However, the levels of disability or disease severity were not correlated with the levels of serum UA in current smoker pwMS, measured by the expanded disability status scale , multiple sclerosis impact scale 29  and MS severity score , respectively. Our result suggests that the reduction in UA levels is more likely a consequence of oxidative stress triggered by many risk factors, including CS, and could be considered a potential indicator of smoking cessation. In addition, the absence of a correlation between UA levels and disease severity and disability suggests that UA is not an optimal biomarker for disease severity and disability prediction among current smoker, ex-smoker or non-smoker pwMS.Objectives: The present study is aimed at determining the effect of cigarette smoking (CS) on serum uric acid (UA) levels quantitatively before and after smoking cessation among people with MS (pwMS). Additionally, a possible correlation between UA levels and both disability progression and disease severity was also investigated. A retrospective cross-sectional study was conducted using the Nottingham University Hospitals MS Clinics database. It involves 127 people with definite MS recorded when reporting the latest smoking status and the clinical diagnosis. All necessary demographics and clinical characteristics were collected. We found that smoker pwMS had significantly lower serum UA levels than non-smoker pwMS ( Accumulated data suggest a major role of oxidative stress in multiple sclerosis (MS) pathogenesis. Reactive oxygen species (ROS) and reactive nitrogen species (RNS) are primarily produced in excess amounts by activated microglial cells and brain-resident macrophages. They are mediators in MS and experimental autoimmune encephalomyelitis (EAE) pathogenesis ,2,3. ROSIn pathological conditions, the natural antioxidant defence levels will be overwhelmed by high levels of generated ROS/RNS and cause oxidative stress . FurtherUric acid (UA), the end product of purine catabolism and a powerful natural antioxidant, is a major part of human plasma’s total antioxidant scavenging activity . The abiMany observations provide convincing evidence for the effective role of UA in MS. Hooper and colleagues found that treatment of EAE with high doses of UA completely protected mice from the disease . In addiDespite the persuasive clinical and experimental data on higher serum UA importance to MS risk, in randomized control trials, oral administration of an inducer of inosine ≤ 10 mg/dl, a precursor of uric acid, maintained for up to two years in combination with IFNβ, as disease-modifying treatment, did not add any additional benefit to disability accumulation compared to IFNβ alone ,26.Cigarette smoking, the leading cause of morbidity worldwide, has been shown to play an important role in the aetiology of MS. Cigarette smoke constituents can contribute to the excessive or dysregulated generation of ROS. Among many potential sources of ROS, xanthine oxidase, a final enzyme of purine metabolism, and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX) have been implicated ,28,29. KTo date, investigations on the relationship between cigarette smoking and serum UA levels in people with MS are lacking; however, a few studies have addressed it in healthy volunteers, people with gout and preeclampsia syndrome, a condition that affects some pregnant women characterized by high blood pressure and other manifestations such as severe headache, nausea or vomiting, excessive weight gain and severe heartburn ,35,36,37This study aimed to determine the effect of cigarette smoking on serum UA levels quantitatively before and after smoking cessation among people with MS and to further determine the possible correlation between serum UA levels and both disability progression and disease severity.Descriptive statistics for demographics and clinical characteristics were used to categorise the data of the patients included in this study .p-value = 0.0001 and 0.0001, respectively), but it is not significant amongst current smokers .Before comparing the effect of smoking on serum UA levels and the correlation between UA and disability and disease severity, the serum UA levels were compared between males and females among all study subjects and each category of smoking status. As shown in p-value = 0.79). The same applies to current smokers and ex-smokers:  and , respectively  or the disability scores, measured by EDSS  and MSIS-29   .p-value = 0.0475). Additionally, among current smokers, there was no correlation between having lower serum UA levels and having severe disease  and high disability scores, measured by EDSS  and MSIS-29  (We found that serum UA was significantly lower in current smoker people with MS compared to non-smokers ( = 0.97) .p-value = 0.0216) . Moreover, there was no correlation found between higher serum UA levels and disease severity  and disability scores when measured by EDSS , but there was a trend towards a negative correlation when measured by MSIS-29   A, but it = 0.07) B,D.To the best of our knowledge, this is the first study investigating the effect of cigarette smoking on UA in people with MS and its impact on disease severity and disability. As expected, based on previous reports where women are known to have lower serum UA, we observed lower levels of serum UA in women compared to men amongst all study subjects ,19,20,21p-value = 0.0001; Lower UA levels in serum from people with MS, when compared with healthy controls or with people with other inflammatory neurological diseases, have been reported in many studies ,20,21,38The studies were inconsistent regarding the correlation between UA levels and clinical measures such as disease activity, severity and disability. We found no overall correlation between UA levels and disease severity, measured by MSSS, or disability, measured by EDSS and MSIS-29, as shown in Chronic cigarette smoking, as one of the modifiable risk factors, has been shown to increase the risk of developing MS ,45,46. AIn the current study, we found that smoker MS patients had significantly lower serum UA levels than non-smoker patients, and this reduction was recovered after smoking cessation. The decrease in serum UA among current smoker people with MS can be partially explained by the increased activity of xanthine oxidoreductase (XOR) in response to cigarette smoke. This enzyme consists of inter-convertible forms, including xanthine dehydrogenase (XDH) and xanthine oxidase (XO), and plays a central role in purine catabolism by converting hypoxanthine to xanthine and xanthine to uric acid and/or ROS.It has been reported that plasma XOR and XO activity is significantly higher in healthy smokers than in non-smokers ,56. FurtVery little is known about the role of ROS-generating enzyme XOR in MS, its relationship with CNS autoimmunity and the effect of smoking on this enzyme. Recently and for the first time, Honorat and colleagues found, in the EAE mouse model of MS, that the activity of XO was increased in the serum and within the CNS of the mice with experimental inflammatory demyelination, and the administration of febuxostat was able to reduce the clinical signs of the disease, suppress the excess ROS production from infiltrating macrophages and microglia, improve mitochondrial function and prevent neurodegeneration in both relapsing–remitting