Advanced Analytical Method Research Articles

Development and Validation of Trace Level Quantification of Nine Azido Genotoxic Impurities in Losartan Potassium Active Pharmaceutical Ingredients by Liquid Chromatography‐Tandem Mass Spectrometry

ABSTRACTThe objective of the study is to develop a sensitive and simple liquid chromatography‐tandem mass spectrometry for the determination of nine azido impurities in Losartan potassium active pharmaceutical ingredients. An advanced analytical method should be developed to determine these nine Azido impurities at 0.5 ppm level. Currently, no such method is reported to control these impurities in losartan potassium drug substance. The nine impurities separation was achieved using Hypersil ODS stationary phase with gradient elution and mobile phase of 0.1 M ammonium acetate as mobile phase A and methanol as mobile phase B. The developed method was found to be sensitive to the limit of detection and limit of quantitation levels as per the International Council for Harmonization M7 guidelines. The method was linear from 0.5 ppm to 15 ppm for all nine azido impurities. This is a single method for the simultaneous determination of all azido impurities in the presence of losartan potassium drug substance at 0.5 ppm and the same method used in the quality control laboratory for sample analysis.

Dual-Mode Immunoassay Constructed by Water-Induced Perylene Diimide Supramolecular Self-Assembly and Enzymatic Biocatalytic Precipitation Strategy.

A water-induced electron-deficient dye, the supramolecule perylene diimide (PDI), has been identified recently. PDI possesses advantages such as easy reduction, nontoxicity, low cost, and simple preparation, making it a promising candidate for electrochemiluminescence (ECL) sensing platforms. In this study, a series of PDI supramolecular systems with morphological changes were prepared by utilizing water molecules to induce PDI self-assembly. This method improves the π-π stacking interactions between PDI molecules and effectively mitigates the aggregation-caused quenching (ACQ) effect on the luminous efficiency of the coplanar polycyclic aromatic hydrocarbon PDI. It is noteworthy that excellent ECL emission performance of the PDI supramolecular system was observed at -0.4 V. This low excitation potential aids in preserving antigen-antibody bioactivity and ensures accurate identification of the immune response. As a proof of concept, a dual-mode immunosensing platform for carcinoembryonic antigen (CEA) detection was constructed using an enzymatic biocatalytic precipitation (EBCP) strategy. The dual-mode immunosensor exhibited good detection performance in the concentration range of 0.001-80 ng·mL-1, presenting an advanced bioprotective analytical method for CEA detection.

On the dynamics of improved perovskite solar cells: Introducing SVM-DNN-GA algorithm to predict dynamical information

This study presents an innovative approach to enhancing the performance of perovskite solar cells through the integration of a functionally graded triply periodic minimal surface (FG-TPMS) layer. The research focuses on the mechanical and vibrational characteristics of doubly curved panels embedded with three distinct iterations of the FG-TPMS model: the primitive, gyroid, and wrapped package graph (IWP). By employing higher-order shear deformation theory (HSDT), the analysis accounts for the complex geometrical and material gradations within the FG-TPMS structures. An advanced analytical method utilizing trigonometric functions is developed to accurately predict the natural frequencies and mode shapes of these novel composite structures. In order to assess the vibrations of TPMS-reinforced perovskite solar cells surrounded by an elastic foundation, this work proposes the implementation of a novel Support Vector Machine (SVM)-deep neural network (DNN)-Genetic Algorithm (GA) employing mathematical modeling datasets. Using the SVM-DNN-GA algorithm, predicted accuracy is improved. In order to simulate and forecast the vibrational behavior of the reinforced solar cells, the integrated methodology makes use of the advantages of each technique. The results indicate that the integration of FG-TPMS layers significantly enhances the mechanical stability of the perovskite solar cells. The application of HSDT reveals detailed insights into the dynamic responses of the doubly curved panels, highlighting the potential for fine-tuning their vibrational characteristics to further improve solar cell performance. This research underscores the potential of FG-TPMS structures in advancing solar cell technology, providing a foundation for future studies to explore the integration of complex geometries and material gradations in photovoltaic applications.

