Ethanol extract of Eclipta prostrata induces multiple myeloma ferroptosis via Keap1/Nrf2/HO-1 axis

BACKGROUND

Multiple myeloma (MM) is an incurable hematological malignancy with limited therapeutic efficacy. Eclipta prostrata is a traditional Chinese medicinal plant reported to possess antitumor properties. However, the effects of E. prostrata in MM have not been explored.

PURPOSE

The aim of this study was to define the mechanism of the ethanol extract of E. prostrata (EEEP) in treating MM and identify its major components.

METHODS

The pro-ferroptotic effects of EEEP on cell death, cell proliferation, iron accumulation, lipid peroxidation, and mitochondrial morphology were determined in RPMI-8226 and U266 cells. The expression levels of nuclear factor erythroid 2-related factor 2 (Nrf2), kelch-like ECH-associated protein 1 (Keap1), heme oxygenase-1 (HO-1), glutathione peroxidase 4 (GPX4), and 4-hydroxynonenal (4HNE) were detected using western blotting during EEEP-mediated ferroptosis regulation. The RPMI-8226 and U266 xenograft mouse models were used to explore the in vivo anticancer effects of EEEP. Finally, high performance liquid chromatography (HPLC) and ultra-high-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry system (UPLC-Q/TOF-MS) were used to identify the major constituents of EEEP.

RESULTS

EEEP inhibited MM cell growth and induced cell death in vitro and in vivo. By promoting malondialdehyde and Fe2+ accumulation, lipid peroxidation, and GSH suppression, EEEP triggers ferroptosis in MM. Mechanistically, EEEP regulates the Keap1/Nrf2/HO-1 axis and stimulates ferroptosis. EEEP-induced lipid peroxidation and malondialdehyde accumulation were blocked by the Nrf2 activator NK-252. In addition, HPLC and UPLC-Q/TOF-MS analysis elucidated the main components of EEEP, including demethylwedelolactone, wedelolactone, chlorogenic acid and apigenin, which may play important roles in the anti-tumor function of EEEP.

CONCLUSION

In summary, EEEP exerts its anti-MM function by inducing MM cell death and inhibiting tumor growth in mice. We also showed that EEEP can induce lipid peroxidation and accumulation of ferrous irons in MM cells both in vivo and in vitro, leading to ferroptosis. In addition, this anti-tumor function may be achieved by the EEEP activation of Keap1/Nrf2/HO-1 axis. This is the first study to reveal that EEEP exerts anti-MM activity through the Keap1/Nrf2/HO-1-dependent ferroptosis regulatory axis, making it a promising candidate for MM treatment.

Family-Centric Applied Behavior Analysis Facilitates Improved Treatment Utilization and Outcomes

Background/Objective: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by lifelong impacts on functional social and daily living skills, and restricted, repetitive behaviors (RRBs). Applied behavior analysis (ABA), the gold-standard treatment for ASD, has been extensively validated. ABA access is hindered by limited availability of qualified professionals and logistical and financial barriers. Scientifically validated, parent-led ABA can fill the accessibility gap by overcoming treatment barriers. This retrospective cohort study examines how our ABA treatment model, utilizing parent behavior technicians (pBTs) to deliver ABA, impacts adaptive behaviors and interfering behaviors (IBs) in a cohort of children on the autism spectrum with varying ASD severity levels, and with or without clinically significant IBs. Methods: Clinical outcomes of 36 patients ages 3-15 years were assessed using longitudinal changes in Vineland-3 after 3+ months of pBT-delivered ABA treatment. Results: Within the pBT model, our patients demonstrated clinically significant improvements in Vineland-3 Composite, domain, and subdomain scores, and utilization was higher in severe ASD. pBTs utilized more prescribed ABA when children initiated treatment with clinically significant IBs, and these children also showed greater gains in their Composite scores. Study limitations include sample size, inter-rater reliability, potential assessment metric bias and schedule variability, and confounding intrinsic or extrinsic factors. Conclusion: Overall, our pBT model facilitated high treatment utilization and showed robust effectiveness, achieving improved adaptive behaviors and reduced IBs when compared to conventional ABA delivery. The pBT model is a strong contender to fill the widening treatment accessibility gap and represents a powerful tool for addressing systemic problems in ABA treatment delivery.

Intratumoural and peritumoural CT-based radiomics for diagnosing lepidic-predominant adenocarcinoma in patients with pure ground-glass nodules: a machine learning approach

AIM

To develop and validate a diagnostic model utilising machine-learning algorithms that differentiates lepidic predominant adenocarcinoma (LPA) from other pathological subtypes in patients with pure ground-glass nodules (pGGNs).

MATERIALS AND METHODS

This bicentric study was conducted across two medical centres and included 151 patients diagnosed with lung adenocarcinoma based on histopathological confirmation of pGGNs. The training cohort consisted of 99 patients from Institution 1, while the test cohort included 52 patients from Institution 2. Radiomics features were extracted from both tumours and the 2 mm peritumoural parenchyma. The tumoural and peritumoural radiomics were designated as Modeltumoural and Modelperitumoural, respectively. The diagnostic efficacy of various models was evaluated through the receiver operating characteristic (ROC) curve analysis. Subsequently, a machine-learning-based prediction model that combined Modeltumoural, Modelperitumoural, and Modelclinical-radiological was developed to differentiate LPA from other pathological subtypes in patients with pGGNs.

RESULTS

Modeltumoural achieved area under the curve (AUC) values of 0.762 and 0.783 in the training and validation sets, respectively. Modelperitumoural attained AUCs of 0.742 and 0.667, and Modelclinical-radiological generated an AUC of 0.727 and 0.739 in the training and validation sets, respectively. Among the machine-learning models evaluated, gradient boosting machines demonstrated the best diagnostic efficacy, with accuracy, AUC, F1 score, and log loss values of 0.885, 0.956, 0.943, and 0.260, respectively.

CONCLUSION

The combined model based on machine learning that incorporated tumour and peritumoural parenchyma, as well as clinical and imaging characteristics, may offer benefits in assessing the pathological subtype of pGGNs.

Sesamin attenuates UVA-induced keratinocyte injury via inhibiting ASK-1-JNK/p38 MAPK pathways

BACKGROUND

Ultraviolet (UV) exposure-stimulated reactive oxygen species (ROS) formation in keratinocytes is a crucial factor in skin aging. Phytochemicals have become widely popular for protecting the skin from UV-induced cell injury. Sesamin (SSM) has been shown to play a role in extensive pharmacological activity and exhibit photoprotective effects.

AIM

To assess the protective effect of SSM on UVA-irradiated keratinocytes and determine its potential antiphotoaging effect.

