Besides the energy harvesting function, with both the temperature sensing ability and the touch perception, this uf‐TEG is demonstrated as the electrical skin when mounted on a robot. Moreover, due to the wind‐sensitive performance and self‐power ability, the uf‐TEGs are assembled on cloth as wearable health and motion monitoring devices.Flexible thermoelectric generators (f‐TEGs) have demonstrated great potential in wearable self‐powered health monitoring devices. However, the existing wearable f‐TEGs are neither flexible enough to bend and stretch while maintaining the device's integrity with a good TE performance nor directly compatible with clothes materials. Here, ultraflexible fabric‐based thermoelectric generators (uf‐TEGs) are proposed with conductive cloth electrodes and elastic fabric substrate. The patterned elastic fabric substrate fits the rigid cuboids well, together with serpentine structured cloth electrodes, rendering uf‐TEG with excellent integrity and flexibility, thereby achieving a highly functional TE performance when strain reaches 30% or on arbitrarily shaped heat sources. The uf‐TEGs show a large peak power of 64.10  An ultraflexible whole‐fabric assisted thermoelectric generator (uf‐TEG) with thermoelectric cuboids, elastic fabric as assembling material, and conductive flexible cloth‐based electrodes is presented here. The uf‐TEG can be applied in different applications such as electrical skin (temperature sensing and touch perception) and human health and motion monitoring . As heat sources on the human body have nonplanar surfaces, softening the TEGs and achieving flexibility to have close contact with the heat source is critical for harvesting heat. Nowadays, pioneer works on flexible TEGs focus on three types of materials, from 1D to 3D, namely TE fibers or nanowires, TE thin‐film based on ink processing methods, and conventional TE bulk materials. To achieve good flexibility, scientists have developed TE fibers woven or sewed on textiles. For instance, Lund et al. fabricated flexible TEGs with an output power of 1.2 µW (ΔT = 65 K) by sewing poly polystyrene sulfonate (PEDOT: PSS) coated threads and silver‐plated polyamide threads on thick wool fabric. A similar sewing process can also be found in Ding et al., where they used gelation extrusion strategy to fabricate p/n TE fibers and woven them onto the textile. The output power for the whole weaved TE textile is at pW level (ΔT = 5–20 K). Although textile renders good flexibility, the unsatisfied TE converting performance from the fiber‐based materials and the hand sewing process limited the TEGs in large‐scale applications. For 2D thin‐film‐based TEG devices, thin films were usually scratched on substrates such as paper or polyimide (PI) to obtain a certain level of bendability. But the in‐plane assembled TE pairs restrict the direction of the along film surface. To solve this problem, researchers used specific kirigami structures to fold the plane substrate and help the former in‐plane device perpendicular to the surface of the heat source. However, the performance is still not ideal, and the device's stretchability was severely affected after folding. Furthermore, the thermoelectric effect of both fiber and 2D films is weak compared with bulk materials. Therefore, researchers are always looking at solutions on flexible devices using 3D bulk TE materials. TE cuboids, which are made of Bi‐Te and Sb‐Te based materials are an option due to their high performance at room temperature. The high figure of merit  of these inorganic compounds guarantee the excellent energy converting performance of the thermoelectrical device. To endow the TEGs of rigid cuboids with a certain stretchability level, Fukuie et al. chose an origami‐like PI substrate and assembled the TE cubes on it, thereby achieving 20% stretching deformation. Lee et al. proposed another method to soften the TEGs by using silver nanowires‐based stretchable interconnects to connect paralleled cuboids to substitute traditional rigid copper electrodes, while the intermediate regions between cuboids were infiltrated with polydimethylsiloxane (PDMS) to hold the cuboids. However, there exist detaching issues between soft PDMS and the rigid TE cuboids in a large bending or stretching situation. Besides, the PDMS material itself is incompatible with fabric when considering wearable TEGs.In the past decade, technological advancement has sped up the development of wearable electronic devices for health and motion monitoring. To substitute the large battery for these wearable electronic devices and acquire a sustainable energy source, the idea of generating power from the human body itself has bred various devices, such as thermoelectric generators (TEGs) and triboelectric nanogenerators (TENGs).2, 3Herein, we present a ultraflexible fabric‐assisted TEGs (uf‐TEGs) combining Bi‐Te and Sb‐Te based TE cuboids, elastic fabric as packaging material, and conductive flexible cloth‐based electrodes to address the above challenges. Cuboids with high TE conversion performance were assembled with elastic fabric for holding cuboids in position to acquire excellent device function. Conductive polyester fiber‐based electrodes were laser‐cut as the serpentine structure to reduce stiffness of electrodes and better fit the fabric substrate under large deformation. Using a scalable pick‐and‐place semi‐automated process, uf‐TEGs were fabricated with different sizes and applicable to heat sources in different areas. The flexibility and stretchability of uf‐TEGs that are achieved from the all‐fabric design conform to the morphological characteristics of human skin, enabling it as a wearable energy harvester. Furthermore, together with functions of temperature sensing and touch perceptions, the uf‐TEGs demonstrate a perfect candidate for electrical skin (e‐skin). Other applications for wearable health and motion monitoring are also included here to show its practicability. However, the PI substrate is not soft and flexible enough (show little bendability in one direction) to cover heat sources with complex surface structures, and it cannot be assembled with clothes and stretched. Others used silicone materials (PDMS or Exoflex) to assemble or package the TEGs, but the soft silicone materials will easily detach from the rigid cuboids after several stretching or bending circles and affect the device's integrity. Besides, the PDMS packaged devices are impossible to combine with clothes. From the perspective of conformal contact and better compatibility with clothes, the uf‐TEG demonstrates its advancement in 1) being able to be directly fabricated on clothes with washability, 2) advanced flexibility and deformability, and 3) showing no detach issue happened in the connected region of elastic fabric and cuboids when compared with the previous research using rigid TE cuboids.In previous research, the flexible printed circuit board with PI substrate is one option for connecting TE cuboids.15 How20.5Sb1.5Te3/n‐type: Bi2Te2.7Se0.3), elastic fabric substrate, and conductive cloth tape electrodes. Figure The uf‐TEGs consist of rigid TE cuboids  and Seebeck coefficient (s) of the Bi0.5Sb1.5Te3 and Bi2Te2.7Se0.3 cuboids versus temperature from 304 to 453 K, respectively. Based on σ and s, the power factor (PF) of two TE materials can be calculated as PF=s2ρ=s2·σ, which is also provided in Figure μW m−1 K−2 at 309.36 K for Bi0.5Sb1.5Te3 and 298.01 μW m−1 K−2 at 310.05 K for Bi2Te2.7Se0.3. High PF of materials ensures high ZT values , guaranteeing a good performance for the uf‐TEGs.The scanning electron microscope (SEM) image of −1Figure . Subsequs Figure . Figure Figure T) was collected via a data acquisition card   of uf‐TEG (48 pairs) and the output voltage (VL) with the load resistance (RL) varying from 10 to 70 Ω were collected in Figure VOC reached a value of 111.49 mV under a temperature difference (ΔT) of 33.24 K, which was superior within the reported flexible TEG devices  was placed on the hot plate for the heating process and then using an electric fan to speed up the cooling process, and Figure VOC variation of 4.09% when ΔT = 33.24 K. Furthermore, the water resistance of the uf‐TEG (16 pairs) was also tested by immersing the uf‐TEG into the water for 30 min followed by a drying process inside an oven for 12 h at 70 °C . Compared with silicone material, our elastic cloth with laser‐cut holes demonstrated good compatibility with a firm holding of the TE elements and no evident gap under a strain of 30.0%, showing its superior stable cuboid holding capability over the silicone materials connected device.The uf‐TEGs demonstrated high deformability owing to the elastic fabric substrate and highly stretchable conductive‐cloth‐based electrodes. As demonstrated on the right of Figure 38 the Finite element analysis (FEA) simulation was used to simulate the electrode deformation under the strain of 12.5%, 25.0%, 37.5%, and 50.0%, respectively. As shown in Figure x and y directions, the resistance variations were relatively small as they increased by 5.55% and 2.82%, respectively, at their largest bending degree (bending radius of 2.5 cm)  that variated from 0 to 60 °C with 5 °C intervals in between. The