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Background High flow oxygen therapy (HFO) is a widely used intervention for pulmonary complications. Amid the coronavirus infectious disease 2019 (COVID-19) pandemic, HFO became a popular alternative to conventional oxygen supplementation therapies. Risk stratification tools have been repurposed –and new ones developed– to estimate outcome risks among COVID-19 patients. This study aims to provide a simple risk stratification system to predict invasive mechanical ventilation (IMV) or death among COVID-19 inpatients on HFO. Methods Among 529 adult inpatients with COVID-19 pneumonia, we selected unadjusted clinical risk factors for developing the composite endpoint of IMV or death. The risk for the primary outcome by each category was estimated using a Cox proportional hazards model. Bootstrapping was used to validate the results. Results Age above 62, eGFR under 60 ml/min, room air SpO2 ≤89 % upon admission, history of hypertension, history of diabetes, and any comorbidity (cancer, cardiovascular disease, COPD/ asthma, hypothyroidism, or autoimmune disease) were considered for the score. Each of the six criteria scored 1 point. The score was further simplified into 4 categories: 1) 0 criteria, 2) 1 criterion, 3) 2-3 criteria, and 4) ≥4 criteria. Taking the first category as the reference, risk estimates for the primary endpoint were HR; 2.94 [1.67 – 5.26], 4.08 [2.63 – 7.05], and 6.63 [3.74 – 11.77], respectively. In ROC analysis, the AUC for the model was 0.72. Conclusions Our score uses simple criteria to estimate the risk for IMV or death among COVID-19 inpatients with HFO. Higher category reflects consistent increases in risk for the endpoint.

Dioxin-like molecules have been associated with endocrine disruption and liver disease. To better understand aryl hydrocarbon receptor (AHR) biology, metabolic phenotyping and liver proteomics were performed in mice following ligand-activation or whole-body genetic ablation of this receptor. Male wild type (WT) and Ahr–/– mice (Taconic) were fed a control diet and exposed to 3,3′,4,4′,5-pentachlorobiphenyl (PCB126) (61 nmol/kg by gavage) or vehicle for two weeks. PCB126 increased expression of canonical AHR targets (Cyp1a1 and Cyp1a2) in WT but not Ahr–/–. Knockouts had increased adiposity with decreased glucose tolerance; smaller livers with increased steatosis and perilipin-2; and paradoxically decreased blood lipids. PCB126 was associated with increased hepatic triglycerides in Ahr–/–. The liver proteome was impacted more so by Ahr–/– genotype than ligand-activation, but top gene ontology (GO) processes were similar. The PCB126-associated liver proteome was Ahr-dependent. Ahr principally regulated liver metabolism (e.g., lipids, xenobiotics, organic acids) and bioenergetics, but it also impacted liver endocrine response (e.g., the insulin receptor) and function, including the production of steroids, hepatokines, and pheromone binding proteins. These effects could have been indirectly mediated by interacting transcription factors or microRNAs. The biologic roles of the AHR and its ligands warrant more research in liver metabolic health and disease., Graphical abstract The aryl hydrocarbon receptor regulated liver metabolism and endocrine function impacting systemic energy homeostasis and body composition. This regulation appeared to involve both direct and indirect mechanisms.Image 1

Background High scores in Medically Necessary, Time-sensitive (MeNTS) scoring system, used for elective surgical prioritization during Coronavirus Disease 2019 pandemic, are assumed to be associated with worse outcomes. We aimed to evaluate the MeNTS scoring system in patients undergoing elective surgery during restricted capacity of our institution with or without moderate or severe postoperative complications. Study Design In this prospective observational study, MeNTS scores of patients undergoing elective operations during May-June 2020 were calculated. Postoperative complication severity (classified as Group Clavien-Dindo, Medically Necessary Time-Sensitive (MeNTS) score was proposed to objectively prioritize elective surgery during COVID-19. Whether higher MeNTS score is associated with worse outcomes was not examined. Despite higher MeNTS score in patients with moderate and severe postoperative complication, it had low prediction capability for these complications.

More than a year after the COVID-19 pandemic was declared, the need still exists for accurate, rapid, inexpensive and non-invasive diagnostic methods that yield high specificity and sensitivity towards the current and newly emerging SARS-CoV-2 strains. Compared to the nasopharyngeal swabs, several studies have established saliva as a more amenable specimen type for early detection of SARS-CoV-2. Considering the limitations and high demand for COVID-19 testing, we employed MALDI-ToF mass spectrometry in the analysis of 60 gargle samples from human donors and compared the resultant spectra against COVID-19 status. Several standards, including isolated human serum immunoglobulins, and controls, such as pre-COVID-19 saliva and heat inactivated SARS-CoV-2 virus, were simultaneously analyzed to provide a relative view of the saliva and viral proteome as they would appear in this workflow. Five potential biomarker peaks were established that demonstrated high concordance with COVID-19 positive individuals. Overall, the agreement of these results with RT-qPCR testing on nasopharyngeal swabs was ≥90% for the studied cohort, which consisted of young and largely asymptomatic student athletes. From a clinical standpoint, the results from this pilot study suggest that MALDI-ToF could be used to develop a relatively rapid and inexpensive COVID-19 assay.

In recent years, increasing evidence indicates the significant roles of circRNAs in carcinogenesis. However, their roles in lung cancer remain largely unclear. We profiled the circRNA expression in 10 paired non-small cell lung cancer (NSCLC) and adjacent non-cancer tissues using high-throughput sequencing. A total of 183 up-regulated and 428 down-regulated circRNAs were identified in the NSCLC tissues (fold change ≥ 2, P < 0.05). Circ_0089823, an up-regulated circRNA (5.4-fold, P = 0.0017), was further investigated through loss-of-function and gain-of-function. The circ_0089823 level in NSCLC samples was related to the gender, tumor size, pathological type, TNM stage and smoking history. Knockdown of circ_0089823 suppressed cell proliferation, induced cell cycle arrest and apoptosis of NSCLC cells in vitro. Additionally, circ_0089823-silenced xenografts grew much slowly. On the contrary, its over-expression promoted the malignant behaviors of NSCLC cells. Furthermore, SOX4, a tumor-promoting transcription factor, was highly expressed in NSCLC tissues and positively regulated by circ_0089823. Bioinformatic analysis revealed several potential binding sites for miR-507, miR-557, miR-579-3p and miR-1287-5p in circ_0089823 and SOX4 3′-untranslated region, which was later confirmed by luciferase reporter assay. Interestingly, silencing SOX4 countervailed the effects of circ_0089823 over-expression on NSCLC cells. Here, we revealed that circ_0089823 might act as a sponge of microRNAs targeting SOX4, thus increasing the expression of SOX4, thereby reinforcing the malignant behaviors of NSCLC cells. This study indicates that circ_0089823 has the potential to become a candidate target for NSCLC treatment.

