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

Polysorbate stability: Effects of packaging materials, buffers, counterions, and pH

Polysorbates, widely used excipients in drug formulations, present a stability challenge due to complex degradation processes. This study investigates the hydrolysis of polysorbate (PS) under temperature stress (50 °C), focusing on the impact of primary packaging materials (glass vs. plastic vials), buffers (histidine and acetic acid), counterions (chloride vs. malate), and pH (4-7). Our findings reveal that leachables from plastic vials inhibit PS degradation in both histidine and acetic acid buffers. Kinetic parameters derived from sigmoidal fitting suggest distinct degradation mechanisms for each buffer. Furthermore, the malate counterion with histidine displays inhibitory effects on PS hydrolysis. Principal component analysis was employed to identify key factors. These results highlight the critical role of excipients and packaging in PS stability, providing valuable insights for biopharmaceutical formulation development and a deeper understanding of PS degradation complexities.

Enzymatic and ultrasound assisted β-cyclodextrin extraction of active ingredients from Forsythia suspensa and their antioxidant and anti-inflammatory activities

With the proposal of the 2030 Agenda for Sustainable Development, the Chinese medicine extraction technology has been innovatively improved to prioritize low energy consumption, sustainability, and minimized organic solvent utilization. Forsythia suspensa (FS) possesses favorable pharmacological properties and is extensively utilized in traditional Chinese medicine. However, due to the limitations of the composition and extraction methods, its potential has not been fully developed. Thus, a combination of ultrasound-assisted extraction (UAE), enzyme-assisted extraction (EAE), and β-cyclodextrin extraction (β-CDE) was employed to isolate and purify rutin, phillyrin, and forsythoside A from FS. The results demonstrated that the efficiency of extracting enzymatic and ultrasound assisted β-cyclodextrin extraction (EUA-β-CDE) was highly influenced by the temperature and duration of hydrolysis, as well as the duration of the extraction process. According to the results of the single-factor experiment, Box-Behnken design (BBD) in Response surface method (RSM) was used to optimize the experimental parameters to achieve the maximum comprehensive evaluation value (CEV) value. The EUA-β-CDE compared with other extraction methods, has good extraction effect and low energy consumption by high performance liquid chromatography (HPLC), scanning electron microscopy (SEM), calculation of power consumption and CO2 emission The EUA-β-CDE compared with other extraction methods, has good extraction effect and low energy consumption by HPLC, SEM, calculation of power consumption and CO2 emission. Then, the structural characteristics of EUA-β-CDE of FS extract had significant interaction with β-CD by Fourier infrared spectroscopy (FT-IR) and differential scanning calorimetry (DSC). In addition, EUA-β-CDE extract has good antioxidant and anti-inflammatory activities. The establishment of EUA-β-CDE of FS provides a new idea for the development and application of other sustainable extraction methods of traditional Chinese medicine.

Method Development for Omics Analyses using Schirmer Strips

PURPOSE

The aim of this article was to investigate whether Schirmer strips gathered during clinical dry eye examinations can be prepared for omics analyses in a standardized way, to adjust for variations in tear volume and enable two separate omics analyses from the same sample. In addition, the intention was to investigate whether fluorescein dye instillation in the eyes gave bias effects on metabolomic analysis.

METHODS

Twelve samples from six individuals, with normal or reduced tear production, were collected. Half of the samples were harvested after instillation of fluorescein in the eye. Each strip was divided in half along the length and prepared with a new method for extracting tear content from the Schirmer strip. The new method was established to compensate for different dilutions of metabolites in varying Schirmer strip wetting levels when using identical extraction volume for all samples. Metabolomic data were compared in samples with and without fluorescein dye and Schirmer strips ranging from 1 to 35 mm wetting levels using a global LC-MS method.

RESULTS

All samples were successfully analyzed with an average of ∼350 relevant features detected per sample after using both positive and negative electrospray ionization mode, despite low tear volumes in some samples and that only one half of the Schirmer strips were used. Principal component analysis plots and heatmaps revealed no bias effects of fluorescein dye presence or different Schirmer strip values when using the proposed method.

CONCLUSION

A high number of relevant metabolomic features can be extracted from longitudinally cut halves of Schirmer strips, which may enable analyses with more than one omics modality from the same sample. With the pre-analytical method described, Schirmer strips can be used for metabolomic analyses even in cases of very low or high tear volume with or without fluorescence.

Alicyclobacillus spp. in fruit-based products: Isolation, identification, quantitative assessment (SPME/GC-MS) of spoilage compounds and spore's resistance to thermal shocks

Alicyclobacillus spp. is the cause of great concern for the food industry due to their spores' resistance (thermal and chemical) and the spoilage potential of some species. Despite this, not all Alicyclobacillus strains can spoil fruit juices. Thus, this study aimed to identify Alicyclobacillus spp. strains isolated from fruit-based products produced in Argentina, Brazil, and Italy by DNA sequencing. All Alicyclobacillus isolates were tested for guaiacol production by the peroxidase method. Positive strains for guaiacol production were individually inoculated at concentration of 103 CFU/mL in 10 mL of orange (pH 3.90) and apple (pH 3.50) juices adjusted to 11°Brix, following incubation at 45 °C for at least 5 days to induce the production of the following spoilage compounds: Guaiacol, 2,6-dichlorophenol (2,6-DCP) and 2,6-dibromophenol (2,6-DBP). The techniques of micro-solid phase extraction by headspace (HS-SPME) and gas-chromatography with mass spectrometry (GC-MS) were used to identify and quantify the spoilage compounds. All GC-MS data was analyzed by principal component analysis (PCA). The effects of different thermal shock conditions on the recovery of Alicyclobacillus spores inoculated in orange and apple juice (11°Brix) were also tested. A total of 484 strains were isolated from 48 brands, and the species A. acidocaldarius and A. acidoterrestris were the most found among all samples analyzed. In some samples from Argentina, the species A. vulcanalis and A. mali were also identified. The incidence of these two main species of Alicyclobacillus in this study was mainly in products from pear (n = 108; 22.3 %), peach (n = 99; 20.5 %), apple (n = 86; 17.8 %), and tomato (n = 63; 13 %). The results indicated that from the total isolates from Argentina (n = 414), Brazil (n = 54) and Italy (n = 16) were able to produce guaiacol: 107 (25.8 %), 33 (61.1 %) and 13 (81.2 %) isolates from each country, respectively. The PCA score plot indicated that the Argentina and Brazil isolates correlate with higher production of guaiacol and 2,6-DCP/2,6-DBP, respectively. Heatmaps of cell survival after heat shock demonstrated that strains with different levels of guaiacol production present different resistances according to spoilage ability. None of the Alicyclobacillus isolates survived heat shocks at 120 °C for 3 min. This work provides insights into the incidence, spoilage potential, and thermal shock resistance of Alicyclobacillus strains isolated from fruit-based products.

Direct Interaction of Minocycline to p47phox Contributes to its Attenuation of TNF-α-Mediated Neuronal PC12 Cell Death: Experimental and Simulation Validation

Minocycline, a repurposed approved medication, shows promise in treating neurodegeneration. However, the specific pathways targeted by minocycline remain unclear despite the identification of molecular targets. This study explores minocycline's potential protective effects against TNF-α-mediated neuronal death in PC12 cells, with a focus on unraveling its interactions with key molecular targets. The study begins by exploring minocycline's protective role against TNF-α-mediated neuronal death in PC12 cells, showcasing a substantial reduction in cleaved caspase-3 expression, DNA fragmentation, and intracellular ROS levels following minocycline pretreatment. Subsequently, a comprehensive analysis utilizing pull-down assays, computational docking, mutation analysis, molecular dynamics simulations, and free energy calculations is conducted to elucidate the direct interaction between minocycline and p47phox-the organizer subunit of NADPH oxidase-2 (NOX2) complex. Computational insights, including a literature survey and analysis of key amino acid residues, reveal a potential binding site for minocycline around Trp193 and Cys196. In silico substitutions of Trp193 and Cys196 further confirm their importance in binding with minocycline. These integrated findings underscore minocycline's protective mechanisms, linking its direct interaction with p47phox to the modulation of NOX2 activity and attenuation of NOX-derived ROS generation. Minocycline demonstrates protective effects against TNF-α-induced PC12 cell death, potentially linked to its direct interaction with p47phox. This interaction leads to a reduction in NOX2 complex assembly, ultimately attenuating NOX-derived ROS generation. These findings hold significance for researchers exploring neuroprotection and the development of p47phox inhibitors.

