Fingerprint Similarity Research Articles

A rapid fingerprint positioning method based on deep convolutional neural network for MIMO-OFDM systems

The combination of fingerprint positioning and 5G (the 5th Generation Mobile Communication Technology) offers broader application prospects for indoor positioning technology, but also brings challenges in real-time performance. In this paper, we propose a fingerprint positioning method based on a deep convolutional neural network (DCNN) using a classification approach in a single-base station scenario for massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation. The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences. First, the DCNN model is used to determine the sub-area to which the mobile terminal belongs, and then the weighted K-nearest neighbor (WKNN) matching algorithm is used to estimate the position within the sub-area. The positioning performance is simulated in a DeepMIMO indoor environment, showing that the classification DCNN method reduces the positioning time by 77.05% compared to the non-classification method, with only a 1.08% increase in average positioning error.

Exploring the role of Yuxuebi tablet in neuropathic pain with the method of similarity research of drug pharmacological effects based on unsupervised machine learning.

Having multiple pharmacological effects is a characteristic of Traditional Chinese Medicine (TCM). Currently, there is a lack of suitable methods to explore and discover modern diseases suitable for TCM treatment using this characteristic. Unsupervised machine learning technology is an efficient strategy to predict the pharmacological activity of drugs. This study takes Yuxuebi Tablet (YXB) as the research object. Using the unsupervised machine learning technology of drug cell functional fingerprint similarity research, the potential pharmacological effects of YXB were discovered and verified. LC-MS combined with the in vitro intestinal absorption method was used to identify components of YXB that could be absorbed by the intestinal tract of rats. Unsupervised learning hierarchical clustering was used to calculate the degree of similarity of cellular functional fingerprints between these components and 121 marketed Western drugs whose indications are diseases and symptoms that YXB is commonly used to treat. Then, based on the Library of Integrated Network-based Cellular Signatures database, pathway analysis was performed for selected Western drugs with high similarity in cellular functional fingerprints with the components of YXB to discover the potential pharmacological effects of YXB, which were validated by animal experiments. We identified 40 intestinally absorbed components of YXB. Through predictive studies, we found that they have pharmacological effects very similar to non-steroidal anti-inflammatory drugs (NSAIDs) and corticosteroids. In addition, we found that they have very similar pharmacological effects to anti-neuropathic pain medications (such as gabapentin, duloxetine, and pethidine) and may inhibit the NF-κB signaling pathway and biological processes related to pain perception. Therefore, YXB may have an antinociceptive effect on neuropathic pain. Finally, we demonstrated that YXB significantly reduced neuropathic pain in a rat model of sciatic nerve chronic constriction injury (CCI). Transcriptome analysis further revealed that YXB regulates the expression of multiple genes involved in nerve injury repair, signal transduction, ion channels, and inflammatory response, with key regulatory targets including Sgk1, Sst, Isl1, and Shh. This study successfully identified and confirmed the previously unknown pharmacological activity of YXB against neuropathic pain through unsupervised learning prediction and experimental verification.

Enhancing protein-ligand binding affinity prediction through sequential fusion of graph and convolutional neural networks.

Predicting protein-ligand binding affinity is a crucial and challenging task in structure-based drug discovery. With the accumulation of complex structures and binding affinity data, various machine-learning scoring functions, particularly those based on deep learning, have been developed for this task, exhibiting superiority over their traditional counterparts. A fusion model sequentially connecting a graph neural network (GNN) and a convolutional neural network (CNN) to predict protein-ligand binding affinity is proposed in this work. In this model, the intermediate outputs of the GNN layers, as supplementary descriptors of atomic chemical environments at different levels, are concatenated with the input features of CNN. The model demonstrates a noticeable improvement in performance on CASF-2016 benchmark compared to its constituent CNN models. The generalization ability of the model is evaluated by setting a series of thresholds for ligand extended-connectivity fingerprint similarity or protein sequence similarity between the training and test sets. Masking experiment reveals that model can capture key interaction regions. Furthermore, the fusion model is applied to a virtual screening task for a novel target, PI5P4Kα. The fusion strategy significantly improves the ability of the constituent CNN model to identify active compounds. This work offers a novel approach to enhancing the accuracy of deep learning models in predicting binding affinity through fusion strategies.

