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What is known and objectives: Spinal Cord Injury (SCI) is defined for the last few decades as patients getting wheelchair bound and under medication for a lifetime [1]. Medical battery for SCI is very limited which often results in heightened frustration for the caregivers who land up with no solid solution to the condition. However, in recent times due to the huge volume of research, neuroscience has progressed massively through GT offering better insight and high hopes of neural regeneration and functional rehabilitation [2]. As GT technology becomes more popular as an emerging therapeutic method, many studies using a manufactured virus (AAV, retro, adeno, lenti) are opening up promising research avenues [3-5]. In this review, we dealt with Direct Lineage Reprogramming (DLR) technology for a novel GT method for SCI patients.

The efficient production of dopaminergic neurons via the direct conversion of other cell types is of interest as a potential therapeutic approach for Parkinson's disease. This study aimed to investigate the use of elongated porous gold nanorods (AuNpRs) as an enhancer of cell fate conversion. We observed that AuNpRs promoted the direct conversion of fibroblasts into dopaminergic neurons in vivo and in vitro. The extent of conversion of fibroblasts into dopaminergic neurons depended on the porosity of AuNpRs, as determined by their aspect ratio. The mechanism underlying these results involves specific AuNpR-induced transcriptional changes that altered the expression of antioxidant-related molecules. The generation of dopaminergic neurons via the direct conversion method will open a new avenue for developing a therapeutic platform for Parkinson's disease treatment. STATEMENT OF SIGNIFICANCE: In this study, we applied modified gold nanoporous materials (AuNpRs) to the direct lineage reprogramming of dopaminergic neurons. The cell reprogramming process is energy-intensive, resulting in an excess of oxidative stress. AuNpRs facilitated the direct conversion of dopaminergic neurons by ameliorating oxidative stress during the reprogramming process. We have found this mechanistic clue from high throughput studies in this research work.

This review summarizes the current developments of microalgal biofuel production based on system design costs such as initial design cost and expected net profit and describes several examples of designs for economic microalgae cultivation.

Neoadjuvant chemoradiation followed by surgery (NCRT+S) has been widely applied to patients with locally advanced esophageal squamous cell carcinoma (ESCC); however, treatment trends and their survival outcomes in a real-world clinical setting are poorly understood. This study aimed to analyze real-world evidence to understand treatment patterns and outcomes for patients with ESCC. We analyzed the treatment pattern and 5-year overall survival (5yOS) by synthesizing the individuals’ general characteristics, cancer information, and treatment records extracted from the Clinical Data Warehouse from 1994 to 2018. Of a total of 2151 patients, most patients received upfront surgery and 5yOS was 36.8% (31.4–43.1%). From 2003 to 2012, the use of NCRT increased, and 5yOS was improved to 42.2% (38.8–45.7%). Notably, after 2013, the proportion of NCRT+S markedly increased up to >50% of patients: 5yOS was much improved to 56.3% (53.2–59.6%). With regard to treatment, patients with NCRT+S had the most favorable 5yOS of 58.1% (53–63.7%), although that for patients with upfront surgery was 48.6% (45.9–51.5%, p < 0.001). Moreover, patients who received adjuvant therapy after surgery had better OS than those with surgery alone (58.4% (52.7–64.7%) vs. 47.3% (44.1–50.7%), p < 0.001). This analysis of real-world data demonstrated a significantly improved survival outcome for locally advanced ESCC over time since NCRT prior to surgery had been routinely applied. We revealed that NCRT+S was the most effective treatment for locally advanced ESCC and that adjuvant chemotherapy may be an encouraging therapeutic option for patients with positive nodes after upfront surgery.