Lipid-based formulations (LBFs) have demonstrated a great potential in enhancing the oral absorption of poorly water-soluble drugs. However, construction of in vitro and in vivo correlations (IVIVCs) for LBFs is quite challenging, owing to a complex in vivo processing of these formulations. In this paper, we start with a brief introduction on the gastrointestinal digestion of lipid/LBFs and its relation to enhanced oral drug absorption; based on the concept of IVIVCs, the current status of in vitro models to establish IVIVCs for LBFs is reviewed, while future perspectives in this field are discussed. In vitro tests, which facilitate the understanding and prediction of the in vivo performance of solid dosage forms, frequently fail to mimic the in vivo processing of LBFs, leading to inconsistent results. In vitro digestion models, which more closely simulate gastrointestinal physiology, are a more promising option. Despite some successes in IVIVC modeling, the accuracy and consistency of these models are yet to be validated, particularly for human data. A reliable IVIVC model can not only reduce the risk, time, and cost of formulation development but can also contribute to the formulation design and optimization, thus promoting the clinical translation of LBFs., Graphical abstract In vitro and in vivo correlations (IVIVCs) are powerful tools for preparation development. Constructing IVIVCs for lipid-based formulations is rather challenging. This article reviews the current status and future perspectives in this field.Image 1

Background The novel severe acute respiratory syndrome coronavirus-2 virus, which has led to the global coronavirus disease-2019 (COVID-19) pandemic is known to adversely affect the cardiovascular system through multiple mechanisms. In this international, multicenter study conducted by the World Alliance Societies of Echocardiography, we aim to determine the clinical and echocardiographic phenotype of acute cardiac disease in COVID-19 patients, to explore phenotypic differences in different geographic regions across the world, and to identify parameters associated with in-hospital mortality. Methods We studied 870 patients with acute COVID-19 infection from 13 medical centers in four world regions (Asia, Europe, United States, Latin America) who had undergone transthoracic echocardiograms. Clinical and laboratory data were collected, including patient outcomes. Anonymized echocardiograms were analyzed with automated, machine learning–derived algorithms to calculate left ventricular (LV) volumes, ejection fraction, and LV longitudinal strain (LS). Right-sided echocardiographic parameters that were measured included right ventricular (RV) LS, RV free-wall strain (FWS), and RV basal diameter. Multivariate regression analysis was performed to identify clinical and echocardiographic parameters associated with in-hospital mortality. Results Significant regional differences were noted in terms of patient comorbidities, severity of illness, clinical biomarkers, and LV and RV echocardiographic metrics. Overall in-hospital mortality was 21.6%. Parameters associated with mortality in a multivariate analysis were age (odds ratio [OR] = 1.12 [1.05, 1.22], P = .003), previous lung disease (OR = 7.32 [1.56, 42.2], P = .015), LVLS (OR = 1.18 [1.05, 1.36], P = .012), lactic dehydrogenase (OR = 6.17 [1.74, 28.7], P = .009), and RVFWS (OR = 1.14 [1.04, 1.26], P = .007). Conclusions Left ventricular dysfunction is noted in approximately 20% and RV dysfunction in approximately 30% of patients with acute COVID-19 illness and portend a poor prognosis. Age at presentation, previous lung disease, lactic dehydrogenase, LVLS, and RVFWS were independently associated with in-hospital mortality. Regional differences in cardiac phenotype highlight the significant differences in patient acuity as well as echocardiographic utilization in different parts of the world.

Post-traumatic stress disorder (PTSD) is a psychiatric disease that seriously affects brain function. Currently, selective serotonin reuptake inhibitors (SSRIs) are used to treat PTSD clinically but have decreased efficiency and increased side effects. In this study, nasal cannabidiol inclusion complex temperature-sensitive hydrogels (CBD TSGs) were prepared and evaluated to treat PTSD. Mice model of PTSD was established with conditional fear box. CBD TSGs could significantly improve the spontaneous behavior, exploratory spirit and alleviate tension in open field box, relieve anxiety and tension in elevated plus maze, and reduce the freezing time. Hematoxylin and eosin and c-FOS immunohistochemistry slides showed that the main injured brain areas in PTSD were the prefrontal cortex, amygdala, and hippocampus CA1. CBD TSGs could reduce the level of tumor necrosis factor-α caused by PTSD. Western blot analysis showed that CBD TSGs increased the expression of the 5-HT1A receptor. Intranasal administration of CBD TSGs was more efficient and had more obvious brain targeting effects than oral administration, as evidenced by the pharmacokinetics and brain tissue distribution of CBD TSGs. Overall, nasal CBD TSGs are safe and effective and have controlled release. There are a novel promising option for the clinical treatment of PTSD., Graphical abstract Nasal delivery of cannabidiol inclusion complex temperature-sensitive hydrogels could effectively target to brains for the treatment of mice with post-traumatic stress disorder (PTSD), which is a novel promising option for the clinical treatment of PTSD.Image 1

The medicinal uses of Calotropis procera are diverse, yet some of them are based on effects that still lack scientific support. Control of diabetes is one of them. Recently, latex proteins from C. procera latex (LP) have been shown to promote in vivo glycemic control by the inhibition of hepatic glucose production via AMP-activated protein kinase (AMPK). Glycemic control has been attributed to an isolated fraction of LP (CpPII), which is composed of cysteine peptidases (95%) and osmotin (5%) isoforms. Those proteins are extensively characterized in terms of chemistry, biochemistry and structural aspects. Furthermore, we evaluated some aspects of the mitochondrial function and cellular mechanisms involved in CpPII activity. The effect of CpPII on glycemic control was evaluated in fasting mice by glycemic curve and glucose and pyruvate tolerance tests. HepG2 cells was treated with CpPII, and cell viability, oxygen consumption, PPAR activity, production of lactate and reactive oxygen species, mitochondrial density and protein and gene expression were analyzed. CpPII reduced fasting glycemia, improved glucose tolerance and inhibited hepatic glucose production in control animals. Additionally, CpPII increased the consumption of ATP-linked oxygen and mitochondrial uncoupling, reduced lactate concentration, increased protein expression of mitochondrial complexes I, III and V, and activity of peroxisome-proliferator-responsive elements (PPRE), reduced the presence of reactive oxygen species (ROS) and increased mitochondrial density in HepG2 cells by activation of AMPK/PPAR. Our findings strongly support the medicinal use of the plant and suggest that CpPII is a potential therapy for prevention and/or treatment of type-2 diabetes. A common epitope sequence shared among the proteases and osmotin is possibly the responsible for the beneficial effects of CpPII.

Graphical abstract, The current 2019 novel coronavirus disease (COVID-19), an emerging infectious disease, is undoubtedly the most challenging pandemic in the 21st century. A total of 92,977,768 confirmed cases of COVID-19 and 1,991,289 deaths were reported globally up to January 14, 2021. COVID-19 also affects people’s mental health and quality of life. At present, there is no effective therapeutic strategy for the management of this disease. Therefore, in the absence of a specific vaccine or curative treatment, it is an urgent need to identify safe, effective and globally available drugs for reducing COVID-19 morbidity and fatalities. In this review, we focus on selective serotonin reuptake inhibitors (SSRIs: a class of antidepressant drugs with widespread availability and an optimal tolerability profile) that can potentially be repurposed for COVID-19 and are currently being tested in clinical trials. We also summarize the existing literature on what is known about the link between serotonin (5-HT) and the immune system. From the evidence reviewed here, we propose fluoxetine as an adjuvant therapeutic agent for COVID-19 based on its known immunomodulatory, anti-inflammatory and antiviral properties. Fluoxetine may potentially reduce pro-inflammatory chemokine/cytokines levels (such as CCL-2, IL-6, and TNF-α) in COVID-19 patients. Furthermore, fluoxetine may help to attenuate neurological complications of COVID-19.

