The extensive functional and structural remodeling that occurs in the brain after amputation often results in phantom limb pain (PLP). These closely related phenomena are still not fully understood.Using magnetic resonance imaging (MRI) and graph theoretical analysis (GTA), we explored how alterations in brain cortical thickness (CTh) and structural covariance networks (SCNs) in upper limb amputees (ULAs) relate to PLP. In all, 45 ULAs and 45 healthy controls (HCs) underwent structural MRI. Regional network properties, including nodal degree, betweenness centrality (BC), and node efficiency, were analyzed with GTA. Similarly, global network properties, including global efficiency (Eglob), local efficiency (Eloc), clustering coefficient (Cp), characteristic path length (Lp), and the small‐worldness index, were evaluated.p < 0.005). There were no group differences in Eglob, Eloc, Cp, and Lp properties . The real‐worldness SCN of ULAs showed a small‐world topology ranging from 2% to 34%, and the area under the curve of the small‐worldness index in ULAs was significantly different compared to HCs (p < 0.001).Compared with HCs, ULAs had reduced CThs in the postcentral and precentral gyri contralateral to the amputated limb; this decrease in CTh was negatively correlated with PLP intensity in ULAs. ULAs showed varying degrees of change in node efficiency in regional network properties compared to HCs  in producing and evaluating structural covariance networks (SCNs) based on the cortical thickness (CTh) of ULA and HC participants. Green‐to‐blue areas in semi‐inflated brain surfaces (left box) represent differing CThs. The HCP 360 Atlas was used to parcellate ROIs for subsequent construction of SCNs using Pearson's correlations between each pair of corrected ROIs in ULA and HC groups. Structural correlation matrices (third from left) were calculated separately for each group to finally obtain binarized, unweighted, and undirected graph (right) used for graph theoretical analysis. Colors in structural correlation matrices represent weakest‐to‐strongest correlation (blue‐to‐red). HCP 360, Human Connectome Project Atlas; HCs, healthy controls; ROI, region of interest; ULAs, upper limb amputees. In the United States, for example, there were an estimated 1.6 million amputations in 2005; this figure is expected to double to 3.6 million by 2050.At present, the most prominent theories about why PLP occurs and develops after amputation are the central nervous system (CNS) theory and the peripheral nervous system theory.The rapid development of functional brain imaging over the past 30 years has fueled extensive study of the patterns of brain reorganization after amputation. CRT theory has played a major role in focusing the field, from functional to structural, from single subject studies to group studies, from study of discrete local brain regions to brain network‐level studies.10 neurons and 10*1013 synapses.The human brain is an extremely complex information processing network comprising 8.6*10Connectivity between brain regions can be identified by measuring structural covariance networks (SCNs).In this study, we investigated how CTh and SCNs reorganize and change in the brains of upper limb amputees (ULAs). Using graph theory analyses, we aimed to provide supplemental structural reorganization information for a richer clinical picture of the brain network characteristics of ULAs. This new information may aid in the discovery of new therapeutics and approaches for the relief of PLP of amputees and improve functional recovery using plasticity‐based therapy.22.1This observational MRI study recruited and enrolled unilateral traumatic ULAs and age‐ and gender‐matched healthy controls (HCs) from September 2020 to December 2021. All potential participants were screened according to the following inclusion criteria: (1) aged 18–60 years; (2) unilateral upper limb traumatic amputation; and (3) time since amputation ≥6 months. Exclusion criteria were as follows: (1) non‐traumatic ULA or ULA accompanied by other limb injuries; (2) history of neurological or psychiatric disease; (3) history of using psychotropic drugs; (4) being naturally left‐handed; and (5) contraindications identified on MRI, such as incompatible heart pacemaker or a brain aneurysm, among others. The ULA group and the HC group each had 45 participants.Handedness was assessed using the Mandarin version of the Edinburgh Handedness Inventory.2.22; voxel size = 1.0 × 1.0 × 1.0 mm3. A total of 176 slices were acquired for each subject. The subject's head was placed in a padded foam collar to reduce head movement during scanning.All images were acquired in our hospital using a 3.0‐Tesla MAGNETOM Prisma MRI scanner  equipped with a 64‐channel phased‐array head coil. A three‐dimensional magnetization‐prepared, rapid gradient echo sequence was used to record T1‐weighted structural images using the following acquisition parameters: repetition time = 2300 ms; slice thickness = 1.0 mm; echo time = 2.46 ms; flip angle = 8°; matrix = 256 × 256 mm2.32.3.1http://www.mricro.com) in preparation for analysis with other brain imaging tools. Prior to structural image preprocessing, we digitally flipped the brain dataset of the 19 left‐side amputees using RESTplus V1.2 (http://restfmri.net/forum/RESTplusV1.2) so that their data would be in the same orientation as those of the right‐side amputees. This would facilitate comparison. All subjects' data were processed with the Computational Anatomy Toolbox 12 (CAT12) software  within SPM12 software  running in a MATLAB R2013b (http://www.mathworks.com/) environment. We estimated CTh, using an automated one‐step approach based on the projection‐based thickness method,The original scanner DICOM image data were converted into NIfTI format using dcm2nii software (2.3.2F < 50%) to excellent (A + =100%).The data processing steps mainly include brain tissue segmentation into gray matter, white matter, and cerebrospinal fluid; topological correction; spatial normalization to standard stereotactic Montreal Neurological Institute space using diffeomorphic anatomical registration using exponentiated Lie algebra; and finally performing nonlinear deformation. CTh was measured by estimating orthogonal (from surface) white matter distances and subsequently by projecting local maxima to other gray matter voxels using neighborhood relationships, which will obtain CTh mapping of every subject.2.3.3Connectivity between brain regions was identified by measuring SCNs.2.3.4http://www.brain‐connectivitytoolbox.net/)R. To obtain a binarized, unweighted, and undirected graph, G; the binarized adjacency matrix, A, in which A = [amn], was generated. The variable amn was set to a value of 1 if the absolute value of rmn between ROIm and ROIn was greater than a specific threshold; otherwise, amn was set to 0. The threshold minimum value set by the 2log(N)/(N‐1) formula (N = ROI numbers) and maximum value set by HC group small‐worldness index (Sigma) were >1.1.G, nodes represent HCP360 ROIs, and edges represent structural connectivity between each pair of nodes. The graph theoretical analysis was applied to quantify the topological properties of G ,ich A = amn, was gich A = amn, was g2.3.5Regional SCN analysisGlobal SCN analysisEglob) of network, local efficiency (Eloc) of network, clustering coefficient (Cp), characteristic path length (Lp), and small‐worldness index (or Sigma) were used to quantify the architecture of the SCN at global network level.Graph theoretical analysis indicators mainly include regional network analysis and global network analysis. 2.42.4.1χ2) for ordinal variables. p < 0.05 was designated as significant.Baseline characteristics of participants were analyzed using GraphPad Prism 9 (GraphPad Software). Continuous variables were summarized as means ± SD, and ordinal variables as counts and percentages (%). After determining whether our data were normally distributed using the D'Agostino‐Pearson method, group differences were evaluated using two sample t‐tests for continuous variables and chi‐square tests . For positive clusters, CTh values were evaluated using two‐sample t‐tests, and p < 0.05 was designated as significant before determining whether CTh values were normally distributed using the D'Agostino‐Pearson method. For assessing correlations of positive clusters with clinical variables , we performed a two‐tailed partial correlation analysis. We used the Desikan–Killiany 40 AtlasStatistical evaluations of CTh were done using two‐sample t‐tests on the right and left hemispheres separately under CAT12. Age, gender, and education were covariates for this analysis. Multiple comparisons were conducted based on the cluster‐level FWE method, with a cluster threshold of 2.4.3p < 0.05 after false discovery rate correction for multiple comparisons of 360 ROIs based on the HCP 360 Atlas in the regional network analysis. Similarly, p < 0.05 was used for analyses of the global network.Graph theoretical analysis for group differences of topological properties in SCNs was based mainly on permutation test theory. For evaluation of the real‐world SCNs of ULAs and HCs, topological properties under the real‐world SCNs were calculated with different densities. Subsequently, we reassigned the two groups randomly to obtain a new permutation group , and then calculated the topological properties in the permutation group. In all, 5000 random permutations were repeated, producing 5000 random SCNs and their corresponding topological properties. In addition, due to different sparsity, we statistically compared the areas under the curve (AUC) of the topological properties under different sparsity values. Then the AUC differences of the topological properties between the ULAs and HCs in the real‐world SCNs were compared with the 5000 random SCNs. Differences greater than the 95% confidence intervals between the two groups were considered as significantly different. The significance level was set at 33.1p > 0.05). Amputations were almost evenly distributed above and below the elbow, with more patients having right‐side amputations. On average, all testing and imaging took place about 2 years after amputation, a time when the mean PLS score was above 6 on a scale of 1–10 (10 being the worst imaginable pain), and the mean PLP score was just under 4 .We found a significant decrease in CTh in the ULA group in two places in the contralateral hemisphere (relative to the amputation side). Compared to the HC group, the CTh of the ULA group was significantly reduced in the left postcentral and precentral gyri  and in the left inferior parietal cortex   increased, whereas the BC of the right orbital and polar frontal cortex  decreased  also increased in ULAs  Figure . Also, td Figure . The nods Figure .3.4Eglob, Eloc, Cp, and Lp of the global network analysis , National Natural Science Foundation of China .The authors declare that they have no conflicts of interests.