A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies

BACKGROUND

RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs. Often, the quality of the data is variable, procured differently, and contains unwanted noise hampering the ability of a predictive model to extract useful information. Data preprocessing methods can be applied in attempts to reduce these systematic variations and harmonize the datasets before they are used to build a machine learning model for resolving tissue of origins.

RESULTS

We aimed to investigate the impact of data preprocessing steps-focusing on normalization, batch effect correction, and data scaling-through trial and comparison. Our goal was to improve the cross-study predictions of tissue of origin for common cancers on large-scale RNA-Seq datasets derived from thousands of patients and over a dozen tumor types. The results showed that the choice of data preprocessing operations affected the performance of the associated classifier models constructed for tissue of origin predictions in cancer.

CONCLUSION

By using TCGA as a training set and applying data preprocessing methods, we demonstrated that batch effect correction improved performance measured by weighted F1-score in resolving tissue of origin against an independent GTEx test dataset. On the other hand, the use of data preprocessing operations worsened classification performance when the independent test dataset was aggregated from separate studies in ICGC and GEO. Therefore, based on our findings with these publicly available large-scale RNA-Seq datasets, the application of data preprocessing techniques to a machine learning pipeline is not always appropriate.

Identification of Helicobacter pylori-carcinogenic TNF-alpha-inducing protein inhibitors via daidzein derivatives through computational approaches

Gastric cancer is considered a class 1 carcinogen that is closely linked to infection with Helicobacter pylori (H. pylori), which affects over 1 million people each year. However, the major challenge to fight against H. pylori and its associated gastric cancer due to drug resistance. This research gap had led our research team to investigate a potential drug candidate targeting the Helicobacter pylori-carcinogenic TNF-alpha-inducing protein. In this study, a total of 45 daidzein derivatives were investigated and the best 10 molecules were comprehensively investigated using in silico approaches for drug development, namely pass prediction, quantum calculations, molecular docking, molecular dynamics simulations, Lipinski rule evaluation, and prediction of pharmacokinetics. The molecular docking study was performed to evaluate the binding affinity between the target protein and the ligands. In addition, the stability of ligand-protein complexes was investigated by molecular dynamics simulations. Various parameters were analysed, including root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA) and dynamic cross-correlation matrix (DCCM). The results has confirmed that the ligand-protein complex CID: 129661094 (07) and 129664277 (08) formed stable interactions with the target protein. It was also found that CID: 129661094 (07) has greater hydrogen bond occupancy and stability, while the ligand-protein complex CID 129664277 (08) has greater conformational flexibility. Principal component analysis revealed that the ligand-protein complex CID: 129661094 (07) is more compact and stable. Hydrogen bond analysis revealed favourable interactions with the reported amino acid residues. Overall, this study suggests that daidzein derivatives in particular show promise as potential inhibitors of H. pylori.

Co-catalpol alleviates fluoxetine-induced main toxicity: Involvement of ATF3/FSP1 signaling-mediated inhibition of ferroptosis

BACKGROUND

Fluoxetine is often used as a well-known first-line antidepressant. However, it is accompanied with hepatogenic injury as its main organ toxicity, thereby limiting its application despite its superior efficacy. Fluoxetine is commonly traditionally used combined with some Chinese antidepressant prescriptions containing Rehmannia glutinosa (Dihuang) for depression therapy and hepatoprotection. Our previous experiments showed that co-Dihuang can alleviate fluoxetine-induced liver injury while efficiencies, and catalpol may be the key ingredient to characterize the toxicity-reducing and synergistic effects. However, whether co-catalpol can alleviate fluoxetine-induced liver injury and its toxicity-reducing mechanism remain unclear.

PURPOSE

On the basis of the first recognition of the dose and duration at which pre-fluoxetine caused hepatic injury, co-catalpol's alleviation of fluoxetine-induced hepatic injury and its pathway was comprehensively elucidated.

METHOD AND RESULTS

The hepatoprotection of co-catalpol was evaluated by serum biochemical indexes sensitive to hepatic injury and multiple staining techniques for hepatic pathologic analysis. Subsequently, the pathway by which catalpol alleviated fluoxetine-induced hepatic injury was predicted by network pharmacology to be predominantly the inhibition of ferroptosis. These were validated and confirmed in subsequent experiments with key technologies and diagnostic reagents related to ferroptosis. Further molecular docking showed that activating transcription factor 3 (ATF3) and ferroptosis suppressor protein 1 (FSP1) were the the most prospective molecules for catalpol and fluoxetine among many ferroptosis-related molecules. The critical role of ATF3/FSP1 signaling was further observed by surface plasmon resonance, diagnostic reagents, transmission electron microscopy, Western blot, real-time PCR, immunofluorescence, and immunohistochemistry. Results showed that fluoxetine directly bound to ATF3 and FSP1; agonisting ATF3 or blocking FSP1 abolished the alleviation of catalpol on fluoxetine-induced liver injury, and both exacerbated ferroptosis. Moreover, co-catalpol significantly enhanced the antidepressant efficacy of fluoxetine against depressive behaviours in mice.

CONCLUSION

The hepatic impairment properties of fluoxetine were largely dependent on ATF3/FSP1 target-mediated ferroptosis. Co-catalpol alleviated fluoxetine-induced hepatic injury while enhancing its antidepressant efficacy, and that ATF3/FSP1 signaling-mediated inhibition of ferroptosis was involved in its co-administration detoxification mechanism. This study was the first to reveal the hepatotoxicity characteristics, targets, and mechanisms of fluoxetine; provide a detoxification and efficiency regimen by co-catalpol; and elucidate the detoxification mechanism.

Distinguishing brain tumors by Label-free confocal micro-Raman spectroscopy

BACKGROUND

Brain tumors have serious adverse effects on public health and social economy. Accurate detection of brain tumor types is critical for effective and proactive treatment, and thus improve the survival of patients.

METHODS

Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors.

RESULTS

Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm-1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %.

CONCLUSIONS

Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy.

Closed, one-stop intelligent and accurate particle characterization based on micro-Raman spectroscopy and digital microfluidics

Monoclonal antibodies are prone to form protein particles through aggregation, fragmentation, and oxidation under varying stress conditions during the manufacturing, shipping, and storage of parenteral drug products. According to pharmacopeia requirements, sub-visible particle levels need to be controlled throughout the shelf life of the product. Therefore, in addition to determining particle counts, it is crucial to accurately characterize particles in drug product to understand the stress condition of exposure and to implement appropriate mitigation actions for a specific formulation. In this study, we developed a new method for intelligent characterization of protein particles using micro-Raman spectroscopy on a digital microfluidic chip (DMF). Several microliters of protein particle solutions induced by stress degradation were loaded onto a DMF chip to generate multiple droplets for Raman spectroscopy testing. By training multiple machine learning classification models on the obtained Raman spectra of protein particles, eight types of protein particles were successfully characterized and predicted with high classification accuracy (93%-100%). The advantages of the novel particle characterization method proposed in this study include a closed system to prevent particle contamination, one-stop testing of morphological and chemical structure information, low sample volume consumption, reusable particle droplets, and simplified data analysis with high classification accuracy. It provides great potential to determine the probable root cause of the particle source or stress conditions by a single testing, so that an accurate particle control strategy can be developed and ultimately extend the product shelf-life.

Principal Component Analysis (PCA) of Molecular Descriptors for Improving Permeation through the Blood-Brain Barrier of Quercetin Analogues

Despite its beneficial pharmacological effects in the brain, partly by modulating inositol phosphate multikinase (IPMK) activity, the therapeutic use of quercetin is limited due to its poor solubility, low oral bioavailability, and low permeability through the blood-brain barrier (BBB). We aimed to identify quercetin analogues with improved BBB permeability and preserved binding affinities towards IPMK and to identify the molecular characteristics required for them to permeate the BBB. Binding affinities of quercetin analogues towards IPMK were determined by molecular docking. Principal component analysis (PCA) was applied to identify the molecular descriptors contributing to efficient permeation through the BBB. Among 34 quercetin analogues, 19 compounds were found to form more stable complexes with IPMK, and the vast majority were found to be more lipophilic than quercetin. Using two distinct in silico techniques, insufficient BBB permeation was determined for all quercetin analogues. However, using the PCA method, the descriptors related to intrinsic solubility and lipophilicity (logP) were identified as mainly responsible for clustering four quercetin analogues (trihydroxyflavones) with the highest BBB permeability. The application of PCA revealed that quercetin analogues could be classified with respect to their structural characteristics, which may be utilized in further analogue syntheses and lead optimization of BBB-penetrating IPMK modulators as neuroprotective agents.

