The application of principal component analysis to drug discovery and biomedical data

Methamphetamine Disturbs Gut Homeostasis and Reshapes Serum Metabolome, Inducing Neurotoxicity and Abnormal Behaviors in Mice

As an illicit psychostimulant, repeated methamphetamine (MA) exposure results in addiction and causes severe neurotoxicity. Studies have revealed complex interactions among gut homeostasis, metabolism, and the central nervous system (CNS). To investigate the disturbance of gut homeostasis and metabolism in MA-induced neurotoxicity, 2 mg/kg MA or equal volume saline was intraperitoneally (i.p.) injected into C57BL/6 mice. Behavioral tests and western blotting were used to evaluate neurotoxicity. To determine alterations of colonic dysbiosis, 16s rRNA gene sequencing was performed to analyze the status of gut microbiota, while RNA-sequencing (RNA-seq) and Western Blot analysis were performed to detect colonic damage. Serum metabolome was profiled by LC–MS analysis. We found that MA induced locomotor sensitization, depression-, and anxiety-like behaviors in mice, along with dysfunction of the dopaminergic system and stimulation of autophagy as well as apoptosis in the striatum. Notably, MA significantly decreased microbial diversity and altered the component of microbiota. Moreover, findings from RNA-seq implied stimulation of the inflammation-related pathway after MA treatment. Western blotting confirmed that MA mediated colonic inflammation by activating the TLR4-MyD88-NF-κB pathway and impaired colonic barrier. In addition, serum metabolome was reshaped after MA treatment. Specifically, bacteroides-derived sphingolipids and serotonin were obviously altered, which were closely correlated with locomotor sensitization, depression-, and anxiety-like behaviors. These findings suggest that MA disrupts gut homeostasis by altering its microbiome and arousing inflammation, and reshapes serum metabolome, which provide new insights into understanding the interactions between gut homeostasis and MA-induced neurotoxicity.

Prebiotic effect of fructans from Agave salmiana on probiotic lactic acid bacteria and in children as a supplement for malnutrition

An imbalanced gut microbiota predisposes the development of nutritional disorders and chronic gastrointestinal diseases. Several studies have shown improvements on the host's health by enhancing the microbiota with prebiotics that stimulate probiotic microorganisms. This study describes the prebiotic effect of fructans from Agave salmiana consumed by children as a supplement for malnutrition. We report the extraction and degree of polymerization of fructans from A. salmiana, food safety and inflammatory analyses, and their effect in vitro on probiotic lactic acid bacteria (LAB). These fructans were included in the diet of 5-year-old normal-weight and malnourished children in double-blind, two-week interventions that compared the effects on their weight, bacterial count, and volatile organic compounds (VOCs). The extracted powdered fructans from A. salmiana had a composition comparable to inulin, were safe for human consumption, and stimulated the growth in vitro of three characteristic LAB. The children who consumed these fructans had a considerable weight gain, an increased number of LAB, and a decreased concentration of VOCs (indicative of less dysbiosis), demonstrating positive effects of this prebiotic on their microbiota, which were more significant in malnourished children. Fructans from A. salmiana induced in malnourished children significant weight gain and improved the functionality of their gut microbiota.

From non-targeted to targeted GC-MS metabolomics strategy for identification of TCM preparations containing natural and artificial musk

BACKGROUND

Moschus is a rare and precious natural medicine. Due to the properties of resources scarcity and expensive price of natural musk, artificial musk has been developed as substitute materials in some prescriptions. Rapid and accurate identification of natural or artificial musk in complex traditional Chinese medicine (TCM) preparations is also a challenge.

METHOD

A strategy from non-targeted to targeted gas chromatography-mass spectrometry (GC-MS) metabolomics was developed for discrimination of natural and artificial musk. Firstly, GC-MS-based non-targeted analysis combined with chemometrics was used to find the potential chemical markers to distinguish natural musk and artificial musk. Subsequently, targeted metabolomics was used to analyze musk in preparations with multiple reaction monitoring (MRM) mode by use gas chromatography coupled with triple quadrupole mass spectrometry (GC-QQQ MS).

RESULTS

Two chemical markers named prasterone and androsterone have been selected and could be detected in all Compound Pien Tze Huang preparations (CPZHs) containing artificial musk, while the CPZHs containing natural musk did not detect two markers with S/N (signal to noise ratio) less than 3.

CONCLUSION

Our work provides an applicable approach to select the practical chemical markers for the assessment of musk in preparations to realize the traceability of musk in TCM and improve the quality control of musk-containing preparations.

Retrograde flow in aortic isthmus in normal and fetal heart disease by principal component analysis and computational fluid dynamics

OBJECTIVES

Reverse flow Retrograde flow (RF) of blood in the aortic isthmus can be observed in different types of fetal heart disease (FHD), including abnormalities in heart structure and function. This study sought to investigate the relationship between RF and blood flow parameters, and develop a computational fluid dynamics (CFD) model to understand the mechanisms underlying this observation.

MATERIAL AND METHODS

A total of 281 fetuses (gestational age [GA] 26.6±.3 weeks) with FHD and 2803 normal fetuses (GA: 26.1±.1 weeks) by fetal echocardiography collected from May 2016 to December 2018. Principal component analysis (PCA) was performed to find the relationship and the CFD model reconstructed from 3D/4D spatio-temporal image correlation (STIC) images to simulate hemodynamics.

RESULTS

There was a significant difference in the percentages of RF between the study (80/201 (39%)) and control (29/2803 (1%)) groups (p < 0.05). The RF occur when the aorta flow rate (left heart) is reduced to 60% by CFD stimulation. Pearson correlation analysis showed significant correlations between flow rate and wall shear stress(WSS) (r = .883, p = 0.047) variables at the AI.

CONCLUSION

Volumetric flow rate of AO or left heart was the main component of the cause of RF. The hemodynamics of the cardiovascular system have highly complex behavior hinge on the turbulent nature of circulating blood flow.

Soft Statistical Mechanics for Biology

The multilevel organization of nature is self-evident: proteins do interact among them to give rise to an organized metabolism and the same hierarchical organization is in action for gene expression, tissue and organ architectures, and ecological systems.The still more common approach to such state of affairs is to think that causally relevant events originate from the lower level in the form of perturbations, that climb up the hierarchy reaching the ultimate layer of macroscopic behavior (e.g., causing a specific disease). Such rigid bottom-up causative model is unable to offer realistic models of many biological phenomena.Complex network approach allows to uncover the nature of multilevel organization, but in order to operationally define the organization principles of biological systems, we need to go further and complement network approach with sensible measures of order and organization. These measures, while keeping their original physical meaning, must not impose theoretical premises not verifiable in biological frameworks. We will show here how relatively simple and largely hypothesis-free multidimensional statistics tools can satisfactorily meet these criteria.

Influence of Salinity on the Microbial Community Composition and Metabolite Profile in Kimchi

Kimchi, a popular traditional Korean fermented food, is produced by fermenting vegetables with various spices and salt. Salt plays an important role in the preparation of kimchi and affects its taste and flavor. This study aimed to investigate the effects of salinity on kimchi fermentation. The salinities of five sets of kimchi samples were adjusted to 1.4%, 1.7%, 2.0%, 2.2%, and 2.5%. The characteristics of each kimchi sample, including its pH, acidity, free sugar content, free amino acid content, organic acid content, and microbial community composition, were evaluated during kimchi fermentation. The low-salinity kimchi sample showed a rapid decline in the pH at the beginning of the fermentation process, a relatively high abundance of Leuconostoc mesenteroides, and high mannitol production. In the late fermentation period, Latilactobacillus sakei had a higher abundance in the kimchi sample with high salinity than in other samples. In the initial stage of fermentation, the metabolite composition did not differ based on salinity, whereas the composition was considerably altered from the third week of fermentation. The findings showed variations in the characteristics and standardized manufacturing processes of kimchi at various salt concentrations. Therefore, salinity significantly affected the types and concentrations of fermentation metabolites in kimchi.

