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لقاء في القرية العالمية = An encounter in the global village : قصص مختارة من المؤتمر الدولي الرابع عشر للقصة القصيرة
هذا الكتاب يحتوي على قصص مختارة من المؤتمر الدولي الرابع عشر للقصة القصيرة وهذا اللقاء الذي نظم من قبل جمعية دراسة القصص القصيرة الإنجليزية (أس أس أس أس إي) وهي جمعية عالمية أنشئت في الولايات المتحدة عام 1992 وينعقد كل عامين ويعتبر اللقاء العالمي الوحيد الذي يركز بشكل خاص على دراسات القصة القصيرة أما القصص المشاركة في اللقاء فهي مكتوبة من قبل 29 كاتبا ينتمون إلى عشرة دول هي الصين وتايوان والهند والولايات المتحدة وكندا ونيوزلندا وفرنسا وإيرلندا والنمسا وسنغافورا وجامايكا.
BOOK
Artificial Intelligence–Based Prediction of Lung Cancer Risk Using Nonimaging Electronic Medical Records: Deep Learning Approach
Background: Artificial intelligence approaches can integrate complex features and can be used to predict a patient’s risk of developing lung cancer, thereby decreasing the need for unnecessary and expensive diagnostic interventions. Objective: The aim of this study was to use electronic medical records to prescreen patients who are at risk of developing lung cancer. Methods: We randomly selected 2 million participants from the Taiwan National Health Insurance Research Database who received care between 1999 and 2013. We built a predictive lung cancer screening model with neural networks that were trained and validated using pre-2012 data, and we tested the model prospectively on post-2012 data. An age- and gender-matched subgroup that was 10 times larger than the original lung cancer group was used to assess the predictive power of the electronic medical record. Discrimination (area under the receiver operating characteristic curve [AUC]) and calibration analyses were performed. Results: The analysis included 11,617 patients with lung cancer and 1,423,154 control patients. The model achieved AUCs of 0.90 for the overall population and 0.87 in patients ≥55 years of age. The AUC in the matched subgroup was 0.82. The positive predictive value was highest (14.3%) among people aged ≥55 years with a pre-existing history of lung disease. Conclusions: Our model achieved excellent performance in predicting lung cancer within 1 year and has potential to be deployed for digital patient screening. Convolution neural networks facilitate the effective use of EMRs to identify individuals at high risk for developing lung cancer.
Journal Article
Innate Immunity of Neonates and Infants
Many important events occur at birth. The fetus is suddenly removed from a protected intra-uterine environment that is aquatic, warm, and nearly sterile, to the dry, cold external world laden with microbes. To survive, the neonate must interact with many organisms, making use of some, while vigorously defending against the others like a nation conducting trade with friendly countries and guarding against hostile ones from invading it, waging wars if necessary. Although, the neonatal immune system is plastic, however, it is highly tolerant which is due to both the fetal development during gestation as well as significant sudden changes in fetal environment and enormous exposure to the new antigens and intestinal bacteria and their products. This \"quiescent mode\" of innate immune system is part of a highly regulated process to fulfill all requirements of multi-layered process of early life, implemented effectively through the cells of innate immune system. While, most of the neonatal innate immune cells (e.g., neutrophils and monocytes) present contained activity and lower frequencies compared to their adult counterparts, innate lymphoid cells (ILCs), a distinct cellular component of innate immunity, show higher level of activity and presence during period of infancy compared to later stages of life and adulthood, which may suggest a role for ILCs in variable susceptibility to certain conditions during life time. In this review, while we focus on the characteristics and status of ILCs in neonatal immune system, we also draw an analogy from a national defense perspective because of the great similarities between that and the immune system by providing the known biological counterparts of all five core operational elements, the five Ds of defense, detection, discrimination, deployment, destruction, and de-escalation, with special focus on innate immunity, maternal support, and influence during the neonatal and infancy periods.
Journal Article
Statins use and its impact in EGFR‐TKIs resistance to prolong the survival of lung cancer patients: A Cancer registry cohort study in Taiwan
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.