Procambarus clarkii could be a good bio-indicator organism for monitoring potentially metals in aquatic systems.Elements accumulation in crayfish is proportional to the increase in bioavailability (direct contact) with the surrounding water, sediment, and feeding. Five heavy metals  and lithium (Li) were analyzed in the sediment, water, and crayfish tissues. Elements  concentrations in sediment, water, and crayfish tissues showed significant differences between the two sampling stations (El-Qanatir and El-Rahawi drain). However, the levels of elements in crayfish tissues were arranged in declining order as hepatopancreas > gills > exoskeleton > muscles for Cu and Cr; hepatopancreas > exoskeleton > gills > muscles for Ni and Ag; and exoskeleton > gills > hepatopancreas > muscles for Li and Mn. The human health hazard evaluation of heavy metals and lithium exposure via edible tissue consumption was assessed for both children and adult consumers. The target hazard quotient THQ values of crayfish edible tissues (less than 1) will not impose any health implications for consumers who ingest edible tissues in sufficient quantities. Furthermore, the hazard index (HI) values reported for children and adult consumers were lower than one, indicating non-carcinogenic and carcinogenic hazards, suggesting that crayfish edible tissues are safe for human ingestion. This evidence also found that  In the delta, the Nile River is divided into two branches: Rosetta and Damietta. The Rosetta branch receives a variety of pollution categories from many sources, including sewage, domestic, industrial, and agricultural waste effluents from El-Rahawai drains, which total more than 5 × 10Procambarus clarkii, receives HMs from the sediments and water in which it lives. Metal accumulation in crayfish tissues has been observed in various scientific studies [HMs pollution is becoming more of a hazard worldwide, particularly in Egypt. HMs levels widely absorbed and accumulated in tissue; they pose a risk to human health when ingested through contaminated food . HMs lev studies , 7. Apar studies . The bio studies , 10. Pol studies .Potential health hazards related to elements exposure have been recognized in recent decades. According to recent studies , elementProcambarus clarkii, individuals were collected from each studied location by fisherman. Crayfish, P. clarkii, samples were obtained at the same time as water and sediment samples and transported to the laboratory of Environmental Physiology, Faculty of Science, Al-Azhar University, Egypt.Sediment and water samples were collected from two locations: the first is upstream (El-Qanatir station) on the Nile River, and the second is the El-Rahawi drain, which discharges into the Nile near Rosetta Branch in El-Qalyubia Province Fig. . SpecimeIn the lab, crayfish were identified. Crayfish samples  were re-washed carefully with potable water, then beheaded, skinned, and dissected (using plastic tools) to obtain exoskeleton, muscles, hepatopancreas, and gills for elements analysis.n = 5) Heavy metals and lithium were determined in water samples using an acid digestion procedure according to [3 , HF , and HClO4  [3 (65%) and 1 mL H2O2 (30%). The mixture was heated to remove acid until the solutions were completely digested [Elements  were measured in water, sediment, and crayfish tissues  . Furtherdigested .n = 5). To estimate the ppm of each analyst in the diluted solution, samples were treated to a multi-element standard curve. The concentrations of elements in the water were reported in µg/L, whereas those in the crayfish tissues and sediment were expressed in µg/g on a dry weight basis.Finally, the digested mixtures of water, sediment, and tissues were transferred to a volumetric flask and diluted with de-ionized water. An inductively coupled plasma optical emission spectrophotometer  was used to measure the heavy metals and lithium in diluted water, tissues, and sediment solutions (The bioaccumulation factor (Bio-AF) was calculated using the following formula  as: Bio-−1 kg−1 day−1 units [−3, where CF stands for the elements concentration in crayfish muscle (mg/kg); IR is the intake rate of crayfish ingested (kg/day), which in this investigation was 7.9 g/day for children and 20.1 g/day for adults; ER stands for the exposure rate (365 days/year); EP stands for the exposure time over a lifetime (assumed to be 70 years old); BW stands for body weight, which is 70 kg for adults and 52.5 kg for children 6–11 years old, which is the 95th percentile [–3) / AT [−1 day−1, respectively [The estimated daily intake (EDI) was computed using the formula below in mg−1 units  as: EDI rcentile ; AT stan–3) / AT , where Cectively .t test was used to investigate the statistical differences between the two locations (El-Qanatir and El-Rahawi drain) based on elements levels in water and sediments. The Pearson’s correlation coefficient between elements levels in the sediment, water, and studied crayfish tissues was investigated. However, statistical significance was performed at p < 0.05.The statistical analyses were performed using IBM SPSS Statistics Version 22; SPSS Inc., IL, USA, and when significant differences of one-way analysis of variance (ANOVA) were found, multivariate, post hoc Tukey evaluations were utilized to quantify the statistical difference between the elements levels in various crayfish tissues for each element. On the other hand, the independent-samples t test, p < 0.05). However, the highest value of elements (54.55 ± 7.00 µg/L) was recorded in the El-Rahawi drain station for copper, and the lowest value (0.1 ± 0.005 µg/L) was recorded in both stations for silver, with elements arranged as Cu > Mn > Li > Ni > Cr > Ag in the El-Rahawi drain, and Mn > Cu > Li > Ni > Cr > Ag in the El-Qanatir station.Table In Egypt and other developing countries, where environmental protection laws have not been implemented, industrial and domestic wastes are dumped indiscriminately, entering water bodies. Toxic and hazardous chemicals, including metals, have been found in these wastes. Because of their toxicity, durability, and bioaccumulative nature, heavy metals poisoning of water resources is a major problem . A compat test, p < 0.05, Table Sediment pollution is one of the most serious environmental issues affecting ecosystems, as sediments serve as both sinks and sources of toxins in aquatic systems. Sediment analysis is crucial for determining the degree of pollution in the environment . Result The concentrations of copper and manganese in sediment of studied stations were lower than the standard acceptable values, according to . HoweverP. clarkii, crayfish in their tissues from the water, and sediments in which they live. Environmental factors affect metal bioaccumulation in aquatic species, but so do internal factors, including life cycle, habitat, and feeding habits [P. clarkii collected from El-Qanatir and El-Rahawi stations, were represented in Table p < 0.05) in the one-way analysis of variance between the different stations and organs, for all metals. Table t test, p < 0.05), which may be due to the increased influence of sewage, industrial, and agricultural discharge, as well as deposition of these metals from the atmosphere. This finding agrees with [Elements are accumulated by freshwater g habits . The levees with , 34Table, p < 0.05). Additionally, heavy metals and lithium in the different tissues of crayfish were ordered in descending order according to; hepatopancreas > gills > exoskeleton > muscles for Cu and Cr; hepatopancreas > exoskeleton > gills > muscles for Ni and Ag; exoskeleton > gills > hepatopancreas > muscles for Li and Mn. This finding agrees with [Procambarus clarkii was hepatopancreas > gills > exoskeleton > muscles. However, Mn was ordered in the following order: exoskeleton > gills > hepatopancreas > muscles of crayfish. Manganese is in direct contact with the crayfish shell, and its level in the shell was higher than in water, due to its assimilation into the calcium carbonate structure [ The presented data reports that one-way analysis of variance (ANOVA) based on heavy metals and lithium in the different tissues of crayfish revealed significant differences between crayfish tissues in both studied locations  to sediment levels . Organisth percentile of the THQ and HI was used to rank the non-carcinogenic risk of each metal among species and tissues [The EDIs of the metal ions under investigation are lower than RfDs  , 22. As  tissues . The val tissues , 22, 39.Elements in water, sediment, and crayfish tissues were significantly higher in El-Rahawi drain than in El-Qanatir station. This indicates that El-Rahawi drain has more elements pollution than El-Qanatir station. The hepatopancreas of crayfish accumulated higher levels of elements than the other tissues, which is consistent with previous research. However, crayfish muscle accumulated elements at the lowest levels. Finally, the studied heavy metals and lithium will not cause any considerable adverse health effects to humans based on estimated daily intake, carcinogenic hazards, and non-carcinogenic hazards. Therefore, crayfish muscles are suitable for human consumption.


Antiretroviral therapy (ART) extends life for patients with human immunodeficiency virus (HIV) infection. However, HIV treatment is lifelong, and adherence presents a special challenge. Suboptimal adherence to ART may lead to disease progression and virologic failure. Earlier studies with combination ART demonstrated that as much as 90%-95% adherence was needed to prevent disease progression.To measure adherence to ART regimens in commercially insured patients with HIV infection and analyze the clinical and demographic factors associated with ≥ 90% adherence.This study used retrospective claims data from a Mid-Atlantic states MCO. Members 18 years and older with an HIV diagnosis identified by medical claims were included in the cohort, and pharmacy claims were retrieved for these members. An ART regimen was established for each patient within a 120-day period after the last physician’s visit occurring between January 1, 2010, and August 31, 2010. For patients who received an ART regimen recommended by the U.S. Department of Health and Human Services (HHS) 2011 Antiretroviral Guidelines, adherence, as measured by medication possession ratio (MPR), was calculated based on pharmacy claims for 12 months after the end of the 120-day period. Logistic regression was used to examine the association between MPR ≥ 90% and age, sex, type of health plan, use of single-tablet regimens (STR), inpatient and outpatient utilization, and direct health care costs.Of the 4,547 adults with HIV diagnosis, 3,528 (77.6%) had received at least 1 antiretroviral. An HHS-recommended ART regimen was identified in 2,377 patients with 1,136 (47.8%) receiving STR. Mean MPR for patients on an HHS-recommended ART regimen was 91.5% ± 14.0 with 73.1% of patients having achieved MPR ≥ 90%. In univariate analyses, sex, number of outpatient visits, cost of inpatient care, and use of STR were significantly associated with MPR ≥ 90%. In multivariate analysis, only male sex (P = 0.027) and the use of STR (P = 0.009) were positively associated with MPR ≥ 90%. Patients on STR were 1.3 times more likely to achieve at least 90% adherence.Adherence is a challenge for patients with HIV, and more than a quarter of patients who were on an HHS-recommended ART regimen failed to achieve an accepted adherence MPR threshold of ≥ 90%. Use of STR was associated with an increased likelihood of achieving adherence of at least 90%. Interventions to improve ART adherence are needed, and STR may be an effective strategy as it decreases pill burden.


