengue outbreak and incidence cases prediction. Utilization of AI approaches that are sensitive with spatiotemporal feasibility to implement in Dengue early warning system application may contribute to increase the policy makers and community attention to do accurate community-based vector control.

Association of Dietary Pattern with Cardiovascular Risk Factors among Postmenopausal Women in Taiwan: A Cross-Sectional Study from 2001 to 2015.

Unhealthy diet and inappropriate lifestyle contribute to an imbalance in cardiometabolic profiles among postmenopausal women. This research aimed to analyze the association between dietary pattern and changes in cardiovascular risk factors among postmenopausal Taiwanese women using binary logistic regression. This cross-sectional study involved 5689 postmenopausal Taiwanese women aged 45 years and above, and the data were obtained from Mei Jau Health Management Institution database between 2001 and 2015. The cardiovascular risk dietary pattern characterized by high intakes of processed food, rice/flour products, organ meat, and sauce was derived by reduced rank regression. Participants in the highest quartile of the cardiovascular risk dietary pattern were more likely to have high levels of systolic blood pressure (OR = 1.29, 95% CI 1.08-1.53), diastolic blood pressure (OR = 1.28, 95% CI 1.01-1.62), atherogenic index of plasma (OR = 1.26, 95% CI 1.06-1.49), triglycerides (OR = 1.38, 95% CI 1.17-1.62), and fasting blood glucose (Q3: OR = 1.45, 95% CI 1.07-1.97). However, this dietary pattern was not correlated with total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and C-reactive protein. Therefore, adherence to the cardiovascular risk dietary pattern increases the risk of having higher levels of blood pressure, triglycerides, fasting blood glucose in postmenopausal Taiwanese women.

Smartphone-based artificial intelligence using a transfer learning algorithm for the detection and diagnosis of middle ear diseases: A retrospective deep learning study.

Background: Middle ear diseases such as otitis media and middle ear effusion, for which diagnoses are often delayed or misdiagnosed, are among the most common issues faced by clinicians providing primary care for children and adolescents. Artificial intelligence (AI) has the potential to assist clinicians in the detection and diagnosis of middle ear diseases through imaging. Methods: Otoendoscopic images obtained by otolaryngologists from Taipei Veterans General Hospital in Taiwan between Jany 1, 2011 to Dec 31, 2019 were collected retrospectively and de-identified. The images were entered into convolutional neural network (CNN) training models after data pre-processing, augmentation and splitting. To differentiate sophisticated middle ear diseases, nine CNN-based models were constructed to recognize middle ear diseases. The best-performing models were chosen and ensembled in a small CNN for mobile device use. The pretrained model was converted into the smartphone-based program, and the utility was evaluated in terms of detecting and classifying ten middle ear diseases based on otoendoscopic images. A class activation map (CAM) was also used to identify key features for CNN classification. The performance of each classifier was determined by its accuracy, precision, recall, and F1-score. Findings: A total of 2820 clinical eardrum images were collected for model training. The programme achieved a high detection accuracy for binary outcomes (pass/refer) of otoendoscopic images and ten different disease categories, with an accuracy reaching 98.0% after model optimisation. Furthermore, the application presented a smooth recognition process and a user-friendly interface and demonstrated excellent performance, with an accuracy of 97.6%. A fifty-question questionnaire related to middle ear diseases was designed for practitioners with different levels of clinical experience. The AI-empowered mobile algorithm's detection accuracy was generally superior to that of general physicians, resident doctors, and otolaryngology specialists (36.0%, 80.0% and 90.0%, respectively). Our results show that the proposed method provides sufficient treatment recommendations that are comparable to those of specialists. Interpretation: We developed a deep learning model that can detect and classify middle ear diseases. The use of smartphone-based point-of-care diagnostic devices with AI-empowered automated classification can provide real-world smart medical solutions for the diagnosis of middle ear diseases and telemedicine. Funding: This study was supported by grants from the Ministry of Science and Technology (MOST110-2622-8-075-001, MOST110-2320-B-075-004-MY3, MOST-110-2634-F-A49 -005, MOST110-2745-B-075A-001A and MOST110-2221-E-075-005), Veterans General Hospitals and University System of Taiwan Joint Research Program (VGHUST111-G6-11-2, VGHUST111c-140), and Taipei Veterans General Hospital (V111E-002-3).

