Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Early and accurate prediction of AD progression is crucial for early intervention and personalized treatment planning. Although AD does not yet have a reliable therapy, several medications help slow down the disease’s progression. However, more study is still needed to develop reliable methods for detecting AD and its phases. In the recent past, biomarkers associated with AD have been identified using neuroimaging methods. To uncover biomarkers, deep learning techniques have quickly emerged as a crucial methodology. A functional molecular imaging technique known as fluorodeoxyglucose positron emission tomography (18F-FDG-PET) has been shown to be effective in assisting researchers in understanding the morphological and neurological alterations to the brain associated with AD. Convolutional neural networks (CNNs) have also long dominated the field of AD progression and have been the subject of substantial research, while more recent approaches like vision transformers (ViT) have not yet been fully investigated. In this paper, we present a self-supervised learning (SSL) method to automatically acquire meaningful AD characteristics using the ViT architecture by pretraining the feature extractor using the self-distillation with no labels (DINO) and extreme learning machine (ELM) as classifier models. In this work, we examined a technique for predicting mild cognitive impairment (MCI) to AD utilizing an SSL model which learns powerful representations from unlabeled 18F-FDG PET images, thus reducing the need for large-labeled datasets. In comparison to several earlier approaches, our strategy showed state-of-the-art classification performance in terms of accuracy (92.31%), specificity (90.21%), and sensitivity (95.50%). Then, to make the suggested model easier to understand, we highlighted the brain regions that significantly influence the prediction of MCI development. Our methods offer a precise and efficient strategy for predicting the transition from MCI to AD. In conclusion, this research presents a novel Explainable SSL-ViT model that can accurately predict AD progress based on 18F-FDG PET scans. SSL, attention, and ELM mechanisms are integrated into the model to make it more predictive and interpretable. Future research will enable the development of viable treatments for neurodegenerative disorders by combining brain areas contributing to projection with observed anatomical traits. Alzheimer’s disease (AD) is a progressive and irreversible neurodegenerative disorder that primarily affects memory, cognition, and behavior . It is tAs researchers and healthcare professionals strive to improve the early detection and management of AD ,19 the iThis journal investigates the application of ViTs in AD-related tasks, such as early detection, disease progression prediction, and biomarker identification. We discuss their strengths and limitations in handling neuroimaging data and explore how combining the strengths of ViTs may lead to more accurate and interpretable results. Additionally, we delve into the emerging area of explainable AI in AD research, in which the understanding of model decisions becomes paramount for clinical acceptance and integration. The exploration of ViTs in AD research opens new possibilities for improved diagnostics and personalized treatment strategies ,37,38,39A transformer model is suggested for the identification of MCI progression. The model expands upon the ViT backbone by utilizing 18F-FDG-PET and self-supervised learning to tackle the issue of MCI progression and disease identification.To address the issue of inadequate data in the field of brain imaging, we suggested a cross-domain transfer learning technique. We used ViT as the backbone with DINO.In the MCI recognition, experimental data show that the proposed method can achieve more competitive outcomes than current models. The model accuracy levels with the ADNI dataset were 92.31%, which is higher than the baseline’s ViT approach. Finally, we visualized important metabolic brain regions, which can assist the physician for proper analysis of MCI.Our method leverages the general ViT architecture as a backbone model to learn valuable Alzheimer’s features from individual 18F-FDG-PET images via the DINO self-supervised learning. These features can then be fed into an ELM classifier to classify individuals. We proved the superiority of the approach in terms of algorithm performance and many medical metrics, including accuracy, specificity, sensitivity, and precision, by validating the suggested framework using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Through this approach, the algorithm learns to discover meaningful biomarkers, subtle cognitive changes, and other relevant factors that might contribute to disease progression. The three things that best describe this paper’s contributions are summarized below:http://adni.loni.usc.edu/ 27 September 2022) provided all the data. ADNI has made significant contributions to our understanding of the early stages of Alzheimer’s disease. The study has provided valuable insights into the biomarkers and cognitive assessments that can help in the early detection and monitoring of MCI and AD. The main objective of ADNI was to determine whether the progression of early AD and mild cogMCI could be monitored using a combination of clinical and neuropsychological assessment, PET, other biological markers, serial magnetic resonance imaging (MRI), and other analyses. This study is multicenter and longitudinal in nature, involving over 63 participating centers. The website http://www.adni-info.org (27 September 2022) offers a comprehensive range of resources, including research findings, study protocols, and data access instructions. Additionally, it serves as a platform for researchers to collaborate and share their findings in the field of AD and related disorders.Based on a public–private partnership led by Principal Investigator Michael W. Weiner, MD, the ADNI database (p ˂ 0.05). It revealed that compared to MCI-s, MCI-c patients