Dielectrophoresis(DEP) is a versatile tool for the precisemicroscalemanipulation of a broad range of substances. To unleash the full potentialof DEP for the manipulation of complex molecular-sized particulatessuch as proteins requires the development of appropriate theoreticalmodels and their comprehensive experimental verification. Here, weconstruct an original DEP platform and test the Hölzel–Pethigempirical model for protein DEP. Three different proteins are studied:lysozyme, BSA, and lactoferrin. Their molecular Clausius–Mossottifunction is obtained by detecting their trapping event via the measurementof the fluorescence intensity to identify the minimum electric fieldgradient required to overcome dispersive forces. We observe a significantdiscrepancy with published theoretical data and, after a very carefulanalysis to rule out experimental errors, conclude that more sophisticatedtheoretical models are required for the response of molecular entitiesin DEP fields. The developed experimental platform, which includesarrays of sawtooth metal electrode pairs with varying gaps and producesvariations of the electric field gradient, provides a versatile toolthat can broaden the utilization of DEP for molecular entities. Dielectrophoresis(DEP), representing a motion of particulates induced by an inhomogeneouselectric field (EF), is a solid candidate for such manipulations,and several experiments have demonstrated the separation, focusing,and immobilization of proteins using DEP.20 There is, however, still a lack of complete understanding of themechanisms involved in this process, and no solid theoretical modelexists. In contrast, colloidal nanoparticles and cell DEP have beenexplained in terms of a well-developed classical dielectric theory,21 which validity has been broadly confirmed experimentally.25The spatial manipulation ofproteins using an external force is of paramount interest for bothfundamental studies and real-world applications,27 Briefly, in these series of studies, they stressed that, startingwith the very first report on protein DEP,28 most experiments have been realized by applying electric field (EF)gradients several orders of magnitude lower than the minimum valuepredicted by the standard DEP theory to overcome dispersive forcesassociated with Brownian motion.29 Thereason for this surprising disagreement is most probably connectedwith the fact that a protein carries net positive or negative charges,which constitute its permanent dipole moment and are responsible forthe so-called dielectric dispersion.31 Hence, thetotal dipole moment of a protein in a DEP experiment should be thesum of the permanent and induced dipole moments, while the classicalDEP theory deals only with the latter. It is important to emphasizethat “the protein′s dipole moment” refers tothat originating from the protein structure (polypeptide chain), theprotein hydration sheath, and the water molecules electrostaticallyattracted by the protein. As a result, the actual polarizability ofthis dynamic molecular conglomerate becomes several orders of magnitudelarger than the polarizability predicted by the standard macroscopictheory,27 and the EF gradient requiredfor DEP is considerably reduced.Recently, Hölzel and Pethig have discussed in detailtheexisting “classical” DEP theory limitations when appliedto interpret the experimental manipulation of proteins.33 This analytical theory, coupled with the massive molecular dynamicssimulations, represents a tremendous step toward a better understandingof protein DEP.Since both dipole moments arelikely to simultaneously exist uponexciting the protein in an inhomogeneous EF, the standard DEP theorythus must describe them jointly in some way, which is currently notthe case. By considering this, Hölzel and Pethig have alsomade an attempt to reconciliate both polarization mechanisms by derivinga simple empirical relation, which involves the dielectric incrementΔε—the bridge between microscale polarization processesin the protein–water mixture and its macroscale experimentaldetermination—available from dielectric spectroscopy data,providing a practical model for analyzing protein DEP, at least untilreliable ab initio calculations are available for all proteins ofinterest. Actually, there are some promising developments in thisdirection: Matyushov and colleagues have developed an analytical theorythat includes the correlation terms between protein and water dipolemoments and accounts for the close interaction between two dielectricsin a mixture, which is neglected in classical DEP theory.26 the required dielectric spectroscopy data exist only forthree of them, namely, bovine serum albumin (BSA),34 concanavalin,35 and ribonuclease.36 Yet, the calculations using the proposed model for these three proteinsare very insightful;27 the estimated DEP forces becomecloser to those observed experimentally.28 It is therefore of practical importance to expand the pool of proteinsthat can be described correctly with this simple model.This excellent idea of linking micro- and macroscalesfor proteinsusing experimentally available data on both the DEP force and thedielectric increment Δε attracts a large interest forpractical verification. Unfortunately, among nearly 22 different proteinsstudied by means of DEP as reviewed elsewhere,34 lysozyme has been partly studied ,35 while no DEP or dielectricspectroscopy data are available for lactoferrin. This way, we areable to both test the reliability of our experimental approach fordetermining the different DEP parameters and generate experimentalDEP data for an additional protein.We thusstudy here the DEP responses for three proteins, namely,lysozyme, lactoferrin, and BSA. The choice of this set is dictatedby the varying availability of dielectric spectroscopy and DEP data:BSA has been completely studied,g between two opposing sawtooth electrodes  finite-elementmethod (FEM) numerical simulation, and state-of-the-art scanning electronmicroscopy (SEM) augmented by focused ion beam (FIB) SEM characterization.All of the results are also carefully analyzed for various possiblesources of experimental errors, showing their minor effect.26Cw and Cp are the molar concentrationsof pure water(55.5 M) and protein, respectively; ρw and ρp are the mass densities of pure water and protein, respectively;and εm is the dielectric constant of the solvent. Values for Δε have been taken from the correspondingdielectric spectroscopy measurements  and ε0 is the vacuum permittivity.We therefore take thischallenge up by utilizing anovel DEP devicedepicted in KTR–1, defined by R. Pethig in his book onp. 352,29 where K is theBoltzmann constant, we estimate the molecular CMmicro functionusingThe experimental conditions are chosen such that other confoundingforces  and proteinaggregation in the sample can be neglected. Thus, proteins are trappedby each successive electrode pair, up to the one that provides insufficientEF gradient magnitude to generate large enough DEP force to overcomedispersive forces associated with the Brownian motion. The trappingevent is observed in terms of fluorescence intensity. Progressingalong the successive electrode pairs with increasing gap distancesshows a sudden fluorescence intensity drop: from that position onward,no protein trapping and therefore no fluorescence is observed forelectrode pairs with larger gaps. The EF gradient magnitude estimatedby numerical calculations for the last electrode pair with observablefluorescence (threshold electrode) gives the minimum EF gradient requiredto trap the chosen protein. Applying the empirical model from micro; however, it is reasonableto assume equality between both terms at the trapping threshold. Theexperimentally obtained molecular CMmicro functions foreach protein are then compared with reported values.27Strictlyspeaking, Fluorescein isothiocyanatemolecules , carbonate–bicarbonatecapsules, 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid powder, sodium hydroxide powder, dimethylsulfoxide(DMSO), lysozyme from chicken egg white , BSA, lactoferrin from bovine colostrum , and dialysis tubes  were purchased from Sigma-Aldrich. Syringe filters(0.1 μm) with alumina membrane were obtained from Whatman Anotop.Imaging spacers were acquired from Grace Bio-Labs SecureSeal.50 First, a 100 mm borosilicate wafer (D263T/DS) was cleaned in piranhasolution (UFT piranha wet bench) followed by treatment in high-frequencyoxygen plasma (Tepla GiGabatch) at 600 W for 3 min with a 400 sccmO2 flow. Next, a negative photoresist AZ nLOF 2020 wasspin-coated at 6000 rpm for 45 s and baked on a hot plate at 105 °Cfor 60 s, producing a 1.4 μm thick photoresist layer for its gradient. Simulations were conducted for each Ne electrode pair by performing a parametric sweep throughthe gap size g. One of the electrodes from the pairwas grounded, while the boundary condition for the second was ϕ= Vpp, where Vpp = 8, 10, 12, and 15 V at an applied frequency of 0.3 MHz. The fieldwas simulated in a water background, assuming a dielectric permittivityεm = 78 and an electrical conductivity σm of 167.5 μS/cm. Gold with σgold =456 kS/cm and εgold = 6.9 was used for the electrodematerial. The mesh size used for the discretization was 5 nm .The electromagnetic finite-elementsimulations for the dielectrophoretic device were performed usingthe AC/DC module of COMSOL Multiphysics 5.6. The calculation procedurewas based on the simulation of the potential ϕ distributionby solving the Laplace equation: ∇All proteins utilized in this workwere labeled with FITC according to the following procedure. First,one carbonate–bicarbonate capsule was dissolved in deionizedwater (50 mL) producing buffer solution with pH 9.5. Next, HEPES (2.38g) was dissolved in deionized water (90 mL) and mixed with NaOH  to adjust the pH. The volume of the buffer was increasedup to 100 mL and then dissolved with deionized water to obtain HEPESsolution (5 mM) with a pH of 7.4 and a conductivity of 167.5 μS/cm.Next, FITC (1 mg) was dissolved in dry DMSO (1 mL) immediately beforeusage to avoid its degradation with time. A solution of lysozyme (7mg/mL), BSA (7 mg/mL), and lactoferrin (7 mg/mL) in carbonate–bicarbonatebuffer was mixed with FITC in DMSO. The mixtures were vigorously stirred for 1 h at room temperaturein the dark. After the reaction occurred, the mixtures were filteredusing 0.1 μm syringe filters and dialyzed against HEPES buffer for 14 h to remove the excess of unbound dye molecules.The dialyzed solution was again filtered and adjusted to the finalconcentration for each protein (500 ng/mL).The reproducibility and morphologyof the fabricated dielectrophoretic device were characterized by field-emissionSEM (Zeiss MERLIN) and FIB SEM (FEI Nova 600 NanoLab) managed at 3and 5 kV, respectively. An ultraviolet–visible–near-infrared(UV–vis–NIR) spectrophotometer (Shimadzu UV-2600) wasutilized to determine the degree of labeling for the proteins.For the proteinDEP experiments, a drop of freshly labeled protein solution (37.7μL) was placed on top of the dielectrophoretic device, coveredwith an imaging spacer , and then sealedwith a microscope coverslip 1e. The dg. In total,four pairs of lines are connected to one pair of contact pads, whilethe whole device contains four such ensembles. Two different designswith various numbers of electrode pairs, Ne, were developed in this work. The first one includes Ne = 215 sawtooth tips separated by gaps varying between2 μm (gmin) and 130.5 μm (gmax). The corresponding gap distance incrementΔg between two adjacent tip pairs is about0.6 μm. The second design includes Ne = 90 sawtooth tips with gmin = 30 μm, gmax = 375.7 μm, and Δg about 3.8 μm. Both devices were connected to a function generatorusing macroelectrodes by making a mechanical contact with the pads.The optical and field-emission SEM images for the fabricated devicesare shown in The design of the dielectrophoreticdevice and the corresponding scanning electron microscopy (SEM) imagesare shown in E0| and ∇|E0|2 ; however, in this case,the magnitude of the electric field intensity gradient is about 2.5times weaker This difference can be explained by the different radiiof curvature observed at the end of the electrode tip, namely, 50nm for the cross-sectional view (S2): the shortest radiusof curvature producing the strongest field gradient.51 Considering the proportionality between the DEP force andthe intensity of the EF gradient  with a concentration of 500 ng/mL. Lysozyme isstudied in Ne = 90 device is used for lysozyme, while