MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics.

Protein structure prediction is a longstanding issue crucial for identifying new drug targets and providing a mechanistic understanding of protein functions. To enhance the progress in this field, a spectrum of computational methodologies has been cultivated. AlphaFold2 has exhibited exceptional precision in predicting wild-type protein structures, with performance exceeding that of other methods. However, predicting the structures of missense mutant proteins using AlphaFold2 remains challenging due to the intricate and substantial structural alterations caused by minor sequence variations in the mutant proteins. Molecular dynamics (MD) has been validated for precisely capturing changes in amino acid interactions attributed to protein mutations. Therefore, for the first time, a strategy entitled 'MoDAFold' was proposed to improve the accuracy and reliability of missense mutant protein structure prediction by combining AlphaFold2 with MD. Multiple case studies have confirmed the superior performance of MoDAFold compared to other methods, particularly AlphaFold2.

Implications of IFNγ SNP rs2069705 in primary Sjögren's syndrome: transcriptional activation and B cell infiltration.

Primary Sjögren's syndrome (pSS) is characterized by its autoimmune nature. This study investigates the role of the IFNγ SNP rs2069705 in modulating the susceptibility to pSS. Differential expression of IFNγ and BAFF was analyzed using the GEO database's mRNA microarray GSE84844. Genotyping of the IFNγ SNP rs2069705 was conducted via the dbSNP website. The JASPAR tool was used for predicting transcription factor bindings. Techniques such as dual-luciferase reporter assays, Chromatin immunoprecipitation, and analysis of a pSS mouse model were applied to study gene and protein interactions. A notable increase in the mutation frequency of IFNγ SNP rs2069705 was observed in MNCs from the exocrine glands of pSS mouse models. Bioinformatics analysis revealed elevated levels of IFNγ and BAFF in pSS samples. The model exhibited an increase in both CD20+ B cells and cells expressing IFNγ and BAFF. Knocking down IFNγ resulted in lowered BAFF expression and less lymphocyte infiltration, with BAFF overexpression reversing this suppression. Activation of the Janus kinase (JAK)/STAT1 pathway was found to enhance transcription in the BAFF promoter region, highlighting IFNγ's involvement in pSS. In addition, rs2069705 was shown to boost IFNγ transcription by promoting interaction between its promoter and STAT4. SNP rs2069705 in the IFNγ gene emerges as a pivotal element in pSS susceptibility, primarily by augmenting IFNγ transcription, activating the JAK/STAT1 pathway, and leading to B-lymphocyte infiltration in the exocrine glands.NEW & NOTEWORTHY The research employed a combination of bioinformatics analysis, genotyping, and experimental models, providing a multifaceted approach to understanding the complex interactions in pSS. We have uncovered that the rs2069705 SNP significantly affects the transcription of IFNγ, leading to altered immune responses and B-lymphocyte activity in pSS.

Large Language Model-Based Natural Language Encoding Could Be All You Need for Drug Biomedical Association Prediction.

Analyzing drug-related interactions in the field of biomedicine has been a critical aspect of drug discovery and development. While various artificial intelligence (AI)-based tools have been proposed to analyze drug biomedical associations (DBAs), their feature encoding did not adequately account for crucial biomedical functions and semantic concepts, thereby still hindering their progress. Since the advent of ChatGPT by OpenAI in 2022, large language models (LLMs) have demonstrated rapid growth and significant success across various applications. Herein, LEDAP was introduced, which uniquely leveraged LLM-based biotext feature encoding for predicting drug-disease associations, drug-drug interactions, and drug-side effect associations. Benefiting from the large-scale knowledgebase pre-training, LLMs had great potential in drug development analysis owing to their holistic understanding of natural language and human topics. LEDAP illustrated its notable competitiveness in comparison with other popular DBA analysis tools. Specifically, even in simple conjunction with classical machine learning methods, LLM-based feature representations consistently enabled satisfactory performance across diverse DBA tasks like binary classification, multiclass classification, and regression. Our findings underpinned the considerable potential of LLMs in drug development research, indicating a catalyst for further progress in related fields.

Evidence linking COVID-19 and the health/well-being of children and adolescents: an umbrella review.