Development of a Robust and Reliable RP-HPLC Method for the Estimation of Finerenone in Tablet Dosage Form

An advanced analytical method was explored, specifically tailored for estimating finerenone, a vital drug found in tablet formulations intended to reduce the risk of chronic kidney disease in patients with type-2 diabetes. This sophisticated technique employs an Inertsil ODS 3V C18 column (5 μm particle size x 250 x 4.6 mm) for precise separation of analyte. The mobile phase consists of an ammonium dihydrogen phosphate buffer (pH 4.5) and acetonitrile (60:40), which is a carefully balanced mixture that flows consistently at 1.0 mL/min. UV detection is achieved at a wavelength of 238 nm. Finerenone’s characteristics are unveiled with a brief retention time of 4.06 minutes, highlighting the efficiency of the method. The method demonstrates linearity within the concentration range of 2.5 to 15 μg/mL, offering a comprehensive scope for accurate analysis. This robust reversed-phase high-performance liquid chromatography (RP-HPLC) method undergoes thorough validation following International Council for Harmonisation (ICH) guidelines, ensuring its reliability. Notably, the accuracy is impressive, with a mean recovery falling comfortably within the acceptable range of 99.76 to 101.09%. In summary, our analytical approach stands out for its simplicity, precision, sensitivity, rapidity, and robustness in estimating finerenone in tablet formulations.

Fractional Fokas-Lenells equation: analyzing travelling waves via advanced analytical method

In this paper, we consider the fractional Fokas-Lenells equation, which allows us to analyze how a nonlinear optic pulse spreads in time as single-mode fiber produces higher-order nonlinear effects. We have computed perfectly accurate travelling wave solutions for the Fokas-Lenells equation using the Riccati-Bernoulli sub-Ode approach. For the corresponding equation, we have established three distinct classes of perfectly accurate travelling wave solutions with different free parameters; hyperbolic, trigonometric, and rational. A sophisticated Backlund transformation is implemented to the equation to change it to ordinary differential equation domain, leading to many extra exact solutions.

Development and validation of simultaneous HPLC-PDA analysis method for quality control of Hwang-ryeon-hae-dok-tang: An analytical quality by design approach

This study introduces an advanced analytical method for quality control of botanical drugs, focusing on Hwang-ryeon-hae-dok-tang (HRHDT), a traditional herbal prescription utilized for inflammation, gastritis, fever, and hypertension. Botanical drugs pose a challenge due to their complex composition, requiring reliable and robust analytical techniques. Our step-by-step procedure included defining the analytical target profile, risk assessment, screening and optimizing design of experiment, measuring the design space, and Monte Carlo simulation. The established HPLC-PDA method enables the simultaneous analysis of four major compounds (geniposide, baicalin, palmatine, and berberine) present in HRHDT. The optimized conditions involve a gradient elution with acetonitrile and water, both containing 0.1% trifluoroacetic acid, a 32 °C column temperature, 254 nm detection wavelength, and 1.0 mL/min flow rate. A successful validation demonstrated excellent linearity (R2: 0.9984–0.9989) and accuracy (recovery: 100.61%–102.44%), with precise repeatability and intermediate precision (%RSD: 0.39%–0.83%). These results further confirm the reliability and accuracy of the analytical method. Overall, our design-based approach not only facilitated the development of robust analytical methods but also allowed for flexible operation ranges. Therefore, this methodology holds significant potential for ensuring the quality of botanical drug products within the pharmaceutical industry.

Deep Learning-Assisted Intelligent Artificial Vision Platform Based on Dual-Luminescence Eu(III)-Functionalized HOF for the Diagnosis of Breast and Ovarian Cancer.

Developing an advanced analytical method to detect spermine (Spm) and N-acetylneuraminic acid (NANA), the biomarkers of breast and ovarian cancers, respectively, is critical for the early diagnosis of the two cancers, which is very meaningful for women's health. Here, a deep learning-assisted artificial vision platform based on a dual-emission ratiometric fluorescence sensor is first constructed to monitor Spm and NANA. The ratiometric fluorescence sensor (Eu@TCBP-HOF, 1) can selectively detect Spm with high sensitivity based on "Turn-on" mode. After adding Spm, the new ratiometric fluorescence sensor (1-Spm, named 2) shows high sensitivity for NANA with "Turn-off" mode. Moreover, the fluorescence sensors can achieve an obvious fluorescence color response to Spm and NANA. Even in real saliva and serum samples, 1 and 2 still show high sensitivity and color responsiveness with limit of detection (LODs) of 0.5 μM for Spm and 0.96 μM for NANA. In virtue of different fluorescence responses, the DenseNet algorithm of deep learning assists the fluorescence sensors, which can simulate the human visual systems to identify fluorescence images and distinguish the concentration of Spm and NANA within 1 s with over 99% recognition accuracy. The intelligent artificial vision platform developed in this work may provide a prospective analytical method for the early diagnosis of female malignant tumors.