thatwith Ne = 215 is used for BSA and lactoferrin.Similar fluorescence images for other applied voltages and electrodepairs can be found in the Supporting Information .Movie S1 for the FITC-labeled lysozyme), indicatingbiomolecular movement toward electrode edges (positive DEP) inducedby various DEP forces generated in the EF gradient . When the gap reaches a specific value, no fluorescence is observedfor the electrode pairs with larger gaps, albeit a weak fluorescencebackground from the molecules randomly adsorbed across the surfaceof the device. This fluorescence threshold indicates the transitionbetween trapping and nontrapping regions, where the DEP or dispersionforce is dominant, and can be correlated with the EF intensity gradientobtained from the corresponding numerical simulations. Since eachprotein has a different polarizability, one needs to select the correctdevice, such that this transition between trapping and nontrappingregions is well visible. Here, we use the device with Ne = 215 for BSA and lactoferrin. For lysosome, no fluorescencethreshold was observed with that device and we had to resort to the Ne = 90 device,which has larger gaps and produces weaker EF intensity gradients toclearly identify the fluorescence threshold for that protein.Upon the application of the externalAC signal,the accumulationof proteins begins in the gap of the first electrode pair and rapidlyprogresses to the neighboring pairs , while its standarddeviation determines the vertical bars. The gray regions in R2 ∼0.97) has the same exponential dependence onthe electrode pair number as the EF intensity gradient .micro values are different from those derived using the dielectricspectroscopy by the empirical model in micro = 3 ±0.5, which is close to that obtained for BSA.It is seen from micro values andthose reported in the literature. The first most obvious source ofexperimental error may be related to the fluorescence intensity measurements.These measurements directly determine the threshold ∇|E0|2 values that are used for CMmicro calculations via 55 This means that we utilizedthe 95th percentile of the background fluorescence distribution asa limit for distinguishing significant fluorescence intensity values.Hence, when the mean fluorescence intensity distribution measuredat a given electrode pair is equal to this limit of the blank, then50% of the measured fluorescence distribution in that region is significant,i.e., statistically different from the background fluorescence.57 This limit is high enough for our purpose because of the followingreasons. Since the mean intensities and corresponding standard deviationsdepicted in Figure S6), the identical number of pixels is always involved in thecalculation. However, the protein distribution near the electrodeschanges upon increasing the gap size: accumulation and therefore fluorescenceemission occur mainly near the electrode tips and drops rapidly alongthe sides of the electrodes. Since our chosen region of interest , where the fluorescence emission iscalculated, encompasses not only the electrode apex but also partof the triangular electrode sides, the total measured mean fluorescenceintensity and standard deviation values for the electrodes with largergaps are thus smeared out by the image pixels without fluorescencealong these sides. As a result, the measured mean intensity valuebecomes closer to the background intensity, while the electrode apexstill exhibits distinguishable fluorescence. Therefore, these electrodescannot be ignored, even if about half of the fluorescence intensityis below the established threshold value.Let us discuss the effectof possible sources for the observeddiscrepancy between the experimental CM55 which is placedtwo standard deviations higher than the limit of the blank and isused, e.g., to eliminate false-positive results in clinical trials.57 In that case, the gray regions in micro becomes smaller for all proteins. This producesa larger statistical difference between the background and fluorescenceof interest but increases the discrepancy between the experimentalCMmicro values and those reported in the literature forlysozyme and BSA. We can argue that if the observed discrepancy wascaused by an inappropriate choice of the fluorescence intensity threshold,then the reinforcement of the threshold should bring the experimentallyobtained CMmicro values closer to the reported ones, whichis not the case here.We could have usedas a significant fluorescence intensity threshold the limit of detectiondefined in ISO 11,843,E0|2 simulationaccuracy. In thiswork, the finite-element method was utilized for the simulations.This means that the values of calculated spatial derivatives for thepotential distribution  strongly dependon the defined model geometry and the chosen mesh resolution. CorrectEF simulations require an accurate knowledge of the electrode geometry,especially its corners. We have taken great care to define the geometryfrom FIB SEM images . Of course, the actual radius of curvaturemay vary between different electrode pairs due to fabrication imperfections.Moreover, applied voltage instabilities may also play a role in thereal ∇|E0|2 values establishedduring experiments. However, repeated measurements confirmed the reproducibilityof the determined DEP threshold, and we can assume that the obtainedCMmicro values are weakly dependent on this source of error.Next, we examine the possible influenceof the ∇|M images 2d,e and m = 167.5 μS/cm)were utilized in the experiments.45 The same low-conductivity conditionsas well as low protein concentrations (500 ng/mL) and physiologicalpH of the utilized buffer (7.4) prevent changes of the protein molecularproperties  caused by aggregation and thus ensurethat DEP is done on a molecular scale rather than the macroscale .60Although it cannot be completely excluded, it is very unlikelythat the interaction between DEP and electrothermal effects couldhave played a role here. Indeed, relatively high-frequency (300 kHz)and low-conductivity buffers .Another importantfactor to consider is the proper definition ofthe protein volume in micro values published in the literature. We have measuredan approx. 3.5 times lower CMmicro value for lysozyme andan approx. 2 orders of magnitude lower value for BSA (micro for lactoferrin, for which no dielectric spectroscopy data exist.In any case, the possible shortcomings of classical theories for describingDEP of proteins observed here are consistent with the observationsthat have prompted the development of more sophisticated theories33 and previous experimental findings,28 indicating that the standardmacroscopic DEP theory fails to describe protein DEP. The proteinDEP response is usually observed upon generating much lower EF intensitygradients compared to those required to overcome dispersive forces.Overall, we have not been able to experimentallyreproduce theCM for BSA 1. We alsmicro function have been calculated according to micro values with those calculated by therecently reported empirical model of In summary, we have experimentally studiedDEP for three modelproteins, namely, lysozyme, BSA, and lactoferrin, using a device builtfrom an array of metal electrode pairs with varying gaps, which producesvariations of the EF gradient intensity. This device can effectivelydetermine the transition between two competing forces: DEP and thedispersive force associated with Brownian motion. This transitionhas been determined by the last electrode pair with observable fluorescencefrom the trapped proteins. The DEP force and molecular CMThe presentedexperimental findings are generally consistent withthe known failure of the classical DEP theory when applied to proteinDEP. We have also shown that even with incorporation of the most recentdevelopments of the protein DEP theory, the experimental results arestill far from the calculations.micro function may be utilized for any proteinmolecule, thus providing an efficient approach to expanding experimentalDEP data and refining protein DEP theory. These advances will setthe ground for developing new types of biosensors able to manipulate,sort, and trap proteins at the microscale.Let us conclude by noting thatthe experimental technique establishedhere to determine the minimum DEP trapping threshold and the correspondingmolecular CM


DNA persistence and DNA transfer are important features in the assessment of a crime scene. The question how long DNA may persist at a certain location is similarly important as the one how the DNA has been transferred to this location. Depending on the source of the DNA as well as the conditions at the crime scene, the answer to this question is quite difficult. In this study, persistence of DNA from epithelial abrasions, blood cells, and saliva cells in indoor and outdoor scenarios has been investigated with regard to exposure time and exposure conditions including sunlight, temperature, and humidity in summer and winter scenarios. Overall, we generated 338 epithelial samples, 572 blood samples, and 572 saliva samples. A complete profile of the cell/DNA donor after exposure could be obtained in 47%, 65%, and 58% of epithelial abrasions, blood samples, and saliva samples, respectively. Regarding blood samples, there were no differences between supporting materials cloth and plastic; however, the percentage of complete profiles was higher for saliva samples on plastic and for epithelial samples on cloth. In indoor scenarios, complete profiles could be recovered from nearly all blood and saliva samples up to 9 months, whereas the amount of epithelial complete profiles already started to decline after 3 months. In outdoor scenarios, we observed a tipping point at an exposure time of 3 months. Blood and saliva samples collected after this period displayed complete profiles in less than 25% of samples. After 12 months, no outdoor sample showed a complete profile. The results of this study facilitate decisions on the relevance of recovered DNA from crime scenes.The online version contains supplementary material available at 10.1007/s00414-022-02800-6. The great progress in forensic DNA analysis has led to DNA testing being conducted regularly and on a large scale during police investigations , since iThe study comprised blood, saliva, and epithelial abrasions from two individuals of different age , without any known skin disease to exclude an influence of the shedder status as described by Kamphausen et al. . SamplesAll samples were obtained after informed consent and with approval of the Medical Ethics Committee at the University of Duisburg-Essen in accordance with the Declaration of Helsinki and national laws .Several different scenarios were set up Table ; in eachCollecting of DNA from plastic surfaces as well as cotton clothes was done as described before . DNA extAll negative controls showed no profiles; DNA amount was either negative (undetermined according to PowerQuant System) or below 0.0005 ng/µl.First, we established expected DNA yields for our samples by analyzing 16 different samples for each chosen amount of blood and saliva as well neck abrasions Table . Here, 2DNA concentrations of samples from the different scenarios measured by Real-time PCR varied widely between the different experimental setups as well as different cell origins Table , but corLooking at the results with no regard to the scenario, complete profiles could be demonstrated in 861 samples . Only 177 samples (12%) showed a partial profile, distributed equally about different scenarios and cell origins. Since the amount of partial profiles in this study is rather low, we decided to concentrate on complete profiles only for all further evaluations. Moreover, although a partial profile may sometimes provide information for a possible assignment of a person to a DNA samples, it does not offer enough certainty to allow a biostatistical calculation in Germany , 25.Considering cellular origin, 373 blood samples (65% of blood samples), 330 saliva samples , and 158 epithelial samples  showed a complete profile Table . These dA higher resilience of saliva samples has already been demonstrated  and may There were no differences in the number of complete profiles between samples on plastic and samples on cloth . However, the results did vary, if the origin of the cells was also considered Supplementary file2 (DOCX 13 KB)Supplementary file3 (DOCX 14 KB)Supplementary file4 (XLSX 16 KB)Supplementary file5 (DOCX 12.6 KB)Below is the link to the electronic supplementary material.