output voltage (Voutput) suddenly increased (25 °C ≤  Twater ≤  60 °C) or decreased (0 °C ≤  Twater  ≤  20 °C) when the uf‐TEG touched the bottle  ≈ 0.99649). Apart from sensing the temperature, the uf‐TEG could also be used for touch perception. As is shown in Figure Human skin, densely covered with the sensory nerve fibers, serves as the sensing function for outside heat, touch, etc. Here, thermoelectric ability endows the uf‐TEG with the functions of temperature sensing and touch perception ability. Together with the device's good flexibility and stretchability that mimic skin morphology, there is great potential for uf‐TEG to be applied as e‐skin in the field of robotic engineering. As shown in e Figure . After tFigure −1. Other applications for human motion monitoring are demonstrated in Figure Instead of being used as e‐skin, uf‐TEGs can also be incorporated into everyday clothing such as cloth, gloves, and wristbands and used for health or motion monitoring. As the output voltage increased with the air velocity on the cold side of uf‐TEG Ultra flexibility and deformability to suit heat sources with complex geometry.2)Compatibility with our everyday clothes.3)Enhanced cuboids connection with no detaching issue by using the elastic fabric substrate.Traditional TEGs also employ rigid TE cuboids, compared with them, our uf‐TEG demonstrates its advancements inIn this paper, uf‐TEGs are demonstrated with practical sensing applications, including electrical skin and wearable health and motion monitoring devices. When used as an e‐skin, both the biological function (temperature sensing and touch perception) and its morphology conform to human skin. Besides, the uf‐TEG can also be applied as wearable health and motion monitoring device, since it can detect, amongst other things, the nose respiration rate, finger bending, and steps counting. However, the energy harvested using uf‐TEGs is not enough to fulfill a fully self‐powered sensing system at the current stage. This is hindered by the devices' internal resistance, which causes energy waste on internal power consumption. Therefore, future efforts will be made to improve the conductivity of the cloth electrode.To conclude, the fabricated uf‐TEGs provide an excellent way to harvest heat from randomly shaped heat sources and reuse the waste heat energy in applications with practical functions, providing inspiration for the future development of fully thermoelectrically self‐powered fabric‐based devices.7−1. The energy and cut speed ensure that the process cuts off the upper polyester conductive tape without totally cutting off the based paper substrate.The serpentine structured electrode arrays were first designed with a line width of 1.30 mm and the pad side length of 5.00 mm in AutoCAD. The polyester conductive tape  was purchased from Shenzhen Shunzhi Technology. A laser‐cut machine cut the polyester conductive tape under the power of 7.6 W (± 0.1 W) and a speed of 10 mm s0.5Sb1.5Te3 (p‐type) and Bi2Te2.7Se0.3 (n‐type) were purchased from Hubei Sagreon New Energy Technology Company, Ltd and the size of the cuboids was custom‐made with the size of 5 mm  ×  5 mm  ×  3 mm. To conduct the pick‐and‐place process, transparent PET thermal‐released film was purchased from Crown Electronics with a releasing temperature of 120 °C.  with a melting point of 138 °C was purchased from Senju Metal Industry Co., Ltd. During the soldering process, the temperature was set as 180 °C and the soldering time was around 5 min.TE cuboids of BiOrdinary elastic thread fabric of pure cotton was purchased online and the elastic fabric was not strictly limited to this specific type. The elastic fabric was laser cut to form a pattern of circular hole arrays  from Beijing Cryoall Science and Technology Co., Ltd. XRD patterns were collected by an X‐ray diffractometer (Rigaku SmartLab) and the infrared image was captured by an infrared camera (Ti489 PRO FLUKE).SEM was used to characterize the surface morphology of the polyester conductive tape and TE cuboids. In the flexibility test, bending molds with different bending radius  were prepared from the laser‐cut acrylic workpieces. Acrylics tubes with different bending radius  were prepared for bending tests of uf‐TEG. Twisting tests were conducted using a pair of rotatable clamps. All resistances were measured with a VICTOR LCR 4091A digital bridge. The sheet resistance of the electrode was tested from the four dimensions 280C Four‐Point‐probe mapping system. To test the output performance of uf‐TEGs, a hot plate from Thermo SCIENTIFIC was used to increase the temperature, and the voltage output and the temperature variation were both collected simultaneously from a data acquisition card of NI. The resistivity and Seeback coefficient of BiThe authors declare no conflict of interest.Supporting InformationClick here for additional data file.


RNA viruses encode essential information in their genomes as conserved structural elements that are involved in efficient viral protein synthesis, replication, and encapsidation. These elements can also establish complex networks of RNA-RNA interactions, the so-called RNA interactome, to shape the viral genome and control different events during intracellular infection. In recent years, targeting these conserved structural elements has become a promising strategy for the development of new antiviral tools due to their sequence and structural conservation. In this context, RNA-based specific therapeutic strategies, such as the use of siRNAs have been extensively pursued to target the genome of different viruses. Importantly, siRNA-mediated targeting is not a straightforward approach and its efficiency is highly dependent on the structure of the target region. Therefore, the knowledge of the viral structure is critical for the identification of potentially good target sites. Here, we describe detailed protocols used in our laboratory for the in vitro study of the structure of viral RNA genomes. These protocols include DMS (dimethylsulfate) probing, SHAPE  analysis, and HMX (2′-hydroxyl molecular interference). These methodologies involve the use of high-throughput analysis techniques that provide extensive information about the 3D folding of the RNA under study and the structural tuning derived from the interactome activity. They are therefore a good tool for the development of new RNA-based antiviral compounds. Viral RNA genomes are dynamic entities. During the infection, they must be translated, copied, and packaged to produce a new generation of virions. Maintaining a proper balance between all these processes involves precise regulatory mechanisms that require the interaction of different genome-encoded elements, giving rise to the so-called interactome ,2. TheseDuring viral replication, a wide spectrum of mutants is generated due to the high error rates of the viral RNA-dependent RNA polymerases. This phenomenon hinders the development of effective antiviral drugs with sustained virological responses. The fact that RNA folding and the RNA interactome in viral genomes are well conserved makes it tempting to propose them as potential targets for the development of new antiviral treatments .RNA-based therapies are promising antiviral strategies. Particularly, the siRNA technology, which has been extensively tested against a wide variety of viruses (for a review see ). These This article describes in detail protocols focused on the application of high-throughput probing methods followed in our laboratory to decipher RNA-RNA interactions in viral genomes.RNA probing focuses on the determination of certain structural features of the target RNA molecule, such as Watson-Crick base pairing, sugar-phosphate backbone flexibility, or solvent accessibility. These properties can be studied independently by using different reagents. Thus, the greater the number of features analyzed, the greater the accuracy of the final structural model ,12. The Most chemical probing reagents show easy access to the reactive groups of RNA due to their small size and can attack a wide range of positions . They alIn vitro, we have applied different probing strategies to analyze subgenomic HCV RNA constructs . DMS treProbing RNA with DMS provides information from Watson–Crick and Hoogsteen pairs. It can be used over a broad pH range with minor changes in reactivity, making it a suitable tool for RNA probing under different experimental conditions, including intracellular environments .DMS modifies unpaired A, C, and G residues by introducing methyl groups at positions N1, N3, and N7, respectively . Methyla1. Denature 1 pmol of purified RNA per reaction by heating at 95 °C for 2 min.2. Transfer the sample to an ice/water bath and incubate for 15 min.3. Distribute 1 pmol aliquot of denatured RNA into new tubes. It should be noted that at least two samples must be prepared to be assayed in the absence (−) or presence (+) of DMS. This is required to compare both reverse transcription (RT) patterns.4. Add folding buffer and proceed to renature the RNA molecules by incubating at the desired temperature for 5 min. 37 °C is usually a good option, although other conditions can be further tested.5. Add 1 µg of tRNA to each reaction