had a higher probability of developing AD. Male dominance prevails in all groups, and the male-to-female ratio is 53:47. Furthermore we also listed the ADNI diagnostic criteria for MCI-s and MCI-c below which can be found details on ADNI website mentioned above.We acquired PET scan data from the ADNI 1, ADNI 2, and ADNI GO cohorts in the ADNI database for this study, comprising 224 MCI-c and 245 MCI-s. Following a minimum of 36 months of clinical follow-up, eligible participants with MCI underwent clinical cognitive evaluations and FDG-PET scanning at baseline. MCI-s criteria: MMSE scores between 24–30 (inclusive), a subjective memory concern reported by subject, informant, or clinician, objective memory loss measured by education-adjusted scores on delayed recall of one paragraph from Wechsler Memory Scale Logical Memory II , a CDR of 0.5, absence of significant levels of impairment in other cognitive domains, essentially preserved activities of daily living, and an absence of dementia.MCI-c criteria: MMSE scores between 24–30 (inclusive), a subjective memory concern reported by subject, informant, or clinician, objective memory loss measured by education-adjusted scores on delayed recall of one paragraph from Wechsler Memory Scale Logical Memory II , a CDR of 0.5, absence of significant levels of impairment in other cognitive domains, essentially preserved activities of daily living, and an absence of dementia.The ADNI project’s web page contains comprehensive information about the PET acquisition procedure. Thirty minutes after injecting 185 ± 18.5 MBq FDG, 469 cases underwent dynamic 3D scans with six 5 min frames. Each frame was motion-corrected to the first frame and then summed to produce a single image file.3, the processed images had a spatial resolution of 91 × 109 × 91. Finally, for the purpose of pre-training the model later, each three-dimensional PET image was divided into two-dimensional images by slicing and tiling it to a size of 224 × 224 pixels.Individual PET scan preprocessing  was carrResearchers’ attention has been drawn to self-supervised learning, a new deep learning paradigm, in recent years. The persistent issue of insufficient data for deep learning model training is the focus of self-supervised learning. Through pretext learning—in which one portion of the input data is learned from another portion of the same input—the model learns without labels when it employs self-supervision. Such self-supervised techniques as ,43 are wstrategy  during t methods ,42. FiguAlthough the standard transformer model was designed for natural language processing, it was given a one-dimensional sequence of word embeddings as input. When the transformer model is used for the computer vision task of image classification, on the other hand, two-dimensional images are used as the input data. It is necessary to divide the input image—which has dimensions of height l words) . The outEach patch of an image is flattened to create a vector The sequence of embedding vectors that results from the operations represents the encoded representation of the image patches. This encoded representation captures both spatial and positional information, enabling effective classification and analysis of the image data. Ultimately, a multilayer perceptron (MLP) model receives the transformer encoder’s output class token for categorization. We employ the  ViT-B moOur suggested method involves training the feature extractor as the second phase. In this work, we address the challenge of learning discriminative MCI characteristics by proposing to use a self-supervised learning paradigm. We employ the recently suggested DINO approach , which hThe cross-domain transfer learning technique is employed in this work as AD datasets don’t include the substantial quantity of data required to train the ViT model from scratch . After bELM has gained popularity in various fields such as pattern recognition, image processing, and data mining due to its efficient learning process . AdditioThe multilayer ELM increases the depth of the network by adding extra layers, resulting in improved feature learning capabilities. The multi-layer ELM’s (MLELM) algorithm can be summarized as follows:ELM algorithm:For each layer Calculate the hidden layer output To get the final hidden layer output The MLELM algorithm offers a proficient approach to train deep architectures, capitalizing on ELM’s rapid learning capabilities while harnessing the expressive potential of multiple hidden layers. As a result, MLELM adeptly captures intricate patterns and extracts high-level features from intricate datasets, thereby bolstering its classification performance.Using the official GitHub repository , the DINUsing the ADNI 18F-FDG-PET imaging datasets for all experiments, we trained the DINO models for 300 epochs with a batch size of 32. With a learning rate of 0.0001, AdamW  was the The findings were assessed using specificity, sensitivity, precision, recall, F1 score, and accuracy; we reported our results in term of mean and standard deviation. These parameters were expressed mathematically as follows:True negatives, true positives, false negatives, and false positives are represented by the letters This study develops and implements a CAD system which is automated for the diagnosis of AD. The suggested approach was used to distinguish between MCI-s and MCI-c patients progressing to AD. The simulation made use of the 18F-FDG-PET image, which was taken from the ADNI database; 469 patients had their 18F-FDG-PET scans taken, comprising 245 MCI-s patients without conversion within 3 years and 224 MCI-c patients who converted to AD within 3 years.It is crucial to identify AD in a timely manner for patient care. To distinguish MCI-s from MCI-c, a 2D ViT base DINO model is utilized in this research. The proposed CAD system’s structure is shown in We utilize transfer learning for ViT by initializing the model with weights that were pre-trained on ImageNet  to enhanFurthermore, we utilized the t-SNE algorithm to reduce the complexity of the features obtained from the