This systematic review evaluated the animal and human evidence for pharmacomicrobiomics (PMx) interactions of antidepressant medications. Studies of gut microbiota effects on functional and behavioral effects of antidepressants in human and animal models were identified from PubMed up to December 2022. Risk of bias was assessed, and results are presented as a systematic review following PRISMA guidelines. A total of 28  studies were included in the review. The reviewed papers converged on three themes: (1) Antidepressants can alter the composition and metabolites of gut microbiota, (2) gut microbiota can alter the bioavailability of certain antidepressants, and (3) gut microbiota may modulate the clinical or modeled mood modifying effects of antidepressants. The majority (n = 22) of studies had at least moderate levels of bias present. While strong evidence is still lacking to understand the clinical role of antidepressant PMx in human health, there is evidence for interactions among antidepressants, microbiota changes, microbiota metabolite changes, and behavior. Well-controlled studies of the mediating and moderating effects of baseline and treatment-emergent changes in microbiota on therapeutic and adverse responses to antidepressants are needed to better establish a potential role of PMx in personalizing antidepressant treatment selection and response prediction.  The clinical response to antidepressants in individuals with major depressive disorder (MDD) is highly variable, both with respect to effectiveness and adverse effects . NotablyAn emerging area of research into response to antidepressants lies in the gut, aptly named pharmacomicrobiomics (PMx), which in this case describes how the microbiome may potentiate or limit the effects of antidepressants in MDD . Animal There is emerging evidence to suggest that antidepressants interact with the gut microbiome to exert therapeutic effects. In preclinical studies, several conventional monoamine-based antidepressants have demonstrated antimicrobial or gut microbiota-altering effects ,20. PrecA systematic search of PubMed  was performed on 18 December 2022. The following search strategy was deployed:  AND  AND (microbiome OR microbiota OR pharmacomicrobiomics OR psychopharmacomicrobiomics or psychobiotic)). The systematic review followed the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting recommendations  in MDD may be particularly impactful as a focus of future work. Although it is highly unlikely that single bacterial species act alone to increase the risk of MDD, recent work has linked increased depression susceptibility with the heightened abundance of several bacterial species, including selected  species . At pres species ,51,52. Aressants ,52, the ressants .An additional aspect of PMx that may be underappreciated is the effect of antidepressant withdrawal on the microbiome and, consequently, behavior. In the case of abusable substances, a recent review suggested that certain alterations in gut microbiota may be associated with reduced stress-associated symptoms including depression during withdrawal, thus reducing the risk of relapse . To our DEFB1, which codes for an antimicrobial peptide that plays a role in gut microbiome homeostasis, was recently associated with variation in plasma kynurenine concentration, a metabolite that was related to the severity of MDD symptoms in a cohort of depressed patients [The role of human genetics may also add yet another complexity to the gut-brain-PMx effect—an area that also awaits future study. For example, genetic variation in patients ,64. The field also lacks studies focused on gut microbiota-derived metabolites as biomarkers for antidepressant treatment effects. Such metabolites are directly produced by microbial species or are by-products of interactions between microbes and dietary substances or naturally produced host materials, and serve as key mediators for a variety of diseases . AlteratThis systematic review was based on a primary search using PubMed. Although secondary identification of additional reports was performed by reviewing bibliographies of included studies and review papers, additional databases were not used. The individual studies reviewed herein are methodologically heterogeneous which would be expected to contribute to the conflicting results presented in this review. The exact time of microbiota samples were not reported in any of the included studies. Recent work suggests that gut microbiota, along with diet, undergo diurnal oscillations which affect gut metabolites and interactions with host cells . The facFuture studies should focus on standardizing and controlling study variables and reporting so that the overall evidence can be evaluated to answer specific scientific and clinical questions. While these initial studies are promising, the field is far from utilizing PMx in personalized prescribing of antidepressant medications to improve outcomes. Additionally, incorporation of other markers including inflammation, imaging, genetics, and neurotransmitters may also increase the precision and understanding of the vast effect of PMx in an entire organism. Gut-microbiota composition may serve as a biomarker for medication selection, treatment outcome prediction, or the need for gut microbiota modulation in conjunction with medications. Baseline gut microbiota composition may be able to predict adverse events or efficacy to specific antidepressants therefore providing a potential clinical tool to personalize antidepressant selection to optimize treatment outcomes. Gut microbiota composition could also be analyzed over time with antidepressant treatment, as a surrogate to understand efficacy of the drug over time in conjunction with clinical measures. Although outside the scope of this review, an important related question is whether probiotic-associated changes in microbiota composition may modify the clinical effects of conventional antidepressants, ketamine, esketamine, or other antidepressant treatments in patients with MDD. From a clinical standpoint, probiotics are generally well tolerated and may be an attractive adjunct to antidepressants if antidepressant benefit can be consistently demonstrated and standardization of treatment can be achieved . A recenInterestingly, recent work in the probiotic space has focused on creating genetically modified bacteria in order to engineer production of specific metabolites or bacterial functional factors that can modulate the gut and therefore affect disease or the interaction of microbiota and drugs . As the The PMx of antidepressants field is expanding rapidly, however, study designs and outcomes reported vary greatly across publications. Clearly there lies an important interaction between the gut microbiota and antidepressants in potentiating adverse events and/or behavior, however, additional well controlled studies are needed to better characterize these interactions and understand how to implement PMx as a part of precision medicine in antidepressant treatment.


A (type A) receptors for benzodiazepine ligands. Flumazenil antagonizes the benzodiazepine-binding site of the GABA/benzodiazepine receptor (BZR) complex in the central nervous system (CNS). The investigation of flumazenil metabolites using liquid chromatography (LC)-tandem mass spectrometry will provide a complete understanding of the in vivo metabolism of flumazenil and accelerate radiopharmaceutical inspection and registration. The main goal of this study was to investigate the use of reversed-phase high performance liquid chromatography (PR-HPLC), coupled with electrospray ionization triple-quadrupole tandem mass spectrometry (ESI-QqQ MS), to identify flumazenil and its metabolites in the hepatic matrix. Carrier-free nucleophilic fluorination with an automatic synthesizer for [18F]flumazenil, combined with nano-positron emission tomography (NanoPET)/computed tomography (CT) imaging, was used to predict the biodistribution in normal rats. The study showed that 50% of the flumazenil was biotransformed by the rat liver homogenate in 60 min, whereas one metabolite (M1) was a methyl transesterification product of flumazenil. In the rat liver microsomal system, two metabolites were identified (M2 and M3), as their carboxylic acid and hydroxylated ethyl ester forms between 10 and 120 min, respectively. A total of 10–30 min post-injection of [18F]flumazenil showed an immediate decreased in the distribution ratio observed in the plasma. Nevertheless, a higher ratio of the complete [18F]flumazenil compound could be used for subsequent animal studies. [18F] According to in vivo nanoPET/CT imaging and ex vivo biodistribution assays, flumazenil also showed significant effects on GABAA receptor availability in the amygdala, prefrontal cortex, cortex, and hippocampus in the rat brain, indicating the formation of metabolites. We reported the completion of the biotransformation of flumazenil by the hepatic system, as well as [18F]flumazenil’s potential as an ideal ligand and PET agent for the determination of the GABAA/BZR complex for multiplex neurological syndromes at the clinical stage.Studies of the neurobiological causes of anxiety disorders have suggested that the γ-aminobutyric acid (GABA) system increases synaptic concentrations and enhances the affinity of GABA A receptors for benzodiazepine ligands fluoro-D-glucose ([18F]F-FDG) imaging that is used for epileptic foci localization [18F]flumazenil was used for in vivo, ex vivo, and in vitro analysis. The investigation of flumazenil metabolites into rat liver homogenates and microsomes was analyzed using liquid chromatography-tandem mass spectrometry. This study aimed to clarify the biotransformation of flumazenil using in vivo and in vitro hepatic systems and to quantify the GABAA/BZR complex distribution of [18F]flumazenil by nano-positron emission tomography (NanoPET). When fully characterized, PET-based GABAA/BZR images provide potential biomarkers for the diagnosis and treatment of patients with multiple neurological syndromes.An increase in the binding capacity of the GABAnsomnia) ,13. It clization ,7. In tht) for flumazenil was 8.2 ± 0.1 min. The column efficiency exceeded 8000 p/m, with a tailing factor of 1.1 , and the flumazenil peak was far from the hepatic matrix peaks, with good resolution to avoid contamination. The dynamic range of flumazenil in the hepatic matrices was 1–100 ppm, with a linear least-squares regression equation of Y = 35.4X—18. The calibration curve (six concentration points without being forced through zero) for flumazenil was sufficiently linear, with a correlation coefficient (r2) of 0.9992. The limits of detection (LOD) and quantification (LOQ) for flumazenil were 0.1 and 0.4 ppm, respectively, using DAD at λ of 250 nm. The incubated solutions were analyzed using HPLC to separate flumazenil and its metabolites. The peak area of flumazenil was measured in triplicate and then averaged to investigate and determine the reaction trend of flumazenil in hepatic biosystems.The ZORBAX Eclipse XDB-C18 column  separated biotransformed flumazenil from the hydrophilic matrices of the liver microsome and the liver homogenate solutions. The mobile phase was composed of aqueous ammonium acetate buffer and acetonitrile (ACN) programmed gradient eluent . After p+ and [M + Na]+ ions were observed at m/z 304 and 326, respectively. Product-ion scans were performed under different collision-activated dissociation conditions to optimize the declustering potential, entrance potential, collision energy, and collision-cell exit potential. A total of 304 product ions were observed at m/z values of 276, 258, 229, 217, and 189 (m/z = −46) and de-ethylation (Δm/z = −28) from the parent molecular ion 304 to form acylium and protonated carboxylic acid, respectively.In the ESI spectrum of flumazenil, the [M + H] and 189 A. Fragme and 189 B. The mot = 7.45 ± 0.1 min) and gradually increased in the peak area (m/z of M1 was 290 amu less 14 (lost CH2) than that of flumazenil, whereas the core structures of M1 and flumazenil were identical  appeared with , were identified in the rat liver microsomal system, with Rt values of 6.5 and 7.7 min, respectively A,B. Flum188Re-MN16ET. In addition to the identification of the flumazenil metabolites M2 and M3, we also detected another metabolite, M1, in the rat liver media; this appeared to be a methyl transesterification product of flumazenil, which has not been discussed previously in the literature. This methyl replacement of the ethyl ester is thought to be an undisclosed flumazenil metabolite.The metabolic pathways of flumazenil in rat liver homogenates and the microsomal enzymatic system are summarized in 18F]flumazenil with a radiochemical purity > 95% after