Association of Angiotensin Receptor Blockers with Incident Parkinson Disease in Patients with Hypertension: A Retrospective Cohort Study.

BACKGROUND: Angiotensin receptor blockers (ARBs), which are commonly used antihypertensives, have been proposed to lower the risk of Parkinson disease by reducing oxidative stress based on animal and in vitro studies. Thus, this study aimed to test this association in patients with newly diagnosed hypertension. METHODS: This retrospective cohort study enrolled 107,207 patients with newly diagnosed hypertension between 2001 and 2013. The hazard ratios for Parkinson disease were calculated for ARB treatment compared with those who never used ARBs and among the 5 subgroups receiving different cumulative ARB dosages. RESULTS: We identified 527 (1.1%) Parkinson disease cases among patients with ARB treatment in a median observation period of 8.4 years compared to the 1,255 (2.2%) Parkinson disease cases among those without ARB treatment in a median observation period of 6.8 years. Overall, risk for developing Parkinson disease was statistically lower in the ARB-treated group with a hazard ratio of 0.56 (95% confidence interval: 0.51-0.63) than those without ARB. CONCLUSIONS: ARB treatment was associated with a statistically important reduction of Parkinson disease risk in patients with newly diagnosed hypertension. Therefore, ARB may constitute an effective neuroprotective strategy to lower Parkinson disease risk in such patients.

Combined Low Plant and High Animal Dietary Pattern Is Associated with a Lower Risk of Anemia among Dyslipidemic Adult Women in Taiwan: A Retrospective Study from 2001 to 2015.

Anemia and dyslipidemia often occurs in premenopausal women. This study investigated the association between dietary patterns and anemia among dyslipidemic women in Taiwan. This study recruited 22,631 dyslipidemic women aged 20-45 years between 2001 and 2015. The dietary assessment was collected by a validated food frequency questionnaire. The biochemical data including blood lipids, red blood cells, hemoglobin, hematocrit, and C-reactive protein (CRP) were retrieved from the database. Women with a combined high plant diet (HP) and low animal diet (LA) were associated with a lower prevalence of obesity (11.7%), central obesity (16.0%), high total cholesterol (16.4%), and high low-density lipoprotein cholesterol (11.9%), and had lower hemoglobin (12.9 ± 1.4 g/dL), hematocrit (38.8 ± 3.6%), and CRP levels (20.6 ± 31.4 nmol/L). The low plant diet (LP) + high animal diet (HA) pattern was negatively associated with moderate to severe anemia (OR: 0.76, 95% CI: 0.64-0.92, p = 0.004) compared to the low plant diet (LP) + low animal diet (LA) pattern. However, the HP + LA pattern was positively correlated with moderate to severe anemia (OR: 1.22, 95% CI: 1.04-1.43, p = 0.015). In conclusion, a low plant and high animal diet plays a role in preventing anemia development among dyslipidemic women.

Predictive Model for Oral Status in Elderly People in a Taiwanese Nursing Home Using a High-Protein Black Soybean Koji Food.

With increased age, the appetite, chewing, swallowing, and digestive ability gradually decrease. Previous studies have shown that poor oral health is associated with an inadequate intake of macro and micronutrients and malnutrition. Therefore, improving the diet of elderly people and promoting nutrient absorption will help to improve the quality of life for elderly people. However, few studies have predicted their oral ability based on different food textures and other factors. The purpose of this study was to explore the correlation between oral assessment and texture parameters of high-protein black soybean koji products in elderly people in a nursing home. We used cross-sectional study design for seventy-nine residents aged 65 years and older were recruited. Three different texture of cookies, including normal cookie hardness (1.4 × 105 N/m2), minced cookie hardness (4.4 × 104 N/m2), and pureed cookie hardness (1.4 × 104 N/m2) were provided to participants to test the oral status. An oral assessment scale was used by a dentist to evaluate the oral status of the elderly participants. Different cookie textures showed a significant positive correlation with pronunciation (r = 0.237, p < 0.05), face (r = 0.371, p < 0.01), tongue (r = 0.362, p < 0.01), pharynx (r = 0.256, p < 0.05), swallowing (r = 0.272, p < 0.05), breathing (r = 0.315, p < 0.01), and the total oral score (r = 0.339, p < 0.01). We also used the high-protein black soybean koji products combined with elderly people's comprehensions in a predictive model that had a moderately high correlation to predict the oral status in the elderly group (r = 0.612). We concluded that the high-protein black soybean koji product was associated with the oral ability of elderly people in a nursing home in Taiwan. Our findings indicated that elderly people could immediately understand the correct food texture.

