Analysis Platform Research Articles

Clinical drug interactions between linezolid and other antibiotics: For adverse drug event monitoring.

Detailed data on safety associated with drug-drug interactions (DDIs) between Linezolid (LZD) and other antibiotics are limited. The aim of this study was to investigate the safety signals related to these DDIs and to provide a reference for clinically related adverse drug event monitoring. Adverse event (AE) information from 1 January 2004 to 16 June 2022 of the target antibiotics including LZD using alone or in combination with LZD was extracted from the OpenVigil FDA data platform for safety signal analysis. The combined risk ratio model, reporting ratio method, Ω shrinkage measure model, and chi-square statistics model were used to analyze the safety signals related to DDIs. Meanwhile, we evaluated the correlation and the influence of sex and age between the drug(s) and the target AE detected. There were 18991 AEs related to LZD. There were 2293, 1726, 4449, 821, 2431, 1053, and 463 AE reports when LZD was combined with amikacin, voriconazole, meropenem, clarithromycin, levofloxacin, piperacillin-tazobactam, and azithromycin, respectively. Except for azithromycin, there were positive safety signals related to DDIs between LZD and these antibiotics. These DDIs might influence the incidence of 13, 16, 7, 7, 6, and 15 types of AEs, respectively, and is associated with higher reporting rates of AEs compared with use alone. Moreover, sex and age might influence the occurrence of AEs. We found that the combinations of LZD and other antibiotics are related to multiple AEs, such as hepatotoxicity, drug resistance and electrocardiogram QT prolonged, but further research is still required to investigate their underlying mechanisms. This study can provide a new reference for the safety monitoring of LZD combined with other antibiotics in clinical practice.

RTModel: A platform for real-time modeling and massive analyses of microlensing events

Context. The microlensing of stars in our Galaxy has long been used to detect and characterize stellar populations, exoplanets, brown dwarfs, stellar remnants, and all other objects that may magnify the source stars with their gravitational fields. The interpretation of microlensing light curves is relatively simple for single lenses and single sources, but it becomes more and more complicated when we add more objects and take their relative motions into account. Aims. RTModel is a modeling platform that has been very active in the real-time investigations of microlensing events, providing preliminary models that have proven very useful for driving follow-up resources towards the most interesting events. The success of RTModel comes from its ability to carry out a thorough and focused exploration of the parameter space in a relatively short time. Methods. This modeling process is based on three key ideas. First, the initial conditions are chosen from a template library including all possible caustic crossing and approaches. The fits are then made using the Levenberg-Marquardt algorithm with the addition of a bumper mechanism to explore multiple minima. Finally, the basic computations of microlensing magnification are performed by the fast and robust VBBinaryLensing package. Results. In this paper, we illustrate all the algorithms of RTModel in detail with the intention to foster new approaches in view of future microlensing pipelines aimed at massive microlensing analyses.

A FAIR, open-source virtual reality platform for dendritic spine analysis

A FAIR, open-source virtual reality platform for dendritic spine analysis

Web-CONEXS: an inroad to theoretical X-ray absorption spectroscopy.

Accurate analysis of the rich information contained within X-ray spectra usually calls for detailed electronic structure theory simulations. However, density functional theory (DFT), time-dependent DFT and many-body perturbation theory calculations increasingly require the use of advanced codes running on high-performance computing (HPC) facilities. Consequently, many researchers who would like to augment their experimental work with such simulations are hampered by the compounding of nontrivial knowledge requirements, specialist training and significant time investment. To this end, we present Web-CONEXS, an intuitive graphical web application for democratizing electronic structure theory simulations. Web-CONEXS generates and submits simulation workflows for theoretical X-ray absorption and X-ray emission spectroscopy to a remote computing cluster. In the present form, Web-CONEXS interfaces with three software packages: ORCA, FDMNES and Quantum ESPRESSO, and an extensive materials database courtesy of the Materials Project API. These software packages have been selected to model diverse materials and properties. Web-CONEXS has been conceived with the novice user in mind; job submission is limited to a subset of simulation parameters. This ensures that much of the simulation complexity is lifted and preliminary theoretical results are generated faster. Web-CONEXS can be leveraged to support beam time proposals and serve as a platform for preliminary analysis of experimental data.