radio-HPLC and radio-thin layer chromatography (TLC). In an amber glass vial, the radiochemical purity of [18F]flumazenil in 30% ethanol was >90% at 8 h after the end of synthesis (EOS) at room temperature. A total of 3.7 MBq of [18F]flumazenil was incubated in Sprague Dawley (SD) rat exsanguinated serum. The in vitro serum stability of the radiochemical purity (RCP%) values of [18F]flumazenil were 95.76%, 93.28%, 91.07%, 87.46%, 85.68%, 79.95%, and 72.13% at 0, 5, 10, 30, 60, 120, and 240 min, respectively. Our in vitro serum stability results showed that intact [18F]flumazenil was >85% at up to 1 h of incubation, under regular conditions , a rapid accumulation in the brain regions was observed at 5 min. The percentage of ID/organs of the brain was 1.92 ± 0.32, 0.39 ± 0.15, 0.03 ± 0.01, 0.03 ± 0.01, 0.001 ± 0.00, and 0.0004 ± 0.00 at 5, 10, 30, 60, 120, and 240 min, respectively. In the high density GABAA regions of the brain, such as the cortex, hippocampus, and thalamus, the SBRs (with the pons/medulla as a reference region) were 1.96 ± 0.49, 1.55 ± 0.37, 1.37 ± 0.37 (at 5 min), 2.65 ± 0.85, 2.06 ± 0.41, 1.70 ± 0.40 (at 10 min), 5.32 ± 0.82, 3.24 ± 0.52, 1.37 ± 0.36 (at 30 min), and 2.22 ± 0.42, 1.78 ± 0.32, 1.57 ± 0.31 (at 60 min), as shown in A visualized in the PET experiments was consistent with the results of the biodistribution assay.At 5, 10, 30, 60, 120, and 240 min post-injection of flumazenil was conducted via a fast and convenient non-carrier nucleophilic substitution combined with the 18F- ion, with nitro-flumazenil as the precursor, using an automatic synthetic technique and solid extraction purification to replace the semi-preparative HPLC. Regarding in vitro serum stability, [18F]flumazenil showed a stable radiopurity when stored at 30% ethanol, with RCP (%) values of up to >85% at up to 1 h of incubation under regular conditions (18F]flumazenil uptake (up to 20%ID/g) in the brains of mice scanned under isoflurane anesthesia in static PET scans from awake animals, and this persistence was two-fold higher in the hippocampus of isoflurane-treated mice than in awake mice [18F]flumazenil, the brain regions showed a rapidly declining trend from 5 to 10 min post-injection , reached the highest SBR ratio in the cortex  and hippocampus regions at a post-injected time of 30 min, relative to those of the pons/medulla   . Because regions ,20. No iobserved . Regardinjection .18F]flumazenil in humans or primates, since different species may have different liver metabolism pathways. 2. The radiochemical purity under regular conditions after only 10 min was >90%, but the radiopurity needed to extend to less than 60 min for clinical use. However, we are still working toward improvements. The advantages of this study are: (1) In flumazenil metabolism, M1–3 biotransformation may occur in its nitrogen-methyl group for demethylation. (2) After administration of [18F]flumazenil for 30 min, we accumulated static PET images at 30–40 min, providing clear brain images, although the window for capturing images rapidly declined. (3) We demonstrated that the specific binding ratios of the GABAA receptors were profoundly influenced in specific brain areas, including the amygdala, prefrontal cortex, cortex, and hippocampus, suggesting that these brain regions may be involved in the processing and storage of conditioned fear memories fluoro-D-glucose ([18F]FDG) imaging that is often used for epileptic foci localization [18F]flumazenil by in vitro, ex vivo and in vivo imaging. [18F]flumazenil is a good ligand and can serve as a PET agent for the determination of the GABAA/BZR complex for multiplex neurological syndromes in future clinical stages.An increase in the binding capacity of the GABAnsomnia) ,13. It clization ,7. In thAnalytical-grade laboratory chemicals for LC/MS were used as received, without further purification. Methanol, acetonitrile (ACN) (both HPLC and MS grade), ammonium acetate, ethyl acetate (EA), phosphate buffer pellets, sucrose, and bovine serum albumin (BSA) were purchased from Merck . Flumazenil was purchased from FutureChem . Deionized water was prepared using a Smart DQ3 reverse osmosis reagent water system  with a 0.22 μm polyvinylidene fluoride (PVDF) filter and a UV light source to produce ultra-pure water . A ZORBAX Eclipse XDB-C18 reversed phase HPLC column  from Agilent Technologies  was used to study flumazenil and its metabolites.All experiments were performed on Sprague Dawley (SD) rats (3–4 weeks old) purchased from a licensed breeder . All the animal procedures and experimental protocols were approved by the Ethical Animal Use Committee of the Institute of Nuclear Energy Research (INER), Atomic Energy Council of Taiwan. This study complied with Taiwan’s laws on the care and use of laboratory animals (LAC-2019-0253). The rats were maintained at 21 ± 2 °C with 50 ± 20% relative humidity under an alternative 12 h light and 12 h dark cycle. The rats were identified as normal using NanoPET/CT imaging, and the serum stability and biodistribution of their brains were evaluated. Fresh rat livers were obtained from healthy male SD rats and frozen at −70 °C. Rat liver microsomes and co-enzymes (NADPH A and B) were purchased from BD Biosciences  and stored at −70 °C.−1. These were mixed to separate the metabolites and flumazenil from the sample matrices, according to the gradient program shown in The quantification of flumazenil metabolism by hepatic enzymes was performed using HPLC , which is comprised of an online degasser, a binary pump, an auto sampler, a thermostat column oven (maintained at 25 °C), and a diode array detector . Data were acquired and processed using the Agilent ChemStation software (ed. 10.02). Separation was achieved using a C18 column with a guard column and an ammonium acetate buffer–acetonitrile gradient elution. The mobile phase consisted of eluents A  and B (ACN only) at a flow rate of 0.6 mL minm/z for flumazenil fragments. Samples were introduced into the spectrometer, either by the HPLC system (Agilent 1100) or by a syringe pump, at a flow rate of 10 µL min−1 . Chromatographic separation was conducted as described in the HPLC conditions section. The analytes were ionized by a turbo spray ion source  in the positive-ion mode at 5000 V and 350 °C. Mass spectra were recorded within a range of 100–1000 amu with unit resolutions of Q1 and Q3. The other experimental parameters are listed in Mass spectrometric analysis of flumazenil in ACN was initially conducted on a 4000 QTrap LC-MS/MS system using API Analyst software 1.4.1  to obtain the appropriate MS parameters and 2 inhalation for 5 min in a closed box. After confirmation of euthanization, the entire liver (approximately 14 g) was removed, washed with ice-cold normal saline solution, and immediately minced in a 0.25 M sucrose solution (30 mL) in an ice bath. The rat liver tissue solution (40 mL) was homogenized in a motor-driven homogenizer, aliquoted (1.5 mL) into sample tubes, and stored at −70 °C. The samples were used for biotransformation studies within 30 d and thawed in an ice bath before use. For the biotransformation, the liver homogenate (1 mL) was placed in a 15 mL test tube and mixed well with a 1:1 (v/v) solution of 0.25% BSA in 0.01 M phosphate-buffered saline solution . The flumazenil solution intended for study  was added to the tube, which was swirled completely and placed in a preheated water bath  to initiate the enzymatic reaction. After specified incubation periods , ACN (2 mL) was added to each tube to stop the reaction and precipitate proteins, which were then centrifuged  to separate the supernatants. Liquid–liquid extraction with EA (400 μL) was performed in triplicate to concentrate the flumazenil in the supernatant and desalt the reaction mixture. The EA fraction (1.2 mL) was collected in a microtube and purged with N2 (thermostat set at 30 °C) to vaporize the EA until the volume was reduced to less than 50 μL. The samples were again dissolved in ACN to generate a final volume of 1.0 mL and filtered through a 0.22 µm PVDF disk filter for LC-DAD/tandem MS analysis. The liver homogenate (2 mL) was mixed well with a buffer solution (2 mL) and DMSO (10 µL) to generate a blank background solution.Normal rat livers were obtained from healthy male SD rats weighing 400–450 g. The samples were homogenized using the following procedure. The rats were euthanized by COg, 4 min, 4°C). The supernatants were filtered through a 0.22 µm PVDF disk filter and analyzed using LC-DAD and tandem MS. The liver microsome solution (25 μL) was also mixed with DMSO (2 μL without flumazenil), buffer, and the co-enzyme solution to generate a blank solution.Incubation solutions were prepared according to the manufacturer’s instructions. First, 2 μL of a 5-mM flumazenil solution in DMSO was added to a reaction mixture buffered with 0.5 M potassium phosphate pH 7.4 (200 μL) containing co-enzyme NADPH solution A (50 μL) and B (10 μL) and 25 μL of liver microsome protein solution (0.5 mg). The final volume of the incubated solution was adjusted to 1.0 mL with deionized water (713 μL). The reaction vials were capped, mixed well, and placed in a pre-heated water bath  to initiate enzymatic reactions. After incubating, the reactions for specified durations , 100 μL of each incubated solution was mixed well with 100 μL of ACN in microcentrifuge tubes to terminate the enzymatic reactions. This was followed by centrifugation to precipitate proteins . [18F]flumazenil was prepared according to a modified version of a previously described method flumazenil in 200 uL of prepared serum at 37 °C. At different time points , each sample was treated with 1 mL of acetonitrile to precipitate serum proteins. After mixing and centrifugation for 2 min at 5000 rpm, the radiochemical purity of [18F]flumazenil was determined using radio-TLC. Radio-TLC was performed on a silica gel plate TLC-SG (1.5 × 6 cm), using ethyl acetate: ethanol (80/20) as the developing agent.Stability in serum: the stability was studied by incubation of 3.7 MBq of [18F]flumazenil via the lateral tail vein. After being distributed for 30 min, isoflurane gas (3%) was used to anesthetize the animals, which were then placed inside a NanoPET/CT PLUS system  for statistical analysis (post-injection 30–40 min). InVivoScope software program (Bioscan Inc.) was used to capture the NanoPET images. Pmod 3.3 software was used to merge the NanoPET/CT images and set them in the brain template. A region of interest (ROI)  was defined in each brain region on the NanoPET images using coronal sections. The average radioactivity concentration within each brain section was calculated by averaging the pixel values within the multiple ROIs. When analyzing the NanoPET data, the medulla was used as the reference region to determine the specific binding ratios for all target regions.The rats were injected with approximately 37 MBq/200 μL of [18F]flumazenil (37 MBq/200 μL) was injected directly into the tail vein. The rats (n = 3 at each time point) were sacrificed by the administration of carbon dioxide at 5, 10, 30, 60, 120, and 240 min post-injection. Different brain regions and blood samples were removed and weighed, and radioactivity was evaluated using an automatic gamma counter . The percentage dose per organ was calculated by comparing tissue counts with suitably diluted aliquots of the injected material. Different brain regions corresponding to the cerebellum, pons/medulla, striatum, cortex/amygdala, hippocampus, thalamus, and hypothalamus were excised. The % dose/g and specific binding ratio (SBR) of the samples were calculated by comparing sample counts with the diluted initial dose.While the rats were anesthetized with isoflurane, [18F]flumazenil and its utilization in PET showed evidence of an animal model expressing the GABAA/BZR receptor. Although the distribution of [18F]flumazenil in metabolism analysis showed about 50% biotransformed metabolites after 30 min, ex vivo biodistribution and in vivo imaging studies still showed significant differences in the target regions. The remaining complete compound structure can continuously accumulate in the target regions of the brain in order to facilitate molecular nuclear medicine imaging. A complete analysis of flumazenil biotransformation by hepatocytes may provide evidence of the efficacy of [18F]flumazenil for use in patients with anxiety at the clinical stage.In conclusion, the flumazenil metabolic pathways were identified in rat liver microsomes and homogenates by HPLC combined with ESI-QqQ MS, revealing the mechanism of flumazenil biotransformation. A new metabolite compound with a methyl replacement at the ethyl ester is thought to be a novel flumazenil metabolite. Through an automatic synthesis process, the successful radiofluorination of [