BACKGROUND Sepsis is diagnosed in millions of people every year, resulting in a high mortality rate. Although patients with sepsis present multimorbid conditions, including cancer, sepsis predictions have mainly focused on patients with severe injuries. OBJECTIVE In this paper, we present a machine learning–based approach to identify the risk of sepsis in patients with cancer using electronic health records (EHRs). METHODS We utilized deidentified anonymized EHRs of 8580 patients with cancer from the Samsung Medical Center in Korea in a longitudinal manner between 2014 and 2019. To build a prediction model based on physical status that would differ between sepsis and nonsepsis patients, we analyzed 2462 laboratory test results and 2266 medication prescriptions using graph network and statistical analyses. The medication relationships and lab test results from each analysis were used as additional learning features to train our predictive model. RESULTS Patients with sepsis showed differential medication trajectories and physical status. For example, in the network-based analysis, narcotic analgesics were prescribed more often in the sepsis group, along with other drugs. Likewise, 35 types of lab tests, including albumin, globulin, and prothrombin time, showed significantly different distributions between sepsis and nonsepsis patients (P<.001). Our model outperformed the model trained using only common EHRs, showing an improved accuracy, area under the receiver operating characteristic (AUROC), and F1 score by 11.9%, 11.3%, and 13.6%, respectively. For the random forest–based model, the accuracy, AUROC, and F1 score were 0.692, 0.753, and 0.602, respectively. CONCLUSIONS We showed that lab tests and medication relationships can be used as efficient features for predicting sepsis in patients with cancer. Consequently, identifying the risk of sepsis in patients with cancer using EHRs and machine learning is feasible.

Background Sepsis is diagnosed in millions of people every year, resulting in a high mortality rate. Although patients with sepsis present multimorbid conditions, including cancer, sepsis predictions have mainly focused on patients with severe injuries. Objective In this paper, we present a machine learning–based approach to identify the risk of sepsis in patients with cancer using electronic health records (EHRs). Methods We utilized deidentified anonymized EHRs of 8580 patients with cancer from the Samsung Medical Center in Korea in a longitudinal manner between 2014 and 2019. To build a prediction model based on physical status that would differ between sepsis and nonsepsis patients, we analyzed 2462 laboratory test results and 2266 medication prescriptions using graph network and statistical analyses. The medication relationships and lab test results from each analysis were used as additional learning features to train our predictive model. Results Patients with sepsis showed differential medication trajectories and physical status. For example, in the network-based analysis, narcotic analgesics were prescribed more often in the sepsis group, along with other drugs. Likewise, 35 types of lab tests, including albumin, globulin, and prothrombin time, showed significantly different distributions between sepsis and nonsepsis patients (P Conclusions We showed that lab tests and medication relationships can be used as efficient features for predicting sepsis in patients with cancer. Consequently, identifying the risk of sepsis in patients with cancer using EHRs and machine learning is feasible.

Objective This study aimed to confirm the accuracy of a machine-learning-based model in predicting the 30-day mortality of patients with pneumonia and evaluating whether they were required to be admitted to the intensive care unit (ICU). Methods The study conducted a retrospective analysis of pneumonia patients at an emergency department (ED) in Seoul, Korea, from January 1, 2016 to December 31, 2017. Patients aged 18 years or older with a pneumonia registry designation on their electronic medical record were enrolled. We collected their demographic information, mental status, and laboratory findings. Three models were used: the pre-existing CURB-65 model, and the CURB-RF and Extensive CURB-RF models, which were machine-learning models that used a random forest algorithm. The primary outcomes were ICU admission from the ED or 30-day mortality. Receiver operating characteristic curves were constructed for the models, and the areas under these curves were compared. Results Out of the 1,974 pneumonia patients, 1,732 patients were eligible to be included in the study; from these, 473 patients died within 30 days or were initially admitted to the ICU from the ED. The area under receiver operating characteristic curves of CURB-65, CURB-RF, and extensive-CURB-RF were 0.615 (0.614-0.616), 0.701 (0.700-0.702), and 0.844 (0.843-0.845), respectively. Conclusion The proposed machine-learning models could predict the mortality of patients with pneumonia more accurately than the pre-existing CURB-65 model and can help decide whether the patient should be admitted to the ICU.