tube to avoid extensive RNA modification by DMS.6. Initiate the probing reaction by adding 1–5 µL of freshly diluted DMS in ethanol (1:5) [(+) DMS reaction] or net ethanol [(-) DMS reaction], and mix by gentle pipetting. In this step, a final reaction volume of 15–20 µL is recommended. Incubate the reactions at 37 °C during 60–90 s. The concentration of the probing reagent should be optimized for each RNA problem. To assay different concentrations of freshly prepared probing reagent, starting with the indicated concentration might be necessary. One to three modified nucleotides per molecule is desirable. A low concentration of the probing reagent results in incomplete probing of the (+)RNA sample, so the probe concentration should be increased. Conversely, an excess of probing reagent may result in the absence of full-length products and a very low signal for distant nucleotides. In this case, reducing the concentration of the chemical reagent (around two-fold) may solve the problem.7. Complete up to 150 µL with sterile RNase-free distilled water and stop DMS-mediated RNA modification by the addition of 0.1 volumes of 3 M sodium acetate, pH 5.2.8. Proceed to RNA precipitation by the addition of three volumes of cold (−20 °C) absolute ethanol and incubate the samples at −80 °C for 30 min or at −20 °C overnight. In this step, an inert carrier such as glycogen can be supplemented to improve RNA precipitation.g at 4 °C.9. Centrifuge RNA samples during 30 min at 12,000× 10. Carefully discard the supernatant and wash the pellet with 250 µL of 80% ethanol. Disruption of the fragile pellet can be avoided by omitting gentle vortexing or pipetting.g, at 4 °C, for 10 min.11. Centrifuge at 12,000× 12. Discard the supernatant and repeat washing steps 9–11.13. Discard the ethanol supernatant and briefly vacuum dry the RNA pellet to eliminate ethanol traces.14. Use sterile RNase-free distilled water to dissolve the samples. The final volume must not exceed 10 µL.In addition to the base-pairing pattern that can be inferred from the RNA probing analysis with DMS, it may also be desirable to obtain data about conformationally dynamic residues . These rSHAPE reagents  can be u1. Denature 1 pmol of the target RNA per reaction, as described above .2) for 5 min at the desired temperature. For timed SHAPE assays, in which conformational dynamics are studied, it may be required to test a wide range of temperatures. Note that NMIA reacts even at 95 °C [2. Proceed to RNA refolding by incubating it with folding buffer , must be performed in the presence of 1 µL of net DMSO.4. Incubate the reactions at the desired temperature for 5X NMIA half-life. The equation:arameter .5. The formation of 2′-O-adducts is stopped by ethanol precipitation, as described (basic protocol 1 step 8).6. The precipitated RNA must be washed twice with 80% ethanol (steps 9–11 from basic protocol 1).7. Discard the supernatant and briefly dry the RNA pellet under vacuum conditions or allow it to air dry at room temperature for a few minutes to remove any ethanol trace amounts.8. Resuspend the pellet in 10 µL of sterile RNase-free distilled water.The analysis of the secondary and tertiary RNA structure proposed in basic protocols 1 and 2 can be further complemented by introducing an additional chemical interference study with SHAPE reagents, as NMIA. This specific methodology is called HMX (2′-hydroxyl molecular interference) B. HMX isWe have followed this strategy for analyzing quaternary structure in the HCV RNA genome . A gener1. RNA modification is accomplished under denaturing conditions by incubating 50 pmol of the construct with freshly diluted NMIA in DMSO for 3 min at 95 °C before cooling on ice for 2 min. The reaction proceeds in the presence of 100 mM HEPES pH 8.0 in a final volume of 20 µL. Note that a non-treated sample, (–) NMIA, must be prepared in net DMSO.In this step, the optimal NMIA concentration must be determined experimentally. In our hands, an NMIA concentration of 20 mM is usually enough for probing ~5 µg of RNA.2. Repeat the modification step twice, paying special attention to the replacement of the evaporated water to achieve the desired final volume.3. The reactions are stopped by cooling on ice and subsequent RNA precipitation in the presence of 0.3 M of sodium acetate, pH 5.2, and three volumes of absolute ethanol.4. Repeat steps 7–12 from Basic protocol 1.260).5. Monitor RNA amount by UV spectrometry  on a non-denaturing polyacrylamide gel . The gel is run at 10 V/cm and 4 °C to avoid overheating.® staining or by UV shadowing. Bands are excised using a razor blade. It is noteworthy that the relative abundance of the different conformers should vary in the NMIA treated samples compared to the non-treated ones.7. RNA conformers are visualized using a UV transilluminator after GelRed8. The gel slices are then soaked in elution buffer  and the RNA is eluted overnight at 4 °C by passive elution.9. Purify the RNA by two consecutive phenol extractions and additional chloroform:isoamyl alcohol extraction.10. Precipitate RNA samples as described in steps 7–12 from Basic protocol 1.11. Use 10 µL of sterile RNase-free distilled water to resuspend the RNA pellet.Modifications included in the RNA molecule during the probing reaction can be detected by end-labeling RNA or by primer extension-dependent procedures . In the Alternatively, a 5′ end-labeled oligonucleotide can be designed to hybridize to the target RNA and then be extended by RT reactions B 23,24,,24,28. I(a)The RNA length is, likely the most determinant feature in choosing the primer extension method. It is useful for long RNAs because multiple primers can be used in separate reactions to analyze modifications along the whole RNA molecule.(b)Primer extension is also suitable for in vivo assays because it does not require previous RNA purification.(c)Finally, the primer extension reaction simplifies the protocol optimization since it can be used for detecting modifications performed by different reagents.The choice of one of these strategies depends on several items:Asp molecules [A main drawback of the primer extension-based strategy is that sequence-specific pausing of the reverse transcription reaction in the non-treated samples generates high background signals, which may be difficult for the analysis. Primer extension-based strategies also omit the information from those nucleotides within the oligonucleotide annealing site and generate intense noise signals caused by the shortest and the full-length cDNA products. These problems were successfully overcome with the strategy designed by Merino et al. . These aolecules , are alsolecules ,19. The 32P and fluorophore labeling are the most common options.Another issue to consider for the readout of probing experiments is the choice of the labeling agent. Currently, a wide variety of labels is available; however, We have optimized the primer extension reactions with fluorescently labeled primers for HCV probing readout. Specifically, we have used the NED fluorophore for mapping both treated and untreated samples, while RNA sequencing reactions were performed with FAM or VIC-labelled oligonucleotides. This strategy allows to resolve of cDNA fragments by capillary electrophoresis and facilitates their quantification by the application of the QuShape software .1. Fluorescently labeled DNA primers used for reverse transcription assays must be purified on denaturing polyacrylamide gels. Briefly, add one volume of denaturing formamide loading buffer  to 200 pmol of each oligonucleotide and heat at 95 °C for 2 min. Then, cool samples on ice and load on 15–20%, high-resolution polyacrylamide-7 M urea gels. Electrophoresis proceeds under denaturing conditions in 1X TBE buffer, 1.2 W/cm, in a darkroom.2. The gel slices containing the fluorescently-labeled full-length primers are excised and soaked in 350 µL of elution buffer.3. Incubate overnight at room temperature, in a darkroom.4. Purify the DNA primers by phenol extraction, followed by chloroform:isoamilic alcohol extraction.5. Extract the aqueous phase and precipitate the primers by the addition of 0.3 M sodium acetate, pH 6.0, and three volumes of absolute ethanol.6. Pellet primer oligonucleotides as noted in steps 7–8 from Basic protocol 1.7. Wash the DNA pellet by supplementing with 300 µL of 70% ethanol and proceed as indicated in steps 9–10 from Basic protocol 1.8. Vacuum dry the samples and dissolve primers in 20 µL of RNase-free distilled water, by vigorous vortexing.260).9. Measure DNA primers concentration by UV spectrophotometry (A1. Add 2.5 pmol of the NED-labelled primer to the (+) and (-) NMIA samples and mix by pipetting. Use 2.5 pmol of FAM- or VIC-labelled primer oligonucleotides for RNA sequencing ladders with 2 pmol of the target construct in separate tubes. An excess of primer may lead to a saturated signal in short-length products and the absence of full-length cDNA. A 1:1 RNA:oligonucleotide ratio is desirable.2. Proceed to primer annealing by heating at 95 °C for 2 min and then snap cooling on ice for 15 min.3. Prepare the RT reaction mix as indicated by the manufacturer and incubate the primer:RNA sample for 1 min at 52 °C. The sequencing reaction of each