Differential fertility preferences for men and women may provide insights into human sexual conflict. We explore whether pairbonded couples have different preferences for future offspring, which socioecological factors are associated with these preferences, and who achieves their desired fertility over time. We utilise the Indonesia Family Life Survey (IFLS), a longitudinal survey which collected data from 1993 to 2015, to compare desired future fertility for 9655 couples and follow couples who had divergent preferences. The majority of couples (64.8%) want the same number of future offspring. In 20.7% of couples, husbands want more future offspring than their wives, while the reverse occurs in 14.5% of couples. Living in villages with the husband's or the wife's parent(s) is associated with having divergent preferences for future offspring, where there is a higher likelihood that women prefer more offspring than their husbands. When examining fertility outcomes, women, particularly those who marry at older ages, are more likely to achieve their desired preference. Contrary to previous research, we do not find that living near one's natal kin or having increased autonomy increases an individual's likelihood of achieving desired fertility outcomes. Social media summary: Who wants more children and who achieves their preference? A longitudinal study of Indonesian couples finds out.Studies that examine fertility intentions find that when differences occur between men and women, it is typically women who want fewer children than men, particularly in Sub-Saharan Africa  may set up a reproductive conflict where daughters-in-law have an advantage over older mothers-in-law, since they have more to lose by forgoing reproduction owing to relatedness asymmetries  instead of directing investment exclusively towards biological offspring. Mathematical modelling has shown that women may delay first births when they have full siblings to invest in as compared with when they have half siblings . If women seek to achieve status, then in contexts where production of offspring is the only route to status – particularly among affinal kin – they may prefer to have more offspring  Is there a difference in (a) desired future reproduction and (b) ideal family size among married men and women in Indonesia (testing Hypothesis 1); and (2) if so, which variables are associated with differential desired fertility (testing Hypotheses 2a–e)?Stated fertility intentions are predictive of future fertility, even though they may be imperfect  will be more likely to achieve their desired fertility, while women living patrilocally  will be less likely to achieve their desired fertility.Women's autonomy may also be associated with their access to natal kin. A frequent empirical finding in the evolutionary literature is that couples have more offspring when living patrilocally . Small provinces and provinces that were politically unstable at the time of the first interview were not sampled. A total of 7224 households were surveyed in 1993/1994  to examine fertility preferences and resolution among Indonesian couples. The IFLS is a longitudinal survey that collects individual- and community-level information on fertility, education, employment, migration and health  to examine questions related to desired fertility. By limiting our sample to couples in their first marriage, we assume that both the wife and husband have the same number of current children , allowing us to compare how many additional children they desire. We restrict the age of women to allow time for future fertility. Including women up to 40 or 45 primarily increases the number of couples who do not desire additional children. The second sample includes couples  who disagree on the number of future offspring desired. There were 3536 couples who had different preferences for future offspring. Of these, 1949 couples remained married, reported birth information and were followed for a minimum of 7 years. This can be further divided into 170 couples who had a number of children that fell between the wife's and husband's preferences and 1779 couples where one person ‘achieved’ their desired fertility.We utilise two samples throughout. The first is the sample of couples  who report their desired number of future offspring ?’ and ‘how many (more) children do you wish to have?’ Starting in 1997/1998, participants could answer with ‘Up to God’. For these analyses, we include couples the first time they are asked, where both answered with a numerical value. We conduct a one-sample test of proportions to see if couples are evenly split (50%/50%) in which partner (wife or husband) prefers more offspring.ever married. Participants are included the first time they answer the question, regardless of their number of marriages or polygyny status. A potential response to the question, ‘if you could choose exactly the number of children to have in your whole life, how many would that be’ is ‘up to God’. This makes analysis more challenging, but we can examine how this changes for men and women across time, to determine whether there are differential preferences for ideal fertility. We also conduct a t-test to compare the ideal family size across men and women for those who provide numeric answers.While ideal family size may be a less reliable indicator of future fertility, we can also examine whether there are differences between men and women. In this case, we do not need to limit the sample to those who are currently married, although the question was only asked of those who were We conduct a multinomial logistic regression model to determine which attributes correspond to having the same preference for future offspring compared with different preferences – either the husband preferring more future children or the wife preferring more future children. We examined the following predictors: age difference between partners (Hypothesis 2a), postmarital residence (Hypotheses 2b–d), proximity to kin (Hypothesis 2d) and wife's education (Hypothesis 2e).Postmarital residence is defined as neolocal if the couple did not live with either the wife's or husband's parents after marriage, matrilocal if they lived with the wife's mother or father after marriage and patrilocal if they lived with the husband's mother or father after marriage. Proximity to kin is a categorical variable indicative of the presence of kin at the time of interview (when the couple reports their preference for future offspring). Given that postmarital residence may be a proxy for kin availability, we use both measures. Our hypotheses are only explicitly about kin availability in Hypothesis 2d, but we believe that hypotheses 2b and 2c could also be framed as hypotheses about kin availability (or lack thereof). The categories of kin availability indicate whether at least one of the wife's parents or husband's parents live in the same village . The options include: neither set of parent(s) are living in the same village, the wife's parent(s) live in the same village, the husband's parent(s) live in the same village or both the husband's and wife's parent(s) live in the same village. Parent's location was not collected in 1997/1998, so we run analyses both with and without the kin availability measure. Wife's education is defined as a categorical variable indicating the highest level of education achieved, including none, grade school, junior high, secondary school, vocational secondary school and postsecondary education.We include a variety of control variables in our multinomial logistic regression model that may influence desired fertility  who had divergent preferences for future offspring. We monitor them for as long as possible by identifying the most recent wave with data on fertility where they remain married , but requiring a minimum duration of 7 years for follow-up to determine who was able to achieve their desired fertility preference . In every wave of data collection, women respond to questions about their number of live births since the previous wave. We include all live births as an indication of the couple's fertility during the follow-up period. We code the couple by who ‘achieved’ their desired fertility. As an example, if a couple had divergent fertility preferences, where the wife wanted two children, but her husband only wanted one, we code the husband as achieving his fertility preference if the couple has zero or one child after the maximum duration of follow-up. We code the wife as achieving her fertility preference if the couple has two or more children over the same time period. We analyse this using a logistic regression model (the event indicates that the wife achieved her desired fertility) and include the same predictors as listed above: spousal age difference (Hypothesis 3b) and postmarital residence/kin availability (Hypothesis 3c), and add wife's autonomy (Hypothesis 3a).In your household, who makes decisions about: (a) your child's health, (b) large expensive purchases for the household, (c) whether you work, (d) whether you (and your spouse) use contraception and (e) your time spent socialising.Wife's autonomy is the average of her autonomy in each wave across the range of years that she is surveyed. Women's autonomy is measured by responses to the following questions:Women report who makes these decisions and can report multiple people. Autonomy is measured as the average of decisions the woman is at least partially involved in, coded as 2 = woman alone makes the decision, 1 = woman makes decision with others, 0 = woman does not make the decision. For any decision that may be irrelevant, such as child's health for women without children, we take the average of decisions that remain. This measure was collected beginning in 1997/1998. For kin availability, there are a lot of possible combinations as we follow couples across the waves of data collection. We categorise this variable into seven categories based on original data collection and final follow-up: (1) no kin present ; (2) wife's parent(s) live in village (both time points); (3) husband's parent(s) live in village (both time points); (4) both sets of parent(s) live in village (both time points); (5) no kin lived in village originally, but kin lived in village by the final follow-up; (6) kin lived in village originally and at follow-up, but which parents had changed; and (7) lived in same village as kin originally, but did not by the final follow-up.We continue to include the control variables: household wealth (averaged across waves where the couple is followed), wife's education, difference in husband and wife's education, religion, region, wife's age at marriage, number of living children (at first interview), arranged marriage status, urban vs. rural residence and duration of time between original interview and maximum possible follow-up. We also include an indicator for whether the couple had experienced the death of a child during the follow-up period since prior research has found that death of a child significantly increases the likelihood of another birth  desired future reproduction and (b) ideal family size among married men and women in Indonesia?z(3823) = 10.75, p < 0.001. This remains if we look at each wave separately, where husbands prefer more future offspring than wives in approximately 58.7% of couples where differences occur.t-test comparing the averages of men and women who report a numeric value shows that the ideal family size is significantly different. Men  report a significantly higher ideal family size than women , t = −6.32, p < 0.001. The distribution of ideal family size changes quite substantially across waves , where the number of people who report that it is ‘up to God’ declines from about 33.6% in 1997/1998 to 3.5% in 2014/2015. In contrast, the number of people who report two as the ideal family size goes from 27.2% in 1997/1998 to 55.4% in 2014/2015. There is clearly a shift towards an ideal family size of two.We can also examine ideal family size, which will depend less on the number of children couples already have. Question 2: Who prefers more offspring, and which socioecological variables are associated with differential desired fertility?and that wives want more offspring than their husbands. If we examine the model predicting the relative risk of the husband preferring more future offspring than the wife, we see that living in an urban area is associated with an increased likelihood that husbands want more children . Since kin availability was not available in the 1997/1998 wave, we report the multinomial logistic model without kin availability in Supplementary Materials Table S2. The results are substantively similar, except that husbands are less likely to desire more