Setting the Theater for Creativity: Proposal for Integrating Temporal and Spatial Artificial Mnemonics as a Qualitative Artificial Development of the Autobiographical Naturalistic Mnemonics (AM).

Mnemonics are not only tools that empower memory but also have a significant role in qualitatively transforming mental functions and, hence, consciousness in general. A specific type of mnemonics is autobiographical mnemonics (AM) constructed of spatial, temporal, and semantic dimensions used in a naturalist form by individuals about their own experiences. This paper proposes a spatial-temporal mnemonic that transforms AM from a naturalist level into an artificial one. We consider allowing the intellect and consciousness to grasp the abstract flow of time in the global context of geography will contribute to setting the stage for creativity. By explicitly representing the abstract time-space theater, the intellect (the world view) is more able to reflect the abstract laws of reality (the world), hence, to make the intellect sphere objectively equipped to externalize the emerged meanings (the internalized reality) that reflect the internal content of experience and, hence, make sense of them as a crucial function in creative activity. The paper is a theoretical and methodological step for the empirical part of the proposal when the mnemonic should be used as a training tool to empower creativity factors.

Modulation of perovskite degradation with multiple-barrier for light-heat stable perovskite solar cells.

The long-term stability of perovskite solar cells remains one of the most important challenges for the commercialization of this emerging photovoltaic technology. Here, we adopt a non-noble metal/metal oxide/polymer multiple-barrier to suppress the halide consumption and gaseous perovskite decomposition products release with the chemically inert bismuth electrode and Al2O3/parylene thin-film encapsulation, as well as the tightly closed system created by the multiple-barrier to jointly suppress the degradation of perovskite solar cells, allowing the corresponding decomposition reactions to reach benign equilibria. The resulting encapsulated formamidinium cesium-based perovskite solar cells with multiple-barrier maintain 90% of their initial efficiencies after continuous operation at 45 °C for 5200 h and 93% of their initial efficiency after continuous operation at 75 °C for 1000 h under 1 sun equivalent white-light LED illumination.

Treatment effects of low-frequency repetitive transcranial magnetic stimulation combined with motor relearning procedure on spasticity and limb motor function in stroke patients.

Objective: Limb paralysis, which is a sequela of stroke, limits patients' activities of daily living and lowers their quality of life. The purpose of this study was to investigate the effects of repetitive transcranial magnetic stimulation (rTMS) combined with a motor relearning procedure (MRP) on motor function and limb spasticity in stroke patients. Methods: Stroke patients were randomly divided into a combined treatment group (rTMS + MRP) and a control group (MRP) (n = 30 per group). The control group was given MRP in addition to conventional rehabilitation, and the combined treatment group was given 1 Hz rTMS combined with MRP. The treatment efficacy was assessed by the modified Ashworth scale (MAS), Fugl-Meyer motor function scale, and motor evoked potential (MEP) testing. Results: After 4 weeks of treatment, the Brunnstrom score, Fugl-Meyer lower extremity motor function, and Fugl-Meyer balance function were significantly higher in the combination treatment group compared to the control group, while the MAS score was lower in the combination treatment group compared to the control group. The MEP extraction rate was higher in the combined treatment group compared to the control group, while the threshold and central motor conduction time (CMCT) were lower in the combined treatment group compared to the control group. Conclusion: Low-frequency rTMS combined with MRP had better efficacy on spasticity and motor function in stroke patients with hemiparesis than MRP alone.

Ginsenoside Rg1 ameliorates Alzheimer's disease pathology via restoring mitophagy.

Background: Alzheimer's disease (AD) is a common form of dementia, and impaired mitophagy is a hallmark of AD. Mitophagy is mitochondrial-specific autophagy. Ginsenosides from Ginseng involve in autophagy in cancer. Ginsenoside Rg1 (Rg1 hereafter), a single compound of Ginseng, has neuroprotective effects on AD. However, few studies have reported whether Rg1 can ameliorate AD pathology by regulating mitophagy. Methods: Human SH-SY5Y cell and a 5XFAD mouse model were used to investigate the effects of Rg1. Rg1 (1µM) was added to ß-amyloid oligomer (AßO)-induced or APPswe-overexpressed cell models for 24 hours. 5XFAD mouse models were intraperitoneally injected with Rg1 (10 mg/kg/d) for 30 days. Expression levels of mitophagy-related markers were analyzed by western blot and immunofluorescent staining. Cognitive function was assessed by Morris water maze. Mitophagic events were observed using transmission electron microscopy, western blot, and immunofluorescent staining from mouse hippocampus. The activation of the PINK1/Parkin pathway was examined using an immunoprecipitation assay. Results: Rg1 could restore mitophagy and ameliorate memory deficits in the AD cellular and/or mouse model through the PINK1-Parkin pathway. Moreover, Rg1 might induce microglial phagocytosis to reduce ß-amyloid (Aß) deposits in the hippocampus of AD mice. Conclusion: Our studies demonstrate the neuroprotective mechanism of ginsenoside Rg1 in AD models. Rg1 induces PINK-Parkin mediated mitophagy and ameliorates memory deficits in 5XFAD mouse models.