RNA sample using the same primer should be run in parallel. Sequencing of only one or two nucleotides could be enough. For that purpose, add 0.5 mM of the desired ddNTPs to each sequencing reaction. The choice of a specific ddNTP will depend on the specific sequence and the features of the RNA tested molecule. For IRES and 3′UTR of HCV, ddCTP, and ddTTP are good starting candidates.4. Initiate primer extension by the addition of 1 µL of the SuperScript™ III enzyme mix and incubate samples at 52 °C for 20 min. Non-specific or premature reverse transcriptase stops by complex structural elements leads to an increase of non-specific signal in the untreated sample. The use of a heat-resistant reverse transcriptase is recommended to increase the temperature of the primer extension reaction. SuperScript™ IV enzyme is a good replacement to solve this problem. Premature signal decay and absence of full-length product may also be due to insufficient primer extension reaction time. Increase up to 1 h the reaction time.5. Stop the reactions on ice.6. Purify DNA samples using the BigDye XTerminator™ Purification kit (Applied Biosystems) and continue with the resolution of the cDNA products by capillary electrophoresis in an Applied Biosystems 3130xl Genetic Analyzer, as described [Resolving cDNA samples by capillary electrophoresis using fluorophore-labeled primers has allowed the development of high-throughput techniques.The extraction of reactivity data from the electropherograms is a challenging and, in many cases, time-consuming process. Different computational strategies can facilitate this task. One of the most useful tools is the QuShape software package . It requhttp://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi, accessed on 21 September 2021) and the module ShapeKnots from RNAStructure [https://rna.urmc.rochester.edu/RNAstructureWeb/, accessed on 2 September 2021). Practical user guides can be found on the corresponding websites.Finally, experimental constraints derived from biochemical and biophysical studies can be used to model RNA secondary structure. These include RNAFold  (http://tructure  (https:/RNA structure knowledge is a constantly growing field. This is reflected by the increasing number of publications reporting the development of new bioinformatics tools helping in the analysis of reactivity data and the interpretation of the results at both structural and functional levels. The RNAProbe web server ,35 has bIn the last years, efforts have also been addressed in designing in silico tools able to predict SHAPE reactivity values for a given RNA sequence, which are then used to create 2D and 3D models. For example, the SHAPER web server can use both experimental and theoretical constraints to refine the folding and correlate it with other existing data . Some toThese are just a few examples of how data derived from RNA probing techniques can be used to infer the folding of RNA molecules at nucleotide resolution, with high accuracy and reliability.The development of new therapeutic strategies against emerging viral infections is, currently, of urgent need. During the last years, the potential of RNA molecules as antiviral drugs has been largely postulated and studied. These studies have allowed to overcome different challenges in the design of inhibitory RNAs, but have also evidenced the importance of the structure in the target region. Different in silico strategies have been designed to identify those regions in the RNA that are susceptible to being targeted by the inhibitors. However, these approaches show limited accuracy since they do not use experimental structure data. The improvements in RNA probing strategies and their combination with user-friendly in silico prediction tools have facilitated the tasks of designing RNA inhibitors, have significantly improved the accuracy of the results, and have also opened the door to the knowledge of RNA folding at a high-order level. In addition, the design of new drugs takes advantage of these experimental approaches to obtain data about optimal drug-binding pockets, including both dsRNA- and loop-binding compounds. New challenges are awaiting, such as deciphering the assembly pathways in viral RNA genomes, a critical step that is responsible for the final 3D RNA conformation, and therefore the function of the viral genome. Future innovations in the field of high-throughput RNA probing, including the synthesis of new chemical reagents and the development of more efficient RNA sequencing platforms, will generate a rich environment in which the analysis of RNA folding will be a requisite for many biomedical applications.


M = 36), filled out a large set of wellbeing scales followed by demographic information including type of school attended each year from 2 to 17. About half the sample had only attended conventional schools and the rest had attended Montessori for between 2 and 16 years (M = 8 years). To reduce the variable set, we first developed a measurement model of wellbeing using the survey data with exploratory then confirmatory factor analyses, arriving at four factors: general wellbeing, engagement, social trust, and self-confidence. A structural equation model that accounted for age, gender, race, childhood SES, and years in private school revealed that attending Montessori for at least two childhood years was associated with significantly higher adult wellbeing on all four factors. A second analysis found that the difference in wellbeing between Montessori and conventional schools existed even among the subsample that had exclusively attended private schools. A third analysis found that the more years one attended Montessori, the higher one’s wellbeing as an adult. Unmeasured selection effects could explain the results, in which case research should determine what third variable associated with Montessori schooling causes adult wellbeing. Several other limitations to the study are also discussed. Although some of these limitations need to be addressed, coupled with other research, including studies in which children were randomly assigned to Montessori schools, this study suggests that attending Montessori as a child might plausibly cause higher adult wellbeing.Wellbeing, or how people think and feel about their lives, predicts important life outcomes from happiness to health to longevity. Montessori pedagogy has features that enhance wellbeing contemporaneously and predictively, including self-determination, meaningful activities, and social stability. Here, 1905 adults, ages 18–81 ( Wellbeing, or the felt experience of health, happiness, and flourishing, predicts several desirable outcomes including better health and work performance, longevity, and more positive social behavior and relations . Low levA logic model for Montessori education  predictsInitiated in 1907, Montessori pedagogy  is the oChildren in Montessori classrooms choose their own work almost all the time, and in this sense are very much in charge of their own educations. A teacher guides children in individual or small group lessons, but children decide which lessons to follow up on, and even whether to sit in on a lesson: they determine their own activities. The classroom is composed of hundreds of potential activities, laid out on shelves, and children choose among them, or even arrange their own field trips during which they leave the classroom to do something else .Self-determination has been conceptualized as fulfilling a fundamental human need for autonomy . Choice We hypothesized that self-determination might lead to two sets of outcomes. A first set concerns what one might call general wellbeing: happiness, finding meaning in life, self-confidence, and (relatedly) a sense of personal competence. A second set of potential outcomes is related to intrinsic motivation: people who experience a high degree of self-determination early in life might later be more intrinsically engaged and seek more challenge. Supporting the idea that self-determination in Montessori schools might cause this array of outcomes, an experience sampling study showed that Montessori middle school students rated themselves more highly than did conventionally schooled students on positive affect, experiences of “flow” , potencyMontessori offers children meaningful activities, by which we mean activities for which the underlying reasons are clear, and thus give people a sense of purpose. Offering meaningful activities is crucial for a school system built on self-determination , becauseWork that is geared at an optimal level—challenging but possible—engages people, putting them in the positive state referred to as “flow” . Under fAnother Montessori education feature that might enhance long term wellbeing is the social environment, which nourishes social-emotional development and sustained relationships. Classrooms span 3 years  during which children have the same teacher and immediate peer group; children just older and younger are classmates for 1–2 years, and are met again as one moves up classroom levels.The practice of staying with the same teacher and classmates, called “looping,” supports positive relationships, self-confidence, and academic performance ; the oneThe Montessori