Patients with early-stage cervical cancer treated by minimally invasive surgery show shorter progression-free and overall survival compared to open surgery. This study integrated minimally invasive and open surgery survival data with surgery costs and utilities and conducted a cost-effectiveness analysis, using a Markovian decision analysis model, to compare the two surgical approaches. Our results show that open radical hysterectomy is not only oncologically superior but also more cost-effective. Until new data regarding the safety of minimally invasive surgery and surgery costs are presented, open radical hysterectomy should be the preferred approach from both the oncological and financial standpoint.We aimed to investigate the cost-effectiveness of open surgery, compared to minimally invasive radical hysterectomy for early-stage cervical cancer, using updated survival data. Costs and utilities of each surgical approach were compared using a Markovian decision analysis model. Survival data stratified by surgical approach and surgery costs were received from recently published data. Average costs were discounted at 3%. The value of health benefits for each strategy was calculated using quality-adjusted life years (QALYs). Incremental cost-effectiveness ratio, calculated using the formula /, was used for cost-effectiveness analysis. One-way sensitivity analysis was conducted for all variables. Open radical hysterectomy was found to be cost-saving compared to minimally invasive surgery with an incremental cost-effectiveness ratio of USD −66 and USD −373 for laparoscopic and robotic surgery, respectively. The most influential parameters in the model were surgery costs, followed by the disutility involved with open surgery. Until further data are generated regarding the survival of patients with early-stage cervical cancer treated by minimally invasive surgery, at current pricing, open radical hysterectomy is cost-saving compared to minimally invasive radical hysterectomy, both laparoscopic and robotic. Following the introduction of laparoscopic radical hysterectomy in the 1990s, this surgical approach gained favor and the number of minimally invasive surgeries for early-stage cervical cancer increased substantially around the world ,3. WhileIn 2018, the first prospective, randomized trial comparing the oncological safety of minimally invasive surgery and open surgery for the treatment of early-stage cervical cancer was published by Ramirez et al. The Laparoscopic Approach to Cervical Cancer (LACC) trial showed inferior oncological outcomes, including shorter progression-free survival and overall survival for the minimally invasive approach . Along wDespite the many advantages of minimally invasive surgery, the total costs of laparoscopic, and particularly robotic radical hysterectomy, have been consistently shown to exceed those of open surgery, mainly due to higher instrument- and procedure-related costs ,2,23. PrIn light of the updated survival data from the prospective randomized LACC trial, the aim of our study was to perform a cost–utility analysis of open radical hysterectomy compared to minimally invasive surgery for the treatment of early-stage cervical cancer, using USA pricing data.As this work is a theoretical, mathematical/financial model, not involving any human or animal subjects in any form, after consultation with our local institutional review board, it was exempt from the need for IRB approval.® 2020b, Natick, MA, USA). The Markov model is presented in In order to compare the costs and utilities of open radical hysterectomy and minimally invasive radical hysterectomy for early-stage cervical cancer, a Markov model was constructed using TreeAgePro software (TreeAge™ Software LLC. version 2022). Additional analyses were performed using MATLAB  and robotic radical hysterectomy (USD −373/QALY gained) . This meHistoric, mainly retrospective and case–control data comparing the safety of minimally invasive radical hysterectomy to open radical hysterectomy did not demonstrate any survival difference between the two approaches. This, along with the many advantages of minimally invasive surgery, made minimally invasive radical hysterectomy an attractive alternative ,32,33,34One of the most influential parameters that affected the incremental cost-effectiveness ratio was the disutility value that accompanies open surgery . Hence, The other most important parameters that affected the incremental cost-effectiveness ratio in our model were surgery costs . The cosAs opposed to cervical cancer, minimally invasive surgery does not seem to jeopardize overall and disease-free survival among endometrial cancer patients . SeveralOur work has several drawbacks. The costs of minimally invasive surgery, including laparoscopic and robotic surgery, vary across reports. Nonetheless, in all reports, minimally invasive radical hysterectomy is more costly than open surgery ,20,23. WFuture research should aim at clarifying the causes of the survival differences observed in the LACC trial and point out specific subgroups of patients that would benefit from the obvious advantages that minimally invasive surgery has to offer. Examples of such patients include patients with small <2 cm tumors, or patients that have conization prior to definitive surgery. Survival data of these patients, integrated into our model, could show different results regarding cost-effectiveness.Until further data are generated regarding the survival of patients with early-stage cervical cancer treated by minimally invasive surgery, at current pricing, open radical hysterectomy is cost-saving compared minimally invasive radical hysterectomy, both laparoscopic and robotic, and should be the preferred surgical approach.


Small extracellular vesicles (sEVs) have burst into biomedicine as a natural therapeutic alternative for different diseases. Considered nanocarriers of biological origin, various studies have demonstrated the feasibility of their systemic administration, even with repeated doses. However, despite being the preferred route of physicians and patients, little is known about the clinical use of sEVs in oral administration. Different reports show that sEVs can resist the degradative conditions of the gastrointestinal tract after oral administration, accumulating regionally in the intestine, where they are absorbed for systemic biodistribution. Notably, observations demonstrate the efficacy of using sEVs as a nanocarrier system for a therapeutic payload to obtain a desired biological (therapeutic) effect. From another perspective, the information to date indicates that food-derived vesicles (FDVs) could be considered future nutraceutical agents since they contain or even overexpress different nutritional compounds of the foods from which they are derived, with potential effects on human health. In this review, we present and critically analyze the current information on the pharmacokinetics and safety profile of sEVs when administered orally. We also address the molecular and cellular mechanisms that promote intestinal absorption and that command the therapeutic effects that have been observed. Finally, we analyze the potential nutraceutical impact that FDVs would have on human health and how their oral use could be an emerging strategy to balance nutrition in people. The enteral route, including oral administration of drugs is the preferred delivery method to treat systemic diseases or local gastrointestinal (GI) pathologies due to its minimal invasiveness (pain-free), relatively low cost, and ability to self-administer . HoweverNanocarriers are a colloidal transport system for drugs with a nanometric particle size  . DefinedSince its introduction as a drug delivery nanosystem, research has focused on understanding the pharmacokinetics and biodistribution of intravenously (i.v.) administered sEVs . The valIn this review, we first discuss the current evidence on the challenges of the ambitious concept of using sEVs as a nanocarrier system for oral prescription, focusing on the stability, bioavailability, and uptake of sEVs by intestinal cells. Second, we present a comparative analysis summarizing the biodistribution and toxicity of orally administered sEVs in murine models. Then, we investigated through the available data molecules or motifs that could explain the endogenous capacity of certain sEVs to transit the GI pathway and cause a therapeutic response, addressing the possible cellular mechanisms by which this response would be mediated. Finally, we discuss the nutraceutical perspective of the consumption of bioactive molecules within sEVs derived from food sources and their potential impact on human health.sEVs are non-replicative lipid-based vesicles classified by a hydrodynamic diameter inferior to 200 nanometers (nm) . They arOrally administered sEVs must survive the harsh degrading conditions of the digestive system as moisture, lubricants, mechanical forces, digestive enzymes, emulsifiers, pH neutralizers , commensTo date, it has