Medical device similarity analysis: a promising approach to medical device equivalence regulation

ABSTRACT Background This study aims to facilitate the identification of similar devices for both, the European Medical Device Regulation (MDR) and the US 510(k) equivalence pathway by leveraging existing data. Both are related to the regulatory pathway of read across for chemicals, where toxicological data from a known substance is transferred to one under investigation, as they aim to streamline the accreditation process for new devices and chemicals. Research design and methods This study employs latent semantic analysis to generate similarity values, harnessing the US Food and Drug Administration 510k-database, utilizing their ‘Device Descriptions’ and ‘Intended Use’ statements. Results For the representative inhaler cluster, similarity values up to 0.999 were generated for devices within a 510(k)-predicate tree, whereas values up to 0.124 were gathered for devices outside this group. Conclusion Traditionally, MDR equivalence involves manual review of many devices, which is laborious. However, our results suggest that the automated calculation of similarity coefficients streamlines this process, thus reducing regulatory effort, which can be beneficial for patients needing medical devices. Although this study is focused on the European perspective, it can find application within 510(k) equivalence regulation. The conceptual approach is reminiscent of chemical fingerprint similarity analysis employed in read-across.

Discovery of new antimicrobial thiophene derivatives with activity against drug-resistant Gram negative-bacteria.

Our aim is to identify new small molecules with antimicrobial potential, especially against colistin-resistant (Col-R) Acinetobacter baumannii and Escherichia coli. After initial hits identification by fingerprint similarity, MIC of 24 heterocyclic derivatives for A. baumannii and E. coli reference strains, and bactericidal activity of selected thiophenes against Col-R strains were determined. We analyzed changes in bacterial membrane permeability and the OMPs profile. Additionally, we determined bacterial adherence to host cells and performed molecular docking studies to assess their binding to bacterial targets. The compounds' MICs ranged from 4 to >64mg/L. Thiophene derivatives 4, 5 and 8 exhibited MIC50 values between 16 and 32mg/L for Col-R A. baumannii and 8 and 32mg/L for Col-R E. coli. The time-kill curve assay demonstrated that thiophenes 4 and 8 had bactericidal effects against Col-R A. baumannii and E. coli. Furthermore, treatment with them resulted in increased membrane permeabilization and reduced adherence of these isolates to host cells. Finally, the docking studies showed a stronger binding affinity to CarO1 and Omp33 of A. baumannii and OmpW and OmpC of E. coli. These findings indicate that thiophene derivatives possess antibacterial activity against Col-R A. baumannii and E. coli, suggesting that they may enhance the repertoire of drug treatments against bacteria.

Optimization and Spectrum-Effect Analysis of Ultrasonically Extracted Antioxidant Flavonoids from Persicae Ramulus.

The objectives of this study were to optimize the ultrasonic-assisted flavonoid extraction process from PR and to establish fingerprints in order to analyze the spectrum-effect relationship of antioxidant activity. The ultrasonic-assisted flavonoid extraction process from PR was optimized using RSM, and the fingerprints of twenty-eight batches of flavonoids from PR were established using UHPLC. Meanwhile, the in vitro antioxidant activity of PR was evaluated in DPPH and ABTS free radical-scavenging experiments. Then, the peaks of the effective antioxidant components were screened using the spectrum-effect relationships. The results show that the optimal extraction yield of flavonoids from PR was 3.24 ± 0.01 mg/g when using 53% ethanol, a 1:26 (g/mL) solid-liquid ratio, and 60 min of ultrasonic extraction. Additionally, the clearance of two antioxidant indices by the flavonoids extracted from PR had different degrees of correlation and showed concentration dependence. Simultaneously, the similarity of the UHPLC fingerprints of twenty-eight batches of PR samples ranged from 0.801 to 0.949, and four characteristic peaks, namely peaks 4, 12, 21, and 24, were screened as the peaks of the components responsible for the antioxidant effect of PR using a GRA, a Pearson correlation analysis, and a PLS-DA. In this study, characteristic peaks of the antioxidant effects of PR were screened in an investigation of the spectrum-effect relationship to provide a scientific basis for the study of pharmacodynamic substances and the elucidation of the mechanism of action of the antioxidant effect of PR.