and secondary progressive EAE ,60. Our Of note, UA is not the only antioxidant mechanism detoxing reactive oxidants. It has been reported that other antioxidant enzymes, such as superoxide dismutase (SOD), catalase and glutathione peroxidase (GPx), are compromised in people with neurological diseases, including MS, suggesting their importance in protection against ROS within the brain. ROS sources in MS are mainly from mitochondria and ROS-producing enzymes such as nicotinamide adenine dinucleotide phosphate (NADPH) oxidases (NOXs), myeloperoxidase (MPO) and inducible nitric oxide (NO) synthase (iNOS), which is upregulated in the active stage of MS . NuclearDespite being the first study to investigate the relationship between smoking and UA levels and its correlation with disease severity and disability in MS directly, this study has some limitations. Firstly, a larger sample size with matched age, sex and MS clinical course to estimate the effect of smoking on UA would be more convincing. Secondly, for an accurate interpretation, confounding variables that possibly modify UA levels, such as BMI, alcohol intake, drugs, number of cigarettes smoked/day, comorbidities and disease activity, using neuroradiological assessment, should be considered to enable the use of more powerful statistical methods and reduce the potential effect of these confounding factors. Finally, information about XOR activity, mitochondrial dysfunction and ATP consumption would be useful.The retrospective study population included people with definite MS registered in Nottingham University Hospitals MS Clinics database, a major regional referral centre for MS in England’s East Midlands counties. All of the clinically definite MS diagnoses were made by an MS specialist neurologist, according to the McDonald and/or Poser criteria , and werThe patients’ smoking status history was obtained during their first visit to the clinic at the time of disease onset or diagnosis. It was updated and recorded more than once during the regular clinical follow-ups, and the latest recorded smoking status was used. The patients were categorised based on their smoking status as non-smokers, current smokers or ex-smokers. The age, sex, disease duration, latest expanded disability status scale (EDSS) score  and serut-test, ordinary one-way ANOVA for normally distributed data and the non-parametric Mann–Whitney U test for non-normally distributed data. To test the correlation between UA levels and both disability progression and disease severity, Pearson’s or Spearman’s product-moment correlation coefficient (r) test was used as appropriate unless otherwise stated. Two-tailed p-values of 0.05 were considered to indicate statistical significance.Normality was tested using both Shapiro–Wilk and D’Agostino and Pearson tests. Where appropriate, differences between mean values were evaluated by the parametric Welch’s Despite being the first study to directly investigate the relationship between smoking and UA levels and its correlation with disease severity and disability in MS, there are some limitations to this study. Firstly, a larger sample size with matched age, sex and MS clinical course to estimate the effect of smoking on UA would be more convincing. Secondly, for an accurate interpretation, confounding variables that possibly modify UA levels, such as BMI, alcohol intake, drugs, number of cigarettes smoked/day, comorbidities and disease activity, using neuroradiological assessment, should be considered to enable the use of more powerful statistical methods and reduce the potential effect of these confounding factors. Finally, information about XOR activity, mitochondrial dysfunction and ATP consumption would be very useful.Considering our results and the literature, we suggest that the reduction in UA levels is more likely a consequence of oxidative stress triggered by many risk factors such as infection, smoking and others preceding disease onset or during the clinical course of the disease. Furthermore, the absence of a correlation between UA levels and disease severity and disability among non-smoker, current smoker or ex-smoker MS patients suggests that UA levels are not a good biomarker for disease severity and/or disability progression. However, it could be recommended as a routine clinical test since it is a simple and cheap indicator that reflects part of the oxidative stress status. In addition, the increase in UA in ex-smokers could allow longitudinal monitoring after smoking cessation.Further prospective studies are needed, considering the above limitations, to evaluate the effect of smoking on serum UA variations and XOR activity during the MS course and the possible relationships with the clinical features of MS including clinical relapses, disease severity and disability progression. In addition, future studies should now explore whether the XOR inhibitors allopurinol or febuxostat will, in practice, at least as adjuvant strategies, reduce the risk of conversion from CIS to definite MS, the number of relapses and disability progression since people with gout very rarely develop MS which we think could be due to the effect of the treatment rather than the elevated UA levels. Despite the possible reduction in UA levels after treatment with XOR inhibitors, the inhibition of XOR may be more important than the reduction in the assumed protection by UA, possibly because peroxynitrite production is reduced. Thus, there is less need for it to be scavenged by UA.


Mass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. However, computational analysis is required to reconstruct such networks with a mechanistic model.We propose our Mass cytometry Signaling Network Analysis Code (McSNAC), a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data. McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption often breaks down as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of protein species involved in signaling.in silico experiments here to show (1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; (2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured.We carry out a series of These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data. Modeling how cells transfer a signal from an extracellular stimulus to different compartments within the cell is critical to understand how different stimuli result in different cellular responses. We designed a software (McSNAC) that proposes a minimal mathematical model to describe biochemical signaling events learning from time-stamped cytometry data. This software makes linear approximations which hold true in many scenarios, but can also break down under several conditions. We explore this approximation in depth to provide guidelines regarding its applicability. In addition, we provide a user interface for non-technical users to analyze their time-stamped cytometry data with this approach. These receptors bind to cognate ligands generated by a stimulation, such as viral infection of cells in the local environment, and initiate a series of biochemical signaling reactions leading to activation of many genes that can generate a variety of cell responses such as secretion of specific proteins , cell proliferation, or cell death x(0) = exp(S−1(DtS)) x(0) = exp(Mt)x(0).If in silico networks were simulated with MATLAB. For the results shown in t1 = 