DINO network and projected them onto a two-dimensional space for visualization purposes. As depicted in Based on the experimental results, we can say that in our task of early prediction classification for MCI, the DINO model can be used instead of the baseline ViT. We observed that when we used the weights obtained from the DINO self-supervised pre-training on the ADNI dataset, which was initialized by ImageNet, all evaluation metrics of the model improved. The accuracy increased by 5.99%, and the F1 score increased by 4.95% compared to the best-performing baseline ViT model. This suggests that self-supervised learning pre-training enables the ViT model to perform better on a small dataset like ADNI. Furthermore, by incorporating the ELM, the model’s performance improved even more. Each of the four metrics showed varying degrees of improvement, with accuracy reaching 92.31% (a 3.95% improvement) F1 score reaching 93.92% (a 5.67% improvement) as compared to KNN classifiers. This indicates that the ELM classifiers effectively classified the MCI pathology using features extracted from DINO model that was previously overlooked.We performed an ablation experiment to examine the impact of transformer design decisions on MCI-to-AD categorization. We investigated the effects of several patch sizes in the experiment. Three variations of patches size were tested: the patch size of 8, the patch size of 16, and the patch size of 32. All models were trained using pre-trained weights from DINO that were included in the Python image model implementation . There wIn recent times, there has been significant research conducted on the use of machine learning techniques for predicting MCI stage using brain imaging. Most of these studies have focused on using structural imaging of the brain, with only a few utilizing functional imaging, specifically 18F-FDG-PET. In this section, we are comparing our results with recent findings in the literature from the ADNI database for diagnosing MCI. Some researchers have attempted to analyze 18F-FDG-PET for AD prediction, but these studies have still relied on manual and supervised features extraction ,56. TablSpecifically, we compare our results with five methods described by Nozadi et al. , Bae et For computer-aided diagnosis, identifying the brain area most closely associated with the deep learning model prediction is crucial. Observing the structural change in the brain is one of the most important factors in the clinical diagnosis of AD and the progression of MCI to AD. We study the potential diseased brain area associated with the prediction of our method as a predictive brain region. To categorize MCI-c and MCI-s classes, we employ self-attention visualization  to look As the population ages, the number of patients with Alzheimer’s disease continues to rise. However, progress in finding a cure for AD has been slow, leading researchers to focus on early diagnosis to delay the progression of the disease through preventive measures. Nevertheless, identifying patients in the prodromal stage of AD remains a difficult task. A neural-network-based model has shown promise in accurately identifying patients with AD at different stages, surpassing the performance of professional radiologists in terms of sensitivity and specificity. Previous studies have identified a specific pattern of reduced brain metabolism in 18F-FDG-PET scans of AD patients, particularly in the bilateral temporo-parietal regions. As the disease advances, reduced FDG uptake is also observed in the frontal, parietal, and lateral temporal lobes. However, 18F-FDG-PET alone is not a definitive biomarker for AD and MCI. While previous attempts to develop CAD diagnostic methods for AD using other imaging modalities have been made, few studies have focused on using machine learning approaches to classify AD patients based on 18F-FDG-PET scans alone. In addition to predicting AD, our model can accurately classify patients with MCI-s and MCI-c, achieving high sensitivity and specificity. The advantages of our model include its ability to dynamically update without retraining from scratch when new imaging studies are added as well as its superior performance in identifying the early stage of AD. Effective and accurate prediction of MCI transitioning into AD holds utmost importance in facilitating timely intervention and disease management. Consequently, numerous studies undertake endeavors to investigate and enhance the predictive capabilities for MCI progression. In this investigation, a comprehensive comparative analysis was conducted to assess the predictive capabilities of DINO-ELM in utilizing 18F-FDG-PET data from the ADNI. Notably, our proposed method exhibited superior performance when compared to the prevailing state-of-the-art MRI-based studies pertaining to MCI progression diagnosis. With an accuracy rate of 92.31%, specificity rate of 95.50%, and sensitivity rate of 90.21% along with 0.96 AUC, our findings demonstrate the potential of employing vision transformers equipped with attention mechanisms with SSL without any human intervention to achieve heightened classification accuracy in contrast to prevailing CNN architectures. This improvement may be attributed to the attention mechanism within vision transformers effectively highlighting distinctions within the brain regions between MCI-c and MCI-s classes.Additionally, we have also examined the brain regions that impact the prediction of our proposed method. Discovering these regions will facilitate the future advancement of deep learning models, enhancing their classification performance. Furthermore, it will aid doctors in effortlessly identifying the regions of interest for diagnosis. We have identified primary regions with the highest attention score: the thalamus, medial frontal, hippocampus, posterior temporal lobe, parietal lobe, posterior cingulate gyrus, left Para hippocampal gyrus, and occipital. Notably, 18F-FDG-PET scans have revealed brain atrophy in these regions. Our model has some limitations. Firstly, the training process is complex and needs