Development and Validation of a Machine Learning Algorithm Using Clinical Pages to Predict Imminent Clinical Deterioration.

BACKGROUND: Early detection of clinical deterioration among hospitalized patients is a clinical priority for patient safety and quality of care. Current automated approaches for identifying these patients perform poorly at identifying imminent events. OBJECTIVE: Develop a machine learning algorithm using pager messages sent between clinical team members to predict imminent clinical deterioration. DESIGN: We conducted a large observational study using long short-term memory machine learning models on the content and frequency of clinical pages. PARTICIPANTS: We included all hospitalizations between January 1, 2018 and December 31, 2020 at Vanderbilt University Medical Center that included at least one page message to physicians. Exclusion criteria included patients receiving palliative care, hospitalizations with a planned intensive care stay, and hospitalizations in the top 2% longest length of stay. MAIN MEASURES: Model classification performance to identify in-hospital cardiac arrest, transfer to intensive care, or Rapid Response activation in the next 3-, 6-, and 12-hours. We compared model performance against three common early warning scores: Modified Early Warning Score, National Early Warning Score, and the Epic Deterioration Index. KEY RESULTS: There were 87,783 patients (mean [SD] age 54.0 [18.8] years; 45,835 [52.2%] women) who experienced 136,778 hospitalizations. 6214 hospitalized patients experienced a deterioration event. The machine learning model accurately identified 62% of deterioration events within 3-hours prior to the event and 47% of events within 12-hours. Across each time horizon, the model surpassed performance of the best early warning score including area under the receiver operating characteristic curve at 6-hours (0.856 vs. 0.781), sensitivity at 6-hours (0.590 vs. 0.505), specificity at 6-hours (0.900 vs. 0.878), and F-score at 6-hours (0.291 vs. 0.220). CONCLUSIONS: Machine learning applied to the content and frequency of clinical pages improves prediction of imminent deterioration. Using clinical pages to monitor patient acuity supports improved detection of imminent deterioration without requiring changes to clinical workflow or nursing documentation.

Porous MOF Featuring 2D Intersecting Channels Based on a Pentanuclear Mn5(COO)10CO3 Cluster with Upgrading of Pipeline Natural Gas.

Natural gas plays a crucial role in daily and industrial production, but the impurities contained in natural gas limit its further use. It is very important to develop adsorbents that can separate CH4 from multicomponent mixtures, but there are still many challenges and problems. Herein, a novel porous MOF {[Mn5(pbdia)2(CO3)(H2O)2] ↔ 5H2O ↔ 2DMF}n (pbdia = 2,2'-(5-carboxy-1,3-phenylene)bis(oxy) diterephthalic acid) was successfully synthesized based on a flexible pentacarboxylic acid ligand and a unique pentanuclear Mn5(COO)10CO3 cluster. The MOF reveals a 3D porous structure with 2D intersecting channels, which shows high C3H8, C2H6, and CO2 adsorption capacities and affinities over CH4. Moreover, the ideal adsorption solution theory selectivities of C3H8/CH4, C2H6/CH4, and CO2/CH4 can reach 263.0, 27.0, and 7.7, respectively, suggesting a potential for removing the low content of C3H8, C2H6, and CO2 from pipeline natural gas, which was further confirmed by breakthrough curves and GCMC simulations.

Comparison of 30-day planned and unplanned readmissions in a tertiary teaching hospital in China.

PURPOSE: The purpose of this study was to analyze and compare the clinical characteristics of patients with 30-day planned and unplanned readmissions and to identify patients at high risk for unplanned readmissions. This will facilitate a better understanding of these readmissions and improve and optimize resource utilization for this patient population. METHODS: A retrospective cohort descriptive study was conducted at the West China Hospital (WCH), Sichuan University from January 1, 2015, to December 31, 2020. Discharged patients (≥ 18 years old) were divided into unplanned readmission and planned readmission groups according to 30-day readmission status. Demographic and related information was collected for each patient. Logistic regression analysis was used to assess the association between unplanned patient characteristics and the risk of readmission. RESULTS: We identified 1,118,437 patients from 1,242,496 discharged patients, including 74,494 (6.7%) 30-day planned readmissions and 9,895 (0.9%) unplanned readmissions. The most common diseases of planned readmissions were antineoplastic chemotherapy (62,756/177,749; 35.3%), radiotherapy sessions for malignancy (919/8,229; 11.2%), and systemic lupus erythematosus (607/4,620; 13.1%). The most common diseases of unplanned readmissions were antineoplastic chemotherapy (2038/177,747; 1.1%), age-related cataract (1061/21,255; 5.0%), and unspecified disorder of refraction (544/5,134; 10.6%). There were statistically significant differences between planned and unplanned readmissions in terms of patient sex, marital status, age, length of initial stay, the time between discharge, ICU stay, surgery, and health insurance. CONCLUSION: Accurate information on 30-day planned and unplanned readmissions facilitates effective planning of healthcare resource allocation. Identifying risk factors for 30-day unplanned readmissions can help develop interventions to reduce readmission rates.