Obesity and its associated complications are highly related to a current public health crisis around the world. A growing body of evidence has indicated that G-protein coupled bile acid (BA) receptor TGR5 (also known as Gpbar-1) is a potential drug target to treat obesity and associated metabolic disorders. We have identified notoginsenoside Ft1 (Ft1) from Panax notoginseng as an agonist of TGR5 in vitro. However, the pharmacological effects of Ft1 on diet-induced obese (DIO) mice and the underlying mechanisms are still elusive. Here we show that Ft1 (100 mg/100 diet) increased adipose lipolysis, promoted fat browning in inguinal adipose tissue and induced glucagon-like peptide-1 (GLP-1) secretion in the ileum of wild type but not Tgr5−/− obese mice. In addition, Ft1 elevated serum free and taurine-conjugated bile acids (BAs) by antagonizing Fxr transcriptional activities in the ileum to activate Tgr5 in the adipose tissues. The metabolic benefits of Ft1 were abolished in Cyp27a1−/− mice which have much lower BA levels. These results identify Ft1 as a single compound with opposite activities on two key BA receptors to alleviate high fat diet-induced obesity and insulin resistance in mice., Graphical abstract Notoginsenoside Ft1 activates Tgr5, but antagonizes Fxr in ileum to enhance bile acid synthesis, thereby imparts metabolic improvement in high fat diet induced obese mice.Image 1

The blood-brain barrier (BBB) is a major anatomical and physiological barrier limiting the passage of drugs into brain. Central nervous system tumors can impair the BBB by changing the tumor microenvironment leading to the formation of a leaky barrier, known as the blood-tumor barrier (BTB). Despite the change in integrity, the BTB remains effective in preventing delivery of chemotherapy into brain tumors. Focused ultrasound is a unique noninvasive technique that can transiently disrupt the BBB and increase accumulation of drugs within targeted areas of the brain. Herein, we summarize the current understanding of different types of targeted ultrasound mediated BBB/BTB disruption techniques. We also discuss influence of the tumor microenvironment on BBB opening, as well as the role of immunological response following disruption. Lastly, we highlight the gaps between evaluation of the parameters governing opening of the BBB/BTB. A deeper understanding of physical opening of the BBB/BTB and the biological effects following disruption can potentially enhance treatment strategies for patients with brain tumors.

Graphical abstract, Highlights • Quantitative ultrasound (QUS) delta-radiomics to predict recurrence. • Machine learning model can predict recurrence with accuracy >80%. • Classification performances improve when features are taken from weeks 1 and 4., Purpose This study investigated the use of quantitative ultrasound (QUS) obtained during radical radiotherapy (RT) as a radiomics biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC). Methods Fifty-one patients with HNSCC were treated with RT (70 Gy/33 fractions) (±concurrent chemotherapy) were included. QUS Data acquisition involved scanning an index neck node with a clinical ultrasound device. Radiofrequency data were collected before starting RT, and after weeks 1, and 4. From this data, 31 spectral and related-texture features were determined for each time and delta (difference) features were computed. Patients were categorized into two groups based on clinical outcomes (recurrence or non-recurrence). Three machine learning classifiers were used for the development of a radiomics model. Features were selected using a forward sequential selection method and validated using leave-one-out cross-validation. Results The median follow up for the entire group was 38 months (range 7–64 months). The disease sites involved neck masses in patients with oropharynx (39), larynx (5), carcinoma unknown primary (5), and hypopharynx carcinoma (2). Concurrent chemotherapy and cetuximab were used in 41 and 1 patient(s), respectively. Recurrence was seen in 17 patients. At week 1 of RT, the support vector machine classifier resulted in the best performance, with accuracy and area under the curve (AUC) of 80% and 0.75, respectively. The accuracy and AUC improved to 82% and 0.81, respectively, at week 4 of treatment. Conclusion QUS Delta-radiomics can predict higher risk of recurrence with reasonable accuracy in HNSCC. Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.

Highlights • DIBH results in cardiac sparing in left sided breast cancer radiotherapy. • Heart volume in field and maximum heart distance are predictors of cardiac sparing. • Thresholds of these anatomical predictors can help to select patients for DIBH., Introduction The risk of radiotherapy-associated cardiovascular disease has been a concern for decades in breast cancer survivors. The objective of our study is to evaluate the dosimetric benefit of Deep Inspiratory Breath-hold technique (DIBH) on organs-at-risk (OAR) sparing in left-sided breast cancer radiotherapy and to find out pre-treatment predictors of cardiac doses for guiding patient selection for DIBH. Material and methods Pre-radiotherapy planning CT scans were done in Free Breathing (FB) and in DIBH [using Active Breathing Coordinator system (ABC™)] in 31 left sided breast cancer patients. 3DCRT plans were generated for both scans. Comparison of anatomical and dosimetric variables were done using paired t test and correlation was evaluated using Pearson correlation. Linear regression was used to get independent predictors of cardiac sparing and Receiver Operating Characteristic (ROC) curve analysis was done to find out the specific threshold of the predictors. Results There was a 39.15% reduction in mean heart dose in DIBH compared to FB (2.4 Gy vs 4.01 Gy) (p

Graphical abstract, Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities.

Highlights • Non-severe COVID-19 patients have abnormal laboratory investigations. • Patients with prolonged pretreatment APTT have a longer isolation length. • Patients with elevated eosinophils after treatment have a shorter isolation length. • A nomogram could help to predict isolation probability at 11-, 16- and 21-day., Background Majority coronavirus disease 2019 (COVID-19) patients are classified as mild and moderate (non-severe) diseases. We aim to develop a model to predict isolation length for non-severe patients. Methods Among 188 non-severe patients, 96 patients were enrolled as training cohort to identify factors associated with isolation length via Cox regression model and develop a nomogram. Other 92 patients formed as validation cohort to validate nomogram. Concordance index (C-index), area under the curve (AUC) and calibration curves were used to evaluated nomogram. Results Increasing absolute eosinophil count (AEC) after admission was correlated with shorter isolation length (P = 0.02). Baseline activated partial thromboplastin time (APTT) > 30 s was correlated with longer isolation length (P = 0.03). A nomogram to predict isolation probability at 11-, 16- and 21-day was developed and validated. The C-indices of training and validation cohort were 0.604 and 0.682 respectively. Both cohorts showed a good discriminative ability (AUC, 11-day: 0.646 vs 0.730; 16-day: 0.663 vs 0.750; 21-day: 0.711 vs 0.783; respectively) and calibration power. Conclusions Baseline APTT and dynamic change of AEC were two significant factors associated with isolation length of non-severe patients. Nomogram could predict isolation probability for each patient to estimate appropriate quarantine length.

