The role of alexithymia in suicide ideation among Taiwanese army military personnel: A serial mediation model investigating the effects of perceived stress and depression.

Maintaining the good mental health of Taiwanese military personnel is crucial, especially in light of incidents such as the Taiwan Strait crisis. Suicide is a leading cause of death among military personnel and alexithymia is a significant risk factor for suicidal ideation. However, the mechanisms linking alexithymia and suicidal ideation in this psychologically burdened population remain poorly understood. In total, 863 voluntary army military personnel from Taiwanese reserve brigades and combined-arms brigades were enroled between May 2020 and February 2021. Structured questionnaires about alexithymia, perceived stress, depression, suicidal ideation, and background characteristics were used. Mediation analyses were conducted to examine the serial mediation roles of perceived stress and depression in the relationship between alexithymia and suicidal ideation. Significant positive correlations were observed between alexithymia, perceived stress, depression, and suicidal ideation in bivariate analyses. Serial mediation analyses revealed that alexithymia significantly predicted higher levels of perceived stress, subsequently leading to depressive symptoms, which were associated with suicidal ideation. Depression served as a significant mediator between alexithymia and suicidal ideation. The strongest mediating effect (71.4%) was observed in the pathway from alexithymia through perceived stress and depression to suicidal ideation. Limitations included the utilization of cross-sectional data and a reliance on retrospective self-report measures. Perceived stress and depression were identified as serial mediators in the association between alexithymia and suicidal ideation. Clinically, it is crucial to prioritise interventions that target emotional regulation skills and assess the presence of alexithymia to effectively reduce suicidal ideation in military personnel.

Factors affecting cognitive frailty improvement and progression in Taiwanese older adults.

BACKGROUND: Knowledge of predictors of cognitive frailty (CF) trajectories is required to develop preventive strategies to delay or reverse the progression from CF to dementia and other adverse outcomes. This 2-year prospective study aimed to investigate factors affecting the progression and improvement of CF in older Taiwanese adults. METHODS: In total, 832 community-dwelling people aged ≥ 65 years were eligible. Fried's five frailty criteria were used to measure prefrailty and frailty, while cognitive performance was assessed by the Clinical Dementia Rating and Mini-Mental State Examination. Each component of reversible CF and potentially reversible CF was assigned a score, with a total score ranging 0 to 5 points. Two annual follow-up CF assessments were conducted. The group-based trajectory model was applied to identify latent CF trajectory groups, and a multinomial logistic regression was used to examine relationships of explanatory variables with CF trajectories. RESULTS: According to data on 482 subjects who completed the two annual follow-ups, three CF trajectories of robust, improvement, and progression were identified. After adjusting for the baseline CF state, CF progression was significantly associated with an older age (odds ratio [OR] = 1.08; 95% confidence interval [CI], 1.02 ~ 1.14), a lower Tinetti balance score (OR = 0.72; 95% CI, 0.54 ~ 0.96), a slower gait (OR = 0.98; 95% CI, 0.97 ~ 0.99), and four or more comorbidities (OR = 2.65; 95% CI, 1.19 ~ 5.90), while CF improvement was not significantly associated with any variable except the baseline CF state. In contrast, without adjusting for the baseline CF state, CF progression was significantly associated with an older age, female sex, balance scores, gait velocity, regular exercise, the number of comorbidities, and depression, while CF improvement was significantly associated with female sex, balance scores, and the number of comorbidities. CONCLUSIONS: The baseline CF state, an older age, poorer balance, slower gait, and a high number of comorbidities may contribute to CF progression, while the baseline CF state may account for associations of engaging in regular exercise and depression with CF development.

Effects of Age, Sex, and Postconcussive Symptoms on Domain-Specific Quality of Life a Year After Mild Traumatic Brain Injury.