Utility of ChatGPT in Clinical Practice.

ChatGPT is receiving increasing attention and has a variety of application scenarios in clinical practice. In clinical decision support, ChatGPT has been used to generate accurate differential diagnosis lists, support clinical decision-making, optimize clinical decision support, and provide insights for cancer screening decisions. In addition, ChatGPT has been used for intelligent question-answering to provide reliable information about diseases and medical queries. In terms of medical documentation, ChatGPT has proven effective in generating patient clinical letters, radiology reports, medical notes, and discharge summaries, improving efficiency and accuracy for health care providers. Future research directions include real-time monitoring and predictive analytics, precision medicine and personalized treatment, the role of ChatGPT in telemedicine and remote health care, and integration with existing health care systems. Overall, ChatGPT is a valuable tool that complements the expertise of health care providers and improves clinical decision-making and patient care. However, ChatGPT is a double-edged sword. We need to carefully consider and study the benefits and potential dangers of ChatGPT. In this viewpoint, we discuss recent advances in ChatGPT research in clinical practice and suggest possible risks and challenges of using ChatGPT in clinical practice. It will help guide and support future artificial intelligence research similar to ChatGPT in health.

Text Dialogue Analysis for Primary Screening of Mild Cognitive Impairment: Development and Validation Study.

BACKGROUND: Artificial intelligence models tailored to diagnose cognitive impairment have shown excellent results. However, it is unclear whether large linguistic models can rival specialized models by text alone. OBJECTIVE: In this study, we explored the performance of ChatGPT for primary screening of mild cognitive impairment (MCI) and standardized the design steps and components of the prompts. METHODS: We gathered a total of 174 participants from the DementiaBank screening and classified 70% of them into the training set and 30% of them into the test set. Only text dialogues were kept. Sentences were cleaned using a macro code, followed by a manual check. The prompt consisted of 5 main parts, including character setting, scoring system setting, indicator setting, output setting, and explanatory information setting. Three dimensions of variables from published studies were included: vocabulary (ie, word frequency and word ratio, phrase frequency and phrase ratio, and lexical complexity), syntax and grammar (ie, syntactic complexity and grammatical components), and semantics (ie, semantic density and semantic coherence). We used R 4.3.0. for the analysis of variables and diagnostic indicators. RESULTS: Three additional indicators related to the severity of MCI were incorporated into the final prompt for the model. These indicators were effective in discriminating between MCI and cognitively normal participants: tip-of-the-tongue phenomenon (P<.001), difficulty with complex ideas (P<.001), and memory issues (P<.001). The final GPT-4 model achieved a sensitivity of 0.8636, a specificity of 0.9487, and an area under the curve of 0.9062 on the training set; on the test set, the sensitivity, specificity, and area under the curve reached 0.7727, 0.8333, and 0.8030, respectively. CONCLUSIONS: ChatGPT was effective in the primary screening of participants with possible MCI. Improved standardization of prompts by clinicians would also improve the performance of the model. It is important to note that ChatGPT is not a substitute for a clinician making a diagnosis.

Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.