Journal Article
Impact of large language model (ChatGPT) in healthcare: an umbrella review and evidence synthesis
Background
The emergence of Artificial Intelligence (AI), particularly Chat Generative Pre-Trained Transformer (ChatGPT), a Large Language Model (LLM), in healthcare promises to reshape patient care, clinical decision-making, and medical education. This review aims to synthesise research findings to consolidate the implications of ChatGPT integration in healthcare and identify research gaps.
Main body
The umbrella review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Cochrane Library, PubMed, Scopus, Web of Science, and Google Scholar were searched from inception until February 2024. Due to the heterogeneity of the included studies, no quantitative analysis was performed. Instead, information was extracted, summarised, synthesised, and presented in a narrative form. Two reviewers undertook title, abstract, and full text screening independently. The methodological quality and overall rating of the included reviews were assessed using the A Measurement Tool to Assess systematic Reviews (AMSTAR-2) checklist. The review examined 17 studies, comprising 15 systematic reviews and 2 meta-analyses, on ChatGPT in healthcare, revealing diverse focuses. The AMSTAR-2 assessment identified 5 moderate and 12 low-quality reviews, with deficiencies like study design justification and funding source reporting. The most reported theme that emerged was ChatGPT's use in disease diagnosis or clinical decision-making. While 82.4% of studies focused on its general usage, 17.6% explored unique topics like its role in medical examinations and conducting systematic reviews. Among these, 52.9% targeted general healthcare, with 41.2% focusing on specific domains like radiology, neurosurgery, gastroenterology, public health dentistry, and ophthalmology. ChatGPT’s use for manuscript review or writing was mentioned in 17.6% of reviews. Promising applications include enhancing patient care and clinical decision-making, though ethical, legal, and accuracy concerns require cautious integration.
Conclusion
We summarise the identified areas in reviews regarding ChatGPT's transformative impact in healthcare, highlighting patient care, decision-making, and medical education. Emphasising the importance of ethical regulations and the involvement of policymakers, we urge further investigation to ensure the reliability of ChatGPT and to promote trust in healthcare and research.
Journal Article
Crowdsource authoring as a tool for enhancing the quality of competency assessments in healthcare professions
The current Objective Structured Clinical Examination (OSCE) is complex, costly, and difficult to provide high-quality assessments. This pilot study employed a focus group and debugging stage to test the Crowdsource Authoring Assessment Tool (CAAT) for the creation and sharing of assessment tools used in editing and customizing, to match specific users’ needs, and to provide higher-quality checklists. Competency assessment international experts (n = 50) were asked to 1) participate in and experience the CAAT system when editing their own checklist, 2) edit a urinary catheterization checklist using CAAT, and 3) complete a Technology Acceptance Model (TAM) questionnaire consisting of 14 items to evaluate its four domains. The study occurred between October 2018 and May 2019. The median time for developing a new checklist using the CAAT was 65.76 minutes whereas the traditional method required 167.90 minutes. The CAAT system enabled quicker checklist creation and editing regardless of the experience and native language of participants. Participants also expressed the CAAT enhanced checklist development with 96% of them willing to recommend this tool to others. The use of a crowdsource authoring tool as revealed by this study has efficiently reduced the time to almost a third it would take when using the traditional method. In addition, it allows collaborations to partake on a simple platform which also promotes contributions in checklist creation, editing, and rating.
Journal Article
A novel method to retrieve alerts from a homegrown Computerized Physician Order Entry (CPOE) system of an academic medical center: Comprehensive alert characteristic analysis
The collection and analysis of alert logs are necessary for hospital administrators to understand the types and distribution of alert categories within the organization and reduce alert fatigue. However, this is not readily available in most homegrown Computerized Physician Order Entry (CPOE) systems.
To present a novel method that can collect alert information from a homegrown CPOE system (at an academic medical center in Taiwan) and conduct a comprehensive analysis of the number of alerts triggered and alert characteristics.