Enriched bread had a hard texture and required a lot of chewing force compared to the control. Furthermore, all samples yielded a shelf-life of 5 days when monitoring the mold growth. From the results, the bread enriched with 20% cricket powder yielded the best consumers’ acceptance score of 77%. It was predominantly high in indispensable amino acids such as leucine, phenylalanine, lysine, and arginine. Therefore, cricket powder could be a novel alternative protein source and successfully utilized in whole wheat bread to enhance its protein content and indispensable amino acids with consumers’ acceptance responding to the current market trend.The current market trends in modern sedentary lifestyles drive the development of new functional products able to fulfill consumers’ demand for a healthy diet. Whole wheat bread contains more protein and fiber than white bread; however, it could be improved in terms of protein content and quality. Cricket powder, which contains high protein , could be used as an alternative source to tackle those deficiencies in such bread. Hence, the study aimed to apply cricket powder in the whole wheat bread formula to enrich protein content, indispensable amino acids and determine their physico-chemical properties, consumers’ acceptance, and shelf-life storage. The results showed that all enriched bread presented high protein , fat , and ash  with the increment of the cricket powder than those of the control bread. Enriched breads’ crust colors were not significantly different ( The steadily increasing global population will reach 9.8 billion people in 2050  resultinBread is a vital staple food and is widely accepted and consumed throughout the world. Increasing bread consumption was reported as a 1.43% Compound Annual Growth Rate (CAGR) between 2019 and 2024 in the world market due to its convenience, portability, nutrition, and taste . The staThe research and development on the application of edible insects is being actively pursued and can be observed by a large number of patents and research articles. Currently, 167 patents and 488 articles related to the applications of edible insects have been filed . In prevAcheta domesticus) was selected due to it being a good source of protein  [There are some species of insects that are very popular to consume in Thailand such as house crickets, bamboo caterpillars, and grasshoppers ,18. Cons70, wt%) , its aff70, wt%) . Additio protein 5–70, wt%70, wt%) . Moreove70, wt%) . These aAcheta domesticus) was purchased from the cricket farm . Pandan leaves were purchased from the local market of Ban Du, Chiang Rai, Thailand. The ingredients for the production of whole wheat bread were purchased from a baking supply shop  including the main ingredients; whole wheat flour , wheat flour  for 12 h before the production of cricket powder. Cricket powder was prepared using the method according to Lucas-González et al.  with sliDeterminations of chemical compositions including moisture, protein, fat, and ash content followed American Association of Cereal Chemists (AACC) approved methods of analysis . MoisturDetermination of physical properties including color using a slightly modified method according to Bawa et al. . The criWater holding capacity (WHC) and oil holding capacity (OHC) were determined by using a slightly modified method according to da Rosa Machado and Thys . One graFoaming capacity (FC) and foaming stability (FS) were conducted by a slightly modified method by Stone, Tanaka and Nickerson . Two graFor foam stability, the samples were allowed to stand for 30 min at room temperature and the remaining foam volume was measured. The following formula was used to calculate FS:®, St. Joseph, MI, USA) and mixed at speed No. 4 for 15 min. while vanilla flavor and milk were gradually added until the dough was obtained. Then, the mixer bowl was covered with plastic wrap, and the dough left to ferment at 40 °C for 1 h. After dough fermentation, the dough was removed by a rolling pin and molded-in aluminum bread mold tray (6.7 cm width × 13.7 cm length × 5.5 cm height) then covered with plastic film wrap and let stand for 20min. The dough was baked at 175 °C for 30 min then the loaves removed from the bread mold tray. Loaves were left to cool down at ambient temperature for 2 h. All loaves were packed in polyethylene bags and stored at −40 °C until analysis.Whole wheat bread was prepared following a formula presented in Determinations of chemical compositions including moisture, protein, fat, ash, and carbohydrate content were performed as previously described above.Amino acid profile was performed only in the cricket powder enriched bread with the best acceptance score and determined using outside analysis service by Central Laboratory  Co., Ltd. employing the in-house method TE-CH-372 based on the Official Journal of the European Journal of communities, L257116 by amino acid analyzer technique.Physical properties in terms of the color of crust and crumb and texture profile including hardness, cohesiveness, springiness, and chewiness were determined. The color of crust and crumb were determined followed the method described by Bawa, Songsermpong, Kaewtapee and Chanput , Loave sDetermination of the shelf-life of loaves was conducted following the method described by Kamaljit . Loaves Sensory evaluation was conducted at the Food Sensory Lab, Department of Food Science and Technology, School of Agro-Industry, Mae Fah Luang University. Ethical approval for the consumer testing and evaluation of the products was granted by Mae Fah Luang University, Chiang Rai, Thailand. The consent of all participants was achieved. Participants for the consumer testing and evaluation of the bread samples comprised 30 untrained panelists, 15 females and 15 males. The panel was asked to rate their preference of how they like the appearance, texture, flavor, taste, and overall acceptability of the products by indicating a numerable value to rate their preference from one (expressing dislike extremely) to nine (like extremely). They were also asked to rate their acceptability of the products via nine points hedonic from one (dislike extremely) to nine (like extremely). To obtain a good understanding of consumer opinions, this stage was evaluated by 100 untrained panelists in the sample which provided the best score from the previous evaluation and commercial whole wheat bread. The panelists were also requested to provide comments and suggestions regarding product development.p < 0.05 was considered significantly different.All data analyses were investigated triplication and expressed as mean ± standard deviation. The different formulas were analyzed using one-factor analysis of variance (ANOVA) by the Statistical Tool for Agricultural Research (STAR) software program . The significance level of The chemical composition of the cricket powder was presented on a dry weight basis  including moisture 2.77 ± 0.11, protein 55.11 ± 3.10, lipid 22.83 ± 0.40, ash 4.80 ± 0.10, carbohydrate 17.26 ± 0.23, respectively. Cricket powder predominantly consists of protein and lipid. The same results were reported by Rumpold and Schlüter , who repThe color of cricket powder showed L* (40.81 ± 0.61), a* (4.9 ± 0.53), b* (15.13 ± 2.02), respectively. The resulting value indicates that the cricket powder has a dark color due to the luminosity (L*) which indicates the lightness is quite low. The same result was reported by other researchers including Lucas-González, Fernández-López, Pérez-Álvarez and Viuda-Martos  who statThe applicability of such protein ingredients depends upon their potential to fulfill one or more functional requirements, viz. solubility, water holding capacity (WHC), and oil holding capacity (OHC) or foam and emulsion stabilization . WHC andThe foaming capacity (FC) and foaming stability (FS) of cricket powder were 8.1 ± 1.20% and 32.43 ± 9.89%, respectively. It was reported that foamability is related to the rate of decrease in the surface tension of the air/water interface caused by the absorption of protein molecules. Low foamability could be related to highly ordered globular proteins, which resist surface denaturation . Ndiritup < 0.05) from the control bread. The moisture content seemed inconsistent with the linear increase in cricket powder. During kneading, protein absorbs water to enable it to flex into gluten. The more protein in the flour, the more water is required to effectively hydrate the dough [The results of moisture, ash, protein, lipids and total carbohydrate on a weight dry basis (g/100 g) are depicted in he dough . Howeverhe dough . Crickethe dough .Amino acid compositions were determined only in the bread enriched with 20% cricket powder which yielded the highest acceptance score from panelists and is depicted in Texture profile analysis (TPA) showed that all cricket powder enriched loaves have a higher value of hardness  and chewiness  than control . This caImages of the cross-section of bread slices are depicted in Generally, homemade bread has a shelf-life of 4–6 days under ambient conditions . In thisBacillus licheniformis, Bacillus subtili and Brevibacillus borstelensis. The first two species are associated with ropy spoilage of bread and B. licheniformis is also potentially pathogenic to humans. Another point is that allergens are a major safety concern surrounding edible insects. Several research groups have identified potential antigens and IgE binding proteins in various insect species, which may correlate to an allergic reaction after consumption [The limitation of edible insects can be perceived by biological hazards associated with the rearing environment and insect’s gut. The presence of spore-forming bacteria in insect-based foods represents a serious threat to consumers’ health since these microorganisms are food-borne pathogens . Howeversumption . The guisumption  stated tp < 0.05) from that of enriched bread with 20% cricket flour, the enriched bread with 20% cricket flour was selected for the further step because of the higher protein content. In the comparison among all bread, it was found that enriched bread with 20% cricket flour yielded a higher score on the overall acceptability. In terms of the food industry and marketing of the product, sensory evaluation with a high overall acceptability score could be used as the key point due to one of the most significant factors influencing the consumer purchase decision and consumption [The result showed that consumers favor the 20% cricket formula the most. Although the results of the 10%, 15%, and 20% cricket formulas were similar in some characteristics such as appearance, texture, color, flavor, taste, and overall acceptability which are demonstrated in sumption . As reposumption , insect-Commercial whole wheat bread was reported to contain total energy , total fat (4%), protein (2%), total carbohydrate (44%), and dietary fiber (4%). It was found that the acceptance score of 77% was derived from the appearance, texture, color, flavor, taste, and overall acceptability of enriched bread with 20% cricket flour compared to those of commercial bread which is 100%. Nevertheless, consumers suggested improving the quality of the product in terms of texture since cricket powder contains high lipid content, thus, making the bread quite crumbly. Balancing the ratio of ingredients and reducing fat content should be improved in future studies.Cricket powder can be utilized as a novel alternative protein source in terms of protein quality, and as an environmentally friendly resource. All enriched bread formulas presented high protein content and the one enriched with 20% cricket powder had the best acceptance without any effect on shelf-life. The derived products successfully combine the compositional and sensory characteristics with some peculiar nutritional properties of cricket. Cricket powder can constitute a novel source of innovative ingredients to be used for the protein enrichment of indispensable amino acids and minerals. In future studies, fatty acid profile analysis is advisable to confirm the functional fatty acids contained in the starting material and final product. Future investigation should also consider improving the texture as well as insect bioactive properties within the food matrix and the effect on allergenicity. Lastly, the current work is not only in response to the current trends in modern sedentary lifestyle with the development of the new functional products able to fulfill consumers’ demand for a healthy diet, but the economics of the country as well. The edible cricket industry will be expanded by adding up the value of processed products. Moreover, bakery industries and entrepreneurs also yield the outcome from this study with the invention of functional health products. The world is also impacted in terms of world food security and environmental issues because insect farming is more pre-eminent in terms of lower greenhouse gas emissions, water, and soil use, but with higher feed conversion and edible mass compared to domestic livestock. Moreover, making consumers respond with positive attitudes toward innovative insect-based foods is one of the most challenging issues facing the industrial sector.