Porphyra 334 (P334), a mycosporine-like amino acid (MAA), is a secondary metabolite found in diverse marine and terrestrial organisms and has several beneficial effects on fibroblast proliferation, wound healing, and antioxidant activity. Here, we report that P334 accelerates the cell reprogramming process of mouse tail-tip fibroblasts (TTFs) and human dermal papilla (HDP) cells into induced pluripotent stem cells (iPSCs). We found that P334 significantly improved the cell reprogramming efficiency by activating the tri-methylation of histone 3 lysine 4 (H3K4me3), which controls mesenchymal to epithelial transition (MET) during the reprogramming process. Thus, we found that P334 directly regulates epigenetic changes, providing an efficient approach for natural compound-based cell reprogramming.

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In the American continent, the most frequently used alternative fuel is ethanol. Especially in Brazil, various blends of gasoline–ethanol fuels are widely spread. The vehicle using blended fuel is called flexible fuel vehicle. Because of several selections for the blending ratios in gas stations, the fuel properties may vary after refueling depending on a driver’s selection. Also, the combustion characteristics of the flexible fuel vehicle engine may change. In order to respond to the flexible fuel vehicle market in Brazil, a study on blended fuels is performed. The main purpose of this study is to enhance performance of the flexible fuel vehicle engine to target Brazilian market. Therefore, we investigated combustion characteristics and optimal spark timings of the blended fuels with various blending ratios to improve the performance of the flexible fuel vehicle engine. As a tool for prediction of the optimal spark timing for the 1.6L flexible fuel vehicle engine, the empirical equation was suggested. The validity of the equation was investigated by comparing the predicted optimal spark timings with the stock spark timings through engine tests. When the stock spark timings of E0 and E100 were optimal, the empirical equation predicted the actual optimal spark timings for blended fuels with a good accuracy. In all conditions, by optimizing spark timing control, performance was improved. Especially, torque improvements of E30 and E50 fuels were 5.4% and 1.8%, respectively, without affecting combustion stability. From these results, it was concluded that the linear interpolation method is not suitable for flexible fuel vehicle engine control. Instead of linear interpolation method, optimal spark timing which reflects specific octane numbers of gasoline–ethanol blended fuels should be applied to maximize performance of the flexible fuel vehicle engine. The results of this study are expected to save the effort required for engine calibration when developing new flexible fuel vehicle engines and to be used as a basic strategy to improve the performance of other flexible fuel vehicle engines.

In vivo gene editing in post-mitotic neurons of the adult brain may be a useful strategy for treating neurological diseases. Here, we develop CRISPR-Cas9 nanocomplexes and show they were effective in the adult mouse brain, with minimal off-target effects. Using this system to target Bace1 suppressed amyloid beta (Aβ)-associated pathologies and cognitive deficits in two mouse models of Alzheimer's disease. These results broaden the potential application of CRISPR-Cas9 systems to neurodegenerative diseases.

Background Physicians’ alert overriding behavior is considered to be the most important factor leading to failure of computerized provider order entry (CPOE) combined with a clinical decision support system (CDSS) in achieving its potential adverse drug events prevention effect. Previous studies on this subject have focused on specific diseases or alert types for well-defined targets and particular settings. The emergency department is an optimal environment to examine physicians’ alert overriding behaviors from a broad perspective because patients have a wider range of severity, and many receive interdisciplinary care in this environment. However, less than one-tenth of related studies have targeted this physician behavior in an emergency department setting. Objective The aim of this study was to describe alert override patterns with a commercial medication CDSS in an academic emergency department. Methods This study was conducted at a tertiary urban academic hospital in the emergency department with an annual census of 80,000 visits. We analyzed data on the patients who visited the emergency department for 18 months and the medical staff who treated them, including the prescription and CPOE alert log. We also performed descriptive analysis and logistic regression for assessing the risk factors for alert overrides. Results During the study period, 611 physicians cared for 71,546 patients with 101,186 visits. The emergency department physicians encountered 13.75 alerts during every 100 orders entered. Of the total 102,887 alerts, almost two-thirds (65,616, 63.77%) were overridden. Univariate and multivariate logistic regression analyses identified 21 statistically significant risk factors for emergency department physicians’ alert override behavior. Conclusions In this retrospective study, we described the alert override patterns with a medication CDSS in an academic emergency department. We found relatively low overrides and assessed their contributing factors, including physicians’ designation and specialty, patients’ severity and chief complaints, and alert and medication type.