OBJECTIVE: To identify the factors influencing longitudinal changes in patients' scores across 6 domains of the Quality of Life after Brain Injury (QOLIBRI) instrument 1 year after mild traumatic brain injury (mTBI). DESIGN: This was a longitudinal cohort study. PARTICIPANTS AND SETTING: Eligible patients with a new diagnosis of mTBI were recruited from the outpatient clinics of the neurosurgery departments of 3 teaching hospitals in Taipei City, Taiwan. In total, 672 patients participated in the baseline assessment. Postinjury follow-up was conducted at 6 and 12 months. MAIN OUTCOME MEASURE: Six domains of the 37-item QOLIBRI: Cognition, Self, Daily Life and Autonomy, Social Relationships, Emotions, and Physical Problems. RESULTS: Linear mixed-effects analyses revealed that, among patients younger than 60 years, the scores of the Cognition, Self, Daily Life and Autonomy, and Social Relationships domains significantly increased 6 months after injury; furthermore, their scores of the Cognition, Self, and Daily Life and Autonomy significantly increased 12 months after injury. By contrast, among patients 60 years and older, the scores of these domains reduced from baseline to 6 and 12 months. No significant sex-based difference was observed in the changes in scores of any QOLIBRI domain. At 6 and 12 months post-injury, the scores of the Cognition, Emotions, and Physical Problems domains were significantly higher for patients with postconcussive symptoms than for those without these symptoms. CONCLUSIONS: Although multiple characteristics of patients significantly affected their baseline scores on the 6 domains of the QOLIBRI, only age and postconcussive symptoms were significantly associated with longitudinal changes in their scores 6 and 12 months after mTBI.

An AI-Enabled Dynamic Risk Stratification for Emergency Department Patients with ECG and CXR Integration.

Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations. This retrospective cohort study included 134,112 ED visits with at least one electrocardiography (ECG) and chest X-ray (CXR) in a medical center from 2012 to 2022. Additionally, an independent community hospital provided 45,614 ED visits as an external validation set. We trained an eXtreme gradient boosting (XGB) model using initial assessment data to predict all-cause mortality in 7 days. Two deep learning models (DLMs) using ECG and CXR were trained to stratify mortality risks. The dynamic triage levels were based on output from the XGB-triage and DLMs from ECG and CXR. During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of the XGB-triage model was >0.866; furthermore, the AUCs of DLMs using ECG and CXR were >0.862 and >0.886, respectively. The dynamic triage scale provided a higher C-index (0.914-0.920 vs. 0.827-0.843) than the original one and demonstrated better predictive ability for 5-year mortality, 30-day ED revisit, and 30-day discharge. The AI-based risk scale provides a more accurate and dynamic stratification of mortality risk in ED patients, particularly in identifying patients who tend to be overlooked due to atypical symptoms.

Effects of home-based computerized cognitive training and tai chi exercise on cognitive functions in older adults with mild cognitive impairment.

BACKGROUND: A randomized trial was conducted to investigate the effects of computerized cognitive training (CCT) and tai chi exercise (TCE) vs. health education (HE) on cognitive functions in 189 older adults with mild cognitive impairment (MCI). METHODS: Cognitive functions were assessed by the five-domain Mattis Dementia Rating Scale (MDRS) (attention, initiation/perseveration, construction, conceptualization, and memory) and the modified Telephone Interview of Cognitive Status (TICS-M), while the timed up and go (TUG), Tinetti's balance, activities of daily living (ADLs), and Activities-specific Balance Confidence (ABC) were also evaluated. Each intervention was delivered once a week for 6 months. All outcomes were followed up at 6 and 12 months of the study. RESULTS: Compared to HE, CCT increased scores on the MDRS's total, initiation/perseveration, construction, and conceptualization domains and on the TICS-M at 6 months and those on the MDRS's total, attention, construction, conceptualization, and memory domains and on the TICS-M at 12 months; TCE increased scores on the MDRS's total and construction domains and on the TICS-M at 6 months and those on the MDRS's total, attention, initiation/perseveration, and conceptualization domains and on the TICS-M at 12 months. Moreover, CCT improved the TUG at 6 and 12 months and Tinetti's balance at 12 months, and TCE improved the TUG at 6 and 12 months, Tinetti's balance, and ABC at 6 and 12 months, and ADLs at 12 months. CONCLUSIONS: The effects of CCT and TCE on improving global cognition and certain cognitive domains for older MCI adults may have been small but they lasted for at least 12 months.