practice of not having grades or tests also might benefit relationships with both teachers and peers. For one, the teacher becomes a guide, rather than one who makes judgments and “grades.” Among students, absence of tests and grades fosters collaboration whereas grades foster competition . Collabovia improved self-esteem  suggesting that peer relationships are stronger in Montessori. This makes sense because, as opposed to conventional schools where students usually work individually, in Montessori schools students often work in small groups. Moreover, research indicates that Montessori student’s social knowledge and skills are more advanced, and the overall school climate is better . Random f-esteem , which rTaken together, findings on social stability led us to predict that Montessori students would have more positive social relationships and a stronger sense of community throughout life see . Such faIn sum, we hypothesized that Montessori pedagogy in childhood might lead to higher wellbeing later in life. The reasoning behind this hypothesis was that the pedagogy has three features  that other studies have shown enhance wellbeing along several dimensions , and because studies of Montessori versus conventionally schooled children  have indicated that Montessori students are different along these dimensions during their school years. In the next section we present those natural experiments.Natural experiments using random lotteries have examined the immediate influence of Montessori on proximal child outcomes, and the results for the two studies which involved highly trained Montessori teachers suggest children have higher wellbeing while in school and are on a trajectory toward higher wellbeing later in their lives. These studies controlled for parent characteristics because admission was determined by a random lottery that parents of the control children had also entered. The studies thus have high internal validity, although the results might not apply to families that do not enter such lotteries, lowering external validity. The first study examined children in Kindergarten and 6th grade , whereasTaking together these findings, as well as the fact that Montessori has conditions that are associated with higher wellbeing, we hypothesized that adults who went to Montessori as children have higher adult wellbeing.SD = 13.12, range = 18–81 years), 79.2% were female, and 83.0% identified as White, 3.4% as Black or African American, 4.5% as Asian, 3.7% as Hispanic or Latino, and less than 1% as American Indian or Alaska Native, Native Hawaiian or Pacific Islander. Another 3.8% aligned with multiple races/ethnicities, and 1% self-identified in other categories  or preferred not to answer. Because of the small sizes of categories other than White, they were grouped for analyses.Participants were recruited through various methods including Facebook ads in cities known to have many Montessori schools, Amazon’s Mechanical Turk, school associations, and snowballing. The final sample consisted of 1905 participants in the US and Canada who had attended Montessori for at least 2 years or who had spent virtually all their school years at conventional schools. Two years of intervention was selected because that duration led to significant outcomes for the Perry Preschool Project , and 2 yns or 6 months, whichever came earlier. Based on The sample size goal was 2000 participants, 1000 in each group , which is well above the threshold needed for an SEM involving 83 paths which is the size of our largest model . Our recn = 1071 (19 had gone to Montessori for just 1 year). Those with at least 2 years of Montessori, n = 834, were classified as such. Although a 2-year cutoff was used, for the Montessori group the average length of attendance was 8 years . An additional group of participants who had attended other alternative schools for two or more years (n = 506) were excluded from analyses to focus here on Montessori versus conventional schooling. (Their results will be reported elsewhere).Participants were partitioned into groups using R. Those who had spent no or only 1 year in an unconventional school (like Montessori) were categorized as conventional, Surveys were administered on the Qualtrics platform with a compensation rate of $0.50/survey. Participants who gave informed consent were then given a series of scales and questions, ending with demographic questions including school types attended. The stated purpose of the research in the informed consent was “to better understand the long-term outcomes of alternative and conventional school education on peoples’ lives.” On the final page, school type history was requested; no specific school system was mentioned until after all other measures had been filled out.The survey included 18 established scales (see below) that were intended to get at a variety of aspects of wellbeing, and a few ordinal scales and other questions getting at issues of interest. Because the ordinal scales explained little variance in wellbeing, they were not used in our analyses, but those results were consistent with the ones below and will be reported elsewhere. Eleven of our 18 scales are subscales of the Psychological and Social Wellbeing scales (SWB). Below, after considering advantages and disadvantages of online surveys, we describe each scale with its original alpha; in the present study, alphas were the same or exceeded the originals in all cases except one .Online surveys are an important source of data for psychology research, with pros and cons . One benThere are many measures of wellbeing , tappingstrongly agree to strongly disagree. The six PWB scales, with their original reported alphas, are Self-Acceptance , Environmental Mastery , and Autonomy , all of which seem to tap into General Wellbeing  , one of absolutely untrue to absolutely true, participants rate five short statements such as, “My life has a clear sense of purpose”; α = 0.86. An aggregate score is calculated by summing up the five items, and a higher score reflects a higher subjective sense of meaning in one’s life. This scale appears to tap into the meaning aspect of engagement.This 10-item scale  measuresnot at all true to very true, participants rate seven short statements such as, “I feel alive and vital”; α = 0.83. An aggregate score is calculated by adding ratings from each of the items. As is recommended, the second item (the only one needing to be reverse scored) was dropped , and Private (religious), and did not attend. The format of this page was as follows: the ages  were listed down the left-most column, the next seven columns held circles one could check for the type of school, and the next four columns were for the funding model.There was no missing data. An exploratory factor analysis (EFA) was done on a randomly selected two-thirds of respondents’ data using all variables and maximum likelihood extraction. A four-factor structure was confirmed in a confirmatory factor analysis (CFA) done with the remaining data. These factors were then entered in a series of Structural Equation Models (SEM) that accounted for age, race, childhood SES, gender, and proportion of years attending private schools. All analyses were run using R, OpenMx 2.19.1 , and SPSThe purpose of using factor analysis here was to reduce the variable set; the variables were selected because they are frequently used to measure wellbeing, rather than with an eye to expected factors. Adequacy of model fit was determined according to the guidelines set by Several scales, particularly the PWB scales, resulted in data that were on visual inspection negatively skewed, reflecting that as a whole, the sample had high wellbeing. Box–Cox transformations were applied to skewed variables . We rounn = 1220). This n allowed covariates (such as gender and childhood SES) to be balanced across conditions . A Promax oblique rotation was used, allowing all factors to intercorrelate. Initially the training set was run using 1–11 factors and all variables. The ordinal items (classified as “Other Questions” in the survey) explained little variance , which made sense in that they were designed to get at issues a step removed from wellbeing. The ordinal variables were removed, and a new set of EFAs were run using the same methods.Exploratory factor analysis using maximum likelihood extraction was run with the correlation matrix from data of approximately 2/3 of the participants . Social Trust and Self confidence each accounted for 16% of the variance; their relation to each other was 0.28.The factor correlations and variance accounted for are shown in Next a confirmatory factor analysis using maximum likelihood estimation was conducted on the remaining 36% of respondents’ data; the results are in The purpose of the factor analyses was to reduce the set of scales to a smaller number of factors and to suggest latent variables emerging from a set of wellbeing measures administered to all participants. Based on prior research concerning the outcomes or at least associates of self-determination, meaningful activities, and social stability (which can exist to varying degrees in any school experience), we hypothesized three latent clusters would emerge: one