Prognostic Impact of Heart Rhythm Shockability Trajectory in Out-of-Hospital Cardiac Arrest: A Multicenter Retrospective Study.

BACKGROUND: This study aimed to investigate the association between the temporal transitions in heart rhythms during cardiopulmonary resuscitation (CPR) and outcomes after out-of-hospital cardiac arrest. METHODS: This was an analysis of the prospectively collected databases in 3 academic hospitals in northern and central Taiwan. Adult patients with out-of-hospital cardiac arrest transported by emergency medical service between 2015 and 2022 were included. Favorable neurological recovery and survival to hospital discharge were the primary and secondary outcomes, respectively. Time-specific heart rhythm shockability was defined as the probability of shockable rhythms at a particular time point during CPR. The temporal changes in the time-specific heart rhythm shockability were calculated by group-based trajectory modeling. Multivariable logistic regression analyses were performed to examine the association between the trajectory group and outcomes. Subgroup analyses examined the effects of extracorporeal CPR in different trajectories. RESULTS: The study comprised 2118 patients. The median patient age was 69.1 years, and 1376 (65.0%) patients were male. Three distinct trajectories were identified: high-shockability (52 patients; 2.5%), intermediate-shockability (262 patients; 12.4%), and low-shockability (1804 patients; 85.2%) trajectories. The median proportion of shockable rhythms over the course of CPR for the 3 trajectories was 81.7% (interquartile range, 73.2%-100.0%), 26.7% (interquartile range, 16.7%-37.5%), and 0% (interquartile range, 0%-0%), respectively. The multivariable analysis indicated both intermediate- and high-shockability trajectories were associated with favorable neurological recovery (intermediate-shockability: adjusted odds ratio [aOR], 4.98 [95% CI, 2.34-10.59]; high-shockability: aOR, 5.40 [95% CI, 2.03-14.32]) and survival (intermediate-shockability: aOR, 2.46 [95% CI, 1.44-4.18]; high-shockability: aOR, 2.76 [95% CI, 1.20-6.38]). The subgroup analysis further indicated extracorporeal CPR was significantly associated with favorable neurological outcomes (aOR, 4.06 [95% CI, 1.11-14.81]) only in the intermediate-shockability trajectory. CONCLUSIONS: Heart rhythm shockability trajectories were associated with out-of-hospital cardiac arrest outcomes, which may be a supplementary factor in guiding the allocation of medical resources, such as extracorporeal CPR.

Nattokinase attenuates endothelial inflammation through the activation of SRF and THBS1.

Natto contains a potent fibrinolytic enzyme called nattokinase (NK), which has thrombolytic, antihypertensive, antiatherosclerotic and lipid-lowering effects. Although NK has been recognized for its beneficial effect on humans with atherosclerotic cardiovascular disease (ASCVD), the underlying mechanisms involved in vascular inflammation-atherosclerosis development remain largely unknown. The current study aimed to explore the effects of NK on gene regulation, autophagy, necroptosis and inflammasome in vascular inflammation. The transcriptional profiles of NK in endothelial cells (ECs) by RNA sequencing (RNA-seq) revealed that NK affected THBS1, SRF and SREBF1 mRNA expression. In Q-PCR analysis, SRF and THBS1 were upregulated but SREBF1 was unaffected in ECs treated with NK. NK treatment induced autophagy and inhibited NLRP3 inflammasome and necroptosis in ECs. Furthermore, the inhibition of SRF or THBS1 by siRNA suppressed autophagy and enhanced the NLRP3 inflammasome and necroptosis. In a mouse model, NK reduced vascular inflammation by activating autophagy and inhibiting NLRP3 inflammasome and necroptosis. Our findings provide the first evidence that NK upregulates SRF and THBS1 genes, subsequently increasing autophagy and decreasing necroptosis and NLRP3 inflammasome formation to reduce vascular inflammation. Therefore, NK could serve as nutraceuticals or adjuvant therapies to reduce vascular inflammation and possible atherosclerosis progression.

Associations between challenging parenting behavior and creative tendencies of children: the chain mediating roles of positive emotion and creative self-efficacy.