Urban Sprawl Analysis Using Landscape Metrics in Chiba Prefecture, Japan from 1990-2021

Abstract Urban sprawl, characterized by unregulated development on the outskirts of urban areas, leads to agricultural land loss, reduced biodiversity, and environmental issues. The study employs landscape metrics to analyze the spatial patterns and effects of urban sprawl by measuring the fragmentation and structure of urban areas in Chiba Prefecture, Japan. The research employs data collected from Landsat imagery 5 (1990) and 8 (2021) and utilizes the Google Earth Engine platform for spatial and temporal analysis. The Land Use Land Cover (LULC) assessment reveals the transformation of Chiba’s landscape from 1990 to 2021, with urban areas expanding significantly and vegetation decreasing. The analysis using 8 selected landscape metrics highlights the varying degrees of urban sprawl across Chiba Prefecture. Cities near Tokyo show higher levels of sprawl, while some areas outside the edge experience growing development. Notably, there is a trend of increasing urban sprawl toward the edge. As Chiba Prefecture faces challenges from an aging population, the findings emphasize the need for controlled urban development to mitigate the negative impacts of sprawl and the importance of sustainable urban planning especially towards the edge area to ensure a balance between development and environmental preservation.

Advances on multiclass pesticide residue determination in citrus fruits and citrus-derived products - A critical review.

The application of agrochemicals in citrus fruits is widely used to improve the quality of crops, increase production yields, and prolong post-harvest life. However, these substances are potentially toxic for humans and the ecosystem due to their widespread use, high stability, and bioaccumulation. Conventional techniques for determining pesticide residues in citrus fruits are chromatographic methods coupled with different detectors. However, in recent years, the need for analytical strategies that are less polluting for the environment has encouraged the appearance of new alternatives, such as sensors and biosensors, which allow selective and sensitive detection of pesticide residues in real time. A comprehensive overview of the analytical platforms used to determine pesticide residues in citrus fruits and citrus-derived products is presented herein. The review focuses on the evolution of these methods since 2015, their limitations, and possible future perspectives for improving pesticide residue determination and reducing environmental contamination.

Metal-Organic Frameworks in Surface Enhanced Raman Spectroscopy-Based Analysis of Volatile Organic Compounds.

Volatile Organic Compounds (VOC) are a major class of environmental pollutants hazardous to human health, but also highly relevant in other fields including early disease diagnostics and organoleptic perception of aliments. Therefore, accurate analysis of VOC is essential, and a need for new analytical methods is witnessed for rapid on-site detection without complex sample preparation. Surface-Enhanced Raman Spectroscopy (SERS) offers a rapidly developing versatile analytical platform for the portable detection of chemical species. Nonetheless, the need for efficient docking of target analytes at the metallic surface significantly narrows the applicability of SERS. This limitation can be circumvented by interfacing the sensor surface with Metal-Organic Frameworks (MOF). These materials featuring chemical and structural versatility can efficiently pre-concentrate low molecular weight species such as VOC through their ordered porous structure. This review presents recent trends in the development of MOF-based SERS substrates with a focus on elucidating respective design rules for maximizing analytical performance. An overview of the status of the detection of harmful VOC is discussed in the context of industrial and environmental monitoring. In addition, a survey of the analysis of VOC biomarkers for medical diagnosis and emerging applications in aroma and flavor profiling is included.

Divergent immune profiles in distinct populations - A vietnamese-german comparison