Influence of resilience on perceived stress and depression among Taiwanese army military personnel.

Resilience has been reported to attenuate psychological burden and promote mental health. Military personnel constitute a population with a high psychological burden and poor sleep quality and are thus at a high risk of depression. This study is aim to examine the mechanism underlying the effects of resilience on perceived stress, sleep quality, and depression among Taiwanese army military personnel. A cross-sectional survey was conducted between May 2020 and February 2021. Participants comprised 1505 voluntary army military service personnel aged 20 years or older; they completed self-reported questionnaires measuring their perceived stress, resilience, sleep quality, and depression. Data were analysed using structural equation modelling. The results indicated that the association between resilience and depression was partially mediated by perceived stress and sleep quality. Perceived stress is a significant mediator on the association between resilience and depression. A high level of sleep disturbance was observed in this population and results found that sleep quality showed a slight partial mediation effect on the association between resilience and depression. Resilience can alleviate the effects of stress, which in turn alleviates depression among military personnel. Promoting resilience-enhancing education and mitigating environmental barriers to sleep is essential for reducing depressive symptoms among military personnel.

Peroxisome proliferator-activated receptor δ improves the features of atherosclerotic plaque vulnerability by regulating smooth muscle cell phenotypic switching.

BACKGROUND AND PURPOSE: Vascular smooth muscle cells (SMCs) undergo phenotypic switching during sustained inflammation, contributing to an unfavourable atherosclerotic plaque phenotype. PPARδ plays an important role in regulating SMC functions; however, its role in atherosclerotic plaque vulnerability remains unclear. Here, we explored the pathological roles of PPARδ in atherosclerotic plaque vulnerability in severe atherosclerosis and elucidated the underlying mechanisms. EXPERIMENTAL APPROACH: Plasma levels of PPARδ were measured in patients with acute coronary syndrome (ACS) and stable angina (SA). SMC contractile and synthetic phenotypic markers, endoplasmic reticulum (ER) stress, and features of atherosclerotic plaque vulnerability were analysed for the brachiocephalic artery of apolipoprotein E-knockout (ApoE-/- ) mice, fed a high-cholesterol diet (HCD) and treated with or without the PPARδ agonist GW501516. In vitro, the role of PPARδ was elucidated using human aortic SMCs (HASMCs). KEY RESULTS: Patients with ACS had significantly lower plasma PPARδ levels than those with SA. GW501516 reduced atherosclerotic plaque vulnerability, a synthetic SMC phenotype, ER stress markers, and NLRP3 inflammasome expression in HCD-fed ApoE-/- mice. ER stress suppressed PPARδ expression in HASMCs. PPARδ activation inhibited ER stress-induced synthetic phenotype development, ER stress-NLRP3 inflammasome axis activation and matrix metalloproteinase 2 (MMP2) expression in HASMCs. PPARδ inhibited NFκB signalling and alleviated ER stress-induced SMC phenotypic switching. CONCLUSIONS AND IMPLICATIONS: Low plasma PPARδ levels may be associated with atherosclerotic plaque vulnerability. Our findings provide new insights into the mechanisms underlying the protective effect of PPARδ on SMC phenotypic switching and improvement the features of atherosclerotic plaque vulnerability.

Green Tea Epigallocatechin Gallate Inhibits Preadipocyte Growth via the microRNA-let-7a/HMGA2 Signaling Pathway.