to be completed in two stages. Additionally, our current method does not utilize a full 3D scan model, instead, it only extracts slices from the brain. This approach may result in missing global anatomical information from other brain regions, which could affect the accuracy of our predictions. The quality of feature extraction in the attention layer also affects the performance of the self-supervised model in the second stage. However, in real clinical scenarios, the causes of hypometabolism observed in 18F-FDG-PET may be more complex. Other types of dementia, such as dementia with Lewy bodies (DLB) or frontotemporal dementia (FTD), can also lead to similar pathological changes such as AD. Further studies on more complex data can provide more reliable clinical aids for the diagnosis of AD. In this work, our study only focuses on MCI to AD progression. In future studies, we should focus on other diagnostic groups, including healthy control, MCI, and AD. Therefore, future studies will focus on incorporating multimodal brain data, including functional MRI (fMRI), structural magnetic resonance imaging (sMRI), and other modalities to identify different diagnostic groups. By integrating multiple imaging modalities, researchers aim to enhance the discriminative power of the models and achieve even better performance in the classification of brain-related conditions.In summary, utilizing brain 18F-FDG-PET, our study has created a ViT-DINO-based features extractor network along with an ELM classifier for diagnostic prediction of MCI. Features were extracted by decomposing the 18F-FDG-PET images into 2D slices. The slices were then arranged at a few intervals without overlapping. The ADNI dataset verified the suggested CAD system. This integrated approach demonstrates strong performance in the MCI classification task following pre-training through DINO self-supervised learning. Additionally, results of the simulations clearly showed that the utilization of ELM enables the vision transformer to achieve enhanced performance in AD tasks with superior classification accuracy and resilience. Furthermore, our approach primarily had a profound effect on specific brain regions that were visually portrayed. The thalamus, medial frontal, hippocampus, and occipital regions of 18F-FDG-PET emerged as the pivotal components within our proposed framework. These discoveries highlight the potential for early identification and classification of individuals with MCI, utilizing patterns of functional atrophy as reliable indicators, prior to subjecting them to interventional clinical studies. Future research will concentrate on expanding the recommended CAD system to include data from additional sources to increase the classification accuracy. Several different samples will be used to assess the overall performance of the proposed CAD system.


Interpersonal Psychotherapy-Adolescent Skills Training (IPT-AST) is a standardized depression prevention program for adolescents conducted in campus settings. The purpose of this review is to examine the randomized controlled trials of IPT-AST for the prevention of adolescent depression in the past 20 years.A systematic search of relevant electronic databases  and study reference lists was conducted. Any study investigating the effectiveness of IPT-AST in 12- to 20-year-olds with depressive symptoms was eligible. Synthesis was via narrative summary and meta-analysis.p < 0.05, I2 = 77%). Six month follow-up data showed that the intervention outcomes of IPT-AST remained significant .A total of 6 studies met the inclusion criteria. Meta-analysis results showed a remarkable improvement in patients’ depressive symptoms after IPT-AST intervention (WMD = −5.05, 95% CI = −8.11 to −1.98, This meta-analysis showed that IPT-AST was effective in adolescents with depressive symptoms at post-prevention and at 6-month follow-up. However, these conclusions are cautious, as they are based on a small number of studies and the presence of author duplication. Future studies should use multi-center, large-sample randomized controlled trials to evaluate the efficacy of IPT-AST for preventing depression in adolescents.https://www.crd.york.ac.uk/prospero/, identifier CRD42023393047. Adolescent depression is a very common mental illness that can seriously damage the physical and mental health of adolescents . EpidemiThe Institute of Medicine (IOM) defines prevention as preventive interventions taken when symptoms have not yet met diagnostic criteria but are present . A randoInterpersonal Psychotherapy-Adolescent Skills Training (IPT-AST) is a school-based preventative approach to manage depression in adolescents . It is bAlthough IPT-AST has been shown to be effective, the duration of its effect is unclear. Researchers have formed two different views based on multiple studies. Some studies suggest that the prevention effect can last for only 6 months. For example, a randomized controlled trial showed a significant preventive effect of IPT-AST, but its effect did not continue beyond 6 months . HoweverThe primary design of present review was registered on PROSPERO CRD42023393047), and was conformed to the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines regarding evidence selection, quality assessment, evidence synthesis, and research reporting [Hutton et al. Participants were adolescents (age from 12 to 20 years old),(2)Depressive symptoms ,(3)IPT-AST or minor modifications of IPT-AST,(4)Compared to any control conditions,(5)Randomized controlled trials (RCTs),(6)When data were duplicated in different studies, newly published and data-complete studies were selected.Because the IPT-AST is a group depression prevention program conducted on campus, we chose the Center for Epidemiologic Studies-Depression Scale (CES-D), a widely used self-report depression measure with strong psychometric properties with adolescents, to evaluate depression symptoms . In the The search results are extracted and stored in the data management software. Two reviewers (KWZ and HMX) independently reviewed the identified studies and