Graphical abstract, Lung adenocarcinoma (LUAD) has a high mortality rate and is difficult to diagnose and treat in its early stage. Previous studies have demonstrated that small nucleolar RNAs (snoRNAs) play a critical role in tumor immune infiltration and the development of a variety of solid tumors. However, there have been no studies on the correlation between tumor-infiltrating immune-related snoRNAs (TIISRs) and LUAD. In this study, we filtered six immune-related snoRNAs based on the tissue specificity index (TSI) and expression profile of all snoRNAs between all LUAD cell lines from the Cancer Cell Line Encyclopedia and 21 types of immune cells from the Gene Expression Omnibus database. Further, we performed real-time quantitative polymerase chain reaction (RT-qPCR) to validate the expression status of these snoRNAs on peripheral blood mononuclear cells (PBMCs) and lung cancer cell lines. Next, we developed a TIISR signature based on the expression profiles of snoRNAs from 479 LUAD patients filtered by the random survival forest algorithm. We then analyzed the value of this TIISR signature (TIISR risk score) for assessing tumor immune infiltration, immune checkpoint inhibitor (ICI) treatment response, and the prognosis of LUAD between groups with high and low TIISR risk score. Further, we found that the TIISR risk score groups showed significant differences in biological characteristics and that the risk score could be used to assess the level of tumor immune cell infiltration, thereby predicting prognosis and responsiveness to immunotherapy in LUAD patients.

Background Inflammatory bowel disease (IBD) is a complex disease with variable presentation, progression, and response to therapies. Current disease classification is based on subjective clinical phenotypes. The peripheral blood immunophenome can reflect local inflammation, and thus we measured 39 circulating immune cell types in a large cohort of IBD and control subjects and performed immunotype:phenotype associations. Methods We performed fluorescence-activated cell sorting or CyTOF analysis on blood from 728 Crohn’s disease, 464 ulcerative colitis, and 334 non-IBD patients, with available demographics, endoscopic and clinical examinations and medication use. Results We observed few immune cell types commonly affected in IBD (lowered natural killer cells, B cells, and CD45RA– CD8 T cells). Generally, the immunophenome was distinct between ulcerative colitis and Crohn’s disease. Within disease subtype, there were further distinctions, with specific immune cell types associating with disease duration, behavior, and location. Thiopurine monotherapy altered abundance of many cell types, often in the same direction as disease association, while anti-tumor necrosis factor (anti-TNF) monotherapy demonstrated an opposing pattern. Concomitant use of an anti-TNF and thiopurine was not synergistic, but rather was additive. For example, thiopurine monotherapy use alone or in combination with anti-TNF was associated with a dramatic reduction in major subclasses of B cells. Conclusions We present a peripheral map of immune cell changes in IBD related to disease entity and therapies as a resource for hypothesis generation. We propose the changes in B cell subsets could affect antibody formation and potentially explain the mechanism behind the superiority of combination therapy through the impact of thiopurines on pharmacokinetics of anti-TNFs., Graphical abstract

Objective This study aimed to improve the accuracy of CT for detection of COVID-19-associated pneumonia and to identify patient subgroups who might benefit most from CT imaging. Methods A total of 269 patients who underwent CT for suspected COVID-19 were included in this retrospective analysis. COVID-19 was confirmed by reverse-transcription-polymerase-chain-reaction. Basic demographics (age and sex) and initial vital parameters (O2-saturation, respiratory rate, and body temperature) were recorded. Generalized mixed models were used to calculate the accuracy of vital parameters for detection of COVID-19 and to evaluate the diagnostic accuracy of CT. A clinical score based on vital parameters, age, and sex was established to estimate the pretest probability of COVID-19 and used to define low, intermediate, and high risk groups. A p-value of, Highlights • A simple score to estimate the pre-test probability of COVID-19 can be calculated using vital parameters, basic vital parameters. • Accuracy of CT for the detection of COVID-19 might be increased by selecting patients with a high-pretest probability. • In patients with mild symptoms, CT examinations are unlikel unlikely to be effective with a very low positive predictive value.

Graphical abstract, The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

Graphical abstract, Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort, resulting in a data matrix containing 3,065 readings for 124 types of measurements over 52 days. A machine learning model was established to predict the disease progression based on the cohort consisting of training, validation, and internal test sets. A panel of eleven routine clinical factors constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 98% in the discovery set. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.70, 0.99, 0.93, and 0.93, respectively. Our model captured predictive dynamics of lactate dehydrogenase (LDH) and creatine kinase (CK) while their levels were in the normal range. This model is accessible at https://www.guomics.com/covidAI/ for research purpose.

Graphical abstract, Highlights • Machine Learning in drug discovery has greatly benefited the pharmaceutical industry. • Application of machine algorithms must entail a robust design in real clinical tasks. • Trending machine learning algorithms in drug design: NB, SVM, RF and ANN., Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Background Despite known clinical risk factors, predicting anthracycline cardiotoxicity remains challenging. Objectives This study sought to develop a clinical and genetic risk prediction model for anthracycline cardiotoxicity in childhood cancer survivors. Methods We performed exome sequencing in 289 childhood cancer survivors at least 3 years from anthracycline exposure. In a nested case-control design, 183 case patients with reduced left ventricular ejection fraction despite low-dose doxorubicin (≤250 mg/m2), and 106 control patients with preserved left ventricular ejection fraction despite doxorubicin >250 mg/m2 were selected as extreme phenotypes. Rare/low-frequency variants were collapsed to identify genes differentially enriched for variants between case patients and control patients. The expression levels of 5 top-ranked genes were evaluated in human induced pluripotent stem cell–derived cardiomyocytes, and variant enrichment was confirmed in a replication cohort. Using random forest, a risk prediction model that included genetic and clinical predictors was developed. Results Thirty-one genes were differentially enriched for variants between case patients and control patients (p < 0.001). Only 42.6% case patients harbored a variant in these genes compared to 89.6% control patients (odds ratio: 0.09; 95% confidence interval: 0.04 to 0.17; p = 3.98 × 10–15). A risk prediction model for cardiotoxicity that included clinical and genetic factors had a higher prediction accuracy and lower misclassification rate compared to the clinical-only model. In vitro inhibition of gene-associated pathways (PI3KR2, ZNF827) provided protection from cardiotoxicity in cardiomyocytes. Conclusions Our study identified variants in cardiac injury pathway genes that protect against cardiotoxicity and informed the development of a prediction model for delayed anthracycline cardiotoxicity, and it also provided new targets in autophagy genes for the development of cardio-protective drugs. (Preventing Cardiac Sequelae in Pediatric Cancer Survivors [PCS2]; NCT01805778), Central Illustration

Highlights • Developed two simple-to use nomograms for identifying COVID-19 positive patients. • Probabilities are provided to allow healthcare leaders to decide suitable cut-offs. • Variables are age, white cell count, chest x-ray appearances and contact history. • Model variables are easily available in the general hospital setting., Objectives To develop:(1) two validated risk prediction models for COVID-19 positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. Methods Patients with and without COVID-19 were included from 4 Hong Kong hospitals. Database was randomly split 2:1 for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4, 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. First prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). Second model developed has same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on H-L test (p = 0.781 and 0.155 respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV. Conclusion Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.