METHODS

HaCaT keratinocytes pretreated with SSM were exposed to UVA radiation at 8 J/cm2 for 10 min. Cell viability and oxidative stress indicators were evaluated using a cell counting kit-8 and lactate dehydrogenase (LDH), malondialdehyde (MDA), glutathione (GSH), and superoxide dismutase (SOD) assay kits. Apoptosis and intracellular ROS levels were analyzed using annexin V-fluorescein isothiocyanate/propyridine iodide and dichlorodihydrofluorescein diacetate staining, respectively. Protein levels of matrix metalloprotein-1 (MMP-1), MMP-9, Bax/Bcl-2, and mitogen-activated protein kinase (MAPK) pathway proteins, phospho-apoptosis signal-regulating kinase-1 (p-ASK-1)/ASK-1, phospho-c-Jun N-terminal protein kinase (p-JNK)/JNK, and p-p38/p38 were determined using western blotting.

RESULTS

Sesamin showed no cytotoxicity until 160 μmol/L on human keratinocytes. Sesamin pretreatment (20 and 40 μM) reversed the suppressed cell viability, increased LDH release and MDA content, decreased cellular antioxidants GSH and SOD, and elevated intracellular ROS levels, which were induced by UVA irradiation. Additionally, SSM inhibited the expression of Bax, MMP-1, and MMP-9 and stimulated Bcl-2 expression. In terms of the regulatory mechanisms, we demonstrated that SSM inhibits the phosphorylation of ASK-1, JNK, and p38.

CONCLUSION

The results suggest that SSM attenuates UVA-induced keratinocyte injury by inhibiting the ASK-1-JNK/p38 MAPK pathways.

Machine Learning Approach for Improved Longitudinal Prediction of Progression from Mild Cognitive Impairment to Alzheimer's Disease

Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of Alzheimer's disease (AD). We developed and validated a machine learning algorithm (MLA), based on a gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55-88 years old (n = 493) diagnosed with MCI. Data were analyzed within multiple prediction windows and averaged to predict progression to AD within 24-48 months. The MLA outperformed the mini-mental state examination (MMSE) and three comparison models at all prediction windows on most metrics. Exceptions include sensitivity at 18 months (MLA and MMSE each achieved 0.600); and sensitivity at 30 and 42 months (MMSE marginally better). For all prediction windows, the MLA achieved AUROC ≥ 0.857 and NPV ≥ 0.800. With averaged data for the 24-48-month lookahead timeframe, the MLA outperformed MMSE on all metrics. This study demonstrates that machine learning may provide a more accurate risk assessment than the standard of care. This may facilitate care coordination, decrease healthcare expenditures, and maintain quality of life for patients at risk of progressing from MCI to AD.

Machine Learning Differentiation of Autism Spectrum Sub-Classifications

PURPOSE

Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum complex. We evaluated machine learning to classify individuals as having one of three disorders of the autism spectrum under DSM-IV, or as non-spectrum.

METHODS

We employed machine learning to analyze retrospective data from 38,560 individuals. Inputs encompassed clinical, demographic, and assessment data.

RESULTS

The algorithm achieved AUROCs ranging from 0.863 to 0.980. The model correctly classified 80.5% individuals; 12.6% of individuals from this dataset were misclassified with another disorder on the autism spectrum.

CONCLUSION

Machine learning can classify individuals as having a disorder on the autism spectrum or as non-spectrum using minimal data inputs.

Blood virome of patients with traumatic sepsis

Sepsis is one of the possible outcomes of severe trauma, and it poses a dire threat to human life, particularly in immunocompromised people. The most prevalent pathogens are bacteria and fungi, but viruses should not be overlooked. For viral metagenomic analysis, we collected blood samples from eight patients with post-traumatic sepsis before and seven days after treatment. The results demonstrated that Anellovirus predominated the viral community, followed by Siphoviridae and Myoviridae, and that the variations in viral community and viral load before and after treatment were not statistically significant. This study allows us to investigate methods for establishing NGS-based viral diagnostic instruments for detecting viral infections in the blood of sepsis patients so that antiviral therapy can be administered quickly.

Workplace bullying and suicidal ideation and behaviour: a systematic review and meta-analysis

OBJECTIVES

Suicidal ideation and behaviour are potential outcomes of workplace bullying. This review aimed to determine the extent of the association between workplace bullying and suicidal ideation and behaviour.

STUDY DESIGN

The study incorporated a systematic review and meta-analysis.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement was followed to conduct a comprehensive systematic review and meta-analysis. A combination of subject terms and free words was used to search nine electronic databases. Two reviewers independently screened articles and extracted information according to the inclusion criteria. A meta-analysis was performed with averaged weighted correlations across samples using the STATA software (version 16.0) from pooled estimates of the main results from all studies.

RESULTS

In total, 25 articles of high or medium quality were included in the systematic review; 15 of these were included in the meta-analysis. The prevalence of suicidal ideation and behaviour was 18% and 4%, respectively. Individuals who experienced workplace bullying had 2.03-times and 2.67-times higher odds of reporting suicidal ideation and behaviour, respectively, after adjustment for confounding factors. Moderating and mediating factors may help reduce the risk of suicidal ideation and behaviour for individuals experiencing workplace bullying.

CONCLUSION

This study indicated that exposure to workplace bullying significantly increased the risk of suicidal ideation and behaviour.

Three New Benzophenone Derivatives from Selaginella tamariscina

Six compounds including three new benzophenones, selagibenzophenones D-F (1-3), two known selaginellins (4-5) and one known flavonoid (6), were isolated from Selaginella tamariscina. The structures of new compounds were established by 1D-, 2D-NMR and HR-ESI-MS spectral analyses. Compound 1 represents the second example of diarylbenzophenone from natural sources. Compound 2 possesses an unusual biphenyl-bisbenzophenone structure. Their cytotoxicity against human hepatocellular carcinoma HepG2 and SMCC-7721 cells and inhibitory activities on lipopolysaccharide-induced nitric oxide (NO) production in RAW264.7 cells were evaluated. Compound 2 showed moderate inhibitory activity against HepG2 and SMCC-7721 cells, and compounds 4 and 5 showed moderate inhibitory activity to HepG2 cells. Compounds 2 and 5 also exhibited inhibitory activities on lipopolysaccharide-induced nitric oxide (NO) production.

Erratum: Corrigendum: Water extract of cacumen platycladi promotes hair growth through the Akt/GSK3β/β-catenin signaling pathway

[This corrects the article DOI: 10.3389/fphar.2023.1038039.].

Clinical Outcomes of a Hybrid Model Approach to Applied Behavioral Analysis Treatment

Objective This study examines the implementation of a hybrid applied behavioral analysis (ABA) treatment model to determine its impact on autism spectrum disorder (ASD) patient outcomes.  Methods Retrospective data were collected for 25 pediatric patients to measure progress before and after the implementation of a hybrid ABA treatment model under which therapists consistently captured session notes electronically regarding goals and patient progress. ABA treatment was streamlined for consistent delivery, with improved software utilization for tracking scheduling and progress. Eleven goals within three domains (behavioral, social, and communication) were examined.  Results After the implementation of the hybrid model, the goal success rate improved by 9.7% compared to the baseline; 41.8% of goals showed improvement, 38.4% showed a flat trend, and 19.8% showed deterioration. Multiple goals trended upwards in 76% of the patients.  Conclusion This pilot study demonstrated that enhancing the consistency with which ABA treatment is monitored/delivered can improve patient outcomes as seen through improved attainment of goals.