Background: Parenting behavior has been reported to be closely associated with children's creativity, yet the association between challenging parenting behavior and children's creative tendencies, as well as the potential mechanisms connecting the two, remains ambiguous. Based on the Social Cognitive Theory and the Self-efficacy Theory, this study aims to examine the correlation between Chinese parents' challenging parenting behaviors and their children's creative tendencies, as well as the chain mediating role of children's positive emotions and creative self-efficacy. Methods: In total, 2,647 families were surveyed with questionnaires completed by parents on the Challenging Parenting Behaviors Scale and by children on the Positive/Negative Emotions Scale, the Creative Self-efficacy Scale, and the Williams Creative Tendency Test Scale, and analyzed using structural equation modeling (SEM) in SPSS 22.0 and Mplus 8.3. Results: The findings indicate that challenging parenting behavior has a positive correlation with children's positive emotions, creative self-efficacy, and creative tendencies. Through positive emotions, creative self-efficacy, and a chain mediated pathway between these two variables, challenging parenting behaviors increase children's creative tendencies. Conclusion: The favorable impacts of challenging parenting behaviors on children's creative tendencies, with the mediating effects of children's positive emotions and creative self-efficacy, may help Chinese parents better grasp the mechanisms underlying this association.

Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach.

BACKGROUND: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5. METHODS: Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3-5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3-5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed. RESULTS: A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model. CONCLUSION: This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3-5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.

The association between parents phubbing and prosocial behavior among Chinese preschool children: a moderated mediation model.

Introduction: The popularization and widespread use of smartphones and other electronic devices have led to the occurrence of "parents phubbing", which may have a negative impact on child-parent relationship and preschoolers' prosocial behavior. Methods: To clarify this process, a questionnaire survey was conducted with 3,834 parents from 20 kindergartens in Zhuhai, China. This study examined the relationship between between parents phubbing, closeness child-parent relationship, authoritative parenting style and children's prosocial behavior. Results: According to the study, we found a significant negative correlation between parents phubbing and preschoolers' prosocial behavior. Closeness child-parent relationship mediated between parents phubbing and preschoolers' prosocial behavior through mediation effects analysis. In other words, parent phubbing was negatively associated with closeness child-parent relationship, which in turn predicted less child prosocial behavior. In addition, authoritative parenting styles have a moderating effect. As the level of authoritative parenting style increases, the negative impact of parent phubbing on the prosocial behavior of preschool children is attenuated. Discussion: This study contributes to the understanding of the relationship between parents phubbing and prosocial behaviors of preschool children, as well as the internal mechanisms at work. Practically, the study suggests that parents should reduce the incidence of phubbing in their contact with their children and, at the same time, work to improve the child-parent relationship and promote the development of prosocial behaviors in children.

Interpretable Deep Learning System for Identifying Critical Patients Through the Prediction of Triage Level, Hospitalization, and Length of Stay: Prospective Study.

BACKGROUND: Triage is the process of accurately assessing patients' symptoms and providing them with proper clinical treatment in the emergency department (ED). While many countries have developed their triage process to stratify patients' clinical severity and thus distribute medical resources, there are still some limitations of the current triage process. Since the triage level is mainly identified by experienced nurses based on a mix of subjective and objective criteria, mis-triage often occurs in the ED. It can not only cause adverse effects on patients, but also impose an undue burden on the health care delivery system. OBJECTIVE: Our study aimed to design a prediction system based on triage information, including demographics, vital signs, and chief complaints. The proposed system can not only handle heterogeneous data, including tabular data and free-text data, but also provide interpretability for better acceptance by the ED staff in the hospital. METHODS: In this study, we proposed a system comprising 3 subsystems, with each of them handling a single task, including triage level prediction, hospitalization prediction, and length of stay prediction. We used a large amount of retrospective data to pretrain the model, and then, we fine-tuned the model on a prospective data set with a golden label. The proposed deep learning framework was built with TabNet and MacBERT (Chinese version of bidirectional encoder representations from transformers [BERT]). RESULTS: The performance of our proposed model was evaluated on data collected from the National Taiwan University Hospital (901 patients were included). The model achieved promising results on the collected data set, with accuracy values of 63%, 82%, and 71% for triage level prediction, hospitalization prediction, and length of stay prediction, respectively. CONCLUSIONS: Our system improved the prediction of 3 different medical outcomes when compared with other machine learning methods. With the pretrained vital sign encoder and repretrained mask language modeling MacBERT encoder, our multimodality model can provide a deeper insight into the characteristics of electronic health records. Additionally, by providing interpretability, we believe that the proposed system can assist nursing staff and physicians in taking appropriate medical decisions.