An alert log collector was developed using the Golang programming language and was implemented to collect all triggered interruptive alerts from a homegrown CPOE system of a 726-bed academic medical center from November 2017 to June 2018. Two physicians categorized the alerts from the log collector as either clinical or non-clinical (administrative).
Overall, 1,625,341 interruptive alerts were collected and classified into 1,474 different categories based on message content. The sum of the top 20, 50, and 100 categories of most frequently triggered alerts accounted for approximately 80, 90 and 97 percent of the total triggered alerts, respectively. Among alerts from the 100 most frequently triggered categories, 1,266,818 (80.2%) were administrative and 312,593 (19.8%) were clinical alerts.
We have successfully developed an alert log collector that can serve as an extended function to retrieve alerts from a homegrown CPOE system. The insight generated from the present study could also potentially bring value to hospital system designers and hospital administrators when redesigning their CPOE system.
Journal Article
Development of a Predictive Model for Optimization of Embryo Transfer Timing Using Blood-Based microRNA Expression Profile
MicroRNAs (miRNAs) can regulate the expression of genes involved in the establishment of the window of implantation (WOI) in the endometrium. Recent studies indicated that cell-free miRNAs in uterine fluid and blood samples could act as alternative and non-invasive sample types for endometrial receptivity analysis. In this study, we attempt to systematically evaluate whether the expression levels of cell-free microRNAs in blood samples could be used as non-invasive biomarkers for assessing endometrial receptivity status. We profiled the miRNA expression levels of 111 blood samples using next-generation sequencing to establish a predictive model for the assessment of endometrial receptivity status. This model was validated with an independent dataset (n = 73). The overall accuracy is 95.9%. Specifically, we achieved accuracies of 95.9%, 95.9%, and 100.0% for the pre-receptive group, the receptive group, and the post-respective group, respectively. Additionally, we identified a set of differentially expressed miRNAs between different endometrial receptivity statuses using the following criteria: p-value < 0.05 and fold change greater than 1.5 or less than −1.5. In conclusion, the expression levels of cell-free miRNAs in blood samples can be utilized in a non-invasive manner to distinguish different endometrial receptivity statuses.
Journal Article
Risk of cancer in long‐term levothyroxine users: Retrospective population‐based study
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.
Journal Article
Use of Mobile Phone App Interventions to Promote Weight Loss: Meta-Analysis
Obesity and lack of physical activity are major health risk factors for many life-threatening diseases, such as cardiovascular diseases, type 2 diabetes, and cancer. The use of mobile app interventions to promote weight loss and boost physical activity among children and adults is fascinating owing to the demand for cutting-edge and more efficient interventions. Previously published studies have examined different types of technology-based interventions and their impact on weight loss and increase in physical activity, but evidence regarding the impact of only a mobile phone app on weight loss and increase in physical activity is still lacking.
The main objective of this study was to assess the efficacy of a mobile phone app intervention for reducing body weight and increasing physical activity among children and adults.
PubMed, Google Scholar, Scopus, EMBASE, and the Web of Science electronic databases were searched for studies published between January 1, 2000, and April 30, 2019, without language restrictions. Two experts independently screened all the titles and abstracts to find the most appropriate studies. To be included, studies had to be either a randomized controlled trial or a case-control study that assessed a mobile phone app intervention with body weight loss and physical activity outcomes. The Cochrane Collaboration Risk of Bias tool was used to examine the risk of publication bias.
A total of 12 studies involving a mobile phone app intervention were included in this meta-analysis. Compared with the control group, the use of a mobile phone app was associated with significant changes in body weight (-1.07 kg, 95% CI -1.92 to -0.21, P=.01) and body mass index (-0.45 kg/m2, 95% CI -0.78 to -0.12, P=.008). Moreover, a nonsignificant increase in physical activity was observed (0.17, 95% CI -2.21 to 2.55, P=.88).
The findings of this study demonstrate the promising and emerging efficacy of using mobile phone app interventions for weight loss. Future studies are needed to explore the long-term efficacy of mobile app interventions in larger samples.
Journal Article