To analyze the clinical characteristics and follow-up data of children with different clinical phenotypes of myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD).The basic demographic and clinical features, laboratory and imaging examination results, and follow-up data of 74 Chinese children with different phenotypes of MOGAD were retrospectively reviewed and analyzed.n = 37), encephalitis (n = 11), optic neuritis , neuromyelitis optica spectrum disorder , transverse myelitis , leukodystrophy-like manifestations (n = 1), and meningitis (n = 1). The mean age of disease onset was 86 months. The number of leukocytes in the cerebrospinal fluid of patients with ADEM was significantly higher than that in patients with ON but lower than that in patients with TM (p < 0.05). The pathogen detection rate among all patients was 36.5%. Recurrence occurred in 17 patients (23%), with the highest recurrence rate in patients with NMOSD and TM. Patients with recurrence had a significantly higher median age than those without any recurrence . The male-to-female ratio in patients with recurrence was 1:4.67, which differed significantly from that at first onset (p < 0.05).The male-to-female ratio in this cohort was 1:1.39. The clinical phenotypes of MOGAD included acute disseminated encephalomyelitis (ADEM; The most common clinical phenotypes of MOGAD in this cohort were ADEM and encephalitis. Recurrence of MOGAD may be related to age and sex, with a higher recurrence rate observed in females. These findings provide a basis for further exploration of the characteristics of different MOGAD phenotypes. It is e2.2.1.In this retrospective study, children who were diagnosed with MOGAD and treated in the Department of Neurology of Children’s Hospital Affiliated to Zhejiang University School of Medicine between March 2017 and February 2021 were qualified for screening. The inclusion criteria were as follows: (1) aged ≤18 years; (2) diagnosed with MOGAD according to the provisional criteria proposed by the International Pediatric Multiple Sclerosis Study Group Criteria (2012) ; (3) pre2.2.The demographic characteristics and clinical data, including clinical manifestations, laboratory and imaging examination results, treatments, and follow-up data of all patients were collected from the medical records and analyzed retrospectively. All children were tested for serum MOG-IgG using fixed cell-based assay  with a full-length human MOG as the target antigen and a low positive cut-off value of 1:10. The samples were sent to the V-Medical Laboratory  for analysis. All patients who tested positive for MOG antibodies underwent magnetic resonance imaging (MRI) examinations of the brain and spine, including T1, T2, and fluid-attenuated inversion recovery (FLAIR).mycoplasma pneumoniae antibody test were performed on 60 and 58 patients, respectively.Cerebrospinal fluid (CSF) samples were collected from 71 patients by lumbar puncture. Testing for Epstein–Barr virus (EBV) and herpes simplex virus (HSV) was performed in 55 and 52 patients, respectively. The serum EBV antibody test and 2.3.All patients received methylprednisolone pulse therapy at a dosage of 10–20 mg/kg/day per day for 3–5 days. Concomitant gamma globulin immunomodulatory therapy at a dosage of 1 g/kg/day was administered to 43 patients for 2 days.2, administered once every 4 weeks for a total of 4 cycles), and intravenous immunoglobulin .The immunosuppressive therapy regime consisted of azathioprine , methotrexate , cyclophosphamide , encephalitis , ON , neuromyelitis optica spectrum disorder , transverse myelitis , leukodystrophy-like manifestations , and meningitis   .p < 0.05).The median age of disease onset was 86 months . The clinical phenotype with the lowest mean age of onset was leukodystrophy-like manifestations (43 months), followed by ADEM (66 months) and meningitis (78 months), and the one with the highest mean age of onset was TM (136 months) . The meap < 0.05).The mean hospital stay was 18.6 days for patients with ADEM, 17.9 days for patients with encephalitis, 12.1 days for patients with ON, 14.9 days for patients with NMOSD, 20.5 days for patients with TM, 18 days for patients with leukodystrophy-like manifestations, and 23 days for patients with meningitis . The mean = 31), headache (n = 19), dizziness (n = 10), vomiting (n = 11), hypersomnia (n = 10), seizures (n = 14), visual impairment (n = 18), ophthalmalgia (n = 2), muscle weakness (n = 16), dysuria (n = 4), constipation (n = 2), ataxia (n = 5), mood changes (n = 1), facial asymmetry (n = 1), and dysarthria (n = 2).The clinical features observed in the various MOGAD presentations included fever . The concentrations of CSF protein fluctuated between 84 and 1,307 mg/L, with a mean value of 295.8 mg/L, and no significant difference was detected among the groups. Among all patients, 3 (5.5%) were positive for EBV; 1 (1.9%) was positive for HSV type II; 9 (15.0%) were positive for serum EBV IgM antibody; and 14 (24.1%) were positive for serum mycoplasma pneumoniae IgM antibody , parietal lobe (n = 35), temporal lobe (n = 21), occipital lobe (n = 21), basal ganglia (n = 21), thalamus (n = 25), brainstem (n = 11), cerebellum (n = 15), corpus callosum (n = 8), and medulla oblongata (n = 4), as well as bilateral involvement of the optic nerve (n = 11) and unilateral involvement of the optic nerve (n = 3). The spinal cord MRI scans revealed affected areas in the cervical cord (n = 19), thoracic cord (n = 21), lumbar and sacral cord (n = 14), and long segment (n = 20).The brain MRI scans indicated involvement in various locations, including the frontal lobe (3.4.p < 0.05), NMOSD versus encephalitis (p < 0.05), ON versus ADEM (p < 0.05), ON versus encephalitis (p < 0.05), and ADEM versus encephalitis (p < 0.05).The shortest mean duration from admission to methylprednisolone therapy was observed in patients with ON and NMOSD . The longest mean duration was found in patient with meningitis (19 days), followed by patients with encephalitis (6.54 days). Significant differences in the mean duration from admission to methylprednisolone therapy were observed in patients with NMOSD versus ADEM (n = 7), mycophenolate mofetil (n = 5), cyclophosphamide (n = 2), and intravenous drip of gamma globulin (n = 2). One patient already taking azathioprine was treated with mycophenolate mofetil due to abnormal liver function. One patient was treated with mycophenolate mofetil due to the poor effect of azathioprine. Recurrence occurred in 13 patients  received immunosuppressive therapy, including 8 patients with ADEM, 5 with NMOSD, 1 with ON, and 1 with TM. Patients were treated monthly with azathioprine . During follow-up, recurrence occurred in 17 (23.0%) children , includip < 0.05). The male-to-female ratio in patients with recurrence was 1:4.67, which was significantly lower than that in patients at first onset (p < 0.05). We further divided 74 patients into four groups according to their serum MOG levels: 1:10, 1:32, 1:100, and 1:320. Pairwise comparison analysis showed no significant difference in the recurrence rate among the four groups (p < 0.05)  ( < 0.05) . The ave6/L, with 64% monocytes. Cranial MRI did not reveal any significant abnormalities, but the electroencephalogram (EEG) indicated diffuse δ activity. The treatment consisted of methylprednisolone, intravenous immunoglobulin infusion, and oral sodium valproate. At the follow-up examination, the CSF WBC count was 32 × 106/L, with 90% monocytes. The first follow-up examination was conducted after a 3-month interval, and the patient experienced recurrent seizures and status epilepticus again, without fever or signs of encephalopathy. The CSF WBC count was 36 × 106/L, and the EEG showed increased δ activity in the left hemisphere and epileptic discharges in the left frontal lobe. Cranial and spinal MRI showed no abnormalities. The patient was treated with methylprednisolone, in addition to combination therapy with oxcarbazepine and sodium valproate to control the seizures. Subsequently, in the fourth month, occasional seizures occurred, and the CSF WBC count was 4 × 106/L. The EEG indicated a basic rhythm with slow waves (≤8 Hz), and increased θ and δ activity was observed in the left hemisphere. Lacosamide was added to the treatment regimen for anti-seizure therapy, and seizure control was achieved. The second recurrence occurred 9 months after the previous one, with seizures again, but without fever or signs of encephalopathy. The CSF WBC count was 45 × 106/L, and the EEG showed a basic rhythm with slow waves (≤8 Hz), as well as increased θ and δ activity in the left hemisphere. Cranial MRI was normal. The treatment included methylprednisolone, intravenous immunoglobulin infusion, and mycophenolate mofetil as immunomodulatory therapy, in addition to continued oral sodium valproate, oxcarbazepine, and lacosamide for anti-seizure therapy. Seizure control was achieved. The third recurrence occurred 11 months after the previous one, with seizures again, but without fever or signs of encephalopathy. The CSF WBC count was 97 × 106/L, and the EEG showed a basic rhythm with slow waves (≤8 Hz), as well as increased θ and δ activity in the left hemisphere. Cranial MRI was normal. Methylprednisolone, mycophenolate mofetil, sodium valproate, oxcarbazepine, and lacosamide were used for anti-seizure therapy. The child remained seizure-free for nearly 1 year.Patient #10 experienced recurrent seizures characterized by frequent and intense episodes, which were controlled after immunomodulatory treatment, with a seizure-free period lasting several months. The main manifestation of the recurrence was seizures without fever or encephalopathy. Multiple cranial MRI showed no notable abnormalities. The initial presentation of this patient was fever and convulsions. The CSF white blood cell (WBC) count was 240 × 104.Pediatric patients with MOGAD exhibit a wide range of monophasic and recurrent disorders . In thisThe CSF leukocyte count was elevated in all clinical phenotypes of MOGAD in comparison with the healthy range, while no significant increase in the CSF protein concentration was observed. Atypical changes in imaging examinations are often observed in patients with MOGAD. In a previous cohort study of 56 children positive for MOG antibodies, 8 14%) exhibited symptoms of area postrema syndrome (APS), with 7 presenting both ADEM and APS, and 1 displaying APS alone . In a mu% exhibitThe incidence of EBV positivity in other patients, such as those with fever, was much lower compared to that in patients with MOGAD. Previous studies have indicated a potential link between viral infection and the development of MOGAD , 16. DesMethylprednisolone pulse therapy and immunoglobulin immunomodulatory therapy are efficient first-line treatments for MOGAD, especially in the acute phase . In this5.The sample size for each clinical phenotype was small, and all patients were from the same hospital. Additionally, the follow-up period was short for some patients. Future investigations with a larger sample size and longer follow-up are needed.6.This study showed that ADEM and encephalitis are the most common clinical phenotypes of MOGAD in Chinese pediatric patients. The age of onset, hospital stay, number of leukocytes in the CSF, and recurrence rate significantly differed in children with different MOGAD phenotypes. Moreover, atypical MRI changes were found in some MOGAD cases. These findings contribute to a better understanding of the characteristics, treatment options, and recurrence rates of each clinical phenotype of MOGAD and may provide insight for improving the diagnosis and treatment of these diseases.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving humans were approved by the Department of Neurology of Children’s Hospital Affiliated to Zhejiang University School of Medicine. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.YH wrote the main manuscript text. XY prepared This study is supported by Key Research and Development Plan of Zhejiang Province (2020C03038); National Natural Science Foundation of China (Grant No. 82001232); Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ19H090002); STI2030-Major Projects+2021ZD 0201700.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