Machine learning determination of applied behavioral analysis treatment plan type

BACKGROUND

Applied behavioral analysis (ABA) is regarded as the gold standard treatment for autism spectrum disorder (ASD) and has the potential to improve outcomes for patients with ASD. It can be delivered at different intensities, which are classified as comprehensive or focused treatment approaches. Comprehensive ABA targets multiple developmental domains and involves 20-40 h/week of treatment. Focused ABA targets individual behaviors and typically involves 10-20 h/week of treatment. Determining the appropriate treatment intensity involves patient assessment by trained therapists, however, the final determination is highly subjective and lacks a standardized approach. In our study, we examined the ability of a machine learning (ML) prediction model to classify which treatment intensity would be most suited individually for patients with ASD who are undergoing ABA treatment.

METHODS

Retrospective data from 359 patients diagnosed with ASD were analyzed and included in the training and testing of an ML model for predicting comprehensive or focused treatment for individuals undergoing ABA treatment. Data inputs included demographics, schooling, behavior, skills, and patient goals. A gradient-boosted tree ensemble method, XGBoost, was used to develop the prediction model, which was then compared against a standard of care comparator encompassing features specified by the Behavior Analyst Certification Board treatment guidelines. Prediction model performance was assessed via area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

The prediction model achieved excellent performance for classifying patients in the comprehensive versus focused treatment groups (AUROC: 0.895; 95% CI 0.811-0.962) and outperformed the standard of care comparator (AUROC 0.767; 95% CI 0.629-0.891). The prediction model also achieved sensitivity of 0.789, specificity of 0.808, PPV of 0.6, and NPV of 0.913. Out of 71 patients whose data were employed to test the prediction model, only 14 misclassifications occurred. A majority of misclassifications (n = 10) indicated comprehensive ABA treatment for patients that had focused ABA treatment as the ground truth, therefore still providing a therapeutic benefit. The three most important features contributing to the model's predictions were bathing ability, age, and hours per week of past ABA treatment.

CONCLUSION

This research demonstrates that the ML prediction model performs well to classify appropriate ABA treatment plan intensity using readily available patient data. This may aid with standardizing the process for determining appropriate ABA treatments, which can facilitate initiation of the most appropriate treatment intensity for patients with ASD and improve resource allocation.

Stimulation of Surface Polysaccharide Production under Aerobic Conditions Confers Aerotolerance in Campylobacter jejuni

The ability of a foodborne pathogen to tolerate environmental stress critically affects food safety by increasing the risk of pathogen survival and transmission in the food supply chain. Campylobacter jejuni, a leading bacterial cause of foodborne illnesses, is an obligate microaerophile and is sensitive to atmospheric levels of oxygen. Currently, the molecular mechanisms of how C. jejuni withstands oxygen toxicity under aerobic conditions have not yet been fully elucidated. Here, we show that when exposed to aerobic conditions, C. jejuni develops a thick layer of bacterial capsules, which in turn protect C. jejuni under aerobic conditions. The presence of both capsular polysaccharides and lipooligosaccharides is required to protect C. jejuni from excess oxygen in oxygen-rich environments by alleviating oxidative stress. Under aerobic conditions, C. jejuni undergoes substantial transcriptomic changes, particularly in the genes of carbon metabolisms involved in amino acid uptake, the tricarboxylic acid (TCA) cycle, and the Embden-Meyerhof-Parnas (EMP) pathway despite the inability of C. jejuni to grow aerobically. Moreover, the stimulation of carbon metabolism by aerobiosis increases the level of glucose-6-phosphate, the EMP pathway intermediate required for the synthesis of surface polysaccharides. The disruption of the TCA cycle eliminates aerobiosis-mediated stimulation of surface polysaccharide production and markedly compromises aerotolerance in C. jejuni. These results in this study provide novel insights into how an oxygen-sensitive microaerophilic pathogen survives in oxygen-rich environments by adapting its metabolism and physiology. IMPORTANCE Oxygen-sensitive foodborne pathogens must withstand oxygen toxicity in aerobic environments during transmission to humans. C. jejuni is a major cause of gastroenteritis, accounting for 400 million to 500 million infection cases worldwide per year. As an obligate microaerophile, C. jejuni is sensitive to air-level oxygen. However, it has not been fully explained how this oxygen-sensitive zoonotic pathogen survives in aerobic environments and is transmitted to humans. Here, we show that under aerobic conditions, C. jejuni boosts its carbon metabolism to produce a thick layer of bacterial capsules, which in turn act as a protective barrier conferring aerotolerance. The new findings in this study improve our understanding of how oxygen-sensitive C. jejuni can survive in aerobic environments.

Water extract of cacumen platycladi promotes hair growth through the Akt/GSK3β/β-catenin signaling pathway

Cacumen Platycladi (CP) consists of the dried needles of Platycladus orientalis L.) Franco. It was clinically demonstrated that it effectively regenerates hair, but the underlying mechanism remains unknown. Thus, we employed shaved mice to verify the hair growth-promoting capability of the water extract of Cacumen Platycladi (WECP). The morphological and histological analyses revealed that WECP application could significantly promote hair growth and hair follicles (HFs) construction, in comparison to that of control group. Additionally, the skin thickness and hair bulb diameter were significantly increased by the application of WECP in a dose-dependent manner. Besides, the high dose of WECP also showed an effect similar to that of finasteride. In an in vitro assay, WECP stimulated dermal papilla cells (DPCs) proliferation and migration. Moreover, the upregulation of cyclins (cyclin D1, cyclin-dependent kinase 2 (CDK2), and cyclin-dependent kinase 4 (CDK4)) and downregulation of P21 in WECP-treated cell assays have been evaluated. We identified the ingredients of WECP using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) and endeavored to predict their relevant molecular mechanisms by network analysis. We found that the Akt (serine/threonine protein kinase) signaling pathway might be a crucial target of WECP. It has been demonstrated that WECP treatment activated the phosphorylation of Akt and glycogen synthase kinase-3-beta (GSK3β), promoted β-Catenin and Wnt10b accumulation, and upregulated the expression of lymphoid enhancer-binding factor 1 (LEF1), vascular endothelial growth factor (VEGF), and insulin-like growth factor 1 (IGF1). We also found that WECP significantly altered the expression levels of apoptosis-related genes in mouse dorsal skin. The enhancement capability of WECP on DPCs proliferation and migration could be abrogated by the Akt-specific inhibitor MK-2206 2HCl. These results suggested that WECP might promote hair growth by modulating DPCs proliferation and migration through the regulation of the Akt/GSK3β/β-Catenin signaling pathway.

Expanding known viral diversity in plants: virome of 161 species alongside an ancient canal

BACKGROUND

Since viral metagenomic approach was applied to discover plant viruses for the first time in 2006, many plant viruses had been identified from cultivated and non-cultivated plants. These previous researches exposed that the viral communities (virome) of plants have still largely uncharacterized. Here, we investigated the virome in 161 species belonging to 38 plant orders found in a riverside ecosystem.