Identifying toggle genes from transcriptome-wide scatter: A new perspective for biological regulation

The study of gene expression variability, especially for cancer and cell differentiation studies, has become important. Here, we investigate transcriptome-wide scatter of 23 cell types and conditions across different levels of biological complexity. We focused on genes that act like toggle switches between pairwise replicates of the same cell type, i.e. genes expressed in one replicate and not expressed in the other, sometimes also referred as ON/OFF genes. The proportion of these toggle genes dramatically increases from unicellular to multicellular organization, especially for development and cancer cells. A relevant portion of toggle switches are non-coding genes: in unicellular systems the most represented classes are tRNA and rRNA, while multicellular systems more frequently show lncRNA, sncRNA and pseudogenes. Notably, disease associated microRNAs (miRNAs), pseudogenes and numerous uncharacterized transcripts are present in both development and cancer cells. On top of the known intrinsic and extrinsic factors, our work indicates toggle genes as a novel collective component creating transcriptome-wide variability. This requires further investigation for elucidating both evolutionary and disease processes.

Preliminary development of a questionnaire to measure the extra-pulmonary symptoms of severe asthma

Background

Research into the effects of asthma treatments on the extra-pulmonary symptoms of severe asthma is limited by the absence of a suitable questionnaire. The aim was to create a questionnaire suitable for intervention studies by selecting symptoms that are statistically associated with asthma pathology and therefore may improve when pathology is reduced.

Methods

Patients attending a specialist asthma clinic completed the 65-item General Symptom Questionnaire (GSQ-65), a questionnaire validated for assessing symptoms of people with multiple medically unexplained symptoms. Lung function (FEV1%) and cumulative oral corticosteroids (OCS) calculated from maintenance dose plus exacerbations were obtained from clinic records. Pathology was represented by the two components of a principal component analysis (PCA) of FEV1% and OCS. LASSO regression was used to select symptoms that had high coefficients with these two principal components and occurred frequently in severe asthma.

Results

100 patients provided data. PCA revealed two components, one where FEV1% and OCS were inversely related and another where they were directly related. LASSO regression revealed 39 symptoms with non-zero coefficients on one or more of the two principal components from which 16 symptoms were selected for the GSQ-A on the basis of magnitude of coefficient and frequency. Asthma symptoms measured by asthma control questionnaires were excluded. The GSQ-A correlated 0.33 and − 0.34 (p = 0.001) with the two principal components.

Conclusion

The GSQ-A assesses the frequency of 16 heterogenous non-respiratory symptoms that are associated with asthma severity using the statistical combination of FEV1% and OCS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-021-01730-0.

Principle component analysis for nonlinear optical properties of thiophene-based metal complexes

The simulation of molecular descriptors of thiophene-based metal complexes has been performed using Gaussian 03 and Atomistic toolkit Virtual Nanolab (ATK-VNL) software. It is found that with respect to the obtained molecular descriptors, the molecules show distinct properties. The dimensions of the data set being large, the principal components (PC1 and PC2) have been obtained using principal component analysis (PCA). Analysis has been done for the Linear regression of principal components with first hyperpolarizability and second hyperpolarizability of the molecules. The results indicate that, of all the calculated molecular descriptors of thiophene-based metal complexes, the molecular energy (E), ionization energy (E_I), and molecular dipole moment (D) plays a dominant role in determining their nonlinear optical properties i.e., the hyperpolarizability value, of the studied molecules. Also, the molecular descriptors, polarizability (P) and molar refractivity (MR), show considerable impact on the nonlinear optical properties of the studied molecules.

Acute hepatotoxicity and nephrotoxicity risk assessment of the Tibetan medicine 25 flavors of the turquoise pill based on 1H-NMR metabonomics

ETHNOPHARMACOLOGICAL RELEVANCE

25 flavors of the turquoise pill, a traditional Tibetan medicine for the treatment of various types of hepatitis, has not been investigated on its safety, especially the component mineral turquoise, which is believed to be essential but worried for its potential toxicity.

AIM OF THE STUDY

To explore the potential acute toxicity and function of 25 flavors of the turquoise pill and turquoise, the possible mechanism of the effects of turquoise and 25 flavors of the turquoise pill were systematically studied based on 1H NMR metabolomics.

MATERIALS AND METHODS

The rats were administered with turquoise and 25 flavors of the turquoise pill by gavage for 7 days, and samples of serum, liver, and kidney were collected. The potential toxicity and function of turquoise and 25 flavors of the turquoise pill on the liver and kidney of SD rats were evaluated by 1H NMR metabonomics, histopathology, and biochemical indexes.

RESULTS

The results demonstrated that 25 flavors of the turquoise pill could scavenge free oxygen radicals, strengthen aerobic respiration and inhibit glycolysis in the liver. It did not cause oxidative stress in the kidney with no obvious damage. By modulation of branched-chain amino acids (BCAAs), 25 flavors of the turquoise pill can improve the utilization of glucose and promote aerobic respiration of the kidney.

CONCLUSION

Considering the high dosage and short duration used in this study relative to their typical clinical usage, administration of 25 flavors of the turquoise pill and its component mineral turquoise are safe to livers and kidneys.

Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation

Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been suggested for the purpose of estimating ID, but no standard package to easily apply them one by one or all at once has been implemented in Python. This technical note introduces scikit-dimension, an open-source Python package for intrinsic dimension estimation. The scikit-dimension package provides a uniform implementation of most of the known ID estimators based on the scikit-learn application programming interface to evaluate the global and local intrinsic dimension, as well as generators of synthetic toy and benchmark datasets widespread in the literature. The package is developed with tools assessing the code quality, coverage, unit testing and continuous integration. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation for real-life and synthetic data.

Doppler Ultrasound under Image Denoising Algorithm in the Diagnosis and Treatment of Fetal Growth Restriction Using Aspirin Combined with Low-Molecular-Weight Heparin

OBJECTIVE

This study explored the clinical application value of image denoising algorithm combined with Doppler ultrasound imaging in evaluation of aspirin combined with low-molecular-weight heparin (LMWH) on fetal growth restriction (FGR).

METHOD

A two-stage image denoising by principal component analysis (PCA) with local pixel grouping (LPG-PCA) denoising algorithm was constructed in this study. Eighty FGR pregnant women were included in the study, and they were rolled into an experimental group (aspirin enteric-coated tablets + LMWH calcium injection) and a control group (LMWH calcium injection) according to the different treatment plans, with 40 cases in each group. All patients were performed with Doppler ultrasound imaging. The blood flow parameters (BFPs) were recorded and compared before and after the treatment in two groups, including power index (PI), resistance index (RI), high systolic blood flow velocity (S), high diastolic blood flow velocity (D), S/D value, and peak systolic velocity (PSV). In addition, the middle cerebral artery (MCA) BFPs, cerebral placental rate (CPR), amniotic fluid index (AFI) and perinatal outcome (PO) of the two groups were compared.