concerning happiness, meaning, and self-confidence; one concerning engagement and seeking challenge; and a third concerning social trust and sense of community. The factor analysis actually resulted in four factors that align reasonably well with what was hypothesized, with the first cluster of outcomes splitting into two ; in the EFA Environmental Mastery also loaded on that factor (0.23) but not at a level that met the threshold of 0.35.The second factor reflects investing oneself in one’s activities and social world. Social Contribution (“My daily activities contribute to something worthwhile to my community”) was set to 1.0 and Social Integration (“I feel close to other people in my community”) had a loading of 0.91; both these scales are from the Social Wellbeing scale. Also loading on Engagement were Personal Growth , Positive Relations , and Purpose in Life , all Psychological Wellbeing scales. Cronbach’s alpha for these five items was 0.83 . Dropping any item resulted in a lower alpha. This factor resembled the second cluster of outcomes we hypothesized would result from the Montessori characteristics of self-determination and meaningful activities, with a stronger social engagement element than was anticipated.The third factor included two Social Wellbeing subscales, Social Acceptance and Social Actualization, which reflect trust in society—items like, “The world is becoming a better place for everyone” and “I believe that people are kind.” Because there were two items for this factor, their loadings were set to be equal (see above) at 0.71. Cronbach’s alpha for these two items was 0.67 . This factor reflects outcomes prior research suggested would result from Montessori’s high degree of social stability.Loading on the fourth factor were three variables that reflect confidence in one’s own thinking . Social Coherence from the Social Wellbeing scale, including items like “I find it easy to predict what will happen next” was set to 1.0, and the Need for Cognition scale loaded highly with it (0.99); this scale includes items like, “I like to have the responsibility of handling a situation that requires a lot of thinking” and “I prefer my life to be full of puzzles that I must solve.” Autonomy from the Psychological Wellbeing scale also loaded on this factor (0.86), with items like, “I have confidence in my own opinions” and “I judge myself by what I think is important, not what others think is important.” Cronbach’s alpha for these three items was 0.57 . Dropping any item resulted in a lower alpha.In sum, two of the three outcome clusters we had hypothesized, based on prior research, would stem from experiences involving high levels of self-determination, meaningful activities, and social stability were upheld, with Engagement having a social aspect that was not expected. The first hypothesized factor, however, split into two, with outcomes pertinent to general wellbeing (including confidence in one’s abilities) falling into one cluster and outcomes more specifically related to self-confidence about one’s thought processes in a fourth cluster.p < 0.001).Having reduced the wellbeing scales to a set of four latent factors, the next step was to examine whether experience with Montessori schooling is associated with participants’ scores on any of those four factors, for the whole sample of 1905 participants, divided into the Montessori and Conventional school groups as explained above. Gender , race (Caucasian/Not), age, childhood SES , and proportion of schooling that was private were accounted for as covariates in the models. There was a significant improvement in model fit when the binary variable of schooling  was added into the model, both overall and ranging across all four factors. Social trust had the largest beta-value (0.32) but even the lowest beta-value, for Self-confidence, showed a highly significant effect of Montessori  has been shown in other research to render happiness and a strong sense of one’s own competence, and which allows one to find and engage in activities that give one a sense of purpose. The second feature we highlighted is that Montessori activities are meaningful, in that they have a clear purpose to which children can relate; this, along with self-determination, allows one to choose work that provides an optimal level of challenge, creating strong engagement. We did not anticipate that higher engagement among people who attended Montessori would include social integration, but it clustered with other variables tapping into engagement in the factor analysis. The third Montessori feature, social stability (including multi-year classrooms), was hypothesized to lead to strong relationships which then predict higher general wellbeing as well as social trust. Classroom looping practices also improve academic performance, which in turn predicts higher wellbeing. Thus, the results are consistent with what we hypothesized, based on prior research.However, an alternative possible explanation for these results is that they stem not from at least 2  years of Montessori education or some associate thereof, but instead from a third variable, perhaps something associated with having parents who make the effort to select and in most cases finance a specific school for their child, as opposed to using the default neighborhood public option. In other words, it may be that having parents who go out of their way to find and fund a different school program leads to higher adult wellbeing, or is associated with other factors that lead to higher wellbeing. Of course, many public school parents also are very intentional about their choice, choosing their domicile (and paying property taxes) based on public school district, but nonetheless they do not pay tuition in addition to taxes. Although we had covaried years of private school in the initial analysis, a more focused way to examine whether something associated with parents choosing a private school explains the results is to limit the dataset to participants who always attended a private school, because private schools are never the default option; every child in the United States and Canada lives in a school district where they could attend a tuition-free public school at least from Kindergarten on. A second analysis therefore analyzed data from the subset of participants who attended private schools for all of their schooling.n = 268 for whom at least 2 of those years were in private Montessori , and Conventional group of n = 171 who went exclusively to conventional private schools. The Montessori group had spent M = 9.22 years (SD = 3.59) in Montessori schools and M = 6.14 (SD = 3.71) in conventional private schools, whereas the exclusively private conventional group had spent M = 14.53 years (SD = 1.30) in school, virtually all of it in conventional private schools. The mean age of participants was 33.99 years , 93 were male (21.2%), and the rest were female; 87.9% identified as White.The second analysis involved the subsample of 439 participants who had exclusively attended private schools: a Montessori group of p = 0.07), suggesting that confidence in one’s own thinking/mind is as strong among those who attended private conventional schools as among those who attended private Montessori schools.The structural equation model of the initial analysis was conducted using only data from the exclusively privately schooled subset of participants. These results, including mean factor scores and SDs, are shown in The standardized Beta values in the SEM were similar to what they were for the whole sample, but slightly stronger for Social Trust and slightly less strong for Self-confidence. Thus, while the initial analysis controlled for the proportion of schooling that was private, this second analysis shows that even among the subsample who only attended private schools, model fit improves when Montessori status is added.Montessori (public or private) for their children that differs from other parents, and that it is those differences that lead to the better outcomes. This possibility was addressed in a third analysis by examining duration effects.Although wellbeing was still higher for Montessori compared to other participants, it is possible that this is because there is something about parents who choose Particularly as children get older, duration of Montessori attendance would often reflect availability rather than parent choice. The option to attend Montessori after age 6 is limited, because Montessori elementary, middle, and high schools are far less prevalent than Montessori preschools; even Montessori elementary schools (for children ages 6–12) were extremely rare 30 years ago (when our average participant age was 6); Montessori elementary schools have gradually become more common, whereas Montessori high schools are still rare today. Because duration of Montessori attendance is often constrained by availability, self-selection is less of an issue in such an analysis, raising the odds that (were any significant effects found) the programs caused effects. Although this analysis was conducted because positive results would strengthen the possibility of causality, we caution that it is only a test of association.SD = 9.73, range 18–61 