been described that different types of nanoparticles can cross the intestinal epithelium using different mechanisms, such as paracellular transport . ParacelThe second mechanism described by which nanoparticles cross the intestinal layers and reach systemic circulation is transcellular transport by epithelial cells (mainly enterocytes that constitute 90–95% of the cells in the GI tract) and M-cells  . The maiMurine studies identifying the biodistribution pattern of orally administered sEVs are few and focus mainly on cow’s milk-derived sEVs, although we have found some studies using plant-derived exosomes-like particles. These studies show that sEVs/exosomes-like particles manage to withstand the hostile environment of the gastrointestinal tract, associated with their transit through acidic conditions in the stomach and degradative conditions in the gut, in various murine models ,25,26. CInterestingly, repeated oral administration of cow’s milk-derived sEVs on mice seems to not alter the biodistribution pattern observed after a single oral intake of sEVs . Betker As mentioned, the intestine is the anatomic site where the absorption of sEVs seems to occur after oral gavage in mice, which results in their entry into the bloodstream. Transendocytosis through intestinal epithelial cells ,38 or paMDR1 (Pgp+) cells in vitro.Little information exists about other pharmacokinetic parameters of orally administered sEVs. Regarding its stability in circulation, Munagala et al.  observedThe studies that evaluated the safety profile of sEVs orally administered consistently showed that the parameters of body weight, plasma cytokine concentration, or tissue damage remain unchanged, suggesting they are well-tolerated and non-immunogenic. Although these studies were performed mainly in milk-derived sEVs, different concentrations, and even at repeated doses, toxicity data confirm preliminarily the potential clinical use of milk-derived sEVs. Very few studies have investigated the use of sEVs formulations for gastric drug delivery. According to Bardonnet et al. , nanoparOf all these sources of sEVs, notably bovine milk has reported increased stability in acidic media that emulate the conditions of the stomach lumen and structural conservation after boiling due to the presence of calcium in comparison to colorectal cancer-derived sEVs (LIM1215 cells) . BesidesAnother approach for increasing the uptake of milk-derived sEVs was published by Warren et al. , where tAnother studied source of sEVs with therapeutic properties after oral delivery is grape juice. Ju et al.  showed tDespite the incipient understanding of the cellular/molecular mechanism that regulates the biological effect of sEVs through oral administration, in the literature, it is possible to find several articles reporting therapeutic efficacy when sEVs are administered orally in models of inflammatory diseases and cancer. In an induced cutaneous delayed-type hypersensitivity (DTH) model, Nazimek et al.  and WasiThis immunological effect mediated by macrophages after sEVs injection is widely described in the literature since the significant clearance of sEVs in systemic circulation occurs through these myeloid cells ,50,51,52B. subtilis-derived bEVs in polarized epithelial Caco-2 cells in vitro, where a fraction of those bEVs did not fuse with the cellular membranes and where secreted to the other side. From this study, three major questions arise: (1) if this described transport mechanism is conserved to all sources of sEVs ; (2) if those secreted “intact” sEVs conserve their capacity to regulate the acceptor cell function and transcriptional expression; (3) if the observed mechanism also occurs in vivo. Regarding the secretion of sEVs from epithelial cells in vivo, Sakhon et al. [Notably, Rubio et al.  demonstrn et al.  reportedThe previously mentioned relationship between epithelial and immune cells from the intestine cannot fully explain the observed phenomena in inflammatory bowel disease (IBD) and cancer models. Tulkens et al.  demonstrApis mellifera hypopharyngeal gland secretomal products  participate in the known antibacterial and pro-regenerative properties of bee-derived products [In the last decade, structures morphologically like extracellular vesicles (EVs), called “food-derived vesicles” or FDVs, have been isolated from different foods . These findings raise the question of whether FDVs contain or overexpress the nutritional compounds or nutraceutical effects of the foods from which they are derived. Several studies have shown that different FDVs have nutraceutical effects. For example, it was observed that the nanovesicles in products . Furtherproducts  describeproducts . In globproducts ,64.Several studies have revealed the effects of sEVs derived from breast milk on the immune function of infants. The analysis of breast milk-derived sEVs showed that the molecules they contain vary depending upon the maternal allergy status . Malnutrhttp://bomiprot.org accessed on 17 november 2022), with over 3100 proteins from whey, milk fat globule membranes (MFGM), and sEVs [Due to its nutritional and immunological benefits, bovine milk-derived sEVs have become an essential fraction of proteomic research. An open online database of bovine milk proteome was established, BoMiProt , phosphatidylethanolamine (PE), cholesterol (Chol), and phosphatidylserine (PS) were measured. Interestingly, sEVs exhibited increased levels of phosphatidylinositol (PI) as compared to sEVs isolated from cultured cells . Other cMany relevant questions need to be answered to understand the value of sEVs in milk formulas since the content of sEVs, and their cargo is modest to not detectable . DietaryAs a final reflection, this interkingdom relationship has been present throughout the entire existence of animals by dietary consumption, but little was known before the identification of the FDVs.While oral administration of sEVs presents various physiological and practical advantages over other routes, there is an existing need to investigate further the safety, stability, pharmacokinetics, and biodistribution attributes before their broad use as drug vehicles or nutritional supplements. Most biodistribution studies rely on DiR-based labeling of the sEVs, while DiR labeling does not affect sEVs morphology ; howeverThe few quality studies presented in the different tables of this review display remarkable features of sEVs being able to resist the harsh acidic environment of the GI tract and reach the intestine. However, most FDVs studies are almost exclusively based on milk-derived sEVs. For this, these findings must be expanded for other FDVs of interest, membrane components, and surface markers depending on the source of isolation. Also, proper readouts must be determined to assess their capacity to elicit significant effects following oral intake. So far, milk-derived sEVs are the most feasible option for a drug delivery application based on their published safety profile, pharmacokinetics, and endogenous stability in the GI tract. Nonetheless, this does not mean, in any case, that these properties are exclusive for this source of sEVs. As we mention in this article, studies evaluating other sources of vesicles in detail could discover an optimal sEVs-based nanocarrier for oral drug delivery. It is interesting to mention that oral administration is getting closer to clinical-stage application. So far, we have found only one clinical trial seeking to characterize the ability of plant-derived exosomes in the delivery of curcumin to colon tissue in the context of colon cancer via oral intake . This study is ongoing, and no data or results are available yet.Several preclinical and clinical studies have failed to show a positive association between milk intake and serum miRNA levels. However, the role of miRNA cargo, the extent to which they are exported via the sEVs route, and whether they contribute to cell-cell communication are still controversial. Albanese et al. found that sEVs did not fuse detectably with cellular membranes to deliver their cargo. They engineered sEVs to be fusogenic and documented their capacity to deliver functional messenger RNAs. Engineered fusogenic sEVs, however, did not detectably alter the functionality of cells exposed to miRNA-carrying sEVs. These results suggest that sEVs-borne miRNAs do not act as effectors of cell-to-cell communication, suggesting that the delivery of different RNA species through the sEVs might be an extremely inefficient process .The interaction of xeno-EVs (plant or bovine to human cells) adds another layer of complication to deciphering the fusion and cargo delivery processes, identifying the glycan features and other pathways on the EVs surface responsible for the interaction between plant/animal EVs and human cells. In conclusion, oral administration presents a reliable delivery route for EVs. The pharmacokinetics and activity of these bioactive compounds will depend on their cellular source, cargo content, and interaction with human cells.