RESULTS

We identified 245 distinct plant-associated virus genomes (88 DNA and 157 RNA viruses) belonging to 27 known viral families, orders, or unclassified virus groups. Some viral genomes were sufficiently divergent to comprise new species, genera, families, or even orders. Some groups of viruses were detected that currently are only known to infect organisms other than plants. It indicates a wider host range for members of these clades than previously recognized theoretically. We cannot rule out that some viruses could be from plant contaminating organisms, although some methods were taken to get rid of them as much as possible. The same viral species could be found in different plants and co-infections were common.

CONCLUSIONS

Our data describe a complex viral community within a single plant ecosystem and expand our understanding of plant-associated viral diversity and their possible host ranges.

Viral metagenomics of pharyngeal secretions from children with acute respiratory diseases with unknown etiology revealed diverse viruses

Acute respiratory tract infections are a major public health problem and the leading cause of morbidity in children younger than 5 years old. This study investigated the potential reasons of unexplained acute respiratory infections in children in Xuzhou and its environs during 2018-2019.We collected pharyngeal swab samples from 411 children under the age of five who presented with symptoms of unexplained acute respiratory infection and were negative for bacteria, mycoplasma, and influenza viruses. Using viral metagenomic techniques, viral nucleic acids were extracted, enriched, and sequenced from the samples. Results indicated that Picornaviridae, Parvoviridae, Paramyxoviridae, Coronaviridae, and Anelloviridae were the five virus families with the highest relative content of sequence reads. And we detected 35 HBoV-positive and 12 HEV-positive samples out of 411 samples by the polymerase chain reaction (PCR). Partial or nearly complete genome sequences of viruses belonging to the families Picornaviridae, Parvoviridae, and Anelloviridae were characterized, and phylogenetic trees were constructed based on the nucleic acid or amino acid sequences of the predicted viral open reading frames (ORFs), as well as genotyping of the viruses. In addition, we observed recombination events in the Saffold virus and Coxsackievirus A9 by analyzing the genetic characteristics of the viruses revealed in this study. This study provides vital information for the prevention and treatment of acute respiratory infections in children younger than five years old.

Early prediction of prostate cancer risk in younger men using polygenic risk scores and electronic health records

BACKGROUND

Prostate cancer (PCa) screening is not routinely conducted in men aged 55 and younger, although this age group accounts for more than 10% of cases. Polygenic risk scores (PRSs) and patient data applied toward early prediction of PCa may lead to earlier interventions and increased survival. We have developed machine learning (ML) models to predict PCa risk in men 55 and under using PRSs combined with patient data.

METHODS

We conducted a retrospective study on 91,106 male patients aged 35-55 using the UK Biobank database. Five gradient boosting models were developed and validated utilizing routine screening data, PRSs, additional clinical data, or combinations of the three.

RESULTS

Combinations of PRSs and patient data outperformed models that utilized PRS or patient data only, and the highest performing models achieved an area under the receiver operating characteristic curve of 0.788. Our models demonstrated a substantially lower false positive rate (35.4%) in comparison to standard screening using prostate-specific antigen (60%-67%).

CONCLUSION

This study provides the first preliminary evidence for the use of PRSs with patient data in a ML algorithm for PCa risk prediction in men aged 55 and under for whom screening is not standard practice.

Novel Urinary Metabolites of Aldehydic Lipid Oxidation Products From Heated Soybean Oil Revealed by Aldehyde Abatement and Metabolomic Fingerprinting

Objectives

Heated cooking oils are a constitutive component of contemporary human diets and a common ingredient in animal feed. Aldehydes, as the most reactive lipid oxidation products (LOPs) in heated oils, are widely considered as a contributor to the adverse health effect associated with heated oil consumption. Aldehydes-derived metabolites in biofluids are valuable for monitoring the exposure of the aldehydes from heated oils, but the efforts for their identification were hampered by the high reactivity of aldehydes as well as the chemical complexity caused by the coexistence of other LOPs in heated oils. To address this challenge, this study investigated the aldehydes-derived metabolites through the metabolomic fingerprinting of mouse urine samples from feeding heated oils with and without aldehyde abatement.

Methods

The heated soybean oil (HSO) was prepared by heating the control soybean oil (CSO) at 185°C for 6 h with constant air flow (50 ml/min). Both HSO and CSO were then mixed with the silica gel with piperazine side chain to remove their aldehyde content, producing the silica gel-treated HSO (HSO-si) and the silica gel-treated CSO (CSO-si), respectively. The urine, serum, fecal samples were collected from the C57BL/6 mice fed with 4 diets containing CSO, CSO-si, HSO, HSO-si, and then analyzed by the liquid chromatography-mass spectrometry (LC-MS) metabolomic analysis.

Results

The silica gel treatment dramatically reduced the aldehyde contents in HSO. Novel urinary metabolites formed by the reactions between 2,4-decadienal and lysine were identified through the metabolomic comparison between the HSO samples and the samples from 3 other treatment groups.

Conclusions

The identification of novel urinary metabolites of aldehydic LOPs warrants further biochemical examination on the chemical and metabolic processes responsible for their formation. These metabolites could become the biomarkers for monitoring the exposure of aldehydes in humans and animals.

Funding Sources

The research is partially supported by the NIFA project MIN-18–125.

Early prediction of central line associated bloodstream infection using machine learning

BACKGROUND

Central line-associated bloodstream infections (CLABSIs) are associated with significant morbidity, mortality, and increased healthcare costs. Despite the high prevalence of CLABSIs in the U.S., there are currently no tools to stratify a patient's risk of developing an infection as the result of central line placement. To this end, we have developed and validated a machine learning algorithm (MLA) that can predict a patient's likelihood of developing CLABSI using only electronic health record data in order to provide clinical decision support.

METHODS

We created three machine learning models to retrospectively analyze electronic health record data from 27,619 patient encounters. The models were trained and validated using an 80:20 split for the train and test data. Patients designated as having a central line procedure based on International Statistical Classification of Diseases and Related Health Problems 10 codes were included.

RESULTS

XGBoost was the highest performing MLA out of the three models, obtaining an AUROC of 0.762 for CLABSI risk prediction at 48 hours after the recorded time for central line placement.

CONCLUSIONS

Our results demonstrate that MLAs may be effective clinical decision support tools for assessment of CLABSI risk and should be explored further for this purpose.

Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm

Background

Strokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety.

Methods

A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke.

Results

After training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure.

Conclusion

MLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment.

Predicting pulmonary embolism among hospitalized patients with machine learning algorithms

Background

Objective

Materials and Methods

Results

Conclusions

Multi-Task Learning with Recurrent Neural Networks for ARDS Prediction using only EHR Data: Model Development and Validation Study (Preprint)

Background

Acute respiratory distress syndrome (ARDS) is a condition that is often considered to have broad and subjective diagnostic criteria and is associated with significant mortality and morbidity. Early and accurate prediction of ARDS and related conditions such as hypoxemia and sepsis could allow timely administration of therapies, leading to improved patient outcomes.

Objective

The aim of this study is to perform an exploration of how multilabel classification in the clinical setting can take advantage of the underlying dependencies between ARDS and related conditions to improve early prediction of ARDS in patients.