BACKGROUND: Deep learning (DL) prediction models hold great promise in the triage of COVID-19. OBJECTIVE: We aimed to evaluate the diagnostic test accuracy of DL prediction models for assessing and predicting the severity of COVID-19. METHODS: We searched PubMed, Scopus, LitCovid, Embase, Ovid, and the Cochrane Library for studies published from December 1, 2019, to April 30, 2022. Studies that used DL prediction models to assess or predict COVID-19 severity were included, while those without diagnostic test accuracy analysis or severity dichotomies were excluded. QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2), PROBAST (Prediction Model Risk of Bias Assessment Tool), and funnel plots were used to estimate the bias and applicability. RESULTS: A total of 12 retrospective studies involving 2006 patients reported the cross-sectionally assessed value of DL on COVID-19 severity. The pooled sensitivity and area under the curve were 0.92 (95% CI 0.89-0.94; I2=0.00%) and 0.95 (95% CI 0.92-0.96), respectively. A total of 13 retrospective studies involving 3951 patients reported the longitudinal predictive value of DL for disease severity. The pooled sensitivity and area under the curve were 0.76 (95% CI 0.74-0.79; I2=0.00%) and 0.80 (95% CI 0.76-0.83), respectively. CONCLUSIONS: DL prediction models can help clinicians identify potentially severe cases for early triage. However, high-quality research is lacking. TRIAL REGISTRATION: PROSPERO CRD42022329252; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD 42022329252.

Ethical Considerations of Using ChatGPT in Health Care.

ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.

The application of Chat Generative Pre-trained Transformer in nursing education.

BACKGROUND: Nursing education is critical for nurses to deliver quality health care. Incorporating AI into education can enhance the learning process and better equip nurses for their health care roles. PURPOSE: This article explores the potential applications and challenges of ChatGPT in nursing education. METHODS: A comprehensive literature review was conducted to explore the potential benefits and challenges of using ChatGPT in nursing education. DISCUSSION: ChatGPT, an advanced large language model, has the potential to make valuable contributions to nursing education in various ways, including personalized learning, simulation scenarios, immediate feedback, and reducing educator workload. However, it is important to address the various challenges and limitations in order to realize its full potential. CONCLUSION: Nursing educators must carefully consider the potential uses, benefits, challenges, drawbacks, and limitations of ChatGPT to make informed decisions about its integration into nursing education.

[Advances in heart failure clinical research based on deep learning].

Heart failure is a disease that seriously threatens human health and has become a global public health problem. Diagnostic and prognostic analysis of heart failure based on medical imaging and clinical data can reveal the progression of heart failure and reduce the risk of death of patients, which has important research value. The traditional analysis methods based on statistics and machine learning have some problems, such as insufficient model capability, poor accuracy due to prior dependence, and poor model adaptability. In recent years, with the development of artificial intelligence technology, deep learning has been gradually applied to clinical data analysis in the field of heart failure, showing a new perspective. This paper reviews the main progress, application methods and major achievements of deep learning in heart failure diagnosis, heart failure mortality and heart failure readmission, summarizes the existing problems and presents the prospects of related research to promote the clinical application of deep learning in heart failure clinical research.

An interpretable DIC risk prediction model based on convolutional neural networks with time series data.

Disseminated intravascular coagulation (DIC) is a complex, life-threatening syndrome associated with the end-stage of different coagulation disorders. Early prediction of the risk of DIC development is an urgent clinical need to reduce adverse outcomes. However, effective approaches and models to identify early DIC are still lacking. In this study, a novel interpretable deep learning based time series is used to predict the risk of DIC. The study cohort included ICU patients from a 4300-bed academic hospital between January 1, 2019, and January 1, 2022. Experimental results show that our model achieves excellent performance (AUC: 0.986, Accuracy: 95.7%, and F1:0.935). Gradient-weighted Class Activation Mapping (Grad-CAM) was used to explain how predictive models identified patients with DIC. The decision basis of the model was displayed in the form of a heat map. The model can be used to identify high-risk patients with DIC early, which will help in the early intervention of DIC patients and improve the treatment effect.

Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study.