Background and aim Chrysin is a flavonoid found in plant extracts from Passiflora species, honey and propolis. It has demonstrated anti-adipogenic activity in vitro but there are no studies substantiating the anti-obesity activity of chrysin in vivo. Experimental procedure The pancreatic lipase (PL) inhibitory potential of chrysin was determined by preliminary in silico screening and further confirmed by in vitro PL inhibitory assay and oral fat tolerance test (OFTT). The effect of chrysin on acute feed intake and sucrose preference test was determined in normal rats. Obesity was induced by feeding of high fructose diet (HFD) to the rats. The rats were divided into six groups: normal control, HFD control, orlistat and three doses of chrysin (25, 50 and 100 mg/kg body weight). Body weight, body mass index (BMI), abdominal circumference/thoracic circumference (AC/TC) ratio, calorie intake, adiposity index, fecal cholesterol, locomotor activity and histopathology of the adipose tissue of the rats were evaluated. Results Chrysin showed good affinity to PL with competitive type of inhibition. It significantly reduced serum triglycerides in OFTT. Chrysin also significantly reduced acute feed intake and sucrose preference in rats. Chrysin significantly decreased the body weight, BMI, AC/TC ratio, adiposity index, calorie intake while it significantly increased the fecal cholesterol and locomotor activity of the rats. Chrysin was found to reduce the size of the adipocytes when compared to the HFD control group. Conclusion Thus, chrysin exerted anti-obesity effect by inhibiting PL, reducing sucrose preference, reducing calorie intake and increasing the locomotor activity of rats., Graphical abstract Image 1021, Highlights • Chrysin was found to be competitive inhibitor of pancreatic lipase. • Chrysin reduced the body weight of rats. • Chrysin reduced calorie intake of rats. • Chrysin increased the locomotor activity of obese rats. • Chrysin reduced the hypertrophy of adipocytes.

ONC201 was originally discovered as TNF-Related Apoptosis Inducing Ligand (TRAIL)-inducing compound TIC10. ONC201 appears to act as a selective antagonist of the G protein coupled receptor (GPCR) dopamine receptor D2 (DRD2), and as an allosteric agonist of mitochondrial protease caseinolytic protease P (ClpP). Downstream of target engagement, ONC201 activates the ATF4/CHOP-mediated integrated stress response leading to TRAIL/Death Receptor 5 (DR5) activation, inhibits oxidative phosphorylation via c-myc, and inactivates Akt/ERK signaling in tumor cells. This typically results in DR5/TRAIL-mediated apoptosis of tumor cells; however, DR5/TRAIL-independent apoptosis, cell cycle arrest, or antiproliferative effects also occur. The effects of ONC201 extend beyond bulk tumor cells to include cancer stem cells, cancer associated fibroblasts and immune cells within the tumor microenvironment that can contribute to its efficacy. ONC201 is orally administered, crosses the intact blood brain barrier, and is under evaluation in clinical trials in patients with advanced solid tumors and hematological malignancies. ONC201 has single agent clinical activity in tumor types that are enriched for DRD2 and/or ClpP expression including specific subtypes of high-grade glioma, endometrial cancer, prostate cancer, mantle cell lymphoma, and adrenal tumors. Synergy with radiation, chemotherapy, targeted therapy and immune-checkpoint agents has been identified in preclinical models and is being evaluated in clinical trials. Structure-activity relationships based on the core pharmacophore of ONC201, termed the imipridone scaffold, revealed novel potent compounds that are being developed. Imipridones represent a novel approach to therapeutically target previously undruggable GPCRs, ClpP, and innate immune pathways in oncology.

Graphical abstract, Highlights • Interactions between sex, diet & Nat2 were investigated in rats congenic for Nat2. • Rapid Nat2 congenic rats developed greater dyslipidemia. • Total cholesterol (TC)-to-HDL ratios were significantly higher in rapid Nat2 rats. • HDL-to-LDL ratios were significantly lower in rapid Nat2 rats. • Rapid Nat2 rats may have increased risk towards metabolic & cardiovascular disease., Recent reports suggest that arylamine N-acetyltransferases (NAT1 and/or NAT2) serve important roles in regulation of energy utility and insulin sensitivity. We investigated the interaction between diet (control vs. high-fat diet) and acetylator phenotype (rapid vs. slow) using previously established congenic rat lines (in F344 background) that exhibit rapid or slow Nat2 (orthologous to human NAT1) acetylator genotypes. Male and female rats of each genotype were fed control or high-fat (Western-style) diet for 26 weeks. We then examined diet- and acetylator genotype-dependent changes in body and liver weights, systemic glucose tolerance, insulin sensitivity, and plasma lipid profile. Male and female rats on the high fat diet weighed approximately 10% more than rats on the control diet and the percentage liver to body weight was consistently higher in rapid than slow acetylator rats. Rapid acetylator rats were more prone to develop dyslipidemia overall (i.e., higher triglyceride; higher LDL; and lower HDL), compared to slow acetylator rats. Total cholesterol (TC)-to-HDL ratios were significantly higher and HDL-to-LDL ratios were significantly lower in rapid acetylator rats. Our data suggest that rats with rapid systemic Nat2 (NAT1 in humans) genotype exhibited higher dyslipidemia conferring risk for metabolic syndrome and cardiovascular dysfunction.

The dysregulation of alternative splicing (AS) has emerged as a mechanism of acute myeloid leukemia (AML). However, the prognostic impact of AS events remains under-explored in AML. Here we report the prognostic value of AS events and associated splicing factors based on three datasets of AML patients. We defined the landscape of AS events in AML and identified 7033 AS events associated with the survival of AML patients. Based on these events, we further developed a composite 15 AS event-based prognostic signature, which was independent of the cytogenetic risk stratification and patient age, and showed a better performance than known gene expression signatures. More importantly, our new signature markedly improved the European LeukemiaNet (ELN) risk classification, indicating a broad applicability in the clinical management of AML. Furthermore, the splicing-regulatory network established the correlations between prognostic AS events and associated splicing factors. The finding was validated by CRISPR-based data, which indicated that the increased expression of RBM39 contributed to the higher exon inclusion of SETD5 and conferred a poor outcome. Together, AS events may serve as a novel assortment of prognosticators for AML and could refine the ELN risk stratification. The splicing regulatory network provides clues regarding the splicing factor-mediated mechanisms of AML.