The design of inorganicmaterials for various applications criticallydepends on our ability to manipulate their synthesis in a rational,robust, and controllable fashion. Different from the conventionaltrial-and-error approach, data-driven techniques such as the designof experiments (DoE) and machine learning are an effective and moreefficient way to predictably control materials synthesis. Here, wepresent a Viewpoint on recent progress in leveraging such techniquesfor predicting and controlling the outcomes of inorganic materialssynthesis. We first compare how the design choice  affects the type of control it can offer overthe resulting product attributes, information elucidated, and experimentalcost. These attributes are supported by discussing select case studiesfrom the recent literature that highlight the power of these techniquesfor materials synthesis. The influence of experimental bias is nextdiscussed, followed finally by our perspectives on the major challengesin the widespread implementation of predictable and controllable materialssynthesis using data-driven techniques. The designof inorganic materials for various applicationscritically depends on our ability to manipulate their synthesis ina rational, robust, and controllable fashion. This Viewpoint presentsthe current progress in data-driven techniques as a more effectiveand efficient way to accomplish such control. This is essential for scientific progress and long-termeconomic growth.1Every example of modern technology thatis enabled by inorganicmaterials is also limited by inorganic materials; for example, thevarious physicochemical properties of inorganic materials limit theconversion of sunlight into electricity or fuel, limit our abilityto efficiently catalyze chemical reactions under mild conditions,and limit how we generate artificial light.2 On the front end, materials informatics and machine-learning-basedcomputational discovery via the Materials Genome Initiative has greatlyaccelerated materials discovery through efforts such as the MaterialsProject, which combines supercomputing and density functional theoryto predict materials and their properties before they are made.5 While this “materials by design” approach, developedover a decade ago, has successfully identified vast numbers of materialswith a wide range of targeted properties, a significant bottlenecknow exists in the next step of the process, which is the actual synthesisof these predicted materials  is slow andrandom. Advances in new combinatorial synthesis methods, like parallelsyntheses via scanning probe lithography or multiple partial cationexchange reactions on metal chalcogenide nanocrystals, can acceleratematerials discovery by producing larger libraries with increasingvolumes of data; however, these techniques are generally low throughputin terms of the absolute amounts of material produced.A solution to this problemlies at the interface between disciplines.Implementing data-driven techniques at the experimental end of materialssynthesis and optimization is a tractable and natural progressionof materials informatics. In this perspective, we describe how properutilization of such techniques can dramatically increase the rateand efficiency of experimental screening and optimization in inorganicmaterials chemistry. Specifically, statistics-based regression techniqueslike the design of experiments (DoE) and response surface methodology(RSM) enable multivariate investigations of the experimental domainfor a specific material system, which can map synthetic outcomes thatare continuous in nature  as a function of input variables. This can provide key mechanisticinsight and a multidimensional view of the experimental design spacefrom only a fraction of the experimental observations typically neededfor OVAT methods. Machine learning can similarly be utilized; thatis, machine-learning classifiers enable the mapping of categorical(or discrete) synthetic outcomes like the crystal phase. Neural networksand iterative active learning can uncover complex synthesis–structure–propertyconnections that human intuition cannot. Herein, we discuss the advantages,challenges, and future directions of leveraging data-driven techniquesto advance the synthesis and optimization of inorganic materials andcritically assess recent developments.An important step toward the implementation of data-driven techniquesis determining which techniques best solve specific materials synthesisproblems. To address this, we will first assess the information affordedvia DoE and the subsequent analysis. We will then similarly comparethe information afforded via machine-learning techniques.10 DoE has since demonstrated its utility in industry, includingmanufacturing and engineering,18 and its application to problems in chemistry has seen a rapid increasein the last two decades.20 Particularly, therehas been a sharp rise in examples of its utility in optimizing materialssyntheses in academic literature, including a recent tutorial outliningthe use of DoE for materials chemistry.34 The increased use of DoE can be attributed to its effectivenessat achieving synthetic control of both dependent and independent variableswhen used in conjunction with RSM, which is an optimization techniquethat provides insight throughout a high-dimensional parameter spaceto formulate a predictive model by using a minimal number of experiments.The basic ideas behind statisticalmodeling and DoE date back to as early as 1935.The ability to maximize the information that can be extracted fromsmall data sets makes DoE ideal for novel, low-throughput, and/orexploratory materials synthesis where little is known about the systemat hand or the ability to collect large amounts of data is limited.Several variables can be screened at once in a rational manner usingtailored screening designs, which allows a broad, multivariate parameterspace to be sampled quickly and systematically in a minimum numberof experiments with the ability to assess different variables withvarying degrees of importance or weights. In the choice of variablesin the experimental design, categorical variables like reagent typerequire an exponential increase in experimental observations comparedto continuous variables, so care must be taken to ensure the mostbeneficial design. Subsequent analyses are regression-based, makingDoE investigations mainly suitable for synthetic outcomes that arecontinuous in nature, such as the emission wavelength or yield. Thisfavors the use of DoE within the parameter space for a specific materialcrystal phase and not mixed-phase systems.response surface of the different product characteristics as a function of the variablesscreened using RSM. These multivariate models quickly identify a setof experimental conditions that lie at the minimum or maximum or hita set target within the response surface parameter space, which typicallywould be missed using one-dimensional experiments or polynomial (higher-order) model to create a eriments 1. This e41 Despite machine-learning techniques requiring notoriously largedata sets, which can be limiting in the field of exploratory materialssynthesis, the raw computing power renders it attractive for mappingcomplex synthesis–structure–property relationships thatare unachievable with traditional approaches. When coupled with newadvancements in self-driving, high-throughput synthetic techniquessuch as multichannel flow reactors or automated robotic systems, whichpermit a larger number of experimental observations to be collectedin a shorter amount of time, the opportunities for systematic explorationof synthetic landscapes are greatly expanded.45The implementation ofmachine-learning techniques in materials chemistry is even more recent,with the majority of its use in academia only coming in recent years.46 As opposed to the DoE, machine-learning techniques can handle bothmixed and categorical variables and synthesis outcomes  had statistically significanteffects, so a second-order design was then performed to model theparameter space at a higher number of levels for these three variables.This model was jointly optimized by using a desirability functionto find the global optimum  within the parameterspace, which predicted the set of conditions to produce the most optimalproduct  and their effectson five different responses: the effect on the percent yield of goldnanorods, the effect on the fraction of rods, the effect on the mediannanorod width, the effect on the medium nanorod length, and the effecton the longitudinal surface plasmon resonance peak wavelength (L) resulted in a 7.3% decrease in the percent yieldof gold nanorods when temperature was increased from low to highlevels. Similarly, the interaction between the temperature and amountof silver promoter (L and M) resulted in an 8.4% decrease in the percentyield of gold nanorods in response to increasing both variables fromthe low to high levels, whereas the silver promoter (M) alone hadno statistically relevant effect on the yield. In contrast, increasingthe amount of ascorbic acid (N) resulted in a 6.2%increase in the percent yield of gold nanorods. All other variables had no significant effect. Afterthe results of the other four responses were analyzed in a similarmanner . Thisenabled the discovery of a new catalytically active and selectivezeolite (ITQ-30) for the alkylation of benzene with propene.70 These examples exemplify the ability of DoEto provide the tools to reopen the synthetic bottleneck of materialsdiscovery, in addition to fine-tuning and optimizing the materialsproperties and reaction conditions. The combination of high-throughputexperimentation with DoE methods, like those used by Corma et al.,has also been extended to other materials systems. Reinhardt and co-workers71 used the well-studied lead halide perovskiteMAPbI3 (MA = methylammonium) as a model system to establishan automated routine for the rapid identification of conditions foroptimal film deposition. In