DDID: a comprehensive resource for visualization and analysis of diet-drug interactions.

Diet-drug interactions (DDIs) are pivotal in drug discovery and pharmacovigilance. DDIs can modify the systemic bioavailability/pharmacokinetics of drugs, posing a threat to public health and patient safety. Therefore, it is crucial to establish a platform to reveal the correlation between diets and drugs. Accordingly, we have established a publicly accessible online platform, known as Diet-Drug Interactions Database (DDID, https://bddg.hznu.edu.cn/ddid/), to systematically detail the correlation and corresponding mechanisms of DDIs. The platform comprises 1338 foods/herbs, encompassing flora and fauna, alongside 1516 widely used drugs and 23 950 interaction records. All interactions are meticulously scrutinized and segmented into five categories, thereby resulting in evaluations (positive, negative, no effect, harmful and possible). Besides, cross-linkages between foods/herbs, drugs and other databases are furnished. In conclusion, DDID is a useful resource for comprehending the correlation between foods, herbs and drugs and holds a promise to enhance drug utilization and research on drug combinations.

CovInter: interaction data between coronavirus RNAs and host proteins.

Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus-host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity. In other words, there is an urgent need to develop a database for providing such IVRHP data. In this study, a new database was therefore constructed to describe the interactions between coronavirus RNAs and host proteins (CovInter). This database is unique in (a) unambiguously characterizing the interactions between virus RNA and host protein, (b) comprehensively providing experimentally validated biological function for hundreds of host proteins key in viral infection and (c) systematically quantifying the differential expression patterns (before and after infection) of these key proteins. Given the devastating and persistent threat of coronaviruses, CovInter is highly expected to fill the gap in the whole process of the 'molecular arms race' between viruses and their hosts, which will then aid in the discovery of new antiviral therapies. It's now free and publicly accessible at: https://idrblab.org/covinter/.

MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery.

In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.

Systematic Description of the Content Variation of Natural Products (NPs): To Prompt the Yield of High-Value NPs and the Discovery of New Therapeutics.

Natural products (NPs) have long been associated with human production and play a key role in the survival of species. Significant variations in NP content may severely affect the "return on investment" of NP-based industries and render ecological systems vulnerable. Thus, it is crucial to construct a platform that relates variations in NP content to their corresponding mechanisms. In this study, a publicly accessible online platform, NPcVar (http://npcvar.idrblab.net/), was developed, which systematically described the variations of NP contents and their corresponding mechanisms. The platform comprises 2201 NPs and 694 biological resources, including plants, bacteria, and fungi, curated using 126 diverse factors with 26,425 records. Each record contains information about the species, NP, and factors involved, as well as NP content data, parts of the plant that produce NPs, the location of the experiment, and reference information. All factors were manually curated and categorized into 42 classes which belong to four mechanisms (molecular regulation, species factor, environmental condition, and combined factor). Additionally, the cross-links of species and NP to well-established databases and the visualization of NP content under various experimental conditions were provided. In conclusion, NPcVar is a valuable resource for understanding the relationship between species, factors, and NP contents and is anticipated to serve as a promising tool for improving the yield of high-value NPs and facilitating the development of new therapeutics.

Association of serum 25-hydroxyvitamin D with the incidence of 16 cancers, cancer mortality, and all-cause mortality among individuals with metabolic syndrome: a prospective cohort study.

PURPOSE: The relationship between vitamin D levels and cancer incidence and mortality in individuals with metabolic syndrome (MetS) remains poorly explored. Herein, we aimed to determine the association between 25-hydroxyvitamin D [25(OH)D] concentrations and the risk of 16 cancer incidence types and cancer/all-cause mortality in patients with MetS. METHODS: We enrolled 97,621 participants with MetS at recruitment from the UK Biobank cohort. The exposure factor was baseline serum 25(OH)D concentrations. The associations were examined using Cox proportional hazards models, which were displayed as hazard ratios (HRs) with 95% confidence intervals (CIs). RESULTS: Over a median follow-up period of 10.92 years for cancer incidence outcomes, 12,137 new cancer cases were recorded. We observed that 25(OH)D concentrations were inversely related to the risk of colon, lung, and kidney cancer, and HRs (95% CI) for 25(OH)D ≥ 75.0 vs. < 25.0 nmol/L were 0.67 (0.45-0.98), 0.64 (0.45-0.91), and 0.54 (0.31-0.95), respectively. The fully adjusted model revealed a null correlation between 25(OH)D and the incidence of stomach, rectum, liver, pancreas, breast, ovary, bladder, brain, multiple myeloma, leukemia, non-Hodgkin lymphoma, esophagus, and corpus uteri cancer. Over a median follow-up period of 12.72 years for mortality outcomes, 8286 fatalities (including 3210 cancer mortalities) were documented. An "L-shaped" nonlinear dose-response correlation was detected between 25(OH)D and cancer/all-cause mortality; the respective HRs (95% CI) were 0.75 (0.64-0.89) and 0.65 (0.58-0.72). CONCLUSION: These findings emphasize the importance of 25(OH)D in cancer prevention and longevity promotion among patients with MetS.