The human immune system exhibits fascinating diversity, sculpted by an intricate interplay of genetic and environmental influences. This study delves into these complexities by comparing the immunological landscapes of healthy individuals from distinct backgrounds: 40 Vietnamese and 24 German participants. Our comprehensive analysis, encompassing 42 lymphocyte populations and 17 cytokines, reveals profound differences in immune profiles between these two populations. Utilizing multicolor flow cytometry and advanced analytical platforms, we conducted a comprehensive characterization of the cellular and molecular components of individual immune systems. Statistical analyses revealed highly significant differences (p < 0.05) between Vietnamese and German cohorts in 33 out of 42 lymphocyte populations and 15 out of 17 cytokines. These disparities encompassed a wide range of immune cell subsets, including T, B and NK cells and involved both activating and inhibitory immune regulators. Healthy Vietnamese subjects exhibited significantly higher numbers of B, T and NK cells compared to their German counterparts. Vietnamese participants displayed higher proportions of plasma cells, immature B cells, and B cells with low CD21 expression (CD21low), which have a phenotypic characteristic of chronic stimulation. Of the 16 subpopulations of CD4+ and CD8+ T cell subpopulations, 11 were significantly elevated in Vietnamese compared to German subjects. Additionally, Vietnamese participants expressed higher proportions of markers for functional and activating NK receptors, indicating the presence of highly cytotoxic NK cells in this population. Almost all of the 17 cytokines examined were significantly lower in Vietnamese subjects. These results demonstrate statistically significant variations in immunological profiles between healthy individuals from Eastern and Western populations. Our findings suggest that caution should be exercised when applying Western reference health profiles to Eastern subjects in clinical settings. This comprehensive analysis underscores the importance of considering population-specific immune profiles in clinical and research contexts, particularly when evaluating immunological parameters across diverse ethnic groups.

Research on risk data analysis of hazardous chemical vessels based on stream computing

Abstract To effectively improve the safety supervision and information management level of vessels carrying hazardous chemicals, the maritime management department is promoting the construction of hazardous chemical vessel monitoring systems based on artificial intelligent technology for hazardous chemical terminals and shipping companies in their jurisdiction. It can identify specific risks and transmit the risk data in real-time to the data center. To analyze data in real-time, a risk data analysis platform for hazardous chemical vessels based on stream computing is designed and implemented. The platform acquires vessel automatic identification system (AIS), risk, basic information, and other data. Based on the stream computing of Flink, it evaluates the risk status of vessels in real time, counts the risk data, and displays the results in the form of charts. The analysis of risk data for hazardous chemical vessels helps maritime management departments to track the real-time navigation status of key vessels, timely identify potential safety hazards, and effectively improve the level and efficiency of safety supervision.

Recent research progress of soil nematode ecology in China.

Soil nematodes are a crucial component of belowground ecosystems. Soil nematode ecology, the studies of community distribution, structural composition, ecological functions, and interaction mechanisms with environment, has always been a hot spot in soil biology research. We systematically elaborated soil nematodes' high diversity and various diet, their advantages as bioindicator and model organisms, and their roles in biological control, ecological functions and soil health. Then, we reviewed the research progress of soil nematode ecology in China, including molecular biology identification methods, responses to global changes, food web structure and function, aboveground and belowground diversity relationship, and large-scale diversity pattern. We put forward the development trend of soil nematology, focusing on the high-throughput sequencing technology in nematode identification and quantification, the necessity of establishing a universal analysis platform to promote soil nematode research, and the importance of strengthening large-scale soil nematode survey.

UHPLC-PDA-Q-TOF-MS-α-amylase-FLD activity detection system and molecular docking.

Traditional and some scientific literature document the antidiabetic effects of the Ziziphi Spinosae Semen (ZSS). However, the bioactive compounds of ZSS responsible for the antidiabetic effects are not well known. This study aimed to investigate the material basis of the antidiabetic effects of ZSS by inhibiting α-amylase. An online analysis platform was established and optimized using an ultra-performance liquid chromatography-photo-diode array-quadrupole-time-of-flight-mass spectrometry-α-amylase-fluorescence detector (UHPLC-PDA-Q-TOF-MS-α-amylase-FLD) system to screen α-amylase inhibitors in ZSS rapidly. The inhibitory effect of these compounds was confirmed by molecular docking screening. and the molecular interactions between α-amylase and active compounds were evaluated, which strongly supported the experimental results. Seventy-eight compounds were identified in the ZSS extract, eleven of which were screened to have significant α-amylase binding activity. This study demonstrated the feasibility of using an established platform to screen for effective components in ZSS, providing a practical method for the rapid screening of potential antidiabetic active ingredients in traditional Chinese medicine.

Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies.