RESULTS

The total effective rate of treatment in group A (87.5%) was greatly higher than that in group B (62.5%), showing statistical difference (P < 0.05). The PI (0.72 ± 0.19), RI (0.57 ± 0.17), and S/D values (2.26 ± 0.43) in group A were dramatically lower than those in group B, which were 0.92 ± 0.21, 0.75 ± 0.14, and 2.64 ± 0.45, respectively (P < 0.05), and the AFI was higher (13.71 ± 2.2 cm vs 11.38 ± 2.16 cm) (P < 0.05). The Apgar score (9.17 ± 0.26), weight (3.57 ± 1.08), and gestational age (38.85 ± 2.50) of group A were all higher in contrast to those of group B, which were 7.33 ± 0.25, 2.61 ± 1.13, and 36.18 ± 2.25, respectively (P < 0.05). In addition, the fetal double parietal diameter (2.4 ± 0.9 mm), femur diameter (2.2 ± 0.6 mm), head circumference (1.2 ± 0.4 mm), abdominal circumference (1.3 ± 0.7 mm), and uterine height (0.8 ± 0.3 mm) in group A were obviously superior to those in group B, which were 1.8 ± 0.4 mm, 1.7 ± 0.5 mm, 0.8 ± 0.2 mm, 0.9 ± 0.4 mm, and 0.4 ± 0.6 mm, respectively, showing statistically observable differences (P < 0.05).

CONCLUSION

Doppler ultrasound based on image denoising algorithm can accurately evaluate the effect of aspirin combined with LMWH on the improvement of FGR and showed good application value.

Integration of Cognitive and Emotional Processing Predicts Poor and Good Outcomes of Psychotherapy

Abstract

With the aim of investigating analogies and differences between psychotherapeutic processes, ten good-outcome and ten poor-outcome cases were selected from a sample of patients treated at the University Hospital of Psychiatry, Salzburg, Austria, and the Department of Psycho-Traumatology of the Clinic St. Irmingard, Prien am Chiemsee, Germany. They were monitored daily using the Therapy Process Questionnaire (TPQ), and their evolution over time was analyzed by means of Principal Components Analysis and Linear Discriminant Analysis. The results highlight that poor-outcome patients show a separation between cognitive processes (Principal Component 1) and relational-emotional processes (Principal Component 2) (r = − 0.25; p = n.s.), while in the good-outcome patients these aspects are well integrated (r = 0.70; p = 0.02). These results corroborate the validity of the daily monitoring procedure and also indicate the need for greater attention to the relational and emotional aspects of the patients rather than merely to their cognitive functioning and well-being.

Key Message

In poor-outcome cases, burdensome emotions and interpersonal experiences on the one hand and cognitive/well-being aspects of the mental processing on the other, stay unrelated. Successful therapeutic processing, as in good-outcome cases, requires an integration of cognitive and affective components.

Quantifying the CVD-grown two-dimensional materials via image clustering

Machine learning (ML) techniques have been recently employed to facilitate the development of novel two-dimensional (2D) materials. Among various synthesis approaches, chemical vapor deposition (CVD) has demonstrated tremendous potential in producing high-quality 2D flakes with good controllability, enabling large-scale production at a relatively low cost. Traditionally, the quality of CVD-grown samples can be manually evaluated based on optical images which is labor-intensive and time-consuming. In this paper, we explored a data-driven unsupervised quality assessment strategy based on image clustering via integrating self-organizing map (SOM) and k-means methods for optical image analysis of CVD-grown 2D materials_. The high matching rate between the clustering results and material experts' labels indicated a good accuracy of the proposed clustering algorithm. The proposed unsupervised ML methodology will provide materials scientists with an effective tool kit for efficient evaluation of CVD-grown materials' quality and has a broad applicability for various material systems.

ADMET Predictability at Boehringer Ingelheim: State-of-the-Art, and Do Bigger Datasets or Algorithms Make a Difference?

Computational methods assisting drug discovery and development are routine in the pharmaceutical industry. Digital recording of ADMET assays has provided a rich source of data for development of predictive models. Despite the accumulation of data and the public availability of advanced modeling algorithms, the utility of prediction in ADMET research is not clear. Here, we present a critical evaluation of the relationships between data volume, modeling algorithm, chemical representation and grouping, and temporal aspect (time sequence of assays) using an in-house ADMET database. We find no large difference in prediction algorithms nor any systemic and substantial gain from increasingly large datasets. Temporal-based data enlargement led to performance improvement in only in a limited number of assays, and with fractional improvement at best. Assays that are well-, intermediately-, or poorly-suited for ADMET predictions and reasons for such behavior are systematically identified, generating realistic expectations for areas in which computational models can be used to guide decision making in molecular design and development.

Exploring the cause of the dual allosteric targeted inhibition attaching to allosteric sites enhancing SHP2 inhibition

SHP2 is a protein tyrosine phosphatase (PTP) that can regulate the tyrosine phosphorylation level. Overexpression of SHP2 will promote the development of cancer diseases, so SHP2 has become one of the popular targets for the treatment of cancer. Studies have reported that both SHP099 and SHP844 are inhibitors of SHP2 and bind to different allosteric sites 1 and 2, respectively. Studies have shown that combining SHP099 with SHP844 will enhance pharmacological pathway inhibition in cells. This study uses molecular dynamic simulations to explore the dual allosteric targeted inhibition mechanism. The result shows that the residues THR108-TRP112 (allosteric site 1) move to LEU236-GLN245 (αB-αC link loop in PTP domain) , the residues of GLN79-GLN87 (allosteric site 2) get close to LEU262-GLN269 (αA-αB link loop in PTP domain) and HIS458-ARG465 (P-loop) come near to ARG501-THR507 (Q-loop) in SHP2-SHP099-SHP844 system, which makes the "inactive conformation" more stable and prevents the substrate from entering the catalytic site. Meanwhile, residue GLU110 (allosteric site 1), ARG265 (allosteric site 2), and ARG501 (Q-loop) are speculated to be the key residues that causing the SHP2 protein in auto-inhibition conformation. It is hoped that this study will provide clues for the development of the dual allosteric targeted inhibition of SHP2.

Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning

Detection of alloreactive anti-HLA antibodies is a frequent and mandatory test before and after organ transplantation to determine the antigenic targets of the antibodies. Nowadays, this test involves the measurement of fluorescent signals generated through antibody-antigen reactions on multi-beads flow cytometers. In this study, in a cohort of 1,066 patients from one country, anti-HLA class I responses were analyzed on a panel of 98 different antigens. Knowing that the immune system responds typically to "shared" antigenic targets, we studied the clustering patterns of antibody responses against HLA class I antigens without any a priori hypothesis, applying two unsupervised machine learning approaches. At first, the principal component analysis (PCA) projections of intra-locus specific responses showed that anti-HLA-A and anti-HLA-C were the most distantly projected responses in the population with the anti-HLA-B responses to be projected between them. When PCA was applied on the responses against antigens belonging to a single locus, some already known groupings were confirmed while several new cross-reactive patterns of alloreactivity were detected. Anti-HLA-A responses projected through PCA suggested that three cross-reactive groups accounted for about 70% of the variance observed in the population, while anti-HLA-B responses were mainly characterized by a distinction between previously described Bw4 and Bw6 cross-reactive groups followed by several yet undocumented or poorly described ones. Furthermore, anti-HLA-C responses could be explained by two major cross-reactive groups completely overlapping with previously described C1 and C2 allelic groups. A second feature-based analysis of all antigenic specificities, projected as a dendrogram, generated a robust measure of allelic antigenic distances depicting bead-array defined cross reactive groups. Finally, amino acid combinations explaining major population specific cross-reactive groups were described. The interpretation of the results was based on the current knowledge of the antigenic targets of the antibodies as they have been characterized either experimentally or computationally and appear at the HLA epitope registry.