[Pollution Characteristics and Ecological Risk Assessment of Microplastics in the Yangtze River Basin].

The Yangtze River, the largest river in China, has not been comprehensively studied for its basin's microplastic pollution status. Therefore, a comprehensive investigation and assessment system of microplastics was developed at the river basin scale to characterize the spatial distribution and composition of microplastics in the Yangtze River Basin in order to analyze their influencing factors and assess their ecological risks. The results showed that the microplastic abundance in the study area ranged from 21 to 44 080 n·m-3, with an average abundance of 4 483 n·m-3. The spatial distribution of microplastic abundance was higher in the tributaries than in the main streams (except the Ganjiang Basin), with the Chengdu of the Minjiang Basin being the tributary area with the highest abundance of microplastics detected. The size of microplastics in the river basin was concentrated in the 0-1 mm range; the shapes were mainly fiber and fragment; and the colors were mainly colored and transparent. Further, introducing the diversity index of microplastics, it was found that both the Simpson index and the Shannon-Wiener index could quantify the diversity of microplastic characteristic composition in the river basin, but there were certain differences in the changing trends between the two. Regression analysis showed that anthropogenic activities were significantly and positively correlated with microplastic abundance (P<0.05), and among the eight anthropogenic activity factors, civilian vehicle ownership and tourism income were the most strongly correlated with microplastic abundance, indicating that transportation and tourism were the main factors influencing microplastic distribution. From the perspective of the potential ecological risk index of microplastics, microplastics in the Yangtze River Basin posed a certain ecological risk, with 68.97% of the area falling within risk zones III and IV, with the ecological risk of microplastics in Taihu Lake warranting more widespread attention.

Oxidative stress and potential effects of metal nanoparticles: A review of biocompatibility and toxicity concerns.

Metal nanoparticles (M-NPs) have garnered significant attention due to their unique properties, driving diverse applications across packaging, biomedicine, electronics, and environmental remediation. However, the potential health risks associated with M-NPs must not be disregarded. M-NPs' ability to accumulate in organs and traverse the blood-brain barrier poses potential health threats to animals, humans, and the environment. The interaction between M-NPs and various cellular components, including DNA, multiple proteins, and mitochondria, triggers the production of reactive oxygen species (ROS), influencing several cellular activities. These interactions have been linked to various effects, such as protein alterations, the buildup of M-NPs in the Golgi apparatus, heightened lysosomal hydrolases, mitochondrial dysfunction, apoptosis, cell membrane impairment, cytoplasmic disruption, and fluctuations in ATP levels. Despite the evident advantages M-NPs offer in diverse applications, gaps in understanding their biocompatibility and toxicity necessitate further research. This review provides an updated assessment of M-NPs' pros and cons across different applications, emphasizing associated hazards and potential toxicity. To ensure the responsible and safe use of M-NPs, comprehensive research is conducted to fully grasp the potential impact of these nanoparticles on both human health and the environment. By delving into their intricate interactions with biological systems, we can navigate the delicate balance between harnessing the benefits of M-NPs and minimizing potential risks. Further exploration will pave the way for informed decision-making, leading to the conscientious development of these nanomaterials and safeguarding the well-being of society and the environment.

Underlying mechanisms of novel cuproptosis-related dihydrolipoamide branched-chain transacylase E2 (DBT) signature in sunitinib-resistant clear-cell renal cell carcinoma.

Renal cell carcinoma (RCC) is the predominant form of malignant kidney cancer. Sunitinib, a primary treatment for advanced, inoperable, recurrent, or metastatic RCC, has shown effectiveness in some patients but is increasingly limited by drug resistance. Recently identified cuproptosis, a copper-ion-dependent form of programmed cell death, holds promise in combating cancer, particularly drug-resistant types. However, its effectiveness in treating drug resistant RCC remains to be determined. Exploring cuproptosis's regulatory mechanisms could enhance RCC treatment strategies. Our analysis of data from the GEO and TCGA databases showed that the cuproptosis-related gene DBT is markedly under expressed in RCC tissues, correlating with worse prognosis and disease progression. In our study, we investigated copper CRGs in ccRCC, noting substantial expression differences, particularly in advanced-stage tumors. We established a connection between CRG expression levels and patient survival, positioning CRGs as potential therapeutic targets for ccRCC. In drug resistant RCC cases, we found distinct expression patterns for DBT and GLS CRGs, linked to treatment resistance. Our experiments demonstrated that increasing DBT expression significantly reduces RCC cell growth and spread, underscoring its potential as a therapeutic target. This research sheds new light on the role of CRGs in ccRCC and their impact on drug resistance.