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

Improving cyber-physical-power system stability through hardware-in-loop co-simulation platform for real-time cyber attack analysis

With advancements in communication systems and measurement technologies, smart grids have become more observable and controllable, evolving into cyber-physical-power systems (CPPS). The impact of network security and secondary equipment on power system stability has become more evident. To support the existing grid toward a smart grid scenario, smart metering plays a vital role at the customer end side. Cyber-Physical systems are vulnerable to cyber-attacks and various techniques have been evolved to detect a cyber attack in the smart grid. Weighted trust-based models are suggested as one of the most effective security mechanisms. A hardware-in-loop CPPS co-simulation platform is established to facilitate the theoretical study of CPPS and the formulation of grid operation strategies. This paper examines current co-simulation platform schemes and highlights the necessity for a real-time hard-ware-in-the-loop platform to accurately simulate cyber-attack processes. This consideration takes into account the fundamental differences in modeling between power and communication systems. The architecture of the co-simulation platform based on RT-LAB and OPNET is described, including detailed modeling of the power system, communication system, and security and stability control devices. Additionally, an analysis of the latency of the co-simulation is provided. The paper focuses on modeling and implementing methods for addressing DDOS attacks and man-in-the-middle at-tacks in the communication network. The results from simulating a 7-bus system show the effectiveness and rationality of the co-simulation platform that has been designed.

Single-molecule epiallelic profiling of DNA derived from routinely collected Pap specimens for noninvasive detection of ovarian cancer.

Recent advances in molecular analyses of ovarian cancer have revealed a wealth of promising tumour-specific biomarkers, including protein, DNA mutations and methylation; however, reliably detecting such alterations at satisfactorily high sensitivity and specificity through low-cost methods remains challenging, especially in early-stage diseases. Here we present PapDREAM, a new approach that enables detection of rare, ovarian-cancer-specific aberrations of DNA methylation from routinely-collected cervical Pap specimens. The PapDREAM approach employs a microfluidic platform that performs highly parallelized digital high-resolution melt to analyze locus-specific DNA methylation patterns on a molecule-by-molecule basis at or near single CpG-site resolution at a fraction (<1/10th) of the cost of next-generation sequencing techniques. We demonstrate the feasibility of the platform by assessing intermolecular heterogeneity of DNA methylation in a panel of methylation biomarker loci using DNA derived from Pap specimens obtained from a cohort of 43 women, including 18 cases with ovarian cancer and 25 cancer-free controls. PapDREAM leverages systematic multidimensional bioinformatic analyses of locus-specific methylation heterogeneity to improve upon Pap-specimen-based detection of ovarian cancer, demonstrating a clinical sensitivity of 50% at 99% specificity in detecting ovarian cancer cases with an area under the receiver operator curve of 0.90. We then establish a logistic regression model that could be used to identify high-risk patients for subsequent clinical follow-up and monitoring. The results of this study support the utility of PapDREAM as a simple, low-cost screening method with the potential to integrate with existing clinical workflows for early detection of ovarian cancer. KEY POINTS: We present a microfluidic platform for detection and analysis of rare, heterogeneously methylated DNA within Pap specimens towards detection of ovarian cancer. The platform achieves high sensitivity (fractions <0.00005%) at a suitably low cost (∼$25) for routine screening applications. Furthermore, it provides molecule-by-molecule quantitative analysis to facilitate further study on the effect of heterogeneous methylation on cancer development.

Research Progress of Biosensor Based on Organic Photoelectrochemical Transistor.

In order to solve the food safety problem better, it is very important to develop a rapid and sensitive technology for detecting food contamination residues. Organic photoelectrochemical transistor (OPECT) biosensor rely on the photovoltage generated by a semiconductor upon excitation by light to regulate the conductivity of the polymer channels and realize biosensor analysis under zero gate bias. This technology integrates the excellent characteristics of photoelectrochemical (PEC) bioanalysis and the high sensitivity and inherent amplification ability of organic electrochemical transistor (OECT). Based on this, OPECT biosensor detection has been proven to be superior to traditional biosensor detection methods. In this review, we summarize the research status of OPECT biosensor in disease markers and food residue analysis, the basic principle, classification, and biosensing mechanism of OPECT biosensor analysis are briefly introduced, and the recent applications of biosensor analysis are discussed according to the signal strategy. We mainly introduced the OPECT biosensor analysis methods applied in different fields, including the detection of disease markers and food hazard residues such as prostate-specific antigen, heart-type fatty acid binding protein, T-2 toxin detection in milk samples, fat mass and objectivity related protein, ciprofloxacin in milk. The OPECT biosensor provides considerable development potential for the construction of safety analysis and detection platforms in many fields, such as agriculture and food, and hopes to provide some reference for the future development of biosensing analysis methods with higher selectivity, faster analysis speed and higher sensitivity.