Autophagy-Related Genes in Atherosclerosis

Background

Atherosclerosis (AS) is a common chronic vascular inflammatory disease and one of the main causes of cardiovascular/cerebrovascular diseases (CVDs). Autophagy-related genes (ARGs) play a crucial part in pathophysiological processes of AS. However, the expression profile of ARGs has rarely been adopted to explore the relationship between autophagy and AS. Therefore, using the expression profile of ARGs to explore the relationship between autophagy and AS may provide new insights for the treatment of CVDs.

Methods

The differentially expressed ARGs of the GSE57691 dataset were obtained from the Human Autophagy Database (HADb) and the Gene Expression Omnibus (GEO) database, and the GSE57691 dataset contains 9 aortic atheroma tissues and 10 normal aortic tissues. The differentially expressed ARGs of the GSE57691 dataset were analyzed by protein-protein interaction (PPI), gene ontology analysis (GO), and Kyoto Encyclopedia of Genes and Genomes analysis (KEGG) and were chosen to explore related miRNAs/transcriptional factors.

Results

The GSE57691 dataset had a total of 41 differentially expressed ARGs. The GO analysis results revealed that ARGs were mainly enriched in autophagy, autophagosome, and protein serine/threonine kinase activity. KEGG analysis results showed that ARGs were mainly enriched in autophagy-animal and longevity regulating signaling pathways. Expressions of ATG5, MAP1LC3B, MAPK3, MAPK8, and RB1CC1 were regarded as focus in the PPI regulatory networks. Furthermore, 11 related miRNAs and 6 related transcription factors were obtained by miRNAs/transcription factor target network analysis.

Conclusions

Autophagy and ARGs may play a vital role in regulating the pathophysiology of AS.

DNA damage is inversely associated to blood levels of DHA and EPA fatty acids in Brazilian children and adolescents

This study aimed to investigate the association between DNA damage and blood levels of docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), retinol, beta-carotene and riboflavin in Brazilian children and adolescents. Subjects (n = 140) were healthy boys and girls aged 9 to 13 years in Ribeirão Preto (SP, Brazil). Data collection included anthropometry, assessment of energy intake and blood sampling. DNA damage was evaluated by single-cell gel electrophoresis (comet assay). Principal component analysis (PCA) was used to verify associations between blood concentrations of vitamins, polyunsaturated fatty acids and DNA damage. Multiple regression analyses, k-means cluster, and analysis of covariance (ANCOVA), adjusted for confounding variables such as age, sex, energy intake, body mass index and total cholesterol (when needed), were applied to confirm the associations. PCA explained 69.4% of the inverse relationships between DNA damage and blood levels of DHA, EPA, retinol, and beta-carotene. Results were confirmed by ANCOVA and multiple regression analyses for DHA and EPA. In conclusion, omega-3-fatty acids were inversely associated with DNA damage in Brazilian children and adolescents and may be a protective factor against the development of future diseases.

Physical Frailty in COPD Patients with Chronic Respiratory Failure

Background

The prevalence of physical frailty and its clinical characteristics in advanced chronic obstructive pulmonary disease (COPD) is unknown, as well as the usefulness of functional capacity tests to screen for physical frailty. The aim of the study was to evaluate the proportion and clinical portrait of COPD patients with chronic respiratory failure exhibiting physical frailty at the time of referral to home-based pulmonary rehabilitation. We also evaluate the usefulness of the short physical performance battery (SPPB) and timed-up and go (TUG) as potential screening tools for physical frailty. Finally, we evaluated the specific contribution of gait speed to the frailty Fried total score.

Methods

This was a prospective observational study in which physical frailty was defined using Fried criteria (body mass loss, exhaustion, low physical activity, slower walking and weakness). Clinical portrait was documented from daily physical activity, exercise tolerance, functional capacity, anxiety and depressive symptoms, health-related quality of life, and fatigue scores. The ability of the SPPB and TUG to predict physical frailty was investigated using receiver operating characteristic curves. Contribution of each Fried criteria was evaluated with a principal component analysis (PCA).

Results

Amongst the 44 included participants (FEV₁, 33 ± 13% of predicted), 19 were physically frail. Frail individuals had lower daily steps number, exercise tolerance and functional capacity, and higher fatigue, anxiety, and depressive symptom scores (p<0.05) compared to non-frail individuals. SPPB and TUG did not have an acceptable detection accuracy for screening physical frailty. PCA indicated that gait speed was the main contributor to the Fried total score of physical frailty.

Conclusion

Physical frailty affects a large proportion of COPD patients with chronic respiratory failure starting a home-based intervention and was associated with worse clinical status. Although the present results need to be confirmed by adequately powered studies, gait speed seems to have the potential to become a simple screening tool for physical frailty in this population.

Pharmacokinetics/pharmacometabolomics-pharmacodynamics reveals the synergistic mechanism of a multicomponent herbal formula, Baoyuan decoction against cardiac hypertrophy

Multicomponent herbal formulas (MCHFs) have earned a wide reputation for their definite efficacy in preventing or treating chronic complex diseases. However, holistic elucidation of the causal relationship between the bioavailable ingredients of MCHFs and their multitarget interactions is very challenging. To solve this problem, pharmacokinetics/pharmacometabolomics-pharmacodynamics (PK/PM-PD) combined with a multivariate biological correlation-network strategy was developed and applied to a classic MCHF, Baoyuan decoction (BYD), to clarify its active components and synergistic mechanism against cardiac hypertrophy (CH). First, multiple plasma metabolic biomarkers for β-adrenergic agonist-induced CH rats were identified by using untargeted metabolomic profiling, and then, these CH-associated endogenous metabolites and the absorbed BYD-compounds in plasma at different treatment stages after oral administration of BYD were analyzed by using targeted PK and PM. Second, the dynamic relationship of BYD-related compounds and CH-associated endogenous metabolites and signaling pathways was built by using multivariate and bioinformatic correlation analysis. Finally, metabolic-related PD indicators were predicted and further verified by biological tests. The results demonstrated that the bioavailable BYD-compounds, such as saponins and flavonoids, presented differentiated and distinctive metabolic features and showed positive or negative correlations with various CH-altered metabolites and PD-indicators related to gut microbiota metabolism, amino acid metabolism, lipid metabolism, energy homeostasis, and oxidative stress at different treatment stages. This study provides a novel strategy for investigating the dynamic interaction between BYD and the biosystem, providing unique insight for disclosing the active components and synergistic mechanisms of BYD against CH, which also supplies a reference for other MCHF related research.

Phytochemicals as potential drug candidates for targeting SARS CoV 2 proteins, an in silico study

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a member of the family Coronaviridae, and the world is currently witnessing a global pandemic outbreak of this viral disease called COVID-19. With no specific treatment regime, this disease is now a serious threat to humanity and claiming several lives daily. In this work, we selected 24 phytochemicals for an in silico docking study as candidate drugs, targeting four essential proteins of SARS-CoV-2 namely Spike glycoprotein (PDB id 5WRG), Nsp9 RNA binding protein (PDB id 6W4B), Main Protease (PDB id 6Y84), and RNA dependent RNA Polymerase (PDB id 6M71). After statistical validation, the results indicated that a total of 11 phytochemicals divided into two clusters might be used as potential drug candidates against SARS-CoV-2.

