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Abstract Background Clinical studies on progressive familial intrahepatic cholestasis (PFIC) type 5 caused by mutations in NR1H4 are limited. Methods New patients with biallelic NR1H4 variants from our center and all patients from literature were retrospectively analyzed. Results Three new patients were identified to be carrying five new variants. Liver phenotypes of our patients manifests as low-γ-glutamyl transferase cholestasis, liver failure and related complications. One patient underwent liver transplantation (LT) and survived, and two other patients died without LT. Nine other patients were collected through literature review. Twelve out of 13 patients showed neonatal jaundice, with the median age of onset being 7 days after birth. Reported clinical manifestations included cholestasis (13/13, 100%), elevated AFP (11/11, 100%), coagulopathy (11/11, 100%), hypoglycemia (9/13, 69%), failure to thrive (8/13, 62%), splenomegaly (7/13, 54%), hyperammonemia (7/13, 54%), and hepatomegaly (6/13, 46%). Six of 13 patients received LT at a median age of 6.2 months, and only one patient died of acute infection at one year after LT. Other 7 patients had no LT and died with a median age of 5 months (range 1.2-8). There were 8 patients with homozygous genotype and 5 patients with compound heterozygous genotype. In total, 13 different variants were detected, and 5 out of 12 single or multiple nucleotides variants were located in exon 5. Conclusions We identified three newly-diagnosed patients and five novel mutations. NR1H4-related PFIC typically cause progressive disease and early death. LT may be the only lifesaving therapy leading to cure.

INTRODUCTION: Children born small for gestational age (SGA) are at a greater risk of developing insulin resistance, type 2 diabetes, and cardiovascular disease in adulthood. Gastrointestinal peptides, some secreted by intestinal L cells, regulate glucose and lipid metabolism and act on the hypothalamus to regulate energy homeostasis. The aim of this study was to explore whether gastrointestinal peptides are involved in metabolic disorders in SGA, which remains unclear. METHODS: The secretion of glucagon-like peptide 1 (GLP-1) and peptide YY (PYY) were investigated in prepubertal children born SGA, the differences between catch-up growth and persistent short stature were compared, and correlation with glucose and lipid metabolism was analyzed. GLP-1, PYY, insulin-like growth factor 1, glucose, insulin, and lipid concentrations were analyzed in prepubertal children aged 4-10 years, stratified into three groups: short-SGA (SGA-s), catch-up growth SGA, and normal growth appropriate for gestational age (AGA). RESULTS: Fasting GLP-1 and PYY concentrations were significantly lower in the SGA group than in the AGA group (p

Objectives: A vast amount of literature has been conducted for investigating the association of different lunar phases with human health; and it has mixed reviews for association and non-association of diseases with lunar phases. This study investigates the existence of any impact of moon phases on humans by exploring the difference in the rate of outpatient visits and type of diseases that prevail in either non-moon or moon phases. Methods: We retrieved dates of non-moon and moon phases for eight years (1st January 2001–31st December 2008) from the timeanddate.com website for Taiwan. The study cohort consisted of 1 million people from Taiwan's National Health Insurance Research Database (NHIRD) followed over eight years (1st January 2001–31st December 2008). We used the two-tailed, paired-t-test to compare the significance of difference among outpatient visits for 1229 moon phase days and 1074 non-moon phase days by using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes from NHIRD records. Results: We found 58 diseases that showed statistical differences in number of outpatient visits in the non-moon and moon phases. Conclusions: The results of our study identified diseases that have significant variations during different lunar phases (non-moon and moon phases) for outpatient visits in the hospital. In order to fully understand the reality of the pervasive myth of lunar effects on human health, behaviors and diseases, more in-depth research investigations are required for providing comprehensive evidence covering all the factors, such as biological, psychological and environmental aspects.

The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities and potentials of AI in healthcare, emphasising the necessity of explainability, trustworthiness, usability, transparency and fairness in developing and implementing AI models. It underscores the ‘black box’ challenge, highlighting the gap between algorithmic outputs and human interpretability, and articulates the pivotal role of explainable AI in enhancing the transparency and accountability of AI applications in healthcare. The discourse extends to ethical considerations, exploring the potential biases and ethical dilemmas that may arise in AI application, with a keen focus on ensuring equitable and ethical AI use across diverse global regions. Furthermore, the paper explores the concept of responsible AI in healthcare, advocating for a balanced approach that leverages AI’s capabilities for enhanced healthcare delivery and ensures ethical, transparent and accountable use of technology, particularly in clinical decision-making and patient care.