SCOPE: This study investigates the effect of epigallocatechin gallate (EGCG) on white and beige preadipocyte growth and explores the involvement of the miR-let-7a/HMGA2 pathway. METHODS AND RESULTS: 3T3-L1 and D12 cells are treated with EGCG. The effect of EGCG on cell proliferation and viability is evaluated, as well as microRNA (miRNA)-related signaling pathways. EGCG inhibits 3T3-L1 and D12 preadipocyte growth, upregulates miR-let-7a expression, and downregulates high-mobility group AT-hook 2 (HMGA2) mRNA and protein levels in a time- and dose-dependent manner. In addition, overexpression of miR-let-7a significantly inhibits the growth of 3T3-L1 and D12 cells and decreases HMGA2 mRNA and protein levels. MiR-let-7a inhibitor antagonizes the inhibitory effects of EGCG on the number and viability of 3T3-L1 and D12 cells. Furthermore, miR-let-7a inhibitor reverses the EGCG-induced increase in miR-let-7a expression levels and decrease in HMGA2 mRNA and protein levels. HMGA2 overexpression induces an increase in cell number and viability and antagonizes EGCG-suppressed cell growth and HMGA2 expression in 3T3-L1 and D12 preadipocytes. CONCLUSION: EGCG inhibits the growth of 3T3-L1 and D12 preadipocytes by modulating the miR-let-7a and HMGA2 pathways.

Predictors of incident reversible and potentially reversible cognitive frailty among Taiwanese older adults.

BACKGROUND: Few studies emphasize on predictors of incident cognitive frailty (CF) and examine relationships between various gait characteristics and CF. Therefore, we conducted a 2-year prospective study to investigate potential predictors, including gait characteristics, of incident reversible CF (RCF) and potentially RCF (PRCF) among Taiwanese older adults. METHODS: Eligible participants were individuals aged ≥ 65 years, who could ambulate independently, and did not have RCF/PRCF at the baseline. The baseline assessment collected information on physical frailty and cognitive measures, in addition to sociodemographic and lifestyle characteristics, preexisting comorbidities and medications, gait characteristics, Tinetti's balance, balance confidence as assessed by Activities-specific Balance Confidence (ABC) scale, and the depressive status as assessed by the Geriatric Depression Scale. The Mini-Mental State Examination (MMSE), Mattis Dementia Rating Scale, and Digit Symbol Substitution Test were used to evaluate cognitive functions. Incident RCF and PRCF were ascertained at a 2-year follow-up assessment. RESULTS: Results of the multinomial logistic regression analysis showed that incident RCF was significantly associated with older age (odds ratio [OR] = 1.05) and lower ABC scores (OR = 0.97). Furthermore, incident PRCF was significantly associated with older age (OR = 1.07), lower ABC scores (OR = 0.96), the presence of depression (OR = 3.61), lower MMSE scores (OR = 0.83), slower gait velocity (OR = 0.97), and greater double-support time variability (OR = 1.09). CONCLUSIONS: Incident RCF was independently associated with older age and lower balance confidence while incident PRCF independently associated with older age, reduced global cognition, the presence of depression, slower gait velocity, and greater double-support time variability. Balance confidence was the only modifiable factor associated with both incident RCF and PRCF.

Specific cognitive domains and frailty trajectories among older Taiwanese adults.

Objective: To investigate the associations of specific cognitive functions and with the frailty trajectory among older Taiwanese adults.Methods: At baseline, 730 community-dwelling older adults were recruited from outpatient clinics of a general hospital. Frailty status was defined using phenotype criteria. Global cognition was assessed using the modified Telephone Interview of Cognitive Status and Mini-Mental State Examination (MMSE). The Mattis Dementia Rating Scale (MDRS) and Digit Symbol Substitution Test were used to evaluate 6 cognitive domains: attention, initiation/perseveration, construction, conceptualization, memory, and processing speed. The group-based trajectory model was used to identify latent frailty trajectory groups and the multinomial logistic regression was to examine the relationships of specific cognitive functions with frailty trajectory.Results: Among 485 participants (168 men, 317 women, and mean age: 71.1 ± 5.5 years) completed 2 annual follow-up assessments, three frailty trajectory groups of improvement, no-change, and progression were identified. After adjusting for baseline frailty status, age, sex, global cognition, regular exercise habit, and number of comorbidities, higher scores on MDRS's initiation/perseveration (odds ratio [OR] = 0.85; 95% CI = 0.75-0.95) and attention (OR = 0.63; 95% CI = 0.38-1.00), respectively, were significantly associated with lower risk of frailty progression. Conversely, no significant association was detected between MMSE or TICSM scores and frailty improvement or progression.Conclusion: Specific cognitive functions of initiation/perseveration and attention, rather than global cognition, may be more useful to predict frailty progression, thus allowing the identification of at-risk older adults.