BACKGROUND: Heart failure (HF) is a common disease and a major public health problem. HF mortality prediction is critical for developing individualized prevention and treatment plans. However, due to their lack of interpretability, most HF mortality prediction models have not yet reached clinical practice. OBJECTIVE: We aimed to develop an interpretable model to predict the mortality risk for patients with HF in intensive care units (ICUs) and used the SHapley Additive exPlanation (SHAP) method to explain the extreme gradient boosting (XGBoost) model and explore prognostic factors for HF. METHODS: In this retrospective cohort study, we achieved model development and performance comparison on the eICU Collaborative Research Database (eICU-CRD). We extracted data during the first 24 hours of each ICU admission, and the data set was randomly divided, with 70% used for model training and 30% used for model validation. The prediction performance of the XGBoost model was compared with three other machine learning models by the area under the curve. We used the SHAP method to explain the XGBoost model. RESULTS: A total of 2798 eligible patients with HF were included in the final cohort for this study. The observed in-hospital mortality of patients with HF was 9.97%. Comparatively, the XGBoost model had the highest predictive performance among four models with an area under the curve (AUC) of 0.824 (95% CI 0.7766-0.8708), whereas support vector machine had the poorest generalization ability (AUC=0.701, 95% CI 0.6433-0.7582). The decision curve showed that the net benefit of the XGBoost model surpassed those of other machine learning models at 10%~28% threshold probabilities. The SHAP method reveals the top 20 predictors of HF according to the importance ranking, and the average of the blood urea nitrogen was recognized as the most important predictor variable. CONCLUSIONS: The interpretable predictive model helps physicians more accurately predict the mortality risk in ICU patients with HF, and therefore, provides better treatment plans and optimal resource allocation for their patients. In addition, the interpretable framework can increase the transparency of the model and facilitate understanding the reliability of the predictive model for the physicians.

Effectiveness of eHealth Self-management Interventions in Patients With Heart Failure: Systematic Review and Meta-analysis.

BACKGROUND: Heart failure (HF) is a common clinical syndrome associated with substantial morbidity, a heavy economic burden, and high risk of readmission. eHealth self-management interventions may be an effective way to improve HF clinical outcomes. OBJECTIVE: The aim of this study was to systematically review the evidence for the effectiveness of eHealth self-management in patients with HF. METHODS: This study included only randomized controlled trials (RCTs) that compared the effects of eHealth interventions with usual care in adult patients with HF using searches of the EMBASE, PubMed, CENTRAL (Cochrane Central Register of Controlled Trials), and CINAHL databases from January 1, 2011, to July 12, 2022. The Cochrane Risk of Bias tool (RoB 2) was used to assess the risk of bias for each study. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria were used to rate the certainty of the evidence for each outcome of interest. Meta-analyses were performed using Review Manager (RevMan v.5.4) and R (v.4.1.0 x64) software. RESULTS: In total, 24 RCTs with 9634 participants met the inclusion criteria. Compared with the usual-care group, eHealth self-management interventions could significantly reduce all-cause mortality (odds ratio [OR] 0.83, 95% CI 0.71-0.98, P=.03; GRADE: low quality) and cardiovascular mortality (OR 0.74, 95% CI 0.59-0.92, P=.008; GRADE: moderate quality), as well as all-cause readmissions (OR 0.82, 95% CI 0.73-0.93, P=.002; GRADE: low quality) and HF-related readmissions (OR 0.77, 95% CI 0.66-0.90, P<.001; GRADE: moderate quality). The meta-analyses also showed that eHealth interventions could increase patients' knowledge of HF and improve their quality of life, but there were no statistically significant effects. However, eHealth interventions could significantly increase medication adherence (OR 1.82, 95% CI 1.42-2.34, P<.001; GRADE: low quality) and improve self-care behaviors (standardized mean difference -1.34, 95% CI -2.46 to -0.22, P=.02; GRADE: very low quality). A subgroup analysis of primary outcomes regarding the enrolled population setting found that eHealth interventions were more effective in patients with HF after discharge compared with those in the ambulatory clinic setting. CONCLUSIONS: eHealth self-management interventions could benefit the health of patients with HF in various ways. However, the clinical effects of eHealth interventions in patients with HF are affected by multiple aspects, and more high-quality studies are needed to demonstrate effectiveness.

Understanding Behavioral Intentions Toward COVID-19 Vaccines: Theory-Based Content Analysis of Tweets.

BACKGROUND: Acceptance rates of COVID-19 vaccines have still not reached the required threshold to achieve herd immunity. Understanding why some people are willing to be vaccinated and others are not is a critical step to develop efficient implementation strategies to promote COVID-19 vaccines. OBJECTIVE: We conducted a theory-based content analysis based on the capability, opportunity, motivation-behavior (COM-B) model to characterize the factors influencing behavioral intentions toward COVID-19 vaccines mentioned on the Twitter platform. METHODS: We collected tweets posted in English from November 1-22, 2020, using a combination of relevant keywords and hashtags. After excluding retweets, we randomly selected 5000 tweets for manual coding and content analysis. We performed a content analysis informed by the adapted COM-B model. RESULTS: Of the 5000 COVID-19 vaccine-related tweets that were coded, 4796 (95.9%) were posted by unique users. A total of 97 tweets carried positive behavioral intent, while 182 tweets contained negative behavioral intent. Of these, 28 tweets were mapped to capability factors, 155 tweets were related to motivation, 23 tweets were related to opportunities, and 74 tweets did not contain any useful information about the reasons for their behavioral intentions (κ=0.73). Some tweets mentioned two or more constructs at the same time. Tweets that were mapped to capability (P<.001), motivation (P<.001), and opportunity (P=.03) factors were more likely to indicate negative behavioral intentions. CONCLUSIONS: Most behavioral intentions regarding COVID-19 vaccines were related to the motivation construct. The themes identified in this study could be used to inform theory-based and evidence-based interventions to improve acceptance of COVID-19 vaccines.