Discrepancies in metabolomic biomarker identification from patient-derived lung cancer revealed by combined variation in data pre-treatment and imputation methods

INTRODUCTION

The identification of metabolomic biomarkers predictive of cancer patient response to therapy and of disease stage has been pursued as a "holy grail" of modern oncology, relying on the metabolic dysfunction that characterizes cancer progression. In spite of the evaluation of many candidate biomarkers, however, determination of a consistent set with practical clinical utility has proven elusive.

OBJECTIVE

In this study, we systematically examine the combined role of data pre-treatment and imputation methods on the performance of multivariate data analysis methods and their identification of potential biomarkers.

METHODS

Uniquely, we are able to systematically evaluate both unsupervised and supervised methods with a metabolomic data set obtained from patient-derived lung cancer core biopsies with true missing values. Eight pre-treatment methods, ten imputation methods, and two data analysis methods were applied in combination.

RESULTS

The combined choice of pre-treatment and imputation methods is critical in the definition of candidate biomarkers, with deficient or inappropriate selection of these methods leading to inconsistent results, and with important biomarkers either being overlooked or reported as a false positive. The log transformation appeared to normalize the original tumor data most effectively, but the performance of the imputation applied after the transformation was highly dependent on the characteristics of the data set.

CONCLUSION

The combined choice of pre-treatment and imputation methods may need careful evaluation prior to metabolomic data analysis of human tumors, in order to enable consistent identification of potential biomarkers predictive of response to therapy and of disease stage.

Optimisation and feature selection of poly-beta-amino-ester as a drug delivery system for cartilage

Drug localisation is one of the main challenges in treating cartilage; poly-beta-amino-esters (PBAEs) drug conjugates are a possible solution; their efficacy depends on the polymer structure hence the full potential of this system is still unknown.

Drug localisation is still one of the main challenges in treating pathologies affecting cartilage; poly-beta-amino-esters (PBAEs) drug conjugates are a possible solution; however, their efficacy highly depends on the polymer structure hence the full potential of this delivery system is still unknown. For the purpose of optimising the delivery system design, a large library of PBAEs was synthesised and dexamethasone (DEX) uptake in cartilage was determined. All three components of PBAE (amine, acrylate and end-capping) impacted the outcome. The most effective PBAE identified enhanced DEX uptake by 8 folds compared to an equivalent dose of the commercial formulation and also prevented, through delivery of DEX, the cartilage degradation caused by IL-1α (interleukine1α). A chemometrics based predictive model was constructed and PBAEs properties most affecting the performance of the drug delivery systems were identified. This model will allow further computer based PBAEs optimisation and fast track the bench to market process for this delivery system.

Screening of Bioequivalent Extended-Release Formulations for Metformin by Principal Component Analysis and Convolution-Based IVIVC Approach

Bioequivalence (BE) is usually hard to achieve for extended-release (ER) dosage form products due to not only its complicated formulation but also to the BCS classification of the investigated drugs. Considering the difficulties in establishing full-scale IVIVC and limited in vivo pharmacokinetics data in the early stage of formulation development, we have selected BCS III drug metformin as a model drug to demonstrate a novel approach for the selection of BE formulations. Firstly, dissolution tests in both standard and biorelevant media were performed followed by identification of the most similar formulation WM to the reference product (GXR) based on principal component analysis (PCA) of the dissolution data. Then, we developed an IVIVC model using the reported GXR pharmacokinetics profiles via a convolution-based approach. Based on our established IVIVC and in vitro dissolution profiles of generic metformin ER products, we were able to predict their in vivo pharmacokinetic profiles and quantitatively compare the differences in AUC and C_max to ensure the correct selection of BE product. Finally, the selection of WM as the BE formulation of GXR was confirmed with a pilot BE study in healthy volunteers under fasting state. Moreover, the in vivo data from the fed state study were further integrated into our IVIVC model to identify FeSSIF-V2 as the biorelevant media for WM. Our novel integrative approach of PCA with a convolution-based IVIVC was successfully adopted for the screening of the BE metformin ER formulation and such an approach could be further utilized for the effective selection of BE formulation for other drugs/formulations with complex in vivo absorption processes.

Luteolin Alleviates Methamphetamine-Induced Hepatotoxicity by Suppressing the p53 Pathway-Mediated Apoptosis, Autophagy, and Inflammation in Rats

Misuse of the psychostimulant methamphetamine (METH) could induce serious hepatotoxicity. Our previous study revealed the effects of luteolin on alleviating METH-induced hepatotoxicity, however, the detailed mechanisms have not been elucidated. In this study, rats were orally pretreated with 100 mg/kg luteolin or sodium dodecyl sulfate water, and then METH (15 mg/kg, intraperitoneal [i.p.]) or saline was administered. Histopathological and biochemical analyses were used to determine the alleviative effects of luteolin. Based on the RNA-sequencing data, METH induced 1859 differentially expressed genes (DEGs) in comparison with the control group, which were enriched into 11 signaling pathways. Among these DEGs, 497 DEGs could be regulated through luteolin treatment and enriched into 16 pathways. The p53 signaling pathway was enriched in both METH administered and luteolin pretreated rats. Meanwhile, luteolin significantly suppressed METH-induced elevation of p53, caspase9, caspase3, cleaved caspase3, the ratio of Bax/Beclin-2, as well as autophagy-related Beclin-1, Atg5, and LC3-II. Luteolin also relieved METH-induced hepatotoxicity by decreasing inflammation factors, including TNF-α, IL-1β, and IL-18. Moreover, the levels of PI3K, p-Akt, and the normalized ratio of p-Akt/Akt declined after METH administration, whereas luteolin pretreatment failed to reverse these effects. Our results suggest that luteolin alleviates METH-induced hepatic apoptosis, autophagy, and inflammation through repressing the p53 pathway. It further illustrates the protective mechanisms of luteolin on METH-induced hepatotoxicity and provides a research basis for clinical treatment.

The swine as a vehicle for research in trauma-induced coagulopathy: Introducing principal component analysis for viscoelastic coagulation tests

BACKGROUND

Uncontrolled bleeding is the leading cause of potentially preventable deaths among trauma patients. Tissue injury and shock result in trauma-induced coagulopathy (TIC). There are still uncertainties regarding detection methods and best practice management for TIC, and a deeper understanding of the pathophysiology requires robust animal models. The applicability of swine in coagulation studies, particularly after trauma has not been sufficiently elucidated. We, therefore, evaluated the swine as a vehicle for TIC research in a selection of trauma modalities.

METHODS

Twenty-six landrace swine (3 females/23 males) (mean weight, 60.0 kg) were anesthetized and randomized to negative controls, receiving no manipulation (n = 5), positive controls by hemodilution (n = 5), pulmonary contusion without hemorrhage (n = 5), pulmonary contusion with hemorrhage (n = 5), and blast polytrauma with hypothermia, hypoperfusion, hypoventilation, and systemic inflammation (n = 6). A comprehensive coagulation panel was analyzed at baseline, 20 minutes and 120 minutes after trauma.