A novel scale for triage assessment of frailty in the emergency department (ED-FraS): a prospective videotaped study.

BACKGROUND: Rapid recognition of frailty in older patients in the ED is an important first step toward better geriatric care in the ED. We aimed to develop and validate a novel frailty assessment scale at ED triage, the Emergency Department Frailty Scale (ED-FraS). METHODS: We conducted a prospective cohort study enrolling adult patients aged 65 years or older who visited the ED at an academic medical center. The entire triage process was recorded, and triage data were collected, including the Taiwan Triage and Acuity Scale (TTAS). Five physician raters provided ED-FraS levels after reviewing videos. A modified TTAS (mTTAS) incorporating ED-FraS was also created. The primary outcome was hospital admission following the ED visit, and secondary outcomes included the ED length of stay (EDLOS) and total ED visit charges. RESULTS: A total of 256 patients were included. Twenty-seven percent of the patients were frail according to the ED-FraS. The majority of ED-FraS was level 2 (57%), while the majority of TTAS was level 3 (81%). There was a weak agreement between the ED-FraS and TTAS (kappa coefficient of 0.02). The hospital admission rate and charge were highest at ED-FraS level 5 (severely frail), whereas the EDLOS was longest at level 4 (moderately frail). The area under the Receiver Operating Characteristic curve (AUROC) in predicting hospital admission for the TTAS, ED-FraS, and mTTAS were 0.57, 0.62, and 0.63, respectively. The ED-FraS explained more variation in EDLOS (R2 = 0.096) compared with the other two methods. CONCLUSIONS: The ED-Fras tool is a simple and valid screening tool for identifying frail older adults in the ED. It also can complement and enhance ED triage systems. Further research is needed to test its real-time use at ED triage internationally.

Development and External Validation of Clinical Features-based Machine Learning Models for Predicting COVID-19 in the Emergency Department.

Introduction: Timely diagnosis of patients affected by an emerging infectious disease plays a crucial role in treating patients and avoiding disease spread. In prior research, we developed an approach by using machine learning (ML) algorithms to predict serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on clinical features of patients visiting an emergency department (ED) during the early coronavirus 2019 (COVID-19) pandemic. In this study, we aimed to externally validate this approach within a distinct ED population. Methods: To create our training/validation cohort (model development) we collected data retrospectively from suspected COVID-19 patients at a US ED from February 23-May 12, 2020. Another dataset was collected as an external validation (testing) cohort from an ED in another country from May 12-June 15, 2021. Clinical features including patient demographics and triage information were used to train and test the models. The primary outcome was the confirmed diagnosis of COVID-19, defined as a positive reverse transcription polymerase chain reaction test result for SARS-CoV-2. We employed three different ML algorithms, including gradient boosting, random forest, and extra trees classifiers, to construct the predictive model. The predictive performances were evaluated with the area under the receiver operating characteristic curve (AUC) in the testing cohort. Results: In total, 580 and 946 ED patients were included in the training and testing cohorts, respectively. Of them, 98 (16.9%) and 180 (19.0%) were diagnosed with COVID-19. All the constructed ML models showed acceptable discrimination, as indicated by the AUC. Among them, random forest (0.785, 95% confidence interval [CI] 0.747-0.822) performed better than gradient boosting (0.774, 95% CI 0.739-0.811) and extra trees classifier (0.72, 95% CI 0.677-0.762). There was no significant difference between the constructed models. Conclusion: Our study validates the use of ML for predicting COVID-19 in the ED and demonstrates its potential for predicting emerging infectious diseases based on models built by clinical features with temporal and spatial heterogeneity. This approach holds promise for scenarios where effective diagnostic tools for an emerging infectious disease may be lacking in the future.

Canola bee pollen is an effective artificial diet additive for improving larval development of predatory coccinellids: a lesson from Harmonia axyridis.