BACKGROUND Appropriate empirical treatment for candidemia is associated with reduced mortality. However, timely diagnosis of candidemia for septic patients remains poor. OBJECTIVE This study aimed to use machine learning algorithms to develop and validate a candidemia prediction model for cancer patients. METHODS This single-center retrospective study used the cancer registry of a tertiary academic hospital. Adult patients with diagnosed malignancies from January 2010 to December 2018 were included. Our study outcome was the prediction of candidemia events. A stratified under-sampling method was used to extract control groups for algorithm learning. Multiple models were developed through a combination of four variable groups and five algorithms (auto-machine learning, deep neural network, gradient boosting, and logistic regression and random forest). The model with the highest area under the receiver operating characteristics (AUROC) was selected as the Candida species detection (CanDETEC) model and compared with the performance indexes of the candida score. RESULTS Among the 273,380 blood culture from 186,404 registered cancer patients, 501 candidemia events and 2000 controls were identified. The AUROC of the developed models varied from 0.771 to 0.889. The random forest model was selected CanDETEC model (AUROC = 0.889, 95% confidence interval: 0.888-0.889). It showed better performance than the candida score (AUROC = 0.677). CONCLUSIONS The CanDETEC model could predict candidemia in cancer patients with high discriminative power. This algorithm could be used for the timely diagnosis and appropriate empirical treatment of candidemia.

BACKGROUND Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care. OBJECTIVE This study aimed to develop and validate prediction models for UE in ICU patients using machine learning. METHODS This study was conducted in an academic tertiary hospital in Seoul, Republic of Korea. The hospital had approximately 2000 inpatient beds and 120 ICU beds. As of January 2019, the hospital had approximately 9000 outpatients on a daily basis. The number of annual ICU admissions was approximately 10,000. We conducted a retrospective study between January 1, 2010, and December 31, 2018. A total of 6914 extubation cases were included. We developed a UE prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model’s performance, we used the area under the receiver operating characteristic curve (AUROC). The sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were also determined for each model. For performance evaluation, we also used a calibration curve, the Brier score, and the integrated calibration index (ICI) to compare different models. The potential clinical usefulness of the best model at the best threshold was assessed through a net benefit approach using a decision curve. RESULTS Among the 6914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was higher during the night shift as compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality were higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.763, and for SVM was 0.740. CONCLUSIONS We successfully developed and validated machine learning–based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787 and the sensitivity was 0.949, which was obtained using the RF algorithm. The RF model was well-calibrated, and the Brier score and ICI were 0.129 and 0.048, respectively. The proposed prediction model uses widely available variables to limit the additional workload on the clinician. Further, this evaluation suggests that the model holds potential for clinical usefulness.