Polydopamine-Induced Modification on the Highly Charged Surface of Asymmetric Nanofluidics: A Strategy for Adjustable Ion Current Rectification Properties.

Surface charge effects in nanoconfines is one of the fundamentals in the ion current rectification (ICR) of nanofluidics, which provides entropic driving force by asymmetric surface charges and causes ion enrichment/depletion by the electrostatic interaction of fixed surface charges. However, the surface charge effect causes a significant electrostatic repulsion in nanoconfines, restricting additional like charge or elaborate chemistry on the highly charged confined surface, which limits ICR manipulation. Here, we use polydopamine (PDA), a nearly universal adhesive, that adheres to the highly positive-charged poly(ethyleneimine) (PEI) gel network in a nanochannel array. PDA enhances the ICR effect from a low rectification ratio of 9.5 to 92.6 by increasing the surface charge and hydrophobicity of the PEI gel network and, meanwhile, shrinking its gap spacing. Theoretical and experimental results demonstrate the determinants of the fixed surface charge in the enrichment/depletion region on ICR properties, which is adjustable by PDA-induced change in a nanoconfined environment. Chemically active PDA brings Au nanoparticles by chloroauric reduction for further hydrophobization and the modification of negative-charged DNA complexes in nanochannels, whereby ICR effects can be manipulated in versatile means. The results describe an adjustable and versatile strategy for adjusting the ICR behaviors of nanofluidics by manipulating local surface charge effects using PDA.

Orthogonal array composite designs for drug combination experiments with applications for tuberculosis.

The aim of this article is to provide an overview of the orthogonal array composite design (OACD) methodology, illustrate the various advantages, and provide a real-world application. An OACD combines a two-level factorial design with a three-level orthogonal array and it can be used as an alternative to existing composite designs for building response surface models. We compare the D$$ D $$ -efficiencies of OACDs relative to the commonly used central composite design (CCD) when there are a few missing observations and demonstrate that OACDs are more robust to missing observations for two scenarios. The first scenario assumes one missing observation either from one factorial point or one additional point. The second scenario assumes two missing observations either from two factorial points or from two additional points, or from one factorial point and one additional point. Furthermore, we compare OACDs and CCDs in terms of I$$ I $$ -optimality for precise predictions. Lastly, a real-world application of an OACD for a tuberculosis drug combination study is provided.

Revealing Ionic Signal Enhancement with Probe Grafting Density on the Outer Surface of Nanochannels.

Probe-modified nanopores/nanochannels are one of the most advanced sensors because the probes interact strongly with ions and targets in nanoconfinement and create a sensitive and selective ionic signal. Recently, ionic signals have been demonstrated to be sensitive to the probe-target interaction on the outer surface of nanopores/nanochannels, which can offer more open space for target recognition and signal conversion than nanoconfined cavities. To enhance the ionic signal, we investigated the effect of grafting density, a critical parameter of the sensing interface, of the probe on the outer surface of nanochannels on the change rate of the ionic signal before and after target recognition (ß). Electroneutral peptide nucleic acids and negatively charged DNA are selected as probes and targets, respectively. The experimental results showed that when adding the same number of targets, the ß value increased with the probe grafting density on the outer surface. A theoretical model with clearly defined physical properties of each probe and target has been established. Numerical simulations suggest that the decrease of the background current and the aggregation of targets at the mouth of nanochannels with increasing probe grafting density contribute to this enhancement. This work reveals the signal mechanism of probe-target recognition on the outer surface of nanochannels and suggests a general approach to the nanochannel/nanopore design leading to sensitivity improvement on the basis of relatively good selectivity.