Evaluating auditory brainstem response to a level-dependent chirp designed based on derived-band latencies.

The best cochlear-neural delay model for designing a chirp that can produce the largest auditory brainstem response (ABR) has not been established. This study comprised two experiments. Experiment I aimed to estimate the delay model by measuring derived-band ABR latencies at different levels. The results demonstrated that, as the level decreased, the delay between the center frequencies of 0.7 and 5.7 kHz increased. The aim of experiment II was to compare ABRs generated by three stimuli: (1) a level-dependent derived-band (DB)-Chirp, designed based on the model in experiment I; (2) a level-dependent level specific (LS)-Chirp from Kristensen and Elberling [(2012). J. Am. Acad. Audiol. 23, 712-721]; and (3) a click. The results demonstrated that the DB-Chirp produced significantly larger wave V than the LS-Chirp at 45 dB normal hearing level (nHL); however, no differences were observed at other levels. The wave I generated by the DB-Chirp and LS-Chirp were significantly larger than those evoked by the click at 45 and 60 dB nHL and at 30 and 45 dB nHL, respectively; however, at all levels, no differences between these two chirps were observed. The DB-Chirp may be a valuable stimulus for producing ABRs for clinical applications such as assessing cochlear synaptopathy and estimating hearing sensitivity.

Design of a Vaccine Passport Validation System Using Blockchain-based Architecture: Development Study.

BACKGROUND: COVID-19 is an ongoing global pandemic caused by SARS-CoV-2. As of June 2021, 5 emergency vaccines were available for COVID-19 prevention, and with the improvement of vaccination rates and the resumption of activities in each country, verification of vaccination has become an important issue. Currently, in most areas, vaccination and reverse transcription polymerase chain reaction (RT-PCR) test results are certified and validated on paper. This leads to the problem of counterfeit documents. Therefore, a global vaccination record is needed. OBJECTIVE: The main objective of this study is to design a vaccine passport (VP) validation system based on a general blockchain architecture for international use in a simulated environment. With decentralized characteristics, the system is expected to have the advantages of low cost, high interoperability, effectiveness, security, and verifiability through blockchain architecture. METHODS: The blockchain decentralized mechanism was used to build an open and anticounterfeiting information platform for VPs. The contents of a vaccination card are recorded according to international Fast Healthcare Interoperability Resource (FHIR) standards, and blockchain smart contracts (SCs) are used for authorization and authentication to achieve hierarchical management of various international hospitals and people receiving injections. The blockchain stores an encrypted vaccination path managed by the user who manages the private key. The blockchain uses the proof-of-authority (PoA) public chain and can access all information through the specified chain. This will achieve the goal of keeping development costs low and streamlining vaccine transit management so that countries in different economies can use them. RESULTS: The openness of the blockchain helps to create transparency and data accuracy. This blockchain architecture contains a total of 3 entities. All approvals are published on Open Ledger. Smart certificates enable authorization and authentication, and encryption and decryption mechanisms guarantee data protection. This proof of concept demonstrates the design of blockchain architecture, which can achieve accurate global VP verification at an affordable price. In this study, an actual VP case was established and demonstrated. An open blockchain, an individually approved certification mechanism, and an international standard vaccination record were introduced. CONCLUSIONS: Blockchain architecture can be used to build a viable international VP authentication process with the advantages of low cost, high interoperability, effectiveness, security, and verifiability.

Prediction Model for Pancreatic Cancer-A Population-Based Study from NHIRD.