Effects of rapid maxillary expansion on anchorage alveolar bone meta-analysis.

PURPOSE: Rapid maxillary expansion (RME) is a routine method for correcting transverse maxillary deficiency. This paper investigated the effect of RME on anchorage alveolar bone and examined the differences between micro-implant-assisted RME and conventional RME. METHODS: Relevant articles were selected from the PubMed, EMBASE and Cochrane Central Register of Controlled Trials databases. Review Manager software (v.5.3) was used for the pooled analysis and Cochran Q and I2 statistic tests were used to assess the heterogeneity. RESULTS: Following conventional RME, the distal buccal alveolar bone thickness and the mesiobuccal alveolar thickness of the maxillary first molars were significantly reduced. Hyrax (standard mean difference [SMD]: -0.93, 95% confidence interval [CI]: -1.20-0.66) and Haas procedures (SMD: -0.88, 95% CI: -1.40-0.36) significantly reduced the buccal vertical alveolar height of the maxillary first molars. Similar results were obtained for the maxillary first premolars following RME. The thickness of the buccal alveolar bone decreased with conventional RME compared to when using the method assisted by micro-implants. CONCLUSIONS: Conventional RME can reduce the thickness and vertical height of maxillary alveolar bone, and there is less loss of alveolar bone when using micro-implant-assisted RME. Further research is needed to validate the findings.

MicroRNA-128 suppresses tau phosphorylation and reduces amyloid-beta accumulation by inhibiting the expression of GSK3ß, APPBP2, and mTOR in Alzheimer's disease.

INTRODUCTION AND AIMS: Alzheimer's disease (AD) is characterized by the abnormal accumulation of hyperphosphorylated tau proteins and amyloid-beta (Aß) peptides. Recent studies have shown that many microRNAs (miRNAs) are dysregulated in AD, and modulation of these miRNAs can influence the development of tau and Aß pathology. The brain-specific miRNA miR-128, encoded by MIR128-1 and MIR128-2, is important for brain development and dysregulated in AD. In this study, the role of miR-128 in tau and Aß pathology as well as the regulatory mechanism underlying its dysregulation were investigated. METHODS: The effect of miR-128 on tau phosphorylation and Aß accumulation was examined in AD cellular models through miR-128 overexpression and inhibition. The therapeutic potential of miR-128 in AD mouse model was assessed by comparing phenotypes of 5XFAD mice administered with miR-128-expressing AAVs with 5XFAD mice administered with control AAVs. Phenotypes examined included behavior, plaque load, and protein expression. The regulatory factor of miR-128 transcription was identified through luciferase reporter assay and validated by siRNA knockdown and ChIP analysis. RESULTS: Both gain-of-function and loss-of-function studies in AD cellular models reveal that miR-128 represses tau phosphorylation and Aß secretion. Subsequent investigations show that miR-128 directly inhibits the expression of tau phosphorylation kinase GSK3ß and Aß modulators APPBP2 and mTOR. Upregulation of miR-128 in the hippocampus of 5XFAD mice ameliorates learning and memory impairments, decreases plaque deposition, and enhances autophagic flux. We further demonstrated that C/EBPα transactivates MIR128-1 transcription, while both C/EBPα and miR-128 expression are inhibited by Aß. CONCLUSION: Our findings suggest that miR-128 suppresses AD pathogenesis, and could be a promising therapeutic target for AD. We also find a possible mechanism underlying the dysregulation of miR-128 in AD, in which Aß reduces miR-128 expression by inhibiting C/EBPα.

An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk.