Abstract Online monitoring of gases dissolved in transformer oil is widely applied. Improving the performance of dissolved gas analysis (DGA)‐based fault diagnosis methods by exploring new features of time‐series data has become an appealing topic. In this study, a new type of correlation features between characteristic gases was extracted from time‐series data based on the maximal information coefficient (MIC), and a fuzzy inference system was established. After the introduction of the principle of the MIC and a method for calculating the MIC‐based correlation features, the dominant symptom features that can be used to classify fault types were extracted through the receiver operating characteristic curve. Then, fuzzy rules were learnt, and a fuzzy inference system was designed. In addition, to improve the feasibility of the method, the Newton interpolation method was used for adaptation to the existing sampling cycle. The diagnostic results of the test data show that the proposed method has excellent performance and outperforms some prevailing traditional rule‐based methods as well as some artificial intelligent methods. The results also show that by exploring new correlation features from time‐series data based on the MIC, the performance of DGA‐based methods can be improved.

Abstract Background The smart hospital's concept of using the Internet of Things (IoT) to reduce human resources demand has become more popular in the aging society. Objective To implement the voice smart care (VSC) system in hospital wards and explore patient acceptance via the Technology Acceptance Model (TAM). Methods A structured questionnaire based on TAM was developed and validated as a research tool. Only the patients hospitalized in the VSC wards and who used it for more than two days were invited to fill the questionnaire. Statistical variables were analyzed using SPSS version 24.0. A total of 30 valid questionnaires were finally obtained after excluding two incomplete questionnaires. Cronbach’s α values for all study constructs were above 0.84. Result We observed that perceived ease of use on perceived usefulness, perceived usefulness on user satisfaction and attitude toward using, and attitude toward using on behavioral intention to use had statistical significance (p

Chest pain is a common complaint in the emergency department, but this may prevent a diagnosis of major adverse cardiac events, a composite of all-cause mortality associated with cardiovascular-related illnesses. To determine potential predictors of major adverse cardiac events in Taiwan, a pilot study was performed, involving the data from 268 patients with major adverse cardiac events, which was by an artificial neural network method. Nine biomarkers were selected for identifying non-ST-elevation myocardial infarction from common chest pain patients. By using a machine learning-based feature selection technique, five biomarkers were chosen from a set of 37 candidate variables. A full and a reduced risk stratification model were built. The full model was based on the characteristics of both invasive (i.e., creatinine and troponin I) and non-invasive (i.e., age, coronary artery disease risk factors, and corrected QT interval) variables, and the reduced model was based only on non-invasive variable characteristics. The full model showed a sensitivity of 0.948 and a specificity of 0.546 when the cutoff was set at 2 points, with a missed major adverse cardiac events rate of 1.32%, a positive predictive value of 0.228, and a negative predictive value of 0.987. High performance was also obtained with the five major biomarkers in the predictor built by the machine learning algorithm. The full model had the highest performance, but the reduced model can be applied as a quick and reasonably performing diagnostic tool.

Medication safety continues to be a problem inside and outside the hospital, partly because new smart technologies can cause new drug-related challenges to prescribers and patients. Better integrated digital and information technology (IT) systems, improved education on prescribing for prescribers and greater patient-centred care that empowers patients to take control of their medications are all vital to safer and more effective prescribing. In July 2021, a roundtable discussion was held as a spin-off meeting of the International Forum on Quality and Safety in Health Care Europe 2021 to discuss challenges and future direction in smart medication management. This manuscript summarises the discussion focusing on the aspects of digital and IT systems, safe prescribing, improved communication and education, and drug adherence.