Methods

The electronic health record data set included 40,703 patient encounters from 7 hospitals from April 20, 2018, to March 17, 2021. A recurrent neural network (RNN) was trained using data from 5 hospitals, and external validation was conducted on data from 2 hospitals. In addition to ARDS, 12 target labels for related conditions such as sepsis, hypoxemia, and COVID-19 were used to train the model to classify a total of 13 outputs. As a comparator, XGBoost models were developed for each of the 13 target labels. Model performance was assessed using the area under the receiver operating characteristic curve. Heat maps to visualize attention scores were generated to provide interpretability to the neural networks. Finally, cluster analysis was performed to identify potential phenotypic subgroups of patients with ARDS.

Results

The single RNN model trained to classify 13 outputs outperformed the individual XGBoost models for ARDS prediction, achieving an area under the receiver operating characteristic curve of 0.842 on the external test sets. Models trained on an increasing number of tasks resulted in improved performance. Earlier prediction of ARDS nearly doubled the rate of in-hospital survival. Cluster analysis revealed distinct ARDS subgroups, some of which had similar mortality rates but different clinical presentations.

Conclusions

The RNN model presented in this paper can be used as an early warning system to stratify patients who are at risk of developing one of the multiple risk outcomes, hence providing practitioners with the means to take early action.

Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus

INTRODUCTION

Diabetic kidney disease (DKD) accounts for the majority of increased risk of mortality for patients with diabetes, and eventually manifests in approximately half of those patients diagnosed with type 2 diabetes mellitus (T2DM). Although increased screening frequency can avoid delayed diagnoses, this is not uniformly implemented. The purpose of this study was to develop and retrospectively validate a machine learning algorithm (MLA) that predicts stages of DKD within 5 years upon diagnosis of T2DM.

RESEARCH DESIGN AND METHODS

Two MLAs were trained to predict stages of DKD severity, and compared with the Centers for Disease Control and Prevention (CDC) risk score to evaluate performance. The models were validated on a hold-out test set as well as an external dataset sourced from separate facilities.

RESULTS

The MLAs outperformed the CDC risk score in both the hold-out test and external datasets. Our algorithms achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 on the hold-out set for prediction of any-stage DKD and an AUROC of over 0.82 for more severe endpoints, compared with the CDC risk score with an AUROC <0.70 on all test sets and endpoints.

CONCLUSION

This retrospective study shows that an MLA can provide timely predictions of DKD among patients with recently diagnosed T2DM.

Machine learning early prediction of respiratory syncytial virus in pediatric hospitalized patients

Respiratory syncytial virus (RSV) causes millions of infections among children in the US each year and can cause severe disease or death. Infections that are not promptly detected can cause outbreaks that put other hospitalized patients at risk. No tools besides diagnostic testing are available to rapidly and reliably predict RSV infections among hospitalized patients. We conducted a retrospective study from pediatric electronic health record (EHR) data and built a machine learning model to predict whether a patient will test positive to RSV by nucleic acid amplification test during their stay. Our model demonstrated excellent discrimination with an area under the receiver-operating curve of 0.919, a sensitivity of 0.802, and specificity of 0.876. Our model can help clinicians identify patients who may have RSV infections rapidly and cost-effectively. Successfully integrating this model into routine pediatric inpatient care may assist efforts in patient care and infection control.

Early prediction of severe acute pancreatitis using machine learning

BACKGROUND

Acute pancreatitis (AP) is one of the most common causes of gastrointestinal-related hospitalizations in the United States. Severe AP (SAP) is associated with a mortality rate of nearly 30% and is distinguished from milder forms of AP. Risk stratification to identify SAP cases needing inpatient treatment is an important aspect of AP diagnosis.

METHODS

We developed machine learning algorithms to predict which patients presenting with AP would require treatment for SAP. Three models were developed using logistic regression, neural networks, and XGBoost. Models were assessed in terms of area under the receiver operating characteristic (AUROC) and compared to the Harmless Acute Pancreatitis Score (HAPS) and Bedside Index for Severity in Acute Pancreatitis (BISAP) scores for AP risk stratification.

RESULTS

61,894 patients were used to train and test the machine learning models. With an AUROC value of 0.921, the model developed using XGBoost outperformed the logistic regression and neural network-based models. The XGBoost model also achieved a higher AUROC than both HAPS and BISAP for identifying patients who would be diagnosed with SAP.

CONCLUSIONS

Machine learning may be able to improve the accuracy of AP risk stratification methods and allow for more timely treatment and initiation of interventions.

Usability of Electronic Health records in Predicting Short-term falls: Machine learning Applications in Senior Care Facilities (Preprint)

Background

Short-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities.

Objective

The aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-month fall risk in residents from a variety of senior care facilities providing different levels of care.

Methods

This retrospective study obtained EHR data (2007-2021) from Juniper Communities’ proprietary database of 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall prediction models and the Juniper Communities’ fall risk assessment. Additional analyses were conducted to examine how changes in the input features, training data sets, and prediction windows affected the performance of these models.

Results

The Extreme Gradient Boosting model exhibited the highest performance, with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.794-0.894), specificity of 0.848, diagnostic odds ratio of 13.40, and sensitivity of 0.706, while achieving the best trade-off in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident’s number of active diseases and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features achieved higher prediction accuracy than using either group of features alone.

Conclusions

This study shows that the Extreme Gradient Boosting technique can use a large number of features from EHR data to make short-term fall predictions with a better performance than that of conventional fall risk assessments and other ML models. The integration of routinely collected EHR data, particularly vital signs, into fall prediction models may generate more accurate fall risk surveillance than models without vital signs. Our data support the use of ML models for dynamic, cost-effective, and automated fall predictions in different types of senior care facilities.

Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification

Diagnosis and appropriate intervention for myocardial infarction (MI) are time-sensitive but rely on clinical measures that can be progressive and initially inconclusive, underscoring the need for an accurate and early predictor of MI to support diagnostic and clinical management decisions. The objective of this study was to develop a machine learning algorithm (MLA) to predict MI diagnosis based on electronic health record data (EHR) readily available during Emergency Department assessment. An MLA was developed using retrospective patient data. The MLA used patient data as they became available in the first 3 h of care to predict MI diagnosis (defined by International Classification of Diseases, 10th revision code) at any time during the encounter. The MLA obtained an area under the receiver operating characteristic curve of 0.87, sensitivity of 87% and specificity of 70%, outperforming the comparator scoring systems TIMI and GRACE on all metrics. An MLA can synthesize complex EHR data to serve as a clinically relevant risk stratification tool for MI.