BACKGROUND: Experiences during childhood and adolescence have enduring impacts on physical and mental well-being, overall quality of life, and socioeconomic status throughout one's lifetime. This underscores the importance of prioritizing the health of children and adolescents to establish an impactful healthcare system that benefits both individuals and society. It is crucial for healthcare providers and policymakers to examine the relationship between COVID-19 and the health of children and adolescents, as this understanding will guide the creation of interventions and policies for the long-term management of the virus. METHODS: In this umbrella review (PROSPERO ID: CRD42023401106), systematic reviews were identified from the Cochrane Database of Systematic Reviews; EMBASE (OvidSP); and MEDLINE (OvidSP) from December 2019 to February 2023. Pairwise and single-arm meta-analyses were extracted from the included systematic reviews. The methodological quality appraisal was completed using the AMSTAR-2 tool. Single-arm meta-analyses were re-presented under six domains associated with COVID-19 condition. Pairwise meta-analyses were classified into five domains according to the evidence classification criteria. Rosenberg's FSN was calculated for both binary and continuous measures. RESULTS: We identified 1551 single-arm and 301 pairwise meta-analyses from 124 systematic reviews that met our predefined criteria for inclusion. The focus of the meta-analytical evidence was predominantly on the physical outcomes of COVID-19, encompassing both single-arm and pairwise study designs. However, the quality of evidence and methodological rigor were suboptimal. Based on the evidence gathered from single-arm meta-analyses, we constructed an illustrative representation of the disease severity, clinical manifestations, laboratory and radiological findings, treatments, and outcomes from 2020 to 2022. Additionally, we discovered 17 instances of strong or highly suggestive pairwise meta-analytical evidence concerning long-COVID, pediatric comorbidity, COVID-19 vaccines, mental health, and depression. CONCLUSIONS: The findings of our study advocate for the implementation of surveillance systems to track health consequences associated with COVID-19 and the establishment of multidisciplinary collaborative rehabilitation programs for affected younger populations. In future research endeavors, it is important to prioritize the investigation of non-physical outcomes to bridge the gap between research findings and clinical application in this field.

Effects of lianas on forest biogeochemistry during their lives and afterlives.

Climate change and other anthropogenic disturbances are increasing liana abundance and biomass in many tropical and subtropical forests. While the effects of living lianas on species diversity, ecosystem carbon, and nutrient dynamics are receiving increasing attention, the role of dead lianas in forest ecosystems has been little studied and is poorly understood. Trees and lianas coexist as the major woody components of forests worldwide, but they have very different ecological strategies, with lianas relying on trees for mechanical support. Consequently, trees and lianas have evolved highly divergent stem, leaf, and root traits. Here we show that this trait divergence is likely to persist after death, into the afterlives of these organs, leading to divergent effects on forest biogeochemistry. We introduce a conceptual framework combining horizontal, vertical, and time dimensions for the effects of liana proliferation and liana tissue decomposition on ecosystem carbon and nutrient cycling. We propose a series of empirical studies comparing traits between lianas and trees to answer questions concerning the influence of trait afterlives on the decomposability of liana and tree organs. Such studies will increase our understanding of the contribution of lianas to terrestrial biogeochemical cycling, and help predict the effects of their increasing abundance.

Fabrication of Electrode Material for Textile-Based Triboelectric Nanogenerators: Research of the Relationship between Output Performance and Dielectric Material Strain.

In this work, a textile-based triboelectric nanogenerator (TENG) device was developed through electroless plating technology to prepare electrode material. Hydrophilic groups on the fiber surface are able to absorb Ag+, which could play a role in the center of a catalyst to reduce Cu2+ to fabricate Cu-coated cotton toward the fabrication of TENG electrode material. The TENG device established admirable performance and good stabilization, and a maximum voltage at 9.6 V was detected when the stress and strain on the polydimethylsiloxane layer are 82.6 kPa and 5.8%, respectively. In addition, the relationships among device properties and strain/thickness of dielectric materials have been explored in depth as well. The output voltage of the device increases gradually with the enhancement of dielectric strain and stress. As expected, the TENG as-fabricated device was installed to various physical behaviors to illustrate the harvesting of power of knee-jerk movements.

Enhancing Chemical Reaction Monitoring with a Deep Learning Model for NMR Spectra Image Matching to Target Compounds.