Exponential Increase in an Ionic Signal: A Dominant Role of the Space Charge Effect on the Outer Surface of Nanochannels.

Nanochannels have advantage in sensitive analyses due to the confinement effects on ionic signal in nano- or sub-nanometric confines but could realize further gains by optimizing signal mechanism. Making target recognitions on the outer surface of nanochannels has been verified to improve target recognitions and signal conversions by maximizing surfaces accessible to targets and ions, but until recently, the signal mechanism has been still unclear. Using electroneutral peptide nucleic acid (PNA) and negative-charged DNA, we verified a dominant space charge effect on an ionic signal on the outer surface of nanochannels. A typical exponential increase of the ionic signal with the charge density on the outer surface has been demonstrated through the PNA-PNA, PNA-DNA, DNA-DNA hybrid, DNA cleavage, and hybridization chain reaction. These results challenge the essential role of steric hindrance on the ionic signal and describe a new ion passageway surrounded and accelerated by the stern layer of charged species on the nanochannel outer surface.

Exploring the contribution of charged species at the outer surface to the ion current signal of nanopores: a theoretical study.

Nanopores attached to charged species realize the artificial regulation of ion transport by the electrostatic effect in nanoconfines, produce a sensitive ion current signal and play a critical role in nanopore-based analyses. However, until now, the contribution of the charged species at the outer surface, an inherent component of nanopores, to the ion current signal has not yet been fully investigated. Here, we theoretically investigate the contribution of the charged species at the outer surface to the ion current signal of a conical nanopore. The results indicate that when the electrostatic effect at the tip of the conical nanopore is strengthened, the contribution from the charged species at the outer surface to the ionic current signal becomes stronger or even predominant compared with that of the inner walls. This effect can be further enhanced using nanopore arrays with small openings and low pore density in a low concentration electrolyte. This work focuses on the working mechanism of nanopores with a high-efficient signal conversion and promotes the performance of nanopores with a regional distribution of charged probes and targets.

Angiotensin II confers resistance to apoptosis in cardiac myofibroblasts through the AT1/ERK1/2/RSK1 pathway.

Myofibroblast apoptosis is essential for normal resolution of wound repair, including cardiac infarction repair. Impaired cardiac myofibroblast (CMF) apoptosis is associated with excessive extracellular matrix (ECM) deposition, which could be responsible for pathological cardiac fibrosis. Conventionally, angiotensin II (Ang II), a soluble peptide, is implicated in fibrogenesis because it induces cardiac fibroblast (CFb) proliferation, differentiation, and collagen synthesis. However, the role of Ang II in regulation of CMF survival and apoptosis has not been fully clarified. In this report, we cultured neonatal rat CFbs, which transform into CMFs after passage 3 (6-8 days), and investigated the effects of Ang II on CMFs challenged by TNF-α combined with cycloheximide and the underlying mechanisms. Here, we show that Ang II rapidly activates MAPKs but not AKT in CMFs and confers apoptosis resistance, as evidenced by the inhibition of caspase-3 cleavage, early apoptotic cells and late apoptotic cells. This inhibitory effect of Ang II was reversed by blockade of AT1 or inactivation of ERK1/2 or RSK1 but not AT2, indicating that activation of the prosurvival AT1/ERK1/2/RSK1 signaling pathway mediates apoptosis resistance. TGF-ß, a latent fibrotic factor, was found to have no relation to Ang II-induced apoptosis resistance in our study. Furthermore, Ang II-mediated apoptosis resistance, which was conferred by activation of the AT1/ERK1/2/RSK1 signaling pathway, was also confirmed in human adult ventricular cardiac myofibroblasts. Collectively, our findings suggest a novel profibrotic mechanism of Ang II in which it promotes myofibroblast resistance to apoptosis in addition to classical mechanisms, providing a potential novel therapeutic approach by targeting prosurvival signaling pathways. © 2018 IUBMB Life, 71(1):261-276, 2019.

Application of kriging models for a drug combination experiment on lung cancer.

Combinatorial drugs have been widely applied in disease treatment, especially chemotherapy for cancer, due to its improved efficacy and reduced toxicity compared with individual drugs. The study of combinatorial drugs requires efficient experimental designs and proper follow-up statistical modeling techniques. Linear and nonlinear models are often used in the response surface modeling for such experiments. We propose the use of kriging models to better depict the response surfaces of combinatorial drugs. We illustrate our method via a drug combination experiment on lung cancer and further show how proper experimental designs can reduce the necessary run size. We demonstrate that only 27 runs are needed to predict all 512 runs in the original experiment and achieve better precision than existing analyses.