Divergent neuroimmune signatures in the cerebrospinal fluid predict differential gender-specific survival among patients with HIV-associated cryptococcal meningitis

Introduction

Survival among people with HIV-associated cryptococcal meningitis (CM) remains low, particularly among women, despite the currently optimal use of antifungal drugs. Cryptococcus dissemination into the central nervous system [brain, spinal cord, and cerebrospinal fluid (CSF)] elicits the local production of cytokines, chemokines, and other biomarkers. However, no consistent diagnostic or prognostic neuroimmune signature is reported to underpin the risk of death or to identify mechanisms to improve treatment and survival. We hypothesized that distinct neuroimmune signatures in the CSF would distinguish survivors from people who died on antifungal treatment and who may benefit from tailored therapy.

Methods

We considered baseline clinical features, CSF cryptococcal fungal burden, and CSF neuroimmune signatures with survival at 18 weeks among 419 consenting adults by "gender" (168 women and 251 men by biological sex defined at birth).

Results

Survival at 18 weeks was significantly lower among women than among men {47% vs. 59%, respectively; hazard ratio (HR) = 1.4 [95% confidence interval (CI), 1.0 to 1.9; p = 0.023]}. Unsupervised principal component analysis (PCA) demonstrated divergent neuroimmune signatures by gender, survival, and intragender-specific survival. Overall, women had lower levels of programmed death ligand 1, Interleukin (IL) (IL-11RA/IL-1F30, and IL-15 (IL-15) than men (all p < 0.028). Female survivors compared with those who died expressed significant elevations in levels of CCL11 and CXCL10 chemokines (both p = 0.001), as well as increased T helper 1, regulatory, and T helper 17 cytokines (all p < 0.041). In contrast, male survivors expressed lower levels of IL-15 and IL-8 compared with men who died (p < 0.044).

Conclusions

Survivors of both genders demonstrated a significant increase in the levels of immune regulatory IL-10. In conclusion, the lower survival among women with CM was accompanied by distinct differential gender-specific neuroimmune signatures. These female and male intragender-specific survival-associated neuroimmune signatures provide potential targets for interventions to advance therapy to improve the low survival among people with HIV-associated CM.

MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects

Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset contains a balanced two-class design based on well-characterized and widely studied cell lines (A549 vs K562 or HCC1806 vs HS578T) with 48 or 36 biological and technical replicates altogether, allowing for investigation of a multitude of technical issues. These datasets allow for investigation of inter-connections between class and batch factors, or to develop approaches to compare and integrate data from DDA and DIA platforms.

Principal Component Analysis of Alternative Splicing Profiles Revealed by Long-Read ONT Sequencing in Human Liver Tissue and Hepatocyte-Derived HepG2 and Huh7 Cell Lines

The long-read RNA sequencing developed by Oxford Nanopore Technology provides a direct quantification of transcript isoforms. That makes the number of transcript isoforms per gene an intrinsically suitable metric for alternative splicing (AS) profiling in the application to this particular type of RNA sequencing. By using this simple metric and recruiting principal component analysis (PCA) as a tool to visualize the high-dimensional transcriptomic data, we were able to group biospecimens of normal human liver tissue and hepatocyte-derived malignant HepG2 and Huh7 cells into clear clusters in a 2D space. For the transcriptome-wide analysis, the clustering was observed regardless whether all genes were included in analysis or only those expressed in all biospecimens tested. However, in the application to a particular set of genes known as pharmacogenes, which are involved in drug metabolism, the clustering worsened dramatically in the latter case. Based on PCA data, the subsets of genes most contributing to biospecimens' grouping into clusters were selected and subjected to gene ontology analysis that allowed us to determine the top 20 biological processes among which translation and processes related to its regulation dominate. The suggested metrics can be a useful addition to the existing metrics for describing AS profiles, especially in application to transcriptome studies with long-read sequencing.

The shift from quantitative to qualitative thinking-problems and prospects as viewed from Husserl's and Hegel's philosophy

This article contrasts the views of the philosophers Husserl and Hegel on quantification in science and compares their proposals for conducting rigorous qualitative research. Both deem quantification integral to science, but furthermore proposed methodologies to investigate qualitative necessities achieved by a shift in conscious activity and awareness. However, their methodologies differ significantly. While Husserl rejects idealization and instead proposes intuitive means to ideate qualitative essential relations, Hegel suggests idealizing less one-sidedly, namely, qualitatively over and above quantitatively. The article first examines how quantification is achieved and how it contrasts with measuring. This contrast reveals that measuring implies knowledge of qualities. These qualities, however, thus far remain oddly external to the mathematical relations linking the various established equations. The article then follows Husserl's reconstruction of the development of science to illustrate the dismissal of many experiential qualities and how philosophy further amplified skepticism about science on qualities. Husserl's notion of the life-world and the method of eidetic variation are then introduced as means to counterbalance mathematical proceedings in science. However, this method reveals both eidetic qualitative structures and psychical structures without being able to distinguish between them. It is thus susceptible to idiosyncratic, traditional, and cultural biases. Subsequently, Hegel's description of the shift in conscious experience that sets qualitative from quantitative thinking apart is introduced. This shift may overcome the biases, but it faces skepticism that calls for further investigation of the experience of different kinds of thinking.

Multicomponent Analysis of Liuwei Dihuang Pills by a Single Marker Quantification Method and Chemometric Discrimination of Fingerprints

An effective and comprehensive quality evaluation method for Liuwei Dihuang pills (LDP) was established by the simultaneous determination of 8 active components in LDP by the quantitative analysis of multicomponents by single marker (QAMS) method and high-performance liquid chromatography (HPLC) fingerprint combined with chemometrics. These 8 active components were determined by QAMS and the external standard method (ESM), and the quantitative results of the two methods were compared to validate the accuracy and feasibility of the QAMS method. 8 active components showed good linear relationships within their ranges, whose average recoveries were 99.7∼102.3%. No significant difference was found (P > 0.05) in the quantitative results determined by QAMS and ESM. Furthermore, the fingerprint of LDP was also established, with 11 common peaks identified, and the similarity of the fingerprints of 21 batches of LDP was greater than 0.95. The 21 batches of LDP were basically divided into 3 groups by hierarchical cluster analysis (HCA) and principal component analysis (PCA), and 3 differential markers were screened out by orthogonal partial least squares discriminant analysis (OPLS-DA). The established QAMS method is accurate, economical, fast, and convenient and can simultaneously determine the content of 8 active components in LDP. HPLC fingerprint combined with chemometric analysis more comprehensively evaluated the quality consistency of different batches of LDP and analyzed the markers that cause quality differences between batches. It can provide a scientific basis and reference of quality consistency evaluation for the manufacturers and drug regulatory departments of the preparation.

Metabolomics analysis of MnO2 nanosheets CDT for breast cancer cells and mechanism of cytotoxic action

Chemodynamic therapy (CDT) has received more and more attention as an emerging therapeutic strategy, especially transition metals with Fenton or Fenton-like activity have good effects in CDT research, manganese dioxide nanosheets (MnO2 NSs) and their complexes have become one of the most favored nanomaterials in CDT of tumors. CDT is mainly based on the role of reactive oxygen species (ROS) in tumor treatment, which have clear chemical properties and produce clear chemical reactions. However, their mechanism of interaction with cells has not been fully elucidated. Here, we performed CDT on mouse breast cancer cells (4T1) based on MnO2 NSs, extracted the metabolites from the 4T1 cells during the treatment, and analyzed the differences in metabolites by using high-resolution liquid chromatography-mass spectrometry (LC-MS). Untargeted metabolomics studies were conducted using the relevant data. This study mainly explored the changes in MnO2 NSs on the metabolite profile of 4T1 cells and their potential impact on tumor therapy, in order to determine the mechanism of action of MnO2 NSs in the treatment of breast cancer. The results of the study showed the presence of 11 different metabolites in MnO2 NSs CDT for 4T1 tumor cells, including phosphoserine, sphingine, phosphocholine, and stearoylcarnitine. These findings provide a deeper understanding of breast cancer treatment, and are beneficial for the further research and clinical application of CDT.