From an early age, children need to gather information to learn about their environment. Deciding which knowledge to pursue can be difficult because information can serve several, sometimes competing, purposes. Here, we examine the developmental trajectories of such diverse information-seeking motives. Over five experiments involving 521 children (aged 4–12), we find that school-age children integrate three key factors into their information-seeking choices: whether information reduces uncertainty, is useful in directing action, and is likely to be positive. Choices that likely reveal positive information and are useful for action emerge as early as age 4, followed by choices that reduce uncertainty (at ~age 5). Our results suggest that motives related to usefulness and uncertainty reduction become stronger with age, while the tendency to seek positive news does not show a statistically significant change throughout development. This study reveals how the relative importance of diverging, sometimes conflicting, information-seeking motives emerges throughout development. Knowledge can impact children’s emotion, cognition, and action. The authors show that when seeking information, children consider if information is useful, positive, and lowers uncertainty, with the emphasis on these considerations changing with age. Once proficient in verbal communication, children may ask up to 95 questions in a single hour to support their learning5. Because information can serve several, sometimes competing, goals, deciding which information to seek out can be a particularly difficult problem to solve. Imagine, for example, a child who suspects their sibling received more pieces of candy than they did. Counting the candy will reduce their uncertainty regarding who received more but may also trigger animosity. As a result, the child will need to arbitrate between different information-seeking motives before choosing a plan of action. Whether the child decides to count the candy may depend on how much they value uncertainty reduction versus their desire to avoid animosity and/or on whether they believe they will be able to correct the potential injustice. Here, we quantify developmental changes to the relative importance of different motives for information-seeking.From a very young age, infants seek information through gestures and vocalization6. According to this theory, when deciding whether to seek information, people first estimate what the information will reveal and then estimate the expected impact of that information on their affect , cognition (how the information will improve their models of the world) and action . In particular, all else being equal, people will be more likely to seek information (1) when they expect knowledge to make them feel better18, (2) when uncertainty is high24, and (3) when it can aid in selecting action that will help gain rewards and avoid harm27.We have recently proposed a theory that characterizes three key motives for information-seeking13. These are integrated into a calculation of the value of information, which results in individual differences in information-seeking behavior13. For example, an individual who puts most of the weight on how information may impact their affective state may decide to skip a medical screening, while someone who puts most of the weight on whether information is useful in avoiding harm may attend them religiously.Different people assign different weights to each of these factors when deciding whether to seek information26. Because subcortical regions mature earlier than frontal and prefrontal areas28, we hypothesize that the weight assigned to affect may be fully apparent earlier in development, while that related to instrumental utility and uncertainty reduction may continue to increase throughout childhood.Studies have shown that the affective evaluation of information is associated with activity in the dopamine-rich midbrain , while the instrumental utility of information and its ability to reduce uncertainty is associated with activity in the frontal and prefrontal cortex29, can guide action31 and reduce uncertainty37, studies have yet to disentangle the importance of these (sometimes) competing motives. Rather, motives have either been studied in isolation or in a situation where they are confounded . In real life, conflicting motives for knowledge are often present; thus, a complete picture of the development of information-seeking requires characterizing the developmental trajectory of multiple, coexisting information-seeking motives.While there are clues in the literature that children, like adults, prefer information that is positiveHere, we examine how the relative importance of different information-seeking motives alters with development from preschool (4-year-olds) to the beginning of teenage years (12-year-olds). We develop a child-friendly task in which, on each trial, we simultaneously vary the instrumental utility of information that children can obtain, the level of uncertainty they have about the information and the likely valence of it. By examining children’s decisions of whether to seek information, we determined the age at which children begin to consider the influence of knowledge on their action, affect and cognition in such multifaceted situations. We find that as early as preschool years, children seek information that is likely to yield positive news, is useful for guiding action, and can reduce uncertainty. The tendency to seek useful and uncertainty-reducing information strengthens with age, while the drive to seek positive news does not show a statistically significant change throughout development.N = 196 children, age M = 7.62 ± 2.27, age range = 4–11, 48% female, 52% male).In Experiment 1, 204 children  completed an information-seeking paradigm online. We then ran a full replication in Experiment 2  was already attached to the fishing hook, but the participant did not know which one. The fishermen’s goal was to catch fish (bigger is better) and avoid catching cans  the likelihood that the information would convey “good news”—this was varied by manipulating the proportion and size of fish and cans and thus the expected value (EV) of the outcome; (2) the amount of uncertainty regarding the outcome—varied by manipulating the number of seaweeds; (3) the instrumental utility of information—varied by manipulating the likelihood that the participant would make the pole-lifting decision versus that the fisherman would make it, which varies the agency a participant has over the outcome decision. If the participant cannot make the pole-lifting decision, then information has no instrumental utility (see ref. 7) because the participants were told the fisherman was not able to see the items in the pond. For full task details, see the “Methods” section and Fig. Similar to our previous experiment in adultsh trial:  the like39; Table To ascertain whether children of all ages correctly understood the task instructions, we included multiple questions to measure their comprehension of the task (see Supplementary Methods). Scores on these questions revealed excellent comprehension. To verify that children of all ages 1) understood the instructions, (2) were able to compare expected values, and (3) performed adequately in the task, we split children into the following age groups 4–5 (preschoolers), 6–7 (early elementary), 8–9 (readers), and 10–12 (older children) year-olds . In these trials, participants were shown two ponds with different combinations of items and had to select the better one (see “Methods”). Participants of all ages performed well above chance , as observed in Table All age groups were also more likely to fish when EV was positive than when it was negative, thus showing both comprehension of the task and an intuition of EV, as observed in Table 6, we expected that participants would want information more when (1) the probability of receiving good news was greater, (2) uncertainty was greater and (3) the instrumental utility of information was greater. We theorized that these three factors would be integrated to guide information-seeking. To test this prediction, we varied the following factors in each of the two available information-seeking options: (1) the expected value (EV) of the outcome ; (2) the amount of uncertainty about the possible outcomes by varying the amount of seaweed in the pond (ranging from zero to six out of six items); and (3) the probability that the participant, rather than the fisherman, would be given the opportunity to make the decision of whether to raise the pole to fish . Importantly, as only participants, but not fishermen, were able to see inside the pond, information on which items were likely hooked to the pole had instrumental utility only when the participant had agency to make the decision on whether to raise the pole. In each trial and pond, uncertainty over the outcome  could be driven not only by the amount of seaweed in the pond but also by the standard deviation of the items in the pond —that is, the standard deviation from the mean value of all visible items in the pond. Thus, we made sure to construct the trials such that the ponds with a greater amount of seaweed also had greater standard deviation (see “Methods”).Based on our theory of information-seeking motivesWe constructed trials such that the difference between the right and left ponds  for each of the three factors described above was orthogonal. That is, the Δs of the three factors did not correlate across trials (see “Methods” for pairwise Pearson’s correlation coefficients). This should maximize decision conflict and allow us to estimate the effect of each factor on information-seeking.In all the analyses below, the key independent variables of interest were ΔEV (EV right pond − EV left pond), Δagency (agency probability in the right pond − agency probability in the left pond), and Δuncertainty (number of seaweeds in the right pond − number of seaweeds in the left pond).To characterize the developmental trajectories of the weight assigned to each information-seeking factor, we first ran a logistic mixed-effects model predicting children’s information-seeking choices (coded as 0 if the participant selected the left pond and 1 for the right pond) from the following factors (as fixed effects and random slopes for each participant): ΔEV, Δagency, and Δuncertainty. Age (in years) was included as a fixed effect, as was its interaction with each of the above Δs. We also controlled for the following fixed effects: participants’ scores in the instruction comprehension test, participants’ scores on the EV comparison task, and the proportion of correct “fishing choices” each participant made.As observed in Table β coefficients of each of the three factors separately with their age in years. We found that, as age increased, participants assigned more weight to Δuncertainty (Experiment 1: r(202) = 0.28, p < 0.001, 95% CI = ; Experiment 2: r(193) = 0.19, p = 0.007, 95% CI = ) and Δagency (Experiment 1: r(202) = 0.32, p < 0.001, 95% CI = ; Experiment 2: r(193) = 0.21, p = 0.003, 95% CI = ) when seeking information. In contrast, the correlation between age and the weight participants’ assigned to ΔEV when seeking information was not significant (Experiment 1: r(202) = 0.09, p = 0.200, 95% CI = ; Experiment 2: r(193) = 0.01, p = 0.856, 95% CI = ). Bayes factors for the correlation between ΔEV β coefficients and age were smaller than 1, signifying anecdotal evidence in favor of the null hypothesis in Experiment 1 (BF = 0.36) and moderate evidence in favor of the null hypothesis in Experiment 2 (BF = 0.17) that ΔEV did not alter with age. These results support the conclusion that preference for information that is likely to convey good news remains relatively stable throughout development, while the preference for information that can reduce uncertainty and has instrumental utility increases with development.To examine the robustness of these results, we performed a complementary analysis. In particular, for each participant, we ran a regularized (ridge) logistic regression predicting information choice from ΔEV, Δuncertainty and Δagency. We then correlated participants’ 40  = 0.38, p < 0.001, 95% CI = ; Experiment 2: r(193) = 0.27, p = 0.001, 95% CI = ).Individual estimates for the effects of Δagency on information-seeking correlated with the proportion of correct fishing choices—defined as the proportion of times the participant chose to fish when the EV of the pond was positive and not to fish when the EV of the pond was negative (Experiment 1: χ2(1) = 9.51, p = 0.002, ω = 0.22), which was characterized by younger children using this strategy more, the effect was not replicated in Experiment 2 (χ2(1) = 2.06, p = 0.151, ω = 0.10). Importantly, even after accounting for this strategy in the model, we still observed in both experiments an interaction between age and Δuncertainty (Experiment 1: χ2(1) = 23.96, p < 0.001, ω = 0.34; Experiment 2: χ2(1) = 9.27, p = 0.002, ω = 0.21) and between age and Δagency (Experiment 1: χ2(1) = 23.92, p < 0.001, ω = 0.34; Experiment 2: χ2(1) = 9.03, p = 0.003, ω = 0.22), and no interaction between age and ΔEV (Experiment 1: χ2(1) = 3.01, p = 0.083, ω = 0.12; Experiment 2: χ2(1) = 0.16, p = 0.686, ω = 0.03; see Supplementary Table We tested whether younger children were more likely to repeat the information-seeking choice they made in the previous trial. To assess this, we ran a mixed-effects logistic model predicting information-seeking as previously described, with one additional variable—the choice made in the previous trial. The results were mixed. While in Experiment 1 there was an interaction between age and previous choice (r(202) = −0.58, p < 0.001, 95% CI = ; Experiment 2: r(194) = −0.57, p < 0.001, 95% CI = ). This suggests that younger children were indeed taking time, presumably to engage with the task.  = −0.50, p < 0.001, 95% CI = ; Experiment 2: r(194) = −0.42, p < 0.001, 95% CI = )).Moreover, even though young children were more likely to use a simple strategy in Experiment 1, in both Experiment 1 and Experiment 2 they took more time in making their information-seeking decision. In particular, relating age to average log reaction times for information-seeking choices revealed that younger children engaged with the task for longer than older children  that show that the same variables that guide children’s behavior also guide adults’ information-seeking.N = 27, age: M = 6.93 years ± 2.6 years, age range = 4–12 years, 59.3% female, 40.7% male) were more likely to select information on the pond where they had higher agency simply because they were drawn to the picture that represented them in the task. To that end, we asked participants to select information on either pond where they had 100% agency  or 50% agency . Given that the participant’s icon was shown on both sides . Importantly, as in the main experiment, Δagency predicted children’s information-seeking choices  = 8.89, p = 0.003, ω = 0.56). These results suggest that our findings cannot be attributed simply to children being drawn to the participants’ icon.Children showed a high level of comprehension of the instructions  preferred information in ponds with more seaweed because they wanted to reduce uncertainty or because they were drawn to seaweed. To distinguish these two possibilities, we introduced a new type of seaweed—seaweed with holes. The holes meant that the children could see that nothing was hiding behind the seaweed . Confirming our findings from the main experiment, Δuncertainty predicted children’s information-seeking choices  = 10.52, p = 0.001, ω = 0.51). Thus, our finding that uncertainty guides children’s choices cannot be attributed to children being drawn to seaweed.Children showed a high level of comprehension of the instructions  were able to track uncertainty within the context of our task. To this end, we conducted a control experiment where children were asked to guess whether each fisherman was more likely to hock a fish or a can and then report their confidence in their guess  as the amount of seaweed. We randomized the side on which the pond with higher uncertainty was displayed. Differences in uncertainty between the two sides covered the same range that participants viewed in the main task. Participants completed an instructions comprehension task before proceeding to the main part of the experiment .M = 0.93 ± 0.11; 5-year-olds: M = 0.93 ± 0.1; 6-year-olds: M = 0.92 ± 0.11; 7-year-olds: M = 0.98 ± 0.04; 8-year-olds: M = 0.97 ± 0.08; 9-year-olds: M = 0.93 ± 0.1; 10-year-olds: M = 1 ± 0; 11-year-olds: M = 1 ± 0; 12-year-olds: M = 1 ± 0). Overall, Δuncertainty was a strong predictor of Δconfidence  = 48.37, p < 0.001), suggesting that children were able to track uncertainty in our task. A Johnson-Neyman analysis performed on the whole data set revealed that the effect of uncertainty on confidence was already significant at age 4 . As there was also an interaction between Δuncertainty and age  = 14.73, p < 0.001, ω = 0.52), we next accounted for these age-related differences in our main model of information-seeking. To that end, we first calculated, for each age, the Pearson correlation coefficient between Δuncertainty and Δconfidence across participants. Then we entered these estimates as a control variable in the main experiment analysis by matching children to the correlation between Δuncertainty and Δconfidence for the corresponding age. Even after including this variable, we found significant effects for the interaction between Δuncertainty and age  = 23.87, p < 0.001, ω = 0.66; Experiment 2: β = 0.14, S.E.M. = 0.04, χ2(1) = 10.31, p < 0.001, ω = 0.44), suggesting that age-related differences in sensitivity to uncertainty during information-seeking cannot be explained by differences in the ability to track uncertainty. All other main effects of interest held true.Children showed a high level of comprehension of the instructions  was to test adults’ behavior on this task to examine if their information-seeking choices are also explained by ΔEV, Δagency, and Δuncertainty. Experiment 7  was a replication of Experiment 6. Adult participants completed the same task as described for Experiment 1 and Experiment 2.The aim of Experiment 6 . Participants performed well above chance in the EV comparisons task  = 406, p < 0.001, r = 0.96; Experiment 7: M = 0.98 ± 0.05, V(28) = 435, p < 0.001, r = 0.94). All participants were more likely to fish when EV was positive than when it was negative  = 406, p < 0.001, r = 0.88; Experiment 7: M = 0.93 ± 0.07, V(29) = 435, p < 0.001, r = 0.88).Comprehension scores were excellent (Experiment 6: χ2(1) = 12.49, p < 0.001, ω = 0.67; Experiment 7: χ2(1) = 13.60, p < 0.001, ω = 0.68), Δagency (Experiment 6: χ2(1) = 34.62, p < 0.001, ω = 1.11; Experiment 7: χ2(1) = 31.94, p < 0.001, ω = 1.05), and Δuncertainty (Experiment 6: χ2(1) = 21.71, p < 0.001, ω = 0.88; Experiment 7: χ2(1) = 20.24, p < 0.001, ω = 0.84; the full model’s results are reported in Table Information-seeking was positively associated with ΔEV , and school-aged children also seek information more when it can reduce the most uncertainty. Thus, like adults (see Experiments 6 and 7), children use information-seeking to improve both internal and external outcomes (see refs. 13). Importantly, the data reveals that these three information-seeking motives follow different development trajectories. While most of our results suggest the tendency to seek positive information was relatively stable from preschool to the pre-teenage years, the tendency to seek information that is useful in guiding actions and when uncertainty was high increased throughout development.Our findings suggest that the three-factor theory of information-seeking41). In other tasks, where motives do not compete, the influence of uncertainty is observed even in younger children . Our results could be related to the finding that young children tend to underestimate uncertainty43; thus, they may not be driven by the same objective uncertainty as older children, as their subjective uncertainty differs.It is interesting that, in children younger than about age 5, information-seeking was predominantly guided by instrumental utility and affect-related considerations. These results could not be explained by a failure to comprehend instructions or an inability to perceive expected value or uncertainty, as preschoolers showed very good comprehension of these aspects, and the one variable that requires an intuition of expected value was already significantly impacting information choices at this age. The findings do not suggest that uncertainty reduction is not important in driving information-seeking at a very young age. Rather, the implication is that in complex situations where multiple information-seeking motives compete, the relative importance of uncertainty in guiding information-seeking may be low in this young population . As the former develops earlier than the latter regions, it is plausible that the behavioral developmental patterns we observed here align with the development of corresponding brain regions and their connectivity28.Here, we were able to test diverging motives for information-seeking concurrently and yet orthogonalize these factors to tease apart their impact on information-seeking. However, future studies are required to test whether the results in children generalize to different information domains and tasks. Indeed, a limitation of this study is that we utilized a single type of task, and the exact developmental stage at which information-seeking motives emerge may be somewhat task-specific. Moreover, future studies may use our task in adolescent populations, as the development of information-seeking motives may not follow linear trajectories from childhood to adulthood. Together with neuroimaging studies, these future endeavors could shed light on the exact mechanisms underlying developmental changes in information-seeking preferences. The affective value of information has been found to correlate with activity in dopaminergic midbrain regions, while instrumental and cognitive  utilities of information may be represented in the frontal and prefrontal cortex , although these remain to be tested. For instance, our findings suggest that even preschoolers will show increased curiosity if information is presented to them as useful for their own goals or likely to yield positive news. Moreover, the model suggests that a low information value from the perspective of one motive may be compensated by a high information value with respect to other motives. For instance, if negative  information needs to be conveyed, student engagement may be increased by presenting it in a way that shows how the information can be useful to achieve goals. We thus believe educators working with children  may find this knowledge useful and use it to guide communication with young students and mentees. Our research may also inspire technological advances that could provide pedagogical tools for children based on their information-seeking preferences, such as artificial learning companions53. To our knowledge, no such artificial systems currently consider hedonic, cognitive, and instrumental goals concurrently.The results may have potential future applications in the fields of public policy, therapy, and education, where information dissemination needs to be targeted appropriately for different age groups  and Experiment 2 (the replication). Therefore, we report them together below.https://gorilla.sc/). All children completed the experiments on Lookit (https://lookit.mit.edu/), an online platform for developmental research. Parents may have assisted children with online navigation but were asked not to interfere with the children’s decisions. All participants received a fixed $10 Amazon voucher for taking part in the experiment. No additional performance bonuses were delivered. In Experiment 1, data from one child were lost due to technical issues with the online hosts. Four children failed one of the attention checks and thus their data were not analyzed. In Experiment 2, four children failed one of the attention checks and were excluded from further analyses. All other participants passed soundchecks, catch trials and instructions comprehension tests. Thus, data from 204 children were analyzed in Experiment 1, and data from 196 children were analyzed in Experiment 2  with either a professional degree  and/or a graduate degree. Legal guardians provided consent for their dependent minors, who then gave their assent. All participants were compensated for their time (approximately 35 min per session). The Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects provided ethical approval for