RESULTS

PT(INR), aPTT, thrombocytes, and fibrinogen did not change after trauma. D-dimer increased (p < 0.0001), prothrombin decreased (p < 0.05) and aPC decreased (p < 0.01) after polytrauma. PAI-1 decreased after pulmonary contusion with hemorrhage (p < 0.05). Positive controls displayed changes in PT(INR), thrombocytes, fibrinogen, prothrombin, aPC (p < 0.05). Principal Component Analysis of rotational thromboelastometry presented pathologic coagulation profiles in both polytrauma and positive control groups with vectors extending outside the 95% confidence interval, which were not detected in negative controls.

CONCLUSION

Coagulopathy was induced after severe porcine blast polytrauma, specifically detected in rotational thromboelastometry. A novel method for principal component analysis of viscoelastic tests was introduced which may increase the detection sensitivity and differentiation of TIC phenotypes and should be further investigated in trauma populations.

Diagnosis of Dry Eye Disease Using Principal Component Analysis: A Study in Animal Models of the Disease

PURPOSE

To evaluate whether principal component analysis (PCA) can assess various diagnostic tests of dry eye disease (DED), providing a simplified, more informative measure of disease status than individual clinical test parameters (ICTP).

MATERIALS AND METHODS

ICTP were analyzed using PCA in two groups of normal rabbits (Groups 1 and 2). Group 3, not truly normal, was also assessed. DED was induced in Group 1 by complete dacryoadenectomy; in Groups 2 and 3 by injection of concanavalin A. Tear break up time, tear osmolarity, Schirmer's tear test and rose bengal staining were the ICTP measured in all groups. Statistical analysis including descriptive statistics, t test, correlation coefficients and PCA was done. PCA using ICTP data from Group 1 generated axes; Group 2 and 3 were plotted over these axes.

RESULTS

All groups had induction of DED. Correlations for all ICTP were in the correct direction and were strongest for Group 1 and weakest in Group 3. PCA clearly separated DED and normal eyes. Principal component (PC) 1, made up of nearly equal contributions from the four clinical tests, explained 73% of the variation and provided a means to separate normal from DED. PC 1 values under 0.52 can be mathematically defined as DED. Of all pairwise comparisons, PC 1 vs PC 2 and PC 1 vs PC 3 were the most informative providing excellent spatial separation and additional information regarding DED status.

CONCLUSIONS

PCA proved useful for evaluating DED providing a simpler, more comprehensive assessment than ICTP. PC 1 is a valuable, clinically relevant, and informative metric for DED status and severity having superior diagnostic value and statistical strength compared to ICTP. Spatial information on biplots of PC 1 vs PC 3 is also informative. PCA, and specifically PC 1, has the potential to serve as a biomarker for DED.

Utilization of Time Series Tools in Life-sciences and Neuroscience

Time series tools are part and parcel of modern day research. Their usage in the biomedical field; specifically, in neuroscience, has not been previously quantified. A quantification of trends can tell about lacunae in the current uses and point towards future uses. We evaluated the principles and applications of few classical time series tools, such as Principal Component Analysis, Neural Networks, common Auto-regression Models, Markov Models, Hidden Markov Models, Fourier Analysis, Spectral Analysis, in addition to diverse work, generically lumped under time series category. We quantified the usage from two perspectives, one, information technology professionals', other, researchers utilizing these tools for biomedical and neuroscience research. For understanding trends from the information technology perspective, we evaluated two of the largest open source question and answer databases of Stack Overflow and Cross Validated. We quantified the trends in their application in the biomedical domain, and specifically neuroscience, by searching literature and application usage on PubMed. While the use of all the time series tools continues to gain popularity in general biomedical and life science research, and also neuroscience, and so have been the total number of questions asked on Stack overflow and Cross Validated, the total views to questions on these are on a decrease in recent years, indicating well established texts, algorithms, and libraries, resulting in engineers not looking for what used to be common questions a few years back. The use of these tools in neuroscience clearly leaves room for improvement.

Phylostratic Shift of Whole-Genome Duplications in Normal Mammalian Tissues towards Unicellularity Is Driven by Developmental Bivalent Genes and Reveals a Link to Cancer

Tumours were recently revealed to undergo a phylostratic and phenotypic shift to unicellularity. As well, aggressive tumours are characterized by an increased proportion of polyploid cells. In order to investigate a possible shared causation of these two features, we performed a comparative phylostratigraphic analysis of ploidy-related genes, obtained from transcriptomic data for polyploid and diploid human and mouse tissues using pairwise cross-species transcriptome comparison and principal component analysis. Our results indicate that polyploidy shifts the evolutionary age balance of the expressed genes from the late metazoan phylostrata towards the upregulation of unicellular and early metazoan phylostrata. The up-regulation of unicellular metabolic and drug-resistance pathways and the downregulation of pathways related to circadian clock were identified. This evolutionary shift was associated with the enrichment of ploidy with bivalent genes (p < 10⁻¹⁶). The protein interactome of activated bivalent genes revealed the increase of the connectivity of unicellulars and (early) multicellulars, while circadian regulators were depressed. The mutual polyploidy-c-MYC-bivalent genes-associated protein network was organized by gene-hubs engaged in both embryonic development and metastatic cancer including driver (proto)-oncogenes of viral origin. Our data suggest that, in cancer, the atavistic shift goes hand-in-hand with polyploidy and is driven by epigenetic mechanisms impinging on development-related bivalent genes.

COSMO-RS-Based Descriptors for the Machine Learning-Enabled Screening of Nucleotide Analogue Drugs against SARS-CoV-2

Chemical similarity-based approaches employed to repurpose or develop new treatments for emerging diseases, such as COVID-19, correlates molecular structure-based descriptors of drugs with those of a physiological counterpart or clinical phenotype. We propose novel descriptors based on a COSMO-RS (short for conductor-like screening model for real solvents) σ-profiles for enhanced drug screening enabled by machine learning (ML). The descriptors' performance is hereby illustrated for nucleotide analogue drugs that inhibit the ribonucleic acid-dependent ribonucleic acid polymerase, key to viral transcription and genome replication. The COSMO-RS-based descriptors account for both chemical reactivity and structure, and are more effective for ML-based screening than fingerprints based on molecular structure and simple physical/chemical properties. The descriptors are evaluated using principal component analysis, an unsupervised ML technique. Our results correlate with the active monophosphate forms of the leading drug remdesivir and the prospective drug EIDD-2801 with nucleotides, followed by other promising drugs, and are superior to those from molecular structure-based descriptors and molecular docking. The COSMO-RS-based descriptors could help accelerate drug discovery for the treatment of emerging diseases.