derived data from the enrolled studies. If there is disagreement, it is resolved through discussion and, if necessary, a third author is consulted to reach a final decision.The outcomes of interest for this review were depression from validated scales. Two reviewers (KWZ and HMX) independently extracted the data. Relevant data for each measure was entered into Stata 14.0. We examined baseline and post-prevention scores between groups to assess the effectiveness of depression and follow-up effects. If sufficient data were not available in the article, the authors were consulted and additional information requested.Two reviewers (KWZ and HMX) independently evaluated the risk of bias in the enrolled studies using the Cochrane Collaboration’s Risk of Bias tool . The folThe following study data were drawn: study characteristics ; population characteristics ; experimental conditions ; and outcomes .2 statistic were used to examine the heterogeneity, with Q > 0.05 and I2 ≤ 50% indicating low heterogeneity. Sensitivity analyses were performed by removing each eligible study to see whether the significance and the pooled effect size would be affected.The extracted data was stored in Stata 14.0. Checked for publication bias by Egger’s test. The Q test and IAfter searching, a total of 119 studies were retrieved; after removing duplicates, 46 studies remained. Two reviewers (KWZ and HMX) performed the initial screening of titles and abstracts, and 31 were excluded, leaving 15 for full-text screening. Finally, 6 studies were included . 757 parAmong the six enrolled studies , 36–39, The quality of included studies was assessed using the Cochrane Risk of Bias tool , and thep < 0.05). See forest plot in For changes in depressive symptoms after the prevention, the results of all six studies were pooled for meta-analysis. The results showed significant improvement in depressive symptoms after the IPT-AST prevention , which may be due, among other things, to the small number of included literature.The results of the meta-analysis of the six trials had a high heterogeneity (n = 729) reported depression at 6-month follow-up; however, 18 individuals were lost to follow-up from prevention. The results showed a significant follow-up effect of IPT-AST for depressive symptoms  and low heterogeneity between studies (I2 = 57%). See forest plot in Six trials  and cultural contexts in future research. Second, five of the six studies included in the meta-analysis belonged to the same research team, but these studies were not conducted at the same time and did not use the same participants and its data; therefore, these studies can be considered as different studies included in the meta-analysis. It is possible that the fact that IPT-AST is in an emerging phase has led to this situation, but this situation may also potentially limit the generalizability of our study findings. Therefore, it should be further explored in future studies. Third, the included studies did not examine the factors  that may influence the effectiveness of IPT-AST prevention, so these factors could be further explored in future studies.Undoubtedly, the current research has systematically evaluated the preventive and follow-up effects of Interpersonal Psychotherapy-Adolescent Skills Training (IPT-AST), and has affirmed its effectiveness within a certain scope. With the outbreak of COVID-19, the concern for physical health has reached a new level along with the concern for mental health. Mental health concerns and research on adolescent populations have increased subsequently . One stuThis meta-analysis showed that IPT-AST was effective in adolescents with depressive symptoms at post-prevention and at 6-month follow-up. However, these conclusions are cautious, as they are based on a small number of studies and the presence of author duplication. Future studies should use multi-center, large-sample randomized controlled trials to evaluate the efficacy of IPT-AST for preventing depression in adolescents.The original contributions presented in this study are included in this article/supplementary material, further inquiries can be directed to the corresponding authors.KZ and HX conceived the study design, searched and screened the article titles and abstracts, confirmed the data and statistical analyses, and responsible for the supervision and quality control. CQ, XS, PS, and NX drafted and supplemented the study methods. PS and NX revised their English and provided the assistance with data analysis. All authors provided the information on the direction of the study and the content of the manuscript and approved the final version of the manuscript.


Several reports suggest that altered mitochondrial DNA copy number (mtDNA-cn), a common biomarker for aberrant mitochondrial function, is implicated in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), but the results are still elusive.A meta-analysis was performed to summarize the current indication and to provide a more precise assessment of the mtDNA-cn in ASD and ADHD. A search in the MEDLINE-PubMed, Scopus, and EMBASE databases was done to identify related studies up to the end of February 2023. The meta-analysis was conducted according to recommendations of the Cochrane Handbook of Systematic Reviews.k = 10) and non-blood samples  suggested significantly higher mtDNA-cn in patients compared to controls (p = 0.0275). Sub-analysis by stratifying studies based on tissue type, showed no significant increase in mtDNA-cn in blood samples among patients and controls (p = 0.284). Conversely, higher mtDNA-cn was observed in non-blood samples in patients than in controls (p = 0.0122). Further stratified analysis based on blood-cell compositions as potential confounds showed no significant difference in mtDNA-cn in peripheral blood samples of patients comparted to controls (p = 0.074). In addition, stratified analysis of aged-matched ASD and ADHD patients and controls revealed no significant difference in mtDNA-cn in blood samples between patients and controls (p = 0.214), whereas a significant increase in mtDNA-cn