Establishment of an HPLC Fingerprint and Cluster Analysis for Miao Ethnic Medicine Osbeckia opipara

: Osbeckia opipara, a traditional Miao medicine, is commonly used by the renowned national-level Chinese Traditional Medicine practitioner Zhengshi Wu for the treatment of diarrhea due to its strong antioxidative, anti-inflammatory, and antidiarrheal effects. This study aimed to establish a high-performance liquid chromatography (HPLC) fingerprint for O. opipara to provide new evidence and technical means for the scientific evaluation and effective quality control of O. opipara. The procedure involved isolation with a Nano ChromCore C18 column (250 mm × 4.6 mm, 5 μm), using a gradient elution of 0.1% formic acid in water and 0.1% formic acid in acetonitrile as the mobile phase, with a flow rate of 1.0 mL/min, a column temperature of 30°C, an injection volume of 10 μL, and detection at a wavelength of 254 nm. Under these chromatographic conditions, fingerprint analysis was conducted on 11 batches of O. opipara collected from different origins. The National Pharmacopoeia Committee developed the 'Chromatographic Fingerprint Similarity Evaluation System' (2004A version) for automated comparison, similarity computation, and analysis of chromatographic data. The results revealed 13 common peaks across the 11 batches of O. opipara samples, with a similarity to the automatically generated reference spectrum exceeding 0.9. SPSS 26.0 software was used to conduct cluster analysis on the peak areas of the 13 common peaks. The observations indicated that the reference spectrum generated from the 11 batches could serve as the standard fingerprint profile for O. opipara, providing sufficient characteristic information extraction.

Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling.

Accurate protein-ligand binding poses are the prerequisites of structure-based binding affinity prediction and provide the structural basis for in-depth lead optimization in small molecule drug design. However, it is challenging to provide reasonable predictions of binding poses for different molecules due to the complexity and diversity of the chemical space of small molecules. Similarity-based molecular alignment techniques can effectively narrow the search range, as structurally similar molecules are likely to have similar binding modes, with higher similarity usually correlated to higher success rates. However, molecular similarity is not consistently high because molecules often require changes to achieve specific purposes, leading to reduced alignment precision. To address this issue, we propose a new alignment method─Z-align. This method uses topological structural information as a criterion for evaluating similarity, reducing the reliance on molecular fingerprint similarity. Our method has achieved success rates significantly higher than those of other methods at moderate levels of similarity. Additionally, our approach can comprehensively and flexibly optimize bond lengths and angles of molecules, maintaining a high accuracy even when dealing with larger molecules. Consequently, our proposed solution helps in achieving more accurate binding poses in protein-ligand docking problems, facilitating the development of small molecule drugs. Z-align is freely available as a web server at https://cloud.zelixir.com/zalign/home.

Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No).

Computational approaches for small-molecule drug discovery now regularly scale to the consideration of libraries containing billions of candidate small molecules. One promising approach to increased the speed of evaluating billion-molecule libraries is to develop succinct representations of each molecule that enable the rapid identification of molecules with similar properties. Molecular fingerprints are thought to provide a mechanism for producing such representations. Here, we explore the utility of commonly used fingerprints in the context of predicting similar molecular activity. We show that fingerprint similarity provides little discriminative power between active and inactive molecules for a target protein based on a known active-while they may sometimes provide some enrichment for active molecules in a drug screen, a screened data set will still be dominated by inactive molecules. We also demonstrate that high-similarity actives appear to share a scaffold with the query active, meaning that they could more easily be identified by structural enumeration. Furthermore, even when limited to only active molecules, fingerprint similarity values do not correlate with compound potency. In sum, these results highlight the need for a new wave of molecular representations that will improve the capacity to detect biologically active molecules based on their similarity to other such molecules.