BACKGROUND Delirium frequently occurs among patients admitted to the intensive care unit (ICU). There is limited evidence to support interventions to treat or resolve delirium in patients who have already developed delirium. Therefore, the early recognition and prevention of delirium are important in the management of critically ill patients. OBJECTIVE This study aims to develop and validate a delirium prediction model within 24 hours of admission to the ICU using electronic health record data. The algorithm was named the Prediction of ICU Delirium (PRIDE). METHODS This is a retrospective cohort study performed at a tertiary referral hospital with 120 ICU beds. We only included patients who were 18 years or older at the time of admission and who stayed in the medical or surgical ICU. Patients were excluded if they lacked a Confusion Assessment Method for the ICU record from the day of ICU admission or if they had a positive Confusion Assessment Method for the ICU record at the time of ICU admission. The algorithm to predict delirium was developed using patient data from the first 2 years of the study period and validated using patient data from the last 6 months. Random forest (RF), Extreme Gradient Boosting (XGBoost), deep neural network (DNN), and logistic regression (LR) were used. The algorithms were externally validated using MIMIC-III data, and the algorithm with the largest area under the receiver operating characteristics (AUROC) curve in the external data set was named the PRIDE algorithm. RESULTS A total of 37,543 cases were collected. After patient exclusion, 12,409 remained as our study population, of which 3816 (30.8%) patients experienced delirium incidents during the study period. Based on the exclusion criteria, out of the 96,016 ICU admission cases in the MIMIC-III data set, 2061 cases were included, and 272 (13.2%) delirium incidents occurred. The average AUROCs and 95% CIs for internal validation were 0.916 (95% CI 0.916-0.916) for RF, 0.919 (95% CI 0.919-0.919) for XGBoost, 0.881 (95% CI 0.878-0.884) for DNN, and 0.875 (95% CI 0.875-0.875) for LR. Regarding the external validation, the best AUROC were 0.721 (95% CI 0.72-0.721) for RF, 0.697 (95% CI 0.695-0.699) for XGBoost, 0.655 (95% CI 0.654-0.657) for DNN, and 0.631 (95% CI 0.631-0.631) for LR. The Brier score of the RF model is 0.168, indicating that it is well-calibrated. CONCLUSIONS A machine learning approach based on electronic health record data can be used to predict delirium within 24 hours of ICU admission. RF, XGBoost, DNN, and LR models were used, and they effectively predicted delirium. However, with the potential to advise ICU physicians and prevent ICU delirium, prospective studies are required to verify the algorithm’s performance.

BACKGROUND Physicians’ alert overriding behavior is considered to be the most important factor leading to failure of computerized provider order entry (CPOE) combined with a clinical decision support system (CDSS) in achieving its potential adverse drug events prevention effect. Previous studies on this subject have focused on specific diseases or alert types for well-defined targets and particular settings. The emergency department is an optimal environment to examine physicians’ alert overriding behaviors from a broad perspective because patients have a wider range of severity, and many receive interdisciplinary care in this environment. However, less than one-tenth of related studies have targeted this physician behavior in an emergency department setting. OBJECTIVE The aim of this study was to describe alert override patterns with a commercial medication CDSS in an academic emergency department. METHODS This study was conducted at a tertiary urban academic hospital in the emergency department with an annual census of 80,000 visits. We analyzed data on the patients who visited the emergency department for 18 months and the medical staff who treated them, including the prescription and CPOE alert log. We also performed descriptive analysis and logistic regression for assessing the risk factors for alert overrides. RESULTS During the study period, 611 physicians cared for 71,546 patients with 101,186 visits. The emergency department physicians encountered 13.75 alerts during every 100 orders entered. Of the total 102,887 alerts, almost two-thirds (65,616, 63.77%) were overridden. Univariate and multivariate logistic regression analyses identified 21 statistically significant risk factors for emergency department physicians’ alert override behavior. CONCLUSIONS In this retrospective study, we described the alert override patterns with a medication CDSS in an academic emergency department. We found relatively low overrides and assessed their contributing factors, including physicians’ designation and specialty, patients’ severity and chief complaints, and alert and medication type.

Adult neurogenesis is the lifelong process by which new neurons are generated in the dentate gyrus. However, adult neurogenesis capacity decreases with age, and this decrease is closely linked to cognitive and memory decline. Our study demonstrated that electromagnetized gold nanoparticles (AuNPs) promote adult hippocampal neurogenesis, thereby improving cognitive function and memory consolidation in aged mice. According to single-cell RNA sequencing data, the numbers of neural stem cells (NSCs) and neural progenitors were significantly increased by electromagnetized AuNPs. Additionally, electromagnetic stimulation resulted in specific activation of the histone acetyltransferase Kat2a, which led to histone H3K9 acetylation in adult NSCs. Moreover, in vivo electromagnetized AuNP stimulation efficiently increased hippocampal neurogenesis in aged and Hutchinson-Gilford progeria mouse brains, thereby alleviating the symptoms of aging. Therefore, our study provides a proof-of-concept for the in vivo stimulation of hippocampal neurogenesis using electromagnetized AuNPs as a promising therapeutic strategy for the treatment of age-related brain diseases.