Inflammatory reactions and pyroptosis play an important role in the pathology of intervertebral disc degeneration (IDD). The aim of the present study was to investigate pyroptosis in the nucleus pulposus cells (NPCs) of inflammatory induced IDD by bioinformatic methods and to search for possible diagnostic biomarkers.  Gene expression profiles related to IDD were downloaded from the GEO database to identify differentially expressed genes (DEGs) between inflammation-induced IDD and non-inflammatory intervention samples. Pyroptosis genes were then searched for, and their expression in IDD was analyzed. Weighted gene co-expression network analysis (WGCNA) was then used to search for modules of IDD genes associated with pyroptosis and intersected with DEGs to discover candidate genes that would be diagnostically valuable. A LASSO model was developed to screen for genes that met the requirements, and ROC curves were created to clarify the diagnostic value of the genetic markers. Ultimately, the screened genes were further validated, and their diagnostic value assessed by selecting gene sets from the GEO database. RT-PCR was used to assess the mRNA expression of diagnostic markers in the nucleus pulposus (NP). Pan-cancer analysis was applied to demonstrate the expression and prognostic value of the screened genes in various tumors.  A total of 733 DEGs were identified in GSE41883 and GSE27494, which were mainly enriched in transmembrane receptor protein serine/threonine, kinase signaling pathway, response to lipopolysaccharide, and other biological processes, and they were mainly related to TGF beta signaling pathway, toll-like receptor signaling pathway, and TNF signaling pathway. A total of 81 genes related to pyroptosis were identified in the literature, and eight genes related to IDD were identified in the Veen diagram, namely, IL1A, IL1B, NOD2, GBP1, IL6, AK1, EEF2K, and PYCARD. Eleven candidate genes were obtained after locating the intersection of pyroptosis-related module genes and DEGs according to WGCNA analysis. A total of six valid genes were obtained after constructing a machine learning model, and five key genes were finally identified after correlation analysis. GSE23132 and GSE56081 validated the candidate genes, and the final IDD-related diagnostic markers were obtained as SMIM1 and SEZ6L2. RT-PCR results indicated that the mRNA expression of both was significantly elevated in IDD. The pan-cancer analysis demonstrated that SMIM1 and SEZ6L2 have important roles in the expression and prognosis of various tumors.  In conclusion, this research identifies SMIM1 and SEZ6L2 as important biomarkers of IDD associated with pyroptosis, which will help to unravel the development and pathogenesis of IDD and determine potential therapeutic targets. Intervertebral disc degeneration (IDD) is a chronic degenerative disease that is an important cause of chronic back and leg pain and even disability , 2. Studβ and tumor necrosis factor-alpha (TNF-α) being the most representative [It has been demonstrated that the inflammatory response is an important factor in IDD –9. With entative . Pyroptoentative . Variousentative , 13. Wheentative . It is eWith the constant maturation and development of information technology and gene sequence technology, important biomarkers for some common clinical diseases have been steadily screened out and can be used as important targets for subsequent diagnosis or treatment . Accordihttps://www.ncbi.nlm. http://nih.gov/geo/). We downloaded the gene expression profiles in GSE41883, GSE27494, GSE23130, GSE124272, and GSE56081. GSE41883, GSE27494, and GSE124272 were used as the training set and GSE23130 and GSE56081 as the validation set. The specific dataset information is displayed in The datasets for this study were all sourced from the GEO database (P < 0.05 and log2 (FC) > 2 or log2 (FC) < −2”was defined as the threshold gene differential expression screening condition. The PCA plot was created with the R package ggord, and the heat map was presented with the R package pheatmap. To further confirm the potential function of the differential genes, GO and KEGG functional enrichment analysis was performed by the ClusterProfiler program package.The R software limma package investigated differential gene expression between the two groups of samples in GSE41883 and GSE27494. “Adjusted http://bioinformatics.psb.ugent.be/webtools/Venn/).This article included 81 genes related to pyroptosis based on previous studies –18. We sβ was selected, and the topological overlap matrix (TOM) was further constructed to generate a hierarchical clustering tree of genes using hierarchical clustering. The gene significance (GS) and module membership (MM) were calculated to measure the significance of genes and clinical information and to analyze the significant associations between modules and models.WGCNA analysis was performed on the selected dataset using the WGCNA package in the R software. First, outliers were filtered to make the model more stable, an appropriate soft threshold The modular genes obtained from WGCNA analysis were intersected with differential genes to obtain core genes associated with pyroptosis. Significant genes were then selected using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm of the glmnet package in R software. The diagnostic value of the obtained genes was clarified by plotting ROC curves. The ultimately obtained genes are of great value in the diagnosis of IDD.To clarify the expression of diagnostic markers, the specific expression of the obtained pivotal genes was validated by the independent datasets GSE23130 and GSE56081.Procell, and we constructed a degeneration model by IL-1β (10 ng/ml). NP tissue and cellular RNA were extracted utilizing the Trizol method and reverse translated into cDNA using ProtoScript II First Strand cDNA Synthesis Kit. RT- PCR was then executed on a Quanstudio DX fluorescent quantitative PCR instrument system utilizing Luna Universal qPCR Master Mix (see Table The NP tissue was obtained from patients who underwent percutaneous endoscope lumbar discectomy (PELD) at the Jiangsu Provincial Hospital of Integrated Chinese and Western Medicine. All patients signed an informed consent form, and the research was approved by the Ethics Committee of the Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine . A total of 25 patients, fourteen with mild degeneration (MD) and others with severe degeneration (SD), were included for relevant molecular biological studies. To further validate SMIM1 and SEZ6L2 mRNA levels, we executed in vitro experiments. Rat NPCs were purchased from N = 19131, G = 60499) in UCSC (https://xenabrowser.net/) database and extracted the expression data of SMIM1 and SEZ6L2 genes. Samples were retrieved from solid tissue normal, primary solid tumor, primary tumor, normal tissue, primary blood-derived cancer-bone marrow, and primary blood-derived cancer. Finally, we executed a log2(x + 0.001) transformation of the expression values. Differences in expression between normal and tumor samples were calculated by R software and subsequently analyzed for the significance of differences using a nonparametric test.We downloaded the pan-cancer dataset from TCGA TARGET GTEx . The Cox proportional hazards regression model was constructed by the coxph function of the survival package in R software to evaluate the prognostic relationship between SMIM1 and SEZ6L2 with pan-cancer [TCGA prognostic dataset the cited from a high-quality research article published in Cell in 2018 . TARGET P < 0.05 indicating a significant difference.All data were statistically analyzed using R and Graph Pad Prism. Measures were expressed as a nonparametric test to compare differences between the two groups, with GSE41883 and GSE27494 were combined, the microarray data were homogenized , and PCAPyroptosis has been demonstrated to play an important role in IDD. Consequently, we searched for 81 pyroptosis Table  related We performed WGCNA analysis on the GSE124272 dataset to further explore the genes associated with the pyroptosis module. Using the WGCNA package of the R software, we constructed the model , and by The DEGs of GSE124272 were obtained by limma package, and 188 differential genes were obtained Figure . It was In order to verify the correlation between the candidate genes and improve the diagnostic value, we conducted correlation analysis on the selected genes  and founP < 0.05). As shown in P < 0.05). In addition, in the SD group, the expression of IL-1β mRNA was significantly increased compared to the MD group  (P = 1.8e − 9) were observed in the results of the survival analysis , and they all showed poor prognosis. In three tumor types , SMIM1 was lowly expressed and showed a poor prognosis . The bes.3e − 6) . In tota.3e − 6) . Signifianalysis . SMIM1 aWith changing lifestyles and the growing aging of the population, the prevalence of IDD is increasing year on year and has become a severe threat to the physical and mental health of patients and the economic development of society. Many risk factors for IDD include aging, smoking, obesity, mechanical loading, genetics, hyperglycemia, and hypoxia. Genetic factors have been reported to account for over 70% , 22. IDDStudies have demonstrated that the intervertebral disc (IVD) is an immune-privileged organ and that immune infiltration plays an important role in developing IDD . In addiFirstly, we conducted enrichment analysis on DEGs and found that the GO function analysis was mainly enriched in transmembrane receiver protein serine/threonine, kinase signaling pathway, response to lipopolysaccharide, and other biological processes. In contrast, KEGG analysis was mainly enriched in the TGF beta signaling pathway, toll-like receiver signaling pathway, TNF signaling pathway, and other pathways. This is essentially the same as the results of the previous study , 38. Theβ also revealed a higher SMIM1 and SEZ6L2 mRNA expression in the degenerated group compared to the control one. This result further illustrates the important value of these two targets in diagnosis and therapy. We subsequently performed a pan-cancer analysis of SMIM1 and SEZ6L2, finding them to be of significant value in cancer diagnosis and prognosis.The mechanism of IDD caused by pyroptosis is definite ; therefoThe small integral membrane protein 1 (SMIM1) is a limited size red blood cell (RBC) membrane protein whose structure is not thoroughly understood and which is associated with a variety of immune responses , 45. It β (transforming growth factor-beta) belongs to a group of growth factors that are members of the TGF superfamily and are important in mediating various cellular functions [β signaling pathway for IDD function remain controversial [β1 expression can repair degenerated discs and improve the inflammatory response within the disc, protect against degradation of the extracellular matrix, promote cell proliferation, and reduce cell death [β signaling pathway may accelerate the progression of IDD [β signaling pathway in IDD. The enrichment analysis demonstrates the importance of the TFGβ signaling pathway in IDD, and the bioinformatics results also indicate that both SMIM1 and SEZ6L2 are important diagnostic targets, but the exact relationship between these two has not been reported in the literature. SMIM1 and SEZ6L2 are importantly linked to cellular immunity and inflammation, and the mechanism by which the TGFβ signaling pathway can regulate IDD by modulating cellular inflammatory responses is also well documented [β signaling pathway is mainly related to the regulation of cellular inflammatory response and pyroptosis, but the exact mechanism needs to be further explored.TGFunctions . It has unctions , 53. Howoversial . Studiesll death –58. Neven of IDD –61. Regacumented , 63. TheSome limitations exist in this article. Although this study has screened SMIM1 and SEZ6L2 as diagnostic markers for LDD through bioinformatics and machine learning methods, it lacks clinical validation in large samples. Second, it is unclear through which pathway these two genes mediate pyroptosis. In the end, the sample size of the gene set in this research screening the GEO database is small and lacks some conventions, and it needs to be expanded later to improve the quality of the study.This research demonstrated the link between IDD and pyroptosis through a bioinformatics approach and combined with machine learning algorithms to identify SMIM1 and SEZ6L2 as important biomarkers, which will help investigate the development pathogenesis of IDD further and identify potential therapeutic targets.