Identification of Antitumor Constituents in Toad Venom by Spectrum-Effect Relationship Analysis and Investigation on Its Pharmacologic Mechanism

(1) Background: Toad venom (Bufonis Venenum, known as ‘Chansu’ in Chinese), the secretion of the ear-side gland and skin gland of Bufo gargarizans cantor or Duttaphrynus melanostictus Schneider, has been utilized to treat several diseases in China for thousands of years. However, due to the chemical variability of the components, systematic chemical composition and the key pharmacophores in toad venom have not yet fully understood. Besides, it contains a variety of effective compounds with different physiological activity and chemotypes, mainly including alkaloids, bufogenins, bufotoxins, and so on. The recent pharmacological researches have demonstrated that several bufogenins have remarkable pharmacological effects, such as anti-inflammatory, analgesic effects, and anti-tumor effects. Aim of the study: To identify the bioactive compounds and pharmacophores originating from toad venom based on analyzing spectrum-effect relationship by chemometrics and to explore the anti-cancer mechanism primarily. (2) Materials and methods: Fingerprint of the 21 batches of samples was established using HPLC (High Performance Liquid Chromatography). The anti-tumor activity of extracts were determined by in-vitro assays. Chemometric analysis was used to establish the spectrum-effect model and screen for active ingredients. Pharmacodynamic tests for the screened active compound monomers were conducted with in-vitro assays. Further anti-tumor mechanisms were investigated using western blot and flow cytometry. (3) Results: The established spectrum-effect model has satisfactory fitting effect and predicting accuracy. The inhibitory effect of major screened compounds on lung carcinoma cells A549 were validated in vitro, demonstrating that arenobufagin, telocinobufogenin, and cinobufotalin had significant anti-tumor effects. Through further investigation of the mechanism by western blotting and flow cytometry, we elucidated that arenobufagin induces apoptosis in A549 cells with the enhanced expression of cleaved PARP (poly (ADP-ribose) polymerase). These results may provide valuable information for further structural modification of bufadienolides to treat lung cancer and a method for discovery of anti-tumor active compounds. Conclusions: Our research offers a more scientific method for screening the principal ingredients dominating the pharmacodynamic function. These screened compounds (arenobufagin, etc.) were proven to induce apoptosis by overactivation of the PARP-pathway, which may be utilized to make BRCA (breast cancer susceptibility gene) mutant cancer cells more vulnerable to DNA damaging agents and kill them.

Bioactive structural basis of proteoglycans from Sarcandra glabra based on spectrum-effect relationship

ETHNOPHARMACOLOGICAL RELEVANCE

Proteoglycans are one of the active ingredients of great importance in Sarcandra glabra. The biological activities of proteoglycans extracted from Sarcandra glabra including suppressing tumor growth and antioxidant activity were studied. However, raw materials from different regions may cause differences in the activity of natural extracts, especially for bioactive biomacromolecules. Conventional identification of S.glabra cannot accurately reflect the distinguishing relationship between internal components and the pharmacological activity. The identification of biologically active structures was obtained by constructing multiple fingerprint and spectrum-effect relationship.

AIM OF THE STUDY

To evaluate the bioactive structural basis of proteoglycans from S.glabra based on spectrum-effect relationship and chemometric methods.

MATERIALS AND METHODS

Multiple fingerprinting including HPSEC, PMP-HPLC, and FT-IR of proteoglycans was established from 18 batches of samples based on the structural characteristics. Both antitumor activity and antioxidant activity were determined. Mathematical analysis was used to analyze the spectrum-effect relationship.

RESULTS

PCA results showed monosaccharides including Xly, Rha, and GlcA, carboxyl group in acidic sugars, peptide bond in proteins, and methylene groups could be used as markers for distinguishing the samples from different sources. The results of the spectrum-effect relationship analysis indicated that the bioactive markers of inhibitory activity on MG63 and U2OS cells by PLS-DA were related to GlcA, Xyl, Fuc, β-glycosidic bonds, peptide linkage, and methylene groups. Markers composing monosaccharide for antioxidant activity were Xyl, GlcA, and GlcN. Meanwhile, the group markers were pyranose ring, carboxyl group, peptide linkage, and methylene structure.

CONCLUSIONS

The material basis that affects the pharmacological efficacy could be found according to the spectrum-effect relationship analysis. This study could lay a foundation for further exploring the relationship between structural characteristics and pharmacodynamics of macromolecular glycoconjugates in Traditional Chinese Medicine.

Patterns of 1,748 Unique Human Alloimmune Responses Seen by Simple Machine Learning Algorithms

Allele specific antibody response against the polymorphic system of HLA is the allogeneic response marker determining the immunological risk for graft acceptance before and after organ transplantation and therefore routinely studied during the patient's workup. Experimentally, bead bound antigen- antibody reactions are detected using a special multicolor flow cytometer (Luminex). Routinely for each sample, antibody responses against 96 different HLA antigen groups are measured simultaneously and a 96-dimensional immune response vector is created. Under a common experimental protocol, using unsupervised clustering algorithms, we analyzed these immune intensity vectors of anti HLA class II responses from a dataset of 1,748 patients before or after renal transplantation residing in a single country. Each patient contributes only one serum sample in the analysis. A population view of linear correlations of hierarchically ordered fluorescence intensities reveals patterns in human immune responses with striking similarities with the previously described CREGs but also brings new information on the antigenic properties of class II HLA molecules. The same analysis affirms that "public" anti-DP antigenic responses are not correlated to anti DR and anti DQ responses which tend to cluster together. Principal Component Analysis (PCA) projections also demonstrate ordering patterns clearly differentiating anti DP responses from anti DR and DQ on several orthogonal planes. We conclude that a computer vision of human alloresponse by use of several dimensionality reduction algorithms rediscovers proven patterns of immune reactivity without any a priori assumption and might prove helpful for a more accurate definition of public immunogenic antigenic structures of HLA molecules. Furthermore, the use of Eigen decomposition on the Immune Response generates new hypotheses that may guide the design of more effective patient monitoring tests.

Integrated biomarker for type 2 diabetes mellitus and impaired fasting glucose based on metabolomics analysis using ultra-high performance liquid chromatography quadrupole-Orbitrap high-resolution accurate mass spectrometry

RATIONALE

The prevalence of type 2 diabetes mellitus (T2DM) is increasing but its early diagnosis in high risk populations remains challenging using only fasting blood glucose (FBG) or hemoglobin A1c measurements. It is, therefore, important to search for an integrated biomarker for early diagnosis by determining metabolites associated with the progression of the disease.

METHODS

We recruited 149 participants (51 T2DM patients, 50 individuals with impaired fasting glucose (IFG) and 48 normal glucose tolerance subjects). Their serum samples were analyzed based on a metabolomics approach using ultra-high-performance liquid chromatography quadrupole-Orbitrap high-resolution accurate mass spectrometry (UHPLC/Q-Orbitrap HRMS). The changes in metabolites were profiled and evaluated using univariate and multivariate analyses. Furthermore, a biomarker model was established and the potential biomarkers were evaluated using binary logistic regression analysis and receiver operating characteristic analysis with AUC (area under the curve). Pathway analysis of differential metabolites was performed to reveal the important biological information.

RESULTS

Thirty-eight differential metabolites were identified as significantly associated with T2DM patients and 23 differential metabolites with IFG individuals, mainly amino acids, carnitines, and phospholipids. By evaluating 17 potential biomarkers, we defined a novel integrated biomarker consisting of 2-acetolactate, 2-hydroxy-2,4-pentadienoate, L-arabinose and L-glutamine. The AUCs of the integrated biomarker with IFG and T2DM patients were 0.874 and 0.994, respectively, which showed a superior diagnostic performance. The levels of 2-acetolactate and 2-hydroxy-2,4-pentadienoate were strongly positively correlated with FBG, while L-glutamine and L-arabinose were strongly negatively associated with FBG. After pathway analysis, it was suggested that the majority of the influenced metabolic pathways associated with diabetes referred to amino acid metabolism.

CONCLUSIONS

The integrated biomarker could diagnose IFG and T2DM with a superior diagnostic performance. This finding provides support for novel biomarkers in the diagnosis and treatment of diabetes.