Risk Factors for Recurrent Violent Injuries Among African Women in The Gambia.

INTRODUCTION: Violence against women remains a major public health concern in African countries. We conducted a matched case-control study to identify risk factors for recurrent violent injuries among African women in The Gambia, a small West African country. METHODS: During the 12-month study period, we recruited study participants from eight emergency departments in the metropolitan areas of the municipality of Kanifing and the West Coast region. We selected women aged ≥15 years who sought medical treatment for an injury due to physical violence at least twice over the study period. Two control groups were used: violence controls (VC), which included those who had experienced a single violence-related injury in the prior 12 months; and nonviolence controls (NVC), which included those who had experienced a nonviolent injury. Control patients were matched based on gender, health facility, injury date, and age (±2 years). RESULTS: In total, 116 case patients and 232 control patients participated in the study. Results of the conditional logistic regression analyses of the VC and NVC control groups individually showed that women with recurrent violent injuries had a significantly higher likelihood of having a secondary education (odds ratio [OR]VC 6.47; ORNVC 4.22), coming from a polygamous family (ORVC 3.81; ORNVC 3.53), and had been raised by a single parent (ORVC 5.25; ORNVC 5.04). Furthermore, compared with the VC group, women with recurrent violent injuries had a significantly higher likelihood of living in a rented house (ORVC 4.74), living with in-laws (ORVC 5.98), and of having experienced childhood abuse (ORVC 2.48). Compared with the NVC group, women with recurrent violent injuries had a significantly higher likelihood of living in an extended family compound (ORVC 4.77), having more than two female siblings (ORVC 4.07), and having been raised by a relative (ORVC 3.52). CONCLUSION: We identified risk factors for recurrent injuries from physical violence among African women in The Gambia. Intervention strategies targeting these risk factors could be effective in preventing recurrent violence against African women.

Identification of Novel Genetic Variants Associated with Insomnia and Migraine Comorbidity.

Purpose: Although insomnia and migraine are often comorbid, the genetic association between insomnia and migraine remains unclear. This study aimed to identify susceptibility loci associated with insomnia and migraine comorbidity. Patients and Methods: We performed a genome-wide association study (GWAS) involving 1063 clinical outpatients at a tertiary hospital in Taiwan. Migraineurs with and without insomnia were genotyped using the Affymetrix Axiom Genome-Wide TWB 2.0. We performed association analyses for the entire cohort and stratified patients into the following subgroups: episodic migraine (EM), chronic migraine (CM), migraine with aura (MA), and migraine without aura (MoA). Potential correlations between SNPs and clinical indices in migraine patients with insomnia were examined using multivariate regression analysis. Results: The SNP rs1178326 in the gene HDAC9 was significantly associated with insomnia. In the EM, CM, MA, and MoA subgroups, we identified 30 additional susceptibility loci. Multivariate regression analysis showed that SNP rs1178326 also correlated with higher migraine frequency and the Migraine Disability Assessment (MIDAS) questionnaire score. Finally, two SNPs that had been previously reported in a major insomnia GWAS were also significant in our migraineurs, showing a concordant effect. Conclusion: In this GWAS, we identified several novel loci associated with insomnia in migraineurs in a Han Chinese population in Taiwan. These results provide insights into the possible genetic basis of insomnia and migraine comorbidity.

Development and Validation of an Artificial Intelligence Electrocardiogram Recommendation System in the Emergency Department.