0) values of n protein abundances drawn from lognormal distributions and the single cell kinetics were simulated using the closed form solution of t2 (= 30 s) were calculated by the software and used for fitting. For the results shown in t2 used for fitting were simply calculated from the closed-form solutions given in First-order t1 = 0 were drawn from lognormal distributions. The rate constants and the parameters for the lognormal distributions are provided in BioNetGen scripts shown in the t2 (= 30 s) were calculated in McSNAC from the synthetic single cell data. Perturbations of abundances of A, B, and C for Nonlinear reaction networks in ground-truth models GT1, GT2, and the NK cell signaling model  were simInitial abundances for the 8 measured phospho-proteins, their 7 unmeasured unphosphorylated counter-parts (activating receptor is measured as both phosphorylated and double-phosphorylated), activating and inhibiting ligand, and a pErk phosphatase were sampled from lognormal distributions for the BioNetGen simulation of the NK cell signaling model. These initial values for all 250 cells can be found in the in silico dataset based on the reaction network in Reaction  of a reaction j→ i is indicated by 1 (or 0) at the ij th element. The ground-truth model given by Reaction  of matrix M* following b is a scalar , and aij is a random number between −1 and 1 drawn from a uniform distribution U. For each sample of M* we calculate a variable d = χ2 ({M*}) − χ2 ({M}), describing the difference in the cost function χ2 defined in M and the sampled M*. Now, we chose a specific element  of the M matrix and create a list of the akl values that were used to generate M*kl, and bin the akl values between −1 to +1. Next, d values that correspond to the sampled {M*kl} and specific bins of akl are collected, thus, each bin of akl would contain multiple values of d. We determine the smallest value of d for each bin in the akl space. Then, starting from the bin that contains akl = 0 and advancing to the bins of akl > 0 (or akl < 0) we determine the first bin that generates a minimum d ≥ 2.71, this first bin corresponds to the upper (or lower) bound of Mkl.We estimate confidence intervals (CIs) for the elements of the approach . First, It is important to note that this method suffers the curse of dimensionality. As the number of dimensions increases, so too must the number of samples to account for coverage of all bins in all dimensions. We report confidence intervals for our 13-dimension parameter benchmarking , but notχ2. The optimization is based on the Metropolis algorithm for simulating a thermodynamic system at temperature T where system variables  associated with an energy E are distributed in a Boltzmann distribution (∝exp(−E/(kBT))). kB is the Boltzmann constant. Slowly cooling or annealing the system takes the system to lower energy states  from higher energy random configuration of system variables . This approach can be adapted for parameter estimation where the ‘energy’ E(= χ2) represents the cost function which is a function of the parameter values. In SA the temperature parameter (kBT) is initiated at larger values  and is decreased slowly to low values. As the temperature parameter is decreased gradually the system is supposed to relax to parameter configuration corresponding to the global minimum of E. However, the landscape of E can contain multiple minima and attaining the global minimum is not guaranteed in such situations. Several improvements on the basic SA algorithm have been proposed to deal with such scenarios. At each value of the temperature parameter, the Metropolis algorithm is run for sufficiently large number of Monte Carlo trials to make sure equilibrium Boltzmann distributions are realized in the simulations. At high temperatures, large increases in cost function can be accepted, while at low temperatures, only insignificant increases will be accepted. Simulated annealing gradually lowers the temperature parameter according to a cooling rate until a ‘global’ minimum is reached. For more information on simulated annealing, refer to [Simulated annealing (SA) is a global optimization algorithm that searches for an optimum value of a cost function, refer to .etc.) found in the All simulations reported in the manuscript used the same hyperparameters (cooling rate, number of Monte Carlo samples, supplementary 1supplementary 2


The physiological processes of cell growth, proliferation, differentiation, and apoptosis are closely related to STAT3, and it has been demonstrated that aberrant STAT3 expression has an impact on the onset and progression of a number of inflammatory immunological disorders, fibrotic diseases, and malignancies. In order to produce the necessary biological effects, macrophages (M0) can be polarized into pro-inflammatory (M1) and anti-inflammatory (M2) types in response to various microenvironmental stimuli. STAT3 signaling is involved in macrophage polarization, and the research of the effect of STAT3 on macrophage polarization has gained attention in recent years. In order to provide references for the treatment and investigation of disorders related to macrophage polarization, this review compiles the pertinent signaling pathways associated with STAT3 and macrophage polarization from many fundamental studies. Almost By phagocytosing of pathogens, presenting exogenous antigens, and secreting cytokines and growth factors, macrophages play a crucial part in the physiopathological processes of immune defense, inflammatory response, tissue remodeling, and homeostasis of the body . Accordi2The STAT family consists of seven structurally similar and highly conserved members: STAT1, STAT2, STAT3, STAT4, STAT5A, STAT5B and STAT6 , all of The gene encoding STAT3 is located on chromosome 17 (17q21) and consists of 750 to 795 amino acids, with a protein molecular mass of approximately 89 kDa . As a doFour different STAT3 isoforms, STAT3α (92 kDa), STAT3β (83 kDa), STAT3γ (72 kDa) and STAT3δ (64 kDa), are regarded to be important factors that influence STAT3 functional heterogeneity . Among tThe distinct structures of STAT3 determine the heterogeneity of its activity. However, there are relatively few studies on STAT3 isoforms, and if we focus our study attention on this, it may explain why STAT3 has varied biological roles under the influence of various local microenvironments.3Macrophages are derived from monocytes that originate from hematopoietic stem cells in the bone marrow. Monocytes grow and mature in the bone marrow for up to 24 hours before moving through the bloodstream . Prior rMacrophages are quite heterogeneous and depending on the tissue they are found in have different names, which including microglia , osteoclasts (found in the bone), Kupffer cells (found in the liver), alveolar macrophages (found in the lung), histiocytes (found in the spleen and connective tissue), intestinal tissue macrophages, and Langerhans cells (found in the skin). Resident macrophages promote tissue homeostasis and monocyte-derived macrophages mainly assist in host defense . The abiAccording to various stimulating factors, M2 macrophages can be further separated into the four subtypes M2a, M2b, M2c, and M2d. M2a macrophages are stimulated by IL-4 or IL-13 to generate and secrete pro-fibrotic substances like TGF-β, insulin-like growth factor, and