future offspring than their wives in 1993/1994 and 1997/1998.Question 3: How are these differences (when they exist) resolved? Under which socioecological contexts do individuals achieve their preferred number of offspring?Of the 1949 couples who disagreed on the number of future offspring , there were 1098 (56%) couples where the wife wanted fewer future offspring than her husband and 851 (44%) where the wife wanted more future offspring than her husband. Examining these couples, we see that in about 48% of couples the wife achieved her desired future fertility, while in only 43% of couples did the husband achieve his desired future fertility see . The remIf we examine the couples where one person ‘achieves their desired fertility’, we can examine whether there are particular traits about a couple that leads the wife or husband to achieve their preference. In this paper, we seek to understand the factors that influence conflict over family size and its resolution in Indonesia. A summary of our research questions, hypotheses and results can be found in When reflecting on these results in light of our original hypotheses, we find that, on average, men are more likely to prefer greater numbers of future offspring than women, but the majority of couples agree on future reproduction. The result is mirrored in ideal family size, where there are minor differences between men and women, with slightly higher ideal family sizes by 0.14 children on average among men. So, counter to our original hypothesis, we do not find differences between ideal family size and desired future fertility. It is impossible to know how people interpret these questions and whether they think only about children within their current partnership or across multiple partners when answering these questions. Our results do not find differences across these measures, providing suggestive evidence that people perceive these questions in similar ways. Overall, we find small, but significant, differential fertility preferences within a minority of couples.We also do not find evidence that men have different fertility intentions than woman when they are significantly older, which we predicted based on their decreasing paternity certainty given higher mortality risks. Perhaps our prediction should have focused on different preferences for interbirth interval. Women, who because of their younger age, can expect to live more future years than their much older spouses, may wish to have offspring at longer birth intervals, spacing out births over their reproductive life. Men, who are older and have fewer future years of life, may prefer to have offspring at a faster rate so as to utilise their wives’ reproductive resources before their death. In this case, we might not observe differences in desired future fertility, just differences in ideal birth intervals. Future research should examine whether different interbirth intervals are found for couples with large spousal age gaps.Hypothesis 2c: Women living matrilocally will have lower desired fertility than their husband but does conform to Hypothesis 2d: Women living near kin will have higher desired fertility than their husbands. This suggests that women desire more offspring than their husbands when they have kin (either their parents or their husband's parents) nearby to help them with allocare or provide financial or emotional support, or are responding to pronatal messaging from kin. An alternative hypothesis is that women who want to have more offspring opt to live near kin. Our results do not support the idea that women are exploited when they live near their husband's kin into having offspring that they do not want to have. Of course, it is possible that women hide their true preferences or report what they view as their inevitable reproductive outcomes.When we examine predictions based on socioecological contexts, we find evidence that women living near their husband's kin have higher desired fertility than their husbands. This may reflect intergenerational reproductive conflict  and the number of children they want tends to be slightly higher overall. For instance, couples that want more than five additional offspring always live in communities with both sets of parents.Education, which we predicted would reduce women's desired fertility, is associated with a reduced likelihood that women want more offspring than their husbands. In couples where women have more education, couples are more likely to either agree on future fertility or have the husband desire more future offspring than the wife. This suggests that women's education – a critically important factor in predicting reduced fertility outcomes  and are unlikely to revise them with time. This also provides evidence against the argument that the fertility transition can be explained as an increase in women's autonomy, whereby women can achieve their lowered desired fertility through greater decision-making power (see also Morgan, Stash, Smith, & Mason, There is no evidence that living near kin influences a person's ability to achieve their desired fertility. This is counter to some prior interpretations and may mean that this context is unique or that women are generally not exploited into having children they do not want. Finally, we find that couples are more likely to have fewer offspring when couples disagree on the number of children they want to have – but that this result is only robust to following couples for seven years. The effect is eliminated when we follow couples for at least 10 years, suggesting that seven years may not be sufficient time for couples to complete their desired fertility.As alluded to previously, these results need to be examined in light of the context of Indonesia. Indonesia's fertility was relatively low by 1993/1994 when the first wave of data was collected, which could limit the level of conflict exhibited between couples. Women's autonomy is generally high in this country, allowing women opportunities that may not be available everywhere, which may reduce or eliminate the exploitation of women. Further, polygyny is allowed in Indonesia and may reduce conflict over reproductive decisions (Mulder & Rauch, There are a variety of limitations to this work. First, people may self-select their partners based on reproductive preference (e.g. McAllister et al., In conclusion, there is some evidence of conflict over fertility preferences, but no evidence that conflict is resolved in favour of men over women systematically in Indonesia. The factor that explains differential fertility preferences best is kin availability, suggesting that women's availability of alloparents for support or exposure to pronatal messages influences their strategic preference for more offspring under favourable social conditions.


Even though 3′–5′-cyclic adenosine monophosphate (cAMP) was discovered over half a century ago, it continues to be an object of scientific interest. This is not surprising, since cAMP signaling plays an important role in a wide variety of physiological processes, such as transcription regulation, metabolism, mitochondrial homeostasis, as well as cell growth and cell death. The importance of cAMP signaling is further highlighted by its evolutionary conservation, spanning from microorganisms to mammals .Over the last decade, research on cAMP signaling has unveiled a highly organized compartmentalization of cAMP within the cell, contributing to the specificity and selectivity to its signaling . This coCattani-Cavalieri et al. published in this Research Topic focuses on understanding the signaling networks in Human Airway Smooth Muscle (HASM) cells, particularly those related to cAMP signaling pools coming from two distinct AC isoforms, i.e., AC2 and AC6. The cAMP signaling in HASM has enormous translational significance, as it involves in bronchodilation by stimulation of β2-adrenoreceptors, e.g., in patients with bronchial asthma and chronic obstructive pulmonary disease. Although G-protein coupled receptors (GPCR) expressed in HASM cells utilize cAMP signaling, activation of different receptors leads to responses that are distinct from those elicited by β2-adrenoreceptors. This phenomenon may be due to the compartmentalization of cAMP signaling within the cell. To investigate this issue, the authors applied Stable Isotope Labeling of Amino acids in Cell culture (SILAC) technique to quantify phosphorylated proteins in HASM. Stimulating the AC activity, the authors found that 14 differentially phosphorylated proteins (DPPs) resulted from AC2 activity and 34 differentially phosphorylated proteins resulted from AC6 activity. They also revealed that AC2 signaling is associated with modifications in RNA/DNA binding proteins and microtubule/spindle body proteins, whereas AC6 signaling is associated with proteins regulating autophagy, calcium-calmodulin signaling, Rho GTPases, and cytoskeletal regulation. Noteworthy, the phosphorylation of OFD1 exhibited contrasting regulation, showing increased phosphorylation at Ser899 in response to AC6 activation, while AC2 activation resulted in decreased phosphorylation of the same site. The study thus provides new insight into the compartmentalization of cAMP signaling within the cell.In this context, a study by Paolocci and Zaccolo delve into the Research Topic of compartmentalized cAMP signaling within the primary cilium and explore the utilization of optogenetic tools to gain insights into the mechanisms underlying ciliopathy in context of autosomal dominant polycystic kidney disease (PKD). The cilium serves as a hub for cell signaling, housing various pathways, including cAMP signaling. Any disruption of cAMP signaling within the cilium can lead to perturbed cytogenesis, potentially resulting in pathological conditions like PKD. Notably, several GPCRs, such as Gpr161 and Gpr175, act as AKAPs facilitating the compartmentalization of cAMP signaling at the primary cilium. Paolocci and Zaccolo discuss various methodologies developed and employed to investigate compartmentalized cAMP signaling in the primary cilium. Among these tools, two FRET-based reporters have been considered: the Epac-based reporter, which measures cAMP production  and Alzheimer’s disease (AD). In neurodegenerative disorders, excessive production of reactive oxygen species (ROS) is regarded as one of the major mechanisms leading to neuronal cell death (Lastly, ll death . The autin vitro assays.In summary, this Research Topic highlights some actual trends in both basic and translational research concerning cAMP signaling. This holds particular significance in the context of diseases such as bronchial asthma, chronic obstructive pulmonary disease and PKD, where the compartmentalization of cAMP signaling plays a crucial role. Moreover, the Research Topic highlights the impact of impaired cAMP signaling in various forms of cell death, ultimately contributing to the development of neurodegenerative diseases. Lastly, the Research Topic presents several methodological approaches, including the utilization of FRET-based reporters to study cAMP compartmentalization, and the identification and characterization of sAC inhibitors for potential therapeutic applications through