Principal component analysis of blood microRNA datasets facilitates diagnosis of diverse diseases

Early, ideally pre-symptomatic, recognition of common diseases (e.g., heart disease, cancer, diabetes, Alzheimer’s disease) facilitates early treatment or lifestyle modifications, such as diet and exercise. Sensitive, specific identification of diseases using blood samples would facilitate early recognition. We explored the potential of disease identification in high dimensional blood microRNA (miRNA) datasets using a powerful data reduction method: principal component analysis (PCA). Using Qlucore Omics Explorer (QOE), a dynamic, interactive visualization-guided bioinformatics program with a built-in statistical platform, we analyzed publicly available blood miRNA datasets from the Gene Expression Omnibus (GEO) maintained at the National Center for Biotechnology Information at the National Institutes of Health (NIH). The miRNA expression profiles were generated from real time PCR arrays, microarrays or next generation sequencing of biologic materials (e.g., blood, serum or blood components such as platelets). PCA identified the top three principal components that distinguished cohorts of patients with specific diseases (e.g., heart disease, stroke, hypertension, sepsis, diabetes, specific types of cancer, HIV, hemophilia, subtypes of meningitis, multiple sclerosis, amyotrophic lateral sclerosis, Alzheimer’s disease, mild cognitive impairment, aging, and autism), from healthy subjects. Literature searches verified the functional relevance of the discriminating miRNAs. Our goal is to assemble PCA and heatmap analyses of existing and future blood miRNA datasets into a clinical reference database to facilitate the diagnosis of diseases using routine blood draws.

SD-OCT peripapillary nerve fibre layer and ganglion cell complex parameters in glaucoma: principal component analysis

BACKGROUND/AIMS

To identify objective glaucoma-related structural features based on peripapillary (p) and macular (m) spectral domain optical coherence tomography (SD-OCT) parameters and assess their discriminative ability between healthy and glaucoma patients.

METHODS

Two hundred and sixty eyes (91 controls and 169 glaucoma) were included in this prospective study. After a complete examination, all participants underwent the posterior pole and the peripapillary retinal nerve fibre layer (pRNFL) protocols of the Spectralis SD-OCT. Principal component analysis (PCA), a data reduction method, was applied to identify and characterise the main information provided by the ganglion cell complex (GCC). The discriminative ability between healthy and glaucomatous eyes of the first principal components (PCs) was compared with that of conventional SD-OCT parameters (pRNFL, macular RNFL (mRNFL), macular ganglion cell layer (mGCL)and macular inner plexiform layer (mIPL)) using 10-fold cross-validated areas under the curve (AUC).

RESULTS

The first PC explained 58% of the total information contained in the GCC and the pRNFL parameters and was the result of a general combination of almost all variables studied (diffuse distribution). Other PCs were driven mainly by pRNFL and mRNFL measurements. PCs and pRNFL had similar AUC (0.95 vs 0.96, p=0.88), and outperformed the other structural measurements: mRNFL (0.91, p=0.002), mGCL (0.92, p=0.02) and mIPL (0.92, p=0.0001).

CONCLUSIONS

PCA identified a diffuse representation of the papillary and macular SD-OCT parameters as the most important PC to summarise structural data in healthy and glaucomatous eyes. PCs and pRNFL parameters showed the greatest discriminative ability between healthy and glaucoma cases.

Avoid Oversimplifications in Machine Learning: Going beyond the Class-Prediction Accuracy

Class-prediction accuracy provides a quick but superficial way of determining classifier performance. It does not inform on the reproducibility of the findings or whether the selected or constructed features used are meaningful and specific. Furthermore, the class-prediction accuracy oversummarizes and does not inform on how training and learning have been accomplished: two classifiers providing the same performance in one validation can disagree on many future validations. It does not provide explainability in its decision-making process and is not objective, as its value is also affected by class proportions in the validation set. Despite these issues, this does not mean we should omit the class-prediction accuracy. Instead, it needs to be enriched with accompanying evidence and tests that supplement and contextualize the reported accuracy. This additional evidence serves as augmentations and can help us perform machine learning better while avoiding naive reliance on oversimplified metrics.

There is a huge potential for machine learning, but blind reliance on oversimplified metrics can mislead. Class-prediction accuracy is a common metric used for determining classifier performance. This article provides examples to show how the class-prediction accuracy is superficial and even misleading. We propose some augmentative measures to supplement the class-prediction accuracy. This in turn helps us to better understand the quality of learning of the classifier.

Cytokine network analysis of immune responses before and after autologous dendritic cell and tumor cell vaccine immunotherapies in a randomized trial

Background

In a randomized phase II trial conducted in patients with metastatic melanoma, patient-specific autologous dendritic cell vaccines (DCV) were associated with longer survival than autologous tumor cell vaccines (TCV). Both vaccines presented antigens from cell-renewing autologous tumor cells. The current analysis was performed to better understand the immune responses induced by these vaccines, and their association with survival.

Methods

110 proteomic markers were measured at a week-0 baseline, 1 week before the first of 3 weekly vaccine injections, and at week-4, 1 week after the third injection. Data was presented as a deviation from normal controls. A two-component principal component (PC) statistical analysis and discriminant analysis were performed on this data set for all patients and for each treatment cohort.

Results

At baseline PC-1 contained 64.4% of the variance and included the majority of cytokines associated with Th1 and Th2 responses, which positively correlated with beta-2-microglobulin (B2M), programmed death protein-1 (PD-1) and transforming growth factor beta (TGFβ1). Results were similar at baseline for both treatment cohorts. After three injections, DCV-treated patients showed correlative grouping among Th1/Th17 cytokines on PC-1, with an inverse correlation with B2M, FAS, and IL-18, and correlations among immunoglobulins in PC-2. TCV-treated patients showed a positive correlation on PC-1 among most of the cytokines and tumor markers B2M and FAS receptor. There were also correlative changes of IL12p40 with both Th1 and Th2 cytokines and TGFβ1. Discriminant analysis provided additional evidence that DCV was associated with innate, Th1/Th17, and Th2 responses while TCV was only associated with innate and Th2 responses.

Conclusions

These analyses confirm that DCV induced a different immune response than that induced by TCV, and these immune responses were associated with improved survival.

Trial registration Clinical trials.gov NCT004936930 retrospectively registered 28 July 2009

Human Clinical Relevance of the Porcine Model of Pseudoallergic Infusion Reactions

Pigs provide a highly sensitive animal model for pseudoallergic infusion reactions, which are mild-to-severe hypersensitivity reactions (HSRs) that arise following intravenous administration of certain nanoparticulate drugs (nanomedicines) and other macromolecular structures. This model has been used in research for three decades and was also proposed by regulatory bodies for preclinical assessment of the risk of HSRs in the clinical stages of nano-drug development. However, there are views challenging the human relevance of the model and its utility in preclinical safety evaluation of nanomedicines. The argument challenging the model refers to the “global response” of pulmonary intravascular macrophages (PIM cells) in the lung of pigs, preventing the distinction of reactogenic from non-reactogenic particles, therefore overestimating the risk of HSRs relative to its occurrence in the normal human population. The goal of this review is to present the large body of experimental and clinical evidence negating the “global response” claim, while also showing the concordance of symptoms caused by different reactogenic nanoparticles in pigs and hypersensitive man. Contrary to the model’s demotion, we propose that the above features, together with the high reproducibility of quantifiable physiological endpoints, validate the porcine “complement activation-related pseudoallergy” (CARPA) model for safety evaluations. However, it needs to be kept in mind that the model is a disease model in the context of hypersensitivity to certain nanomedicines. Rather than toxicity screening, its main purpose is specific identification of HSR hazard, also enabling studies on the mechanism and mitigation of potentially serious HSRs.