fibronectin in order to promote tissue repair; M2b macrophages are triggered by immune complexes and Toll-like receptor (TLR) agonists and produce high amounts of the anti-inflammatory cytokine IL-10, which has strong immunomodulatory and anti-inflammatory effects;M2c macrophages are triggered by IL-10, TGF-β, or glucocorticoids and exert phagocytosis and clearance of apoptotic cells, immunosuppression, stimulation of angiogenesis, and development of tissue fibrosis through high expression of Mer receptor tyrosine kinase and high levels of TGF-β and IL-10 release;M2d macrophages are triggered by IL-6, TLR and A2 adenosine receptor agonists and characterized by high expression of IL-10, TGF-β, and VEGF and low expression of IL-12, TNF-α, and IL-1β, which promotes angiogenesis and cancer metastasis –36.Since various macrophage subpopulations have different secretion profiles, macrophages can regulate a variety of pathophysiological processes, including various inflammatory and immune diseases, fibrotic diseases, and tumors. This coincides with the effects of STAT3 signaling pathway, and numerous studies have also demonstrated that macrophages can act as STAT3 effectors, secreting different cytokines, chemokines and growth factors through macrophage polarization, which play important roles in tissue homeostasis, tissue remodeling, immune response and cancer progression. However, in actuality, macrophage polarization is a dynamic process, and the distinction between M1 and M2 macrophages is a simplified explanation of the complex process of polarization, which involves the balancing of various cytokines and chemokines as well as the recruitment of other immune cells or cancer cells . Moreove4Macrophage polarization is regulated through the activation of several interrelated cellular signaling pathways. STAT3 plays an extremely important role in macrophage polarization as a crossroads of multiple signaling pathways. The macrophage polarization-related pathways involving STAT3 include: Janus kinases (JAK)/STAT signaling pathway, NF-κB signaling pathway, phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway, Notch signaling pathway, Hedgehog (Hh) signaling pathway, Wnt signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, etc Figure 24.1JAK/STAT is a main signaling pathway for the activation of more than 70 cytokines, and it plays an important role in numerous crucial biological processes such as cell proliferation, differentiation, apoptosis and immune regulation . JAK1, Jin vivo that cucurbitacin B enhanced antitumor immunity through regulating M2 polarization of macrophages, but the specific mechanism of action by which macrophage polarization affects tumor progression was not described  Degradation of SOCS-bound signaling proteins such as JAK and STAT via the ubiquitination pathway, thereby inhibiting cytokine signaling. This negative regulation of SOCS assists the regulation of inflammatory immune responses and suppresses cell proliferation  on the surface of macrophages binds to LPS through a pathway dependent on the myeloid differentiation factor 88 (MyD88) or the interferon regulatory factor (IRF) 3. This pathway includes IkappaB kinase (IKK) phosphorylation, degradation of IkappaBalpha (IκBα) after phosphorylation modification, and entry of p65/p50 of NF-κB into the nucleus to mediate transcription of pro-inflammatory factors such as IL-1β, IL-6 and TNF-α, amplifying inflammatory signals . In gene pathway . The NF-4.3.2via phosphorylation , c-Jun amino-terminal kinases, p38, and ERK5 families of conventional MAPKs are the most well-known . The croIn summary, multiple signaling pathways can regulate macrophage polarization by affecting STAT3, which in turn affects the development of disease. Therefore, activation or inhibition of these signaling pathways could regulate the M1/M2 ratio and provide new therapeutic strategies for related diseases Table 1.5Macrophages are highly plastic and can be polarized into M1 and M2 types under various environmental stimuli, and the functions of the two are almost antagonistic to each other. It is well known that macrophage polarization is a key to the pathogenic development of many disorders. As an extremely important transcription factor with multiple biological functions, STAT3 is the convergence point of several major signaling pathways involved in inflammatory, immunity, fibrosis, and oncogenic diseases, as well as a key regulator of macrophage different biological functions. STAT3 is a heterogeneous protein that can influence macrophage polarization in both directions, with the activation of it mostly regulating macrophage M2 polarization. However, there are no relevant studies to explain this paradoxical phenomenon, therefore, more exploration is needed to clarify the specific mechanism of STAT3’s role in macrophage polarization. The JAK/STAT signaling pathway is the most prominent pathway involved in STAT3 signaling, and the negative regulators SOCS, PIAS and PTP can affect macrophage polarization by inhibiting different targets of this signaling pathway, suggesting that targeting the STAT3 signaling pathway to alter the M1/M2 ratio appears to be effective in controlling disease progression. The current studies showed that the NF-κB signaling pathway, PI3K/AKT signaling pathway, Notch signaling pathway, Hedgehog signaling pathway, Wnt signaling pathway and MAPK signaling pathway crosstalk with JAK/STAT3 signaling pathway, which regulates gene transcription by affecting the phosphorylation and nuclear translocation of STAT3, have effects on macrophage polarization. This review summarized these signaling pathways with relevant diseases involved in this process to providing new ideas for the interested readers. However, most studies focused on single signaling pathway regulating macrophage M1 or M2 polarization, and very few studies investigated how other macrophage subtypes and multiple signaling pathways work together to regulate the direction of macrophage polarization and thus influence disease progression. Because of the diversity of STAT3 and macrophage activities, the complexity of signaling pathway transduction and regulation, and the network connectivity of these signaling pathways, the phenotypic alteration of macrophages is uncertain. The signaling pathways and regulatory mechanisms of STAT3 and macrophage polarization have not yet been thoroughly and systematically studied. In future work, the fundamental questions are whether there are new subtypes of STAT3 and what other important intermediate types of macrophages exist in addition to M1 and M2. The exploration of STAT3 and various subtypes of macrophages is the primary challenge. On this basis, more research should investigate how crosstalk of multiple signaling pathways affects the direction of macrophage polarization and how to achieve therapeutic goals by modulating macrophage phenotypic transition. These results will then be applied to study the pathogenesis of human-related diseases and explore more targeted treatment options. At present, most of the research is based on animal and cell experiments, and there is still a long way to go to translate them into clinical applications.TX wrote the original review and MZ, WL and TZ reviewed edited and assisted with figures. All authors contributed to the article and approved the submitted version.