BACKGROUND: Pollen is a common plant-derived food source for predatory ladybird beetles under field conditions, yet the potential for pollen to improve the quality of artificial diets remains largely unexplored. In this study, we developed three pollen diets by incorporating varying proportions of canola bee pollen (7.5%, 15.0% and 22.5% with 2.5%, 5.0%, and 7.5% of water, respectively) into a conventional diet. The feeding efficiency of Harmonia axyridis, an omnivorous predator, was evaluated and compared on three pollen diets, a conventional nonpollen diet and pea aphids. RESULTS: The larvae fed a medium or high pollen diet exhibited significantly higher survival in the 4th instar, pupa and adult stages than those fed a nonpollen diet. These larvae also developed into significantly heavier adults, and their survival rates in adulthood were comparable to those fed pea aphids. Specifically, we revealed the underlying mechanisms through which a high pollen diet enhances pupal development. Consumption of high pollen diet versus nonpollen diet resulted not only in a significant decrease in pupal glycogen content, but also an increase in adult lipid content. Both diet treatments induced similar changes in carbohydrate and glycogen content compared to the aphid diet while exhibiting different alterations in pupal protein content and adult lipid content. Furthermore, the transcriptome analysis revealed that the nutrient metabolism, immune response, and cuticle development pathways were predominantly enriched among the differentially expressed genes (DEGs). CONCLUSION: Canola bee pollen offers diverse advantages in terms of rearing H. axyridis larvae with an artificial diet, which will advance the development of effective diets for predaceous coccinellids. © 2024 Society of Chemical Industry.

The incidence, predictors, and causes of cardiac arrest in United States emergency departments.

Background: Emergency department cardiac arrest (EDCA) is a global public health challenge associated with high mortality rates and poor neurological outcomes. This study aimed to describe the incidence, risk factors, and causes of EDCA during emergency department (ED) visits in the U.S. Methods: This retrospective cohort study used data from the 2019 Nationwide Emergency Department Sample (NEDS). Adult ED visits with EDCA were identified using the cardiopulmonary resuscitation code. We used descriptive statistics and multivariable logistic regression, considering NEDS's complex survey design. The primary outcome measure was EDCA incidence. Results: In 2019, there were approximately 232,000 ED visits with cardiac arrest in the U.S. The incidence rate of EDCA was approximately 0.2%. Older age, being male, black race, low median household income, weekend ED visits, having Medicare insurance, and ED visits in non-summer seasons were associated with a higher risk of EDCA. Hispanic race was associated with a lower risk of EDCA. Certain comorbidities (e.g., diabetes and cancer), trauma centers, hospitals with a metropolitan and/or teaching program, and hospitals in the South were associated with a higher risk of EDCA. Depression, dementia, and hypothyroidism were associated with a lower risk of EDCA. Septicemia, acute myocardial infarction, and respiratory failure, followed by drug overdose, were the predominant causes of EDCA. Conclusions: Some patients were disproportionately affected by EDCA. Strategies should be developed to target these modifiable risk factors, specifically factors within ED's control, to reduce the subsequent disease burden.

Currently Used Methods to Evaluate the Efficacy of Therapeutic Drugs and Kidney Safety.

Kidney diseases with kidney failure or damage, such as chronic kidney disease (CKD) and acute kidney injury (AKI), are common clinical problems worldwide and have rapidly increased in prevalence, affecting millions of people in recent decades. A series of novel diagnostic or predictive biomarkers have been discovered over the past decade, enhancing the investigation of renal dysfunction in preclinical studies and clinical risk assessment for humans. Since multiple causes lead to renal failure, animal studies have been extensively used to identify specific disease biomarkers for understanding the potential targets and nephropathy events in therapeutic insights into disease progression. Mice are the most commonly used model to investigate the mechanism of human nephropathy, and the current alternative methods, including in vitro and in silico models, can offer quicker, cheaper, and more effective methods to avoid or reduce the unethical procedures of animal usage. This review provides modern approaches, including animal and nonanimal assays, that can be applied to study chronic nonclinical safety. These specific situations could be utilized in nonclinical or clinical drug development to provide information on kidney disease.

Prospective study of pain and patient outcomes in the emergency department: a tale of two pain assessment methods.