(1) Background: Cancer has been the leading cause of death in Taiwan for 39 years, and among them, pancreatic cancer has been ranked seventh in the top ten cancer mortality rates for the past three years. While the incidence rate of pancreatic cancer is ranked at the bottom of the top 10 cancers, the survival rate is very low. Pancreatic cancer is one of the more difficult cancers to detect early due to the lack of early diagnostic tools. Early screening is important for the treatment of pancreatic cancer. Only a few studies have designed predictive models for pancreatic cancer. (2) Methods: The Taiwan Health Insurance Database was used in this study, covering over 99% of the population in Taiwan. The subset sample was not significantly different from the original NHIRD sample. A machine learning approach was used to develop a predictive model for pancreatic cancer disease. Four models, including logistic regression, deep neural networks, ensemble learning, and voting ensemble were used in this study. The ROC curve and a confusion matrix were used to evaluate the accuracy of the pancreatic cancer prediction models. (3) Results: The AUC of the LR model was higher than the other three models in the external testing set for all three of the factor combinations. Sensitivity was best measured by the stacking model for the first factor combinations, and specificity was best measured by the DNN model for the second factor combination. The result of the model that used only nine factors (third factor combinations) was equal to the other two factor combinations. The AUC of the previous models for the early assessment of pancreatic cancer ranged from approximately 0.57 to 0.71. The AUC of this study was higher than that of previous studies and ranged from 0.71 to 0.76, which provides higher accuracy. (4) Conclusions: This study compared the performances of LR, DNN, stacking, and voting models for pancreatic cancer prediction and constructed a pancreatic cancer prediction model with accuracy higher than that of previous studies. This predictive model will improve awareness of the risk of pancreatic cancer and give patients with pancreatic cancer a simpler tool for early screening in the golden period when the disease can still be eradicated.

SEMRES - A Triple Security Protected Blockchain Based Medical Record Exchange Structure.

BACKGROUND AND OBJECTIVE: COVID-19, a serious infectious disease outbreak started in the end of 2019, has caused a strong impact on the overall medical system, which reflects the gap in the volume and capacity of medical services and highlights the importance of clinical data ex-change and application. The most important concerns of medical records in the medical field include data privacy, data correctness, and data security. By realizing these three goals, medical records can be made available to different hospital information systems to achieve the most complete medical care services. The privacy and protection of health data require detailed specification and usage requirements, which is particularly important for cross-agency data exchange. METHODS: This research is composed of three main modules. "Combined Encryption and Decryption Architecture", which includes the hybrid double encryption mechanism of AES and RSA, and encrypts medical records to produce "Secured Encrypted Medical Record". "Decentralize EMR Repository", which includes data decryption and an exchange mechanism. After a data transmission is completed, the content verification and data decryption process will be launched to confirm the correctness of the data and obtain the data. A blockchain architecture is used to store the hash value of the encrypted EMR, and completes the correctness verification of the EMR after transmission through the hash value. RESULTS: The results of this study provide an efficient triple encryption mechanism for electronic medical records. SEMRES ensures the correctness of data through the non-repudiation feature of a blockchain open ledger, and complete integrated information security protection and data verification architecture, in order that medical data can be exchanged, verified, and applied in different locations. After the patient receives medical services, the medical record is re-encrypted and verified and stored in the patient's medical record. The blockchain architecture is used to ensure the verification of non-repudiation of medical service, and finally to complete the payment for medical services. CONCLUSIONS: The main aim of this study was to complete a security architecture for medical data, and develop a triple encryption authentication architecture to help data owners easily and securely share personal medical records with medical service personnel.

The effects of medications for treating COPD and allied conditions on stroke: a population-based cohort study.

Patients with chronic obstructive pulmonary disease (COPD) are at higher risk of stroke. This study aimed to investigate the clinical factors of stroke risk in COPD and allied conditions patients and associations between medications for treating COPD and allied conditions. The population-based study cohort comprised 24,173 patients diagnosed with COPD and allied conditions between 2000 and 2013, and 24,170 selected matched patients without COPD comprised the comparison cohort from a nationwide database. Cox-proportional hazard regression was performed to determine the impact of medical therapies, comorbidities, and other clinical factors on stroke risk. Of the 48,343 included patients, 1394 (2.9%) experienced stroke during follow-up, with a significant difference between COPD and allied conditions cohort (1003/4.2%) and comparison cohort (391/1.6%) (adjusted hazard ratio [aHR]: 2.72, p < 0.001). Cox-regression analysis revealed that COPD and allied conditions patients who were older (>65 years) (HR: 1.06); male (HR: 1.39); with hypertension (HR: 1.46), diabetes mellitus (HR: 1.33) and atrial fibrillation (HR: 1.63) had increased stroke risk. Mucolytics (HR: 0.44) and combination therapy with inhaled corticosteroids (ICS) and long-acting ß2-agonists (LABA) (HR: 0.75) were associated with decreased stroke risk in COPD and allied conditions patients. Among COPD and allied conditions patients, major comorbidities increase risk of stroke. Therapy with mucolytic agents and combination ICS/LABA is associated with risk reduction.