Using blocked fractional factorial designs to construct discrete choice experiments for healthcare studies.

Discrete choice experiments (DCEs) are increasingly used for studying and quantifying subjects preferences in a wide variety of healthcare applications. They provide a rich source of data to assess real-life decision-making processes, which involve trade-offs between desirable characteristics pertaining to health and healthcare and identification of key attributes affecting healthcare. The choice of the design for a DCE is critical because it determines which attributes' effects and their interactions are identifiable. We apply blocked fractional factorial designs to construct DCEs and address some identification issues by utilizing the known structure of blocked fractional factorial designs. Our design techniques can be applied to several situations including DCEs where attributes have different number of levels. We demonstrate our design methodology using two healthcare studies to evaluate (i) asthma patients' preferences for symptom-based outcome measures and (ii) patient preference for breast screening services. Copyright © 2016 John Wiley & Sons, Ltd.

Discovery of a low order drug-cell response surface for applications in personalized medicine.

The cell is a complex system involving numerous components, which may often interact in a non-linear dynamic manner. Diseases at the cellular level are thus likely to involve multiple cellular constituents and pathways. As some drugs, or drug combinations, may act synergistically on these multiple pathways, they might be more effective than the respective single target agents. Optimizing a drug mixture for a given disease in a particular patient is particularly challenging due to both the difficulty in the selection of the drug mixture components to start out with, and the all-important doses of these drugs to be applied. For n concentrations of m drugs, in principle, n(m) combinations will have to be tested. As this may lead to a costly and time-consuming investigation for each individual patient, we have developed a Feedback System Control (FSC) technique which can rapidly select the optimal drug-dose combination from the often millions of possible combinations. By testing this FSC technique in a number of experimental systems representing different disease states, we found that the response of cells to multiple drugs is well described by a low order, rather smooth, drug-mixture-input/drug-effect-output multidimensional surface. The main consequences of this are that optimal drug combinations can be found in a surprisingly small number of tests, and that translation from in vitro to in vivo is simplified. This points to the possibility of personalized optimal drug mixtures in the near future. This unexpectedly simple input-output relationship may also lead to a simple solution for handling the issue of human diversity in cancer therapeutics.

An application of a Hill-based response surface model for a drug combination experiment on lung cancer.

Combination chemotherapy with multiple drugs has been widely applied to cancer treatment owing to enhanced efficacy and reduced drug resistance. For drug combination experiment analysis, response surface modeling has been commonly adopted. In this paper, we introduce a Hill-based global response surface model and provide an application of the model to a 512-run drug combination experiment with three chemicals, namely AG490, U0126, and indirubin-3 ' -monoxime (I-3-M), on lung cancer cells. The results demonstrate generally improved goodness of fit of our model from the traditional polynomial model, as well as the original Hill model on the basis of fixed-ratio drug combinations. We identify different dose-effect patterns between normal and cancer cells on the basis of our model, which indicates the potential effectiveness of the drug combination in cancer treatment. Meanwhile, drug interactions are analyzed both qualitatively and quantitatively. The distinct interaction patterns between U0126 and I-3-M on two types of cells uncovered by the model could be a further indicator of the efficacy of the drug combination.

Design superhydrophobic no-noble metal substrates for highly sensitive and signal stable SERS sensing.

Surface-enhanced Raman spectroscopy (SERS) is an analytical technique with a broad range of potential applications in fields such as biomedicine, electrochemistry, and hazardous chemicals. However, it is challenging to develop SERS substrates that are both good sensitive and signal stable. Here we designed a superhydrophobic Nd doped MoS2 uniformly assembled on the activated carbon fiber cloth (CFC) to avoid the coffee ring effect and enrich the analyte, improving the enhancement factor (EF) to 3.9 × 109 and pesticide endosulfan (<10-10) analyte detection. We demonstrate our strategy by density-functional theory (DFT) calculations confirming that both adsorption energy and density of states are enhanced after doping Nd leading to SERS enhancement. Beside DFT calculations, our experiments also provide an effective means to demonstrate that the high SERS sensitivity is based on multiple charge transfer processes combined with the activated carbon cloth. Our results address the limitations of low sensitivity and limit of detection (LOD) of semiconductor SERS substrates for trace analysis and are a step towards practical applications.