In the synthetic laboratory, researchers typically rely on nuclear magnetic resonance (NMR) spectra to elucidate structures of synthesized products and confirm whether they match the desired target compounds. As chemical synthesis technology evolves toward intelligence and continuity, efficient computer-assisted structure elucidation (CASE) techniques are required to replace time-consuming manual analysis and provide the necessary speed. However, current CASE methods typically aim to derive precise chemical structures from spectroscopic data, yet they suffer from drawbacks such as low accuracy, high computational cost, and reliance on chemical libraries. In meticulously designed chemical synthesis reactions, researchers prioritize confirming the attainment of the target product based on NMR spectra, rather than focusing on identifying the specific product obtained. For this purpose, we innovatively developed a binary classification model, termed as MatCS, to directly predict the relationship between NMR spectra image (including 1H NMR and 13C NMR) and the molecular structure of the target compound. After evaluating various feature extraction methods, MatCS employs a combination of the Graph Attention Networks and Graph Convolutional Networks to learn the structural features of molecular graphs and the pretrained ResNet101 network with a Convolutional Block Attention Module to extract features from NMR spectra images. The results show that on a challenging Testsim data set, which poses difficulty in distinguishing spectra of similar molecular structures, MatCS achieves comprehensive evaluation metrics with an F1-score of 0.81 and an AUC value of 0.87. Simultaneously, it exhibited commendable performance on an external SDBS data set containing experimental NMR spectra, showcasing substantial potential for structural verification tasks in real automated chemical synthesis.

Otilonium bromide ameliorates pulmonary fibrosis in mice through activating phosphatase PPM1A.

Pulmonary fibrosis (PF) is a chronic, progressive and irreversible interstitial lung disease characterized by unremitting pulmonary myofibroblasts activation, extracellular matrix (ECM) deposition and inflammatory recruitment. PF has no curable medication yet. In this study we investigated the molecular pathogenesis and potential therapeutic targets of PF and discovered drug lead compounds for PF therapy. A murine PF model was established in mice by intratracheal instillation of bleomycin (BLM, 5 mg/kg). We showed that the protein level of pulmonary protein phosphatase magnesium-dependent 1A (PPM1A, also known as PP2Cα) was significantly downregulated in PF patients and BLM-induced PF mice. We demonstrated that TRIM47 promoted ubiquitination and decreased PPM1A protein in PF progression. By screening the lab in-house compound library, we discovered otilonium bromide (OB, clinically used for treating irritable bowel syndrome) as a PPM1A enzymatic activator with an EC50 value of 4.23 µM. Treatment with OB (2.5, 5 mg·kg-1·d-1, i.p., for 20 days) significantly ameliorated PF-like pathology in mice. We constructed PF mice with PPM1A-specific knockdown in the lung tissues, and determined that by targeting PPM1A, OB treatment suppressed ECM deposition through TGF-ß/SMAD3 pathway in fibroblasts, repressed inflammatory responses through NF-κB/NLRP3 pathway in alveolar epithelial cells, and blunted the crosstalk between inflammation in alveolar epithelial cells and ECM deposition in fibroblasts. Together, our results demonstrate that pulmonary PPM1A activation is a promising therapeutic strategy for PF and highlighted the potential of OB in the treatment of the disease.

Ratiometric electrochemical aptasensor based on split aptamer and Au-rGO for detection of aflatoxin M1.

A novel ratiometric electrochemical aptasensor based on split aptamer and Au-reduced graphene oxide (Au-rGO) nanomaterials was proposed to detect aflatoxin M1 (AFM1). In this work, Au-rGO nanomaterials were coated on the electrode through the electrodeposition method to increase the aptamer enrichment. We split the aptamer of AFM1 into 2 sequences (S1 and S2), where S1 was immobilized on the electrode due to the Au-S bond, and S2 was tagged with methylene blue (MB) and acted as a response signal. A complementary strand to S1 (CS1) labeled with ferrocene (Fc) was introduced as another reporter. In the presence of AFM1, CS1 was released from the electrode surface due to the formation of the S1-AFM1-S2 complex, leading to a decrease in Fc and an increase in the MB signal. The developed ratiometric aptasensor exhibited a linear range of 0.03 µg L-1 to 2.00 µg L-1, with a detection limit of 0.015 µg L-1 for AFM1 detection. The ratiometric aptasensor also showed a linear relationship from 0.2 µg L-1 to 1.00 µg L-1, with a detection limit of 0.05 µg L-1 in natural milk after sample pretreatment, indicating the successful application of the developed ratiometric aptasensor. Our proposed strategy provides a new way to construct aptasensors with high sensitivity and selectivity.

Association between semen parameters and recurrent pregnancy loss: An umbrella review of meta-analyses.