The 2025–2030 United States Dietary Guidelines process is currently underway, and the 2025 Dietary Guidelines Advisory Committee is examining and evaluating a list of prioritized scientific questions identified by the United States Department of Health and Human Services and the United States Department of Agriculture. One of the questions that will be evaluated is if changes should be made to USDA Dietary Patterns based on whether starchy vegetables and grains are, or can be, consumed interchangeably. These foods have historically been classified in distinct food groups. Menu modeling analyses evaluating the impact of replacing starchy vegetables with grains result in declines in key nutrients of concern. Given their unique nutrient contributions and the fact that many cultural foodways within the United States population include both starchy vegetables and grains, it is important for dietary recommendations to continue to categorize starchy vegetables and grains separately. Foods rich in carbohydrates, such as grains, fruits, legumes, nuts and seeds, and vegetables, including starchy vegetables, provide the bulk of dietary intake for most global cuisines and food patterns . Grain cAs the work of the 2025–2030 U.S. Dietary Guidelines Advisory Committee (DGAC) continues, the U.S. Department of Health and Human Services (HHS) and U.S. Department of Agriculture (USDA) released a list of proposed scientific questions that will inform the work of the DGAC , and should additional Dietary Patterns be developed/proposed based on:Findings from systematic reviews, data analysis, and/or food pattern modeling analyses.Population norms , preferences , or needs  of the diverse individuals and cultural foodways within the United States population?Notably, the DGAC is considering if starchy vegetables can be considered as interchangeable with grains. This perspective substantiates the need to consider the diversity of complex carbohydrate-containing foods, each with a unique role to contribute to overall nutrition and dietary health. Nutrient-dense dietary patterns include both grain foods and starchy vegetables. These food groups are currently considered separately, and they must remain separate to ensure people are encouraged to consume complementary nutrients from each of these food groups.Further, this paper includes a menu modeling analysis which demonstrates the nutritional differences between starchy vegetables and grains in an overall dietary pattern. Using complex carbohydrate foods, specifically starchy vegetables  and grains, interchangeably is at best, not a useful strategy, but at worst, may increase the risk of micronutrient inadequacy and/or dietary imbalances.Carbohydrate foods are often lumped into one macronutrient category, despite their wide nutritional variations. Simple carbohydrates, such as mono-and disaccharides, can be intrinsically present in foods, as in fruit, or isolated and used as an ingredient, as for sucrose from cane sugar.“Complex” carbohydrates can be even more varied. Starches and fibers can have different lengths and compositions. Even fiber, a subclass of complex carbohydrates, is far from homogenous, with its various soluble, insoluble, cellulose, hemicellulose, pectins, lignins, and variants . DietaryStructurally, carbohydrates have a common glycerol backbone, but otherwise their chemical structures are broad, divided into various mono-, di-, and polysaccharides, resulting in differing degrees of digestibility, bioavailability, and function.The term “starch” can also have multiple meanings. Most starch is easily broken down into its component simple carbohydrates, but “resistant starch” (RS) passes to the large intestine intact and undigested. Even RS is known to have five variants . Two sucRS3: also known as retrograded starch, forms after cooking high-starch foods like potatoes or rice, then cooling and refrigerating them, causing their long amylose chains to recrystallize into double helices that amylase cannot break down.RS5: formed when starchy foods are fried, producing a starch-lipid matrix.Resistant starch has been widely studied in recent years, with studies showing possible benefits to lipid metabolism, body weight, and as a prebiotic to improve gut health . ResistaSome carbohydrate foods, such as fruits and vegetables, can be eaten alone, even raw, while others are more commonly used as ingredients, either in cooked meals or snacks, or as ingredients, such as grain flour in breads and baked goods. Carbohydrate-containing foods can be eaten in savory or sweet preparations, allowing them a versatile role in the diet.Wheat, rice, and maize (corn) are the three most commonly consumed grains in the world . Even inIn many cultures, starchy vegetables and grains have also provided an economical means of “stretching” modest amounts of proteins while still providing a satiating and nutrient-dense meal. Examples include combining potatoes with eggs in a Spanish frittata (Tortilla de Patatas) or a skillet meal that mixes lean ground beef with potatoes and vegetables. These two dishes have their variations in many cuisines and provide a way to extend more moderate amounts of proteins, while increasing overall vegetable consumption.Starchy vegetables, in contrast to grains, are seldom consumed with added sugar, either as a staple vegetable in a meal, or as a snack, the exception being candied sweet potatoes or sweet potato pie, but overall consumption of these is low. The term “grain-based desserts,” for instance, does not have a potato equivalent. Whether starchy vegetable or starchy grain, either can contain added fats and/or sugars. Dietary balance, which includes consideration for quantity and frequency of consumption, remains a cornerstone of dietary guidelines.Grains and starchy vegetables share the commonality of being complex carbohydrates, and in the case of whole grains, fiber as well. However, that is where much of their similarities end.6, and vitamin C content. Potatoes are a good source of potassium, one of the “nutrients of public health concern” identified by the 2020–2025 United States Dietary Guidelines for Americans Whole wheat bread: 3% DVVitamin C:Medium-sized potato: 30% DVWhole wheat bread: 0%Vitamin B6:Medium-sized potato: 10% DVWhole wheat bread: 8% DVIron:Medium-sized potato: 6% of the DVNotably, the low phosphorous content of potatoes makes their iron more bioavailable than the iron in plant foods with higher phytate content . Their hWhole wheat bread: 9% DVFiber:Medium-sized potato: 8% of the DVWhole wheat bread: 14% of the DVGrains and starchy vegetables both lose fiber when processed by removal of the bran and germ from grains, and removal of the skin from potatoes. If potatoes are boiled, there can be additional loss of potassium through leeching. Mineral content is well retained whether potatoes are baked, fried, roasted, or microwaved . Frying Compared with starchy vegetables, such as potatoes, grains tend to be lower in potassium and vitamin C, but intrinsically provide higher amounts of other nutrients, such as thiamine, zinc, and vitamin E. If grains are consumed in “whole” form, their bran and germ components can be sources of phytochemicals, mostly as polyphenols . The majAmong macronutrient differences between grains and potatoes, whole wheat bread contains 7.2 g of protein in two slices (64 g), compared with 3 g in a medium-sized potato (5.3 oz) of similar energy content , 10. WhiTools for collecting dietary intake data have varied over time, and have included various questionnaires, 24-h recalls, and interviews. The National Health and Nutrition Examination Survey (NHANES) uses trained interviews to conduct dietary recalls on two separate days . Other lThese data collection tools do not always consistently classify foods, especially starchy vegetables. The USDA and the DGAs consider all white potatoes as vegetables. Yet, the FFQ used in many large observational studies, groups white potatoes with breads, cereals, and starches, but other starchy vegetables, such as sweet potatoes, including sweet potato fries, and butternut and acorn squashes, with the vegetable group . The nutGiven these inconsistencies, a tool that addresses the quality of carbohydrate-containing foods would be a meaningful addition to inform the categorization of foods for development of food-based dietary guidelines.At a time when most Americans do not eat enough vegetables, moving a group of vegetables to a different food group, could result in vegetable consumption in food assistance programs and other efforts affected by DGA. Given the diverse variety and nutrient composition of carbohydrate-containing foods, others have suggested the usefulness of an evidence-based metric for measuring carbohydrate food quality . It has Among these justifications for a more holistic metric of carbohydrate quality was better alignment with current dietary guidance, including a focus on micronutrients to encourage, as well as ones to limit. Current global dietary guidelines encourage consumption of whole grains and many, including the 2020–2025 DGA, note fiber and potassium among the “nutrients of concern” because more than half the population consumes insufficient amounts. Additionally, the DGAs have consistently encouraged reduction of saturated fat, sodium, and added sugars, due to historical overconsumption by most consumers.The result was the development of a metric known as the Carbohydrate Food Quality Score (CFQS), which builds onto the 10:1:1 model of carbohydrate:fiber:free sugar first developed by Liu et al., and considers sodium, potassium, added sugars, and whole grains, all of which were addressed in the 2020–2025 DGAs . More reThe question regarding interchangeability of starches and grains suggests a need to evaluate their unique nutrient contributions. A comparison of the nutrients these foods provide through menu modeling is one way to understand the differences between starchy vegetables and grains and demonstrate how they are not nutritionally similar or interchangeable.Decreased intake of potassium (−21%).Shortfalls in dietary fiber intake (−10%).Reduced intake of choline (−2%) and vitamins B6 (−17%), C (−11%), and E (−5%).Of particular concern are fiber and potassium, considered as “nutrients of public health concern” by the 2020–2025 DGA (page 49), because “low intakes are associated with health concerns.” Replacing potatoes with grains resulted in a considerable decrease in potassium content (−21%). Many starchy vegetables are good sources of potassium . Consuming grains in place of starchy vegetables may further widen the gap between recommended and actual intakes of potassium. A meaningful decrease in fiber intake (−10%) also occurred when grains replaced potatoes. This finding reinforces the need to include both starchy vegetables and grains in the diet to obtain adequate intake of fiber, especially given that currently 90% of women and 97% of men do not meet dietary fiber intake recommendations .To minimize confusion about carbohydrate recommendations, it is critical to clarify the unique differences between starchy vegetables and grains given their significant nutrient contributions to the American diet. Nutrition analysis clearly demonstrates that starchy vegetables, including potatoes, are not nutritionally interchangeable with grains. Both are high in carbohydrates, but their micronutrient contributions are different and deserve dietary recommendations that respects this difference.As with grains, a diverse intake of starchy vegetables should be encouraged. The limitation of these menu modeling results is that potatoes were the only starchy vegetable included. Additional modeling done with other culturally relevant starchy vegetables, and over 7 days or more, may show different results.This perspective addresses white potatoes, in part because they are the most commonly consumed starchy vegetable and have the same nutritional strengths as other starchy vegetables, particularly with respect to their content of potassium, vitamin C, and dietary fiber. Those with color in the edible portion, such as butternut and acorn squash, also contain beta-carotene, as do sweet potatoes, but other starchy vegetables do not, such as beets and cassava (yuca). Yet all, except white potatoes, are grouped as vegetables in much of the scientific literature that uses the aforementioned FFQ.Carbohydrate guidance should focus not only on the unique nutritional assets of each, but also their varied culinary uses and cultural relevance within diverse foodways. All starchy vegetables have nutrient profiles that are distinct from those of grain foods and should retain their categorizations as separate food groups in dietary guidance.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.KA: Writing – original draft, Writing – review & editing.The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Funding for this perspective was provided by Potatoes USA.This study received funding from Potatoes USA. The funder had the following involvement with the study: composition and data analysis of the menu modeling. KA was compensated by Potatoes USA for preparation and revision of the manuscript.The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