was observed in non-blood samples between patients and controls (p < 0.001). Finally, when the mtDNA-cn was analyzed in blood samples of aged-matched patients with ASD  or ADHD , no significant difference in mtDNA-cn was observed between ASD patients and controls (p = 0.385), while a significant increase in mtDNA-cn was found between ADHD patients and controls (p = 0.033).Fourteen studies involving 666 cases with ASD and ADHD and 585 controls were collected and judged relevant for the systematic review and meta-analysis. The pooled results by a random effects meta-analysis was reported as a geometric mean of the estimated average response ratio and 95% confidence interval. Overall analysis of studies reported differences in mtDNA-cn in blood samples (In this first meta-analysis of the evaluation of mtDNA-cn in ASD/ADHD, our results show elevated mtDNA-cn in ASD and ADHD, further emphasizing the implication of mitochondrial dysfunction in neurodevelopmental disorders. However, our results indicate that the mtDNA-cn in blood is not reflected in other tissues in ASD/ADHD, and the true relationship between blood-derived mtDNA-cn and ASD/ADHD remains to be defined in future studies. The importance of blood-cell compositions as confounders of blood-based mtDNA-cn measurement and the advantages of salivary mtDNA-cn should be considered in future studies. Moreover, the potential of mtDNA-cn as a biomarker for mitochondrial malfunction in neurodevelopmental disorders deserves further investigations. Publications were excluded if they were: 1) reference abstracts, review articles, editorials or case reports, 2) animal studies, 3) studies with unavailable data for extraction or estimation of mtDNA-cn, and 4) studies focused only on mitochondrial biochemical markers or mtDNA variants/haplogroups.2.3.Two investigators first independently assessed the eligibility of studies at the level of title and abstract, with a third reviewer determining the divergences. The full text of suitable articles was retrieved after the agreement was reached by consensus. Discrepancies were discussed and resolved among the investigators and a third reviewer. Relevant data regarding study characteristics were collected on an EXCEL sheet. All identified references and list of publications were transferred to ENDNOTE X 8.2 for Windows.2.4.The Newcastle–Ottawa quality assessment scale (NOS) was used for assessment of study quality . Higher In the present meta-analysis, the quality assessment of included studies was conducted to evaluate the reliability and validity of the findings . We usedThe summary plot is bar plot displaying the percentage of studies within each topic that meet a certain risk of bias judgment .2.5.2) was done using the restricted maximum-likelihood estimator (2 statistics were reported. When heterogeneity (tau2 > 0) was detected regardless of the Q-test results, a prediction interval for the true outcomes was testified. Studentized residuals and Cook’s distances were used to examine the outliers and/or influential of studies. Studies were considered potentially outliers when a studentized residual was more than 100 x percentile of a standard normal distribution, a Bonferroni correction with two-sided alpha = 0.05 for included studies in the meta-analysis. Studies were considered to be influential when a Cook’s distance was more than the median plus six times the interquartile range of the Cook’s distances. Check for funnel plot asymmetry was done using the rank correlation test and regression test with the standard error (SE) of the observed outcome.The analysis was performed using mtDNA-cn as the primary outcome. Statistics were presented after a transformation was applied to the raw data. The geometric mean and its 95% confidence interval (95% CI) were used to perform a random-effects meta-analysis. The estimation of heterogeneity  is shown in 3.2.Two authors extracted the data independently with a standard extraction table. The extracted information include first author’s name, publication year, disease, subject’s ethnicity, sample used, sample size, age, gender, age of disease onset, disease duration, clinical assessment, and outcome.3.3.A total of 14 studies involving 666 cases and 585 controls were used in the current systematic review and meta-analysis. 3.4.Description and outcome of the included studies are summarized in 3.5.n = 666) and controls (n = 585) regardless of the used biological samples (k = 14). The overall meta-analysis of pooled studies by dichotomizing mtDNA-cn into high and low, showed a significant relationship between increased mtDNA-cn and ASD/ADHD    . Convers< 0.001) . Geometr< 0.001) .k = 5). The results showed no significant difference in peripheral blood mtDNA-cn between patients and controls   .k = 9), the mtDNA-cn did not reach statistical significance  (k = 3) was significantly higher in patients than in controls   . On the controls . Geometr< 0.001) .k = 6) or ADHD only (k = 3). In this sub-analysis, only blood samples were considered since all ADHD studies used blood to measure mtDNA-cn. Notably, measurement of mtDNA-cn in ASD patients was done using peripheral blood, leukocytes, and PBMCs, whereas only peripheral blood was used to measure the mtDNA-cn in ADHD patients. The results showed no significant difference in blood mtDNA-cn between ASD patients and controls    . On the < 0.001) . Geometr= 0.072) .3.6.The risk of bias assessment in this meta-analysis is presented in graphical format as a traffic light plot  and a su4.proximity to ETC, the source of oxidants, lack of histones protective proteins and DNA repair capacity. Oxidative stress may ultimately lead to mutations or copy number alteration that could impair the mitochondrial function, triggering a vicious cycle of ROS generation and mtDNA oxidative damage (Mitochondria play a key role in energy production and cellular function. They are the only organelles that have their own DNA (mtDNA), which presents in multiple copies per cell. The mtDNA encodes only 37 genes, and 13 proteins translated by these genes are subunits of