the recruitment of children in this study. Peer Lookit users and the Lookit team also approved the study for data collection on the Lookit website. Sex information  was provided by parents. Because we did not find significant effects of sex on our measures of interest, we analyzed data from all participants jointly. The full procedure is illustrated in Fig. Participants completed three practice trials before proceeding to the main task. An auditory recording guided children throughout the experiment.The main task was set around a story of two temporarily blinded fishermen fishing in two separate ponds—presented to participants simultaneously on the left and right sides of the screen. Each fisherman wanted to catch fish while avoiding cans, and it was the participants’ job to help them Fig. . Within The likelihood that the information would convey “good news” was defined as the expected value (EV) of the outcome . The amount of uncertainty in each trial was varied by changing the number of seaweeds (from 0 to 6 out of 6 items). The instrumental utility of information was manipulated by varying the likelihood that the participant would decide whether to reel in the hook .and the standard deviation of items was low and (2) ponds in which the number of seaweed was low and the standard deviation of items was high. This was done because we defined uncertainty as a high number of seaweeds. However, standard deviation also influences uncertainty , and we wanted to make sure that any pond in which uncertainty is high due to a large number of seaweeds was also high in terms of standard deviation. High/low was defined as greater/lower than the mean of all generated ponds. Thirty trials were semi-randomly generated for each participant, as detailed above. The correlation between standard deviation and the number of seaweeds across trials was significant (Experiment 1: r(6118) = 0.28, p < 0.001, 95% CI = , Experiment 2: r(5878) = 0.28, p < 0.001, 95% CI = ).The combination of items presented in each pond was assigned semi-randomly on each trial. First, we randomly generated ponds with different combinations of items. We then excluded ponds in which (1) the number of seaweeds was high Our task was piloted on 4- and 6-year-olds, who followed the game easily and enjoyed playing. While the task may seem complex on paper , once played with the verbal story instructions, it seems similar to the popular digital games children play online. We performed a series of validation checks to ensure that children engaged with the task appropriately and understood each of its relevant components.13, the outcome (fish/can) on each trial was not revealed to the participant. This is crucial to model real-world situations where people seek information that provides clues about future outcomes , but where those outcomes will only be known in the far future (and sometimes never). This is done because if the outcomes were revealed immediately, there would likely be a strong instrumental utility for information but little non-instrumental utility for advanced information in the form of reduced uncertainty and positive affect .Note that following all previously published similar tasks in adultsSound check. After providing consent, participants completed a soundcheck wherein they were presented with a simple animal sound and asked to choose the corresponding animal from four illustrated options. All participants passed this check.Comprehension check. After reading and/or listening to instructions (available in the Supplementary Methods), participants completed multiple-choice questions that tested their comprehension (see Supplementary Methods for the complete comprehension check). Participants were given up to three opportunities to answer each question correctly. Participants’ responses were coded as 2 when answered correctly at the first attempt, 1 when answered correctly after one attempt, and 0 when answered correctly after two attempts. No one answered incorrectly more than twice for a single question. The sum of these scores was then divided by the maximum possible score , with 1 indicating perfect performance and 0 indicating participants answered each question incorrectly twice before selecting the correct answer.Catch trials. To verify participants’ attention during the task, two catch trials were displayed in which participants were prompted to click on an image of a fish or a can. Participants who failed at least one catch trial were excluded from all analyses. Two children failed one of the catch trials and were thus excluded from the analyses.Expected value (EV) comparison check. Upon completion of the information-seeking task, participants were asked to complete 10 trials which measured the children’s ability to compare the expected value of potential outcomes. Participants were presented with ten trials from the information-seeking task and were asked to indicate which pond was better (see Supplementary Methods for instructions). We calculated the proportion of trials each participant answered correctly .Fishing choices check. To further assess participants’ understanding of the task, we measured the proportion of correct “fishing” choices—that is, the proportion of times a participant chose to “fish” when the expected value of that pond was positive and not to fish when negative .r(6118) = 0.007, p = 0.604, 95% CI = ; Experiment 2: r(5878) = −0.004, p = 0.737, 95% CI = ), Δuncertainty and Δagency (Experiment 1: r(6118) = −0.003, p = 0.02, 95% CI = ; Experiment 2: r(5878) = −0.007, p = 0.604, 95% CI = ), ΔEV and Δuncertainty (Experiment 1: r(6118) = 0.008, p = 0.507, 95% CI = ; Experiment 2: r(5878) = 0.01, p = 0.316, 95% CI = ) across trials. Δs fluctuations across trials for an example participant are shown in Supplementary Fig. For each pond, we calculated 1) the expected value of the pond , (2) the number of seaweed (varied from 0 to 6) and (3) the likelihood that the participant will be given the possibility to decide whether to fish . Then for each trial, we calculated the difference between the two ponds on each of the three measures above (right pond – left pond). This gave us (1) ΔEV, (2) Δuncertainty and (3) Δagency, respectively. These Δs were not correlated with each other. In particular, there was no significant correlation between ΔEV and Δagency , Δuncertainty (right-left), Δagency (right-left). We also included fixed effects of instructions comprehension score, EV comparisons task score, and correct fishing choices as covariates. To determine the developmental trajectory of each motive, the model also included age in years and its interaction with each of the three motives. Results remain consistent if age is log-transformed to account for potential non-linear patterns and/or measured in months rather than years. Three additional models were run. One model was similar to the one described above but with the inclusion of a factor representing information-seeking choices made on the previous trial . A second model was run on the same data as the first one but included triple interactions between age and each pair of information-seeking motives . A third model, similar to the main one described above, was run on trials in which Δagency was equal to 0, i.e., trials in which the agency probability was identical for the two sides of the screen. The intention here was to test whether the effects of ΔEV and Δuncertainty would remain significant in the absence of agency. Therefore, this model did not include Δagency or its interaction with age as predictors.57 (version 4.1–4) to avoid overestimating the coefficients. Individual participants’ coefficients were then correlated (via Pearson correlation) with participants’ age to further assess the relationship between the effects of each information-seeking motive and developmental stage. The BayesFactor R package58 (version 0.9.12-4.3) was used to calculate Bayes factors. All default options were used, including non-informative Jeffreys’s priors59.As complementary analyses, we fit the children’s data at the individual level by estimating their β coefficients for ΔEV, Δagency, and Δuncertainty from information-seeking choices on a trial-by-trial basis. Regularized ridge regression was conducted with the glmnet R package60, all numeric variables were rescaled using the R scale function61, which divides a variable by its standard deviation. For ΔEV, Δuncertainty, and Δagency the center argument was set to false to preserve the original sign, in accordance with the fact that values of 0 were meaningful for these variables62. For all other numeric variables, the center argument was set to true so that predictors would have a mean of 0, essentially z-scoring those variables. None of our analyses were affected by rescaling. For illustrative purposes, age was converted back to raw values by inverting the z-score transformation , which was used to obtain the statistical estimates for each effect. Additionally, we tested the significance of each effect of interest by performing likelihood ratio tests using the full model and a nested model missing the target factor. For these tests, the R (version 4.2.0) anova function was used61.To ease interpretability and comparison among predictorson Figs. . Models 64 (version 1.2.0). The Johnson-Neyman technique40 was used to estimate the age at which the effects of each information-seeking motive  became significant (p < 0.05). These values were estimated from the same model as described above using the modelbased R package65 (version 0.8.5). To ease interpretability, we report the age in years instead of z-scored age in years, at which effects became significant.For illustrative purposes, we plotted the estimated probability of seeking information on the right side as a function of each of the three key variables while keeping the other variables constant for each age Fig.  using thThe data met the assumptions of the statistical tests used. Where applicable, normality and equal variances were formally tested, and when assumptions of normality were violated, non-parametric tests were conducted.https://github.com/affective-brain-lab/information-seeking-children/.Custom data analysis codes are provided at M = 6.93 years ± 2.6 years, age range = 4–12 years, 59.3% female, 40.7% male). Consent, assent, and ethical approval were obtained as in the main experiment. N was based on the main experiment.Twenty-nine children completed the experiment online. Two participants failed one of the attention checks. Thus, data from 27 children were analyzed , factors related to uncertainty and EV were not included in the model. The same rescaling and centering procedures were applied as in the main experiment. Significance tests were performed through model selection via chi-square difference tests as detailed for the main experiment.M = 7.45 years ± 2.57 years, age range = 4–12 years, 42.5% female, 57.5% male). Consent, assent, and ethical approval were obtained as in the main experiment. N was calculated based on the main experiment.Forty-one children completed the experiment online. One participant failed one of the attention checks. Thus, data from 40 children were analyzed , factors related to EV and agency were not included in the model. The same rescaling and centering procedures were applied as in the main experiment. Significance tests were performed through model selection via chi-square difference tests as detailed for the main experiment.M = 7.24 years ± 2.49 years, age range = 4–12 years, 51.85% female, 48.5% male). Consent, assent, and ethical approval were obtained as in the main experiment. Required N for a power of 80% and an alpha of 0.05 was calculated based on the main experiment.Fifty-five children completed the experiment online. One participant failed the instructions comprehension task and was therefore excluded from all further analyses. Thus, data from 54 children were analyzed .For each trial, we calculated the difference in uncertainty (Δuncertainty) between the two sides of the screen . We then calculated the difference in confidence (Δconfidence) between the two sides .40 was used to estimate the age at which the effects of Δuncertainty on Δconfidence became significant.A logistic mixed-effects model was run to predict Δconfidence from Δuncertainty (entered as fixed effect and random slope for each participant), scores on the instructions comprehension task (as fixed effect), and age and its interaction with Δuncertainty (as fixed effect). The same rescaling and centering procedures were applied as in the main experiment. Significance tests were performed through model selection via chi-square difference tests as detailed for the main experiment. The Johnson-Neyman techniqueAdditionally, we tested whether accounting for uncertainty tracking could explain differences in sensitivity to Δuncertainty across ages in the main experiment. To that end, we calculated the correlation between Δconfidence and Δuncertainty across participants separately for each age and included the Pearson R as covariates in the main experiment. This allowed us to control for age-specific uncertainty tracking ability in the main experiment based on measurements from Experiment 5.https://www.prolific.co/) and received a fixed compensation of £7.50. In Experiment 6, the data of two participants were lost due to technical issues with the online host. All other participants passed soundchecks, catch trials and instructions comprehension tests. Thus, data from 28 adults were analyzed in Experiment 6, and data from 29 participants were analyzed in Experiment 7 . 