BACKGROUND: Accurate pain assessment is essential in the emergency department (ED) triage process. Overestimation of pain intensity, however, can lead to unnecessary overtriage. The study aimed to investigate the influence of pain on patient outcomes and how pain intensity modulates the triage's predictive capabilities on these outcomes. METHODS: A prospective observational cohort study was conducted at a tertiary care hospital, enrolling adult patients in the triage station. The entire triage process was captured on video. Two pain assessment methods were employed: (1) Self-reported pain score in the Taiwan Triage and Acuity Scale, referred to as the system-based method; (2) Five physicians independently assigned triage levels and assessed pain scores from video footage, termed the physician-based method. The primary outcome was hospitalization, and secondary outcomes included ED length of stay (EDLOS) and ED charges. RESULTS: Of the 656 patients evaluated, the median self-reported pain score was 4 (interquartile range, 0-7), while the median physician-rated pain score was 1.5 (interquartile range, 0-3). Increased self-reported pain severity was not associated with prolonged EDLOS and increased ED charges, but a positive association was identified with physician-rated pain scores. Using the system-based method, the predictive efficacy of triage scales was lower in the pain groups than in the pain-free group (area under the receiver operating curve, [AUROC]: 0.615 vs. 0.637). However, with the physician-based method, triage scales were more effective in predicting hospitalization among patients with pain than those without (AUROC: 0.650 vs. 0.636). CONCLUSIONS: Self-reported pain seemed to diminish the predictive accuracy of triage for hospitalization. In contrast, physician-rated pain scores were positively associated with longer EDLOS, increased ED charges, and enhanced triage predictive capability for hospitalization. Pain, therefore, appears to modulate the relationship between triage and patient outcomes, highlighting the need for careful pain evaluation in the ED.

What drives the spatial heterogeneity of cropping patterns in the Northeast China: The natural environment, the agricultural economy, or policy?

Understanding the spatiotemporal dynamic of crop cover types and the driving forces of cropping patterns in the Northeast China (NEC) is essential for establishing suitable and sustainable cropping patterns that are adapted to local conditions, and for promoting the optimal use of black soil resources. Here, we classified the major grain crop cover types and investigated their spatiotemporal dynamic in the NEC by combining multi-source remote sensing imagery and phenological information based on the Google Earth Engine (GEE) platform. A number of typical cropping patterns from 2017 to 2021 were defined and extracted, and the characteristics of their spatial heterogeneity were analyzed. Driving mechanisms for the spatial heterogeneity of cropping patterns were revealed using Geodetector. The results concluded that over the past five years (2017-2021), there has been a shift from soybean to maize in the NEC, while rice has remained stable in terms of spatiotemporal dynamics. Seven dominant cropping patterns showed high spatial heterogeneity and positive spatial agglomeration. The center of gravity of the cropping pattern shifted southwards as the frequency of maize planting increased, while the center of gravity shifted northwards as the frequency of soybean planting increased, while the rice cropping pattern remained stable. The interaction between black-soil productivity index (BPI) and total grain income trend (TGIT) exhibits the most pronounced impact on the spatial heterogeneity of cropping patterns, with a q statistic of 0.523. Following closely are the interactions of soybean subsidies trend (SST), rice subsidies trend (RST), and maize subsidies trend (MST) with TGIT, with q statistics of 0.481, 0.472, and 0.452, respectively. Among the seven dominant cropping patterns, the soybean-based cropping pattern had the highest level of TGIT and BPI, followed by the maize-based cropping pattern, while the rice-based cropping pattern had the lowest level. All of the natural environmental, agri-economic and policy factors have a synergistic effect in contributing to the spatial heterogeneity of cropping patterns. Natural environmental factors determine the overall spatial distribution of cropping patterns in the NEC, while economic and policy factors combine to influence farmers' decisions, resulting in diverse regional cropping patterns. It is recommended that maize-soybean rotations such as Maize-Soybean Alternate Cropping (MSAC) and Maize-Soybean Rotational Cropping (MSRC) should be promoted, especially in the central and southern regions of the NEC, to meet agricultural market demand and stabilize soil productivity.

A nomogram for predicting hospital mortality of critical ill patients with sepsis and cancer: a retrospective cohort study based on MIMIC-IV and eICU-CRD.