AIM: Recurrent pregnancy loss (RPL) is a common clinical reproductive problem. With research advancements, an increasing number of studies have suggested that male factors play an important role in RPL. However, the evaluation results of male sperm quality in published meta-analyses are inconsistent. We aimed to summarize the evidence of the association between semen factors and RPL and evaluate the level and validity of the evidence. METHODS: We searched PubMed, Cochrane Library, EMBASE, Web of Science, and Scopus databases for systematic reviews or meta-analyses to evaluate the association between male semen parameters and RPL. The methodological quality of the included meta-analyses was assessed, and data and evidence were re-synthesized and stratified using a random-effects model. RESULTS: Seven meta-analyses and nine semen parameters were included in the final analysis. The methodological quality of all publications was considered low or very low. There was highly suggestive evidence for the association between sperm DNA fragmentation (SDF), sperm progressive motility rate, and RPL (class II). The evidence level for the association between sperm concentration, normal sperm morphology, sperm deformity rate, total motility, and RPL was suggestive evidence (class III). The evidence level for the association between sperm volume and sperm count and RPL was weak (class IV). There was no significant association between sperm pH and RPL (class NS). CONCLUSIONS: Our results suggest level II evidence for the association between male SDF and RPL, while the evidence level for the association between conventional semen routine parameters and RPL was low (classes III and IV).

Application of Oral Retractor for Lip Injury Protection in Oral and Maxillofacial Surgeries: A Randomized Controlled Trial.

PURPOSE: Iatrogenic lip injury may occur during oral and maxillofacial surgical procedures. This study aimed to evaluate the effect of oral retractors on iatrogenic lip injury prevention during intraoral procedures of oral and maxillofacial surgery. METHODS: We conducted a randomized controlled trial and included patients who underwent intraoral procedures of oral and maxillofacial surgery. Patients were randomly allocated to receive oral retractor (intervention group) or traditional procedure without lip protection (control group). The incidence of lip injury was the outcome variable. Other study variables included surgical time and satisfaction of patients and surgeons with treatment experience evaluated by visual analog scale (VAS). Student t test and χ 2 test were used to compare both groups' variables and measure the relationship between the predictor variable and the outcome variable. P <0.05 was considered significant for all analyses. RESULTS: A total of 114 patients were included, with 56 allocated to intervention group and 58 to control group. The results showed that the application of an oral retractor did not significantly increase surgical time ( P =0.318). A total of 12 patients had lip injury, with 1 in the intervention group and 11 in the control group ( P =0.003). For the assessment of satisfaction with treatment experience, the intervention group had significantly higher VAS scores for doctors and patients ( P <0.05). CONCLUSIONS: We found that the oral retractor was a good tool for iatrogenic lip injury prevention in oral and maxillofacial surgical procedures and could be considered in clinical treatment.

Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors.

The accurate prediction of binding free energy is a major challenge in structure-based drug design. Quantum mechanics (QM)-based approaches show promising potential in predicting ligand-protein binding affinity by accurately describing the behavior and structure of electrons. However, traditional QM calculations face computational limitations, hindering their practical application in drug design. Nevertheless, the fragment molecular orbital (FMO) method has gained widespread application in drug design due to its ability to reduce computational costs and achieve efficient ab initio QM calculations. Although the FMO method has demonstrated its reliability in calculating the gas phase potential energy, the binding of proteins and ligands also involves other contributing energy terms, such as solvent effects, the 'deformation energy' of a ligand's bioactive conformations, and entropy. Particularly in cases involving ionized fragments, the calculation of solvation free energy becomes particularly crucial. We conducted an evaluation of some previously reported implicit solvent methods on the same data set to assess their potential for improving the performance of the FMO method. Herein, we develop a new QM-based binding free energy calculation method called FMOScore, which enhances the performance of the FMO method. The FMOScore method incorporates linear fitting of various terms, including gas-phase potential energy, deformation energy, and solvation free energy. Compared to other widely used traditional prediction methods such as FEP+, MM/PBSA, MM/GBSA, and Autodock vina, FMOScore showed good performance in prediction accuracies. By constructing a retrospective case study, it was observed that incorporating calculations for solvation free energy and deformation energy can further enhance the precision of FMO predictions for binding affinity. Furthermore, using FMOScore-guided lead optimization against Src homology-2-containing protein tyrosine phosphatase 2 (SHP-2), we discovered a novel and potent allosteric SHP-2 inhibitor (compound 8).

Effects of fuel and driving conditions on particle number emissions of China-VI gasoline vehicles: based on corrections to test results.