42% of participants reported their education level. Of those, 0.04% had less than a high school diploma; 0.04% had no more than a high school degree; 0.16% had at most a 2-year degree; 36% had at most a 4-year degree; 40% had a professional degree  and/or a graduate degree.Thirty adults took part in Experiment 6, and 29 adults took part in Experiment 7. All participated online through Prolific (The procedure was identical to the one described for children, except participants gave consent for themselves. The University College London Research Ethics Committee approved these studies.The analysis was the same as described in the main study, except that age was not included as a factor.Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review File


Each year, an estimated 15 million babies are born preterm. Micronutrient deficiencies, including vitamin D deficiency (VDD), are common in many low- and middle-income countries (LMICs), and these conditions are often associated with adverse pregnancy outcomes. Bangladesh experiences a high prevalence of VDD. The country also has a high preterm birth (PTB) rate. Using data from a population-based pregnancy cohort, we estimated the burden of VDD during pregnancy and its association with PTB.N = 3,000) were enrolled after ultrasound confirmation of gestational age at 8–19 weeks of gestation. Trained health workers prospectively collected phenotypic and epidemiological data at scheduled home visits. Trained phlebotomists collected maternal blood samples at enrollment and 24 -28 weeks of gestation. Aliquots of serum were stored at -800 C. We conducted a nested case–control study with all PTB (n = 262) and a random sample of term births (n = 668). The outcome, PTB, was defined as live births < 37 weeks of gestation, based on ultrasound. The main exposure was vitamin D concentrations of 24–28 weeks maternal blood samples. The analysis was adjusted for other PTB risk factors. Women were categorized as VDD (lowest quartile of 25(OH)D; <  = 30.25 nmol/L) or not deficient (upper-three quartiles of 25(OH)D; > 30.25 nmol/L). We used logistic regression to determine the association of VDD with PTB, adjusting for potential confounders.Pregnant women (The median and interquartile range of serum 25(OH)D was 38.0 nmol/L; 30.18 to 48.52 (nmol/L). After adjusting for co-variates, VDD was significantly associated with PTB . The risk of PTB was also higher among women who were shorter , primiparous , passive smokers , and those who received iron supplementation during pregnancy .VDD is common in Bangladeshi pregnant women and is associated with an increased risk of PTB. Globally, an estimated 15 million babies are born preterm each year, which is increasing. Preterm birth (born before 37 completed weeks of gestation) and its complications are the leading cause of neonatal deaths  and deatThe ability to find markers that correlate with PTB can aid in decreasing child morbidity and mortality rates. PTB is of multifactorial origin, partially associated with immunologic, genetic, nutritional, and environmental factors –10. The Micronutrient deficiencies, including vitamin D deficiency (VDD), are common in many LMICs, and these deficiencies are often associated with adverse pregnancy outcomes , 14. PreVDD is highly prevalent in Bangladesh, with 66% to 94.2% adult women in their childbearing age experiencing the deficiency . BangladThis is a nested case–control study that uses data from a population-based cohort of pregnant women and their children; the study is known as the Alliance for Maternal and Newborn Health Improvement (AMANHI). As part of AMANHI, we established a biorepository and enrolled 3,000 pregnant women between 2014 and 2018 in two sub-districts of Sylhet district in northeast Bangladesh and followed them until day 42–60 postpartum. The details of the study methodology were published earlier . TrainedMaternal blood samples were collected twice during pregnancy (8–19 weeks and 24–28 weeks or 32–36 weeks of gestation) and once during the postpartum period (42–60 days postpartum). The second pregnancy sample was collected from about three-fourths of the randomly selected women. The third sample was collected from women who were not selected for the second blood sample. The blood samples were collected by trained phlebotomists at the study clinic, and serum samples were separated by centrifugation, aliquoted, and stored at -80 ℃ using standard procedures .n = 2,287), had at least one antenatal and one postnatal visit and the pregnancy outcome data , and had a live-born baby . In this nested case–control study, all women who had a PTB (between 24 and < 37 weeks of gestation) were included as cases (n = 263) and a random sample of term births were included as controls (n = 671). Vitamin D was measured from cases and controls only. Vitamin D measurement was missing in one case and three control mothers; these mothers were excluded from the analysis. Thus, 262 cases and 668 controls were included in the analysis  for analysis. Serum 25(OH)D concentrations were measured by electrochemiluminescence immunoassay (ECLIA) with Roche automated immunoassay analyzers Cobas e601 using a commercial kit  according to the manufacturer’s instruction. The method for vitamin D was standardized against LC MS/MS which in turn was standardized to the NIST standard. The measuring range for 25(OH)D was 7.50‑175 nmol/L. Commercial control based on human serum in two concentration ranges was run in each lot/day to monitor the accuracy and precision of this assay.The outcome, PTB, was defined as live births < 37 weeks of gestation, based on ultrasound. The main exposure was vitamin D concentrations of 24–28 weeks maternal blood samples. Using Serum 25(OH)D concentrations, we categorized women into quartiles . Table Maternal age was categorized into < 30 and ≥ 30 years. Parity was categorized as 0/primiparous, 1–3, and ≥ 4 children. Maternal and paternal education was categorized into 0–5 years and > 5 years of schooling. Maternal height was categorized into height < 145 cm and >  = 145 cm. Household crowding index was created by dividing the number of persons by number of sleeping rooms. We then categorized the household crowding into <  = 2 and > 2. Using principal component analysis, we calculated scores for household wealth based on housing materials and household contents. The households were divided into tertiles using the wealth scores.Since the number of cases were fixed, using Kelsey et al.  formula We conducted two sets of bi-variate analyses using Pearson’s chi-squared test for independence. First, we examined the association of vitamin D concentrations (deficient vs not deficient) with the mother’s age, education, occupation, maternal height, tobacco consumption, husband’s education, husband’s occupation, household crowding, and household wealth index. In the second set of bivariate analysis, we examined the association of infant’s preterm or term status with vitamin D concentrations and selected socioeconomic, demographic, and care-seeking characteristics of mothers. Multivariable logistic regression was used to calculate unadjusted and adjusted odds ratios (aORs) and 95% confidence intervals (CIs) to identify factors significantly associated with PTB. Variables with a p-value of < 0.2 in the bivariate analyses with PTB were included in the multivariable logistic regression model. We used Stata V.17 to analyze the data (StataCorp 2017).The association of maternal serum vitamin D concentrations with selected characteristics of mothers and households is presented in Table In bivariate analysis, the mother’s serum vitamin D concentrations, mother’s height, parity, iron supplementation during pregnancy, and passive smoking were associated with the infant’s term and preterm birth status Table .Table 3DIn the unadjusted logistic regression analysis examining the factors associated with PTB, mothers with VDD , mother’s height , primiparity , mothers with history of passive smoking during pregnancy , and mothers who received iron supplements  showed a higher risk of PTB D deficiency during the second trimester of pregnancy. A vitamin D concentration of < 50 nmol/L or 20 ng/ml is considered deficient and associated with unfavorable outcomes . In the In addition to VDD, maternal short stature, primiparity, passive smoking, and iron supplementation during pregnancy were also significantly associated with higher risks of PTB. The significantly higher risk of PTB among women who consumed iron during pregnancy is counter-intuitive. An earlier study conducted in the same population found a similar result . The benn = 333) and Thrasher Research Fund (n = 154), Wagner et al., 2015 found an increased risk of PTB in women with lower vitamin D concentrations [Our study showed that there was increased risk of PTB among women who had VDD. However, the current literature on vitamin D concentrations and PTB is conflicting. Some studies showed an association while others did not. In a study conducted in South Carolina, USA, using two datasets from National Institute of Child Health and Human Development  found that pregnant women who had vitamin D concentrations > 40 ng/mL had 62% lower odds of having PTB when compared to women who had vitamin D concentrations < 20 ng/mL . A Meta-However, several other studies did not show an association between VDD and PTB. A prospective cohort study by Wang S et al., (2021) in 3,465 pregnant women in Zhoushan Maternal and Child Health Hospital, Zhejiang, China did not show a significant association between maternal VDD and risk for PTB . A retroRandomized clinical trials (RCT) conducted to assess whether vitamin D supplementation to those who are deficient can improve the rate of PTB has also been inconclusive. Sablok et al., (2015) performed a RCT in 180 pregnant women in Delhi, India and found an 8.3% PTB rate among women who received Vitamin D compared to a rate of 21.1% among those who did not . RostamiThe mechanism of maternal vitamin D deficiency and PTB has not been fully elucidated. However, recent studies suggested several pathways including oxidative stress, imbalance in the regulation of the inflammatory response, and compromise of placental function during pregnancy , 46. YamThe strength of the study is its population-based prospective design. The main outcome variable, PTB, was based on gestational age dating by early pregnancy ultrasound conducted by trained ultrasonologists. Thus, the term/preterm classification was accurate compared to classification based on reported date of last menstrual period. The study also has several limitations. This is a case–control study, which can be subject to bias and confounding . Since wThe study showed a high prevalence of VDD in Bangladeshi pregnant women, and that these women had an increased risk of PTB. This findings from a case–control study should be cautiously interpreted to infer causality. Additional research is warranted, both observational to further explore the associations between VDD and the risk of PTB and randomized trials to examine if the supplementation is beneficial.


