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.

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.

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.

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.

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.

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.

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.

Using electronic health record collected clinical variables to predict medical intensive care unit mortality.

BACKGROUND: Clinical decision support systems are used to help predict patient stability and mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians with patient management and in allocating finite resources. However, systems currently in common use have limited predictive value in the clinical setting. The increasing availability of Electronic Health Records (EHR) provides an opportunity to use medical information for more accurate patient stability and mortality prediction in the ICU. OBJECTIVE: Develop and evaluate an algorithm which more accurately predicts patient mortality in the ICU, using the correlations between widely available clinical variables from the EHR. METHODS: We have developed an algorithm, AutoTriage, which uses eight common clinical variables from the EHR to assign patient mortality risk scores. Each clinical variable produces a subscore, and combinations of two or three discretized clinical variables also produce subscores. A combination of weighted subscores produces the overall score. We validated the performance of this algorithm in a retrospective study on the MIMIC III medical ICU dataset. RESULTS: AutoTriage 12 h mortality prediction yields an Area Under Receiver Operating Characteristic value of 0.88 (95% confidence interval 0.86 to 0.88). At a sensitivity of 80%, AutoTriage maintains a specificity of 81% with a diagnostic odds ratio of 16.26. CONCLUSIONS: Through the multidimensional analysis of the correlations between eight common clinical variables, AutoTriage provides an improvement in the specificity and sensitivity of patient mortality prediction over existing prediction methods.

High-performance detection and early prediction of septic shock for alcohol-use disorder patients.

BACKGROUND: The presence of Alcohol Use Disorder (AUD) complicates the medical conditions of patients and increases the difficulty of detecting and predicting the onset of septic shock for patients in the ICU. METHODS: We have developed a high-performance sepsis prediction algorithm, InSight, which outperforms existing methods for AUD patient populations. InSight analyses a combination of singlets, doublets, and triplets of clinical measurements over time to generate a septic shock risk score. AUD patients obtained from the MIMIC III database were used in this retrospective study to train InSight and compare performance with the Modified Early Warning Score (MEWS), the Simplified Acute Physiology Score (SAPS II), and the Systemic Inflammatory Response Syndrome (SIRS) for septic shock prediction and detection. RESULTS: From 4-fold cross validation, InSight performs particularly well on diagnostic odds ratio and demonstrates a relatively high Area Under the Receiver Operating Characteristic (AUROC) metric. Four hours prior to onset, InSight had an average AUROC of 0.815, and at the time of onset, InSight had an average AUROC value of 0.965. When applied to patient populations where AUD may complicate prediction methods of sepsis, InSight outperforms existing diagnostic tools. CONCLUSIONS: Analysis of the higher order correlations and trends between relevant clinical measurements using the InSight algorithm leads to more accurate detection and prediction of septic shock, even in cases where diagnosis may be confounded by AUD.