Leveraging Transfer Learning to Analyze Opinions, Attitudes, and Behavioral Intentions Toward COVID-19 Vaccines: Social Media Content and Temporal Analysis.

BACKGROUND: The COVID-19 vaccine is considered to be the most promising approach to alleviate the pandemic. However, in recent surveys, acceptance of the COVID-19 vaccine has been low. To design more effective outreach interventions, there is an urgent need to understand public perceptions of COVID-19 vaccines. OBJECTIVE: Our objective was to analyze the potential of leveraging transfer learning to detect tweets containing opinions, attitudes, and behavioral intentions toward COVID-19 vaccines, and to explore temporal trends as well as automatically extract topics across a large number of tweets. METHODS: We developed machine learning and transfer learning models to classify tweets, followed by temporal analysis and topic modeling on a dataset of COVID-19 vaccine-related tweets posted from November 1, 2020 to January 31, 2021. We used the F1 values as the primary outcome to compare the performance of machine learning and transfer learning models. The statistical values and P values from the Augmented Dickey-Fuller test were used to assess whether users' perceptions changed over time. The main topics in tweets were extracted by latent Dirichlet allocation analysis. RESULTS: We collected 2,678,372 tweets related to COVID-19 vaccines from 841,978 unique users and annotated 5000 tweets. The F1 values of transfer learning models were 0.792 (95% CI 0.789-0.795), 0.578 (95% CI 0.572-0.584), and 0.614 (95% CI 0.606-0.622) for these three tasks, which significantly outperformed the machine learning models (logistic regression, random forest, and support vector machine). The prevalence of tweets containing attitudes and behavioral intentions varied significantly over time. Specifically, tweets containing positive behavioral intentions increased significantly in December 2020. In addition, we selected tweets in the following categories: positive attitudes, negative attitudes, positive behavioral intentions, and negative behavioral intentions. We then identified 10 main topics and relevant terms for each category. CONCLUSIONS: Overall, we provided a method to automatically analyze the public understanding of COVID-19 vaccines from real-time data in social media, which can be used to tailor educational programs and other interventions to effectively promote the public acceptance of COVID-19 vaccines.

A systematic review of theoretical constructs in CDS literature.

BACKGROUND: Studies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning. The Unified Theory of Acceptance and Use of Technology (UTAUT) model may provide such a theory-based explanation; however, it is unknown if the model can be applied to the CDS literature. OBJECTIVE: Our overall goal was to develop a taxonomy based on UTAUT constructs that could reliably characterize CDS interventions. METHODS: We used a two-step process: (1) identified randomized controlled trials meeting comparative effectiveness criteria, e.g., evaluating the impact of CDS interventions with and without specific features or implementation strategies; (2) iteratively developed and validated a taxonomy for characterizing differential CDS features or implementation strategies using three raters. RESULTS: Twenty-five studies with 48 comparison arms were identified. We applied three constructs from the UTAUT model and added motivational control to characterize CDS interventions. Inter-rater reliability was as follows for model constructs: performance expectancy (κ = 0.79), effort expectancy (κ = 0.85), social influence (κ = 0.71), and motivational control (κ = 0.87). CONCLUSION: We found that constructs from the UTAUT model and motivational control can reliably characterize features and associated implementation strategies. Our next step is to examine the quantitative relationships between constructs and CDS adoption.

The management of coronavirus disease 2019 (COVID-19).