The machine learning-assisted electrocardiogram (ECG) is increasingly recognized for its unprecedented capabilities in diagnosing and predicting cardiovascular diseases. Identifying the need for ECG examination early in emergency department (ED) triage is key to timely artificial intelligence-assisted analysis. We used machine learning to develop and validate a clinical decision support tool to predict ED triage patients' need for ECG. Data from 301,658 ED visits from August 2017 to November 2020 in a tertiary hospital were divided into a development cohort, validation cohort, and two test cohorts that included admissions before and during the COVID-19 pandemic. Models were developed using logistic regression, decision tree, random forest, and XGBoost methods. Their areas under the receiver operating characteristic curves (AUCs), positive predictive values (PPVs), and negative predictive values (NPVs) were compared and validated. In the validation cohort, the AUCs were 0.887 for the XGBoost model, 0.885 for the logistic regression model, 0.878 for the random forest model, and 0.845 for the decision tree model. The XGBoost model was selected for subsequent application. In test cohort 1, the AUC was 0.891, with sensitivity of 0.812, specificity of 0.814, PPV of 0.708 and NPV of 0.886. In test cohort 2, the AUC was 0.885, with sensitivity of 0.816, specificity of 0.812, PPV of 0.659, and NPV of 0.908. In the cumulative incidence analysis, patients not receiving an ECG yet positively predicted by the model had significantly higher probability of receiving the examination within 48 h compared with those negatively predicted by the model. A machine learning model based on triage datasets was developed to predict ECG acquisition with high accuracy. The ECG recommendation can effectively predict whether patients presenting at ED triage will require an ECG, prompting subsequent analysis and decision-making in the ED.

The risk and timing of acute ischemic stroke after electrical cardioversion for atrial fibrillation in Taiwan: A nationwide population-based cohort study.

BACKGROUND: There is a positive association between electrical cardioversion (ECV) and acute ischemic stroke (AIS). Although 4 weeks of anticoagulation therapy after ECV in atrial fibrillation (AF) patients is generally suggested by current guidelines to reduce the risk of AIS, limited studies have been conducted in Asian populations to determine the risk and timing of AIS after ECV for AF in recent years. Therefore, we aim to use the National Health Insurance Research Database (NHIRD) in Taiwan to determine the risk and timing of AIS after ECV for AF. METHODS: The data analyzed in this nationwide population-based retrospective cohort study were obtained from the NHIRD in Taiwan. The outcome in this study was the cumulative incidence of AIS in patients with AF during 7-day and 30-day follow-up periods after the patients underwent ECV. RESULTS: Our analysis included 39,697 patients with AF, of whom 5723 received ECV and 5723 were propensity score-matched controls. Compared to the controls, patients who received ECV exhibited a significantly increased incidence of 7-day AIS development (adjusted hazard ratio [HR] = 1.524, p = 0.003). In contrast, the incidence of 30-day AIS development showed no significant increase (adjusted HR = 1.301, p = 0.426). CONCLUSIONS: AF patients who underwent ECV had a higher incidence of 7-day AIS development but not 30-day AIS development. Considering the timing of AIS development after ECV in AF patients, the optimal duration of antithrombotic therapy after ECV deserves further investigation.

An Adapted Hybrid Model for Hands-On Practice on Disaster and Military Medicine Education in Undergraduate Medical Students During the COVID-19 Pandemic.