Case Study: Leveraging Databricks to Process Health Care Claims Data and Detect Risks

The insurance industry heavily relies on efficient data processing and risk detection to maintain operational effectiveness and customer satisfaction. This case study defines the use of Databricks, a unified analytics platform, combined with PySpark, for processing claims data and detecting potential risks. We detail the setup of the Databricks environment, the import and cleaning of claims data, feature engineering, building a risk detection model, and evaluating its performance. Visual aids such as diagrams and flowcharts are included to illustrate the process. Key Words: Claims Data, Risk Detection, Databricks, PySpark, Data Processing, Machine Learning

Predictions of Aboveground Herbaceous Production from Satellite-Derived APAR Are More Sensitive to Ecosite than Grazing Management Strategy in Shortgrass Steppe

The accurate estimation of aboveground net herbaceous production (ANHP) is crucial in rangeland management and monitoring. Remote and rural rangelands typically lack direct observation infrastructure, making satellite-derived methods essential. When ground data are available, a simple and effective way to estimate ANHP from satellites is to derive the empirical relationship between ANHP and plant-absorbed photosynthetically active radiation (APAR), which can be estimated from the normalized difference vegetation index (NDVI). While there is some evidence that this relationship will differ across rangeland vegetation types, it is unclear whether this relationship will change across grazing management regimes. This study aimed to assess the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR, considering variations in plant communities across ecological sites in the shortgrass steppe of northeastern Colorado. Additionally, we compared satellite-predicted biomass production from the process-based Rangeland Analysis Platform (RAP) model to our empirical APAR-based model. We found that APAR could be used to predict ANHP in the shortgrass steppe, with the relationship being influenced by ecosite characteristics rather than grazing management practices. For each unit of added APAR (MJ m−2 day−1), ANHP increased by 9.39 kg ha−1, and ecosites with taller structured herbaceous vegetation produced, on average, 3.92–5.71 kg ha−1 more ANHP per unit APAR than an ecosite dominated by shorter vegetation. This was likely due to the increased allocation of plant resources aboveground for C3 mid-grasses in taller structured ecosites compared to the C4 short-grasses that dominate the shorter structured ecosites. Moreover, we found that our locally calibrated empirical model generally performed better than the continentally calibrated process-based RAP model, though RAP performed reasonably well for the dominant ecosite. For our empirical models, R2 values varied by ecosite ranging from 0.49 to 0.67, while RAP R2 values ranged from 0.07 to 0.4. Managers in the shortgrass steppe can use satellites to estimate herbaceous production even without detailed information on short-term grazing management practices. The results from our study underscore the importance of understanding plant community composition for enhancing the accuracy of remotely sensed predictions of ANHP.

INTERNATIONAL EXPERIENCE IN USING INNOVATIVE TOOLS OF STATE FINANCIAL CONTROL OVER ILLEGAL FINANCIAL FLOWS

Effective financial control often requires cooperation between countries and international organizations. The study of international experience contributes to the development of mechanisms of international cooperation and information exchange to combat illegal financial flows. The article examines the international experience of using innovative tools of state financial control to combat illegal financial flows. Emphasis is placed on the analysis of modern practices in countries with developed systems of financial control, such as the USA, Great Britain, Germany, France, and Japan. The main tools used to detect and prevent illegal financial flows are described, including automated transaction monitoring systems, analytical platforms based on artificial intelligence, as well as methods of a risk-based approach to auditing. Special attention is paid to the role of international cooperation and information exchange between financial institutions of different countries, which allows more effective detection and termination of activities related to money laundering and terrorist financing. The article also analyzes the legal acts that regulate the use of innovative financial control tools in different countries and their compliance with international standards, such as FATF recommendations. The challenges and risks associated with the introduction of the latest technologies into the financial control system, including issues of data confidentiality and protection of human rights, were studied. The analysis of indicators and methods of assessment of illegal financial flows was carried out, the formation of illegal financial flows in modern globalized conditions was analyzed, the experience of other countries with regard to state financial control and its effectiveness was studied, innovative approaches and technologies that could be used to improve the financial control system and the possibility of their implementation of financial control in Ukraine. In addition, the evaluation of the effectiveness of the proposed innovative tools in the fight against illegal financial flows was carried out and the need for the development of such an assessment methodology, which would allow measuring the real impact of new technologies on reducing the volume of illegal financial flows, was substantiated.