In December 2019, a novel coronavirus causing severe acute respiratory disease occurred in Wuhan, China. It is an emerging infectious disease with widespread and rapid infectiousness. The World Health Organization declared the coronavirus outbreak to be a public health emergency of international concern on 31 January 2020. Severe COVID-19 patients should be managed and treated in a critical care unit. Performing a chest X-ray/CT can judge the severity of the disease. The management of COVID-19 patients includes epidemiological risk and patient isolation; treatment entails general supportive care, respiratory support, symptomatic treatment, nutritional support, psychological intervention, etc. The prognosis of the patients depends upon the severity of the disease, the patient's age, the underlying diseases of the patients, and the patient's overall medical condition. The management of COVID-19 should focus on early diagnosis, immediate isolation, general and optimized supportive care, and infection prevention and control.

Epidemiology, clinical characteristics of the first cases of COVID-19.

BACKGROUND: COVID-19 is currently the most urgent threat to public health in the world. The aim of this study is to provide an overview of the first cases of COVID-19 to make further improvements in health policies and prevention measurements in response to the outbreak of COVID-19. METHODS: We performed a search in PubMed, the CNKI (China National Knowledge Infrastructure), Web of Science and the WHO database of publications on COVID-19 for peer-reviewed papers from 1 December 2019 to 9 July 2020. We analysed the demographics, epidemiological characteristics, clinical features, signs and symptoms of the disease at the onset. RESULTS: We identified the first cases of COVID-19 in 16 different countries/regions from Asia, Europe, North America and South America. Of these 16 cases, 8 (50.0%) were male, with a mean of age 43.38 ± 15.19 years. All the cases had a history of travel or exposure. Twelve cases (75.0%) occurred in January, eight patients were Chinese, two patients were international students in Wuhan, one patient had a history of travelling in Wuhan, and one patient was in contact with Chinese patient. The longest hospital stay was 24 days (1 patient), and the shortest was 5 days (1 patient). The usual hospital stay was 9 days (4 patients). CONCLUSION: Understanding the epidemiological characteristics, clinical characteristics, and diagnosis and treatment of the first patients in various countries are of great significance for the identification, prevention and control of COVID-19.

Resistance to FGFR1-targeted therapy leads to autophagy via TAK1/AMPK activation in gastric cancer.

BACKGROUND: Fibroblast growth factor receptor 1 (FGFR1) is frequently dysregulated in various tumors. FGFR inhibitors have shown promising therapeutic value in several preclinical models. However, tumors resistant to FGFR inhibitors have emerged, compromising therapeutic outcomes by demonstrating markedly aggressive metastatic progression; however, the underlying signaling mechanism of resistance remains unknown. METHODS: We established FGFR inhibitor-resistant cell models using two gastric cancer (GC) cell lines, MGC-803 and BGC-823. RNA-seq was performed to determine the continuous cellular transcriptome changes between parental and resistant cells. We explored the mechanism of resistance to FGFR inhibitor, using a subcutaneous tumor model and GC patient-derived tumor organotypic culture. RESULTS: We observed that FGFR1 was highly expressed in GC and FGFR1 inhibitor-resistant cell lines, demonstrating elevated levels of autophagic activity. These resistant cells were characterized by epithelial-mesenchymal transition (EMT) required to facilitate metastatic outgrowth. In drug-resistant cells, the FGFR1 inhibitor regulated GC cell autophagy via AMPK/mTOR signal activation, which could be blocked using either pharmacological inhibitors or essential gene knockdown. Furthermore, TGF-ß-activated kinase 1 (TAK1) amplification and metabolic restrictions led to AMPK pathway activation and autophagy. In vitro and in vivo results demonstrated that the FGFR inhibitor AZD4547 and TAK1 inhibitor NG25 synergistically inhibited proliferation and autophagy in AZD4547-resistant cell lines and patient-derived GC organotypic cultures. CONCLUSIONS: We elucidated the molecular mechanisms underlying primary resistance to FGFR1 inhibitors in GC, and revealed that the inhibition of FGFR1 and TAK1 signaling could present a potential novel therapeutic strategy for FGFR1 inhibitor-resistant GC patients.

Telemedicine and COVID-19 Pandemic: Experiences of Western China Hospital.

COVID-19 has brought unprecedented challenges to the healthcare system. In response to COVID-19, hospitals can replace some routine medical services with telemedicine. At the beginning of the pandemic, West China Hospital developed a new model of telemedicine platform against COVID-19. The telemedicine platform played a critical role in fighting the pandemic in Sichuan Province and significantly improved healthcare outcomes.