Objective Oral verrucous squamous cell carcinoma or oral verrucous carcinoma (OVC) is a rare verrucous variant of oral squamous cell carcinoma (OSCC), which accounts for 2 to 12% of all oral carcinomas. Oral verrucous hyperplasia (OVH) is clinically similar to OVC and has been proposed to be a precursor lesion of OVC. Etiopathogenesis of both lesions is still inconspicuous. Oncogenic viruses such as human papillomavirus (HPV) and Epstein–Barr virus (EBV) have been reported to be associated with some cases of OSCC, and we hypothesized that it may act as a causative agent of these verrucous lesions. This study aimed to investigate frequency of HPV and EBV infections in OVC and OVH.Material and Methods Using polymerase chain reaction (PCR), a total of 35 formalin-fixed paraffin-embedded (FFPE) tissue samples, including 27 OVC samples and 8 OVH samples, were investigated for HPV and EBV infection. HeLa and B95-8 cell lines were used as positive controls of HPV and EBV PCR, respectively.Results All OVC and OVH samples show a positivity toGAPDH, whereas neither HPV nor EBV PCR products was detected in both OVC and OVH samples.Conclusions In summary, our study demonstrated that HPV and EBV are not involved in pathogenesis of OVC and OVH. Other etiologic factors contributing to OVC and OVH need to be further clarified. The clinical features of OVC appear as well-demarcated, painless, thick plaque with papillary or verruciform surface projections. This histopathologic feature may be mistaken for benign epithelial hyperplasia. Interestingly, the clinical and histopathological morphologies of OVC are closely similar to oral verrucous hyperplasia (OVH). Both OVC and OVH show similar clinical appearance, and the common oral sites of these lesions are also similar.In the present study, OVC and OVH specimens were examined for the presence of HPV and EBV infections by PCR. The result will support whether OVC and OVH are associated with HPV and EBV.Ethical consideration of this study was approved by the Institutional Review Board of Faculty of the Dentistry/Faculty of Pharmacy, Mahidol University (COE.No.MU-DT/PY-IRB 2018/060.1312 and 2021/014.0402). Archival pathological specimens of the Department of Oral and Maxillofacial Pathology, Faculty of Dentistry, Mahidol University were assessed, and a total of 35 samples, including 27 cases of OVC and 8 cases of OVH, were retrieved. The clinicopathological information of each case, including age, sex, and tumor location, was collected from pathology requested forms. Histopathological slides of these cases were confirmed by two oral pathologists (P.S. and P.L.). Histopathological diagnosis of OVH and OVC was made, based on criteria recommended by Lin et al.GAPDH) was served as the internal control to test the quality of DNA. GP5 +/GP6+ primers encoding major capsid protein were examined to detect HPV infection. Finally, LMP1 primers encoding EBV latent membrane protein was used to detect EBV infection. The properties of these primers were shown in2, 200 mM dNTPs , 0.2 mM primers, 0.5 UTaqDNA polymerase , and 50 ng/µl of template DNA. All PCR reactions were done in the thermal cycler with the following conditions: 10-minute initial denaturation at 95 °C, 45 cycles of denaturation at 95 °C for 45 seconds, annealing at 55 °C for 45 seconds, extension at 72 °C for 45 seconds, and a final extension at 72 °C for 10 minutes. Following amplification, the PCR products were separated by gel electrophoresis using 1% (w/v) agarose gel in 1x TBE buffer, and then stained with RedSafe Nucleic Acid Staining Solution .The wax blocks of paraffin-embedded tissue of desired samples were sectioned to obtain tissue sections, approximately three to four ribbons with 10-µm thickness, using a microtome (LEICA RM 2255). The tissue sections were placed into a sterile 1.5 mL microcentrifuge tube for tissue DNA extraction. Genomic DNA extraction was performed, according to the phenol-chloroform protocol previously proposed by Kasperbauer et al.A total of 35 histopathological samples consisting of 27 specimens of OVC and 8 specimens of OVH were obtained from the Department of Oral and Maxillofacial Pathology, Faculty of Dentistry, Mahidol University. The clinicopathological features of OVC patients were presented inGAPDHwere successfully amplified in all samples of OVC and OVH in our study. In addition, internal control amplifications were also negative. Our study found that all 35 samples (100%) of OVC and OVH were negative for both HPV and EBV infections. Representatives of the PCR amplification for each gene fragments were shown inThe DNA fragments of humanOur results demonstrating an absence of HPV and EBV DNA in OVC and OVH is surprising and contradicts our hypothesis that these viral infections would take a role in the promotion of epithelial verrucous proliferation is some OVC or OVH cases. Regarding the previous studies , the roHPV detection in OVC is also highly controversial. One previous study demonstrated that HPV DNA was detected in all lip verrucous carcinoma (100%), using PCR with sequence analysis and restriction fragment length polymorphism analysis. However, only five samples were investigated in their study.With regard to EBV infection, only a few studies investigated EBV in OVC, and very few cases were performed in those studies.The pathogenesis of these verrucous lesions may be contributed to other factors. The etiology of OVC is complex and strong associations between OVC and alcohol consumption, smoking, areca nut chewing, and oral microbiota have been found. In addition, OVC also shows an association with poor prosthesis, earlier injuries and chronic inflammation, or may result from worsening of premalignant lesions such as oral verrucous leukoplakia.GAPDH, a housekeeping gene, was successful in all cases. This indicated that DNA of our samples were good quality for DNA amplification.Numerous studies investigated HPV and EBV infections using PCR method, a laboratory technique used to synthesize the multiple copies of a fragment of DNA. This technique is widely accepted due to its more sensitivity and reliability. It has been proposed that in comparison to DNA isolated from fresh or frozen tissue, the quality of DNA extracted from formalin-fixed paraffin-embedded (FFPE) was inferior and may lead to the negative PCR result.In conclusion, our results do not support HPV and EBV as a potential cause for the development of OVH and OVC in Thai patients. Therefore, further studies may be needed to elucidate the etiology of these lesions.