Association between metabolic parameters and risks of anemia and electrolyte disturbances among stages 3-5 chronic kidney disease patients in Taiwan.

BACKGROUND: Anemia and electrolyte disturbances are adverse outcomes of chronic kidney disease (CKD). This study explored the association between metabolic parameters with anemia and electrolyte and mineral disorders among CKD patients in Taiwan. METHODS: This cross-sectional study with a total of 2176 CKD stages 3-5 patients were collected from the Department of Nephrology at Shuang Ho Hospital, Taipei Medical University through the "Chronic Kidney Disease Common Care Network" database from December 2008 to April 2019. A multivariable-adjusted logistic regression expressed as odd ratios (OR) was performed to assess the association of metabolic parameters with anemia and electrolyte and mineral disorders. RESULTS: Elevated diastolic blood pressure, fasting blood glucose, and glycated hemoglobin A1c (HbA1c) were associated with presence of anemia. Similarly, elevated fasting blood glucose and HbA1c were associated with hyponatremia (OR = 1.59 and 1.58, P for both < 0.01) and hypercalcemia (OR = 1.38 and 1.33, P for both < 0.05). There was no significant association in serum lipid levels with presence of anemia. However, total triglycerides, total cholesterol and low-density lipoprotein-cholesterol were only associated with presence of hypercalcemia (OR = 1.43, 1.95 and 3.08, respectively, P for all < 0.05). CONCLUSIONS: Elevated diastolic blood pressure, fasting blood glucose, HbA1c and blood lipids are associated with anemia or electrolyte and mineral disorders in CKD patients.

Synergistic Interaction of Dietary Pattern and Concordance Lifestyle with Abnormal Liver Function among Young Adults in Taiwan: A Population-Based Longitudinal Study.

While diet and lifestyle are independently implicated in the etiology of liver disease, the interaction of diet and lifestyle may be more helpful for determining the risk of liver abnormality. Thus, our study aimed to evaluate the interaction between the dietary pattern associated with liver biomarkers and lifestyle factors among Taiwanese adults with abnormal liver enzymes. A liver-associated dietary pattern, generated using reduced rank regression, was characterized by high intake of soy sauce or other dips, sugar sweetened beverages, and preserved and processed foods, but low intake of seafood, fruits, eggs, and dark-colored vegetables. In the fully adjusted model, liver-associated dietary patterns or unhealthy concordance lifestyle factors were associated with an increased risk of having liver function abnormality (OR = 1.08, 95% CI: 1.04, 1.12 and OR = 1.42, 95% CI: 1.31, 1.53, respectively). Moreover, the interaction between liver-associated dietary pattern and unhealthy concordance lifestyle factors showed more significant correlation, with an elevated risk of abnormal liver function (OR = 2.14, 95% CI: 2.02, 2.26). Therefore, our study suggests that participants who have a strong liver-associated dietary pattern along with unhealthy concordance lifestyles are likely to have increased odds of abnormal liver function.

Logical Observation Identifiers Names and Codes (LOINC®) Applied to Microbiology: A National Laboratory Mapping Experience in Taiwan.

BACKGROUND AND OBJECTIVE: Logical Observation Identifiers Names and Codes (LOINC) is a universal standard for identifying laboratory tests and clinical observations. It facilitates a smooth information exchange between hospitals, locally and internationally. Although it offers immense benefits for patient care, LOINC coding is complex, resource-intensive, and requires substantial domain expertise. Our objective was to provide training and evaluate the performance of LOINC mapping of 20 pathogens from 53 hospitals participating in the National Notifiable Disease Surveillance System (NNDSS). METHODS: Complete mapping codes for 20 pathogens (nine bacteria and 11 viruses) were requested from all participating hospitals to review between January 2014 and December 2016. Participating hospitals mapped those pathogens to LOINC terminology, utilizing the Regenstrief LOINC mapping assistant (RELMA) and reported to the NNDSS, beginning in January 2014. The mapping problems were identified by expert panels that classified frequently asked questionnaires (FAQs) into seven LOINC categories. Finally, proper and meaningful suggestions were provided based on the error pattern in the FAQs. A general meeting was organized if the error pattern proved to be difficult to resolve. If the experts did not conclude the local issue's error pattern, a request was sent to the LOINC committee for resolution. RESULTS: A total of 53 hospitals participated in our study. Of these, 26 (49.05%) used homegrown and 27 (50.95%) used outsourced LOINC mapping. Hospitals who participated in 2015 had a greater improvement in LOINC mapping than those of 2016 (26.5% vs. 3.9%). Most FAQs were related to notification principles (47%), LOINC system (42%), and LOINC property (26%) in 2014, 2015, and 2016, respectively. CONCLUSIONS: The findings of our study show that multiple stage approaches improved LOINC mapping by up to 26.5%.