Background: The coronavirus disease 2019 (COVID-19) pandemic has resulted in substantial impacts on all aspects of medical education. Modern health systems must prepare for a wide variety of catastrophic scenarios, including emerging infectious disease outbreaks and human and natural disasters. During the COVID-19 pandemic, while the use of traditional teaching methods has decreased, the use of online-based teaching methods has increased. COVID-19 itself and the accompanying infection control measures have restricted full-scale practice. Therefore, we developed an adapted hybrid model that retained adequate hands-on practice and educational equality, and we applied it with a group of undergraduate medical students participating in a mandatory disaster education course in a military medical school. Methods: The course covered the acquisition of skills used in emergency and trauma scenarios through designated interdisciplinary modules on disaster responses. Several asynchronous and synchronous online webinars were used in this one-credit mandatory disaster and military medicine education course. To allow opportunities for hands-on practice and ensure education equality, the students were divided into 15 groups, with 12 students in each group. The hands-on practice exercises were also recorded and disseminated to the students in the designated area for online learning. Results: A total of 164 3rd-year medical students participated in this mandatory disaster and military medicine course during the COVID-19 pandemic. The satisfaction survey response rate was 96.5%. The students were satisfied with the whole curriculum (3.8/5). Most of the free-text comments regarding the course represented a high level of appreciation. The students felt more confident in the knowledge and skills they gained in hands-on exercises than they did in the knowledge and skills they gained in online exercises. The students showed significant improvements in knowledge after the course. Conclusions: We demonstrated that this adapted hybrid arrangement provided an enhanced learning experience, but we also found that medical students were more confident in their knowledge and skills when they had real hands-on practice.

Man on Noninvasive Positive Airway Pressure Ventilation With Throat Pain.

Dentures dislodged into throat on bilevel positive airway pressure (BiPAP) ventilation can be overlooked and potentially compromise airway patency. An 81-year-old man with a history of chronic obstructive pulmonary disease (COPD) presented with increased shortness of breath and productive cough for 1 week. Inhaled bronchodilators, parenteral steroids, and BiPAP ventilation were administered for acute exacerbation of COPD complicated with acute hypercapnic respiratory failure. Fifty minutes after receiving BiPAP ventilation, his respiratory condition improved; however, he started to complain of neck pain. The patient remained intolerant to the device 3 hours later, despite receiving assurance that the discomfort might be caused by air pressure through mask ventilation. His throat did not exhibit any abnormality during visual examination. Neck radiographs were subsequently obtained and demonstrated a denture impacted in the hypopharynx. His neck pain resolved after the removal of the dislodged maxillary denture. Denture dislodgement can occur in mask ventilation and compromise airway patency if stuck in the hypopharynx or respiratory tract. Such adverse events may be overlooked on the coexistence of respiratory and pulmonary diseases. A precisely pharyngolaryngeal inspection and complete imaging studies must be performed to facilitate early identification and further retrieval intervention.

Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury.

Traumatic brain injury (TBI) can lead to severe adverse clinical outcomes, including death and disability. Early detection of in-hospital mortality in high-risk populations may enable early treatment and potentially reduce mortality using machine learning. However, there is limited information on in-hospital mortality prediction models for TBI patients admitted to emergency departments. The aim of this study was to create a model that successfully predicts, from clinical measures and demographics, in-hospital mortality in a sample of TBI patients admitted to the emergency department. Of the 4881 TBI patients who were screened at the emergency department at a high-level first aid duty hospital in northern Taiwan, 3331 were assigned in triage to Level I or Level II using the Taiwan Triage and Acuity Scale from January 2008 to June 2018. The most significant predictors of in-hospital mortality in TBI patients were the scores on the Glasgow coma scale, the injury severity scale, and systolic blood pressure in the emergency department admission. This study demonstrated the effective cutoff values for clinical measures when using machine learning to predict in-hospital mortality of patients with TBI. The prediction model has the potential to further accelerate the development of innovative care-delivery protocols for high-risk patients.

An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction.