Recommender systems are gaining traction in healthcare because they can tailor recommendations based on users’ feedback concerning their appreciation of previous health-related messages. However, recommender systems are often not grounded in behavioral change theories, which may further increase the effectiveness of their recommendations. This paper’s objective is to describe principles for designing and developing a health recommender system grounded in the I-Change behavioral change model that shall be implemented through a mobile app for a smoking cessation support clinical trial. We built upon an existing smoking cessation health recommender system that delivered motivational messages through a mobile app. A group of experts assessed how the system may be improved to address the behavioral change determinants of the I-Change behavioral change model. The resulting system features a hybrid recommender algorithm for computer tailoring smoking cessation messages. A total of 331 different motivational messages were designed using 10 health communication methods. The algorithm was designed to match 58 message characteristics to each user profile by following the principles of the I-Change model and maintaining the benefits of the recommender system algorithms. The mobile app resulted in a streamlined version that aimed to improve the user experience, and this system’s design bridges the gap between health recommender systems and the use of behavioral change theories. This article presents a novel approach integrating recommender system technology, health behavior technology, and computer-tailored technology. Future researchers will be able to build upon the principles applied in this case study.

Starting renal replacement therapy (RRT) for patients with chronic kidney disease (CKD) at an optimal time, either with hemodialysis or kidney transplantation, is crucial for patient's well-being and for successful management of the condition. In this paper, we explore the possibilities of creating forecasting models to predict the onset of RRT 3, 6, and 12 months from the time of the patient's first diagnosis with CKD, using only the comorbidities data from National Health Insurance from Taiwan. The goal of this study was to see whether a limited amount of data (including comorbidities but not considering laboratory values which are expensive to obtain in low- and medium-income countries) can provide a good basis for such predictive models. On the other hand, in developed countries, such models could allow policy-makers better planning and allocation of resources for treatment. Using data from 8,492 patients, we obtained the area under the receiver operating characteristic curve (AUC) of 0.773 for predicting RRT within 12 months from the time of CKD diagnosis. The results also show that there is no additional advantage in focusing only on patients with diabetes in terms of prediction performance. Although these results are not as such suitable for adoption into clinical practice, the study provides a strong basis and a variety of approaches for future studies of forecasting models in healthcare.

Abstract Background Transient infantile hypertriglyceridemia (HTGTI) is an autosomal recessive disorder caused by mutations in the glycerol-3-phosphate dehydrogenase 1 (GPD1) gene. Case presentation We report a case of HTGTI in a Chinese female infant. She presented with hepatomegaly, hypertriglyceridemia, moderately elevated transaminases, and hepatic steatosis at 3.5 months of age. A novel mutation c.523C>T, p. (Q175*) was identified in GPD1. The patient was a homozygote and her parents were heterozygous for the mutation. Ultrastructural study showed intrahepatocytic lipid droplets. Conclusions This is the first reported case of HTGTI in Chinese, expanding the worldwide distribution of HTGTI and the mutation spectrum of GPD1.

Alert dwell time, defined as the time elapsed from the generation of an interruptive alert to its closure, has rarely been used to describe the time required by clinicians to respond to interruptive alerts. Our study aimed to develop a tool to retrieve alert dwell times from a homegrown CPOE (computerized physician order entry) system, and to conduct exploratory analysis on the impact of various alert characteristics on alert dwell time. Additionally, we compared this impact between various professional groups. With these aims, a dominant window detector was developed using the Golang programming language and was implemented to collect all alert dwell times from the homegrown CPOE system of a 726-bed, Taiwanese academic medical center from December 2019 to February 2021. Overall, 3,737,697 interruptive alerts were collected. Correlation analysis was performed for alerts corresponding to the 100 most frequent alert categories. Our results showed that there was a negative correlation (ρ = −0.244, p = 0.015) between the number of alerts and alert dwell times. Alert dwell times were strongly correlated between different professional groups (physician vs. nurse, ρ = 0.739, p < 0.001). A tool that retrieves alert dwell times can provide important insights to hospitals attempting to improve clinical workflows.