Integrating Digital Health Solutions with Immunization Strategies: Improving Immunization Coverage and Monitoring in the Post-COVID-19 Era.

The COVID-19 pandemic underscored the critical importance of vaccination to global health security and highlighted the potential of digital health solutions to improve immunization strategies. This article explores integrating digital health technologies with immunization programs to improve coverage, monitoring, and public health outcomes. It examines the current landscape of digital tools used in immunization initiatives, such as mobile health apps, electronic health records, and data analytics platforms. Case studies from different regions demonstrate the effectiveness of these technologies in addressing challenges such as vaccine hesitancy, logistics, and real-time monitoring of vaccine distribution and adverse events. The paper also examines ethical considerations, data privacy issues, and the need for a robust digital infrastructure to support these innovations. By analyzing the successes and limitations of digital health interventions in immunization campaigns during and after the COVID-19 pandemic, we provide recommendations for future integration strategies to ensure resilient and responsive immunization systems. This research aims to guide policymakers, health professionals, and technologists in leveraging digital health to strengthen immunization efforts and prepare for future public health emergencies.

Network pharmacology-based exploration of molecular mechanisms underlying therapeutic effects of Jianpi Huatan Quyu recipe on chronic heart failure with spleen Qi deficiency syndrome.

Chronic heart failure is a complex clinical syndrome. The Chinese herbal compound preparation Jianpi Huatan Quyu recipe has been used to treat chronic heart failure; however, the underlying molecular mechanism is still not clear. To identify the effective active ingredients of Jianpi Huatan Quyu recipe and explore its molecular mechanism in the treatment of chronic heart failure. The effective active ingredients of eight herbs composing Jianpi Huatan Quyu recipe were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The target genes of chronic heart failure were searched in the Genecards database. The target proteins of active ingredients were mapped to chronic heart failure target genes to obtain the common drug-disease targets, which were then used to construct a key chemical component-target network using Cytoscape 3.7.2 software. The protein-protein interaction network was constructed using the String database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed through the Metascape database. Finally, our previously published relevant articles were searched to verify the results obtained via network pharmacology. A total of 227 effective active ingredients for Jianpi Huatan Quyu recipe were identified, of which quercetin, kaempferol, 7-methoxy-2-methyl isoflavone, formononetin, and isorhamnetin may be key active ingredients and involved in the therapeutic effects of TCM by acting on STAT3, MAPK3, AKT1, JUN, MAPK1, TP53, TNF, HSP90AA1, p65, MAPK8, MAPK14, IL6, EGFR, EDN1, FOS, and other proteins. The pathways identified by KEGG enrichment analysis include pathways in cancer, IL-17 signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, calcium signaling pathway, cAMP signaling pathway, NF-kappaB signaling pathway, AMPK signaling pathway, etc. Previous studies on Jianpi Huatan Quyu recipe suggested that this Chinese compound preparation can regulate the TNF-α, IL-6, MAPK, cAMP, and AMPK pathways to affect the mitochondrial structure of myocardial cells, oxidative stress, and energy metabolism, thus achieving the therapeutic effects on chronic heart failure. The Chinese medicine compound preparation Jianpi Huatan Quyu recipe exerts therapeutic effects on chronic heart failure possibly by influencing the mitochondrial structure of cardiomyocytes, oxidative stress, energy metabolism, and other processes. Future studies are warranted to investigate the role of the IL-17 signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and other pathways in mediating the therapeutic effects of Jianpi Huatan Quyu recipe on chronic heart failure.