OBJECTIVE: Sepsis remains a high cause of death, particularly in immunocompromised patients with cancer. The study was to develop a model to predict hospital mortality of septic patients with cancer in intensive care unit (ICU). DESIGN: Retrospective observational study. SETTING: Medical Information Mart for Intensive Care IV (MIMIC IV) and eICU Collaborative Research Database (eICU-CRD). PARTICIPANTS: A total of 3796 patients in MIMIC IV and 549 patients in eICU-CRD were included. PRIMARY OUTCOME MEASURES: The model was developed based on MIMIC IV. The internal validation and external validation were based on MIMIC IV and eICU-CRD, respectively. Candidate factors were processed with the least absolute shrinkage and selection operator regression and cross-validation. Hospital mortality was predicted by the multivariable logistical regression and visualised by the nomogram. The model was assessed by the area under the curve (AUC), calibration curve and decision curve analysis curve. RESULTS: The model exhibited favourable discrimination (AUC: 0.726 (95% CI: 0.709 to 0.744) and 0.756 (95% CI: 0.712 to 0.801)) in the internal and external validation sets, respectively, and better calibration capacity than Acute Physiology and Chronic Health Evaluation IV in external validation. CONCLUSIONS: Despite that the predicted model was based on a retrospective study, it may also be helpful to predict the hospital morality of patients with solid cancer and sepsis.

Enhancing drug-drug Interaction Prediction by Integrating Physiologically-Based Pharmacokinetic Model with Fraction Metabolized by CYP3A4.

BACKGROUND: Enhancing the precision of drug-drug interaction (DDI) prediction is essential for improving drug safety and efficacy. The aim is to identify the most effective fraction metabolized by CY3A4 (fm) for improving DDI prediction using physiologically based pharmacokinetic (PBPK) models. RESEARCH DESIGN AND METHODS: The fm values were determined for 33 approved drugs using a human liver microsome for in vitro measurements and the ADMET Predictor software for in silico predictions. Subsequently, these fm values were integrated into PBPK models using the GastroPlus platform. The PBPK models, combined with a ketoconazole model, were utilized to predict AUCR (AUCcombo with ketoconazole/AUCdosing alone), and the accuracy of these predictions was evaluated by comparison with observed AUCR. RESULTS: The integration of in vitro fm method demonstrates superior performance compared to the in silico fm method and fm of 100% method. Under the Guest-limits criteria, the integration of in vitro fm achieves an accuracy of 76%, while the in silico fm and fm of 100% methods achieve accuracies of 67% and 58%, respectively. CONCLUSIONS: Our study highlights the importance of in vitro fm data to improve the accuracy of predicting DDIs and demonstrates the promising potential of in silico fm in predicting DDIs.

Applying a Smartwatch to Predict Work-related Fatigue for Emergency Healthcare Professionals: Machine Learning Method.

INTRODUCTION: Healthcare professionals frequently experience work-related fatigue, which may jeopardize their health and put patient safety at risk. In this study, we applied a machine learning (ML) approach based on data collected from a smartwatch to construct prediction models of work-related fatigue for emergency clinicians. METHODS: We conducted this prospective study at the emergency department (ED) of a tertiary teaching hospital from March 10-June 20, 2021, where we recruited physicians, nurses, and nurse practitioners. All participants wore a commercially available smartwatch capable of measuring various physiological data during the experiment. Participants completed the Multidimensional Fatigue Inventory (MFI) web form before and after each of their work shifts. We calculated and labeled the before-and-after-shift score differences between each pair of scores. Using several tree-based algorithms, we constructed the prediction models based on features collected from the smartwatch. Records were split into training/validation and testing sets at a 70:30 ratio, and we evaluated the performances using the area under the curve (AUC) measure of receiver operating characteristic on the test set. RESULTS: In total, 110 participants were included in this study, contributing to a set of 1,542 effective records. Of these records, 85 (5.5%) were labeled as having work-related fatigue when setting the MFI difference between two standard deviations as the threshold. The mean age of the participants was 29.6. Most of the records were collected from nurses (87.7%) and females (77.5%). We selected a union of 31 features to construct the models. For total participants, CatBoost classifier achieved the best performances of AUC (0.838, 95% confidence interval [CI] 0.742-0.918) to identify work-related fatigue. By focusing on a subgroup of nurses <35 years in age, XGBoost classifier obtained excellent performance of AUC (0.928, 95% CI 0.839-0.991) on the test set. CONCLUSION: By using features derived from a smartwatch, we successfully built ML models capable of classifying the risk of work-related fatigue in the ED. By collecting more data to optimize the models, it should be possible to use smartwatch-based ML models in the future to predict work-related fatigue and adopt preventive measures for emergency clinicians.