Background: Feature extraction from photoplethysmography (PPG) signals is an essential step to analyze vascular and hemodynamic information. Different morphologies of PPG waveforms from different measurement sites appear. Various phenomena of missing or ambiguous features exist, which limit subsequent signal processing. Methods: The reasons that cause missing or ambiguous features of finger and wrist PPG pulses are analyzed based on the concept of component waves from pulse decomposition. Then, a systematic approach for missing-feature imputation and ambiguous-feature resolution is proposed. Results: From the experimental results, with the imputation and ambiguity resolution technique, features from 35,036 (98.7%) of 35,502 finger PPG cycles and 36307 (99.1%) of 36,652 wrist PPG cycles can be successfully identified. The extracted features became more stable and the standard deviations of their distributions were reduced. Furthermore, significant correlations up to 0.92 were shown between the finger and wrist PPG waveforms regarding the positions and widths of the third to fifth component waves. Conclusion: The proposed missing-feature imputation and ambiguous-feature resolution solve the problems encountered during PPG feature extraction and expand the feature availability for further processing. More intrinsic properties of finger and wrist PPG are revealed. The coherence between the finger and wrist PPG waveforms enhances the applicability of the wrist PPG.

Background: A potential evidence from previous epidemiological studies remains conflicting findings regarding the association between atrial fibrillation (AF) and dementia risk. We, therefore, carried out a meta-analysis of relevant studies to investigate the magnitude of the association between AF and dementia risk.Methods: We performed a systematic literature search of PubMed, EMBASE, and Google Scholar for potential studies between January 1, 1990, and December 31, 2018, with no restriction on the publication language. All potential studies were independently assessed by two reviewers. We only included observational studies that calculated the odds ratio (OR)/hazards ratio (HR) for dementia associated with atrial fibrillation. We first assessed the heterogeneity among study-specific HRs using the Q statistic and I2 statistic. We then used the random-effects model to obtain the overall HR and its 95% CI for all studies. We also tested and corrected for publication bias by funnel plot–based methods. The quality of each study was assessed with the Newcastle Ottawa Scale.Results: A total of 16 studies with 2,415,356 individuals, and approximately 200,653 cases of incidence dementia were included in this study. Patients with AF had a greater risk of incidence dementia than those without AF (random-effect hazard ratio HR: 1.36, 95% CI: 1.23–1.51, p < 0.0001; I2 = 83.58). Funnel plot and Egger test did not reveal significant publication bias. However, limitations of the study included high heterogeneity and varying degrees of confounder adjustment across individual studies.Conclusion: This study serves as added evidence supporting the hypothesis that AF is associated with an increased risk of dementia. More studies are needed to establish whether optimal treatment of AF can reduce or mitigate the risk of dementia.

Familial glucocorticoid deficiency type 1 (FGD1) is an autosomal recessive disorder caused by mutations in the melanocortin 2 receptor (MC2R) gene, characterized by a low or undetectable serum cortisol level and a high adrenocorticotropic hormone (ACTH) level. Clinical manifestations include hypoglycemia, seizure, skin hyperpigmentation, hyperbilirubinemia, cholestasis, and a tall stature. Some dysmorphic features such as, a prominent forehead, hypertelorism, a broad nasal bridge, and small tapering fingers, have been reported. Children with FGD1 may have other isolated endocrine abnormalities. To date, no patient with FGD1 has been reported in mainland China. Here we report on a Chinese patient with FGD1 having a novel MC2R gene variant, a mild transverse palm crease, hypertelorism, and subtle/transient endocrine abnormalities relating to all three zones of the adrenal cortex and thyroid gland. We also reviewed cases with dysmorphic features or additional endocrine abnormalities.

Comma hair has been described as a specific dermoscopic feature of tinea capitis, however, it is not always present. Recognition of the additional dermoscopic features is therefore important. Furthermore, comma hair has never been shown to be applicable in fungal infection of the hair follicles on other parts of the body such as the eyebrows, axillary, or pubic areas. We encountered 13 Taiwanese patients who had been diagnosed with tinea capitis and evaluated their dermoscopic features using a nonpolarized, contact-type dermoscopic instrument with alcohol as the interface medium. Herein, we describe a woman with fungal infection of the eyebrow and present the under-recognized zigzag hair and bar code-like hairs for the first time.

We detected Bartonella quintana in 48.6% of captive rhesus macaques from an animal facility in Beijing, China. Prevalence of infection increased over the period of observation. Our findings suggest that macaques may serve as reservoir hosts for B. quintana and that Pedicinus obtusus lice might act as efficient vectors.