Highlights • Radiomics studies in NSCLC suffer from a number of limitations. • No single radiomic signature has been translated into clinical use. • Identification of limitations can help future studies to expedite biomarker translation., Radiomics has become a popular image analysis method in the last few years. Its key hypothesis is that medical images harbor biological, prognostic and predictive information that is not revealed upon visual inspection. In contrast to previous work with a priori defined imaging biomarkers, radiomics instead calculates image features at scale and uses statistical methods to identify those most strongly associated to outcome. This builds on years of research into computer aided diagnosis and pattern recognition. While the potential of radiomics to aid personalized medicine is widely recognized, several technical limitations exist which hinder biomarker translation. Aspects of the radiomic workflow lack repeatability or reproducibility under particular circumstances, which is a key requirement for the translation of imaging biomarkers into clinical practice. One of the most commonly studied uses of radiomics is for personalized medicine applications in Non-Small Cell Lung Cancer (NSCLC). In this review, we summarize reported methodological limitations in CT based radiomic analyses together with suggested solutions. We then evaluate the current NSCLC radiomics literature to assess the risk associated with accepting the published conclusions with respect to these limitations. We review different complementary scoring systems and initiatives that can be used to critically appraise data from radiomics studies. Wider awareness should improve the quality of ongoing and future radiomics studies and advance their potential as clinically relevant biomarkers for personalized medicine in patients with NSCLC.

Graphical abstract, Our previous study indicated that intravenous vitamin C (IVC) treatment concurrent with modulated electrohyperthermia (mEHT) was safe and improved the quality of life (QoL) of non-small-cell lung cancer (NSCLC) patients. The aim of this trial was to further verify the efficacy of the above combination therapy in previously treated patients with refractory advanced (stage IIIb or IV) NSCLC. A total of 97 patients were randomized to receive IVC and mEHT plus best supportive care (BSC) (n = 49 in the active arm, receiving 1 g/kg * d IVC concurrently with mEHT, three times a week for 25 treatments in total) or BSC alone (n = 48 in the control arm). After a median follow-up of 24 months, progression-free survival (PFS) and overall survival (OS) were significantly prolonged by combination therapy compared to BSC alone (PFS: 3 months vs 1.85 months, P

Introduction Ablating the slow pathway (SP) is the superior treatment for atrioventricular nodal reentrant tachycardia (AVNRT) with a low complication rate. However, the ablation of the SP could result in either complete elimination or modification of the SP. We aimed to investigate whether the duration of AH jump pre-ablation associated with the outcome of elimination of SP. Methods We included 56 patients with typical AVNRT (slow-fast), 20 males and 36 females, aged 44.2 ± 15.1 years. Slow pathway ablation was performed using classical approach. Univariate and multivariate analysis was performed for potential predictors of SP elimination. Results Typical AVNRT was inducible in all patients. Post-ablation, non-inducibility of AVNRT was obtained in all 56 (100%) patients, with SP elimination in 33 (61%) patients and SP modification in 23 (39%) patients. Patients with SP elimination had significantly longer AH jump than patients with SP modification. Cox regression analysis showed that AH jump duration was the independent predictor of SP elimination, in which every 20 ms increase in AH jump duration was associated with 1.30 higher rate of SP elimination. Furthermore, ROC curve analysis indicated that the AH jump duration of ≥100 ms had 6.14 times higher probability for complete elimination of the SP with a sensitivity of 79%, specificity of 70%, PPV of 79% and NPV of 70%. Conclusions AH jump duration pre-ablation is associated with complete elimination of slow pathway during AVNRT ablation.

In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR), some achieve a partial response, and some do not respond at all or even progress. Accurate prediction of treatment response has the potential to improve patient care by improving prognostication, enabling de-escalation of toxic treatment that has little benefit, facilitating upfront use of novel targeted therapies, and avoiding delays to surgery. Visual inspection of a patient’s tumor on multiparametric MRI is insufficient to predict that patient’s response to NAC. However, machine learning and deep learning approaches using a mix of qualitative and quantitative MRI features have recently been applied to predict treatment response early in the course of or even before the start of NAC. This is a novel field but the data published so far has shown promising results. We provide an overview of the machine learning and deep learning models developed to date, as well as discuss some of the challenges to clinical implementation., Highlights • Machine and deep learning have shown potential in predicting neoadjuvant treatment outcomes using multiparametric MRI data. • Machine and deep learning-based early identification of chemotherapy non-responders could improve patient management. • Deep learning techniques using CNN may prove to be more powerful and more robust than traditional machine learning classifiers.

As part of the United States Pharmacopeia’s ongoing review of dietary supplement safety data, a new comprehensive systematic review on green tea extracts (GTE) has been completed. GTEs may contain hepatotoxic solvent residues, pesticide residues, pyrrolizidine alkaloids and elemental impurities, but no evidence of their involvement in GTE-induced liver injury was found during this review. GTE catechin profiles vary significantly with manufacturing processes. Animal and human data indicate that repeated oral administration of bolus doses of GTE during fasting significantly increases bioavailability of catechins, specifically EGCG, possibly involving saturation of first-pass elimination mechanisms. Toxicological studies show a hepatocellular pattern of liver injury. Published adverse event case reports associate hepatotoxicity with EGCG intake amounts from 140 mg to ∼1000 mg/day and substantial inter-individual variability in susceptibility, possibly due to genetic factors. Based on these findings, USP included a cautionary labeling requirement in its Powdered Decaffeinated Green Tea Extract monograph that reads as follows: “Do not take on an empty stomach. Take with food. Do not use if you have a liver problem and discontinue use and consult a healthcare practitioner if you develop symptoms of liver trouble, such as abdominal pain, dark urine, or jaundice (yellowing of the skin or eyes).”

Graphical abstract, Microsatellite instability (MSI) is a genomic property of the cancers with defective DNA mismatch repair and is a useful marker for cancer diagnosis and treatment in diverse cancer types. In particular, MSI has been associated with the active immune checkpoint blockade therapy response in cancer. Most of computational methods for predicting MSI are based on DNA sequencing data and a few are based on mRNA expression data. Using the RNA-Seq pan-cancer datasets for three cancer cohorts (colon, gastric, and endometrial cancers) from The Cancer Genome Atlas (TCGA) program, we developed an algorithm (PreMSIm) for predicting MSI from the expression profiling of a 15-gene panel in cancer. We demonstrated that PreMSIm had high prediction performance in predicting MSI in most cases using both RNA-Seq and microarray gene expression datasets. Moreover, PreMSIm displayed superior or comparable performance versus other DNA or mRNA-based methods. We conclude that PreMSIm has the potential to provide an alternative approach for identifying MSI in cancer.

Graphical abstract, Highlights • The suitability of methods for analysing distress in laboratory mice was assessed. • Non-invasive vs. telemetric methods were compared in an orthotopic pancreatic cancer model. • Transmitter implantation caused higher distress than laparotomy plus carcinoma cell injection. • Tumour growth provoked very mild distress. • Non-invasive methods had a better performance than telemetry for distress analysis., Prospective severity assessment is legally required in many countries to ensure high-quality research along with high welfare standards for laboratory animals. Mice and rats, the most common laboratory species, are prey animals that usually suppress signs of pain and suffering. Therefore, highly sensitive readout parameters are necessary to adequately quantify distress. The present study compared the performance of different non-invasive methods in determining animal distress, such as measuring body weight, distress score, faecal corticosterone metabolites, burrowing, and nesting behaviour, with continuous monitoring of heart rate, body temperature and activity by telemetry. The distress caused by two surgical interventions was compared and the burden caused by tumour growth was described. Transmitter implantation caused higher distress than laparotomy plus carcinoma cell injection into the pancreas. Surprisingly, no significant increase in distress was observed during tumour growth. The receiver operating characteristic curve analysis revealed that some non-invasive distress-parameters, i.e., distress-score and burrowing activity, exhibited slightly better performance to quantify distress than the most suitable parameters measured by telemetry. Due to the high burden caused by the implantation of the telemetric device, the use of non-invasive methods to assess distress in laboratory animals after surgical interventions should be favoured in future studies.