Lettuce is sensitive to high temperature, and exogenous spermidine can improve heat tolerance in lettuce, but its intrinsic mechanism is still unclear. We analyzed the effects of exogenous spermidine on the leaf physiological metabolism, transcriptome and metabolome of lettuce seedlings under high-temperature stress using the heat-sensitive lettuce variety ‘Beisansheng No. 3′ as the material. The results showed that exogenous spermidine increased the total fresh weight, total dry weight, root length, chlorophyll content and total flavonoid content, increased the activities of antioxidant enzymes such as superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), and decreased malondialdehyde (MDA) content in lettuce under high temperature stress. Transcriptome and metabolome analyses revealed 818 differentially expressed genes (DEGs) and 393 metabolites between water spray and spermidine spray treatments under high temperature stress, and 75 genes from 13 transcription factors (TF) families were included in the DEGs. The Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis of DEG contains pathways for plant–pathogen interactions, photosynthesis-antennal proteins, mitogen-activated protein kinase (MAPK) signaling pathway and flavonoid biosynthesis. A total of 19 genes related to flavonoid synthesis were detected. Most of these 19 DEGs were down-regulated under high temperature stress and up-regulated after spermidine application, which may be responsible for the increase in total flavonoid content. We provide a possible source and conjecture for exploring the mechanism of exogenous spermidine-mediated heat tolerance in lettuce. Environmental temperatures are increasing and causing one of the most severe plant abiotic stresses. Global temperatures are projected to increase by 0.2 °C per decade, which will result in temperatures ranging from 1.8 to 4.0 °C above current levels by 2100 . Heat stIn recent decades, exogenous applications of protectants, such as osmoprotectants, phytohormones, signaling molecules, and trace elements, have shown beneficial effects on plants grown at high temperatures because of their growth-promoting and antioxidant abilities . PolyamiLactuca sativa L.) is one of the most popular leafy vegetables in daily life. It originates from the Mediterranean coast and is highly intolerant to high temperatures, with 15~25 °C being the preferred growth temperature and growth being hindered above 30 °C. High temperatures are one of the main factors limiting the growth of lettuce. High temperatures always result in thinner leaves and longer internodes of lettuce, which leads to a decrease in its nutritional quality and commercial value [Lettuce  content was determined by the thiobarbituric acid method and calculated from the absorption values at 450, 532 and 600 nm. The chlorophyll content was determined by the method of Madhava Rao and Sresty : 0.3 g oThe chloroplast pigment extract was poured into a cuvette, and 95% ethanol was used as a blank control. The optical density (OD) values were measured on a spectrophotometer  at 665 and 649 nm. Chlorophylls were calculated according to the following equations:The flavonoid content was determined by the sodium nitrite-aluminum nitrate chromogenic method. The flavonoid content was determined using a kit . After the sample was dried to a constant weight, 0.1 g was dissolved in 1 mL of the extraction solution. The sample was extracted by ultrasonication at 60 °C for 30 min and then centrifuged at 12,000 rpm for 10 min, and the supernatant was taken for measurement. The absorbance value of the sample extract at 470 nm was measured.g. The supernatant was collected, and the above steps were repeated twice.Place 0.5 g of lettuce sample in a cooled mortar and add in 5 mL of phosphate buffer (pH 7.0) for grinding. The homogenate was collected and centrifuged at 4 °C for ten minutes at a force of 11,000× −1 cm−1) [Superoxide dismutase (SOD) activity was measured by the NBT (nitrogen blue tetrazolium) photochemical reduction method , with 50−1 cm−1) .g, 4 °C) and 600 µL of the supernatant was collected, and it was repeated once. The supernatant was placed in a freeze extractor to drain. Then, 600 µL of 10% methanol solution was added, it was placed in an ice bath and sonicated for 10 min (power 80 HZ) and then centrifuged for 15 min . The supernatant was collected, and 50 µL of each sample was injected into a QC using an ACQUITYUPLCHSST3 column  for chromatographic separation, and the small molecules that eluted from the column were collected in positive and negative ion modes using high-resolution tandem mass spectrometry . Peak extraction was mainly implemented by the commercial software Progenesis QI , including peak alignment, peak extraction, normalization, deconvolution and compound identification steps. Three biological replicates were performed for each sample.The tissue sample (25 mg) was placed in a 1.5 mL tube with 800 µL of precooled precipitant (methanol: acetonitrile: distilled water = 2:2:1) and two small steel beads, then it was ground in a grinder . After the removal of the beads, it was sonicated in an ice bath for 10 min (power 80 HZ), allowed to rest for 120 min at −20 °C, and then it was centrifuged for 15 min , following their standard procedures.The steps of total RNA extraction, RNA purity assessment, library construction, library quality control, and up sequencing were performed at UW . The clean reads were aligned to the genomic sequences using Bowtie2, and then the gene expression levels were calculated for each sample using RSEM . FPKM (Fragments Per Kilobases per Million reads) was used to express the gene expression levels. We defined genes with more than a two-fold difference and Q-value ≤ 0.001 to be screened as significantly differentially expressed genes . Based on the annotation results of GO  and KEGG  and the official classification, we functionally classified the differentially expressed genes and performed enrichment analysis using the phyper function in R software, with FDR correction for p values. Functions with Q values ≤ 0.05 were considered significantly enriched.The high-throughput sequencing offline data (Raw Data) were filtered to obtain high-quality data (Clean Data), and the data were filtered using UW’s self-developed filtering software SOAPnuke  for statistics and trimmomatic  for filtering. The clean reads were compared to the reference genome sequence using HISAT . The first cDNA strand was synthesized using a reverse transcription kit . The primer sequences of the related genes were downloaded from the GenBank library of NCBI. The primer design is shown in p < 0.05).One-way ANOVA was performed on all data using SPSS 22.0 software , and Duncan’s test was used to test the significance of the differences between samples  database to get the matched KEGG Orthology (KO). The KEGG enrichment analysis of different genes in each treatment group is shown in The KEGG analysis of DEGs between the HS and CK enrichment analyses is shown in The KEGG enrichment analysis of DEGs between HS and H is shown in The different transcription factors of differentially expressed genes (DEGs) were different in the different treatment comparisons . SeventeAs shown in High temperature (HT) is a widespread environmental stress that affects most plants at all periods of growth and can limit plant growth and development and reduce productivity. Plant growth and development involve many temperature-sensitive biochemical responses . One appIn our experiments, heat stress resulted in wilting of lettuce, curling of leaves, a reduction in biomass, and an increase in malondialdehyde content. The application of spermidine improved the growth of lettuce leaves, reduced the malondialdehyde content under heat stress, and increased the chlorophyll content. Previous studies showed that Spd treatment significantly promoted FW and DW in white clover seedlings under PEG-simulated water stress conditions . Zeng foChlorophyll is an important indicator of the ability of leaves to maintain their green color, and the decrease in chlorophyll concentration is generally considered to be a response mechanism to reduced light uptake by chloroplasts under stress; furthermore, the decrease in chlorophyll content during stress may be related to impaired chlorophyll synthesis or pigment protein degradation and could be a result of ROS production . The resCrop tolerance to HT stress is associated with an increase in antioxidant capacity . PreviouUpregulation of many genes has been reported to help plants resist stress conditions that lead to plant adaptation . Under vSignaling molecules are involved in the activation of many stress-responsive genes, and various signal transduction molecules associated with the activation of stress-responsive genes exist depending on the plant type and type of stress. Among them, widely used are Ca-dependent protein kinases (CDPKs), mitogen-activated protein kinases (MPKs), NO, sugar , and phytohormones . These mTFs stimulate and regulate multiple stress response pathways in plants subjected to high-temperature stress ,49. FlavFlavonoids may protect plants from oxidative stress by blocking ROS production through their ability to chelate metal ions and scavenge ROS, thus achieving their antioxidant function ,55,56. MExogenous spermidine increased the content of chlorophylls and antioxidant enzymes and decreased the content of malondialdehyde under high-temperature stress. By integrating transcriptome and metabolite analysis, we found that spermidine may be involved in or induce the expression of TF families such as AP2-EREBP, WRKY, MYB, and NAM to transmit information, and combined with the metabolic pathways identified in KEGG enrichment, spermidine influenced the biosynthesis of flavonoid synthesis and ultimately alleviated the damage to lettuce from high-temperature stress. A hypothetical model of exogenous spermidine-mediated heat stress tolerance in lettuce is shown in


Estimating and understanding the yield variability within an individual field is critical for precision agriculture resource management of high value tree crops. Recent advancements in sensor technologies and machine learning make it possible to monitor orchards at very high spatial resolution and estimate yield at individual tree level.This study evaluates the potential of utilizing deep learning methods to predict tree-level almond yield with multi-spectral imagery. We focused on an almond orchard with the ‘Independence’ cultivar in California, where individual tree harvesting and yield monitoring was conducted for ~2,000 trees and summer aerial imagery at 30cm was acquired for four spectral bands in 2021. We developed a Convolutional Neural Network (CNN) model with a spatial attention module to take the multi-spectral reflectance imagery directly for almond fresh weight estimation at the tree level.The deep learning model was shown to predict the tree level yield very well, with a R2 of 0.96 (±0.002) and Normalized Root Mean Square Error (NRMSE) of 6.6% (±0.2%), based on 5-fold cross validation. The CNN estimation captured well the patterns of yield variation between orchard rows, along the transects, and from tree to tree, when compared to the harvest data. The reflectance at the red edge band was found to play the most important role in the CNN yield estimation.This study demonstrates the significant improvement of deep learning over traditional linear regression and machine learning methods for accurate and robust tree level yield estimation, highlighting the potential for data-driven site-specific resource management to ensure agriculture sustainability. About 8In response to these challenges, various regulatory programs have been implemented in California over the past decade, requiring growers to increase the efficiency of irrigation and nitrogen use . MeetingBoth mechanistic simulation models and statistical approaches have been used for yield estimation . The proStatistical models, on the other hand, are based on the empirical relationships learned from the observed yield data and the factors affecting production, instead of simulating complex biophysical processes . RegressRecent advancement of remote sensing technologies enables plant monitoring across a range of spatial and temporal resolutions, opening doors for data-driven yield estimation at the field scale . Both tr2 = 0.77), using VI time series from MODIS imagery, climate variables, soil properties, and historical average yield , and Recurrent Neural Network have been introduced to improve yield estimation with large remote sensing datasets, due to their improved performance over traditional statistical approaches . The Bayge yield . A limitge yield . Shahhosge yield . HoweverIn order to capture variations of crop yield among individual plants for precision management, higher spatial resolution observations of canopy structure and conditions are required, such as those from very high-resolution commercial satellite and aerial imagery . Recent We here took advantage of a unique individual tree harvesting data and aerial imagery of multiple spectral bands at 30cm spatial resolution over an almond orchard in California’s central valley, to explore the potential of deep learning for tree level almond yield estimation. Specifically, we aimed to address the following questions: (i) how CNN model can be used to estimate almond yield for each individual tree, based on very high resolution