their work, they provided a direct comparisonbetween the manual optimization protocols and high-throughput experimentation,combined with the DoE statistical analysis. This work highlightedthat automation and peak ratio analysis outperformed manual techniquesby only requiring about 60% of the experimental time. This shows promisefor the combination of high-throughput techniques with DoE; however,for a larger materials space, such as the screening of vast compositionallibraries for new materials compositions, machine learning would bethe technique of choice over DoE, as discussed below.DoE has also been applied to large data sets todirectly screendata libraries for possible next-generation materials properties.For example, Maier et al. screened the catalytic performance of 467different materials for propene oxidation.72 The sparsity of data sets and the substantialcosts of experiments have limited its application in the explorationof synthetic design spaces,35 but for thosesyntheses with abundant data, machine-learning algorithms can effectivelygenerate models despite the random distribution of data points. Forexample, the recent work of Nguyen et al. extracted data from 72 publicationsto define a set of extended and condensed synthetic conditions todetermine the best approach for predicting synthesis outcomes wherethe available data are limited phosphine, the presence of trioctylphosphine(TOP) redshifts the emission and absorption maxima, while the presenceof a zinc halide salt results in spectral blueshifts  algorithms can provide more efficient sampling within the designspace compared to DoE and capture nonlinear relationships from limiteddata. Braham et al. utilized SVM models to predict the thickness ofquantum-confined CsPbBr75 where scanning transmission electron microscopy–energy-dispersiveX-ray data including the elemental compositions and number of interfaceswere fed into the algorithm after each experiment. This approach resultedin the synthesis of quaternary to senary biphasic heterostructureswith a high elemental complexity and potentially superior catalyticproperties. The authors demonstrated that machine-learning-assistedoptimization is a viable approach to identifying nanostructures withtarget morphological qualities, even with limited data sets, as thisplatform enabled the synthesis of new and complex materials, includingthe purported most complex biphasic nanocrystal ever made, with successrates (95%) significantly better than random selection. The limitationsfor expanding such a design feedback loop to higher-throughput synthesisand experimentation and into more complex properties, such as catalyticactivity or stability, are almost exclusively experimental, illustratingthe need for new automated characterization techniques to extractmore information from the megalibraries of nanomaterials at much higherthroughputs.Another suitable optionfor smaller data sets is iterative optimizationsthat utilize active learning. Bayesian optimization algorithms canreceive feedback from experiments in an iterative manner to guidethe exploration of the design space. A visualization of this typeof iterative exploration of a parameter is given in 76 Spectral characterizationwas accomplished in a high-throughput manner via a microplate reader,followed by the implementation of a least absolute shrinkage and selectionoperator with a cross-validation (LASSO-CV) algorithm, which automaticallydeconvoluted the UV–vis absorption spectra into ratios of variouscomponents (Besides predicting the outcomesof materials syntheses, machine-learningtechniques have also proven to be useful for batch-processing theexperimental data. For example, Dahl et al. developed a phase mapof the Cs–Pb–Br perovskite nanocrystal synthesis from2337 reactions using a high-throughput liquid handling robot, whichadded dodecane, oleylamine, lead oleate, oleic acid, and cesium oleatein varying proportions to a 96-well plate to react and subsequentlyobtained optical spectra.mponents 6a. The cmponents 6b. By coBoth statistical techniques and machine learning can accelerateour understanding of materials design, synthesis, and discovery. Theexamples given in this Viewpoint show that these approaches have thecapability of being transferred to other complex inorganic systemsfor a range of problems. DoE addresses several of the problems fornovel, low-throughput, and/or exploratory materials synthesis, wherelittle is known about the system at hand or the ability to collectlarge amounts of data is limited by its ability to maximize the informationthat can be extracted from small data sets using multivariate screeningtechniques. When dealing with more complex materials systems wheredesign space exploration, rather than optimization, is the focus,machine learning is a more appropriate option. The raw computing powerrenders it attractive for mapping complex synthesis–structure–propertyrelationships that are unachievable using traditional approaches.Both techniques offer a more unsupervised route to materials synthesisby removing a large portion of manual inputs from the experimenter,which inherently alleviates problems with experimental bias and expeditesthe synthesis of novel materials.Despite this, after assessmentof the current state of this up-and-comingfield, there are clear challenges that remain in their widespreadadoption. Bias must continue to be carefully considered at every stepof implementation. DoE is limited to linear regression, preventingit from establishing more complex nonlinear models, or classificationmodels for categorical responses, so revealing the mechanistic insightsof a reaction relies on researcher capability and accurate analysisof the results. In machine learning, both supervised and unsupervisedmodels may be biased due to the way they were constructed or how theyanalyze and learn from the data. The remaining issues of overfittingand underfitting in machine-learning models demonstrate this. Additionally,the trade-off between time, expense, and accuracy must be carefullyweighed for each specific investigation. For example, if the maingoal is the realistic use of a certain material in a target application,time and expenses must be rationalized for that goal, which oftencome at the expense of the level of accuracy. The limitations in datacollection when constructing training data sets are directly influencedby this, where the expense and cost of large data sets may outweighthe insights gained. This is exacerbated by the fact that data miningfrom the published literature is extremely challenging because ofinconsistent experimental reporting, compounded by challenges in theautomatic extraction of machine-readable data. These issues oftenlead to data-mining efforts being incomplete or requiring a largeamount of manual cleaning.78 but despite the growing popularity of robotic systems for chemicalsynthesis, they can be difficult to run and maintain due to a lackof standard operating systems and a capacity for direct access tothe literature through natural language processing.79 In the future, implementing widespread guidelines for reportingscientific results, creating a universal system for data digitization,and making raw data freely available will advance our ability to data-mineand construct the large data sets needed for machine learning. Althoughthe intuition of experienced chemists will always be required to appropriatelyidentify problems and design the experiments that such autonomoussystems will carry out,80 such advanceswill ultimately lead to a greater feasibility to model multivariatesystems and solve problems that include mixed variable types and unitsof measurement. Additionally, computer vision will play a big rolein improved characterization for electron microscopy analysis andcould open the door for the implementation of machine learning acrossa wide range of analytical methods and problems in materials chemistry.83 Efforts to develop more user-friendly software tointegrate common machine-learning models and toolboxes can make thesetechniques more accessible to chemists by reducing the time and effortspent on coding.Because of this, at present, thedevelopment of high-throughput,high-data volume experimentation and characterization methods arecritically needed to solve limitations with machine learning for materialsdesign, synthesis, and discovery. Initial efforts include combiningrobotic synthesis and self-driven laboratories guided by machine learningfor autonomous materials discovery and optimization,