Graphical abstract, Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein–protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico.

Graphical abstract, Highlights • Medication overuse is related to chronicization and medication-overuse headache. • Prediction of medication overuse (MO) is a challenge in the management of migraine. • Machine learning and random optimization could help to estimate MO risk in migraine. • A customized decision support system was devised for migraine clinical management. • This approach may exploit significant patterns in data connoting causality., Machine learning (ML) is largely used to develop automatic predictors in migraine classification but automatic predictors for medication overuse (MO) in migraine are still in their infancy. Thus, to understand the benefits of ML in MO prediction, we explored an automated predictor to estimate MO risk in migraine. To achieve this objective, a study was designed to analyze the performance of a customized ML-based decision support system that combines support vector machines and Random Optimization (RO-MO). We used RO-MO to extract prognostic information from demographic, clinical and biochemical data. Using a dataset of 777 consecutive migraine patients we derived a set of predictors with discriminatory power for MO higher than that observed for baseline SVM. The best four were incorporated into the final RO-MO decision support system and risk evaluation on a five-level stratification was performed. ROC analysis resulted in a c-statistic of 0.83 with a sensitivity and specificity of 0.69 and 0.87, respectively, and an accuracy of 0.87 when MO was predicted by at least three RO-MO models. Logistic regression analysis confirmed that the derived RO-MO system could effectively predict MO with ORs of 5.7 and 21.0 for patients classified as probably (3 predictors positive), or definitely at risk of MO (4 predictors positive), respectively. In conclusion, a combination of ML and RO – taking into consideration clinical/biochemical features, drug exposure and lifestyle – might represent a valuable approach to MO prediction in migraine and holds the potential for improving model precision through weighting the relative importance of attributes.

Background Panax ginseng Meyer has been used as a nourishing edible herb in East Asia for thousands of years. 25-OH-PPT was first discovered as a natural rare triterpenoid saponin in ginseng stems and leaves by our group. Research found that it showed strong inhibitory effects on α-glucosidase and protein tyrosine phosphatase 1B, and protected cardiocytes (H9c2) through PI3K/Akt pathway. Methods In the research, in order to optimize the 25-OH-PPT enrichment process, optimal macroporous resins and optimal purification conditions were studied. Meanwhile, the hypoglycemic effect and mechanism of 25-OH-PPT were evaluated by using STZ to establish insulin-dependent diabetic mice and the spontaneous type 2 diabetes DB/DB mice. Results and Conclusion Research found that 25-OH-PPT can reduce blood glucose and enhance glucose tolerance in STZ model mice. It increases insulin sensitivity by upregulating GLUT4 and AMPK in skeletal muscle, and activating insulin signaling pathways. In DB/DB mice, 25-OH-PPT achieves hypoglycemic effects mainly by activating the insulin signaling pathway. Meanwhile, through the influence of liver inflammatory factors and lipids in serum, it can be seen that 25-OH-PPT has obvious anti-inflammatory and lipid-lowering effects. These results provide new insights into the study of ginseng as a functional food.

Background: Alzheimer's Disease (AD) is a complex and multifactorial disease and novel approaches are needed to illuminate the underlying pathology. Metabolites comprise the end-product of genes, transcripts, and protein regulations and might reflect disease pathogenesis. Blood is a common biofluid used in metabolomics; however, since extracellular vesicles (EVs) hold cell-specific biological material and can cross the blood-brain barrier, their utilization as biological material warrants further investigation. We aimed to investigate blood- and EV-derived metabolites to add insigts to the pathological mechanisms of AD.Methods: Blood samples were collected from 10 AD and 10 Mild Cognitive Impairment (MCI) patients, and 10 healthy controls. EVs were enriched from plasma using 100,000×g, 1 h, 4 °C with a wash. Metabolites from serum and EVs were measured using liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. Multivariate and univariate analyses were employed to identify altered metabolites in cognitively impaired individuals.Results: While no significant EV-derived metabolites were found differentiating patients from healthy individuals, six serum metabolites were found important; valine (p = 0.001, fold change, FC = 0.8), histidine (p = 0.001, FC = 0.9), allopurinol riboside (p = 0.002, FC = 0.2), inosine (p = 0.002, FC = 0.3), 4-pyridoxic acid (p = 0.006, FC = 1.6), and guanosine (p = 0.004, FC = 0.3). Pathway analysis revealed branched-chain amino acids, purine and histidine metabolisms to be downregulated, and vitamin B6 metabolism upregulated in patients compared to controls.Conclusion: Using a combination of LC-MS and NMR methodologies we identified several altered mechanisms possibly related to AD pathology. EVs require additional optimization prior to their possible utilization as a biological material for AD-related metabolomics studies.

Objective Accumulating evidence indicates that high uric acid (UA) is strongly associated with obesity and metabolic syndrome and drives the development of nonalcoholic fatty liver disease (NAFLD) and insulin resistance. Although urate transporter-1 (URAT1), which is primarily expressed in the kidneys, plays a critical role in the development of hyperuricemia, its pathophysiological implication in NAFLD and insulin resistance remains unclear. We herein investigated the role and functional significance of URAT1 in diet-induced obese mice. Methods Mice fed a high-fat diet (HFD) for 16–18 weeks or a normal-fat diet (NFD) were treated with or without a novel oral URAT1-selective inhibitor (dotinurad [50 mg/kg/day]) for another 4 weeks. Results We found that URAT1 was also expressed in the liver and brown adipose tissue (BAT) other than the kidneys. Dotinurad administration significantly ameliorated HFD-induced obesity and insulin resistance. HFD markedly induced NAFLD, which was characterized by severe hepatic steatosis as well as the elevation of serum ALT activity and tissue inflammatory cytokine genes (chemokine ligand 2 (Ccl2) and tissue necrosis factor α (TNFα)), all of which were attenuated by dotinurad. Similarly, HFD significantly increased URAT1 expression in BAT, resulting in lipid accumulation (whitening of BAT), and increased the production of tissue reactive oxygen species (ROS), which were reduced by dotinurad via UCP1 activation. Conclusions In conclusion, a novel URAT1-selective inhibitor, dotinurad, ameliorates insulin resistance by attenuating hepatic steatosis and promoting rebrowning of lipid-rich BAT in HFD-induced obese mice. URAT1 serves as a key regulator of the pathophysiology of metabolic syndrome and may be a new therapeutic target for insulin-resistant individuals, particularly those with concomitant NAFLD., Graphical abstract Vicious cycle of URAT1 activity and insulin resistance in the pathophysiology of metabolic syndrome. The impacts of URAT1 is amplified in metabolic syndrome via persistent exposure to hyperinsulinemia. Conversely, enhanced URAT1 action induces insulin resistance in metabolic syndrome, leading to a vicious cycle. URAT1-selective inhibitor may be particularly effective in reducing hyperuricemia associated with insulin-resistant metabolic disorders by severing this vicious cycle.Image 1, Highlights • URAT1 is expressed in the liver and brown adipose tissue other than in the kidneys. • URAT1-selective inhibitor ameliorates HFD-induced insulin resistance. • URAT1-selective inhibitor improves NAFLD through the inhibition of Ccl2 and TNFα. • URAT1-selective inhibitor promotes rebrowning of HFD-induced lipid-rich BAT. • URAT1 serves as a key regulator of the pathophysiology of metabolic syndrome.