the electron transport chain (ETC), involved in the oxidative phosphorylation of ATP, providing the body with a steady stream of energy , 15. Thee damage . Many fae damage . One of e damage , 21. Thee damage , 20–22. In previous studies, both increased and decreased mtDNA-cn in blood and other tissues have been linked to neurodevelopmental disorders, suggesting a diverse and complicated role of altered mtDNA-cn in these disorders. In this study, we performed a meta-analysis to summarize the current evidence and to provide more conclusive results regarding the relationship between mtDNA-cn and ASD/ADHD. We first evaluated the relationship between mtDNA-cn and ASD/ADHD in an overall meta-analysis. The results showed a significant relationship between higher mtDNA-cn and ASD/ADHD. Although there was a large heterogeneity across the studies, further evaluation showed that the data did not significantly influence the pooled results. The variations in the range of mtDNA-cn could be explained by many factors. While real time qPCR is an accurate and reliable method for mtDNA-cn evaluation, quantification can be affected by the methodological variability in the process. For instance, the techniques used for total DNA extraction and different sets of primers used to amplify the mtDNA genes may affect the range of mtDNA-cn . NoticeaThe mtDNA-cn varies widely among tissues and can adapt to the energy demand and physiological condition of each individual , 17, we In the current meta-analysis, blood was the most common tissue examined for mtDNA-cn changes in ASD and ADHD. To reduce the uncertainty induced by blood compositions as confounding factors, further analysis was performed by stratifying studies based on peripheral blood samples only. No significant association was found between higher mtDNA-cn in peripheral blood samples of ASD/ADHD patients compared to controls.Studies have shown that changes in mtDNA-cn may be influenced by the age of individuals , 19. ThuBlood-derived mtDNA-cn has been proposed as a potential biomarker for many diseases . HoweverVariations in mtDNA-cn measured in blood can be caused by differences in blood-cell compositions due to different mtDNA content in different blood-cell types. For instance, no consistent correlation was found between mtDNA-cn in blood leukocytes and cell-free mtDNA in plasma . In factASD and ADHD are the two most common neurodevelopmental disorders observed in childhood. These disorders affect the brain function and neurological development, causing impairments in cognition, communication, behavior, and motor functioning , 2. The Neurons are extremely sensitive to free radicals , 70. IncImpaired mitochondrial function can alter brain energy metabolism and cause neurological disorders. The increase in mtDNA-cn may be a compensatory adaptive response to overcome oxidatively defective mitochondria in order to correct for energy deficit , 21. Concommon themes in ASD and ADHD. However, the exact genetic etiology of mitochondrial dysfunction in the pathophysiology of these disorders remains uncertain.Mitochondrial dysfunction can be classified as either primary or secondary defects. Primary mitochondrial dysfunction are caused by defects in mtDNA and/or nuclear DNA (nDNA) genes directly involved in mitochondrial function and ATP producing . SecondaASD and ADHD share some clinical features , which mAlthough brain tissues are considered as the standard target tissue in studying neurodevelopmental disorder and other brain disorders, blood is minimally invasive for mtDNA-cn measurement. Nevertheless, blood collection may cause patient’s discomfort and requires a standard level of training. Saliva is less invasive, accessible biofluid that can be collected by patients following simple instructions. Salivary mtDNA is also independent of the number of cells in saliva, thus it is not confounded by cellular content . Recentlstudies investigating the pathogenesis of ASD and ADHD have suggested an important role of mtDNA-cn in these conditions, making mitochondria a potential target for effective therapeutic strategies  suggest elevated mtDNA-cn in patients than in controls, further emphasizing the implication of mitochondrial dysfunction in neurodevelopmental disorders. However, our results indicate that the mtDNA-cn in blood is not reflected in other tissues in ASD/ADHD, and the true relationship of blood derived mtDNA-cn remains to be elucidate in future studies. Our results also highlight the importance blood-cell compositions as confounder of blood-based mtDNA-cn measurement and the advantages of salivary mtDNA-cn over blood and other invasive tests. Finally, the potential of mtDNA-cn as a biomarker for mitochondrial malfunction in neurodevelopmental disorders deserves further investigations.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.GA-K: conceptualization, methodology, study design, retrieving, processing and analysing data, writing the original draft, and reviewing and editing the manuscript. HJ: conceptualization, methodology, study design, retrieving, processing and analysing data, and editing and reviewing the manuscript. YA, MA and MH: supporting processing and analysing data, and editing and reviewing the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The study examined factors associated with food and water stockpiling (FWS) during the COVID-19 pandemic.A secondary analysis of online survey data collected in two waves: April 2020 (wave 1) and June/July 2020 (wave 2), was conducted through REDCap web application. A total of 2,271 Non-Latino Black and Latino adults (mean age: 36.8 years (SD = 16.0); 64.3% female) living in Illinois were recruited. Participants self-reported if they stockpiled food and/or water (FWS) seven days prior to survey completion because of the pandemic. Logistic regression was used to determine if each variable was associated with the odds of reporting FWS.Nearly a quarter (23.3%) of participants reported FWS. The adjusted model revealed that odds of FWS increase as the number of household members increased . Odds of FWS were