Crystalline metals generally exhibit good deformability but low strength and poor irradiation tolerance. Amorphous materials in general display poor deformability but high strength and good irradiation tolerance. Interestingly, refining characteristic size can enhance the flow strength of crystalline metals and the deformability of amorphous materials. Thus, crystalline–amorphous nanostructures can exhibit an enhanced strength and an improved plastic flow stability. In addition, high-density interfaces can trap radiation-induced defects and accommodate free volume fluctuation. In this article, we review crystalline–amorphous nanocomposites with characteristic microstructures including nanolaminates, core–shell microstructures, and crystalline/amorphous-based dual-phase nanocomposites. The focus is put on synthesis of characteristic microstructures, deformation behaviors, and multiscale materials modelling. Strong, ductile, thermally stable, and irradiation tolerant materials are in urgent demand for improving the safety and efficiency of advanced nuclear reactors ,2,3. MatIn contrast, amorphous materials generally exhibit superior resistance to wear and corrosion, and unique mechanical and chemical properties due to the absence of GBs and crystalline structures ,27,28,292O3 nanolayers with individual layer thicknesses of 10 and 50 nm ..63]. TEMreported . Xu et areported  discoverreported .For metal matrix dual-phase nanocomposites, amorphous nanoparticles were proved to play an important role in enhancing both strength and plasticity of NC metals. Firstly, the amorphous nanoparticles act as strengthening second phase to impede dislocation motion, pin GBs and prevent grain coarsening, contributing to high strength. Secondly, the nanosized amorphous phase can co-deform with NC metals by STZs or viscoplastic flow to accommodate the shape change in NC metals, achieving superb plastic flow stability. Such a deformation mechanism was demonstrated in 600 °C annealed Ni(75 at.%)-SiOC nanocomposite, which exhibited a characteristic microstructure of 9 nm amorphous SiOC nanoparticles embedded in NC Ni matrix [Due to the lack of strain hardening ability in amorphous phase, amorphous phase does not exhibit any ductility. Consequently, cracking often occurs in amorphous phase or amorphous–crystal interfaces when amorphous phase or the interfaces are subject to an effective tension loading . As demoMechanistic models incorporating deformation mechanisms related to microstructure characters are urgently demanded in order to accelerate the design and discovery of nanostructured composites with specific properties. Given the structural characteristics of dual-phase nanostructures, which include a significant volume fraction of boundaries and nanosized crystalline–amorphous constituents, a multiscale perspective of mechanical behavior is crucial, including but not limited to atomic-scale studies using atomistic simulations and the development of models at different length scales.Atomistic models offer valuable insight into the deformation mechanisms of defect–interface interactions, including GB sliding/shear/migration , slip tr2Cu eutectic alloy. The simulated stress–strain responses indicated that the buckling strength and critical strain decreased with increasing layer thickness.Microscale models with the presumed deformation mechanisms and corresponding physical parameters and functions are able to capture the influence of microstructure characteristics on mechanical responses of NCs ,140,141.In contrast to the deformation of crystalline phases, amorphous materials can be modelled using isotropic shear transformation. To capture the diverse deformation behaviors of amorphous metals, a mesoscale STZ dynamics modelling framework has been developed ,41. Thisf to describe the change in the state at a material point with I as the input from the macroscale model and O as the output from the model. The basic idea of using machine learning models to accelerate multiscale modeling is to replace the micromechanics model with a surrogate model constructed from data. The surrogate model can be used to approximate the mechanical behavior of nanocomposites, which is based on the data computed from a series of microscale analyses with various microstructural features. Previous work has demonstrated the feasibility of using neural networks to model constitutive materials, such as hyper-elasticity [The application of microscale models to predict macroscale behaviors is crucial for the use of large-scale nanostructured materials. While analytical micromechanics methods with reduced orders can provide high efficiency in bridging the gap and homogenizing the heterogeneous material behaviors, they generally lose accuracy or require extensive model calibration when irregular complex morphologies, nonlinear history-dependent properties, or large deformation are present. Consequently, the advancement of machine learning models, such as artificial neural networks and deep neural networks, has emerged as a promising alternative to train surrogate models. As illustrated in asticity , viscoelasticity , elasto-asticity ,152, andasticity ,154. TheThe mechanical behaviors and properties of the crystalline–amorphous dual-phase nanostructures could be optimized by tailoring the morphology, characteristic dimension, and misorientation of nanosized constituents that determine deformation accommodation mechanisms in adjacent constituents and the characteristic size, structure and properties of amorphous phase boundaries that determine the deformation mechanisms within/at/across boundaries. Accelerating the design and discovery of such nanostructured composites with desired properties is thus urgently demanding mechanistic models that can capture microstructure-dominated deformation mechanics and predict the mechanical behaviors and properties. Multiscale material modelling has been extensively employed to understand the mechanical behaviors of nanostructured materials. Atomistic modelling and microscopies at the atomic level can elucidate deformation processes occurring within/at/across boundaries but cannot predict macroscopic properties. Micro-scale mechanistic models have been developed for some characteristic nanostructures (such as nanolaminates) to predict the microstructure-mechanical property relations, but a general approach for the development of microscale models is still missing. More importantly, atomic-level studies and micro-scale mechanistic models cannot directly predict the mechanical response of NCs. This calls for a breakthrough in materials modelling to bridge the length-scale gap from atomic-scale to macro-scale. Machine learning techniques can offer the potential to uncover underlying physics quantities and functions associated with deformation events across different length-scale models and accelerate the development of these constitutive laws.


Extracellular Genomic Materials (EGMs) are the nucleic acids secreted or released from all types of cells by endogenous or exogenous stimuli through varying mechanisms into the extracellular region and inevitably to all biological fluids. EGMs could be found as free, protein-bound, and/ or with vesicles. EGMs can potentially have immunophenotypic and/or genotypic characteristics of a cell of origin, travel to distant organs, and interact with the new microenvironment. To achieve all, EGMs might bi-directionally transit through varying membranes, including the blood–brain barrier. Such ability provides the transfer of any information related to the pathophysiological changes in psychiatric disorders in the brain to the other distant organ systems or vice versa. In this article, many aspects of EGMs have been elegantly reviewed, including their potential in diagnosis as biomarkers, application in treatment modalities, and functional effects in the pathophysiology of psychiatric disorders. The psychiatric disorders were studied under subgroups of Schizophrenia spectrum disorders, bipolar disorder, depressive disorders, and an autism spectrum disorders. EGMs provide a robust and promising tool in clinics for prognosis and diagnosis. The successful application of EGMs into treatment modalities might further provide encouraging outcomes for researchers and clinicians in psychiatric disorders. Psychiatric disorders are a group of complex diseases affecting mood, thinking, perception and behavior. They rank among the most disabling disorders with prevalence estimates and disability weights comparatively higher than many other diseases . World HDespite their massive burden on individuals, families, and society, there is still no curative treatments for these disorders. Treatment options are a few and limited with the most studies barely indicating moderate response rates. The lack of definitive information regarding the cause and pathophysiology, coupled with the limited availability of biological-based guidance for clinicians, poses a significant challenge to the development of more effective treatment modalities. Uncovering pathophysiological fingerprints of psychiatric disorders is lagging behind due to the involvement of complex multifactorial mechanisms, heterogeneous presentation of disorders and difficulties in classification of patients. Therefore, further research is essential for ushering to shed light on the underlying mechanisms and to explore and develop molecular characteristics of psychiatric disorders with the aim of providing more accurate diagnosis and achieving early and personalized treatment.Extracellular Genomic Materials (EGMs) are the general nomenclature of all extracellular molecules of nucleic acids found in a variety of biological fluids, including blood, cerebrospinal fluid (CSF), amniotic fluid, lymphatic circulation, saliva, urine, milk, etc. , 6. The This narrative review intends to evaluate and spotlight the potential roles of EGMs' in psychiatric disorders. In this regard, we first described the concept of EGMs and then reviewed the relevant literature for psychiatric disorders. Finally, we discussed the potential applications of EGMs in the clinical aspects of psychiatry.Nucleic acids including DNA, RNA, and related molecules, are released by all cell types into the extracellular environment during both physiological and pathological processes and subsequently pass through the borders . CentralThe nucleic acids in the extracellular environment differ by their genetic materials, nucleic acid structures, the complexes they form with other molecules, and the assemblies in which they are involved. In this context, EGMs can be divided into three main groups including free, vesicular, or protein-bound Fig. . Free EGThe release mechanism of EGMs, whether active or passive, depends on the type of EGMs being delivered. For instance, cfDNA tends to be passively released, whereas EVs exhibit a preference for active release , 19. TheEGMs exhibit characteristics that are reflective of the cells from which they originate and provide profound information about the specific cell or tissue of origin , 30, 31.EGMs possess the capability to cross blood–brain barrier in both directions.EGMs carry specific immune/genetics/epigenetics signatures that reflect the cells/tissues from which they have originated.EGMs play an important role in intercellular communication within the CNS and between the CNS and other systems.EGMs can be easily isolated from the