Mitochondrial dysfunction is associated with neuronal damage in Huntington's disease (HD), but the precise mechanism of mitochondria-dependent pathogenesis is not understood yet. Herein, we found that colocalization of XIAP and p53 was prominent in the cytosolic compartments of normal subjects but reduced in HD patients and HD transgenic animal models. Overexpression of mutant Huntingtin (mHTT) reduced XIAP levels and elevated mitochondrial localization of p53 in striatal cells in vitro and in vivo. Interestingly, XIAP interacted directly with the C-terminal domain of p53 and decreased its stability via autophagy. Overexpression of XIAP prevented mitochondrially targeted-p53 (Mito-p53)-induced mitochondrial oxidative stress and striatal cell death, whereas, knockdown of XIAP exacerbated Mito-p53-induced neuronal damage in vitro. In vivo transduction of AAV-shRNA XIAP in the dorsal striatum induced rapid onset of disease and reduced the lifespan of HD transgenic (N171-82Q) mice compared to WT littermate mice. XIAP dysfunction led to ultrastructural changes of the mitochondrial cristae and nucleus morphology in striatal cells. Knockdown of XIAP exacerbated neuropathology and motor dysfunctions in N171-82Q mice. In contrast, XIAP overexpression improved neuropathology and motor behaviors in both AAV-mHTT-transduced mice and N171-82Q mice. Our data provides a molecular and pathological mechanism that deregulation of XIAP triggers mitochondria dysfunction and other neuropathological processes via the neurotoxic effect of p53 in HD. Together, the XIAP-p53 pathway is a novel pathological marker and can be a therapeutic target for improving the symptoms in HD.

Background Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care. Objective This study aimed to develop and validate prediction models for UE in ICU patients using machine learning. Methods This study was conducted in an academic tertiary hospital in Seoul, Republic of Korea. The hospital had approximately 2000 inpatient beds and 120 ICU beds. As of January 2019, the hospital had approximately 9000 outpatients on a daily basis. The number of annual ICU admissions was approximately 10,000. We conducted a retrospective study between January 1, 2010, and December 31, 2018. A total of 6914 extubation cases were included. We developed a UE prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model’s performance, we used the area under the receiver operating characteristic curve (AUROC). The sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were also determined for each model. For performance evaluation, we also used a calibration curve, the Brier score, and the integrated calibration index (ICI) to compare different models. The potential clinical usefulness of the best model at the best threshold was assessed through a net benefit approach using a decision curve. Results Among the 6914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was higher during the night shift as compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality were higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.763, and for SVM was 0.740. Conclusions We successfully developed and validated machine learning–based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787 and the sensitivity was 0.949, which was obtained using the RF algorithm. The RF model was well-calibrated, and the Brier score and ICI were 0.129 and 0.048, respectively. The proposed prediction model uses widely available variables to limit the additional workload on the clinician. Further, this evaluation suggests that the model holds potential for clinical usefulness.