Background and Aims The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the health care systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized (inpatients) or discharged (outpatients). Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration, and discharged patients often returned to the hospital for aggravation of their condition. Here, we have developed a new combined approach of omics to identify factors that could distinguish coronavirus disease 19 (COVID-19) inpatients from outpatients. Methods Saliva and blood samples were collected over the course of two observational cohort studies. By using machine learning approaches, we compared salivary metabolome of 50 COVID-19 patients with that of 270 healthy individuals having previously been exposed or not to SARS-CoV-2. We then correlated the salivary metabolites that allowed separating COVID-19 inpatients from outpatients with serum biomarkers and salivary microbiota taxa differentially represented in the two groups of patients. Results We identified nine salivary metabolites that allowed assessing the need of hospitalization. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrrolidineacetic acid) and one serum protein, chitinase 3-like-1 (CHI3L1), were sufficient to separate inpatients from outpatients completely and correlated with modulated microbiota taxa. In particular, we found Corynebacterium 1 to be overrepresented in inpatients, whereas Actinomycetaceae F0332, Candidatus Saccharimonas, and Haemophilus were all underrepresented in the hospitalized population. Conclusion This is a proof of concept that a combined omic analysis can be used to stratify patients independently from COVID-19.

To determine the ability of the Activity Measure for Post-Acute Care (AM-PAC) "6-Clicks" assessments of mobility and activity to predict key clinical outcomes in patients hospitalized with coronavirus disease 2019 (COVID-19).Retrospective cohort study.An academic health system in the United States consisting of 5 inpatient hospitals.Adult patients (N=1486) urgently or emergently admitted who tested positive for COVID-19 and had at least 1 AM-PAC assessment.Not applicable.Discharge destination, hospital length of stay, in-hospital mortality, and readmission.A total of 1486 admission records were included in the analysis. After controlling for covariates, initial and final mobility (odds ratio, 0.867 and 0.833, respectively) and activity scores (odds ratio, 0.892 and 0.862, respectively) were both independent predictors of discharge destination with a high accuracy of prediction (area under the curve [AUC]=0.819-0.847). Using a threshold score of 17.5, sensitivity ranged from 0.72-0.79, whereas specificity ranged from 0.74-0.83. Both initial AM-PAC mobility and activity scores were independent predictors of mortality (odds ratio, 0.885 and 0.877, respectively). Initial mobility, but not activity, scores were predictive of prolonged length of stay (odds ratio, 0.957 and 0.980, respectively). However, the accuracy of prediction for both outcomes was weak (AUC=0.659-0.679). AM-PAC scores did not predict rehospitalization.Functional status as measured by the AM-PAC "6-Clicks" mobility and activity scores are independent predictors of key clinical outcomes individual hospitalized with COVID-19.

Highlights • Easily measurable parameters from chest CT examinations enable prediction of pulmonary hemodynamics. • ECG-gated CTPA is superior to non-gated CT. • Non-invasive pH therapy monitoring or follow-up might be implemented in the future., Purpose The aim of the study was to investigate the potential of electrocardiogram (ECG)-gated computed tomography pulmonary angiography (CTPA) as a predictor of disease severity in patients with chronic thromboembolic pulmonary hypertension (CTEPH). Method Forty-five CTEPH patients with a mean age of 63.8 years±12.7 y (±standard deviation) who had undergone ECG-gated CTPA and right heart catheterization (RHC) were included in the study. Right ventricular to left ventricular volume ratio (RVV/LVV), diameter ratio on 4-chamber view (RVD4CH/LVD4CH), pulmonary trunk (PT) diameter, PT to aortic diameter ratio (PT/A), and septal angle were correlated to mean pulmonary artery pressure (mPAP). Moreover, RVV/LVV and RVD4CH/LVD4CH were adjusted to pulmonary diameter index (PADi) and PT/A index. Areas under the curve (AUC) for predicting mPAP above 40 mmHg, 35 mmHg, and 30 mmHg were calculated. Results RVD4CH/LVD4CH revealed the strongest correlation to mPAP before (r = 0.6507) and after (r = 0.7650; p < 0.0001) PT/A adjustment. The AUCs for predicting pH with mPAP over 40 mmHg and 30 mmHg were 0.9229 and 0.864, respectively. A cutoff value of 1.298 enabled prediction of pH with mPAP over 40 mmHg with a sensitivity, specificity, positive predictive, and negative predictive value of 80.00 %, 95.83 %, 88.46 %, and 94.12 %, respectively. Intra- and interobserver variability were excellent for all parameters. Conclusion Combining different and easily evaluable ECG-gated CTPA parameters enables excellent prediction of pulmonary hemodynamics in CTEPH patients. Ventricular diameter ratio on 4-chamber view adjusted by the PT/A ratio yielded the best correlation to mPAP.

OBJECTIVE: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG).METHODS: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site.RESULTS: The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%.CONCLUSION: Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.

The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 patients and differentiating them from patients with pulmonary edema. Although systemically administered pulmonary vasodilators and acetazolamide are of great benefit for treating pulmonary edema, they should not be used to treat COVID-19 as they carry the risk of several adverse consequences, including worsening the matching of ventilation and perfusion, impaired carbon dioxide transport, systemic hypotension, and increased work of breathing. This study proposes a machine learning-based method (EDECOVID-net) that automatically differentiates the COVID-19 symptoms from pulmonary edema in lung CT scans using radiomic features. To the best of our knowledge, EDECOVID-net is the first method to differentiate COVID-19 from pulmonary edema and a helpful tool for diagnosing COVID-19 at early stages. The EDECOVID-net has been proposed as a new machine learning-based method with some advantages, such as having simple structure and few mathematical calculations. In total, 13 717 imaging patches, including 5759 COVID-19 and 7958 edema images, were extracted using a CT incision by a specialist radiologist. The EDECOVID-net can distinguish the patients with COVID-19 from those with pulmonary edema with an accuracy of 0.98. In addition, the accuracy of the EDECOVID-net algorithm is compared with other machine learning methods, such as VGG-16 (Acc = 0.94), VGG-19 (Acc = 0.96), Xception (Acc = 0.95), ResNet101 (Acc = 0.97), and DenseNet20l (Acc = 0.97).