BackgroundMedical faculty development programs (FDPs) often lack hands-on training in teaching skills. Microteaching, a short, focused teaching practice, could be a feasible way to bridge this gap. This study aimed to explore the feasibility of including hands-on microteaching in a medical FDP in India.MethodologyThis mixed-methods study involved fresh medical faculty members who never attended FDP on the basics of medical education technologies, stakeholders, and students from a recently established autonomous medical institute of national importance in India. Participants completed a pre-test survey on their teaching skills and received a hands-on microteaching session during an FDP. After the session, participants completed a post-test survey and provided feedback on the feasibility and acceptability of hands-on microteaching in the program. Program evaluation was done by application of Kirkpatrick’s Model levels one, two, and three.ResultsAccording to the Kirkpatrick Model of Evaluation level one (Reaction), the participants reported improved teaching skills and greater confidence in their ability to teach after the microteaching session. They also reported that hands-on microteaching was an effective way to learn teaching skills and receive feedback. Stakeholders opined that microteaching is a very effective tool for improving teaching skills and should be a part of FDP. Evaluation at level two (Learning) shows that there was a significant improvement in the mean score of post-tests. As per level three (Behaviour Change) evaluation, the majority of the students informed that there is observable improvement in the effectiveness of teaching of faculties in the past two months, i.e., since the participation of faculty in hands-on microteaching in FDP.ConclusionsHands-on microteaching could be a feasible and effective way to enhance the teaching skills of medical faculty members in India. The study findings suggest that including hands-on microteaching in FDPs could help bridge the gap between theoretical knowledge and practical teaching skills. Medical education is an ever-evolving field, with new technologies and teaching methods constantly emerging. The quality of medical education is critical to society’s overall health, and one way to ensure high-quality education is through effective faculty development programs (FDPs) ,2. MedicThere is a growing recognition of the importance of incorporating hands-on teaching methods, including microteaching, to improve the quality of medical education . In IndiMost medical institutes, not under the purview of NMC, conduct FDPs with somewhat varied plans. The Medical Education Unit (MEU) of All India Institute of Medical Sciences (AIIMS), Kalyani, West Bengal, conducted two FDPs including hands-on microteaching sessions by the participants. The data were analyzed to observe the effect of microteaching on the teaching skills of medical faculty and the feasibility to include it in routine FDPs. The findings were presented as a poster at the National Conference of Health Professional Education 2022 held in Dehradun, India on October 31, 2022.Study settingThis study was conducted at the AIIMS Kalyani, West Bengal, 50 km away from the city of Kolkata. The institute is one of the 22 AIIMS newly established under Pradhan Mantri Swasthya Suraksha Yojana with the aim to correct the imbalances in the availability of affordable healthcare facilities and to strengthen the facilities for quality medical education in the underserved states of India. All the AIIMS are autonomous bodies and are not regulated by the NMC. The study proposal was approved by Institutional Ethics Committee vide Ref. IEC/AIIMS/Kalyani/Meeting/2022/95.Study participantsThe institute was recently established in 2019, and new faculty members of different medical disciplines, dental, and nursing joined the institute. All faculty members who had never attended any FDP on the basics of health professional education technology were invited to attend the three-day FDP organized by the MEU of AIIMS Kalyani. Stakeholders  and undergraduate medical students were also included for their views.Study typeThis is a triangulation-type, mixed-methods study of four months duration, where both quantitative  and qualitative (open-ended responses) methods were involved simultaneously for evaluating the improvement in knowledge and teaching skills.A quasi-experimental design was adopted to evaluate the effectiveness of the intervention. The population studied was the medical faculty of AIIMS Kalyani. The intervention used was a hands-on microteaching session during FDP. A comparison was made between pre and post-intervention scores, and the outcome of the microteaching session was hypothesized as an enhancement of knowledge on the concept of the lesson plan, framing of learning objectives, and the use of appropriate teaching-learning media and effective teaching method, which is the prerequisite for developing teaching skills.The qualitative part of the study was performed by open-ended questions used to take the views of participants and stakeholders on including microteaching sessions in the FDP.Planning and intervention implementationThe FDP was planned with 22 topics shortlisted after reviewing the NMC document and the FDP schedule of other institutes of national importance. The average session duration was 45 minutes, and two sessions were taken as flipped classrooms (FCs). It also included three hours of mandatory microteaching sessions by following a checklist of six teaching skills, as used by Deshpande, et al., i.e., set induction, teaching planning, presentation, interaction with students, use of audio-visual (AV) aids, and summarization .The microteaching was implemented in two consecutive FDPs of three days each with 20 and 17 participants (n = 37). The pre-test was conducted on 10 multiple-choice questions (MCQs) based on the concept of the lesson plan, learning objectives, and the use of appropriate teaching-learning media and other effective teaching methods. The topic of microteaching was spread across three days.On the first day, an introductory session on microteaching was conducted through FC. On the second day, along with other topics, a detailed session on microteaching was conducted during which a handout of a checklist of six teaching skills was given to the participants. Instructions were conveyed to prepare a seven-minute topic of choice from their specialty for the microteaching session by following the teaching skills checklist. On the third day, a mandatory microteaching session in front of other participants was conducted. Four junior residents (JRs) from other departments were invited to observe the microteaching session along with members of the MEU. JRs are fresh medical graduates, and their views on how they would have preferred a particular session as students are valuable. The MEU members, JRs, and participants were divided into two groups and microteaching sessions were conducted in two parallel rooms during the same period. The classrooms were equipped with a computer, projector, whiteboard, and flip chart. The environment of the classroom was friendly and non-threatening where each participant presented a microteaching session within seven minutes. Each observer was allowed to share constructive feedback in the form of positive points about the presentation and how could it have been made more effective. Post-test was conducted on the same 10 MCQs based on teaching skills.Feedback on strengths/opportunities and challenges/weaknesses of microteaching was taken separately from participants and stakeholders in the form of open-ended questions through Google Forms.After two months of training, the feedback on improvement in the teaching skills of teachers was evaluated from feedback by undergraduate medical students. Online feedback forms were sent to two batches of undergraduate medical students (n = 175), 50 students in the senior and 125 in the junior batch. Information was taken on 32 faculty members who taught them in the last two months. Feedback on five faculty was not taken as they did not teach MBBS students in the past two months. They were excluded for the evaluation of Kirkpatrick’s level three. Informed consent was taken, and confidentiality was maintained at every step.EvaluationThe evaluation of hands-on microteaching was performed to analyze its effectiveness. Because the course was evaluated in terms of learning and as it was retrospective, we used Kirkpatrick’s framework levels one, two, and three for evaluation .We used participants’ feedback given for the course (level one) to decide the effectiveness of the course. Views on the strengths/opportunities and challenges/ threats from stakeholders were also evaluated. Participants’ (n = 37) and stakeholders’ (n = 7) feedback was taken via open-ended questions through Google Forms. Qualitative analysis was done using the manual content analysis method. Similar responses across the study participants were coded. Next, the codes related to similar areas were clustered together to form the categories, which were grouped under the selected themes. The Standards for Reporting Qualitative Research (SRQR) guidelines were followed while reporting .The pre and post-tests were assessed manually by referring to a common answer key. The pre and end-of-course scores were compared using paired t-tests on Microsoft Excel to evaluate the gain in knowledge about teaching methods (level two).After two months of training, online feedback from MBBS students were taken to study if there was an observable improvement in the teaching skills of the teachers in the past two months (level three). The response, yes/no, was depicted in percentages. The study plan, implementation, and evaluation have been explained in Figure The results of the present study are described under three levels of the Kirkpatrick evaluation model.Level one: reactionThe content analysis of participants’ feedback about microteaching was depicted into two themes, which are shown in Table Figure Level two: learning gained by the microteaching session in FDPFigure Level three: behavior change after the microteaching session in FDPOut of 175 students, 152 responded. The response rate of undergraduate medical students was 86.8%. Figure Mere knowledge of pedagogy does not ensure the acquisition of teaching skills. Hands-on practice of these skills is required to build confidence and adopt them. Conducting microteaching during FDP provides a platform to acquire and practice a variety of teaching skills in front of peer groups and students/residents to get their points of view. This study was conducted to evaluate the feasibility of including hands-on microteaching in the medical FDP in India. The study used a mixed-methods approach, including both quantitative and qualitative data collection and analysis, to explore the perceptions of medical educators regarding the use of hands-on microteaching in FDPs and the change in the behavior of participants after the session.Our findings suggest that hands-on microteaching is a feasible and effective way to improve teaching skills among medical educators in India. The quantitative data showed a significant improvement in the teaching skills of participants after they underwent hands-on microteaching sessions. This is consistent with previous research that has shown microteaching to be an effective teaching technique for improving the teaching skills of educators in various fields -5.For the microteaching session, the presenter was given a checklist of the six core teaching skills and their components for consideration while preparing the presentation similar to a study reported by Deshpande et al. . StudiesOur results are consistent with several studies that show that microteaching is a very effective method for improving teaching skills, communication, and time management skills. Moreover, it enhances confidence, better lesson plans, appropriate use of AV aids, students’ engagement, and active teaching-learning ,8,11-18.Studies have reported that microteaching should be video recorded and provided to participants as it is useful in self-introspection and reflection on one’s performance. Participants felt it was very important to provide them with recordings of their presentations to enable them to get a 360-degree assessment of their teaching which serves as a very strong trigger for behavioral change -21. The Limitations of the studyIt is a challenge to ensure the homogeneity of the study participants as the teaching faculty have varying proficiency levels and prior teaching experiences. Longitudinal follow-up is required to evaluate level four of Kirkpatrick’s model to study the effect of microteaching on the outcome, that is, the performance of undergraduate medical students during their assessment.This mixed-methods study evaluated the feasibility of including hands-on microteaching in the medical FDP in India. The study found that hands-on microteaching is a feasible and effective tool for enhancing the teaching skills of medical educators. Our study contributes to the existing literature on FDPs by demonstrating the potential of hands-on microteaching as a cost-effective and scalable approach for improving the teaching skills of medical educators in India. All medical institutions conduct FDPs that consume organizational time and resources. A little adjustment in the given schedule to include hands-on microteaching sessions in a non-threatening environment will lead to better teaching skills with confidence and behavior change making lessons more interesting and effective.We recommend that NMC India considers incorporating hands-on microteaching into the schedule of FDPs, as it can help bridge the gap between theoretical knowledge and practical application of teaching skills.