Background Delirium frequently occurs among patients admitted to the intensive care unit (ICU). There is limited evidence to support interventions to treat or resolve delirium in patients who have already developed delirium. Therefore, the early recognition and prevention of delirium are important in the management of critically ill patients. Objective This study aims to develop and validate a delirium prediction model within 24 hours of admission to the ICU using electronic health record data. The algorithm was named the Prediction of ICU Delirium (PRIDE). Methods This is a retrospective cohort study performed at a tertiary referral hospital with 120 ICU beds. We only included patients who were 18 years or older at the time of admission and who stayed in the medical or surgical ICU. Patients were excluded if they lacked a Confusion Assessment Method for the ICU record from the day of ICU admission or if they had a positive Confusion Assessment Method for the ICU record at the time of ICU admission. The algorithm to predict delirium was developed using patient data from the first 2 years of the study period and validated using patient data from the last 6 months. Random forest (RF), Extreme Gradient Boosting (XGBoost), deep neural network (DNN), and logistic regression (LR) were used. The algorithms were externally validated using MIMIC-III data, and the algorithm with the largest area under the receiver operating characteristics (AUROC) curve in the external data set was named the PRIDE algorithm. Results A total of 37,543 cases were collected. After patient exclusion, 12,409 remained as our study population, of which 3816 (30.8%) patients experienced delirium incidents during the study period. Based on the exclusion criteria, out of the 96,016 ICU admission cases in the MIMIC-III data set, 2061 cases were included, and 272 (13.2%) delirium incidents occurred. The average AUROCs and 95% CIs for internal validation were 0.916 (95% CI 0.916-0.916) for RF, 0.919 (95% CI 0.919-0.919) for XGBoost, 0.881 (95% CI 0.878-0.884) for DNN, and 0.875 (95% CI 0.875-0.875) for LR. Regarding the external validation, the best AUROC were 0.721 (95% CI 0.72-0.721) for RF, 0.697 (95% CI 0.695-0.699) for XGBoost, 0.655 (95% CI 0.654-0.657) for DNN, and 0.631 (95% CI 0.631-0.631) for LR. The Brier score of the RF model is 0.168, indicating that it is well-calibrated. Conclusions A machine learning approach based on electronic health record data can be used to predict delirium within 24 hours of ICU admission. RF, XGBoost, DNN, and LR models were used, and they effectively predicted delirium. However, with the potential to advise ICU physicians and prevent ICU delirium, prospective studies are required to verify the algorithm’s performance.

Objectives The aim of this study was to test the applicability of haptic feedback using a smartwatch to the delivery of cardiac compression (CC) by professional healthcare providers. Methods A prospective, randomized, controlled, case-crossover, standardized simulation study of 20 medical professionals was conducted. The participants were randomly assigned into haptic-first and non-haptic-first groups. The primary outcome was an adequate rate of 100-120/min of CC. The secondary outcome was a comparison of CC rate and adequate duration between the good and bad performance groups. Results The mean interval between CCs and the number of haptic and non-haptic feedback-assisted CCs with an adequate duration were insignificant. In the subgroup analysis, both the good and bad performance groups showed a significant difference in the mean CC interval between the haptic and non-haptic feedback-assisted CC groups-good: haptic feedback-assisted (0.57-0.06) vs. non-haptic feedback-assisted (0.54-0.03), p < 0.001; bad: haptic feedback-assisted (0.57-0.07) vs. non-haptic feedback-assisted (0.58-0.18), p = 0.005-and the adequate chest compression number showed significant differences- good: haptic feedback-assisted (1,597/75.1%) vs. non-haptic feedback-assisted (1,951/92.2%), p < 0.001; bad: haptic feedbackassisted (1,341/63.5%) vs. non-haptic feedback-assisted (523/25.4%), p < 0.001. Conclusions A smartwatch cardiopulmonary resuscitation feedback system could not improve rescuers' CC rate. According to our subgroup analysis, participants might be aided by the device to increase the percentage of adequate compressions after one minute.

The worldwide demand for Li-ion batteries (LIBs) has risen sharply, and the number of spent LIBs is increasing proportionally. The current situation makes the recycling of spent LIBs indispensable for the conservation of the environment and the raw materials used in the LIBs. The pretreatment process in LIB recycling can enhance the recovery efficiency of the valuable elements in the LIBs and reduce the energy consumption in the subsequent processes. The pretreatment process has been adopted from the early stage of LIB recycling research, a large number of articles that focus on the pretreatment methods of spent LIBs have emerged over the last few years. Therefore, it would be appropriate to categorize various emerging methods in the pretreatment process and summarize the relevant literature in the current stage. This review focuses exclusively on the pretreatment process itself by comprehensively analyzing the relevant literature, which is unlike most review papers that covered the pretreatment process as a part of the general LIB recycling technology. We establish the scope and the sequence of the pretreatment processes, which are classified into discharge, dismantling, comminution, classification, separation, dissolution, and thermal treatment. The technology development and the current status of each category are discussed in depth.