peripheral sources in a relatively noninvasive manner.In light of these knowledge, there are four common key features of EGMs which make them indispensable for psychiatric research:Investigating the quantity and composition of EGMs holds significant potential in various applications. The concentration of EGMs in circulation is dynamic and can be influenced by a range of physiological and pathological processes. Factors such as exercise, stress, pregnancy, inflammation, sepsis, cancerogenesis, and transplantation and CNS-related disorders can increase EGM levels –41. ConvCfDNAs depending on its origin might include genomic (cfDNA), mitochondrial (cf-mtDNA) and ribosomal (cf-rDNA) DNA all of which are detectable nucleic acid fragments that are either released from CNS cells through varying essential physiological processes or apoptosis or necrosis of the cells. Studies indicated that up to 80% of CNS cells induce apoptosis which has been proven to be involved in the pathophysiological processes of various psychiatric disorders . CfDNA iIn this section, we summarized cfDNA, cf-rDNA, and cf-mtDNA studies in psychiatric disorders Table .Table 1AThe first study investigating cfDNA levels in schizophrenia spectrum disorders (SSD) was published in 2017. The study included 83 male patients with acute psychotic disorders  and 30 male healthy control subjects. It explored the role of oxidative DNA damage and dysregulation of apoptosis in the disease pathophysiology by evaluating the concentration of plasma cfDNA and the levels of 8-oxodG in cfDNA and lymphocytes. They found no difference in the cfDNA index between study groups. However, patients had elevated 8-oxodG levels in both cfDNA and lymphocytes. Moreover, the cfDNA/ FL1-8-oxodG ratio, an indicator of apoptosis in damaged cells, was elevated in one-third of patients with schizophrenia . ErshovacfDNA studies in bipolar disorder mainly consist of studies focusing on circulating cf-mtDNA. The only study investigating cf-nuclear DNA levels in bipolar disorder showed that cf-nuclear DNA levels were higher in the patient group compared to the controls. However, there was no difference between groups regarding cf-mtDNA , 58. AnoThere are a few studies investigating cf-mtDNA levels in major depressive disorder. Lindqvist et al. found that patients with major depressive disorder had higher cf-mtDNA levels in comparison to controls, and there was a positive correlation between cf-mtDNA and antioxidant glutathione peroxidase levels in the whole sample. Among a subgroup of patients who underwent an eight-week treatment with selective serotonin reuptake inhibitors, contrasting changes in cf-mtDNA levels were observed between treatment non-responders and responders. Non-responders exhibited an increase in cf-mtDNA levels, while cf-mtDNA levels in the responders remained unchanged . In a seOnly one study investigates cfDNA concentrations in Autism spectrum disorder. Shmarina et al. showed that patients with Autism spectrum disorder had elevated plasma cfDNA levels compared to controls and contained a high amount of oxidative modification. It was shown that the expression of the NFкB1, nuclear factor gene and levels of proinflammatory cytokines were also higher in the peripheral lymphocytes of the patients, and it was hypothesized that oxidized cfDNA fragments lead to the activation of inflammatory processes . AnotherIn a recent study, Lindquvist et al. showed a decreased cf-mtDNA level in patients with social anxiety disorder in comparison to healthy controls. Cf-mtDNA level did not change in the patient group after CBT, and was not associated with the severity of symptoms .MicroRNAs (miRNA) are small noncoding RNAs that consist of 19–23 nucleotides. They exert functional effects on cells by negatively regulating gene expression and/or potentially interacting with cell receptors as ligands. They can inactivate a target mRNA by binding its 3′- UTR side and diminish their post-transcriptional output. Approximately 70% of known miRNAs are expressed in the CNS, contributing to various biological processes including neurogenesis, neuroplasticity, neuronal differentiation, function and maintenance, and overall brain development. The ability of miRNAs to regulate gene function, coupled with their abundant expression levels in CNS suggest their potential role as epigenetic modifiers in the pathophysiology of psychiatric disorders . As cimiMounting evidence suggests that miRNAs have the potential to serve as biomarkers for early and precise diagnosis of schizophrenia . There aThere are several studies in the literature investigating cimiRNAs as a biomarker for bipolar disorders Table . VariousBDNF gene, which plays a crucial role in the pathophysiology of disorder, was decreased in patients with major depressive disorder, while the levels of miR-182-5p and miR-132-3p were increased [Studies conducted on cimiRNAs have revealed that miR-124-3p, miR-132-3p, miR-182-5p, miR 345-5p, and miR-425-3p are frequently upregulated and miR-636 is downregulated in patients with major depressive disorder  , and four circRNAs were confirmed to show positive results. Bioinformatics analysis of the identified circRNAs revealed their potential involvement in pathways relevant to pathophysiology of diseases, including metabolic processes, stress response, and histone ubiquitination [Tan et al. explored alterations in plasma exosome circular RNA (circRNAs) expressions in patients with schizophrenia. 44 circRNAs exhibited differential expression pattern in patients compared to controls with 38 upregulated and six downregulated circRNAs. Further validation was performed on eight circRNAs with high fold change using qRT-PCR in a separate sample  and showed that miR-142-3p, -484 and -652-3p were consistently upregulated while miR-185-5p was downregulated in patients, irrespectively of the disease state. Bioinformatics analysis highlighted some pathways for those dysregulated miRNAs, such as PI3K/Akt signaling pathway, fatty acid metabolism, extracellular matrix, and adhesion pathways [Ceylan et al. examined circulating exosome miRNA expressions in bipolar disorder  and methamphetamine-dependent patients (MDPs). They found no difference in exosome size distribution or numbers between controls and patient with substance use disorders. However, 34 and 19 miRNAs were identified as differentially expressed in MDPs and HDPS compared to HCs. There was no significant difference in miRNA expression levels of HDPs and MDPs . Lee et Ranganathan et al. examined the plasma exosome levels of various proteins that are more specific to CNS cells and are known to play a role in neurodevelopment in a sample of 24 patients with schizophrenias and 12 controls and showed that GFAP concentrations were higher and a-II-Spectrin levels were lower in the patient group. No difference was found between the groups in synaptophysin levels. There was no difference in the mean exosome concentrations or sizes of the groups . In anotDu et al. conducted a study analyzing metabolites in serum exosomes of patients with bipolar disorder. They findings revealed that 15 specific metabolites  demonstrated good to excellent performance in differantiating bipolar disorders from both healthy controls and other major mental disorders. Bioinformatics analysis revealed that these metabolites are related to sugar metabolism, which suggests that dysfunction in sugar metabolism may potentially contribute to the onset and/or development of bipolar disorder . In an iIn a study with patients with major depressive disorder it was found that BDNF levels were low and proBDNF levels were high in the serum and exosomes of patients compared to controls, and BDNF levels increased, and proBDNF levels decreased with treatment . Nasca eThe etiology of psychiatric disorders has not been fully elucidated, and most of these disorders still lack curative treatments. Current treatment modalities only offer a certain level of improvement in response, highlighting the pressing need for studies on the neurobiological mechanisms of psychiatric disorders. Moreover, the diverse clinical presentations of the same diseases, substantial variability in treatment response among patients, and the potential involvement of distinct etiological biological processes even within patients sharing the same diagnosis highlight the importance of personalized and individualized treatment options and the search for specific biomarkers. The emergence of personalized medicine and the integration of various molecular techniques such as genomics, transcriptomics, and epigenomics into clinical practice have revolutionized the diagnosis, prognosis and therapeutic monitoring of diseases. EGMs have garnered significant attention in medicine in recent years, as they hold the potential to serve as biomarkers for diagnosis or companion diagnostics and be utilized as tools in treatment approaches. This paper aimed to provide a comprehensive review of EGM studies, which represent a relatively novel area of interest in psychiatric research.One of the primary advantages of integrating EGMs into clinical practice is their application from peripheral blood to advance the diagnosis, improve prognosis, and monitor disease progression, instead of requiring more invasive procedures such as tissue biopsy or lumber punction. Despite the recent advancements in studying the potential role of EGMs as biomarkers for various diseases, the translation of these scientific findings into clinical practice has been limited, with notably progress primarily seen in the field of oncology. However, considering the findings of the aforementioned studies and the fact that EGMs can easily cross the BBB in various forms, including cfDNA, cf-mtDNA, noncoding RNAs, and EVs, EGMs hold considerable promise as valuable candidates for the psychiatric research.Investigation of EGMs according to disease stages (such as acute exacerbation-remission or different stages