Interactive Effects of Unhealthy Lifestyle Behaviors on Testicular Function among Healthy Adult Men: A Cross-Sectional Study in Taiwan.

Recently, the role of lifestyle factors in testicular function has developed into a growing area of interest. Based on cross-sectional data on 3283 Taiwanese men, we investigated whether interactive effects of unhealthy lifestyle behaviors were associated with testicular function. The men were recruited from a private screening institute between 2009 and 2015. Lifestyle behaviors (smoking, alcohol drinking, physical activity (PA), sleeping habits, and diet) were obtained by a validated self-reported questionnaire. The men provided a semen sample and had blood drawn for sex hormone measurement. Men who smoked and drank had higher testosterone (T) levels (ß = 0.81, p < 0.001) than those who neither smoked nor drank. Men who smoked and had high Western dietary pattern scores had higher T levels-by 0.38 ng/mL (p = 0.03). Those who drank and did not get enough sleep or had high Western dietary pattern scores had elevated T levels-by 0.60 ng/mL (p = 0.005) or 0.45 ng/mL (p = 0.02), respectively. Light PA and insomnia were associated with decreased T levels-by 0.64 ng/mL (p < 0.001). Those who smoked and drank or had light PA or had high Western dietary pattern scores had lower normal sperm morphologies (NSMs)-by 2.08%, 1.77%, and 2.29%, respectively. Moreover, drinkers who had high Western dietary pattern scores had higher sperm concentrations-by 4.63 M/mL (p = 0.04). Awareness and recognition of the long-term impact of lifestyle behaviors and better lifestyle choices may help to optimize the chance of conception amongst couples.

Design and evaluation of a knowledge-based clinical decision support system for the psychiatric nursing process.

BACKGROUND AND OBJECTIVES: The nursing assessment in the psychiatric department differ from those used in other departments considerably. We developed a psychiatric knowledge-based clinical decision support system (Psy-KBCDSS), which may aid nurses in solving patients' problems in the psychiatric department. In addition, we compared the sensitivity and specificity for the nursing diagnoses between the psychiatric nursing process system (Psy-NPS) and Psy-KBCDSS to determine that the Psy-KBCDSS can assist nurses in performing the nursing assessment and diagnosis. METHODS: Visual Studio 2019 was adopted as the primary software development tool, and C# as the main development language. The concept of the nursing process was applied to develop the Psy-KBCDSS user interface. We developed a clinical diagnostic validity inference engine to calculate the frequencies of the nursing assessment items and nursing diagnoses in clinical tasks in the Psy-NPS for generating a knowledge-based database of the Psy-KBCDSS. The sensitivity and specificity for nursing diagnoses formulated by senior and junior nurses were used to determining the effectiveness of adopting Psy-NPS and Psy-KBCDSS. RESULTS: This study include 22 nursing diagnoses commonly encountered in psychiatric wards. The top eight most common diagnoses in the Psy-NPS and Psy-KBCDSS were altered thought processes, ineffective coping, sensory and perceptual alterations, insomnia, risk for other-directed violence, anxiety, impaired social interaction, and risk for suicide. Compared with the Psy-NPS, the Psy-KBCDSS had significantly higher sensitivity for sensory and perceptual alterations, ineffective coping, and insomnia and significantly higher specificity for ineffective coping. CONCLUSIONS: Considering its high sensitivity and specificity for various nursing diagnoses, the Psy-KBCDSS, as an empirical patient-oriented nursing clinical decision-making support system, can assist nurses in clinical nursing tasks including nursing process-based patient assessment and nursing diagnosis.

Association between Dietary Pattern, Lifestyle, Anthropometric Status, and Anemia-Related Biomarkers among Adults: A Population-Based Study from 2001 to 2015.