BACKGROUND: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a machine learning (ML) model trained to predict AF risk from 12­lead electrocardiogram (ECG) would be more efficient than criteria based on clinical variables in indicating a population for AF screening to potentially prevent AF-related stroke. METHODS: We retrospectively included all patients with clinical encounters in Geisinger without a prior history of AF. Incidence of AF within 1 year and AF-related strokes within 3 years of the encounter were identified. AF-related stroke was defined as a stroke where AF was diagnosed at the time of stroke or within a year after the stroke. The efficiency of five methods was evaluated for selecting a cohort for AF screening. The methods were selected from four clinical trials (mSToPS, GUARD-AF, SCREEN-AF and STROKESTOP) and the ECG-based ML model. We simulated patient selection for the five methods between the years 2011 and 2014 and evaluated outcomes for 1 year intervals between 2012 and 2015, resulting in a total of twenty 1-year periods. Patients were considered eligible if they met the criteria before the start of the given 1-year period or within that period. The primary outcomes were numbers needed to screen (NNS) for AF and AF-associated stroke. RESULTS: The clinical trial models indicated large proportions of the population with a prior ECG for AF screening (up to 31%), coinciding with NNS ranging from 14 to 18 for AF and 249-359 for AF-associated stroke. At comparable sensitivity, the ECG ML model indicated a modest number of patients for screening (14%) and had the highest efficiency in NNS for AF (7.3; up to 60% reduction) and AF-associated stroke (223; up to 38% reduction). CONCLUSIONS: An ECG-based ML risk prediction model is more efficient than contemporary AF-screening criteria based on age alone or age and clinical features at indicating a population for AF screening to potentially prevent AF-related strokes.

PVT1 promotes resistance to 5-FU in colon cancer via the miR-486-5p/CDK4 axis.

Drug resistance in tumors is a major issue, limiting the curative efficacy of currently available cancer chemotherapeutics. 5-Fluorouracil (5-FU) is a commonly applied therapeutic drug in colon cancer patient regimens; however, the majority of patients develop resistance to 5-FU in the later stages of the disease, rendering this chemotherapy ineffective. Drug resistance is the main factor underlying the poor prognosis of patients with colon cancer. In recent years, a number of studies have confirmed that long non-coding (lnc)RNAs may play vital roles in tumor resistance. In the present study, the Gene Expression Omnibus (GEO) and lncRNADisease2 databases were screened for colon cancer-associated expression patterns of lncRNA plasmacytoma variant translocation 1 (PVT1). Subsequently, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect changes in PVT1 expression in resistant cell lines, and a Cell Counting Kit-8 (CCK-8) assay kit was used to assess the effects of PVT1 knockdown on the half maximal inhibitory concentrations of parental and 5-FU-resistant HCT116 cells. Subsequently, CCK-8, clone formation, and flow cytometric assays were performed to investigate the effects of PVT1 knockdown on the sensitivity of HCT116-5FU-resistant cells to 5-FU. Dual-luciferase assay, RNA pull-down and RNA immunoprecipitation assays verified the interactive regulation of PVT1, miR-486-5p and cyclin dependent kinase 4 (CDK4). PVT1 was highly expressed in HCT116-5FU-resistant cells, as compared to its expression in HCT116 parental cells. PVT1 knockdown significantly reduced the resistance of HCT116-5FU-resistant cells to 5-FU. In addition, PVT1 upregulated CDK4 expression by adsorbing miR-486-5p; however, CDK4 overexpression restored the effects of miR-486-5p inhibition on HCT116-5-FU-resistant cells. Additionally, PVT1 knockdown partially rescued CDK4 overexpression in HCT116-5-FU-resistant cells. On the whole, the findings of the present study suggest that PVT1 promotes the resistance of colon cancer cells to 5-FU by regulating the miR-486-5p/CDK4 axis. Therefore, PVT1 may prove to be a potential target for counteracting resistance to 5-FU in colon cancer therapy.

Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies.