Comprehensive molecular docking and dynamic simulations for drug repurposing of clinical drugs against multiple cancer kinase targets

Drug repurposing is a method to identify novel therapeutic agents from the existing drugs and clinical compounds. In the present comprehensive work, molecular docking, virtual screening and dynamics simulations were carried out for ten cancer types viz breast, colon, central nervous system, leukaemia, melanoma, ovarian, prostate, renal and lung (non-small and small cell) against validated eighteen kinase targets. The study aims to understand the action of chemotherapy drugs mechanism through binding interactions against selected targets via comparative docking simulations with the state-art molecular modelling suits such as MOE, Cresset-Flare, AutoDock Vina, GOLD and GLIDE. Chemotherapeutic drugs (n = 112) were shortlisted from standard drug databases with appropriate chemoinformatic filters. Based on docking studies it was revealed that leucovorin, nilotinib, ellence, thalomid and carfilzomib drugs possessed potential against other cancer targets. A library was built to enumerate novel molecules based on the scaffold and functional groups extracted from known drugs and clinical compounds. Twenty novel molecules were prioritised further based on drug-like attributes. These were cross docked against 1MQ4 Aurora-A Protein Kinase for prostate cancer and 4UYA Mitogen-activated protein kinase for renal cancer. All docking programs yielded similar results but interestingly AutoDock Vina yielded the lowest RMSD with the native ligand. To further validate the final docking results at atomistic level, molecular dynamics simulations were performed to ascertain the stability of the protein-ligand complex. The study enables repurposing of drugs and lead identification by employing a host of structure and ligand based virtual screening tools and techniques.Communicated by Ramaswamy H. Sarma.

Divergent Neuroimmune Signatures in the Cerebrospinal Fluid Predict Differential Gender-Specific Survival Among Patients With HIV-Associated Cryptococcal Meningitis

Survival among people with HIV-associated cryptococcal meningitis (CM) remains low, exceptionally among women with the increased threat of death on current optimal use of antifungal drugs. Cryptococcus dissemination into the central nervous system (CNS) prompts a neuroimmune reaction to activate pathogen concomitant factors. However, no consistent diagnostic or prognostic immune-mediated signature is reported to underpin the risk of death or mechanism to improve treatment or survival. We theorized that the distinct neuroimmune cytokine or chemokine signatures in the cerebrospinal fluid (CSF), distinguish survivors from people who died on antifungal treatment, who may benefit from tailored therapy. We considered the baseline clinical disease features, cryptococcal microbiologic factors, and CSF neuroimmune modulated signatures among 419 consenting adults by gender (biological sex assigned at birth) (168 females and 251 males) by 18 weeks of survival on antifungal management. Survival at 18 weeks was inferior among females than males (47% vs. 59%; hazard ratio HR=1.4, 95% CI: 1.0 to 1.9, and p=0.023). Unsupervised principal component analysis (PCA) demonstrated the divergent neuroimmune signatures by gender, survival, and intragender-specific survival. Overall, females displayed lower levels of PD-L1, IL-1RA, and IL-15 than males (all p≤0.028). Female survivors compared with those who died, expressed significant fold elevations in levels of CSF (CCL11 - myeloid and CXCL10 - lymphoid chemokine (in both p=0.001), and CSF Th1, Th2, and Th17 cytokines. In contrast, male survivors expressed distinctly lower levels of CSF IL-15 and IL-8 compared with those who died. Survivors of either gender demonstrated a significant increase in the levels of immune regulatory element, IL-10. In the finale, we classified divergent neuroimmune key signatures in CSF by gender, survival, and intragender-specific survival among people with HIV-associated cryptococcal meningitis. These intragender-specific survival associated-neuroimmune signatures, suggests the discrete role of gender immune regulating mechanisms as the possible targets for interventions to advance therapy to improve survival among people with HIV-associated cryptococcal meningitis.

Visualising structural and functional characteristics distinguishing between newly diagnosed high-tension and low-tension glaucoma patients

PURPOSE

To determine whether there are quantifiable structural or functional differences that can distinguish between high-tension glaucoma (HTG; intraocular pressure [IOP] > 21 mm Hg) and low-tension glaucoma (LTG; IOP ≤ 21 mm Hg) at diagnosis.

METHOD

This was a retrospective, cross-sectional study. Clinical results of one eye from 90 newly diagnosed HTG and 319 newly diagnosed LTG patients (117 with very-low-tension glaucoma [vLTG; ≤15 mm Hg] and 202 with middling LTG [mLTG; >15 mm Hg, ≤21 mm Hg]) were extracted, which included relevant demographic covariates of glaucoma, quantitative optical coherence tomography (including the optic nerve head, retinal nerve fibre layer and ganglion cell-inner plexiform layer) measurements and standard automated perimetry global metrics. We used binary logistic regression analysis to identify statistically significant clinical parameters distinguishing between phenotypic groups for inclusion in principal component (PC) (factor) analysis (PCA). The separability between each centroid for each cohort was calculated using the Euclidean distance (d(x,y)).

RESULTS

The binary logistic regression comparing HTG and all LTG identified eight statistically significant clinical parameters. Subsequent PCA results included three PCs with an eigenvalue >1. PCs 1 and 2 accounted for 21.2% and 20.2% of the model, respectively, with a d(x,y) = 0.468, indicating low separability between HTG and LTG. The analysis comparing vLTG, mLTG and HTG identified 15 significant clinical parameters, which were subsequently grouped into five PCs. PCs 1 and 2 accounted for 24.1% and 17.8%, respectively. The largest separation was observed between vLTG and HTG (d(x,y) = 0.581), followed by vLTG and mLTG (d(x,y) = 0.435) and lastly mLTG and HTG (d(x,y) = 0.210).

CONCLUSION

Conventional quantitative structural or functional parameters could not distinguish between pressure-defined glaucoma phenotypes at the point of diagnosis and are therefore not contributory to separating cohorts. The overlap in findings highlights the heterogeneity of the primary open-angle glaucoma clinical presentations among pressure-defined groups at the cohort level.

The colonic metabolism differences of main alkaloids in normal and colitis mice treated with Coptis chinensis Franch. and Sophora flavescens Ait. herbal pair using liquid chromatography-high resolution mass spectrometry method combined with chemometrics

Coptis chinensis Franch. and Sophora flavescens Ait. is a herbal pair frequently used in treating ulcerative colitis. However, the bio-disposition profile of the major components in the inflamed gut remains unclear, which is essential to understand the pharmacological material basis of this herb pair. Here we established an integral quantitative and chemometric method to deduce the colonic metabolism differences of this herbal pair in normal and colitis mice. With this LC-MS method, a total of 41 components have been found in the Coptis chinensis Franch. and Sophora flavescens Ait. extract, and 28 metabolites were found in the colon after oral administration. Alkaloid and its phase I metabolites were the main components in the colon of normal and colitis mice. The results of principal component analysis at 6 h after oral administration showed significant colonic metabolism differences between normal and colitis mice. Heamap results showed that colitis induced significant changes in the colonic bio-disposition of this herbal pair extract. In particular, in the context of colitis, the phase I metabolism of berberine, coptisine, jatrorrhizine, palmatine,and epiberberine has been inhibited. These results may provide a basis for understanding the pharmacological material basis of Coptis chinensis Franch. and Sophora flavescens Ait. in treating ulcerative colitis.

CNS-dominant human FMRP isoform rescues seizures, fear, and sleep abnormalities in Fmr1-KO mice

Fragile X syndrome is a neurodevelopmental disorder caused by the absence of the mRNA-binding protein fragile X messenger ribonucleoprotein (FMRP). Because FMRP is a highly pleiotropic protein controlling the expression of hundreds of genes, viral vector-mediated gene replacement therapy is viewed as a potential viable treatment to correct the fundamental underlying molecular pathology inherent in the disorder. Here, we studied the safety profile and therapeutic effects of a clinically relevant dose of a self-complementary adeno-associated viral (AAV) vector containing a major human brain isoform of FMRP after intrathecal injection into wild-type and fragile X-KO mice. Analysis of the cellular transduction in the brain indicated primarily neuronal transduction with relatively sparse glial expression, similar to endogenous FMRP expression in untreated wild-type mice. AAV vector-treated KO mice showed recovery from epileptic seizures, normalization of fear conditioning, reversal of slow-wave deficits as measured via electroencephalographic recordings, and restoration of abnormal circadian motor activity and sleep. Further assessment of vector efficacy by tracking and analyzing individual responses demonstrated correlations between the level and distribution of brain transduction and drug response. These preclinical findings further demonstrate the validity of AAV vector-mediated gene therapy for treating the most common genetic cause of cognitive impairment and autism in children.