Photometric Determination of Trace Amounts of Aluminum in Nearly Saturated Rock Salt Solutions Used by Chlor-alkali Industry

The previously widespread mercury cell technology in chlorine production has now been replaced by more environmentally friendly membrane cell electrolysis which is a Best Available Techniques (BAT) technology. However, this requires a much cleaner brine containing contaminants (Al, Ca, Mg, etc.) in the order of ng/g at most. For this reason, it’s very important to detect trace amounts of aluminum in concentrated saline media in the simplest and fastest way. To the best of our knowledge, no one has previously developed a spectrophotometric method capable of detecting aluminum in ionic forms selectively in the order of ng/g in concentrated saline media, without any preconcentration or separation step. Our advanced analytical method provides an opportunity for this. During the analytical procedure, a colored complex ion is formed from the dissolved aluminum content of the sample with eriochrome cyanine R (ECR) ligand in buffered pH medium. The sensitivity of the measurement is increased by adding quaternary ammonium salt. The colored complex ion is formed in 15 minutes, then the absorbance measurement can be performed for 90 minutes. The effect of rock salt interference was eliminated by proper calibration. In our work the dependence of the signal on temperature, pH, time elapsed after the addition of reactants, the dosing sequence, the salinity of the medium was examined, furthermore, we studied which wavelength-absorbance values give the best fit (highest R2 value) and the highest sensitivity in case of linear calibration. Surprisingly, increasing the salinity significantly improves the sensitivity of the measurement.

Environmental-Related Trigger for Asthma in East Java: An Advance Analysis of the Risk Factor

Background: According to the Indonesian Ministry of Data and Information Center (2019), the main cause of asthma is not yet known. The national prevalence of asthma is 4.0%. Meanwhile, according to Basic Health Research in 2019, the incidence of asthma in Indonesia was 2.4%, and in East Java, it was 2.5%. Several risk factors for the prevalence of asthma due to unhealthy behaviours, namely smoking consumption habits, physical activities, triggers for depression, and processed chicken/meat/fish foods that are given preservatives. Methods: The design of this study is the advanced analytical method. An approach of utilizing secondary data from the 2019 National Riskesdas (Basic Health Research) report. This study looked at the relationship between asthma prevalence and smoking habits, physical activity, triggers for depression, and consumption of processed chicken/meat/fish food with preservatives. Results: Based on the results, cigarette consumption habits and triggers of depression had a relationship with risk factors for asthma prevalence. Meanwhile, physical activity and consumption of processed meat/chicken/fish foods with preservatives did not have a relationship with the incidence of asthma. Conclusion: The increasing prevalence of asthma was closely related to smoking habits and a trigger for depressive disorders. However, based on the cross-tabulation results, there was no relationship between physical activity and consumption of processed meat/chicken/fish foods with preservatives and the prevalence of asthma. It is hoped that with this research, and seeing that there were still exposure factors from smoking habits and depression triggers, prevention efforts can be made, starting with education about asthma recurrence by avoiding smoking or air pollution, allergens, stress, and emotions. In addition, reducing asthma can be done by asking for support from the closest people to minimize these behaviors and changing smoking behavior with other positive habits such as getting used to replacing nicotine with candy.

Alterations in the gastric microbiota and metabolites in gastric cancer: An update review.

Gastric cancer (GC) is one of the leading causes of cancer mortality worldwide. Numerous studies have shown that the gastric microbiota can contribute to the occurrence and development of GC by generating harmful microbial metabolites, suggesting the possibility of discovering biomarkers. Metabolomics has emerged as an advanced promising analytical method for the analysis of microbiota-derived metabolites, which have greatly accelerated our understanding of host-microbiota metabolic interactions in GC. In this review, we briefly compiled recent research progress on the changes of gastric microbiota and its metabolites associated with GC. And we further explored the application of metabolomics and gastric microbiome association analysis in the diagnosis, prevention and treatment of GC.

Plant toxin poisoning–a disguised diagnosis?