Personalized stratification of hospitalization risk amidst COVID-19: A machine learning approach

Objective: In the wake of COVID-19, the United States (U.S.) developed a three stage plan to outline the parameters to determine when states may reopen businesses and ease travel restrictions. The guidelines also identify subpopulations of Americans deemed to be at high risk for severe disease should they contract COVID-19. These guidelines were based on population level demographics, rather than individual-level risk factors. As such, they may misidentify individuals at high risk for severe illness, and may therefore be of limited use in decisions surrounding resource allocation to vulnerable populations. The objective of this study was to evaluate a machine learning algorithm for prediction of serious illness due to COVID-19 using inpatient data collected from electronic health records. Methods: The algorithm was trained to identify patients for whom a diagnosis of COVID-19 was likely to result in hospitalization, and compared against four U.S. policy-based criteria: age over 65; having a serious underlying health condition; age over 65 or having a serious underlying health condition; and age over 65 and having a serious underlying health condition. Results: This algorithm identified 80% of patients at risk for hospitalization due to COVID-19, versus 62% identified by government guidelines. The algorithm also achieved a high specificity of 95%, outperforming government guidelines. Conclusions: This algorithm may identify individuals likely to require hospitalization should they contract COVID-19. This information may be useful to guide vaccine distribution, anticipate hospital resource needs, and assist health care policymakers to make care decisions in a more principled manner.

Semi-supervised deep learning from time series clinical data for acute respiratory distress syndrome prediction: model development and validation study

Background

A high number of patients who are hospitalized with COVID-19 develop acute respiratory distress syndrome (ARDS).

Objective

In response to the need for clinical decision support tools to help manage the next pandemic during the early stages (ie, when limited labeled data are present), we developed machine learning algorithms that use semisupervised learning (SSL) techniques to predict ARDS development in general and COVID-19 populations based on limited labeled data.

Methods

SSL techniques were applied to 29,127 encounters with patients who were admitted to 7 US hospitals from May 1, 2019, to May 1, 2021. A recurrent neural network that used a time series of electronic health record data was applied to data that were collected when a patient’s peripheral oxygen saturation level fell below the normal range (<97%) to predict the subsequent development of ARDS during the remaining duration of patients’ hospital stay. Model performance was assessed with the area under the receiver operating characteristic curve and area under the precision recall curve of an external hold-out test set.

Results

For the whole data set, the median time between the first peripheral oxygen saturation measurement of <97% and subsequent respiratory failure was 21 hours. The area under the receiver operating characteristic curve for predicting subsequent ARDS development was 0.73 when the model was trained on a labeled data set of 6930 patients, 0.78 when the model was trained on the labeled data set that had been augmented with the unlabeled data set of 16,173 patients by using SSL techniques, and 0.84 when the model was trained on the entire training set of 23,103 labeled patients.

Conclusions

In the context of using time-series inpatient data and a careful model training design, unlabeled data can be used to improve the performance of machine learning models when labeled data for predicting ARDS development are scarce or expensive.

Predicting ventilator-associated pneumonia with machine learning

Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. VAP diagnostics derived from machine learning (ML) methods that utilize electronic health record (EHR) data have not yet been explored. The objective of this study is to compare the performance of a variety of ML models trained to predict whether VAP will be diagnosed during the patient stay.A retrospective study examined data from 6126 adult ICU encounters lasting at least 48 hours following the initiation of mechanical ventilation. The gold standard was the presence of a diagnostic code for VAP. Five different ML models were trained to predict VAP 48 hours after initiation of mechanical ventilation. Model performance was evaluated with regard to the area under the receiver operating characteristic (AUROC) curve on a 20% hold-out test set. Feature importance was measured in terms of Shapley values.The highest performing model achieved an AUROC value of 0.854. The most important features for the best-performing model were the length of time on mechanical ventilation, the presence of antibiotics, sputum test frequency, and the most recent Glasgow Coma Scale assessment.Supervised ML using patient EHR data is promising for VAP diagnosis and warrants further validation. This tool has the potential to aid the timely diagnosis of VAP.

A machine learning approach to predicting risk of myelodysplastic syndrome

BACKGROUND

Early myelodysplastic syndrome (MDS) diagnosis can allow physicians to provide early treatment, which may delay advancement of MDS and improve quality of life. However, MDS often goes unrecognized and is difficult to distinguish from other disorders. We developed a machine learning algorithm for the prediction of MDS one year prior to clinical diagnosis of the disease.

METHODS

Retrospective analysis was performed on 790,470 patients over the age of 45 seen in the United States between 2007 and 2020. A gradient boosted decision tree model (XGB) was built to predict MDS diagnosis using vital signs, lab results, and demographics from the prior two years of patient data. The XGB model was compared to logistic regression (LR) and artificial neural network (ANN) models. The models did not use blast percentage and cytogenetics information as inputs. Predictions were made one year prior to MDS diagnosis as determined by International Classification of Diseases (ICD) codes, 9th and 10th revisions. Performance was assessed with regard to area under the receiver operating characteristic curve (AUROC).

RESULTS

On a hold-out test set, the XGB model achieved an AUROC value of 0.87 for prediction of MDS one year prior to diagnosis, with a sensitivity of 0.79 and specificity of 0.80. The XGB model was compared against LR and ANN models, which achieved an AUROC of 0.838 and 0.832, respectively.

CONCLUSIONS

Machine learning may allow for early MDS diagnosis MDS and more appropriate treatment administration.

Correlation of Population SARS-CoV-2 Cycle Threshold Values to Local Disease Dynamics: Exploratory Observational Study

BACKGROUND

Despite the limitations in the use of cycle threshold (CT) values for individual patient care, population distributions of CT values may be useful indicators of local outbreaks.

OBJECTIVE

We aimed to conduct an exploratory analysis of potential correlations between the population distribution of cycle threshold (CT) values and COVID-19 dynamics, which were operationalized as percent positivity, transmission rate (Rt), and COVID-19 hospitalization count.

METHODS

In total, 148,410 specimens collected between September 15, 2020, and January 11, 2021, from the greater El Paso area were processed in the Dascena COVID-19 Laboratory. The daily median CT value, daily Rt, daily count of COVID-19 hospitalizations, daily change in percent positivity, and rolling averages of these features were plotted over time. Two-way scatterplots and linear regression were used to evaluate possible associations between daily median CT values and outbreak measures. Cross-correlation plots were used to determine whether a time delay existed between changes in daily median CT values and measures of community disease dynamics.

RESULTS

Daily median CT values negatively correlated with the daily Rt values (P<.001), the daily COVID-19 hospitalization counts (with a 33-day time delay; P<.001), and the daily changes in percent positivity among testing samples (P<.001). Despite visual trends suggesting time delays in the plots for median CT values and outbreak measures, a statistically significant delay was only detected between changes in median CT values and COVID-19 hospitalization counts (P<.001).

CONCLUSIONS

This study adds to the literature by analyzing samples collected from an entire geographical area and contextualizing the results with other research investigating population CT values.

Low-Fat, High-Fiber Diet Reduces Markers of Inflammation and Dysbiosis and Improves Quality of Life in Patients With Ulcerative Colitis

BACKGROUND & AIMS

A high-fat diet has been associated with an increased risk of ulcerative colitis (UC). We studied the effects of a low-fat, high-fiber diet (LFD) vs an improved standard American diet (iSAD, included higher quantities of fruits, vegetables, and fiber than a typical SAD). We collected data on quality of life, markers of inflammation, and fecal markers of intestinal dysbiosis in patients with UC.