Snowflake Cu2S@ZIF-67: A novel heterostructure substrate for enhanced adsorption and sensitive detection in BPA.

Developing semiconductor substrates with superior stability and sensitivity is challenging in surface-enhanced Raman scattering (SERS) research. Here, a snowflake Cu2S@ZIF-67 heterostructure was fabricated using a straightforward method, exhibiting a notable enhancement factor of 9.0 × 109 and a limit of detection (LOD) of 10-14 M for methylene blue (MB). In addition, the Cu2S@ZIF-67 heterostructure substrate demonstrates outstanding homogeneity (relative standard deviation (RSD) = 9.2%) and stability (120 days). Employing Cu2S generates highly sensitive hotspots via an electromagnetic (EM) mechanism, and the growth of ZIF-67 on its surface augments the adsorption capacity and charge transfer capability (chemical mechanism, CM), thereby enhancing the SERS detection sensitivity. Furthermore, the Cu2S@ZIF-67 heterostructure, which was used as a SERS substrate, facilitated the detection of bisphenol A (BPA) with an LOD of 10-11 M. The Cu2S@ZIF-67 heterostructure substrate has excellent selectivity and anti-interference, which is very suitable for BPA detection in complex environment applications. The accuracy of the Cu2S@ZIF-67 heterostructure as a SERS substrate for detecting BPA in real water samples (water bottles, tap water, and pure milk) was confirmed by comparison with high-performance liquid chromatography (HPLC). These results demonstrate that through the rational design of heterostructures can achieve the quantitative and accurate detection of hazardous substances in food and the environment can be achieved.

Engineering a Programmed Death-Ligand 1-Targeting Monobody Via Directed Evolution for SynNotch-Gated Cell Therapy.

Programmed death-ligand 1 (PD-L1) is a promising target for cancer immunotherapy due to its ability to inhibit T cell activation; however, its expression on various noncancer cells may cause on-target off-tumor toxicity when designing PD-L1-targeting Chimeric Antigen Receptor (CAR) T cell therapies. Combining rational design and directed evolution of the human fibronectin-derived monobody scaffold, "PDbody" was engineered to bind to PD-L1 with a preference for a slightly lower pH, which is typical in the tumor microenvironment. PDbody was further utilized as a CAR to target the PD-L1-expressing triple negative MDA-MB-231 breast cancer cell line. To mitigate on-target off-tumor toxicity associated with targeting PD-L1, a Cluster of Differentiation 19 (CD19)-recognizing SynNotch IF THEN gate was integrated into the system. This CD19-SynNotch PDbody-CAR system was then expressed in primary human T cells to target CD19-expressing MDA-MB-231 cancer cells. These CD19-SynNotch PDbody-CAR T cells demonstrated both specificity and efficacy in vitro, accurately eradicating cancer targets in cytotoxicity assays. Moreover, in an in vivo bilateral murine tumor model, they exhibited the capability to effectively restrain tumor growth. Overall, CD19-SynNotch PDbody-CAR T cells represent a distinct development over previously published designs due to their increased efficacy, proliferative capability, and mitigation of off-tumor toxicity for solid tumor treatment.

Application of fractional factorial designs to study drug combinations.

Herpes simplex virus type 1 (HSV-1) is known to cause diseases of various severities. There is increasing interest to find drug combinations to treat HSV-1 by reducing drug resistance and cytotoxicity. Drug combinations offer potentially higher efficacy and lower individual drug dosage. In this paper, we report a new application of fractional factorial designs to investigate a biological system with HSV-1 and six antiviral drugs, namely, interferon alpha, interferon beta, interferon gamma, ribavirin, acyclovir, and tumor necrosis factor alpha. We show how the sequential use of two-level and three-level fractional factorial designs can screen for important drugs and drug interactions, as well as determine potential optimal drug dosages through the use of contour plots. Our initial experiment using a two-level fractional factorial design suggests that there is model inadequacy and that drug dosages should be reduced. A follow-up experiment using a blocked three-level fractional factorial design indicates that tumor necrosis factor alpha has little effect and that HSV-1 infection can be suppressed effectively by using the right combination of the other five antiviral drugs. These observations have practical implications in the understanding of antiviral drug mechanism that can result in better design of antiviral drug therapy.

A systematic review on the state-of-the-art strategies for protein representation.

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.