Many very poisonous plants grow outdoors, or can be purchased at the store, and some popular wild edible plants can easily be confused with a toxic ‘twin’ plant. Diagnosis of plant poisoning can, however, be challenging. Would yew (Taxus baccata) intoxication have crossed your mind, when finding a person unconscious with signs of acute heart failure, just a few hours after last observed physically fit? We present a challenging analytical investigation, of what turned out to be a fatal yew poisoning. Further, we will present our development of a new comprehensive screening method for plant toxins in blood, using UHPLC-QTOF-MS (ultra-high performance liquid chromatography quadrupole time-of-flight/mass spectrometry). Thanks to excellent interdisciplinary cooperation and new advanced technology, we have identified a death caused by yew toxins. No substances were initially detected by our extensive routine chromatographic toxicological analysis in postmortem blood and urine, including high-resolution mass spectrometry (UHPLC-QTOF-MS) with a repertoire of approximately 850 substances in blood. However, when comparing MS findings in the postmortem blood sample against a large library of approximately 3000 toxicologically relevant compounds, the substance 3,5-dimethoxyphenol (3,5-DMP) was found. 3,5-DMP is a non-toxic marker of yew poisoning. Since the toxic substances in yew, taxine B and taxine A, were not available for purchase as pure reference materials, we produced and analyzed extracts of needles, bark and twigs of three types of yew from our own gardens. The identities of taxine B and taxine A in the deceased's blood and urine sample were then confirmed by comparison with the results for these extracts. The concentration of 3,5-DMP in postmortem blood was found to be 0.7 μmol/L (0.11 mg/L), using purchased reference material for 3,5-DMP. The breakthrough experienced in this yew case spurred us on to develop a new advanced UHPLC-QTOF-MS method for screening of a broad range of plant toxins in blood samples. For many toxins, we will also be able to perform quantitative analysis. A seldom cause of death that could easily have been overlooked was revealed, due to perseverance and some luck. Yew has a high content of cardiotoxic alkaloids, which can lead to death within few hours after ingestion. Taxine B mediates cardiotoxicity by blocking calcium channels, and to some extent sodium channels, in heart muscle cells, and this can induce refractory ventricular fibrillation. In order to improve diagnostics, knowledge and national cooperation on plant and mushroom poisonings in Norway, we are developing a new advanced analytical screening method, and we have also established a nationwide and tightly cooperating ‘Competence group on plant and mushroom toxins’. The group is composed of medical pharmacologists, toxicologists, an anesthesiologist, chemists, veterinarians, and botanists. Plant toxin poisoning, like the presented fatality caused by yew, is a diagnosis that can easily be missed. We are developing an advanced screening method (UHPLC-QTOF-MS) for a large selection of plant toxins in blood, to improve diagnostics and knowledge on such poisonings.

Archaeal Viruses: Production of Virus Particles and Vesicle-like Viruses and Purification Using Asymmetrical Flow Field-Flow Fractionation.

Asymmetrical flow field-flow fractionation (AF4) is a separation method based on hydrodynamic size of the sample components. It can separate a broad size range of components (~103 to 109Da; particle diameter from ~1nm to ~1μm), but is especially well suited for high molecular weight samples such as virus-sized particles and extracellular vesicles. Separation takes place in an open channel where the flows control sample elution. Separation does not involve stationary phase, allowing gentle separation and good recoveries. The method is compatible with a wide variety of buffers. Coupling to various analytical detectors enables rapid assays on the molecular weight and size and their distribution, degradation, and aggregation of the sample components giving information on the sample quality. In addition to being an advanced analytical method, AF4 can be used in a semipreparative mode for purification. Here, we summarize archaeal virus production methods and virus purification by AF4 and provide examples on the steps that need optimization for obtaining good separation with the focus on halophilic archaeal viruses. Importantly, AF4 method is suitable for a variety of viruses and extracellular vesicles regardless of their host organism.

Electrochemical Method for Ease Determination of Sodium Diclofenac Trace Levels in Water Using Graphene-Multi-Walled Carbon Nanotubes Paste Electrode.

Sodium diclofenac (DCF) presence reported in water use cycle at various concentrations including trace levels necessitates continuous development of advanced analytical method for its determination. In this work, ease electrochemical methods for DCF determination based on voltammetric and amperometric techniques were proposed using a simple combination of graphene with multi-walled carbon nanotubes as paste electrode. Integration of the graphene with multi-walled carbon nanotubes enlarged the electroactive surface area of the electrode and implicitly enhanced the electrochemical response for DCF determination. On the basis of the sorption autocatalytic effect manifested at low concentration of DCF, we found that the preconcentration step applied prior to differential-pulsed voltammetry (DPV) and multiple-pulsed amperometry (MPA) allowed for the enhancement of the electroanalytical performance of the DCF electrochemical detections, which were validated by testing in tap water. The lowest limit of detection (LOD) of 1.40 ng·L−1 was found using preconcentration prior to DPV under optimized operating conditions, which is better than that reached by other carbon-based electrodes reported in the literature.