years). They had attended Montessori for a mean of 7.88 years  and conventional school for a mean of 7.36 years (SD = 3.83); 23% were male and 84% were White.The subset of 853 respondents from the sample who had attended Montessori for at least 1 year in childhood was examined, omitting those who had never attended it . This subset had an average age of 32.07  might be strengthened by more time in a Montessori environment. Alternatively, it may be that parents who choose to and are able to (because they live in communities where it is available) keep their children in Montessori longer also give their children other experiences that promote even more General Wellbeing and Engagement, but not more experiences that promote more Social Trust and Self-confidence than were indexed by the initial choice.plausibly lead to higher wellbeing in adulthood, because many features of Montessori schooling are known to cause higher wellbeing contemporaneously or predictively in other school and non-school situations. Based on this existing research, we developed a hypothesized model of how three Montessori program features—self-determination, meaningful activities, and social stability—might lead to three clusters of wellbeing outcomes—a general cluster including happiness, finding meaning in life, and feeling competent; another including engaging and seeking challenge in one’s activities; and a third around a strong sense of community and social trust. Although we could not test that model directly, we could test its plausibility by seeing whether Montessori schooling was associated with latent factors aligned with those outcomes. Over 1900 individuals filled out a large set of wellbeing surveys and their responses were subjected to exploratory and confirmatory factor analyses, which arranged into four latent factors similar to those originally hypothesized, but with confidence in one’s own thoughts and mind emerging as a distinct fourth factor, and the engagement factor including social engagement as well , showed a trend. Attending Montessori schooling for at least two childhood years was associated with higher wellbeing on these factors even among people who only attended private schools their entire pre-college lives.Having parents who always had selected a private school was thus not responsible for the generally higher wellbeing associated with having attended Montessori observed in the initial analysis. Perhaps there is something associated with selecting Montessori specifically (whether public or private) that is associated with higher wellbeing. Although three studies that used dozens of measures (like parenting styles measures) to discriminate Montessori from other parents found no significant differences , there mHowever, General Wellbeing and Engagement were hypothesized to be influenced by Montessori features see , and areAlthough the associations with General Wellbeing and Engagement held across all three analyses, the study design does not allow one to determine if Montessori schooling caused higher wellbeing. An experimental design, in which children were randomly assigned to Montessori and then tested as adults, would be needed.Although we know of no long-term lottery control studies of Montessori education, two short term ones were described in the section “Introduction.” The two natural experiments conducted in the US show that high-fidelity public Montessori causes features associated with higher wellbeing, like stronger mastery orientation, executive function, social knowledge/skills, and academic performance . It is aAnother support for the plausibility of the hypothesized model is that several of Montessori programs’ features, including the three highlighted here, predict higher wellbeing even when implemented in conventional school settings; these were discussed in the section “Introduction.” For example in classrooms where students are given more agency and opportunities for self-determination, they also have higher sense of their own competence and overall wellbeing , and thiIn sum, although this study only shows an association between Montessori schooling in childhood and higher adult wellbeing, lottery control studies and studies showing that features of Montessori schooling are associated with higher wellbeing in other settings lend weight to the possibility that Montessori might cause higher adult wellbeing. But if this is not the case—if in fact features of Montessori parents or some other third variable associated with Montessori attendance is the cause—then it would be very interesting to determine what the underlying cause for the discovered association is.This study has several limitations in addition to its being a study of association rather than an experiment. One is that the sample was largely female and White, which is often the case for internet survey samples . Other sA second limitation concerns variation in Montessori implementation. Although the core Montessori features we discussed—self-determination, meaningful activities, and social stability—likely characterize all Montessori schools, variations in implementation might accentuate or mitigate them. For example, we have seen Montessori elementary classrooms that require students to fill out checklists of their work activities in ways that likely reduce feelings of self-determination in those classrooms. Studies showing the strongest Montessori outcomes involve high fidelity Montessori implementation . Here, wAnother limitation is that participants knew the purpose was to consider the impact of alternative schooling on one’s life, and this could have biased people’s responding, although the direction such bias might take is unclear. If one has fond memories of school, whether it was conventional or Montessori, and one knows one is doing a survey about the impact of school on current wellbeing, one might answer more positively; if one has negative memories, one might answer more negatively. Thus, while it is unclear the direction in which knowledge about the survey’s purpose might bias any individual’s responses, in the future one might administer surveys without providing a description of the purpose. This might be difficult to do while obtaining a large sample of alternatively schooled individuals, since alternative schooling is relatively rare . Participants did not answer questions about the types of schools they attended and when until the end of the survey, which might have reduced attention to this aspect of the study until the surveys were complete, but biased responding is a limitation in survey research, particularly when it concerns subjective qualities.Yet another limitation comes from recruitment itself. In order to get a large enough Montessori sample, Facebook ads were run in communities where Montessori schools are more abundant, such as Washington D.C., Minneapolis/St. Paul, and Milwaukee which have long had Montessori teacher training programs. Although these ads should also have recruited conventionally schooled people in those cities, it is conceivable that the study’s external validity is compromised by the strategy, because there are regional differences in wellbeing . For exaA further limitation concerns internal validity: We asked people to recall what type of school they attended each year from when they were 2–17, with seven options ranging from Did Not Attend to Homeschool. Some people might not have remembered their school type but guessed a type, which would produce noise in the data, rendering our results less reliable. In general, people’s memories for childhood experiences are thought to be “substantially accurate” , p. 84, In sum, wellbeing is a multidetermined but very important human outcome. If childhood schooling were to influence adult wellbeing, the public health implications would be very important. Pedagogical environments that support children to become adults with high levels of wellbeing are desirable. Although there are important limitations, the research presented here suggests that Montessori schooling might be associated with higher adult wellbeing, and that a causal relation between Montessori schooling in childhood and wellbeing in adulthood is at least plausible.