The low birth rates and rapid population aging has drawn considerable attention from scholars and policymakers in China and around the world. In 2021, China launched the policy and supportive measures that allow up to 3 children per couple. This study aims to explore the influencing factors of the third-child fertility intention among women aged 20–34 years in China.We draw data from the National Fertility Survey conducted in 2017. The nationally representative survey adopts a stratified, 3-stage, and probabilities proportional to size sampling method. A total of 61,588 valid samples aged 20–34 years old were obtained. Fertility desire and behavior, childbearing and service use, and potential influencing factors of fertility intention such as the history of pregnancy were assessed.In general, 5.01% of Chinese women of prime childbearing age had fertility intention for a third child, and the proportion varies by region across mainland China. Individual characteristics such as being ethnic minorities, being rural residents, and having more siblings are significantly positively correlated with the third-child fertility intention, while the intention was significantly lower among women with a higher income or education level, migrant women, and those engaged in the non-agricultural labor force. Women who already had a son had lower fertility intention for a third child. Moreover, it was the perceived acceptable costs of childcare services rather than the actual costs that mattered more for the fertility intention.Our study concludes a series of socioeconomic factors, and previous childbearing and childrearing experiences are crucial for women’s fertility intention for a third child. These findings highlight the importance of launching supportive measures in addition to the introduction of the 3-child policy in promoting a fertility-friendly society.The online version contains supplementary material available at 10.1186/s12889-023-15719-3. The low birth rates and rapid population aging has drawn considerable attention from scholars and policymakers in China and around the world. Recent census data in 2020 reveals that the population aged 65 and above has surged to 191 million, accounting for 13.52% of China’s total population, up from 8.92% in 2010 [Multiple survey results have concluded that the ideal number of children for the majority of the Chinese population of childbearing age is usually 2, while the proportion of women with fertility intention for a third child is less than 10%, and the number is even lower for young people below 30 years old [Despite the policy efforts, fertility intention and behavior should not be expected to boost shortly, since many other socioeconomic factors may have greater impacts . At the Compared with giving birth to the first child, the marginal utility, i.e. the additional benefits or satisfaction a family perceived for having the second or the third child compared to the first one, could be significantly different for a family , 11. TheThough the reproduction willingness of families that already have 1 child has been well studied –14, lessWith the high cost and inconsistent quality, childcare services in China are still far from ideal , 19. StaUsing nationally representative survey data, this study investigates the influencing factors and their marginal impacts on the third-child fertility intention among women aged 20–34 years. We pay special attention to women in the labor force and women with childbearing and childrearing experience. Since the survey was conducted prior to the introduction of the 3-child policy, our research serves as a baseline result for evaluating fertility support measures and contributes to the literature as a comparative reference for future research on related topics using more updated data.We draw data from the National Fertility Survey conducted by the former National Health and Family Planning Commission in 2017. The nationally representative survey adopted a stratified, 3-stage, and probabilities proportional to size sampling method, covering approximately 250,000 women aged 15–60 years from 12,500 villages/communities across all provincial-level administrative units in mainland China. With an aim to better understand the fertility intention of women and the need for fertility-related public services, the questionnaire included questions on fertility desire and behavior, childbearing and service use, and potential influencing factors of fertility intention such as the history of pregnancy.The survey was conducted through both face-to-face interviews and online questionnaires and was subject to strict quality control measures such as effective management, standardized operational guidelines, comprehensive training, onsite monitoring and technical support, ex post quality checks, and data comparisons with other sources. The sampling design weights were constructed at the national level according to women’s age, marital status, and household registration status. These rigorous measures played a critical role in the successful implementation of the survey and ensured the high quality of the data , 22.In this study, we focus on the fertility intention of women aged between 20 and 34, which is considered the primary childbearing age by the National Health Commission of China and is commonly used in the context of many countries. We exclude the younger age group due to their early stage of reproductive life, which may result in an irrational perception of childbearing. Many women in this age group are still pursuing higher education or starting their careers and lack a stable partner or financial stability. As such, their fertility intentions are subject to various factors that are highly likely to change over time . AlthougThe current analysis was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-22049). The research was performed in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants during the interview.The survey collected information on the intended number of children by asking “How many children do you plan to have?”, the answers to which include the number of children already have and children plan to have in the future. We recategorize the answers to generate a binary variable of “intend to have 3 children or more”: 0 if the answer is below 3, i.e., there was no fertility intention for a third child; and 1 if the answer is 3 or above, i.e., there was fertility intention for a third child. We construct the indicator of “ideal number of children is 3 or more” following the same logic, based on the question “How many children do you think is ideal for a family?”. Though the 2 indicators are strongly correlated, the intended number of children are individual’s rational choice based on real conditions, which is a better predictor for the actual fertility behavior , 26. On hukou address, many of whom are rural-to-urban migrant workers. Rural and urban residence areas are defined based on the administrative classification of the respondents’ current residing address.To capture the characteristics of the interviewees and their families, we use age, age-squared, number of siblings, marital status (0 = “unmarried (widowed/divorced/never married)”, 1 = “married or cohabiting”), ethnicity , migration status , and residence area , as indicators. We assume a curvilinear association between age and fertility intention among women to capture the mixed impact of concerns related to age. For instance, as women age, they may become more financially and psychologically prepared for motherhood, which could lead to a growing desire for children. However, they may also face different social and cultural pressures related to childbearing. Migrant population refers to respondents whose current residing place was different from their registered We include the educational and employment statuses of both the women and their spouses: educational status was categorized into 4 groups ; employment status was categorized into 4 groups . We identified the homeownership status of the current residence as “rented”, “self-built”, or “purchased” and coded 0, 1, and 2, respectively. “Self-built” residence refers to houses that are constructed by the homeowner themselves, which are typically found in rural areas in China and are more affordable options compared to pre-built houses.For analyses of the fertility intention of women in the labor force, we further include income level and occupation type. The annual individual income of female respondents themselves was divided into 4 groups , with each group accounting for roughly a quarter of the samples. Occupations were broadly identified as 6 types  personnel”, 1 = “clerks and related personnel”, 