Quality evaluation of compounds in leaves of six Taxus species based on UPLC-MS/MS and chemometrics

Introduction: Taxus species are used as medicinal plants all over the world. The leaves of Taxus species are sustainable medicinal resources that are rich in taxoids and flavonoids. However, traditional identification methods cannot effectively identify Taxus species on the basis of leaces used as raw medicinal materials, because their appearance and morphological characteristics are almost the same, and the probability of error identification increases in accordance with the subjective consciousness of the experimenter. Moreover, although the leaves of different Taxus species have been widely used, their chemical components are similar and lack systematic comparative research. Such a situation is challenging for quality assessment. Materials and methods: In this study, ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry combined with chemometrics was applied for the simultaneous determination of eight taxoids, four flavanols, five flavonols, two dihydroflavones, and five biflavones in the leaves of six Taxus species, namely, T. mairei, T. chinensis, T. yunnanensis, T. wallichiana, T. cuspidata, and T. media. Chemometric methods, including hierarchical cluster analysis, principal component analysis, orthogonal partial least squares-discriminate analysis, random forest iterative modeling, and fisher linear discriminant analysis, were utilized to differentiate and evaluate the six Taxus species. Results: This proposed method exhibited good linearity (R ² = 0.9999-0.9972) with a lower quantification limits of 0.94-3.05 ng/mL for all analytes. The intra- and inter-day precisions were within 6.83%. Six compounds, namely, 7-xylosyl-10-deacetyltaxol, ginkgetin, rutin, aromadendrin, 10-deacetyl baccatin III, and epigallocatechin, were identified through chemometrics for the first time. These compounds can be used as important chemical markers to distinguish the above six Taxus species rapidly. Conclusion: This study established a method for determination of the leaves of six Taxus species, and revealing the differences in the chemical components of these six Taxus species.

Dual functionality of pyrimidine and flavone in targeting genomic variants of EGFR and ER receptors to influence the differential survival rates in breast cancer patients

Breast cancer ranks as one of the most prevalent forms of cancer and stands as the primary global cause of mortality among women. Overexpression of EGFR and ER receptors or their genomic alterations leads to malignant transformation, disease aggression and is linked to poor patient survival outcomes. The clinical breast cancer patient's genomic expression, survival analysis, and computational drug-targeting approaches were used to identify best-hit phytochemicals for therapeutic purposes. Breast cancer patients have genomic alterations in EGFR (4%, n = 5699) and ER (9%, n = 8461), with the highest proportion being missense mutations. No statistically significant difference was observed in the patient survival rates between the altered and unaltered ER groups, unlike EGFR, with the lowest survival rates in the altered group. Computational screening of natural compound libraries (7711) against each EGFR (3POZ) and ER (3ERT) receptor shortlists the best-hit 3 compounds with minimum docking score (ΔG = -7.9 to -10.8), MMGBSA (-40.16 to -51.91 kcal/mol), strong intermolecular H-bonding, drug-like properties with least kd, and ki. MD simulation studies display stable RMSD, RMSF, and good residual correlation of best-hit common compounds (PubChem ID: 5281672 and 5280863) targeting both EGFR and ER receptors. In vitro, studies revealed that these common drugs exhibited a high anti-proliferative effect on MCF-7 and MDA-MB-231 breast cancer cells, with effective IC50 values (15-40 μM) and lower free energy, kd, and ki (5281672 > 5280863 > 5330286) much affecting HEK-293 non-cancerous cells, indicating the safety profile. The experimental and computational correlation studies suggest that the highly expressed EGFR and ER receptors in breast cancer patients having poor survival rates can be effectively targeted with best-hit common potent drugs with a multi-target therapeutic approach. Insight Box: The findings of this study provide valuable insights into the genomic/proteomic data, breast cancer patient's survival analysis, and EGFR and ER receptor variants structural analysis. The genetic alterations analysis of EGFR and ER/ESR1 in breast cancer patients reveals the high frequency of mutation types, which affect patient's survival rate and targeted therapies. The common best-hit compounds affect the cell survival patterns with effective IC50, drug-like properties having lower equilibrium and dissociation constants demonstrating the anti-proliferative effects. This work integrates altered receptor structural analysis, molecular interaction-based simulations, and ADMET properties to illuminate the identified best hits phytochemicals potential efficacy targeting both EGFR and ER receptors, demonstrating a multi-target therapeutic approach.

Repurposing FDA-approved drugs as FXR agonists: a structure based in silico pharmacological study

Farnesoid X receptor (FXR) modulates the expression of genes involved in lipid and carbohydrate homeostasis and inflammatory processes. This nuclear receptor is likely a tumor suppressor in several cancers, but its molecular mechanism of suppression is still under study. Several studies reported that FXR agonism increases the survival of colorectal, biliary tract, and liver cancer patients. In addition, FXR expression was shown to be down-regulated in many diseases such as obesity, irritable bowel syndrome, glomerular inflammation, diabetes, proteinuria, and ulcerative colitis. Therefore, development of novel FXR agonists may have significant potential in the prevention and treatment of these diseases. In this scenario, computer-aided drug design procedures can be resourcefully applied for the rapid identification of promising drug candidates. In the present study, we applied the molecular docking method in conjunction with molecular dynamics (MD) simulations to find out potential agonists for FXR based on structural similarity with the drug that is currently used as FXR agonist, obeticholic acid. Our results showed that alvimopan and montelukast could be used as potent FXR activators and outperform the binding affinity of obeticholic acid by forming stable conformation with the protein in silico. However, further investigational studies and validations of the selected drugs are essential to figure out their suitability for preclinical and clinical trials.

Insights Into The Effects of Amino Acid Substitutions on The Stability of Reteplase Structure: A Molecular Dynamics Simulation Study

Background

Reteplase (recombinant plasminogen activator, r-PA) is a recombinant protein designed to imitate the endogenous tissue plasminogen activator and catalyze the plasmin production. It is known that the application of reteplase is limited by the complex production processes and protein's stability challenges. Computational redesign of proteins has gained momentum in recent years, particularly as a powerful tool for improving protein stability and consequently its production efficiency. Hence, in the current study, we implemented computational approaches to improve r-PA conformational stability, which fairly correlates with protein's resistance to proteolysis.

Objectives

The current study was developed in order to evaluate the effect of amino acid substitutions on the stability of reteplase structure using molecular dynamic simulations and computational predictions.

Materials and Methods

Several web servers designed for mutation analysis were utilized to select appropriate mutations. Additionally, the experimentally reported mutation, R103S, converting wild type r-PA into non-cleavable form, was also employed. Firstly, mutant collection, consisting of 15 structures, was constructed based on the combinations of four designated mutations. Then, 3D structures were generated using MODELLER. Finally, 17 independent 20-ns molecular dynamics (MD) simulations were conducted and different analysis were performed like root-mean-square deviation (RMSD), root-mean-square fluctuations (RMSF), secondary structure analysis, number of hydrogen bonds, principal components analysis (PCA), eigenvector projection, and density analysis.

Results

Predicted mutations successfully compensated the more flexible conformation caused by R103S substitution, so, improved conformational stability was analyzed from MD simulations. In particular, R103S/A286I/G322I indicated the best results and remarkably enhanced the protein stability.

Conclusion

The conformational stability conferred by these mutations will probably lead to more protection of r-PA in protease-rich environments in various recombinant systems and potentially enhance its production and expression level.

Domain Adaptation Principal Component Analysis: Base Linear Method for Learning with Out-of-Distribution Data

Domain adaptation is a popular paradigm in modern machine learning which aims at tackling the problem of divergence (or shift) between the labeled training and validation datasets (source domain) and a potentially large unlabeled dataset (target domain). The task is to embed both datasets into a common space in which the source dataset is informative for training while the divergence between source and target is minimized. The most popular domain adaptation solutions are based on training neural networks that combine classification and adversarial learning modules, frequently making them both data-hungry and difficult to train. We present a method called Domain Adaptation Principal Component Analysis (DAPCA) that identifies a linear reduced data representation useful for solving the domain adaptation task. DAPCA algorithm introduces positive and negative weights between pairs of data points, and generalizes the supervised extension of principal component analysis. DAPCA is an iterative algorithm that solves a simple quadratic optimization problem at each iteration. The convergence of the algorithm is guaranteed, and the number of iterations is small in practice. We validate the suggested algorithm on previously proposed benchmarks for solving the domain adaptation task. We also show the benefit of using DAPCA in analyzing single-cell omics datasets in biomedical applications. Overall, DAPCA can serve as a practical preprocessing step in many machine learning applications leading to reduced dataset representations, taking into account possible divergence between source and target domains.