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the University of Virginia Institutional Review Board for the Social and Behavioral Sciences. The patients/participants provided their written informed consent to participate in this study.AL conceived of the study, contributed theory, raised funds, oversaw the entire project, did some data analysis, and wrote the manuscript. MM conducted the data analyses. DV and EF selected stimuli in collaboration with AL, recruited participants, put the surveys on Qualtrics, ran the study, worked with MM to prepare the data file, and contributed to the manuscript. DV ran some analyses. All the authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Membrane transporters play an important role in the absorption, distribution, metabolism, and excretion of xenobiotic substrates, as well as endogenous compounds. The evaluation of transporter-mediated drug-drug interactions (DDIs) is an important consideration during the drug development process and can guide the safe use of polypharmacy regimens in clinical practice. In recent years, several endogenous substrates of drug transporters have been identified as potential biomarkers for predicting changes in drug transport function and the potential for DDIs associated with drug candidates in early phases of drug development. These biomarker-driven investigations have been applied in both preclinical and clinical studies and proposed as a predictive strategy that can be supplanted in order to conduct prospective DDIs trials. Here we provide an overview of this rapidly emerging field, with particular emphasis on endogenous biomarkers recently proposed for clinically relevant uptake transporters. Membrane transporters of the ATP-binding cassette (ABC) and solute carrier (SLC) super families are now widely recognized as important determinants of the absorption, distribution, metabolism, and excretion of many xenobiotic compounds, and for many of these transporters, physiological substrates have been identified ,2. The iOrganic anion transporters (OATs), along with organic anion-transporting polypeptides (OATPs), organic cation transporter (OCTs), organic cation and carnitine transporters (OCTNs), and multidrug and toxin extrusions proteins (MATEs) , are a SSLCO1B1] and OATP1B3 [SLCO1B3] (collectively referred to as OATP1B hereafter) are partially redundant transporters that are highly expressed on the sinusoidal membrane of hepatocytes and play key roles in the hepatic uptake of drugs , but some of the conjugated bilirubin is effluxed back into the sinusoidal blood by MRP3 [ABCC3] and enter a shuttling-loop  and be taken up by neighbouring hepatocytes in an OATP1B-dependent manner , OCT2 [SLC22A2], OAT3 [SLC22A8], or NTCP [SLC10A1]; CP-III is identified to be OATP2B1 substrate  and OAT3 [SLC22A8] are mainly expressed on the basolateral membrane of renal proximal tubular cells and mediate cellular uptake of substrates from the blood into the kidney [SLC22A11] (PDA only), MRP4 [ABCC4], GCDCA-S was a substrate of OAT3 and MRP2, and taurine was not a substrate of the tested transporters. In addition, the same study found that PDA was the most sensitive plasma biomarker in response to strong and selective OAT inhibitor probenecid (500 mg every 6 h), while GCDCA-S was the most sensitive OAT biomarker based on renal clearance , OCT2 [SLC22A2], OCT3 [SLC22A3], OCTN1 [SLC22A4], OCTN2 [SLC22A5], MATE1 [SLC47A1], and MATE2-K [SLC47A2]. Their function is to transport organic cations, zwitterions, and some uncharged compounds as facilitated diffusion systems and/or antiporters, and they modulate the distribution of many endogenous compounds such as thiamine, creatinine, and neurotransmitters  and THTR2 [SLC19A3]  [(–/–) mice, which could be indicative of OCT1 involvement in thiamine disposition [Thiamine is reported to be a substrate of MATE1 ( 3.9 µM) ,29,85, O59.9 µM) ,86, thiactively)  through ctively) . Clinicactively) . Comparectively) . β-oxidaposition . In wildposition . The varN1-Methylnicotinamide (NMN) is produced as a result of tryptophan and niacin metabolism [Km =300–318 µM), MATE1 (Km = 301 µM), and MATE2K (Km = 422 µM) in overexpressed HEK293 cells or Xenopus laevis oocytes [Ki values of 9.4 µM (OCT2), 83 nM (MATE1), and 56 nM (MATE2K) and by trimethoprim with IC50 values of 134 µM (OCT2), 29 µM (MATE1), and 0.61 µM (MATE2K) as well as by other OCT2/MATE substrates and inhibitors such as metformin, tetraethyl ammonium (TEA), quinine, and cimetidine [tabolism , and was oocytes ,90,91. Tmetidine .The contribution of transporters to the uptake of NMN is species-dependent , and plaNMN has been explored as a potential endogenous OCT2/MATEs transporter biomarker in several human DDI studies ,32. In aN1- Methyladenosine (m1A) as a novel OCT2 substrate [1A is a substrate of mouse Oct1, Oct2, Mate1, human OCT1, OCT2, and MATE2-K, but not human MATE1 [1A in mice and renal clearance of exogenously administered m1A is decreased in Oct1/2 double knockout or by Mate1 inhibition by pyrimethamine to near the glomerular filtration rates while plasma concentrations are increased [1A (1.72-fold) as well as metformin (2.18-fold) [1A in healthy individuals is higher than the GFR, which supports the significant contribution of tubular secretion to urinary excretion. With low diurnal and inter-individual variation in plasma concentrations in healthy volunteers, m1A could be further explored as a surrogate probe for the evaluation of DDIs involving OCT2/MATE2K [Metabolomics data using wild-type and Oct1/2 double knockout mice identified ubstrate . In vitran MATE1 . Urinaryncreased . In cyno18-fold) . In huma2/MATE2K .Carnitine, and its derivatives acetylcarnitine and propionyl carnitine showed a substantial and consistent decrease of urinary excretion in both mice and healthy volunteers in untargeted metabolomics analysis when pyrimethamine was given as a MATE inhibitor. In healthy volunteers treated with metformin at a micro dose or therapeutic dose, renal clearance of carnitine and acetylcarnitine was decreased by 90–94% and 87–91%, respectively, when pyrimethamine was co-administered , and pyrThe urinary excretion of both carnitine and acetylcarnitine is reduced in Oct1/Oct2 double-knockout mice in comparison with wild-type mice, and carnitine was found to be a substrate of OCT2, which suggests the involvement of OCT2 in the renal secretion of carnitine and possibly acetylcarnitine . NonetheIsobutyryl-carnitine (IBC) has also been recently suggested as a potential OCT1 biomarker from this family, and while genomic data in mouse and humans with active OCT1 genotypes suggests that is a substrate of OCT1 with carriers of high-activity OCT1 genotypes having almost 3-fold higher IBC concentrations in blood and 2-fold higher in urine compared to deficient OCT1, more specific data for IBC are lacking ,98.Creatinine is produced during muscle metabolism and excreted primarily through glomerular filtration in the kidney. In vitro, creatinine has low affinity as a substrate of OCT2, MATE1, MATE2K, and OCT3 ,100,101,Dopamine is a MATE substrate. As an in vitro MATE inhibitor, its renal excretion is reduced in Mate1 knockout mice or with the administration of imatinib ,102. DopSLC22A2 [The amino acid, tryptophan, is an OCT1 and OCT2 substrate, which was identified in an untargeted metabolomics analysis of urinary samples of 21 subjects who had participated in the clinical trial on metformin pharmacokinetics, in subjects carrying different functional variants in the OCT2 gene, SLC22A2 . A positSLC22A2 .The family of solute carriers is increasingly recognized as having an important role in the pharmacokinetics and pharmacodynamics profiles of many prescription drugs, and as a site of relevance to DDIs. Mechanistically, most of these DDIs can be linked directly to effects of a select set of xenobiotic transporters that are critically important in mediating drug disposition patterns. These transporters include OATPs, OATs, OCTs, and MATEs, and current regulatory requirements  propose Such potential biomarkers should be thoroughly investigated and validated and should include considerations related to selectivity and specificity (determining exactly which transporters are involved in a biomarker’s distribution), sensitivity , reliability, and reproducibility  ,105. AltOne particularly fruitful area of research that could help resolve the above problems is related to the implementation of a bottom-to-top approach that involves the selection of endogenous transporter substrates from untargeted metabolomics approaches in transporter-deficient and humanized transgenic mice in the presence and absence of specific inhibitors. This would allow for the identification of putative biomarkers in a more unbiased manner that does not depend on top-to-bottom strategies that are initiated by observations in human subjects where the likelihood of identifying selective and specific biomarkers is intrinsically compromised. In such bottom-up approaches, substrates with the most robust differences can be more easily identified and subsequently used to ensure that a sufficient degree of sensitivity is achieved. Another useful tool that requires further investigation is the application of endogenous biomarker cocktails to highlight the individual and collective importance of multiple transporters simultaneously in the context of DDIs prediction . CarefulAlthough the use of cocktail strategies together with multiple biomarkers opposed to testing single biomarkers to predict certain DDIs has been proposed and may have clinical importance, this remains to be clarified for most perpetrator drugs. In addition, more detailed investigations into the influence of ethnicity and racial ancestry on the functional utility of particular transporter biomarkers, and on the basal function and expression of relevant transporters, is urgently required. Despite these reservations, it is anticipated that, over the next decade, the importance of transporter biomarkers and their role in DDI prediction will be more clearly defined, and that continued investigation in this area will likely have a profound impact on attempts to refine treatment regimens involving polypharmacy with transporter substrates.