2 = “business and service personnel”, 3 = “production and transportation equipment operators”, 4 = “professional and technical personnel”, and 5 = “personnel in charge of state agencies, enterprises, and institutions”).For women with at least 1 child, we include an indicator of whether women have already had a son . To examine the impact of short-term reproductive experience on the willingness to have another child, the survey questions regarding childbirth costs and reimbursement were only posed to those respondents with a child under 1 year old. We include whether they received free folic acid before or during pregnancy  and calculated the proportion of reimbursement of childbirth cost for women. Additionally, questions pertaining to the actual and acceptable costs for nurseries and kindergartens were only directed toward respondents with a child under 6 years old. We employ the ratio of nursery/kindergarten fees deemed acceptable by mothers to household income as a proxy for the “acceptable cost of childcare services”, and we use the ratio of actual nursery/kindergarten fees to household income as a proxy for the “actual cost of childcare services”.We first presented the geographical distribution of the proportion of women aged 20–34 years with the third-child fertility intention at the provincial level. We compared women of prime childbearing age with or without the intention, illustrated the distribution of characteristics, and conducted t-tests and chi-square tests. Weighted linear probability model analyses were conducted for women and the married or cohabitating subsample to estimate the marginal effects of the influencing factors and their 95% confidence intervals (95% CIs). To further analyze the influencing factors for women in the labor force, we repeated the exercise and include individual income levels and occupation types. We also paid special attention to those who have already had 1 or 2 children and evaluated the marginal impact of their childbearing and childrearing experience on their third-child fertility intention. Statistical analyses were performed using Stata 16.Table Table The composition of women with the third-child fertility intention was different from that of the other group in many terms: they were comparatively younger and had more siblings, and a larger proportion of them were married or cohabiting, ethnic minorities, and rural residents. On the other hand, a lower proportion of them were migrant population or living in a rented residence. Their education level and individual income level were lower, and the patterns are similar for their spouse. Among women with childbearing and childcaring experiences, those with third-child fertility intention have a higher proportion ever received free folic acid, and on average, their proportion of childbirth costs reimbursed, actual cost of childcare services, and acceptable cost of childcare services are higher as well. The t-test and chi-square test results in column (4) indicate that the differences were statistically significant.The regression results of individual and family characteristics are shown in Table Column (2) reports the regression results for married or cohabiting women, with the characteristics of their spouse controlled. The probability of the respondents having a third-child fertility intention was 1.4% points (95% CI: -0.026 to -0.002) lower if the spouse had a college degree or above than had an education level of primary school or below.Since the intention to have a third child could be restricted by the universal 2-child policy when the survey was conducted, as a robustness check, we further narrowed down the samples based on the respondent’s answer to the question “Does the universal 2-child policy affect your (re)fertility intention?”. Only those 9,344 married respondents who answered “no” were retained, and we assume that these respondents’ fertility intention would not be affected by the 3-child policy as well. Column (3) shows a consistent result for this subgroup, and the coefficient magnitudes are larger in general. In terms of influencing factors of spouses, the higher the education level of the spouse, the lower the third-child fertility intention for the women  and (6), only the null hypotheses of Wald chi-square tests on the estimates of having a son are rejected, meaning that the impact of having a son on lowering third-child fertility intention is significantly stronger for rural residents than for urban residents, while other childbearing and childcare services utilization experience does not yield a different effect on women living in rural and urban areas. Moreover, results from the relative importance analysis suggest that having a son is the most important influencing factor for third-child fertility intention in our models, while other childbearing and childrearing experiences show less explanatory power (Appendix Table To our knowledge, this is the first study with a large nationally representative population sample to explore the socioeconomic factors on the third-child fertility intention among women of prime childbearing age in China. Consistent with previous literature, our results suggest that rural residents, ethnic minorities, non-migrant women, and homeowners have stronger intentions for having more than 1 child –32. Due Our findings show that having more siblings increase the intention to have more children, implying the importance of trusting relationships with siblings and their fertility-relevant supportive resources in influencing fertility willingness , 37. HowOur findings provide further evidence that women in the non-agricultural labor force have less incentive to raise more children. Since childbearing and parenting inevitably take up a large amount of time and energy, the opportunity cost of having more children for professional women is higher than that for women in homemaking or farming . A growiThe marginal role of fertility policies in guiding fertility behavior has been significantly weakened in China , 50–52, Our findings provide evidence that compared with the actual cost, it is the perceived affordability of childcare services that plays a more important role in fertility intention. Women of prime childbearing age attach great importance to the quality of childcare and could repress their fertility intention due to concerns about service costs. This result echoes the quality-quantity tradeoffs proposed by Becker and Lewis in such that parents who value children’s quality raise the cost of having children, thereby reducing fertility intention , 57.The strengths of our study include the large sample size, the population-based design, and the adjustment for a wide range of demographic and socioeconomic characteristics. Several limitations of this study should be mentioned and taken into consideration by future researchers. Our data comes from a survey conducted in 2017, prior to the introduction of the 3-child policy in 2021. The policy and supportive measures may have changed the social norms and child-raising environment, thus the level of fertility intention reflected in the survey may be underestimated. As a cross-sectional study, we cannot deduce causative pathways between socioeconomic factors and fertility intention. Nevertheless, our research serves as a baseline result for evaluating fertility support measures and contributes to the literature as a comparative reference for future research on related topics using more updated data.Using nationally representative data from the National Fertility Survey in 2017, we explore the influencing factors and their marginal impacts on the third-child fertility intention among women aged 20–34 years in China. Our findings indicate that individual characteristics such as being ethnic minorities, being rural residents, and having more siblings are significantly positively correlated with the third-child fertility intention, while the intention was significantly lower among women with a higher income or education level, migrant women, and those in the non-agricultural labor force. Further investigation on the impact of childbearing and childrearing experiences showed that women who already had a son had a lower third-child fertility intention. Moreover, it was the perceived affordability of childcare services rather than the actual costs that mattered more for the fertility intention.To promote long-term balanced development of the population, it is necessary to implement relevant supportive measures to build a fertility-friendly society. Our findings suggest that acceptable childbearing and childcare supportive services with satisfactory quality should cover all individuals, including rural-to-urban migrant workers and other economically disadvantaged groups.In addition, further efforts should be made to reduce the implicit employment discrimination against professional women due to childbirth. As a series of measures such as providing childcare support at work and flexible parental leave support are being promoted after the launch of the 3-child policy, future research may focus on evaluating how these measures help working women balance their careers and family responsibilities and how it may impact fertility intention.Below is the link to the electronic supplementary material.Supplementary Material 1