INTRODUCTION: Therapeutic options for type 2 diabetes mellitus (T2DM) have expanded over the last decade with the emergence of cardioprotective novel agents, but without such data for older drugs, leaving a critical gap in our understanding of the relative effects of T2DM agents on cardiovascular risk. METHODS AND ANALYSIS: The large-scale evidence generations across a network of databases for T2DM (LEGEND-T2DM) initiative is a series of systematic, large-scale, multinational, real-world comparative cardiovascular effectiveness and safety studies of all four major second-line anti-hyperglycaemic agents, including sodium-glucose co-transporter-2 inhibitor, glucagon-like peptide-1 receptor agonist, dipeptidyl peptidase-4 inhibitor and sulfonylureas. LEGEND-T2DM will leverage the Observational Health Data Sciences and Informatics (OHDSI) community that provides access to a global network of administrative claims and electronic health record data sources, representing 190 million patients in the USA and about 50 million internationally. LEGEND-T2DM will identify all adult, patients with T2DM who newly initiate a traditionally second-line T2DM agent. Using an active comparator, new-user cohort design, LEGEND-T2DM will execute all pairwise class-versus-class and drug-versus-drug comparisons in each data source, producing extensive study diagnostics that assess reliability and generalisability through cohort balance and equipoise to examine the relative risk of cardiovascular and safety outcomes. The primary cardiovascular outcomes include a composite of major adverse cardiovascular events and a series of safety outcomes. The study will pursue data-driven, large-scale propensity adjustment for measured confounding, a large set of negative control outcome experiments to address unmeasured and systematic bias. ETHICS AND DISSEMINATION: The study ensures data safety through a federated analytic approach and follows research best practices, including prespecification and full disclosure of results. LEGEND-T2DM is dedicated to open science and transparency and will publicly share all analytic code from reproducible cohort definitions through turn-key software, enabling other research groups to leverage our methods, data and results to verify and extend our findings.

rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography.

BACKGROUND: Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values to facilitate meaningful recommendations for echocardiography. METHODS: Using 2 232 130 ECGs linked to electronic health records and echocardiography reports from 484 765 adults between 1984 to 2021, we trained machine learning models to predict the presence or absence of any of 7 echocardiography-confirmed diseases within 1 year. This composite label included the following: moderate or severe valvular disease (aortic/mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction <50%, or interventricular septal thickness >15 mm. We tested various combinations of input features (demographics, laboratory values, structured ECG data, ECG traces) and evaluated model performance using 5-fold cross-validation, multisite validation trained on 1 site and tested on 10 independent sites, and simulated retrospective deployment trained on pre-2010 data and deployed in 2010. RESULTS: Our composite rECHOmmend model used age, sex, and ECG traces and had a 0.91 area under the receiver operating characteristic curve and a 42% positive predictive value at 90% sensitivity, with a composite label prevalence of 17.9%. Individual disease models had area under the receiver operating characteristic curves from 0.86 to 0.93 and lower positive predictive values from 1% to 31%. Area under the receiver operating characteristic curves for models using different input features ranged from 0.80 to 0.93, increasing with additional features. Multisite validation showed similar results to cross-validation, with an aggregate area under the receiver operating characteristic curve of 0.91 across our independent test set of 10 clinical sites after training on a separate site. Our simulated retrospective deployment showed that for ECGs acquired in patients without preexisting structural heart disease in the year 2010, 11% were classified as high risk and 41% (4.5% of total patients) developed true echocardiography-confirmed disease within 1 year. CONCLUSIONS: An ECG-based machine learning model using a composite end point can identify a high-risk population for having undiagnosed, clinically significant structural heart disease while outperforming single-disease models and improving practical utility with higher positive predictive values. This approach can facilitate targeted screening with echocardiography to improve underdiagnosis of structural heart disease.

Obesity is independently associated with septic shock, renal complications, and mortality in a multiracial patient cohort hospitalized with COVID-19.

BACKGROUND: Obesity has emerged as a risk factor for severe coronavirus disease 2019 (COVID-19) infection. To inform treatment considerations the relationship between obesity and COVID-19 complications and the influence of race, ethnicity, and socioeconomic factors deserves continued attention. OBJECTIVE: To determine if obesity is an independent risk factor for severe COVID-19 complications and mortality and examine the relationship between BMI, race, ethnicity, distressed community index and COVID-19 complications and mortality. METHODS: A retrospective cohort study of 1,019 SARS-CoV-2 positive adult admitted to an academic medical center (n = 928) and its affiliated community hospital (n-91) in New York City from March 1 to April 18, 2020. RESULTS: Median age was 64 years (IQR 52-75), 58.7% were men, 23.0% were Black, and 52.8% were Hispanic. The prevalence of overweight and obesity was 75.2%; median BMI was 28.5 kg/m2 (25.1-33.0). Over the study period 23.7% patients died, 27.3% required invasive mechanical ventilation, 22.7% developed septic shock, and 9.1% required renal replacement therapy (RRT). In the multivariable logistic regression model, BMI was associated with complications including intubation (Odds Ratio [OR]1.03, 95% Confidence Interval [CI]1.01-1.05), septic shock (OR 1.04, CI 1.01-1.06), and RRT (OR1.07, CI 1.04-1.10), and mortality (OR 1.04, CI 1.01-1.06). The odds of death were highest among those with BMI ≥ 40 kg/m2 (OR 2.05, CI 1.04-4.04). Mortality did not differ by race, ethnicity, or socioeconomic distress score, though Black and Asian patients were more likely to require RRT. CONCLUSIONS AND RELEVANCE: Severe complications of COVID-19 and death are more likely in patients with obesity, independent of age and comorbidities. While race, ethnicity, and socioeconomic status did not impact COVID-19 related mortality, Black and Asian patients were more likely to require RRT. The presence of obesity, and in some instances race, should inform resource allocation and risk stratification in patients hospitalized with COVID-19.