(1) Background: While an artificial intelligence (AI)-based, cardiologist-level, deep-learning model for detecting acute myocardial infarction (AMI), based on a 12-lead electrocardiogram (ECG), has been established to have extraordinary capabilities, its real-world performance and clinical applications are currently unknown. (2) Methods and Results: To set up an artificial intelligence-based alarm strategy (AI-S) for detecting AMI, we assembled a strategy development cohort including 25,002 visits from August 2019 to April 2020 and a prospective validation cohort including 14,296 visits from May to August 2020 at an emergency department. The components of AI-S consisted of chest pain symptoms, a 12-lead ECG, and high-sensitivity troponin I. The primary endpoint was to assess the performance of AI-S in the prospective validation cohort by evaluating F-measure, precision, and recall. The secondary endpoint was to evaluate the impact on door-to-balloon (DtoB) time before and after AI-S implementation in STEMI patients treated with primary percutaneous coronary intervention (PPCI). Patients with STEMI were alerted precisely by AI-S (F-measure = 0.932, precision of 93.2%, recall of 93.2%). Strikingly, in comparison with pre-AI-S (N = 57) and post-AI-S (N = 32) implantation in STEMI protocol, the median ECG-to-cardiac catheterization laboratory activation (EtoCCLA) time was significantly reduced from 6.0 (IQR, 5.0-8.0 min) to 4.0 min (IQR, 3.0-5.0 min) (p < 0.01). The median DtoB time was shortened from 69 (IQR, 61.0-82.0 min) to 61 min (IQR, 56.8-73.2 min) (p = 0.037). (3) Conclusions: AI-S offers front-line physicians a timely and reliable diagnostic decision-support system, thereby significantly reducing EtoCCLA and DtoB time, and facilitating the PPCI process. Nevertheless, large-scale, multi-institute, prospective, or randomized control studies are necessary to further confirm its real-world performance.

Potential Role of Protein Kinase C in the Pathophysiology of Diabetes-Associated Atherosclerosis.

Diabetes mellitus is a metabolic syndrome that affects millions of people worldwide. Recent studies have demonstrated that protein kinase C (PKC) activation plays an important role in hyperglycemia-induced atherosclerosis. PKC activation is involved in several cellular responses such as the expression of various growth factors, activation of signaling pathways, and enhancement of oxidative stress in hyperglycemia. However, the role of PKC activation in pro-atherogenic and anti-atherogenic mechanisms remains controversial, especially under hyperglycemic condition. In this review, we discuss the role of different PKC isoforms in lipid regulation, oxidative stress, inflammatory response, and apoptosis. These intracellular events are linked to the pathogenesis of atherosclerosis in diabetes. PKC deletion or treatment with PKC inhibitors has been studied in the regulation of atherosclerotic plaque formation and evolution. Furthermore, some preclinical and clinical studies have indicated that PKCß and PKCδ are potential targets for the treatment of diabetic vascular complications. The current review summarizes these multiple signaling pathways and cellular responses regulated by PKC activation and the potential therapeutic targets of PKC in diabetic complications.

Artificial Intelligence-Assisted Electrocardiography for Early Diagnosis of Thyrotoxic Periodic Paralysis.

CONTEXT: Thyrotoxic periodic paralysis (TPP) characterized by acute weakness, hypokalemia, and hyperthyroidism is a medical emergency with a great challenge in early diagnosis since most TPP patients do not have overt symptoms. OBJECTIVE: This work aims to assess artificial intelligence (AI)-assisted electrocardiography (ECG) combined with routine laboratory data in the early diagnosis of TPP. METHODS: A deep learning model (DLM) based on ECG12Net, an 82-layer convolutional neural network, was constructed to detect hypokalemia and hyperthyroidism. The development cohort consisted of 39 ECGs from patients with TPP and 502 ECGs of hypokalemic controls; the validation cohort consisted of 11 ECGs of TPP patients and 36 ECGs of non-TPP individuals with weakness. The AI-ECG-based TPP diagnostic process was then consecutively evaluated in 22 male patients with TTP-like features. RESULTS: In the validation cohort, the DLM-based ECG system detected all cases of hypokalemia in TPP patients with a mean absolute error of 0.26 mEq/L and diagnosed TPP with an area under curve (AUC) of approximately 80%, surpassing the best standard ECG parameter (AUC = 0.7285 for the QR interval). Combining the AI predictions with the estimated glomerular filtration rate and serum chloride boosted the diagnostic accuracy of the algorithm to AUC 0.986. In the prospective study, the integrated AI and routine laboratory diagnostic system had a PPV of 100% and F-measure of 87.5%. CONCLUSION: An AI-ECG system reliably identifies hypokalemia in patients with paralysis, and integration with routine blood chemistries provides valuable decision support for the early diagnosis of TPP.