In this study, we examine whether the long-term structural changes in the labour market, driven by automation, affect fertility. The adoption of industrial robots is used as a proxy for these changes. It has tripled since the mid-1990s in the EU, tremendously changing the conditions of participating in the labour market. On the one hand, new jobs are created, benefitting largely the highly skilled workers. On the other hand, the growing turnover in the labour market and changing content of jobs induce fears of job displacement and make workers continuously adjust to new requirements . The consequences of these changes are particularly strong for the employment and earning prospects of low and middle-educated workers. Our focus is on six European countries: Czechia, France, Germany, Italy, Poland and the UK. We link regional data on fertility and employment structures by industry from Eurostat (NUTS-2) with data on robot adoption from the International Federation of Robotics. We estimate fixed effects linear models with instrumental variables in order to account for the external shocks which may affect fertility and robot adoption in parallel. Our findings suggest robots tend to exert a negative impact on fertility in highly industrialised regions, regions with relatively low educated populations and those which are technologically less advanced. At the same time, better educated and prospering regions may even experience fertility improvements as a result of technological change. The family and labour market institutions of the country may further moderate these effects.The online version contains supplementary material available at 10.1007/s10680-023-09657-4. Over the last two decades, technological advancements in production, including cutting-edge industrial robots, have tremendously transformed the labour markets in advanced market economies, creating new career opportunities, but also inducing fears of job displacement OECD, . Only inIn our view, automation may affect fertility since it alters the conditions of participating in the labour market and with it the economic well-being of the family and the strategies of its adult members adopted to combine paid work with care. Past research has clearly demonstrated that individuals tend to postpone or even abstain from having children during economic downturns employment or work conditions. Much less has been done on how fertility reacts to long-term structural changes in the labour markets, driven, for instance, by globalisation or technological change. These changes may not necessarily affect (un)employment, but rather change the demand for workers’ skills. They may increase uncertainty, push workers into poorly paid low quality jobs or increase workers’ effort to catch up with quickly changing work guidelines and skill requirements employment rate, wages or proportion of persons on specific contracts (e.g. temporary or part time). These indicators excel in identifying short-term cyclical economic conditions, but are less able to capture long-term structural changes in the labour markets, driven for instance by globalisation or technological change. These changes may not necessarily affect (un)employment, but may require workers to adjust to the changing demand for skills. They may thus increase uncertainty and workers’ effort to adapt new work guidelines and protocols or undertake training. New employment opportunities may open in front of some workers, while others may be pushed into poorly paid low quality jobs , though on a slow but gradual decline since the onset of the Great Recession. Germany and Italy had been the lowest low fertility countries (with TFR below 1.35) since the mid-1980s and Czechia and Poland since the late 1990s/early 2000s. However, while Germany and in particular Czechia experienced some increase in fertility over the last 15 years, Italy and Poland remained at the fairly low levels with TFR oscillating between 1.25 and 1.45  and the UK started in the early 1990s see Fig. . Germany(H1). This expectation is formed due to the fact that industrial robots are largely employed in manufacturing, leading to a larger job destruction, turnover and uncertainty there rather than in the service sector. Second, we hypothesise that the negative (positive) fertility effects of robot adoption will be more (less) evident in regions where the proportion of men employed in manufacturing at early stages of automation was larger, making men more exposed to robotisation (H2). This is because fertility is less likely to decline in a reaction to a deterioration in women’s than men’s employment conditions. Next, we expect stronger fertility declines/weaker fertility increases in response to robot adoption in regions with a larger proportion of low and middle educated workers (H3) since they are the ones which are mainly negatively affected by automation, either by being at risk of job displacement or having to compete with displaced workers for jobs. Last but not least, we anticipate that fertility effects of robot adoption depend on the region’s capacity to embrace technological change. Consistently with past research showing that employment effects of robot adoption are weaker or even positive in regions which invest in modern technologies, we expect that fertility will be less likely to decline/more likely to increase in response to automation in technology- and knowledge-intensive regions (H4). Finally, fertility effects of robot adoption may also vary across the studied countries since they display substantial differences in welfare regimes, the gender normative context and penetration of automation. We abstain, however, from formulating specific hypotheses on the role of the specific cross-country differences for our findings since a comparison of only six countries which vary in numerous important dimensions precludes testing such hypotheses. We rather discuss our findings from the perspective of the cross-country differences presented in Sect. In this study, we extend the work by Anelli et al.  and examworkers H since thOur study is based on regional NUTS-2 data. The nomenclature of territorial units for statistics (NUTS) is a hierarchical system for dividing up the economic territory of the European Economic Area, the UK, and Switzerland for the purpose of data collection and socio-economic analyses. NUTS-2 regions are roughly equally populated, with population ranging from 0.8—3 million, and these are the smallest geographical units for which employment data are available in Eurostat for all 6 countries of our interest. We observe the countries fairly since the start of the robotisation till 2017. This means we cover the years 1997–2017 for the old EU member states and the UK and 2007–2017 for Czechia and Poland. Covering fully the 1990s for the old EU member states was not possible due to data availability.To measure fertility, we use TFR and the age-specific fertility rates for the following age groups: 20–24, 25–29, 30–34, 35–39, 40–44, 45 + . These data have been provided by Eurostat at the NUTS-2 level since 1990. They are computed by combining national statistics on births by mother’s age and population of women by age. They are fairly complete with some missing data in fertility of women aged 45 + . We use simple linear interpolation to supply them.To measure worker’s exposure to automation we use data on industrial robot stocks provided by the International Federation of Robotics (henceforth: IFR). Industrial robots are defined by IFR as fully autonomous machines that do not require a human operator. Their main tasks are handling operations and machine tending  and welding and soldering  , ratio of share of highly educated women to share of highly educated men, the square of the latter and women’s economic activity rate. The variables denoting population structure by age are introduced to control for any variation in population exposed to childbearing. We also account for the population education level given the educational gradient in fertility  proportion of workers employed outside of manufacturing (used to test H1), the initial proportion of women employed in manufacturing over the proportion of men in manufacturing (H2), proportion of highly educated persons (time-varying) (H3) and the proportion of workers employed in technology- and knowledge-intensive sectors (time-varying) (H4). The control and moderating variables are fairly complete. Any missing values were imputed via linear interpolation. This was done in 14% of cases for population structure by education, and max. 25% for employment data. There are no cases when the entire time series for specific regions are missing.After accounting for the NUTS reclassifications and excluding foreign territories . The respective findings are presented in Table With few exceptions, our findings are largely consistent with our hypothesis H1.We observe a clearly negative effect of robot adoption on total fertility in those German regions which were initially highly industrialised. It is strongly driven by fertility reduction at young ages (20–24 and 25–29). This negative effect is significantly weaker in regions with a smaller initial proportion of workers employed in manufacturing. We also detect some negative fertility effects of robots in the French and British regions with initially large manufacturing sectors. In the UK, the negative effects on age-specific fertility in those regions are not significant but the negative effect on total fertility is significant. In France, they emerge at the highest reproductive ages: 40–44 and 45 + . In Italy, most of the effects in highly industrialised regions are insignificant except for those at higher reproductive ages where the pattern is unclear . Some inconsistency is also detected in Czechia and Poland though it seems that the effects of robot adoption there tend to be rather positive in highly industrialised regions: The main effects at all reproductive ages, but for 25–29, are positive though significant only at ages 20–24 and 35–39.Next, we expected that fertility effects of robot adoption will be more negative in regions where men were more exposed to automation than women (H2). The findings which allow to verify this hypothesis are presented in Table  Apart from the UK and the cluster built by Czechia and Poland, we do not find evidence for hypothesis H2. Our findings even suggest the reverse, namely that robot adoption in Germany, France and Italy leads to stronger fertility decline in regions where the initial ratio of women’s to men’s employment share in manufacturing was larger. These negative effects, obtained net of the regional employment in manufacturing and women’s activity rate, are largely significant at young reproductive ages. Interestingly, in Italy and to some extent in France we even find traces of positive effects of robot adoption in regions with initially large manufacturing sectors which are dominated by men.The findings for the UK and Czechia and Poland are more consistent with our expectations. In the UK, the interaction between exposure to robotisation and the ratio of women’s and men’s employment in manufacturing is positive at all reproductive ages and significant in the models for the total fertility. At the same time, the effect of robot adoption on fertility in highly industrialised regions where employment in manufacturing is dominated by men is negative, suggesting that robotisation reduces fertility in such regions. In Czechia and Poland, the interaction between exposure to robotisation and ratio of women’s and men’s employment in manufacturing is positive at all reproductive ages (like in the UK), but significant only at ages 20–24.Subsequently, we test the hypothesis that robots exert a more negative impact on fertility in lower educated regions (H3). We present our findings in Table We find clear support for hypothesis H3 in Germany and Italy. There is some evidence for this hypothesis also in the remaining countries but for France where our findings suggest the opposite.In Germany, we identify a significantly negative effect of exposure to robots on fertility in regions characterised by lower educational attainment of the population: An increase in the exposure to robotisation by 1 robot per 10,000 workers leads to a decline in total fertility by 0.0016 there. Negative and mostly significant fertility effects are found at all reproductive ages. They clearly weaken with an increase in the proportion of highly educated individuals in a region. We find some traces of a similar pattern in Italy and Czechia and Poland, but the estimated effects are significant only at some ages and in Czechia and Poland some reversed findings are also obtained for the age group 35–39. The educational attainment of the regional population does not seem to matter for the effects of robotisation on fertility in the UK (except for highest reproductive ages where the findings are consistent with our expectations). Finally, in France we find that robotisation has a positive influence on fertility in regions with fairly low educated populations, which is in contrast to our hypothesis H3.Finally, we expected the fertility effects of robotisation to be less negative or more positive in regions which are better able to embrace technological change. We operationalise this ability with the regional investment in technology- and knowledge-intensive sectors, measured by its employment. Only a few findings are consistent with this hypothesis Table .Table 5EOn the one hand, we find the interaction term between exposure to robotisation and employment in technology- and knowledge-intensive sectors to be significantly negative at lower reproductive ages (25–29) in Germany and the UK. On the other hand, however, the interaction term turns often positive and significant at high reproductive ages. This latter finding emerges clearly in Germany, but also to a lower extent in France, UK and Czechia and Poland, suggesting fertility recuperation (or higher-order fertility) encouraged by increasing employment/earning opportunities and growing prosperity of the region.Industrial robots substantially change the conditions of participating in the labour markets and thereby may also affect fertility. On the one hand, there is evidence that robots destroy jobs, increase turnover in the labour market and make workers adjust to the new demands in the labour markets . On the other hand, however, robots may also increase productivity and thereby contribute to the expansion of new jobs, in particular in regions with highly educated workforce open to technological innovations. In this study, we examined whether these long-term structural changes, driven by adoption of industrial robots, affect regional fertility rates in six European countries. We find that fertility effects of robot adoption are rather small and vary across regions, depending on workforce education, employment structure and region’s capacity to embrace technological change. Briefly, our findings suggest that robots tend to exert a negative influence on fertility in regions where substantial numbers of workers are exposed to losing their jobs due to automation, i.e. highly industrialised regions (except for Czechia and Poland) and regions with relatively low educated populations (except for France). We also find the fertility effects to be more negative in less technologically advanced regions where robotisation is unlikely to boost productivity and create new jobs. The negative fertility effects are clearly most evident at young ages, especially in regions with large manufacturing sectors and to some extent in regions with lower educated populations. This finding may suggest postponement of fertility to higher ages, though fertility recuperation at older ages does not emerge clearly from our study, except for regions which are strongly oriented at knowledge and technological innovations. These findings are consistent with past research, showing that highly educated individuals, whose skills are valued in the labour market, tend to postpone childbearing into higher ages  and support for the unemployed is more limited. We also find the effects of robot adoption to be less disruptive for fertility and even to encourage it in Czechia and Poland. This finding is seemingly striking, but we explain it by the fact that robots are less likely to replace labour in countries with lower labour costs Supplementary file2 (XLSX 660 KB)Below is the link to the electronic supplementary material. Tables 1-20 (located in the Word file) present full results from interaction IV models presented in the paper. They supplement the basic models presented in the Appendix. Tables 21-45 in Excel present full results from the basic and interaction OLS models.