multi-spectral imagery; and (ii) what is the capability of the trained CNN models in capturing the within-field almond yield variation; and (iii) what is the relative importance or added value of the observations in the red edge part of the spectrum, a spectral band increasingly available in recent imaging systems, with regard to almond yield estimation.22.1This study was conducted over an almond orchard with a size of 2 squared kilometers in Vacaville, California, USA Figure 1The orchard was planted with a self-fertile productive almond cultivar, ‘Independence’, between 2015 and 2017. Within the orchard, rows are oriented northeast to southwest in parallel with prevailing winds, and the average row spacing is about 6 m and the average spacing between trees along the same row is about 4.5 m. Almond trees bloom between late February and early March, followed by leaf out, fruit set and rapid growth, reaches full canopy typically in June or early-July, and fruit maturity progresses through summer. Almonds are typically harvested from August to October, and trees become dormancy during the winter season.We designed an automatic weighing system attached to the commercial almond harvester to measure the almond yield of an individual tree Figure 12.2Multi-spectral aerial imagery was acquired on July 29, 2021, about one month ahead of the harvest, by CERES Imaging  A multi-spectral imaging camera was integrated with a crop duster plane flying at 6,000 ft above the ground, resulting in images with a 0.3-meter spatial resolution. Four spectral bands are centered around 800 nm (near infrared), 717 nm (red edge), 671 nm (red), and 550 nm (green), with a spectral resolution of 10 nm . The image was acquired near local solar noon to minimize the shadow effects.2.3For each individual tree, extracting its center location from CERES imagery is needed in order to match the tree yield record from the harvester and to clip the corresponding image block as CNN input. We developed a multi-stage segmentation method to identify all individual crowns with varying canopy sizes, especially over a mature orchard. First, Normalized Difference Vegetation Index (NDVI) was calculated for each pixel from the red and near infrared bands of the CERES aerial imagery Table S1For quality control, the extracted tree locations were plotted over the CERES imagery for visual examination. For example, those trees with very small or large crowns were carefully examined against CERES imagery to ensure the location accuracy. To further ensure the alignment with the locations of the individually harvested trees, a visual check of the locations of starting, ending, and some randomly selected trees within the harvested rows was also conducted. All these processes were done in Python and QGIS.33.1The Convolutional neural network (CNN), a most established deep learning algorithm, is developed to estimate fresh almond yield with multi-spectral aerial images as inputs. CNN has a unique ability to automatically and adaptively learn spatial hierarchies of important features that summarize the presence of detected features in the input image for a particular predictive modeling problem . The extA CNN is typically composed of a stacking of three types of layers, i.e., convolution, pooling, and fully connected layers . The firvia model training. The combination of a global average pooling layer and the following dense layers helps the CNN model focus more on the relevant features and thus improves.To improve CNN model’s overall performance, the spatial attention module is recently introduced into the CNN architecture by combining a global average pooling layer and the following dense layers . Global 3.2TensorFlow , Keras , and KerThe CNN model training process is to find kernels in the convolutional layers and weights in the dense layers to minimize the differences between model estimations and ground measurements on a training dataset. The Mean Squared Error (MSE) loss function was applied for the CNN model training, which calculates the average of the squared differences between model estimations and actual values. To efficiently optimize the kernels and weights within the CNN model, the Adam optimization algorithm  is used,-4 to 10-2 with Adam optimizer. For the Bayesian optimization algorithm itself, the maximum trail number was set to 50, and for each trail, the batch size is 128 with 100 epochs.The general setup of the possible CNN structures for the Bayesian optimization algorithm are as follows: three to four convolutional blocks followed by a spatial attention module with a global average pooling layer and two fully connected dense layers. For the first dense layer, there are 30 to 100 neurons followed by a dropout layer. For each convolutional block, there are 16 to 128 convolutional layers (kernels) followed by a batch normalization and pooling layers, then another 16 to 128 convolutional layers followed by a batch normalization, pooling and ReLU activation layers. The pooling layers in each convolutional block can be either average pooling or max pooling. The overall architecture of the CNN model for the Bayesian optimization algorithm is shown in To investigate the impact of input image block size used for the CNN model and explore how the neighboring trees potentially influence yield estimation, another two separate CNN models were built with an input image size of 41 × 41 pixels (roughly 6m radius) and 61 × 61 pixels (9m radius), respectively. To understand the contribution of the red edge band to the yield estimation, a reduced CNN model was constructed by excluding red edge reflectance as input, hereafter called “reduced CNN model”, considering that red edge band is not as widely used for aerial imaging as the other three bands. Similarly, another 14 sets of reduced CNN models were further built with all the combinations of different reflectance bands as input and compared how they influenced model’s yield estimation accuracy Table S23.3For comparison purposes, Other statistical models were also built for individual tree level almond yield estimation, including stepwise linear regression as a baseline for linear relationships and four traditional machine learning approaches. The Scikit-learn  and hypeRegression models were built using features at individual tree level as inputs, including VIs and texture. 13 commonly used vegetation indices (VIs) were calculated from CERES multi-spectral imagery, including those sensitive to structure, greenness, and chlorophyll content   was appl3.42), Root Mean Squared Error (RMSE), and RMSE normalized by averaged yield measurement (NRMSE) were calculated. Statistics of these metrics were reported based on 5-fold cross validation.To evaluate models’ performance in predicting almond yield, the predicted and observed individual tree yield from the reserved testing samples were compared, and the coefficient of determination  of tree-to-tree variation in almond yield, with a RMSE of 3.5 kg/tree ( ± 0.11) and a normalized RMSE of 6.60% ( ± 0.2%) Figure 5The performance of the full CNN models with all four bands varied, very slightly, with the size of input image blocks Table 1.4.22 of 0.68 ( ± 0.08) and higher NRMSE of 18.7% ( ± 2.3%) than the full CNN model with four band imagery as input (2 of 0.85 ( ± 0.01) and NRMSE of 12.6% ( ± 0.7%). For two band combinations, the reduced model with NIR and red edge bands or NIR and green bands had similar performance (R2 0.85 ( ± 0.02) and 12.6% ( ± 0.8%)). When driven by only one single band imagery, the red edge based CNN model still captured 83% ( ± 2%) of yield variability among individual trees, and NRMSE only increased slightly to 13.8% ( ± 1.0%). These results demonstrated the importance of red edge imagery in almond yield estimation.When removing the red edge imagery from the input imagery, the accuracy of the reduced CNN model was reduced significantly, with a lower Ras input Figure 74.32, the CNN model was found more robust and stable as shown by much lower standard deviation of R2 among different folds of test sets, compared with other models .Third, super high spatial resolution imagery may improve yield estimation accuracy with more details, especially for deep learning approaches. 5.2From the CNN model result, reflectance in the red edge band was found to play a vital role in almond yield estimation. The red edge spectral band covers a transitional wavelength region from the red band, where the absorption by chlorophyll is dominant, to near infrared where strong scattering by leaf cell structure is further enhanced by multiple scattering among layers of leaves. Reflectance in the red edge band serves as a critical proxy for canopy size and leaf volume. Previous study shows that the red edge band is less saturated at high biomass condition than its adjacent wavelengths and the common vegetation indices such as NDVI . Moreove5.3This is the first study attempted for the tree-level yield estimation, especially capturing the spatial variability of almond yield within an individual orchard. Although it proves the concept of integrating aerial and drone-based images with deep learning techniques for high resolution yield estimation, some uncertainties still exist. Potential errors, for example, may exist in the harvest yield records used for the model training and testing, as this was the first time the individual tree harvester was designed and tested in the almond field. The sampling strategy, designed by the other group for individual tree harvesting, i.e., every seventh row, prevented us from taking full advantage of the spatial information from neighboring trees for yield estimation in the model building process.The success of integrating the CNN model with multi-spectral imagery in estimating the within field variability is likely because the imagery at various wavelengths captures the information on the tree structure and plant conditions due to the light-matter interaction. The structural variability such as canopy size can result from cumulative impacts on plant growth by soil properties and long-term climate, while weather variability can also affect the plant health during a particular season. Nonetheless, our study was still constrained by the availability of the yield records for individual trees in one orchard over one single year. Although the unique yield dataset provided sample data covering the gradient of spatial yield variation within a single orchard, it does not represent the yield variability across different orchards where climate and soils may vary significantly. Similarly, the lack of yield record at the tree level from multiple years has prevented us to incorporate weather information in our modeling approach. Future work is needed to collect more ground truthing data and include additional predictors such as soil properties and weather variables for more robust yield estimation and prediction .With rapid advancement in deep learning technology, an important next step is to explore the potential and utility of other powerful approaches such as transformer networks  and gene62 of 0.83 ( ± 0.02) and NRMSE of 13.8% ( ± 1.0%). The CNN model performed significantly better than traditional machine learning methods and stepwise linear regression driven by tree-level features such as VIs and texture.Individual tree level yield estimation is critical for precision on-farm management and for improving our understanding of yield variability within a field. The challenge of matching efficient supply of inputs like water and fertilizer with tree scale demand is hampered by a lack of understanding of yield variation within orchard blocks. Our work makes a significant step toward bringing awareness to the problem by coupling high-resolution imagery and modeling and paves the way for future innovation in precision orchard management. A CNN deep learning models in estimating almond yield was developed and evaluated, by taking advantage of a unique tree yield data and super high resolution of multi-spectral aerial imagery in 2021 over a single cultivar almond orchard in California’s Central Valley. The 5-fold cross validation showed that the CNN model with spatial attention module, driven by 4-band block imagery of 21 by 21 pixels, captured 96% (±0.2%) of tree-to-tree variation within the study almond orchard with a very low RMSE 3.50 kg/tree and NRMSE of 6.6% ( ± 0.2%). The reduced CNN model with the red edge band reflectance alone had a RThe almond yield for all individual trees predicted by the CNN model also captured well the spatial patterns and variability of almond yield from row-to-row and from tree-to-tree both within a row and along a transect perpendicular to the row orientation. Our findings demonstrated the potential of applying deep learning technology to integrate high resolution multi-spectral aerial images for accurate and robust tree level yield estimation. The data-driven approach developed here fills an important gap in tree level yield estimation critical for site-specific orchard resource management, ultimately contributing to agriculture sustainability.The original contributions presented in the study are included in the article/MT: conceptualization, methodology, analysis, writing, review and editing. DS, CP, SV, BK, SK, PB: data collection, project coordination, review and editing. YJ: conceptualization, methodology, writing, review and editing. All authors contributed to the article and approved the submitted version.


Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes – the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system—the Microbial Containment Device (MCD)—that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical. Metabolomics is a mainstream strategy for understanding complex biological processes and mapping the molecular underpinnings of disease . Liquid in vitro cell cultures—to map intracellular metabolic activities  device, but it addresses two shortcomings of the Transwell ® for use in high-throughput microbial metabolomics studies: (1) the pore size of the Transwell ® (0.4 μm) allows some bacteria to pass through the membrane and (2) the high cost of the Transwell ® is incompatible with the needs of large-scale metabolomics studies. To demonstrate the applicability of the MCD in replacing a traditional microbial culture workflow, we used the MCD to differentiate four bacteria species  and measure their antibiotic susceptibility profiles. Our data show that the MCD can (1) accurately reproduce traditional microbial culture workflows; (2) allow the user to analyze samples with no additional sample preparation steps once samples are loaded into the device; (3) facilitate pathogen identification (ID) and empirically measure antibiotic susceptibility profiles of these pathogens; and (4) maintain a sterile mass spectrometry analysis chamber. In summary, we introduce this tool as a simple device for enabling large-scale metabolomics projects where the emphasis is on quantifying metabolic boundary fluxes.One of the primary advantages of studying microbes via their metabolic boundary fluxes is its simplicity. The metabolites that are consumed or secreted by abolites . Moreoveabolites . These aThe MCD is a plastic insert that fits into a standard-format 96-well microplate. The MCD has 96 sampling inserts and the bottom of each is enclosed, with a 0.2-μm semi-permeable membrane. When the MCD is inserted into a 96-well microplate, the membrane creates a lower microbial growth chamber and upper analytical chamber. Media components, including small molecules such as metabolites, can freely diffuse through the membrane between the upper and the lower compartments . The MCD1). A Universal Laser Systems VLS3.60DT2  was used to cut the filter and double-sided tape for filter assembly. We used an Anprolene AN74j ethylene oxide gas sterilization machine  to sterilize the prototype MCD before testing and validation. After we performed proof-of-concept testing and validation of the device, we designed a custom mold for the injection molding and mass manufacturing processes. This required us to develop a new semi-automatic machine and assembly technique for polycarbonate filter (0.2 μm pore size) assembly in our lab. We also developed a Quality Control (QC) procedure  to ensure repeatability and reliability of the manufactured MCD. Commercial MCD platforms can be ordered from Fluidome, Inc.3 . All steps of development are shown in We used a rapid prototyping workflow to design, fabricate, and test the MCD. We used Fusion 360 software to design the MCD and performed 3D printing with high temp resin and a Form 3 SLA 3D printer . We then post-washed the 3D printed component in an isopropyl alcohol bath to dissolve liquid resin followed by post-curing using 405 nm light along with a heating system  based on the polycarbonate filter membrane specification datasheet. For the boundary condition for this study, we assumed initial concentrations of 0 and 2 mM on the top and bottom wells, respectively . We considered the ambient pressure and temperature to be 100 kPa and 310 K, respectively, and water as the liquid material. Furthermore, we considered free tetrahedral mesh (extra fine), including 149,756 mesh elements, in this study. We used the transport of diluted species physics and a time-dependent study to monitor the concentration change over 6 h on the top and bottom of a single 3D well. The dynamic change of concentration was simulated in the 3D model over the cross-sectional surface and over the centerline of the well from bottom to top. Additionally, to ensure that the size of mesh and number of mesh elements had no impact on our result, we ran the mesh dependency test. The purpose of the mesh dependency test is to determine the correct mesh size (ranging from coarse to extra fine mesh) that leads to a consistent result  in the simulation. This means that the simulation result remains independent from the mesh size. After running the simulation and gradually increasing the mesh density, we determined that free tetrahedral mesh (extra fine), including 149,756 mesh elements, produced the most consistent result for our simulation.To optimize the MCD design, we computationally modeled the metabolite diffusion and dynamic changes of metabolite concentration over time within the proposed device. We used COMSOL Multiphysics softwareBacterial strains were grown on BD BBL™ Mueller Hinton II Broth , prepared as per the manufacturer’s instructions. The Mueller Hinton broth was autoclaved at 121.5°C for 20 min  and cooled to room temperature. The prepared medium was stored at 4°C. Mueller Hinton (MH) agar plates were prepared in the same way but with supplementation of 1% agar before autoclaving.Escherichia coli , Klebsiella pneumoniae , Enterococcus faecalis , and Staphylococcus aureus  were purchased from the American Type Culture Collection (ATCC) or provided by the Center for Disease Control (CDC)  see .2 and 17.5% O2) on Mueller Hinton (MH) agar plates prior to experiments, and 17.5% O2. All experiments were performed in the Thermo 1,300 Series A2 biosafety cabinet. To create initial stock solutions, we picked a bacterial colony from an agar plate and diluted it in 4 mL saline to reach 0.5 McFarland (∼7.5E6 CFU/mL). Optical density values at 600 nm (OD600) were measured in a microplate reader (Thermo Scientific™ Multiskan™ GO).Bacteria were incubated overnight , gentamicin , ampicillin , ciprofloxacin , ceftriaxone , and meropenem  at Clinical and Laboratory Standards Institute (CLSI) breakpoint concentrations were made by adding the antibiotic solution to each well of a 96-well plate and drying the plates for 4 h in the biosafety cabinet.2 and 17.5% O2. After 4 h, a 10 μl aliquot from each well was diluted with 90 μl 50% methanol (Thermo Scientific™ Optima™ LC/MS grade reagent)/50% water  in a 96-well PCR plate . The plate was centrifuged at 4,000 rpm for 10 min . Subsequently, 70 μl of the supernatant was transferred to a new 96-well sampling plate  and diluted with 70 μl 50% methanol for a total sample dilution of 1:20 from the starting concentration. The samples were analyzed via LC-MS.270 μl MH medium was added to each well of the MCD receiver (96-well plate). Wells were inoculated using 30 μl of bacterial suspension (0.5 McFarland). The plate was sealed using a gas permeable sealer  and incubated at 36.5°C, 5% COThe following workflow was used to compare the results from the MCD to the conventional metabolomics workflow. However, the MCD is designed to be used with the Thermo Scientific™ Vanquish™ Charger Module to enable the optimized workflow, as shown in °C, spray voltage of −2000 V, and capillary temperature of 275°C. Data were acquired in full scan mode (50–750 m/z) with a 240,000 resolving power, an automatic gain control target of 3e6, and a maximum injection time of 200 ms. All standards were purchased from Sigma-Aldrich  and used to verify the biomarkers described in All metabolomics testing and characterization steps were carried out in the Calgary Metabolomics Research Facility (CMRF). Our methods have been described elsewhere . Brieflypost hoc Tukey correction, where *p < 0.05; **p < 0.01; ***p < 0.001; and ****p < 0.0001. All the statistical methods for finding microbial biomarkers were reported in our previous study  the volume of liquid in the MCD insert and (2) the concentration of metabolites in the MCD receiver. We used succinate as a representative small molecule metabolite for this study since its size, cross-sectional area, and chemical properties are broadly representative of soluble metabolites found in microbial growth media; 250 μl of succinate solutions at varying concentrations  was introduced into the MCD receiver (bottom well). Then, 50, 75, or 100 μl of water was added to the MCD insert (top well). Samples were incubated for 4 h and aliquots were then taken from each MCD insert, diluted 20-fold with 50% methanol, and succinate concentrations were measured by UHPLC-MS . The resA critical consideration in conducting metabolomics experiments in the MCD is the time required for a microbe to reach equilibrium between the lower and upper wells. We used COMSOL Multiphysics software to conduct a numerical simulation of diffusion and to quantify the diffusion rate between 0 and 6 h for molecules dissolved in liquids in an aqueous (water) solution. Since samples were taken within a 1 mm distance from the filter membrane of the MCD insert during empirical testing, we considered this same sampling zone for evaluation of the diffusion rate in the MCD for the numerical simulation. The 3D schematic showing the diffusion of metabolites in one well including the MCD receiver and insert along the centerline is shown in E. coli. For bottom-to-top sterility testing, 250 μl of MH medium containing either E. coli or K. pneumoniae (OD600 0.01) was loaded into the MCD receiver. The MCD insert was placed on top of the MCD receiver and 100 μl of MH broth was added to each well of the MCD insert. Optical density measurements after 8 h on the MCD receiver were much higher than the MCD insert  sterility tests using D insert , demonst chamber .E. coli, K. pneumoniae, E. faecalis, and S. aureus; three to four unique isolates per species) and analyzed the analytical chamber for the presence of diagnostic biomarkers following a 4-h incubation. It has been reported previously  after a 4-h incubation in our antibiotic panel  metabolites produced by microbes diffused freely through the MCD membrane over a 4 h incubation; (2) the membrane maintains the sterility of upper wells in the device; and (3) this tool can be used to both identify microbial species and empirically measure antibiotic susceptibility profiles. In summary, this study shows that the MCD functions as a tool for single-step metabolomics experiments and in vitro cell cultures. This wider range of applications includes biofuel production studies, such as for quantifying butyric acid and other products of fermentation by Clostridium acetobutylicum  may freely pass through the MCD filter, the longer incubation times needed to equilibrate these large molecules  may makeIn summary, we introduce here a simple consumable device that enables metabolic boundary fluxes to be studied via a streamlined sample handling procedure. We illustrate the utility of the MCD platform for microbial identification and antibiotic susceptibility testing and propose its applicability to a wide range of studies.All of the data used in this article are available on request from the authors.MM, TR, and IAL designed the experiments. MM designed, manufactured, and assembled the MCD and conducted the microbial growth, identification, and antibiotic susceptibility testing assays. MM, RA, and SL characterized the MCD. MM, RAG, and DGB collected mass spectrometry data. MM, SLB, TR, and IAL wrote the manuscript. All authors have read and approved the final version of the manuscript.