METHODS

We analyzed data from a parallel-group, cross-over study of 17 patients with UC in remission or with mild disease (with a flare within the past 18 mo), from February 25, 2015, through September 11, 2018. Participants were assigned randomly to 2 groups and received a LFD (10% of calories from fat) or an iSAD (35%-40% of calories from fat) for the first 4-week period, followed by a 2-week washout period, and then switched to the other diet for 4 weeks. All diets were catered and delivered to patients' homes, and each participant served as her or his own control. Serum and stool samples were collected at baseline and week 4 of each diet and analyzed for markers of inflammation. We performed 16s ribosomal RNA sequencing and untargeted and targeted metabolomic analyses on stool samples. The primary outcome was quality of life, which was measured by the short inflammatory bowel disease (IBD) questionnaire at baseline and week 4 of the diets. Secondary outcomes included changes in the Short-Form 36 health survey, partial Mayo score, markers of inflammation, microbiome and metabolome analysis, and adherence to the diet.

RESULTS

Participants' baseline diets were unhealthier than either study diet. All patients remained in remission throughout the study period. Compared with baseline, the iSAD and LFD each increased quality of life, based on the short IBD questionnaire and Short-Form 36 health survey scores (baseline short IBD questionnaire score, 4.98; iSAD, 5.55; LFD, 5.77; baseline vs iSAD, P = .02; baseline vs LFD, P = .001). Serum amyloid A decreased significantly from 7.99 mg/L at baseline to 4.50 mg/L after LFD (P = .02), but did not decrease significantly compared with iSAD (7.20 mg/L; iSAD vs LFD, P = .07). The serum level of C-reactive protein decreased numerically from 3.23 mg/L at baseline to 2.51 mg/L after LFD (P = .07). The relative abundance of Actinobacteria in fecal samples decreased from 13.69% at baseline to 7.82% after LFD (P = .017), whereas the relative abundance of Bacteroidetes increased from 14.6% at baseline to 24.02% on LFD (P = .015). The relative abundance of Faecalibacterium prausnitzii was higher after 4 weeks on the LFD (7.20%) compared with iSAD (5.37%; P = .04). Fecal levels of acetate (an anti-inflammatory metabolite) increased from a relative abundance of 40.37 at baseline to 42.52 on the iSAD and 53.98 on the LFD (baseline vs LFD, P = .05; iSAD vs LFD, P = .09). The fecal level of tryptophan decreased from a relative abundance of 1.33 at baseline to 1.08 on the iSAD (P = .43), but increased to a relative abundance of 2.27 on the LFD (baseline vs LFD, P = .04; iSAD vs LFD, P = .08); fecal levels of lauric acid decreased after LFD (baseline, 203.4; iSAD, 381.4; LFD, 29.91; baseline vs LFD, P = .04; iSAD vs LFD, P = .02).

CONCLUSIONS

In a cross-over study of patients with UC in remission, we found that a catered LFD or iSAD were each well tolerated and increased quality of life. However, the LFD decreased markers of inflammation and reduced intestinal dysbiosis in fecal samples. Dietary interventions therefore might benefit patients with UC in remission. ClinicalTrials.gov no: NCT04147598.

Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data

Background

Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.

Results

When trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO₂, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG.

Conclusions

A model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights into individual patient’s risk profiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00255-w.

Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids

Purpose: Coronavirus disease–2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time.

Methods: Gradient-boosted decision-tree models used for predicting treatment benefit were trained and tested on data from electronic health records dated between December 18, 2019, and October 18, 2020, from adult patients (age ≥18 years) with COVID-19 in 10 US hospitals. Models were evaluated for performance in identifying patients with longer survival times when treated with a corticosteroid versus remdesivir. Fine and Gray proportional-hazards models were used for identifying significant findings in treated and nontreated patients, in a subset of patients who received supplemental oxygen, and in patients identified by the algorithm. Inverse probability-of-treatment weights were used to adjust for confounding. Models were trained and tested separately for each treatment.

Findings: Data from 2364 patients were included, with men comprising slightly more than 50% of the sample; 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. After adjustment for confounding, neither corticosteroids nor remdesivir use was associated with increased survival time in the overall population or in the subpopulation that received supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04).

Implications: Machine-learning methods have the capacity to identify hospitalized patients with COVID-19 in whom treatment with a corticosteroid or remdesivir is associated with an increase in survival time. These methods may help to improve patient outcomes and allocate resources during the COVID-19 crisis.

COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients

BACKGROUND

The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments.

METHODS

Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined.

RESULTS

Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events.

CONCLUSION

Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

Identification of Quinone Degradation as a Triggering Event for Intense Pulsed Light-Elicited Metabolic Changes in Escherichia coli by Metabolomic Fingerprinting

Intense pulsed light (IPL) is becoming a new technical platform for disinfecting food against pathogenic bacteria. Metabolic changes are deemed to occur in bacteria as either the causes or the consequences of IPL-elicited bactericidal and bacteriostatic effects. However, little is known about the influences of IPL on bacterial metabolome. In this study, the IPL treatment was applied to E. coli K-12 for 0–20 s, leading to time- and dose-dependent reductions in colony-forming units (CFU) and morphological changes. Both membrane lipids and cytoplasmic metabolites of the control and IPL-treated E. coli were examined by the liquid chromatography-mass spectrometry (LC-MS)-based metabolomic fingerprinting. The results from multivariate modeling and marker identification indicate that the metabolites in electron transport chain (ETC), redox response, glycolysis, amino acid, and nucleotide metabolism were selectively affected by the IPL treatments. The time courses and scales of these metabolic changes, together with the biochemical connections among them, revealed a cascade of events that might be initiated by the degradation of quinone electron carriers and then followed by oxidative stress, disruption of intermediary metabolism, nucleotide degradation, and morphological changes. Therefore, the degradations of membrane quinones, especially the rapid depletion of menaquinone-8 (MK-8), can be considered as a triggering event in the IPL-elicited metabolic changes in E. coli.

[Feasibility of near-infrared fluorescence imaging in assisting with the determination of the resection range of radiation intestinal injury]