The increasing number of vehicles are emitting a large amount of particles into the atmosphere, causing serious harm to the ecological environment and human health. This study conducted the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) to investigate the emission characteristics of particle number (PN) of China-VI gasoline vehicles with different gasoline. The gasoline with lower aromatic hydrocarbons and olefins reduced particulate matter (PM) and PN emissions by 24% and 52% respectively. The average PN emission rate of the four vehicles during the first 300 s (the cold start period) was 7.2 times that of the 300 s-1800s. Additionally, because the particle transmission time and instrument response time, the test results of instantaneous emissions of PN were not synchronized with vehicle specific power (VSP). By calculating the Spearman correlation coefficient between pre-average vehicle specific power (PAVSP) and the test results of PN instantaneous emissions, the delay time was determined as 10s. After the PN emissions results were corrected, the PN emissions were found to be more related to VSP. By analyzing the influence of driving status on emission, this study found that vehicles in acceleration mode increased PN emissions by 76% compared to those in constant speed mode.

How Seeds Attract and Protect: Seed Coat Development of Magnolia.

Seeds are one of the most important characteristics of plant evolution. Within a seed, the embryo, which will grow into a plant, can survive harsh environments. When the seeds are mature, the mother plant will disperse them from its body, allowing them to be taken away to grow in a new place. Otherwise, if the young generation grows alongside the mother plants in the same place, they will compete for sunlight and nutrition. The mother plants use different strategies to send away their seeds. One of these strategies is endozoochory, which means that the seeds disperse via ingestion by animals. There is a conflict between the seeds' abilities to attract animals and protect the embryo within the digestion systems of animals. Magnolia seeds exhibit typical endozoochory. The seed coats of Magnolia feature sarcotestas and sclerotestas. The sarcotesta, which is fleshy, bright-colored, and edible, attracts animals. The sclerotesta is hard and woody, protecting the embryo from the digestive systems of animals. In this study, we used scanning electron and light microscopes to examine the development of the sarcotesta and sclerotesta of Magnolia stellata seed coats. The results showed that the sarcotesta and sclerotesta come from the outer integument. This result confirms the hypothesis of Asa Gray from 1848. The dependence of the seed dispersal strategy on structural development is discussed.

Acupuncture for radicular pain: a review of analgesic mechanism.

Radicular pain, a common and complex form of neuropathic pain, presents significant challenges in treatment. Acupuncture, a therapy originating from ancient traditional Chinese medicine and widely utilized for various pain types, including radicular pain, has shown promising outcomes in the management of lumbar radicular pain, cervical radicular pain, and radicular pain due to spinal stenosis. Despite its efficacy, the exact mechanisms through which acupuncture achieves analgesia are not fully elucidated and are the subject of ongoing research. This review sheds light on the current understanding of the analgesic mechanisms of acupuncture for radicular pain, offering valuable perspectives for both clinical application and basic scientific research. Acupuncture is postulated to relieve radicular pain by several mechanisms: peripherally, it reduces muscle spasms, lessens mechanical pressure on nerve roots, and improves microcirculation; at the molecular level, it inhibits the HMGB1/RAGE and TLR4/NF-κB signaling pathways, thereby decreasing the release of pro-inflammatory cytokines; within the spinal cord, it influences synaptic plasticity; and centrally, it modulates brain function, particularly affecting the medial prefrontal cortex, anterior cingulate cortex, and thalamus within the default mode network. By acting across these diverse biological domains, acupuncture presents an effective treatment modality for radicular pain, and deepening our understanding of the underlying mechanisms regarding analgesia for radicular pain is crucial for enhancing its clinical efficacy and advancement in pain management.

Event-triggered hybrid impulsive control for synchronization of fractional-order multilayer signed networks under cyber attacks.

In this paper, we consider the exponential bipartite synchronization (EBS) problem of fractional-order multilayer signed networks with time-varying delays (FO-MSNT) under random cyber attacks. In contrast to the existing literature, the proposed hybrid event-triggered controller combines the advantages of feedback controller and impulsive controller, and the event-triggered condition is constructed by applying the network topology and the Lyapunov function of the subsystem, rather than the state function of the subsystem. Based on the Lyapunov-Razumikhin method and the graph theory, some sufficient conditions for achieving EBS of FO-MSNT under cyber attacks which are related to the topology of networks, the event-triggered parameters, the order of fractional derivative and the signal sent by the enemy are obtained. Furthermore, fractional-order coupled Chua's circuits model and fractional-order power systems built on MSNT are established and the EBS issues under cyber attacks are analyzed. Numerical examples and simulations are provided to show the validity of our theories.