Functional neurological disorder (FND) may mimic various kinds of neurologic diseases and may coexist with other neurologic disorders. In cases overlapped by FND, it might be challenging to distinguish symptoms induced by FND and those induced by other underlying neurological disorders, especially when patients show no positive signs indicative of FND. Here, we present the case of a patient who was genetically diagnosed with paroxysmal kinesigenic dyskinesia (PKD). However, most of the patient’s symptoms were considered to indicate FND. To our knowledge, there are no reports of FND overlapping PKD. This case illustrates the possibility that FND can coexist with and mimic symptoms of other diseases. It is necessary to rule out coexisting FND symptoms that may modify clinical presentations that cannot simply be explained by a recognized neurological disease. Functional neurological disorder (FND) refers to various genuinely involuntary neurologic symptoms and signs such as seizures, weakness, and sensory disturbance, which have characteristic clinical features, as well as to the difficulty in voluntarily controlling these in line with a normal basic structure of the nervous system . FND indA 24-year-old woman presented to our hospital with involuntary movement, especially when walking. This involuntary movement was typically present in one foot and on the adduction of the ankle. This only continued for a few seconds if she made any rapid voluntary movement or tensed her muscles. This transient movement affected either leg but was not found on both legs simultaneously. There were no symptoms preceding this involuntary movement, and the patient claimed that she could not tell when the involuntary movement would occur. The patient had noticed these symptoms when she was young but had not sought medical attention because the symptoms were minimal and did not affect her daily activities. However, she had attended our clinic the previous year because she felt the symptoms had aggravated over the past few years. On her first visit to our outpatient clinic, besides the paroxysmal involuntary movement, the patient reported always having difficulty with ambulation but being able to walk unassisted despite her muscle weakness, as described below. On examination using the Medical Research Council (MRC) scale, some muscles showed weakness; the deltoid was MRC grade 4/5, biceps was MRC grade 5/5, wrist flexion MRC grade 4/5, extensor digitorum communis MRC grade 5/5, hamstrings MRC grade 5/5, and the gluteus maximus was MRC grade 2/5, without laterality. All weak muscles were affected by give-way weakness and her weak wrist flexion was compatible with the features of “paradoxical wrist flexion” . All othShe had never been diagnosed with any neurological diseases. The patient only had one sibling, an older half-brother with a different mother, who had a similar clinical presentation in terms of involuntary movement, especially when he was young. No symptoms were reported by her other family members, including her father, who was common to both siblings. Based on the clinical findings and family anamnesis, we considered that the patient might have paroxysmal kinesigenic dyskinesia (PKD) in addition to FND. Genetic analysis showed a typical mutation in individuals who have PKD within the proline-rich transmembrane protein 2 (PRRT2) gene (NM_145239.2): c.649dupC (p.Arg217Profs*8) . We starAfter a definite diagnosis of PKD with a gene mutation in this patient, her half-brother visited our clinic for a final diagnosis of his condition. He told us that he had symptoms similar to those of his sister when he was young, but they had gradually decreased. At his first visit, he reported taking 100 mg of carbamazepine every day and having no symptoms. His neurological examination was unremarkable. A slight involuntary movement similar to that of his sister was noticeable when he did not take carbamazepine. He was finally diagnosed as having PKD with the same gene mutation as his half-sister.FND has neurological symptoms that cause significant functional impairment, and these symptoms are not compatible with a currently recognized neurological or psychological disease. The DSM-5 diagnostic criteria for FND no longer require the existence of preceding stressors and instead focus on positive symptoms . Thus, tPKD is a disorder characterized by episodes of various abnormal involuntary movements typically triggered by voluntary movement. This disorder should be considered in individuals with sudden episodes of unilateral or bilateral involuntary movement and a positive family history of similar symptoms . PRRT2 mThis case illustrates typical features of both PKD and FND and the possibility that FND overlaps or can coexist across a broader range of neurological diseases than has been known until now. This report presents just one example of FND and structural diseases. Therefore, further accumulation and assessment of cases that could be accompanied by FND are necessary because the treatment could differ according to whether FND affects symptoms.Our patient presented with FND and was genetically diagnosed with PKD. We could not find any cases in the literature describing patients with PKD accompanied by FND. It is important for physicians to consider the possibility that FND can coexist with other structural diseases, especially when the latter cannot explain all symptoms. Further studies would be beneficial in determining the prevalence of unrecognized FND overlapping other diseases.


Reactive oxygen species (ROS) were originally described as toxic by-products of aerobic cellular energy metabolism associated with the development of several diseases, such as cancer, neurodegenerative diseases, and diabetes ,2,3. In 2O2) best fits the properties of a signaling molecule and is recognizable as the major ROS in the oxidative regulation of physiological activity [2O2 is mainly produced by NAPDH oxidases and the mitochondrial electron transport chain [However, recent findings have shown that ROS can also contribute to bona fide physiological processes, leading to a new paradigm in reversible post-translational modifications involved in signal transduction, defined as oxidative eustress ,5. Amongactivity . H2O2 isrt chain ,8. This 2O2 signaling in E. coli, plants and animals. It has recently been shown that H2O2 can regulate Shh signaling during development and regeneration [2O2 levels through a noncanonical Boc-Rac1 pathway [2O2 regulates Shh trafficking. Thus, Shh directly impacts its own distribution and potentially the distribution of other morphogens via H2O2 level modulation. These founding results provide a molecular explanation for the robustness of morphogenesis and open a new path toward the integration of ROS regulation in morphogens signaling. It has been shown that ROS level oscillation is involved in cell cycle regulation in vertebrate early development [Xenopus laevis oocytes [2O2 signaling crosstalk is also at work in plants, as shown by Cheng et al. in this Issue [2O2 and Ca2+ in melon and Arabidopsis seed germination. They show that H2O2 and Ca2+ form a reciprocal positive-regulatory loop to maintain a balance between abscisic acid (ABA) and gibberellic acid (GA3) essential to promoting seed germination under ABA stress. Finally, Roth et al. performed a transcriptomic analysis of E. coli response to different concentrations of H2O2 [2O2 exposure and emphasize the role of cysteine synthesis as an antioxidant response. This Special Issue highlights the most recent advances in all the aspects of ROS signaling with examples of Hneration . Thauvin pathway . In a poelopment . Tokmako oocytes . This cais Issue . In this of H2O2 . This anWe would like to acknowledge the authors that have contributed to this Special Issue, “Hydrogen peroxide signaling in physiology and pathology”. This Special Issue has highlighted the need for further research into the mechanisms of ROS signaling.