Elucidation of anti-pneumonia pharmacodynamic material basis and potential mechanisms of Xiebai San by combining spectrum–efficacy relationship and surface plasmon resonance

Ethnopharmacological relevanceXiebai San (XBS), a classic Chinese prescription, has been used for the clinical treatment of pneumonia-related diseases for thousands of years. However, the anti-pneumonia pharmacodynamic material basis of XBS and its underlying mechanisms remain unclear. Aim of the studyThis study aimed to comprehensively investigate and verify the anti-pneumonia pharmacodynamic material basis and mechanisms of XBS. Materials and methodsThis study explored the anti-pneumonia activity and key pneumonia targets of XBS in lipopolysaccharide (LPS)-induced zebrafish and RAW264.7 cells in vivo and in vitro through transcriptomics, western blotting, and reverse transcription–quantitative polymerase chain reaction (RT–qPCR). The chemical fingerprint of XBS was established using high-performance liquid chromatography, and the similarities and areas of characteristic peaks of 15 batches of XBS were analyzed. Based on the spectrum–efficacy relationship, the potential anti-inflammatory components were screened according to their peak areas and efficacy using principal component analysis (PCA), bivariate correlation, and partial least squares regression analysis. Active components that bind to core targets were further screened based on surface plasmon resonance (SPR). The binding mode of proteins and components was simulated via molecular docking, which enabled the identification of the primary active components of XBS, thereby elucidating its anti-pneumonia properties. Finally, the anti-inflammatory activities of these components were verified in vitro. ResultsXBS decreased neutrophil aggregation in zebrafish and nitric oxide (NO) secretion in RAW264.7 cells as well as suppressed the release of downstream inflammatory cytokines such as iNOS, TNF-α, IL-1β, IL-18, and CXCL10 related to TNF and JAK–STAT signaling pathways. The phosphorylation of IκBα, Akt, and Stat3 was alleviated after XBS in cells. The fingerprint similarities of 15 batches of XBS ranged from 0.381 to 0.994, with a large difference. A total of 15 characteristic peaks were identified, and the relative standard deviation of their peak areas ranged from 24.1% to 70.7%. The results of in vitro anti-inflammatory activities of 15 batches of XBS showed that all samples inhibited the expression levels of NO and nine inflammatory markers. The anti-inflammatory index of 15 batches of XBS was determined to be 0.69–0.96 based on transformation of the anti-inflammatory rate and composite index method via PCA. The spectrum–efficacy relationship model of 15 characteristic peak areas and the anti-inflammatory index showed that 7 main potential active components were related to the anti-inflammatory activity of XBS. Moreover, four components (mulberroside A, isoquercitrin, liquiritigenin, and glycyrrhizic acid) screened based on SPR had different affinities toward TNFR1, Akt1, and Stat3 proteins, and the binding modes were elucidated via molecular docking. Finally, in LPS-induced RAW264.7 cells, all four active components (at a concentration of 60 μM) significantly inhibited the expression levels of NO and inflammatory markers. ConclusionsBased on the comprehensive strategy of spectrum–efficacy relationship and SPR, mulberroside A, isoquercitrin, liquiritigenin, and glycyrrhizic acid were identified as the primary pharmacodynamic active components involved in the anti-pneumonia activity of XBS and were found to intervene in TNF and JAK–STAT signaling pathways.

Optimization of the extraction process for Shenshou Taiyi powder based on Box-Behnken experimental design, standard relation, and FAHP-CRITIC methods

BackgroundAncient classic prescription play a crucial role in the preservation and advancement of traditional Chinese medicine (TCM) theories. They represent a significant milestone in the ongoing development and transmission of TCM knowledge and practices and are considered one of the breakthroughs in the development of TCM inheritance. In the process of developing ancient classic prescriptions, many problems may still arise in ensuring quality consistency between traditional methods and modern production processes, among which the extraction process poses major challenges. This paper introduces a practical approach extracting an ancient classic prescription using a modern extraction process. The technique is demonstrated through the study of the extraction process of Shenshou Taiyi powder (STP).MethodsThis study focuses on optimising the STP extraction process to ensure consistency in the quality of the product obtained through ancient and modern processes using the standard relation and fuzzy analytic hierarchical process (FAHP) and criteria importance through intercriteria correlation (CRITIC) method integrated weights combined with the Box-Behnken response surface test. Using the contents of rosmarinic acid, isoimperatorin, puerarin, as well as the extract yield and fingerprint similarity as evaluation indexes of STP, the Box-Behnken response surface method was employed to examine the varying extraction parameters, including water addition ratio, extraction duration, and number of extractions. The weighted coefficients for each parameter were calculated by combining the benchmark correlation and FAHP-CRITIC method, deriving a comprehensive score.ResultsThe optimal extraction process for STP consisted of a two extractions, each using at a tenfold quantity of water, performed for one hour. Process verification across three separate batches yielded a comprehensive score of 94.7, with a relative standard deviation of 0.76%.ConclusionsThe application of the Box-Behnken response surface method combined with standard relation and FAHP-CRITIC approach proved to be stable and feasible for optimising the extraction process of STP.