Development of a core descriptor set for Crohn's anal fistula

AIM

Crohn's anal fistula (CAF) is a complex condition, with no agreement on which patient characteristics should be routinely reported in studies. The aim of this study was to develop a core descriptor set of key patient characteristics for reporting in all CAF research.

METHOD

Candidate descriptors were generated from published literature and stakeholder suggestions. Colorectal surgeons, gastroenterologists and specialist nurses in inflammatory bowel disease took part in three rounds of an international modified Delphi process using nine-point Likert scales to rank the importance of descriptors. Feedback was provided between rounds to allow refinement of the next ratings. Patterns in descriptor voting were assessed using principal component analysis (PCA). Resulting PCA groups were used to organize items in rounds two and three. Consensus descriptors were submitted to a patient panel for feedback. Items meeting predetermined thresholds were included in the final set and ratified at the consensus meeting.

RESULTS

One hundred and thirty three respondents from 22 countries completed round one, of whom 67.0% completed round three. Ninety seven descriptors were rated across three rounds in 11 PCA-based groups. Forty descriptors were shortlisted. The consensus meeting ratified a core descriptor set of 37 descriptors within six domains: fistula anatomy, current disease activity and phenotype, risk factors, medical interventions for CAF, surgical interventions for CAF, and patient symptoms and impact on quality of life.

CONCLUSION

The core descriptor set proposed for all future CAF research reflects characteristics important to gastroenterologists and surgeons. This might aid transparent reporting in future studies.

An epithelial-mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis

OBJECTIVE

To identify susceptibility modules and genes for colorectal cancer (CRC) using weighted gene co-expression network analysis (WGCNA).

METHODS

Four microarray datasets were downloaded from the Gene Expression Omnibus database. We divided the tumor samples into three subgroups based on consensus clustering of gene expression, and analyzed the correlations between the subgroups and clinical features. The genetic features of the subgroups were investigated by gene set enrichment analysis (GSEA). A gene expression network was constructed using WGCNA, and a protein-protein interaction (PPI) network was used to identify the key genes. Gene modules were annotated by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses.

RESULTS

We divided the cancer cases into three subgroups based on consensus clustering (subgroups I, II, III). The green module identified by WGCNA was correlated with clinical characteristics. Ten key genes were identified according to their degree of connectivity in the protein-protein interaction network: FYN, SEMA3A, AP2M1, L1CAM, NRP1, TLN1, VWF, ITGB3, ILK, and ACTN1.

CONCLUSION

We identified 10 hub genes as candidate biomarkers for CRC. These key genes may provide a theoretical basis for targeted therapy against CRC.

An Approach to Identify HLA Class II Immunogenic Epitopes in the Greek Population through Machine Learning Algorithms

Current pre-transplantation routine matching involves serum anti-HLA antibodies quantification but cannot always preclude unfavorable graft outcomes. Epitope-based matching is proposed as a more precise approach, but to date no epitope-matching algorithm provides a satisfactory predictive tool for transplantation outcomes. In this study, anti-HLA-II loci responses from 1748 patients were analyzed with unsupervised machine learning algorithms, namely principal component analysis (PCA) and antigenic distances, projected as dendrograms. PCA for anti-HLA-DR anti-bodies revealed three main clusters of responses: anti-HLA-DR51 combined with anti-HLA-DRB1*01, anti-HLA-DR52 combined with anti-HLA-DRB1*08 and anti-HLA-DR53 combined with anti-HLA-DRB1*10. The dendrogram for anti-HLA-DR confirmed the pattern and showed further bisection of each cluster. Common epitopes present exclusively in all HLA molecules of each cluster were determined following the HLA epitope registry. Thus, we propose that 19 out of 123 HLA-DR epitopes are those that mainly lead anti-HLA-DR responses in the studied population. Likewise, we identified 22 out of 83 epitopes responsible for anti-HLA-DQ and 13 out of 62 responsible for anti-HLA-DP responses. Interpretation of these results may elucidate mechanisms of interlocus cross-reactivity, providing an alternative way of estimating the significance of each epitope in a population and thus suggesting a novel strategy towards optimal donor selection.

Discovery of the key active compounds in Citri Reticulatae Pericarpium (Citrus reticulata "Chachi") and their therapeutic potential for the treatment of COVID-19 based on comparative metabolomics and network pharmacology

Edible herbal medicines contain macro- and micronutrients and active metabolites that can take part in biochemical processes to help achieve or maintain a state of well-being. Citri Reticulatae Pericarpium (CRP) is an edible and medicinal herb used as a component of the traditional Chinese medicine (TCM) approach to treating COVID-19 in China. However, the material basis and related mechanistic research regarding this herb for the treatment of COVID-19 are still unclear. First, a wide-targeted UPLC-ESI-MS/MS-based comparative metabolomics analysis was conducted to screen for the active metabolites of CRP. Second, network pharmacology was used to uncover the initial linkages among these metabolites, their possible targets, and COVID-19. Each metabolite was then further studied via molecular docking with the identified potential SARS-CoV-2 targets 3CL hydrolase, host cell target angiotensin-converting enzyme II, spike protein, and RNA-dependent RNA polymerase. Finally, the most potential small molecule compound was verified by in vitro and in vivo experiments, and the mechanism of its treatment of COVID-19 was further explored. In total, 399 metabolites were identified and nine upregulated differential metabolites were screened out as potential key active metabolites, among which isorhamnetin have anti-inflammatory activity in vitro validation assays. In addition, the molecular docking results also showed that isorhamnetin had a good binding ability with the key targets of COVID-19. Furthermore, in vivo results showed that isorhamnetin could significantly reduced the lung pathological injury and inflammatory injury by regulating ATK1, EGFR, MAPK8, and MAPK14 to involve in TNF signaling pathway, PI3K-Akt signalling pathway, and T cell receptor signaling pathway. Our results indicated that isorhamnetin, as screened from CRP, may have great potential for use in the treatment of patients with COVID-19. This study has also demonstrated that comparative metabolomics combined with network pharmacology strategy could be used as an effective approach for discovering potential compounds in herbal medicines that are effective against COVID-19.

A multi-marker integrative analysis reveals benefits and risks of bariatric surgery

Bariatric surgery (BS) is an effective intervention for severe obesity and associated comorbidities. Although several studies have addressed the clinical and metabolic effects of BS, an integrative analysis of the complex body response to surgery is still lacking. We conducted a longitudinal data study with 36 patients with severe obesity who were tested before, 6 and 12 months after restrictive BS for more than one hundred blood biomarkers, including clinical, oxidative stress and metabolic markers, peptide mediators and red blood cell membrane lipids. By using a synthetic data-driven modeling based on principal component and correlation analyses, we provided evidence that, besides the early, well-known glucose metabolism- and weight loss-associated beneficial effects of BS, a tardive, weight-independent increase of the hepatic cholesterol metabolism occurs that is associated with potentially detrimental inflammatory and metabolic effects. Canonical correlation analysis indicated that oxidative stress is the most predictive feature of the BS-induced changes of both glucose and lipids metabolism. Our results show the power of multi-level correlation analysis to uncover the network of biological pathways affected by BS. This approach highlighted potential health risks of restrictive BS that are disregarded with the current practice to use weight loss as surrogate of BS success.