We investigated the expression and the potential value of plasma transfer RNA-derived fragments (tRFs) of children with obstructive sleep apnea–hypopnea syndrome (OSAHS) as screening biomarkers.At first, we randomly selected five plasma samples from the case group and the control group for high-throughput RNA sequencing. Secondly, we screened two tRFs with different expression between the two groups, amplified it by quantitative reverse transcription-PCR (qRT-PCR) on all samples. Then we analyzed the diagnostic value of the tRFs and their correlation with the clinical data.A total of 50 OSAHS children and 38 healthy controls were included. Our results demonstrated that the plasma levels of tRF-16-79MP9PD and tRF-28-OB1690PQR304 were significantly down-regulated in OSAHS children. Receiver operating characteristic curve (ROC) showed that the area under the curve (AUC) of tRF-16-79MP9PD and tRF-28-OB1690PQR304 was 0.7945 and 0.8276. In addition, the AUC of the combination reached 0.8303 with 73.46% and 76.42% sensitivity and specificity. Correlation analysis showed that the degree of tonsil enlargement, hemoglobin (Hb) and triglyceride (TG). were related to the expression levels of tRF-16-79MP9PD and tRF-28-OB1690PQR304. Multivariable linear regression analysis showed that degree of tonsil enlargement, Hb and TG related to tRF-16-79MP9PD while degree of tonsil enlargement and Hb related to tRF-28-OB1690PQR304.The expression levels of tRF-16-79MP9PD and tRF-28-OB1690PQR304 in the plasma of OSAHS children decreased significantly which were closely related to the degree of tonsil enlargement, Hb and TG, may become novel biomarkers for the diagnosis of pediatric OSAHS. It has Transfer RNAs (tRNAs) are a class of non-coding RNAs that transport amino acids and assist protein synthesis . Under sIntermittent hypoxia is one of the typical characteristics of OSAHS, which can increase the expression of reactive oxygen species, resulting in systemic inflammation state, endothelial dysfunction and metabolic dysregulation . Therefo2.2.1.This study was an observational clinical study. We collected data from 50 patients aged 4–7 years old who were diagnosed with OSAHS after PSG based on medical history in Ningbo Women and Children's Hospital  from November 2020 to December 2021. The diagnostic criteria of OSAHS refer to Chinese Guideline for the Diagnosis and Treatment of Childhood Obstructive Sleep Apnea (2020) , which dIn this study, basic information such as age, gender, body weight and height of all subjects were collected. Body mass index (BMI) was calculated by dividing weight by height squared.This study has been approved by the ethical committee of Ningbo Women and Children's Hospital on November 5th, 2020  and all participants have signed the informed written consent forms.2.2.All blood samples were collected from the peripheral venous blood of the children in Ningbo Women and Children's Hospital. For tsRNA studies, blood was firstly harvested into an ethylenediaminetetraacetic acid anticoagulation tube (EDTA). Within 1 h of blood collection, all specimens were centrifuged at 3,000 rpm/10 mins under 4°C to obtain plasma. The supernatant was transferred to RNase-free Eppendorf tubes and then stored at −80°C until RNA extraction.2.3.Total RNA, including tsRNA, was extracted and isolated from plasma samples by using TRIzol LS reagent  according to the instructions of manufacturer. RNA concentration was measured on NanoDrop™ Nd-1000 spectrophotometer  at 260 nm, 280 nm. The acceptance ratio standard for the study is 1.8–2.0. The extracted total RNA was stored at −80°C for subsequent experiments.2.4.In this study, plasma samples of 5 pairs of children with OSAHS and healthy controls were firstly selected. After extracting total RNA, small isolated RNA was processed with the rtStarTM tRF and tiRNA Pretreatment Kit  to remove the superfluous modification groups. Furthermore, compared to the original RNA, pretreated RNA had been purified twice to make it meet the concentration requirements for subsequent experiments. Purified RNA was then sent to Shanghai Kangcheng Company  for constructing small RNA libraries and performing small RNA sequencing analysis. Briefly, according to the requirements of the commercial kit NEB Next® Multiplex Small RNA Library Prep Set for Illumina , the pretreated RNA was bound with 3′ and 5′ adapters, and small RNA library was established after adding reverse transcription primers to synthesize cDNA. The Illumina NextSeq 500  system was then used for sequencing.http://www.mirbase.org/), Genomic tRNA database (http://gtrnadb.ucsc.edu/), tRFdb (http://genome.bioch.virginia.edu/trfdb/) and MintBase (https://cm.jefferson.edu/MINTbase/). In the expression of tsRNAs between OSAHS and healthy controls, the clean data was measured after the expression level was normalized. We believed that tsRNAs with a fold change of ≥2.0 and P < 0.05 are significantly different expressions. We chose tRF-16-79MP9PD and tRF-28-OB1690PQR304 for follow-up research.In the original sequencing data, low-quality reads and short reads (<15 nt) should be firstly filtered. After this step, all selected RNA reads were aligned to several non-coding RNA database, such as miRBase database . First step, input 1 µl of 3′ Adaptor, 0.5 µl of RNA Spike-in and nuclease-free water into 2000 ng RNA to make a total volume of 8.8 µl, then incubate the mixture at 70°C for 2 min. Second, we added 7.2 µl of 3′ Ligation Reaction Buffer, 1 µl 3′ Ligation Enzyme Mix and 1 µl of RNase Inhibitor in sequence, which was then incubated for 1 h at 25°C. Then the procedure involves addition of a 1 µl of nuclease-free water and 1 µl of RT-primer, and incubation for 5 min at 75°C, 15 min at 37°C, 15 min at 25°C. Third, 1 µl of 5′Adaptor (denatured), 2.5 µl of 10 mM ATP, 0.5 µl of 5′ Ligation Reaction Buffer, 1 µl of 5′ Ligation Enzyme Mix and 20 µl samples were mixed and incubated at 25°C at 1 h. Lastly, 25 µl of Adaptor-Ligated RNA, 8 µl of First-Strand synthesis reaction buffer, 3 µl of 0.1 MDTT, 2 µl of 2.5 mM dNTP Mix, 1 µl of RNase Inhibitor and 1 µl of Reverse Transcriptase were mixed in a nuclease-free tube, and the sample was heated for 60 min at 45°C, and then to 75°C for 15 min. qRT-PCR was firstly conducted on plasma samples of the previous 5 pairs of children with OSAHS and healthy controls, and the sequencing results of PCR products showed complete matching with tsRNA original sequence . After t2.6.X ± SD) and the counting data were expressed in percentage (%). independent samples t-test was used to evaluate the significance of measurement data between the two groups. χ2 test was used for comparison of counting data. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were carried out to evaluate the diagnostic value of tRFs in OSAHS. Correlations between tRFs and relevant clinical parameters were analyzed by Pearson and Spearman correlation analysis. Multivariate linear regression analysis was performed to assess the association between plasma tRFs levels and several clinical parameters. All experiments were repeated at least three times independently. P value < 0.05 was considered statistically significant.Statistical analysis was performed using SPSS version 24.0 and GraphPad Prism 8.0. The measurement data were expressed as mean ± standard deviation (3.3.1.2) between the OSAHS children and the controls. Besides, there were differences in height, serum creatinine (Scr) and total cholesterol (TC) between the two groups.A total of 50 OSAHS children and 38 healthy controls were enrolled. Parameters of all subjects are summarized in 3.2.P value and tsRNAs expression difference between two groups, we selected tRF-16-79MP9PD and tRF-28-OB1690PQR304 as our research objects. After that, we designed primers for the two tsRNAs, and the sequencing results of the qRT-PCR products proved the specificity of the primers . The average reads of mature tRNA and precursor tRNA were 156,419.3 (3.04%) and 5,973.1 (0.12%). A total of 499 tsRNAs were detected, of which 66 were recorded in the tRF database . Through primers .3.3.P < 0.0001), which was consistent with the results of high-throughput sequencing (To further investigate the differential expression of tRF-16-79MP9PD and tRF-28-OB1690PQR304 in OSAHS children, we verified the expression of both tRFs in an extended cohort. As expected, our results showed that the expression levels of tRF-16-79MP9PD and tRF-28-OB1690PQR304 were significantly lower in patients with OSAHS than in controls (both, quencing .3.4.The ROC analysis revealed that the AUCs of tRF-16-79MP9PD and tRF-28-OB1690PQR304 were 0.7945 with 52% sensitivity and 94.74% specificity , 0.8276 3.5.Firstly, we used simple linear correlation to analyze the correlation between tRF-16-79MP9PD and tRF-28-OB1690PQR304 and the clinical parameters we collected. The visual correlation heat map  showed ty = −10.309 + 0.902x1 + 1.887x2 − 0.160x3 + 1.656x4. Differently, Hb and the degree of tonsil enlargement were related to tRF-28-OB1690PQR304 (y = −14.383 + 1.353x1 + 2.503x2.Subsequently, we performed multiple linear regression using the regression method. The results showed that degree of tonsil enlargement, Hb and TG levels were related to the tRF-16-79MP9PD expression  whose ma90PQR304  whose ma4.As one of the most serious diseases among childhood sleep disordered breathing, OSAHS is characterized by hypoventilation, decreased blood oxygen saturation and disrupted sleep structure . CurrenttsRNAs exist widely in tissues and cells of various organisms with tissue specificity and disease correlation and can play roles in cell proliferation, regulation of gene expression, RNA processing, modulation of DNA damage response and tumor suppression . tRFs caP value and tsRNAs expression differences. Subsequently, qRT-PCR was performed in the extended cohort to verify the authenticity of the sequencing results. It was proved that the expression levels of tRF-16-79MP9PD and tRF-28-OB1690PQR304 were down-regulated in the OSAHS group compared with the HC group, which was consistent with the results of small RNA sequencing, indicating that the decreased expression of tRF-16-79MP9PD and tRF-28-OB1690PQR304 were closely related to OSAHS.To our knowledge, this study was the first to investigate the plasma tRFs levels in children with OSAHS. Specifically, we first analyzed general baseline data of the OSAHS and HC groups. The results showed that the height of children with OSAHS was lower than that of HC group, which was consistent with the research of Johnson C  and may In addition, we further evaluated the diagnostic efficacy of tRF-16-79MP9PD and tRF-28-OB1690PQR304 by ROC curve analysis, and the results demonstrated that tRF-16-79MP9PD and tRF-28-OB1690PQR304 had high sensitivity and specificity to distinguish OSAHS from healthy children, processing AUCs of 0.7945 with 52% sensitivity and 94.74% specificity, 0.8276 with 79.59% sensitivity and 71.05% specificity, respectively. More importantly, the AUC of their combination increased to 0.8303 with 73.46% sensitivity and 76.32% specificity, suggesting the great potential of tRF-16-79MP9PD and tRF-28-OB1690PQR304 as diagnosis biomarkers for childhood OSAHS. At present, studies on OSAHS in children have not obtained clear biomarkers, and existing studies also have some limitations, such as lack of specificity and sensitivity analysis (Furthermore, we analyzed the correlation between tRFs levels and laboratory parameters in OSAHS children. We found that the expression levels of tRF-16-79MP9PD and tRF-28-OB1690PQR304 were closely related to the degree of tonsil enlargement. At the same time, we found that the expression level of tRF-16-79MP9PD was also significantly correlated with Hb, TG and CK-MB level. Multivariable linear regression analysis showed that Hb, TG and degree of tonsil enlargement were related to tRF-16-79MP9PD. In addition, tRF-28-OB1690PQR304 was significantly correlated with Hb and TG. The degree of tonsil enlargement and Hb were related to the tRF-28-OB1690PQR304 level through multivariable linear regression analysis. Thus, the plasma levels of tRF-16-79MP9PD and tRF-28-OB1690PQR304 are related to a variety of laboratory indicators in OSHAS. More experiments are needed to investigate the role of tRF-16-79MP9PD and tRF-28-OB1690PQR304 in OSAHS.Nevertheless, there are still some limitations to our study. Firstly, the sample size of this study was 50 OSAHS children and 38 healthy controls. Further expansion of sample size is needed to evaluate the diagnostic value of tRFs. Secondly, this study did not explore the function of tRFs by combining the pathological and physiological mechanisms of OSAHS. In the future, we will explore the role of tRFs in the pathogenesis of OSAHS based on this research. Thirdly, this study was limited to children in Ningbo City. Considering regional and racial differences, future multicenter and larger sample size studies are needed to evaluate the value of tRF as a biomarker.5.In conclusion, our study identified that the plasma levels of tRF-16-79MP9PD and tRF-28-OB1690PQR304 were down-regulated in 4- to 7-year-old children with OSAHS and were closely related to the clinical parameters of OSAHS children. tRF-16-79MP9PD and tRF-28-OB1690PQR304 may be used as a new potential biomarker for the diagnosis of pediatric OSAHS.