Impact of electronic cigarette usage on the onset of respiratory symptoms and COPD among Chinese adults.

The prevalence of dual usage and the relatively low cessation rate among e-cigarette (EC) users suggest that ECs have not demonstrated significant effectiveness as a smoking cessation tool. Furthermore, there has been a substantial increase in the prevalence of EC usage in recent years. Therefore, the objective of this study is to investigate the association between EC use and the incidence of respiratory symptoms and chronic obstructive pulmonary disease (COPD). A total of 10,326 participants aged between 20 and 55 years, without any respiratory diseases or COPD, were recruited for the study. These individuals attended employee physical examinations conducted at 16 public hospitals in Hebei province, China from 2015 to 2020. Logistic regression models were utilized to assess the association between EC use and the risk of respiratory symptoms and COPD using risk ratios along with their corresponding 95% confidence intervals. Restricted cubic spline functions were employed to investigate the dose-response non-linear relationship. The robustness of the logistic regression models was evaluated through subgroup analyses, and sensitivity analyses. During the 5-year follow-up period, a total of 1071 incident cases of respiratory symptoms and 146 incident cases of COPD were identified in this cohort study. After adjusting for relevant confounding factors, EC users demonstrated a respective increase in the risk of reporting respiratory symptoms and COPD by 28% and 8%. Furthermore, dual users who used both ECs and combustible cigarettes exhibited an elevated risk of incident respiratory symptoms and COPD by 41% and 18%, respectively, compared to those who had never used non-users of any cigarette products. The association between daily EC consumption and the development of respiratory symptoms, as well as COPD, demonstrated a significant J-shaped pattern. The potential adverse association between the consumption of ECs, particularly when used in combination with combustible cigarettes, and the development of respiratory symptoms and COPD necessitates careful consideration. Policymakers should approach ECs cautiously as a prospective smoking cessation tool.

Streptomyces Strains and Their Metabolites for Biocontrol of Phytopathogens in Agriculture.

Sustainable agriculture is increasingly linked to biological pesticides as alternatives to agro-chemicals. Streptomyces species suppress plant diseases through their unique traits and numerous metabolites. Although many Streptomyces strains have been developed into commercial products, their roles in the biocontrol of phytopathogens and mechanisms of functional metabolite synthesis remain poorly understood. In this review, biocontrol of plant diseases by Streptomyces is summarized on the basis of classification of fungal and bacterial diseases and secondary metabolites produced by Streptomyces that act on phytopathogenic microorganisms are discussed. The associated non-ribosomal peptide synthetases and polyketide synthetases responsible for biosynthesis of these secondary metabolites are also investigated, and advances in fermentation of Streptomyces are described. Finally, the need to develop precise and effective biocontrol methods for plant diseases is highlighted.

Associations between cytokines and the risk of female and male infertility: A two-sample Mendelian randomization analysis.

PURPOSE: Observational studies have linked cytokines to the occurrence of female and male infertility. However, it is not clear how biomarkers of inflammation are causally related to infertility. To explore whether genetic variants in circulating cytokines are associated with the pathogenesis of infertility, we performed two-sample Mendelian randomization (MR) analysis. METHODS: A total of 31,112 individuals of European ancestry were included in a genome-wide association study (GWAS) of 47 circulating cytokines as instrumental variables (IVs). Outcome data were female infertility, including four different subtypes, and male infertility, from the FinnGen consortium. Five MR methods, including inverse-variance weighted (IVW), MR-Egger, simple mode, weighted median, and weighted mode were employed to examine the genetic association between cytokines and the risk of female infertility and male infertility. The false discovery rate (FDR) was controlled using the Benjamini-Hochberg method. RESULTS: After FDR correction, cis-protein quantitative trait locus (cis-pQTL) instruments showed that the cytokines GROa and MCSF were positively associated with female infertility. In analyses of subtypes of female infertility, eotaxin and sICAM were inversely associated with ovulation-related infertility; MCP3 alone was positively associated with uterus-related infertility; GROa and MCSF were positively correlated with infertility of cervical, vaginal, and other or unspecified origin; and MIP1a was negatively correlated with tubal origin infertility. The cytokines HGF, IL-2ra, and RANTES were positively correlated with male infertility. Similar findings were obtained in sensitivity analyses. There was no evidence of pleiotropy or heterogeneity in the results. CONCLUSION: These findings contribute to current understanding of the role of cytokine biomarkers in the etiology of female and male infertility. Furthermore clinical experimental validation is required to evaluate the potential of these cytokines as biomarkers.