Identification of Reaction Network Hypotheses for Complex Feedstocks from Spectroscopic Measurements with Minimal Human Intervention.

In this work, we detail an automated reaction network hypothesis generation protocol for processes involving complex feedstocks where information about the species and reactions involved is unknown. Our methodology is process agnostic and can be utilized in any reactive process with spectroscopic measurements that provide information on the evolution of the components in the mixture. We decompose the mixture spectra to obtain spectroscopic signatures of the individual components and use a 1-D convolutional neural network to automatically identify functional groups indicated by them. We employ atom-atom mapping to automatically recover reaction rules that are applied on candidate molecules identified from chemistry databases through fingerprint similarity. The method is tested on synthetic data and on spectroscopic measurements of lab-scale batch hydrothermal liquefaction (HTL) of biomass to determine the accuracy of prediction across datasets of varying complexities. Our methodology is able to identify reaction network hypotheses containing reaction networks close to the ground truth in the case of synthetic data, and we are also able to recover candidate molecules and reaction networks close to the ones reported in the previous literature studies for biomass pyrolysis.

Fingerprint Correlation Between Twins

CODY BARNES, KRISTY HENSON, MARK FLOOD, Department of Natural Sciences, Fairmont State University, Fairmont, WV, 26201 Fingerprint correlation between identical twins When teaching about the uniqueness of fingerprints it is commonly said that identical twin fingerprint characteristics are not identical however, limited research has been conducted to determine if there are any correlations between similar print characteristics. In the recent past unrelated individuals have been misidentified based on fingerprint similarities in forensics cases and this may be more likely among twins. This research case study’s objective is to determine if there are any correlations between fingerprint characteristics of identical twins and the probability of misidentification. Identical twin fingerprints were rolled and collected onto 10 print cards then blindly analyzed using Henry Classification, AFIS, and NCIC techniques. Preliminary results indicate a strong relationship between characteristics, but it is not significant (0.49; p = 0.35); fingerprint characteristics between hands were not significantly different (R: p = 0.75, L: p = 0.24). Results indicate there could be a possibility of misidentification, but more twin fingerprints should be analyzed.

Computer-Assisted Drug Discovery of Potential African Anti-SARS-CoV-2 Natural Products Targeting the Helicase Protein

Objectives: In our continuous efforts to combat COVID-19, our objective was to conduct a comprehensive computer-aided drug design study utilizing 4924 African natural metabolites sourced from diverse databases across various African regions from 1962 to 2019. The primary goal was to target the SARS-CoV-2 helicase, a crucial enzyme in viral replication. Methods: We employed structural fingerprint and molecular similarity studies with VXG, the co-crystallized ligand, as a reference. Subsequently, docking and absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies were conducted. Results: The structural fingerprint analysis identified 200 structurally similar compounds, then the molecular similarity studies selected 40 compounds. Among them, 15 metabolites with low free energies, favorable binding modes, and promising ADMET properties were identified. Four compounds were excluded according to the toxicity studies. Compound 1552, 1-(( S)-2,3-dihydro-2-(( R)-1-hydroxypropan-2-yl)benzofuran-5-yl)ethenone, exhibited the most favorable docking results. Molecular dynamics simulations conclusively demonstrated its stable binding to the SARS-CoV-2 helicase, characterized by low energy and optimal dynamics. Conclusion: The findings suggest promising avenues for potential COVID-19 cures, encouraging further exploration through in vitro and in vivo studies of the identified compounds, particularly compound 1552.