Inadequate dietary intake, poor nutritional status, heavy smoking, and alcohol consumption are associated with the risk of anemia. The objective of this study was to investigate the associations between dietary patterns, lifestyle, nutritional status, and anemia-related biomarkers among adults using a multivariable regression model. Taiwanese adults aged 20-45 years (n = 118,924, 43,055 men and 75,869 women) were obtained from the Mei Jau Health Management Institution database, between 2001 and 2015, for data analysis. The anemia-inflammation-related dietary pattern was derived by reduced rank regression analysis. Dietary patterns with high intakes of eggs, meat, organ meats, rice or flour products, fried foods, sugary beverages, and processed foods significantly increased the risk of anemia, and was associated with decreased hemoglobin, hematocrit, and red blood cells, but increased white blood cells and C-reactive protein levels. Moreover, current alcohol drinkers, as well as people who were underweight, overweight, obese, and central obese, were more likely to increase their risk of anemia by 46%, 20%, 23%, 34%, and 28%, respectively. Interestingly, participants who are current or past smokers were inversely associated with risk of anemia. In conclusion, adherence to the anemia-inflammation dietary pattern was associated with an increased risk of anemia in Taiwanese adults. Furthermore, abnormal weight status and alcohol drinking were correlated with an increased risk of anemia.

Association between Dietary Patterns and Serum Hepatic Enzyme Levels in Adults with Dyslipidemia and Impaired Fasting Plasma Glucose.

We investigated the association between dietary patterns and serum hepatic enzyme levels in adults with dyslipidemia and impaired fasting glucose in Taiwan. A total of 15,005 subjects (5452 men and 9553 women) aged 35-69 years were selected. Two major dietary patterns were identified by principal component analysis: Western dietary pattern and Mediterranean dietary pattern. Subjects in the highest quartile (Q4) of the Western dietary pattern showed an increased risk of elevated serum alanine aminotransferase (ALT) levels (OR: 1.24, 95% CI: 1.06-1.45, p-trend = 0.01). Fur-thermore, in the highest quartile of the Western dietary pattern, subjects with high waist circum-ference were observed to have a greater risk for developing abnormal serum ALT levels compared to those in the lowest quartile (Q1) (OR: 1.43, 95% CI: 1.04-1.97, p-trend = 0.01). In the highest quartile of the Western dietary pattern, only women were at an increased risk for having abnormal serum ALT levels (OR: 1.28, 95% CI: 1.04-1.59, p-trend = 0.03). By contrast, in the highest quartile of the Mediterranean dietary pattern, only men were at a reduced risk for having abnormal serum gamma-glutamyl transferase (GGT) levels (OR: 0.72, 95% CI: 0.53-0.97, p-trend = 0.048). We report a positive association between the Western dietary pattern and abnormal serum ALT levels.

Calcium Channel blockers are associated with reduced risk of Parkinson's disease in patients with hypertension: A population-based retrospective cohort study.

BACKGROUND: The use of dihydropyridine calcium channel blockers (DCCBs) was proposed to reduce the risk of Parkinson's disease (PD). This study aimed to evaluate the association between DCCB and its dose effect and the risk of PD in patients with newly diagnosed hypertension. METHODS: This population-based retrospective cohort study enrolled 107,207 patients with newly diagnosed hypertension, between 2001 and 2013, from Taiwan's National Health Insurance Research Database. Patients who had PD before hypertension or were taking antipsychotics for more than 30 days in the 6 months prior to the end of the observation period were excluded. A Cox proportional hazard model was used to estimate the risk of PD in different groups. The dose-related effects of DCCB on the risk of PD were evaluated according to the cumulative defined daily dose (DDD). RESULTS: We observed 832 (1.2%) PD cases in patients treated with DCCB as compared to 950 (2.4%) PD cases in those not treated with DCCB, during a median follow-up duration of 8.3 years and 6.2 years, respectively. The risk of PD in the DCCB-treated group (hazard ratio [HR] = 0.50) was significantly lower than that in the group without DCCB treatment. DCCB reduced the risk of PD in a dose-dependent manner, with HRs ranging from 0.61 to 0.37 for DDDs of 90-180 to >720. CONCLUSIONS: DCCB treatment was associated with a significantly reduced risk of PD in patients with newly diagnosed hypertension. Further clinical trials are needed to confirm the proposed neuroprotective effects of DCCB in PD.