Transcriptome Analysis of Protection by Dendrobium Nobile Alkaloids (DNLA) against Chronic Alcoholic Liver Injury in Mice

Objective: To investigate the protective effects of Dendrobium nobile Lindl. alkaloids (DNLA) against chronic alcoholic liver injury. C57BL/6J mice were fed with the Lieber−DeCarli alcohol diet to induce chronic alcoholic liver injury. DNLA (20 mg/kg/day) was gavaged along with the alcohol diet for 28 days. Liver injury was evaluated by serum enzymes. Triglyceride levels, histopathology, and transcriptome changes were examined by RNA-Seq and qPCR. DNLA decreased serum triglyceride levels in mice receiving alcohol. Hepatocyte degeneration and steatosis were ameliorated by DNLA, as evidenced by H&E and Oil-red O staining. DNLA brought the alcohol-induced aberrant gene expression pattern towards normal. Alcohol induced 787 differentially expressed genes (padj < 0.01). DNLA induced 280 differentially expressed genes to a much less extent. Ingenuity pathway analysis showed that DNLA ameliorated alcohol-induced oxidative stress and xenobiotic metabolism disruption. qPCR verified that DNLA alleviated over-activation of Cyp2a4, Cyp2b10, and Abcc4; attenuated oxidative stress (Hmox1, Gstm3, Nupr1), reduced the expression of Nrf2 genes (Nqo1, Gclc, Vldlr); and rescued some metabolic genes (Insig1, Xbp1, Socs3, Slc10a2). In conclusion, DNLA was effective against alcohol-induced fatty liver disease, and the protection may be attributed to alleviated oxidative stress and restored metabolism homeostasis, probably through modulating nuclear receptor CAR-, PXR-, and Nrf2-mediated gene expression pathways.

On component-wise dissimilarity measures and metric properties in pattern recognition

In many real-world applications concerning pattern recognition techniques, it is of utmost importance the automatic learning of the most appropriate dissimilarity measure to be used in object comparison. Real-world objects are often complex entities and need a specific representation grounded on a composition of different heterogeneous features, leading to a non-metric starting space where Machine Learning algorithms operate. However, in the so-called unconventional spaces a family of dissimilarity measures can be still exploited, that is, the set of component-wise dissimilarity measures, in which each component is treated with a specific sub-dissimilarity that depends on the nature of the data at hand. These dissimilarities are likely to be non-Euclidean, hence the underlying dissimilarity matrix is not isometrically embeddable in a standard Euclidean space because it may not be structurally rich enough. On the other hand, in many metric learning problems, a component-wise dissimilarity measure can be defined as a weighted linear convex combination and weights can be suitably learned. This article, after introducing some hints on the relation between distances and the metric learning paradigm, provides a discussion along with some experiments on how weights, intended as mathematical operators, interact with the Euclidean behavior of dissimilarity matrices.

Applications of machine learning in tumor-associated macrophages

Evaluation of tumor-host interaction and intratumoral heterogeneity in the tumor microenvironment (TME) is gaining increasing attention in modern cancer therapies because it can reveal unique information about the tumor status. As tumor-associated macrophages (TAMs) are the major immune cells infiltrating in TME, a better understanding of TAMs could help us further elucidate the cellular and molecular mechanisms responsible for cancer development. However, the high-dimensional and heterogeneous data in biology limit the extensive integrative analysis of cancer research. Machine learning algorithms are particularly suitable for oncology data analysis due to their flexibility and scalability to analyze diverse data types and strong computation power to learn underlying patterns from massive data sets. With the application of machine learning in analyzing TME, especially TAM’s traceable status, we could better understand the role of TAMs in tumor biology. Furthermore, we envision that the promotion of machine learning in this field could revolutionize tumor diagnosis, treatment stratification, and survival predictions in cancer research. In this article, we described key terms and concepts of machine learning, reviewed the applications of common methods in TAMs, and highlighted the challenges and future direction for TAMs in machine learning.

Post-traumatic stress disorder among healthcare workers during the COVID-19 pandemic in Italy: Effectiveness of an eye movement desensitization and reprocessing intervention protocol

Aim

The Coronavirus 2019 (COVID-19) pandemic represents one of the most catastrophic events of recent times. Due to the hospitals’ emergency situation, the population of healthcare workers was the most affected. Healthcare workers who were exposed to COVID-19 patients are most likely to develop psychological distress and post-traumatic stress disorder (PTSD). The present study aimed at investigating PTSD in a sample of Italian healthcare workers during this outbreak and to evaluate the effectiveness of the Eye Movement Desensitization and Reprocessing (EMDR) Therapy with this population.

Methods

A total of 744 healthcare workers were included. 587 healthcare workers were treated with EMDR, while the other 157 were not treated. Participants were asked to provide sociodemographic information; the post-traumatic symptomatology was evaluated through Impact of Event Scale-Revised (IES-R) and to investigate the level of intensity of emotional activation was used The Emotion Thermometer (THERMO) at two time points (pre-post treatment).

Results

The results obtained between EMDR treatment and non-EMDR treatment were evaluated on only 2 hospitals. Treatment group n = 68 vs. waitlist non-treatment group n = 157. All scores pre- and post-EMDR decreased significantly (p < 0.001) showing an evident effect of EMDR. The differences between pre- and post-treatment of the IES-R scores of subjects in which EMDR was performed as compared to the scores pre- and post-12 weeks of waiting list subjects in which it was not performed were significantly different (p < 0.001).

Limitation

The emergency situation did not provide an opportunity to explore further aspects that would have been important for research. One limitation is the use and analysis of only two standardized tests. In addition, other psychopathologies were not investigated as outcome measures. A limitation is the comparison of subjects treated online and de visu. Although the protocol used was the same, the mode of intervention may have influenced the results. In addition, the effectiveness of EMDR treatment was only evaluated at two time points (pre-post) with no possibility of follow-up and the lack of a control group.

Discussion/conclusion

The findings of the present study suggest that healthcare workers were at high risk of developing PTSD when confronted with COVID-19 outbreak and suggest the importance of psychological support during this humanitarian emergency.

Perspectives for better batch effect correction in mass-spectrometry-based proteomics

Mass-spectrometry-based proteomics presents some unique challenges for batch effect correction. Batch effects are technical sources of variation, can confound analysis and usually non-biological in nature. As proteomic analysis involves several stages of data transformation from spectra to protein, the decision on when and what to apply batch correction on is often unclear. Here, we explore several relevant issues pertinent to batch effect correct considerations. The first involves applications of batch effect correction requiring prior knowledge on batch factors and exploring data to uncover new/unknown batch factors. The second considers recent literature that suggests there is no single best batch effect correction algorithm---i.e., instead of a best approach, one may instead ask, what is a suitable approach. The third section considers issues of batch effect detection. And finally, we look at potential developments for proteomic-specific batch effect correction methods and how to do better functional evaluations on batch corrected data.

Integrated comparative metabolomics and network pharmacology approach to uncover the key active ingredients of Polygonati rhizoma and their therapeutic potential for the treatment of Alzheimer’s disease

Alzheimer’s disease (AD) has become a worldwide disease affecting human health and resulting in a heavy economic burden on the healthcare system. Polygonati rhizoma (PR), a kind of traditional Chinese medicine (TCM), is known to improve learning and memory abilities. However, its AD-treating material basis and therapeutic potential for the treatment of AD have remained unclear. Therefore, the present study aimed to uncover the key active ingredients of PR and its therapeutic potential for the treatment of AD. First, we used comparative metabolomics to identify the potential key active ingredients in the edible and medicinal PR. Second, network pharmacology was used to decipher the effects and potential targets of key active ingredients in the PR for the treatment of AD, and molecular docking was further used to identify the binding ability of those active ingredients with AD-related target of AChE. The rate of acetylcholinesterase (AChE) inhibition, oxidative stress, neuroprotective effects, and anti-inflammatory activity were assessed in vitro to screen the potential active ingredients in the PR with therapeutic potential against AD. Finally, APPswe/PS1dE9 AD mice were used to screen the therapeutic components in the PR. Seven overlapping upregulated differential metabolites were identified as the key active ingredients, among which cafestol, isorhamnetin, and rutin have AChE inhibitory activity, anti-inflammatory activity, and neuroprotective effects in vitro validation assays. Furthermore, in vivo results showed that cafestol, isorhamnetin, and rutin displayed several beneficial effects in AD transgenic mice by reducing the number of Aβ-positive spots and the levels of inflammatory cytokines, inhibiting the AChE activity, and increasing the antioxidant levels. Each compound is involved in a different function in the early stages of AD. In conclusion, our results corroborate the current understanding of the therapeutic effects of PR on AD. In addition, our work demonstrated that the proposed network pharmacology-integrated comparative metabolomics strategy is a powerful way of identifying key active ingredients and mechanisms contributing to the pharmacological effects of TCM.