Identification of Vernonia patula Merr. and Its Similar Varieties Based on a Combination of HPLC Fingerprinting and Chemical Pattern Recognition.

Vernonia patula Merr. (VP) is a traditional medicine used by the Zhuang and Yao people, known for its therapeutic properties in treating anemopyretic cold and other diseases. Distinguishing VP from similar varieties such as Praxelis clematidea (PC), Ageratum conyzoides L. (AC) and Ageratum houstonianum Mill (AH) was challenging due to their similar traits and plant morphology. The HPLC fingerprints of 40 batches of VP and three similar varieties were established. SPSS 20.0 and SIMCA-P 13.0 were used to statistically analyze the chromatographic peak areas of 37 components. The results showed that the similarity of the HPLC fingerprints for each of the four varieties was >0.9, while the similarity between the control chromatogram of VP and its similar varieties was <0.678. Cluster analysis and partial least squares discriminant analysis provided consistent results, indicating that all four varieties could be individually clustered together. Through further analysis, we found isochlorogenic acid A and isochlorogenic acid C were present only in the original VP, while preconene II was present in the three similar varieties of VP. These three components are expected to be identification points for accurately distinguishing VP from PC, AC and AH.

Mix-Key: graph mixup with key structures for molecular property prediction.

Molecular property prediction faces the challenge of limited labeled data as it necessitates a series of specialized experiments to annotate target molecules. Data augmentation techniques can effectively address the issue of data scarcity. In recent years, Mixup has achieved significant success in traditional domains such as image processing. However, its application in molecular property prediction is relatively limited due to the irregular, non-Euclidean nature of graphs and the fact that minor variations in molecular structures can lead to alterations in their properties. To address these challenges, we propose a novel data augmentation method called Mix-Key tailored for molecular property prediction. Mix-Key aims to capture crucial features of molecular graphs, focusing separately on the molecular scaffolds and functional groups. By generating isomers that are relatively invariant to the scaffolds or functional groups, we effectively preserve the core information of molecules. Additionally, to capture interactive information between the scaffolds and functional groups while ensuring correlation between the original and augmented graphs, we introduce molecular fingerprint similarity and node similarity. Through these steps, Mix-Key determines the mixup ratio between the original graph and two isomers, thus generating more informative augmented molecular graphs. We extensively validate our approach on molecular datasets of different scales with several Graph Neural Network architectures. The results demonstrate that Mix-Key consistently outperforms other data augmentation methods in enhancing molecular property prediction on several datasets.

Exploring and Designing Potential Inhibitors of SIRT2 in Natural Products by Artificial Intelligence (AI) and Molecular Dynamics Methods

Background:: The histone deacetylase family of proteins, which includes the sirtuins, participates in a wide range of cellular processes, and is intimately involved in neurodegenerative illnesses. The research on sirtuins has garnered a lot of interest. However, there are currently no effective therapeutic drugs. Methods:: In order to explore the potential inhibitors of SIRTs, we first screened four potential lead compounds of SIRT2 in Traditional Chinese Medicine (TCM) for nervous disease using the Auto- Dock Vina method. Then, with Molecular Dynamics (MD) simulation method, we discovered how these inhibitors from Traditional Chinese herbal medicines affect this protein at the atomic level. Results and Discussion:: We found hydrophobic interactions between inhibitors and SIRT2 to be crucial. The small molecules have been found to have strong effect on the residues in the zincbinding domain, exhibiting relationship with the signaling pathway. Finally, based on the conformational characteristics and the MD properties of the four potential inhibitors in TCM, we have designed the new skeleton molecules according to the parameters of binding energy, fingerprint similarity, 3D similarity, and RO5, with AI method using MolAICal software. Conclusion:: We have proposed the candidate inhibitor of SIRT2. Our research has provided a new approach that can be used to explore potential inhibitors from TCM. This could potentially pave the way for the creation of effective medicines.

HPLC Fingerprint Analysis of Cibotii rhizoma from Different Regions and Identification of Common Peaks by LC-MS.