Acupuncture has shown the therapeutic effect on various neurodegenerative disorders including Parkinson's disease (PD). While investigating the neuroprotective mechanism of acupuncture, we firstly found the novel function of melanin-concentrating hormone (MCH) as a potent neuroprotective candidate. Here, we explored whether hypothalamic MCH mediates the neuroprotective action of acupuncture. In addition, we aimed at evaluating the neuroprotective effects of MCH and elucidating underlying mechanism in vitro and in vivo PD models. First, we tested whether hypothalamic MCH mediates the neuroprotective effects of acupuncture by challenging MCH-R1 antagonist (i.p.) in mice PD model. We also investigated whether MCH has a beneficial role in dopaminergic neuronal protection in vitro primary midbrain and human neuronal cultures and in vivo MPTP-induced, Pitx3-/-, and A53T mutant mice PD models. Transcriptomics followed by quantitative PCR and western blot analyses were performed to reveal the neuroprotective mechanism of MCH. We first found that hypothalamic MCH biosynthesis was directly activated by acupuncture treatment and that administration of an MCH-R1 antagonist reverses the neuroprotective effects of acupuncture. A novel finding is that MCH showed a beneficial role in dopaminergic neuron protection via downstream pathways related to neuronal survival. This is the first study to suggest the novel neuroprotective action of MCH as well as the involvement of hypothalamic MCH in the acupuncture effects in PD, which holds great promise for the application of MCH in the therapy of neurodegenerative diseases.

Recent work generating induced dopaminergic (iDA) neurons using direct lineage reprogramming potentially provides a novel platform for the study and treatment Parkinson's disease (PD). However, one of the most important issues for iDA-based applications is the degree to which iDA neurons resemble the molecular and functional properties of their endogenous DA neuron counterparts. Here we report that the homogeneity of the reprogramming gene expression system is critical for the generation of iDA neuron cultures that are highly similar to endogenous DA neurons. We employed an inducible system that carries iDA-inducing factors as defined transgenes for direct lineage reprogramming to iDA neurons. This system circumvents the need for viral transduction, enabling a more efficient and reproducible reprogramming process for the generation of genetically homogenous iDA neurons. We showed that this inducible system generates iDA neurons with high similarity to their bona fide in vivo counterparts in comparison to direct infection methods. Thus, our results suggest that homogenous expression of exogenous genes in direct lineage reprogramming is critical for the generation of high quality iDA neuron cultures, making such culture systems a valuable resource for iDA-based drug screening and, ultimately, potential therapeutic intervention in PD.

BackgroundThe task of monitoring and managing the entire emergency department (ED) is becoming more important due to increasing pressure on the ED. Recently, dashboards have received the spotlight as health information technology to support these tasks. ObjectiveThis study aimed to describe the development of a real-time autonomous dashboard for the ED and to evaluate perspectives of clinical staff on its usability. MethodsWe developed a dashboard based on three principles—“anytime, anywhere, at a glance;” “minimal interruption to workflow;” and “protect patient privacy”—and 3 design features—“geographical layout,” “patient-level alert,” and “real-time summary data.” Items to evaluate the dashboard were selected based on the throughput factor of the conceptual model of ED crowding. Moreover, ED physicians and nurses were surveyed using the system usability scale (SUS) and situation awareness index as well as a questionnaire we created on the basis of the construct of the Situation Awareness Rating Technique. ResultsThe first version of the ED dashboard was successfully launched in 2013, and it has undergone 3 major revisions since then because of geographical changes in ED and modifications to improve usability. A total of 52 ED staff members participated in the survey. The average SUS score of the dashboard was 67.6 points, which indicates “OK-to-Good” usability. The participants also reported that the dashboard provided efficient “concentration support” (4.15 points), “complexity representation” (4.02 points), “variability representation” (3.96 points), “information quality” (3.94 points), and “familiarity” (3.94 points). However, the “division of attention” was rated at 2.25 points. ConclusionsWe developed a real-time autonomous ED dashboard and successfully used it for 5 years with good evaluation from users.