Iron Loss Calculation for High-Speed Permanent Magnet Machines Considering Rotating Magnetic Field and Thermal Effects

Thanks to their many merits such as high power density and fast dynamic response, the high-speed permanent magnet machines (HSPMMs) have attracted increasing industrial and domestic applications. However, the iron loss may become significantly higher at higher operating speed and frequency and it should be carefully considered in the machine design and analysis. In this paper, an advanced iron loss analytical calculation method is applied for HSPMMs in which the influences of rotating magnetic field and thermal field are both considered. A 30 kW, 45000 r/min HSPMM is studied to demonstrate that the proposed model is feasible and advantageous. Analysis results reveal that the predicted iron loss by using the proposed method has satisfactory accuracy with small errors (maximum error of 3.73% and absolute average error of 3.03%) under different operating conditions. The proposed method can also be applied in other electromagnetic devices such as superconducting electrical machines.

Development and optimization of analytical methods for the detection of 18 taste and odor compounds in drinking water utilities

Taste and odor (T&O) issues have been a major concern among drinking water utilities as source waters are becoming increasingly vulnerable to compounds released during algal blooms as well as non-algal compounds. While most of the literature focuses on the two most common T&O compounds – 2-MIB and geosmin, there are other compounds that have the potential to cause T&O events. The aim of this study was to develop an advanced analytical method using solid phase microextraction (SPME) and gas chromatography-tandem mass spectrometry (GC-MS/MS) to identify 18 T&O compounds belonging to various odor classes. The developed method was optimized for the 18 analytes and implemented to determine the holding time of the compounds in raw and treated (distribution system point-of-entry or PoE) drinking water matrices. Compounds belonging to certain classes such as pyrazines and anisoles were found to be “stable” (< 30% loss) in all tested waters for up to two weeks, while aldehydes, ketones, esters and alkyl sulfides showed > 30% loss within 96 h in raw water. Preservation of samples at low pH (< 2) using hydrochloric acid increased the holding times and reduced losses within 96 h for aldehydes, ketones and esters. The paper also discusses the occurrence of these compounds with water utilities from the Midwest and Eastern US during the summer months. The study detected eight T&O compounds - 2-MIB, geosmin, β-cyclocitral, β-ionone, hexanal, indole, dimethyl disulfide and dimethyl trisulfide. While five compounds were detected above their threshold concentrations in the raw water, two of them (2-MIB and geosmin) were detected above threshold in the PoE samples.

High Performance Thin Layer Chromatography Fingerprinting Analysis of Piper betle L. Leaves

Introduction: Many modern medicines used today based on plants and plant products. Piper betle is generally known as the betle vine, it is an important medicinal and recreational plant. High performance thin layer chromatography (HPTLC) is an advanced powerful analytical method with more separation power, high performance and superior reproducibility than classic thin layer chromatography (TLC). A chromatographic fingerprint of a plant extract is a chromatographic pattern of some common chemical constituents of pharmacologically active and/or chemical characteristics. Chromatographic fingerprints are useful in authentication and identification of plant.&#x0D; Objectives: Objectives of present research was to establish HPTLC fingerprinting of methanolic extract of Piper betle L. leaves.&#x0D; Materials and Methods: Methanolic extract of Piper betle leaves was prepared using soxhlet apparatus. HPTLC studies were performed using a CAMAG HPTLC system equipped with automatic TLC sampler-4 (ATS 4), TLC scanner 4, and vision CATS 3.0 software.&#x0D; Results: The study revealed the presence of alkaloids with Rf value 0.65, flavonoids with Rf values 0.19, 0.29, 0.72, 0.95., and phenolic compound with Rf value 0.7.&#x0D; Conclusion: The HPTLC fingerprinting profile developed for the methanolic extract of Piper betle L. leaves will help in proper identification of the plant.Piper betle

Evaluation and authentication for metformin HCL, Glimepride, atorvastatine by using bulk HPLC pharmaceutical dosage form