Data Consult Service: Can we use observational data to address immediate clinical needs?

OBJECTIVE: A number of clinical decision support tools aim to use observational data to address immediate clinical needs, but few of them address challenges and biases inherent in such data. The goal of this article is to describe the experience of running a data consult service that generates clinical evidence in real time and characterize the challenges related to its use of observational data. MATERIALS AND METHODS: In 2019, we launched the Data Consult Service pilot with clinicians affiliated with Columbia University Irving Medical Center. We created and implemented a pipeline (question gathering, data exploration, iterative patient phenotyping, study execution, and assessing validity of results) for generating new evidence in real time. We collected user feedback and assessed issues related to producing reliable evidence. RESULTS: We collected 29 questions from 22 clinicians through clinical rounds, emails, and in-person communication. We used validated practices to ensure reliability of evidence and answered 24 of them. Questions differed depending on the collection method, with clinical rounds supporting proactive team involvement and gathering more patient characterization questions and questions related to a current patient. The main challenges we encountered included missing and incomplete data, underreported conditions, and nonspecific coding and accurate identification of drug regimens. CONCLUSIONS: While the Data Consult Service has the potential to generate evidence and facilitate decision making, only a portion of questions can be answered in real time. Recognizing challenges in patient phenotyping and designing studies along with using validated practices for observational research are mandatory to produce reliable evidence.

Comparative First-Line Effectiveness and Safety of ACE (Angiotensin-Converting Enzyme) Inhibitors and Angiotensin Receptor Blockers: A Multinational Cohort Study.

[Figure: see text].

Characterising the long-term clinical outcomes of 1190 hospitalised patients with COVID-19 in New York City: a retrospective case series.

OBJECTIVE: To characterise the long-term outcomes of patients with COVID-19 admitted to a large New York City medical centre at 3 and 6 months after hospitalisation and describe their healthcare usage, symptoms, morbidity and mortality. DESIGN: Retrospective cohort through manual chart review of the electronic medical record. SETTING: NewYork-Presbyterian/Columbia University Irving Medical Center, a quaternary care academic medical centre in New York City. PARTICIPANTS: The first 1190 consecutive patients with symptoms of COVID-19 who presented to the hospital for care between 1 March and 8 April 2020 and tested positive for SARS-CoV-2 on reverse transcriptase PCR assay. MAIN OUTCOME MEASURES: Type and frequency of follow-up encounters, self-reported symptoms, morbidity and mortality at 3 and 6 months after presentation, respectively; patient disposition information prior to admission, at discharge, and at 3 and 6 months after hospital presentation. RESULTS: Of the 1190 reviewed patients, 929 survived their initial hospitalisation and 261 died. Among survivors, 570 had follow-up encounters (488 at 3 months and 364 at 6 months). An additional 33 patients died in the follow-up period. In the first 3 months after admission, most encounters were telehealth visits (59%). Cardiopulmonary symptoms (35.7% and 28%), especially dyspnoea (22.1% and 15.9%), were the most common reported symptoms at 3-month and 6-month encounters, respectively. Additionally, a large number of patients reported generalised (26.4%) or neuropsychiatric (24.2%) symptoms 6 months after hospitalisation. Patients with severe COVID-19 were more likely to have reduced mobility, reduced independence or a new dialysis requirement in the 6 months after hospitalisation. CONCLUSIONS: Patients hospitalised with SARS-CoV-2 infection reported persistent symptoms up to 6 months after diagnosis. These results highlight the long-term morbidity of COVID-19 and its burden on patients and healthcare resources.

Clinical Features and Outcomes of Patients with Dementia Compared to an Aging Cohort Hospitalized During the Initial New York City COVID-19 Wave.