of bipolar), disease subgroups, and phenotypic characteristics of patients holds the potential to uncover the related biological mechanisms and target more specific relevant pathways for treatment. Studies exploring the changes in expression levels of free EGMs, such as cimiRNAs and lncRNAs, as well as variations in metabolite and protein cargoes of EVs throughout different stages of the disease and in association with clinical phenotypes, would provide valuable insight into underlying mechanisms and pathways. Moreover, considering the overlapping symptomatology observed in different psychiatric diseases and the common genetic, neuroanatomical, and neurobiological findings, cross-disorders studies can also provide important information about at what points these diseases overlap and differ from each other, both biological and epigenetic wise. Furthermore, prospective studies with high-risk clinical individuals and prodromal patients may shed light on the biological processes associated with disease progression and transformation. Additionally, EGM studies have the potential to contribute valuable information on the neurobiological and epigenetic mechanisms underlying some environmental risk factors, such as stress, trauma and drug abuse, that contribute to the development of the diseases.Another potential application of EGMs is to investigate the mechanisms of action of various treatment methods, such as lithium and electroconvulsive therapy (ECT), which are highly effective and widely used in treatment but still lack a complete understanding of underlying mechanisms. In this regard, EVs studies are very promising in particular. Analyzing changes in cargo contents following these treatments could help bridge the gap between neurobiology and clinical outcomes. For example, it has been hypothesized that ECT may act by inducing CNS cells to secrete EVs containing harmful and neurotoxic proteins/metabolites .The research on CNS including post-mortem or preclinical studies which allow in situ analysis of the organ system has certain limitations, such as ethical concerns and criticism regarding to the extent to which these studies accurately reflect real diseases in vivo. However, the concept of analyzing the peripheral markers that are expected to reflect disease status in the CNS remains a subject of debate. Nevertheless, the potential of EGMs may overcome such obstacles. As previously mentioned, EGMs released from CNS cells can be detected peripherally, and their cell of origin can be distinguished using specific markers. These characteristics make them valuable tool for investigating the pathological processes occurring in the CNS in real time. Previous studies have investigated the involvement of EVs in transcription regulation, neurogenesis, synaptic plasticity, and neuroinflammation, all of which have been associated with psychiatric diseases . TherefoPsychiatric disorders such as schizophrenia and bipolar disorder have some peripheral effects and are associated with comorbidities such as metabolic syndrome . Given tEGMs, particularly EVs, hold great promise for therapeutic advances in psychiatric disorders. EVs offer several advantages, including low immunogenicity and toxicity, biodegradability, the capacity to cross BBB while protecting their internal materials and the ability of being navigated to target cells and receptors. EV mediated drug delivery presents an exciting opportunity to develop more effective target-specific treatment approach while minimizing the systemic side effects . StudiesOverall, the use of EGMs presents exciting opportunities for advancing therapeutic interventions in psychiatric disorders, ranging from targeted drug delivery to gene therapy and immune modulation. Continued research in this field holds promise for developing innovative and personalized treatment strategies that address the complex pathophysiology of psychiatric disorders. Furthermore, it is widely acknowledged that relying on a single biomarker may not suffice to attain the desired level of specificity and sensitivity necessary for clinical application. Hence, although EGMs hold great promise for unraveling the mechanisms underlying psychiatric disorders, the translation of ongoing endeavors to identify molecular biomarkers for disease diagnosis and management necessitates further collaborative efforts from the research and medical communities. We hope that this review will provide a comprehensive foundation for future investigations and will serve as a guiding resource for researchers in this field.PubMed was searched for all published articles through March 31st 2023, to include all articles examining any type of EGMs in patients with psychiatric disorders in comparison to healthy controls. The search was limited to English-written articles. The following search strategy were used: psychiatry* OR psychosis OR psychotic OR schizophrenia OR depression OR bipolar OR mania OR manic OR autism OR autistic OR Asperger OR anxiety OR ocd OR “obsessive compulsive” OR panic AND exosome OR microparticle OR virtosome OR nucleosome OR polynucleosome OR “cell-free DNA" OR cfDNA OR "cell-free DNA" OR "circulating DNA"OR "mitochondrial DNA" OR mtDNA OR "circular DNA"OR "cell-free RNA" OR "cell-free RNA" OR OR "extracellular trap*" OR "neutrophil-extracellular" OR “extracellular RNA" OR siRNA OR "circular RNA" OR miRNA OR microvesicles*After merging and curating our results, we found 78 studies that met inclusion criteria: 22 for schizophrenia spectrum disorder, 10 for bipolar disorder, 22 for depressive disorders, 15 for autism spectrum disorder and 9 for other psychiatric disorders. Review articles were manually searched for any additional relevant studies.


Iodine supplementation during pregnancy in areas with mild-moderate deficiency is still a matter of debate. The present study aimed at systematically reviewing currently available evidences provided by meta-analyses with the aim to further clarify controversial aspects regarding the need of iodine supplementation in pregnancy as well as to provide guidance on clinical decision-making, even in areas with mild-moderate deficiency. Medline, Embase and Cochrane search from 1969 to 2022 were performed. For the purpose of this review, only studies containing meta-analytic data were selected. A total of 7 meta-analyses were retrieved. Four meta-analyses evaluated the relationship between iodine status during pregnancy and neonatal and maternal outcomes suggesting the existence of a U-shaped correlation between iodine status and several maternal and neonatal consequences, especially if iodine status is evaluated at the beginning of pregnancy. Three meta-analyses evaluating the results of intervention trials failed to provide straightforward conclusions on the benefits of iodine supplementation in pregnant women in areas with mild-moderate iodine deficiency. Although evidence coming from meta-analyses suggests a role of iodine status during pregnancy in determining maternal and child outcomes, results of meta-analyses of intervention trials are still controversial. Several factors including, degree of iodine deficiency, and pooling studies conducted in areas with different iodine intake, may account for the lack of benefits reported by meta-analyses of intervention trials. More high-quality, randomized, controlled trials including information on timing, dose and regimen of iodine supplementation are needed to further elucidate this issue. Iodine is an essential trace element required for the synthesis of thyroid hormones , 2. DuriEven in countries where effective salt iodization programs are established, several studies indicate that pregnant and lactating women may be at high risk of having inadequate iodine levels , 8. SincMoreover, recent evidence suggests that excessive iodine intake could be detrimental both for maternal thyroid status or for pregnancy outcomes , 20–22.Due to the relevant number of retrospective cohort studies and clinical trials on the effects of iodine supplementation in pregnancy, several meta-analyses attempted to clarify the current state of art, but questions remain unanswered. The present study collected and systematically reviewed currently available data from meta-analyses with the aim to clarify controversial aspects regarding the multifaceted role of iodine in pregnancy as well as to provide guidance on clinical decision-making.A comprehensive narrative review was performed. We searched for relevant literature using Medline, Embase and Cochrane search and including the following terms: (“iodine“[MeSH Terms] OR (“iodine“[All Fields]) AND (“pregnancy” [MeSH Terms] OR “pregnancy”[All fields]) AND . Publications from 1969 up to 2022 were included. For the purpose of this review, only studies containing meta-analytic data were selected. Nine meta-analyses meeting the required selection criteria were found –31. One In the last eight years, four different meta-analyses have evaluated relationships between iodine status during pregnancy and neonatal and maternal outcomes –26 and wFirst, the four different studies used different thresholds and measurement methods for urinary iodine concentration (UIC) in pregnant women. To define iodine deficiency, the 2016 and the 2020 meta-analyses both by Nazeri et al. , 24 evalA second important difference between the four studies stems from different outcomes evaluated by each meta-analysis. The 2016 meta-analysis by Nazeri et al., evaluated the associations between pregnancy and post-partum UIC values and several neonatal outcomes, including neonatal TSH as assessed by heel punch and cord blood samples. The 2019 meta-analysis by Levie et al. evaluated offspring neurodevelopment assessed by verbal and non-verbal Intelligence Quotient (IQ) testing at a median 1.5 to 8.6 years of age. The 2020 meta-analysis by Nazeri et al. considered growth parameters at birth, including birth weight, length, and head circumference. Lastly, the study by Wan