Objective: To investigate the feasibility of near-infrared fluorescence imaging (NIRFI) to assist in determining the resection range of radiation intestinal injury (RII). Methods: A descriptive cohort study was conducted. Clinical data of 10 RII patients who presented intestinal obstruction and received operation with more than 100 cm of small intestine had been resected atGeneral Department of Jinling Hospital from October 2014 to January 2015 were retrospectively analyzed. The Novadaq SPY Intra-operative Imaging System was used in capturing and viewing fluorescent images. Firstly, the dense adhesion was mobilized and the obstructive intestine was fully freed under laparoscopy, then entering into abdomen from the corresponding incision. The surgeon determined the resection range according to the color of the intestinal serous layer of the diseased intestinal wall, the thickness of the intestinal wall, and the degree of swelling of the mesentery. Afterwards, intra-operative NIRFI was performed by intravenous injection of 2 ml indocyanine green (ICG) and the imaging results of the diseased intestinal arteriovenous phase were observed and recorded. The evaluation criteria for the final resection range were mainly based on the changes in mesenteric arterial phase imaging. In RII lesions, mesenteric vessels in mesenteric artery phase were disordered, and the comb-like distribution of normal mesenteric vessels completely disappeared. Only the clouded appearance in the intestinal wall was observed. Imaging results of the diseased intestinal tissue during the development phase and mesenteric vein phase were not significantly different from normal intestinal tissue. Intraoperative and postoperative conditions under NIRFI-assisted positioning, including the resection range, anastomosis site, operation-related complications, hospitalization time and cost were recorded. Data of abdominal contrast-enhanced CT and gastrointestinal angiography during 5 years of follow-up were collected to evaluate whether there was anastomotic stenosis or insufficient resection of diseased bowel. Results: Based on the imaging of mesenteric arterial phase of NIRFI, the median resection length of the small intestine was 185 (120-260) cm. After NIRFI imaging, only local lesion of ileum was excised in 6 patients, and jejunum-ileum anastomosis was performed to preserve ileocecal flap. No serious complications such as anastomotic leakage and anastomotic hemorrhage, or chronic intestinal failure such as short bowel syndrome occurred. The median hospitalization time was 32 (22-51) days, and the median hospitalization cost was 142 000 (90 000-175 000) RMB. The hospitalization time and cost were mainly used for the enteral and parenteral nutrition support treatment during the perioperative period. All the patients had normal oral diet and/or oral enteral nutrient. After 5 years of follow-up, no recurrence was found. Abdominal contrast-enhanced CT and gastrointestinal angiography showed no thickening of the intestinal wall or stenosis of the lumen. Conclusion: Mesenteric arterial phase imagingof NIRFI can help surgeons to determine the site and range of resection of RII lesions.

A Machine Learning Approach for Predicting Early Phase Postoperative Hypertension in Patients Undergoing Carotid Endarterectomy

BACKGROUND

This study aimed to establish and validate a machine learning-based model for the prediction of early phase postoperative hypertension (EPOH) requiring the administration of intravenous vasodilators after carotid endarterectomy (CEA).

METHODS

Perioperative data from consecutive CEA procedures performed from January 2013 to August 2019 were retrospectively collected. EPOH was defined in post-CEA patients as hypertension involving a systolic blood pressure above 160 mm Hg and requiring the administration of any intravenous vasodilator medications in the first 24 hr after a return to the vascular ward. Gradient boosted regression trees were used to construct the predictive model, and the featured importance scores were generated by using each feature's contribution to each tree in the model. To evaluate the model performance, the area under the receiver operating characteristic curve was used as the main metric. Four-fold stratified cross-validation was performed on the data set, and the average performance of the 4 folds was reported as the final model performance.

RESULTS

A total of 406 CEA operations were performed under general anesthesia. Fifty-three patients (13.1%) met the definition of EPOH. There was no significant difference in the percentage of postoperative stroke/death between patients with and without EPOH during the hospital stay. Patients with EPOH exhibited a higher incidence of postoperative cerebral hyperperfusion syndrome (7.5% vs. 0, P < 0.001), as well as a higher incidence of cerebral hemorrhage (3.8% vs. 0, P < 0.001). The gradient boosted regression trees prediction model achieved an average AUC of 0.77 (95% CI 0.62 to 0.92). When the sensitivity was fixed near 0.90, the model achieved an average specificity of 0.52 (95% CI 0.28 to 0.75).

CONCLUSIONS

We have built the first-ever machine learning-based prediction model for EPOH after CEA. The validation result from our single-center database was very promising. This novel prediction model has the potential to help vascular surgeons identify high-risk patients and reduce related complications more efficiently.

Characterization of Urinary N-acetyltaurine as a Biomarker of Serum Acetate in Experimental Animal Models of Hyperacetatemia

Objectives

Acetate is an intermediate metabolite originated from multiple important metabolic pathways. Even though blood acetate level has been associated with many health events, it is not commonly monitored in clinical practice, partially due to the needs for invasive blood collection and the challenges in acetate analysis. N-acetyltaurine (NAT) was earlier identified as a novel urinary metabolite of ethanol from the reaction between taurine and ethanol-derived acetate. As a direct metabolite of acetate, NAT has the potential to function as a urinary biomarker that reflects the acetate level inside the body. To test this hypothesis, this study examined the correlations between serum acetate level and urinary NAT level in three experimental animal models of hyperacetatemia.

Methods

Glycerol-triacetate (GTA) dosing, ethanol dosing, and streptozotocin (STZ)-induced Type 1 diabetes were used to achieve hyperacetatemia in mice. In GTA model, serum samples were collected at 2 h and urine samples were collected for 24 h after dosing the mice with 5.8 g/kg GTA. In ethanol dosing, serum were collected at 2 h after intraperitoneal injection of 4 g/kg ethanol, while 24 h urine samples were collected before and after 14-day feeding of modified semi-solid diet containing 2.2%–6.7% (v/v) ethanol. In the Type I diabetes model, urine and serum samples were collected before and 5 days after the intraperitoneal injection of 180 mg/kg STZ. The concentrations of NAT and creatinine in urine, as well as acetate in serum, were measured using their respective liquid chromatography-mass spectrometry (LC-MS) methods.

Results

The occurrence of hyperacetatemia in three animal models was confirmed by the clear elevation of serum acetate concentrations. The concentrations of urinary NAT were also dramatically increased in three animal models, suggesting the correlations between serum acetate and urinary NAT.

Conclusions

Urinary NAT is an effective metabolic marker of hyperacetatemia in three experimental models. The results warrant further investigation on its application in other pathophysiological conditions and in humans.

Funding Sources

This research was partially supported by the Agricultural Experiment Station project MIN-18–125 (C. C.) from the United States Department of Agriculture (USDA).

Investigating ultrasound-induced acoustic softening in aluminum and its alloys

Ultrasonic vibration has been observed to lower the flow stress necessary to initiate plastic deformation, a phenomenon known as "acoustic softening". This unique effect of ultrasound has been extensively applied in welding, machining, forming of metals, and ultrasonic additive manufacturing to lower the yield stress necessary to initiate plastic deformation, it nevertheless lacks fundamental investigation. Some prior studies showed experimental errors due to the design of experimental setups and the associated testing methods that have been introduced, leading to questions about their observations and conclusions. Therefore, an experimental setup described in this paper is designed to minimize the constraints identified from the setups in prior studies. Three types of aluminum are studied: Al 1100-O a commercially pure aluminum, Al 6061-O an aluminum alloy without precipitate strengthening, and Al 6061-T6 a precipitate-strengthened aluminum alloy. The acoustic softening and residual effect are compared based on the similarities and differences in microstructures of the three types of aluminum. In both acoustic softening and residual effect, linear relations are obtained between stress change and ultrasound intensities. The slope defined by the linear relations, i.e. the acoustic softening factor, depends on the microstructure of the specific material. The underlying mechanism of acoustic softening is associated with the activation of dislocations by ultrasonic energy and subsequently their interactions with other dislocations and precipitates, whereas the residual effects are attributed to the permanent changes in dislocation density due to dislocation annihilation, dynamic annealing, and dislocation-precipitate interaction.