BACKGROUND: Patients with dementia are vulnerable during the coronavirus disease 2019 (COVID-19) pandemic, yet few studies describe their hospital course and outcomes. OBJECTIVE: To describe and compare the hospital course for COVID-19 patients with dementia to an aging cohort without dementia in a large New York City academic medical center. METHODS: This was a single-center retrospective cohort study describing all consecutive patients age 65 or older with confirmed COVID-19 who presented to the emergency department or were hospitalized at New York-Presbyterian/Columbia University Irving Medical Center between March 6 and April 7, 2020. RESULTS: A total of 531 patients were evaluated, including 116 (21.8%) with previously diagnosed dementia, and 415 without dementia. Patients with dementia had higher mortality (50.0%versus 35.4%, p = 0.006); despite similar comorbidities and complications, multivariate analysis indicated the association was dependent on age, sex, comorbidities, and code status. Patients with dementia more often presented with delirium (36.2%versus 11.6%, p < 0.001) but less often presented with multiple other COVID-19 symptoms, and these findings remained after adjusting for age and sex. CONCLUSION: Hospitalized COVID-19 patients with dementia had higher mortality, but dementia was not an independent risk factor for death. These patients were approximately 3 times more likely to present with delirium but less often manifested or communicated other common COVID-19 symptoms. For this high-risk population in a worsening pandemic, understanding the unique manifestations and course in dementia and aging populations may help guide earlier diagnosis and optimize medical management.

Comprehensive Comparative Effectiveness and Safety of First-Line ß-Blocker Monotherapy in Hypertensive Patients: A Large-Scale Multicenter Observational Study.

[Figure: see text].

Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. MATERIALS AND METHODS: For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output. RESULTS: The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. DISCUSSION: Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. CONCLUSIONS: We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.

Exploring the multifunctional role of melatonin in regulating autophagy and sleep to mitigate Alzheimer's disease neuropathology.

Melatonin (MLT) is a neurohormone that is regulated by the circadian clock and plays multifunctional roles in numerous neurodegenerative disorders, such as Alzheimer's disease (AD). AD is the most common form of dementia and is associated with the degradation of axons and synapses resulting in memory loss and cognitive impairment. Despite extensive research, there is still no effective cure or specific treatment to prevent the progression of AD. The pathogenesis of AD involves atrophic alterations in the brain that also result in circadian alterations, sleep disruption, and autophagic dysfunction. In this scenario, MLT and autophagy play a central role in removing the misfolded protein aggregations. MLT also promotes autophagy through inhibiting methamphetamine toxicity to protect against neuronal cell death in AD brain. Besides, MLT plays critical roles as either a pro-autophagic indicator or anti-autophagic regulator depending on the phase of autophagy. MLT also has antioxidant properties that can counteract mitochondrial damage, oxidative stress, and apoptosis. Aging, a major risk factor for AD, can change sleep patterns and sleep quality, and MLT can improve sleep quality through regulating sleep cycles. The primary purpose of this review is to explore the putative mechanisms of the beneficial effects of MLT in AD patients. Furthermore, we also summarize the findings from preclinical and clinical studies on the multifunctional roles of MLT on autophagic regulation, the control of the circadian clock-associated genes, and sleep regulation.

Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review.

BACKGROUND: . OBJECTIVE: Electronic health records (EHRs) are linked with documentation burden resulting in clinician burnout. While clear classifications and validated measures of burnout exist, documentation burden remains ill-defined and inconsistently measured. We aim to conduct a scoping review focused on identifying approaches to documentation burden measurement and their characteristics. MATERIALS AND METHODS: Based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Extension for Scoping Reviews (ScR) guidelines, we conducted a scoping review assessing MEDLINE, Embase, Web of Science, and CINAHL from inception to April 2020 for studies investigating documentation burden among physicians and nurses in ambulatory or inpatient settings. Two reviewers evaluated each potentially relevant study for inclusion/exclusion criteria. RESULTS: Of the 3482 articles retrieved, 35 studies met inclusion criteria. We identified 15 measurement characteristics, including 7 effort constructs: EHR usage and workload, clinical documentation/review, EHR work after hours and remotely, administrative tasks, cognitively cumbersome work, fragmentation of workflow, and patient interaction. We uncovered 4 time constructs: average time, proportion of time, timeliness of completion, activity rate, and 11 units of analysis. Only 45.0% of studies assessed the impact of EHRs on clinicians and/or patients and 40.0% mentioned clinician burnout. DISCUSSION: Standard and validated measures of documentation burden are lacking. While time and effort were the core concepts measured, there appears to be no consensus on the best approach nor degree of rigor to study documentation burden. CONCLUSION: Further research is needed to reliably operationalize the concept of documentation burden, explore best practices for measurement, and standardize its use.