Managing mixed acid–base disorders can be diagnostically challenging, particularly when metabolic acidosis and metabolic alkalosis occur simultaneously. When dealing with metabolic alkalosis, a comprehensive approach involves taking a detailed medical history, assessing volume status, and performing urine chloride analysis. Routine calculation of the anion gap is important to identify masked wide anion gap metabolic acidosis. We report a case of a 32‐year‐old female with type 1 diabetes mellitus, presented with intractable vomiting for 2 days with hyperglycemia, hypokalemia, and metabolic alkalosis, along with a wide anion gap. She was diagnosed with “diabetic ketoalkalosis” due to diabetic ketoacidosis combined with vomiting‐induced metabolic alkalosis. She became clinically stable after resuscitation with normal saline, intravenous potassium, and intravenous insulin. Algorithm for an approach to metabolic alkalosis. The main pathogenesis of DKA is the insufficiency of effective insulin and increased levels of counterregulatory hormones leading to hyperglycemia and ketosis. DKA is diagnosed when patients have hyperglycemia (plasma glucose concentration ≥250 mg/dL), with wide anion gap metabolic acidosis (pH <7.30 and serum bicarbonate ≤18 mmol/L), and the presence of ketonemia or ketonuria. Promptly appropriate treatment with the administration of intravenous fluid and insulin, along with monitoring of an electrolyte disturbance, leads to better outcomes and fewer complications. However, some DKA patients might present with metabolic alkalosis resulting in delayed diagnosis and treatment. The uncommon manifestation of DKA in this patient is known as ketoalkalosis or alkaline ketoacidosis which can occur when acidosis is overridden by coexisting alkalosis.2A 32‐year‐old female with type 1 diabetes mellitus was admitted due to intractable vomiting and fatigue for 2 days. Three days prior to admission, she used methamphetamine and got euphoria. She denied having any food and missed the insulin. One day later, she developed nausea and intractable vomiting more than 10 times. On admission, she was afebrile but lethargic. Her blood pressure was 100/70 mmHg, her pulse rate was 128 beats per minute, and her respiratory rate was 18 per minute without Kussmaul breathing. She had dry lips and a dry tongue. Her abdomen was soft with no tenderness.3− 37 mmol/L. Urine analysis showed ketone 2+ and glucose 2+. Her urine sodium was 32 mmol/L, urine potassium 30 mmol/L, and urine chloride <20 mmol/L . Venous blood gas analysis showed pH 7.502, pCO2 47.7 mmHg, and HCO3The initial investigations demonstrated the presence of hyperglycemia, hyponatremia, hypokalemia, and metabolic alkalosis. Hyponatremia, which corrected sodium was 128.4 mmol/L after considering the effect of hyperglycemia, along with signs of volume depletion, may arise from hypovolemia hyponatremia. However, urine sodium, which is commonly used as an indicator of effective intravascular volume, may not be as reliable in the setting of metabolic alkalosis. The presence of bicarbonaturia from metabolic alkalosis leads to increasing excretion of cations, including sodium and potassium, in urine. Instead, urine chloride is a more suitable marker to use. Low urine chloride, high urine sodium, and signs of hypovolemia in the presence of metabolic alkalosis as seen in this patient, support that metabolic alkalosis is resulted from recent vomiting. The approach to addressing metabolic alkalosis is described in the discussion section.Additionally, despite the presence of alkalemia, it is recommended to calculate the anion gap to explore the possibility of concurrent mixed‐wide anion gap metabolic acidosis. In our case, the calculated anion gap of 19 mmol/L, with the patient's underlying disease of type 1 diabetes mellitus and hyperglycemia, led the medical team to suspect diabetic ketoacidosis as a potential cause. An elevated serum beta‐hydroxybutyrate level of 3.4 mmol/L confirmed the diagnosis of diabetic ketoacidosis.4After being diagnosed with diabetes ketoacidosis with alkalemia, she was initially treated with intravenous normal saline 2500 mL in the first 2 h. The insulin drip was started after the potassium was raised to 3.8 mmol/L by potassium intravenous supplement . After 18 h, the patient received 4 L of intravenous fluid  and an insulin intravenous drip of 1.5 units/h. The patient's symptoms, including nausea and vomiting, were resolved. Her blood glucose was 131 mg/dL, serum sodium 137 mmol/L, potassium 3.4 mmol/L, chloride 102 mmol/L, bicarbonate 28 mmol/L, anion gap 7 mmol/L, and beta‐hydroxybutyrate 0.7 mmol/L. Regular insulin of 8 units subcutaneously was prescribed and intravenous fluid, and insulin were discontinued 1 h later. After 3 days of admission, the patient was discharged with premixed human insulin (70/30) 14‐0‐10‐unit subcutaneous premeal.One month after discharge, she was appointed at the outpatient clinic and was clinically stable. She complied with medication and denied any substance use. Fasting plasma glucose was 155 mg/dL, and serum electrolytes became normal. Insulin was adjusted according to self‐monitoring blood glucose and carbohydrate counting.5In our patient, multiple electrolyte disturbances were observed, requiring a careful approach to each problem. Her main electrolyte disorders, which are metabolic alkalosis with masked wide anion gap metabolic acidosis and hypokalemia, and their pathophysiology are discussed as follows.5.1+) despite volume depletion in metabolic alkalosis, urine Cl− is used instead of urine Na+ to represent volume status. Low urine chloride (Cl−) indicates low effective circulating volume (ECV), while high urine Cl− suggests a volume‐expanded state or defect in renal tubular reabsorption. Based on urine Cl−, causes of metabolic alkalosis can be divided into chloride‐responsive (urine Cl− <20 mmol/L) and chloride‐resistant (urine Cl− ≥20 mmol/L)To determine the causes of metabolic alkalosis, a comprehensive evaluation including history, volume status, kidney function, and urine electrolytes should be performed. Since bicarbonaturia can induce high urine sodium  accumulation from endogenous or exogenous sources. The loss of acid through the gastrointestinal tract or kidney is directly correlated with the generation of HCO3−. Normally, the kidney has a great capacity to excrete excess HCO3− into the urine. The presence of the maintenance factors which are the mechanisms that impair kidney HCO3− excretion results in persistent metabolic alkalosis. Table The pathogenesis of metabolic alkalosis consists of two distinct factors: generation and maintenance. The generation of metabolic alkalosis is mainly due to excess bicarbonate . Normal gastric acid production, however, does not result in metabolic alkalosis. While gastric H+ enters the small bowel, it will be neutralized by HCO3− secreted primarily by the pancreas. The secretion of pancreatic HCO3− into the intestinal lumen also adds the same amount of H+ to the body fluids as the amount of HCO3− gained from gastric production thereby maintaining the acid–base balance.Gastric parietal cells produce gastric acid by generating intracellular H3− secretion into the intestinal lumen. Without H+ gained during pancreas HCO3− secretion, HCO3− added from gastric production leads to HCO3− excess and generates metabolic alkalosis . This state reduces the amount of filtered HCO3− and impairs excess HCO3− removal from intravascular part.Hyperaldosteronism: Volume contraction activates the renin‐angiotensin‐aldosterone system (RAAS) which secondary hyperaldosteronism stimulates Na+ reabsorption exchanged with potassium (K+) and H+ secretion in distal tubule resulting in hypokalemia and metabolic alkalosis.Hypochloremia: Chloride loss from vomiting or chloride‐rich diarrhea results in hypochloremia which impair HCO3− excretion via pendrin in β‐intercalated cell.Hypokalemia: Hypokalemia contributes to metabolic alkalosis via the following mechanisms: (1) stimulate ammoniogenesis in proximal tubule due to intracellular acidosis state, (2) increasing proximal tubular reabsorption of filtered HCO3−, (3) enhancing H+ secretion via H+‐ATPase and AE1 activation in α‐intercalated cells, and (4) decreasing HCO3− secretion by downregulating the expression of pendrin in β‐intercalated cells.Vomiting maintains metabolic alkalosis through four factors Figure : (1) decactive or recent vomiting, bicarbonaturia results in high urine Na+ and high urine pH. However, in the event of inactive or remote vomiting which vomiting is resolved but volume status is still depleted, HCO3− excretion is further worsening due to GFR reduction, hypochloremia, and hypokalemia. Low urine pH (pH 6 or lower) occurs despite the presence of metabolic alkalosis, called “paradoxical aciduria.” Low urine Na+ and low urine K+ are also noted due to the hyperaldosteronism effect and absence of bicarbonaturia.During 5.4+.+ loss. This is supported by high urine K+ and urine K/urine creatinine as shown in her laboratory result. As discussed above, vomiting leads to bicarbonaturia which increases the luminal electrochemical gradient and enhances tubular K+ excretion. Volume depletion after vomiting activates RAAS resulting in secondary hyperaldosteronism which stimulates H+ and K+ excretion in the cortical collecting duct. In addition, DKA also contributes to tubular K+ loss via osmotic diuresis in hyperglycemia and the anionic property of ketoacidosis.The gastric fluid typically contains 5–10 mmol/L of K5.5This insulin‐dependent diabetic patient with a history of insulin omission presented with high blood sugar levels and elevated ketonemia, making DKA suspicious. However, due to the absence of metabolic acidosis as a diagnostic criterion, physicians may delay or be hesitant to make a DKA diagnosis.An anion gap is an important tool in this situation. A high anion gap represents an increase in unmeasured anions or a decrease in unmeasured cations. Theoretically, severe metabolic alkalosis may be associated with a small increment of anion gap by the effect of alkaline pH on the electrical charge of albumin.Diabetic alkalosis, the setting of alkalemia combined with diabetic ketoacidosis, was reported in several patients with diabetes mellitus. The majority of these patients had insulin‐dependent diabetes, accompanied by severe vomiting, and signs of hypovolemia and tachycardia at initial presentation. Following the administration of insulin and intravenous fluid, clinical symptoms were rapidly improved.Metabolic alkalosis is a frequent metabolic disturbance encountered in emergency departments. Generally, reduced intravascular volume is the common etiology of this condition, which leads to many general practitioners to address it solely with intravenous normal saline administration. Nonetheless, the absence of an anion gap calculation can result in a delay in diagnosing and effectively managing underlying causes of wide anion gap metabolic acidosis, such as DKA or exposure to toxins. General practitioners should remain vigilant about the possibility of masked wide anion gap metabolic acidosis within the context of metabolic alkalosis.6The approach to patients with metabolic alkalosis should include a detailed history, volume status assessment, and urine electrolyte to guide the etiology of metabolic alkalosis. Understanding these mechanisms that generate and maintain alkalosis may support diagnosis and treatment more precisely. Moreover, even in documented metabolic alkalosis, the anion gap should be regularly calculated to detect masked wide anion gap metabolic acidosis as shown in this case.Thunyatorn Wuttiputhanun: Conceptualization; investigation; visualization; writing – original draft. Natavudh Townamchai: Supervision; writing – review and editing. Somchai Eiam‐Ong: Supervision; writing – review and editing. Kullaya Takkavatakarn: Conceptualization; supervision; writing – review and editing.The authors received no financial support for the research, authorship, and/or publication of this article.The authors have no conflict of interest to declare.Written informed consent was obtained from the patient to publish this report in accordance with the journal's patient consent policy.