A Review of Pharmaceutical Robot based on Hyperspectral Technology

The quality and safety of medicinal products are related to patients’ lives and health. Therefore, quality inspection takes a key role in the pharmaceutical industry. Most of the previous solutions are based on machine vision, however, their performance is limited by the RGB sensor. The pharmaceutical visual inspection robot combined with hyperspectral imaging technology is becoming a new trend in the high-end medical quality inspection process since the hyperspectral data can provide spectral information with spatial knowledge. Yet, there is no comprehensive review about hyperspectral imaging-based medicinal products inspection. This paper focuses on the pivotal pharmaceutical applications, including counterfeit drugs detection, active component analysis of tables, and quality testing of herbal medicines and other medical materials. We discuss the technology and hardware of Raman spectroscopy and hyperspectral imaging, firstly. Furthermore, we review these technologies in pharmaceutical scenarios. Finally, the development tendency and prospect of hyperspectral imaging technology-based robots in the field of pharmaceutical quality inspection is summarized.

Searching for Novel Anaplastic Lymphoma Kinase Inhibitors: Structure-Guided Screening of Natural Compounds for a Tyrosine Kinase Therapeutic Target in Cancers

Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase molecular target with broad importance for drug discovery, especially in the field of cancer therapeutics. ALK belongs to the insulin receptor superfamily that is involved in various malignancies, including non-small cell lung cancer, anaplastic large cell lymphoma, and neuroblastoma. ALK has been shown to play a role in cancer progression and metastasis, making it one of the prime targets to develop novel anticancer therapeutics. In this context, natural compounds can be an important resource to unravel novel ALK inhibitors. In this study, we report a structure-based virtual screening of natural compounds from the ZINC database, with an eye to potential inhibitors of ALK. Molecular docking was performed on a natural compound library, and top hits holding good binding affinity, docking score, and specificity toward ALK were selected. The hits were further evaluated based on the PAINS (pan-assay interference compounds) filter, ADMET (absorption, distribution, metabolism, excretion, toxicity) properties, PASS (prediction of activity spectra for substances) analysis, and two-dimensional interaction of protein-ligand complexes. Importantly, two natural compounds (ZINC03845566 and ZINC03999625) were identified as potential candidates for ALK, having appreciable affinity and specificity toward the ALK binding pocket and depicting drug-like properties as predicted from ADMET analysis and their physicochemical parameters. An all-atom molecular dynamics simulation for 100 ns on ALK promised stable ALK-ligand complexes. Hence, we conclude that ZINC03845566 and ZINC03999625 can act as potential ALK inhibitors against cancers where ALK plays a role, for example, in lung cancer, among others. All in all, these findings inform future discovery and translational research for ALK inhibitors as anticancer drugs.

Molecular insights into the differential dynamics of SARS-CoV-2 variants of concern

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has affected the lives and livelihood of millions of individuals around the world. It has mutated several times after its first inception, with an estimated two mutations occurring every month. Although we have been successful in developing vaccines against the virus, the emergence of variants has enabled it to escape therapy. Few of the generated variants are also reported to be more infectious than the wild-type (WT). In this study, we analyze the attributes of all RBD/ACE2 complexes for the reported VOCs, namely, Alpha, Beta, Gamma, and Delta through computer simulations. Results indicate differences in orientation and binding energies of the VOCs from the WT. Overall, it was observed that electrostatic interactions play a major role in the binding of the complexes. Detailed residue level energetics revealed that the most prominent changes in interaction energies were seen particularly at the mutated residues which were present at RBD/ACE2 interface. We found that the Delta variant is one of the most tightly bound variants of SARS-CoV-2 with dynamics similar to WT. The high binding affinity of RBD towards ACE2 is indicative of an increase in viral transmission and infectivity. The details presented in our study provide additional information for the design and development of effective therapeutic strategies for the emerging variants of the virus in the future.

METTL3 Is Associated With the Malignancy of Esophageal Squamous Cell Carcinoma and Serves as a Potential Immunotherapy Biomarker

Methyltransferase-like 3 (METTL3) is an RNA methyltransferase mediating N6 methyladenosine (m6A) modification. Its role in cancer pathogenesis and progression has attracted increasing attention. However, the immunological role, possible immune mechanism, and clinical significance of METTL3 in esophageal squamous cell carcinoma (ESCC) remain to be confirmed. The Tumor Genome Atlas (TCGA) provided clinical and transcriptome sequencing data for this study (162 tumor tissue samples and 11 normal tissue samples), while the Immunology Database and Analysis Portal (immport, https://www.immport.org/home) provided 2483 immune-related genes. METTL3 was substantially expressed in ESCC and linked to poor prognosis in ESCC, according to the findings. Functional analysis revealed that METTL3 is mainly involved in chromosomal homologous recombination and DNA mismatch repair processes, which could be potential mechanisms for tumor disease development and progression. Analysis on the TISIDB website shows that effector memory CD8 T cells, NK cells, neutrophils and other cells are highly correlated with METTL3 expression. We screened immune genes associated with METTL3 by Spearman’s analysis and performed functional analysis. These immune genes were mostly linked with immune processes, such as cytokine receptors, the MAPK signaling pathway, and natural killer cell-mediated cytotoxicity, indicating that METTL3 is a key molecule in the immune regulation of esophageal cancer. In addition, based on METTL3-related immune genes, we separated the patients into several subgroups and constructed a prognostic prediction model consisting of six immune genes. As an independent prognostic indicator for ESCC, the risk score of this model can be employed. A nomogram was also developed to accurately evaluate individual prognoses based on clinical indicators and risk scores. In summary, this study suggests that METTL3 is not only a potential pathogenic molecule for esophageal carcinogenesis and progression but also a potential biological marker for forecasting ESCC patient prognosis and could serve as a basis for clinical decision making.

Clustering of End Stage Renal Disease Patients by Dimensionality Reduction Algorithms According to Lymphocyte Senescence Markers

End stage renal disease (ESRD) engenders detrimental effects in the Immune system, manifested as quantitative alterations of lymphocyte subpopulations, akin, albeit not identical to those observed during the ageing process. We performed dimensionality reduction of an extended lymphocyte phenotype panel of senescent and exhaustion related markers in ESRD patients and controls with Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). The plane defined by the first two principal components of PCA showed two fuzzy clusters, for patients and controls, respectively, with loadings of non-senescent markers pointing towards the controls’ centroid. Naive lymphocytes were reduced in ESRD patients compared to controls (CD4+CD45RA+CCR7+ 200(150-328) vs. 426(260-585cells/μl respectively, P = 0.001, CD19+IgD+CD27- 54(26-85) vs. 130(83-262)cells/μl respectively, P < 0.001). PCA projections of the multidimensional ESRD immune phenotype suggested a more senescent phenotype in hemodialysis compared to hemodiafiltration treated patients. Lastly, clustering based on UMAP revealed three distinct patient groups, exhibiting gradual changes for naive, senescent, and exhausted lymphocyte markers. Machine learning algorithms can distinguish ESRD patients from controls, based on their immune-phenotypes and also, unveil distinct immunological groups within patients’ cohort, determined possibly by dialysis prescription.

DeePred-BBB: A Blood Brain Barrier Permeability Prediction Model With Improved Accuracy

The blood-brain barrier (BBB) is a selective and semipermeable boundary that maintains homeostasis inside the central nervous system (CNS). The BBB permeability of compounds is an important consideration during CNS-acting drug development and is difficult to formulate in a succinct manner. Clinical experiments are the most accurate method of measuring BBB permeability. However, they are time taking and labor-intensive. Therefore, numerous efforts have been made to predict the BBB permeability of compounds using computational methods. However, the accuracy of BBB permeability prediction models has always been an issue. To improve the accuracy of the BBB permeability prediction, we applied deep learning and machine learning algorithms to a dataset of 3,605 diverse compounds. Each compound was encoded with 1,917 features containing 1,444 physicochemical (1D and 2D) properties, 166 molecular access system fingerprints (MACCS), and 307 substructure fingerprints. The prediction performance metrics of the developed models were compared and analyzed. The prediction accuracy of the deep neural network (DNN), one-dimensional convolutional neural network, and convolutional neural network by transfer learning was found to be 98.07, 97.44, and 97.61%, respectively. The best performing DNN-based model was selected for the development of the “DeePred-BBB” model, which can predict the BBB permeability of compounds using their simplified molecular input line entry system (SMILES) notations. It could be useful in the screening of compounds based on their BBB permeability at the preliminary stages of drug development. The DeePred-BBB is made available at https://github.com/12rajnish/DeePred-BBB.

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.