et al. considered maternal TSH and anti-thyroperoxidase (TPO) and anti-thyroglobulin (Tg) antibody positivity as their main outcome.These differences clearly impact the conclusions reached by the meta-analyses. The first meta-analysis by Nazeri et al. demonstrated that while there was no correlation between maternal iodine status and neonatal TSH evaluated in heel blood samples, cord blood TSH was significantly higher in newborns of iodine-deficient mothers when compared with children of mothers with adequate iodine levels. The authors concluded that cord blood TSH was a more sensitive parameter for evaluating maternal iodine status when compared with heel blood TSH. The meta-analysis by Levie et al. showed a non-linear correlation between the maternal UI/creatinine ratio and mean verbal IQ of offspring, while no correlation was observed with non-verbal IQ. Stratification of data according to the timing of measurement of UI/creatinine ratio revealed that only values assessed before the 14th gestational week were related to verbal IQ, with no associations when samples were obtained later in pregnancy. These data highlight that the events of the first weeks of pregnancy are essential in determining fetal brain development. These results are particularly relevant since the model employed was adjusted for multiple variables that are known to influence neurodevelopment, including gestational age, child sex, maternal ethnicity/country of birth, maternal education, parity, maternal age, pre-pregnancy body mass index, and smoking during pregnancy. The 2020 meta-analysis by Nazeri et al. failed to show any association between UIC and growth parameters at birth . The analysis by Wan et al. showed that excessive iodine levels in pregnant women were associated with a significant increase in the rate of anti-Tg and anti-TPO antibody positivity. Moreover, TSH levels tended to be higher in women with UIC > 500 µg/L as compared to women with lower UIC levels.The abundance of observational data regarding the possible detrimental effects of maternal iodine deficiency on neurological development prompted several investigators to design prospective randomized trials to assess the benefits of iodine supplementation during pregnancy. Owing major discrepancies in the results provided by these trials, more recently, several meta-analyses have tried to draw more definitive conclusions Table . It shouThe most comprehensive meta-analysis is represented by the 2017 Cochrane systematic review performed by Harding et al. , which wper day starting in the first trimester, and assessed maternal TSH and FT4 values in the second and third trimesters [More recently, two other meta-analyses attempted to summarize findings of randomized clinical trials of iodine supplementation, with similarly inconclusive results. The meta-analysis by Dineva et al.,  aimed toimesters , 33. TheAs for child neurodevelopmental outcomes, the authors were able to perform a meta-analysis on only two RCTs that were comparable in study design, since they both administered iodine as potassium-iodide (KI) tablets in a similar dosage from early pregnancy until delivery, used a placebo as the control treatment, assessed child cognition using the same cognitive tool , and children were tested at similar ages (1.5 or 2 y) , 34. TheFinally, another very recent meta-analysis by Nazeri et al.  aimed toOne hundred years have passed since the initiation of iodine supplementation in the United States and in Switzerland . Over thThe first and most important problem resides in the fact that most studies are currently performed in countries which are only mildly to moderately iodine-deficient. While the damage due to severe iodine deficiency is undoubtable, any neurological consequences of moderate iodine deficiency are likely more subtle and the commonly used outcomes may be insensitive to small differences. The lack of observed effects after iodine supplementation in randomized clinical trials is probably due to the fact that the differences between treated and non-treated subjects are rather small in populations where iodine is widespread . On the other hand, performing placebo-controlled clinical trials in countries where severe iodine deficiency is still a reality (where we could expect to observe more evident results) is not considered ethically acceptable . AnotherAs a second point, the available epidemiological studies and clinical trials are extremely variable in terms of study design. First, no standard definition of iodine deficiency during pregnancy is available, and the lack of a common nomenclature makes the comparison across studies difficult, if not impossible. Indeed, while on a population basis a UIC threshold of 150 mcg/L of is conventionally used, this value is not applicable to the individual due to extremely variable within-day and between- day UIC levels, reflecting shifting hydration status and episodic iodine intake . Even whA further aspect stems from the timing of UIC assessment. Indeed, UIC levels vary considerably during pregnancy, with a transient increase in UIC levels in the first trimester due to increased renal iodine clearance and reduced UIC in late pregnancy due to depletion of intra-thyroidal iodine stores, as well as to the iodine shift from the maternal circulation to the growing fetal-placental unit . The timAlthough some studies try to adjust for variations in glomerular filtration rate using the UIC/creatinine ratio, this is not universally used in epidemiological studies on iodine deficiency . FurtherSubstantial variability is also present in terms of the outcomes evaluated in the various studies. While some studies have evaluated maternal outcomes (such as thyroid function or obstetric complications), others also consider the neonatal ones . In assessing offspring, timing of evaluation has differed widely, since some studies assess outcomes at birth, while other studies include also evaluations later in life. It is worth noting that the meta-analysis by Levie et al. the only one evaluating child outcomes at older ages, demonstrated a non-linear association between maternal urinary iodine and complex child neurological function, i.e. verbal IQ. This finding is particularly relevant since this is the only meta-analysis to date that has adjusted for multiple confounding factors that may impact neurological development.The timing of outcome evaluation plays an even more relevant role in the intervention trials. Indeed, it is highly probable that the lack of any observed effect in existing trials is in part due to the evaluation of neurological effects too early in the life of the studied children. The consequences of moderate iodine deficiency are best assessed later in life (at least 5 years of age), when more advanced neurological skills can be assessed , but manFinally, there is likely a relatively narrow window for optimal iodine status in pregnancy, with excess as well as inadequate intakes having the potential to adversely affect maternal thyroid function and child neurological development. Indeed, it is probable that there is a U-shaped association between maternal iodine status and neurological consequences. This U-shaped association was demonstrated by Levie et al. regarding neurological outcomes of offspring. Also the meta-analysis of Wan et al. although limited to the evaluation of the risk related with excess iodine, indirectly supports the need for avoiding over-supplementation. The non-monotonic nature of the relationship between iodine status and clinical outcomes further increases the difficulty in data interpretation, given the lack of a simple linear threshold for defining iodine deficiency.The reason underlying the detrimental effect of excessive iodine exposure during pregnancy on neurological outcomes could reside in the increase in the incidence of thyroid autoimmunity that is sometimes observed after implementation of iodine-supplementation programs in iodine-deficient countries . NeverthIn conclusion, the four meta-analyses, which have evaluated epidemiological studies on iodine status, suggest that there is a probable correlation between iodine status and several maternal and neonatal outcomes. It is likely that a U-shaped relation exists between UIC and both the risk for maternal thyroid function alterations and children neurological outcomes. Evaluating UIC at the beginning of pregnancy appears to be the most valuable strategy. On the other hand, even if it seems reasonable to state that iodine supplementation during pregnancy might be beneficial, the meta-analyses that took into account the results provided by intervention trials, failed to provide a straightforward conclusion.However, it is important remembering that there is no controversy about the importance of preventing severe iodine deficiency in pregnancy. Although performed before the advent of modern clinical trial strategies, early supplementation campaigns, as well as studies performed in the 1960 and 1970 s conclusively demonstrated that iodine supplementation in severely deficient regions, can prevent cretinism . The neeTo strengthen the evidence supporting iodine supplementation in pregnancy, future trials will need to have the following characteristics i) a randomized-controlled study design ii)collection of precise information on timing, dosing and formulation of iodine supplementation iii) evaluation of the baseline iodine status of the included population, possibly using alternatives to UIC iv) inclusion of information on possible confounders that can impact the offspring neurological outcomes  v) follow-up information on children neurological development that extends beyond the neonatal period.In the absence of definitive data coming from meta-analyses, it should be reminded that the current ATA guidelines recommend to start low-dose iodine supplements, optimally 3 months prior to pregnancy in women living in regions of known mild to moderate iodine deficiency . In ligh