Reverse phase chromatographic techniques are used for analyzing drugs. It is advanced analytical instrumentation method used for analysis of drugs. A novel accurate, precise, simple, &amp; selective RP- HPLC technique is established and authorized for stability representing RP – HPLC technique for simultaneous assessment of Metformin HCL, Glimepiride &amp; Atorvastatin in bulk &amp; pharmaceutical dosage form by RP– HPLC technique. The technique on Luna C 18, 250mm × 4.6 mm, 15 µm. Column with a mobile phase comprising of buffer and acetonitrile in ratio of (85:15 v/v) and 1.0ml/ minflow rate. The detection is 258 nm. The retention time for metformin HCL, Atorvastatin &amp; Glimepiride are discovered to be 3.266, 5.237 and 8.457 correspondingly. The technique authorized accordingly to ICH rules for precision, specificity, LOQ, LOD, accuracy, robustness, &amp; linearity. The technique demonstrates best recovery &amp;reproducibility with % RSD lower than 2. The suggested technique is discovered to be precise, simple, accurate, specific, &amp;linear. The results are within the acceptance limits of lower than 2% signified that suggested technique has best reproducibility. The toughness of developed technique is validated by altering mobile phase composition, flow rate, and changing wavelength. The % RSD was found within the limits i.e., should not be more than 2.0.

Occurrence, Fate and Adverse Effects of Residual Levels of Preservatives and Active Whitening Agents from Wasterwater of Personal Care Products Industries

The present study was carried out to estimate the residual level of preservatives and active whitening agents from personal care products (PCPs) wastewater by using high performance liquid chromatography (HPLC) along with characterization of wastewater. Diverse types of pollutants are being detected in aqueous environment with PCPs and related compounds containing large variety of groups, including parent molecules and their respective metabolites. Intensive research is required on this subject to gain reliable information about their occurrence, fate and adverse effects on health. For this purpose, development of extremely sensitive analytical methods of determination in industrial wastewater samples are not only need of the hour but also a challenge. Liquid chromatography is an advanced analytical method for such pollutants. In this study, samples were collected from industrial sites located at distant points of Lahore and characterization was done through different parameters to illustrate wastewater quality. It has been found that values were within limits of National Environmental Quality Standard (NEQS) except for turbidity and total suspended solids (TSS) in some samples. Six preservatives including methylparaben, benzoic acid, salicylic acid, sodium benzoate, propylene glycol and formic acid along with five whitening agents like kojic acid, arbutin, ascorbic acid, nicotinamide and hydroquinone were quantified. It was interpreted that residues are present in almost all collected PCPs wastewater samples. The concentration of selected preservatives and active whitening agents in wastewater was ranged between 0.91–4.17 and 1.13–15.65 mg/L respectively. The most quantified concentrations were of methylparaben and sodium benzoate among preservative residues. In whitening agents, kojic acid and ascorbic acid were quantified in the highest concentrations.

Identification of key gene modules and genes in colorectal cancer by co-expression analysis weighted gene co-expression network analysis

Colorectal cancer (CRC) has been one of the most common malignancies worldwide, which tends to get worse for the growth and aging of the population and westernized lifestyle. However, there is no effective treatment due to the complexity of its etiology. Hence, the pathogenic mechanisms remain to be clearly defined. In the present study, we adopted an advanced analytical method—Weighted Gene Co-expression Network Analysis (WGCNA) to identify the key gene modules and hub genes associated with CRC. In total, five gene co-expression modules were highly associated with CRC, of which, one gene module correlated with CRC significantly positive (R = 0.88). Functional enrichment analysis of genes in primary gene module found metabolic pathways, which might be a potentially important pathway involved in CRC. Further, we identified and verified some hub genes positively correlated with CRC by using Cytoscape software and UALCAN databases, including PAICS, ATR, AASDHPPT, DDX18, NUP107 and TOMM6. The present study discovered key gene modules and hub genes associated with CRC, which provide references to understand the pathogenesis of CRC and may be novel candidate target genes of CRC.

Injection-Induced Seismic Risk Management Using Machine Learning Methodology – A Perspective Study

Effective identification of induced seismicity and real-time management of seismic risks are hot topics due to increasing induced seismicity in areas related to energy exploitation. Existing decision-making tool for managing seismic risks, known as the traffic light system, is not robust enough. To meet the increasing needs for safe mining of energy at production sites, finding an advanced and efficient method to improve the traffic light system is essential. In recent years, machine learning, an advanced inductive and analytical method, has been widely used in seismology. In this context, research gaps associated with the identification and management of induced seismicity, as well as the current achievements of machine learning in addressing induced seismicity problems, are reviewed. A basic framework of using machine learning method to optimize the traffic light system in the industrial production process is first proposed. Then, its feasibility and rationality are demonstrated by similar cases. This framework may provide a reference for the development of a risk-based adaptive traffic light management system.