To establish the fingerprint of Cibotii rhizoma using high-performance liquid chromatography (HPLC) and evaluate the quality of Cibotii rhizoma from different regions using chemometrics to identify the potential quality markers, thirteen batches of Cibotii rhizoma samples were analyzed. the similarity evaluation system of TCM chromatographic fingerprint similarity evaluation was used to confirm common peaks. The SPSS 27 software was used for hierarchical cluster analysis (HCA), and SIMCA 14.1 software was used for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Moreover, a batch of Cibotii rhizoma was selected for LC-MS analysis and speculated on 15 common components. HPLC fingerprint were established, 15 common peaks were matched, two chromatographic peaks were identified using standard substances (protocatechuic acid and protocatechuic aldehyde), and 13 common components were inferred through liquid chromatograph-mass spectrometer (LC-MS). The 13 batches of the samples showed good similarities (>0.910). The results of HCA, PCA and OPLS-DA showed that 13 batches of samples were divided into three groups, and different markers were selected. The method is simple, rapid and reproducible, and can provide a reference for the overall quality evaluation of Cibotii rhizoma.

High-Throughput Screening of Sulfur Reduction Reaction Catalysts Utilizing Electronic Fingerprint Similarity.

The catalytic performance is determined by the electronic structure near the Fermi level. This study presents an effective and simple screening descriptor, i.e., the one-dimensional density of states (1D-DOS) fingerprint similarity, to identify potential catalysts for the sulfur reduction reaction (SRR) in lithium-sulfur batteries. The Δ1D-DOS in relation to the benchmark W2CS2 was calculated. This method effectively distinguishes and identifies 30 potential candidates for the SRR from 420 types of MXenes. Further analysis of the Gibbs free energy profiles reveals that MXene candidates exhibit promising thermodynamic properties for SRR, with the protocol achieving an accuracy rate exceeding 93%. Based on the crystal orbital Hamilton population (COHP) and differential charge analysis, it is confirmed that the Δ1D-DOS could effectively differentiate the interaction between MXenes and lithium polysulfide (LiPS) intermediates. This study underscores the importance of the electronic fingerprint in catalytic performance and thus may pave a new way for future high-throughput material screening for energy storage applications.

Herb induced liver injury by Xianling Gubao Tablets: A case assessed for causality using updated RUCAM and integrated evidence chain

ObjectiveA typical case of Xianling Gubao (XLGB) Tablets-induced liver injury was systematically studied in the clinic and the laboratory. MethodsA patient with herb-induced liver injury (HILI) and a history of taking XLGB Tablets before disease onset was engaged as the study subject, and the case was diagnosed according to the updated Roussel Uclaf Causality Assessment Method (RUCAM) and the integrated evidence chain (iEC) method recommended by the Guidelines for Diagnosis and Treatment of Herb-induced Liver Injury (HILI Guidelines). ResultsClinical history, biochemical indexes and imaging tests were used to exclude the influence of fundamental diseases and confusing liver diseases such as viral, alcoholic and autoimmune liver diseases on the diagnosis. Based on an investigation of the patient’s medication history, she was suspected to have HILI caused by XLGB Tablets, as the patient was only taking an oral preparation of XLGB Tablets, and the influence of other drugs on the diagnosis was excluded. This patient with alanine aminotransferase (ALT) ≥ 3 × upper limit of normal (ULN) and a calculated R of 6 was diagnosed with possible acute drug-induced hepatocellular injury. The relationship was considered “highly probable” (score of 9) using the updated RUCAM of 2016. Moreover, the fingerprint similarity between the preparation taken by the patient and a commercially available preparation was 0.99, suggesting that the patient was consuming XLGB Tablets rather than another drug. LC-MS technology and the Agilent Fake TCM-Drugs database were used to investigate the drug, and no chemical additions were found. Examination of the drug for pesticide residues, heavy metals, aflatoxins and other exogenous substances indicated compliance with the content limits of the Chinese Pharmacopoeia. ConclusionIn summary, the final diagnosis of XLGB-induced liver injury reached the clinical diagnosis of HILI and was acute severe hepatocellular injury type by the updated RUCAM and iEC. Therefore, this study provides scientific evidence regarding the causality evaluation of compound preparations of traditional Chinese medicines-induced liver injury.