Objectives: A vast amount of literature has been conducted for investigating the association of different lunar phases with human health; and it has mixed reviews for association and non-association of diseases with lunar phases. This study investigates the existence of any impact of moon phases on humans by exploring the difference in the rate of outpatient visits and type of diseases that prevail in either non-moon or moon phases. Methods: We retrieved dates of non-moon and moon phases for eight years (1st January 2001-31st December 2008) from the timeanddate.com website for Taiwan. The study cohort consisted of 1 million people from Taiwan's National Health Insurance Research Database (NHIRD) followed over eight years (1st January 2001-31st December 2008). We used the two-tailed, paired-t-test to compare the significance of difference among outpatient visits for 1229 moon phase days and 1074 non-moon phase days by using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes from NHIRD records. Results: We found 58 diseases that showed statistical differences in number of outpatient visits in the non-moon and moon phases. Conclusions: The results of our study identified diseases that have significant variations during different lunar phases (non-moon and moon phases) for outpatient visits in the hospital. In order to fully understand the reality of the pervasive myth of lunar effects on human health, behaviors and diseases, more in-depth research investigations are required for providing comprehensive evidence covering all the factors, such as biological, psychological and environmental aspects. [ABSTRACT FROM AUTHOR]

Background: Adverse drug reactions increase morbidity and mortality, and potential drug interactions (DIs) increase the probability of adverse drug reactions. Objectives: To survey the potential DIs of dermatologic outpatient prescriptions from the National Health Insurance Research Database of Taiwan. Methods: All prescriptions written by dermatologists in 2000 were analyzed to identify potential DIs among drugs appearing on the same prescription sheet. Results: Of 150.6 million prescription sheets with 669.5 million prescriptions registered in the National Health Insurance Research Database of Taiwan, we identified 6.6 million (4.4%) dermatology prescription sheets with 19 million (2.8%) prescriptions. The findings of the study showed that 283,458 potential DIs were found in this category, accounting for 1.49% per prescription. The most common significance Level 1 interaction (1.1%) was between the less-sedative antihistamines (terfenadine/astemizole) and azole antifungal agents. Among the category of severity, the most common was terfenadine interacting with cimetidine and ketoconazole (4.4%), followed by astemizole interacting with cimetidine and ketoconazole (2.9%). The most common drug class interaction occurred between corticosteroids and antacids (48.5%). Overall, DI incidence in dermatologic patients was lower than that of the general patient population. Conclusions: Based on the study findings, we concluded that dermatologists need to be reminded of having possible potential DIs when prescribing medications. Introducing information technology into the computerized physician order entry system into the daily practice may reduce potential DIs.

Recent years have witnessed an increased prevalence of knee osteoarthritis (KOA) among diabetes mellitus (DM) patients—conditions which might share common risk factors such as obesity and advanced aging. Therefore, we conducted dry-to-wet lab research approaches to assess the correlation of type 1 DM (T1DM) and type 2 DM (T2DM) with KOA among all age and genders of Taiwanese population. The strength of association (odds ratio: OR) was analyzed using a phenome-wide association study portal. Populations of 37,353 T1DM and 1,218,254 T2DM were included. We observed a significant association of KOA with T1DM (OR: 1.40 (1.33–1.47), p< 0.0001) and T2DM (OR: 2.75 (2.72–2.78), p< 0.0001). The association between T1DM and KOA among the obese (OR: 0.99 (0.54–1.67), p = 0.0477) was insignificant compared to the non-obese (OR: 1.40 (1.33–1.48), p < 0.0001). Interestingly, a higher association between T2DM and KOA among non-obese persons (OR: 2.75, (2.72–2.79), p < 0.0001) compared to the obese (OR: 1.71 (1.55–1.89), p < 0.0001) was noted. Further, histopathologic and Western blot studies of diabetic mice knee joints revealed enhanced carboxymethyl lysine (advanced glycation end product), matrix metalloproteinase-1, and reduced cartilage-specific proteins, including type II collagen (Col II), SOX9, and aggrecan (AGN), indicating deteriorated articular cartilage and proteoglycans. Results indicate that DM is strongly associated with KOA, and obesity may not be a confounding factor.