lower among participants who were not self-quarantining compared to those self-quarantining all the time . Furthermore, individuals with lower levels of concern about COVID-19 had lower odds of FWS than those extremely concerned.Household size, self-quarantine status, and concern about COVID-19 were significantly associated with FWS. These findings highlight the need to address the concerns of marginalized individuals to promote healthy behaviors. In the early months of the COVID-19 pandemic, many businesses closed temporarily or permanently, and non-essential workers were forced to comply with local and/ or, state stay-at-home orders. As consumers began quarantining at home, they grew concerned about possible food shortages and price gouging. As a result, consumers across the world started stockpiling food and water.The Coronavirus Disease (COVID-19) has affected the lives of people and economies worldwide since being declared a global pandemic by the World Health Organization (WHO) in March 2020. Although there are studies of stockpiling behavior and preferences related to COVID-19, it is unclear how food and water stockpiling (FWS) behaviors are associated with pandemic-related factors, such as job loss and quarantining behaviors among marginalized groups  in the US. Therefore, we sought to identify individual, household, and COVID-19 related factors associated with FWS early in the pandemic among non-Latino Black people and Latino adults living in Illinois, to identify potentially vulnerable individuals in future large-scale health crises.The economic fallout of the COVID-19 pandemic has disproportionately affected racial and ethnic minorities in the US. For example, Black and Latino adults have experienced higher rates of pandemic-related job loss. They have a higher risk of contracting the disease because they are more likely to hold essential jobs , making it difficult to quarantine and avoid exposure.Self-administered survey data was collected through the Research Electronic Data Capture web application to measure the early effects of the COVID-19 pandemic among adults (aged ≥ 18 years) residing in Illinois. Surveys were collected in April, 2020 (n = 2294) and June/ July, 2020 (n = 2143). For the current study, only Latino and non-Latino Black participants were included . The Institutional Review Board at the University of Illinois Urbana-Champaign (protocol number: 20868) approved this research. Anxiety symptoms were assessed using the validated, 2-item Generalized Anxiety Disorder-2 (GAD-2) scale.This secondary data analysis included participants’ self-reported FWS behavior, sociodemographic and household characteristics, anxiety symptoms, and Supplemental Nutrition Assistance Program (SNAP) participation, as well as self-quarantine behaviors, concern about COVID-19, and job changes due to the pandemic. Self-reported household food security status was measured using the US Department of Agriculture’s 6-item food security module.Descriptive statistics  were calculated and stratified by FWS status. Logistic regressions tested the association between individual, household, and COVID-19 related variables, and the odds of reporting FWS. Variables that were statistically significant in the crude models were included in the adjusted model. Statistical significance was assessed at α = 0.05. All analyses were performed with SAS version 9.4 . and other European and Asian countries, after the WHO declaration of COVID-19 as a pandemic. The findings from this study showed that nearly a quarter of non-Latino Black people and Latinos reported FWS. The results also showed that household and pandemic-related factors were associated with FWS. The results indicated that a higher number of family members in the household increased the odds of FWS in this diverse sample with varied levels of education, pre-pandemic annual income, levels of food insecurity, and pandemic-related job changes. These findings likely reflect the need to ensure that every household member had sufficient food and water. Individuals with relatively lower levels of concern about COVID-19 had lower odds of FWS compared to those who endorsed feeling ‘extremely concerned’ about the pandemic. These results support other reports that American, Canadian, and European adults who feared more severe pandemic threats were more likely to stockpile essentials during COVID-19 lockdowns. Similarly, less frequent quarantining significantly lowered the odds of FWS. It is possible that individuals who were willing to leave their homes during the lockdowns felt more confident about accessing food and water than others in our sample.There was an upsurge in food stockpiling behavior in countries such as China, These findings can inform clinicians and healthcare professionals about effective strategies to minimize COVID-19 adverse outcomes for these marginalized groups now and in post-COVID-19 safety efforts. However, future studies should further examine the important variables reported here to inform initiatives that meet marginalized communities’ food and water needs during a large-scale crisis.Given the findings, it is imperative that researchers, policymakers, and practitioners further examine FWS behavior and how pandemic-related fear can lead to FWS. Stockpiling may negatively impact supply chains and cause shortages that would be dangerous for vulnerable or marginalized groups due to food inaccessibility. Stockpiling of perishable foods may lead to food that is no longer safe  in terms of nutrient content, which may compromise nutrition security.


Urinary tract infections while common in the general population are frequently overdiagnosed in patients with asymptomatic bacteriuria. Overtreatment of asymptomatic bacteriuria leads to increasing levels of antibiotic resistance. Demographic factors including age and sex are associated with increased antibiotic resistance. There has been little to no prior research on the association of race or ethnicity with antibiotic resistance. Here we report the results of a retrospective analysis of urine cultures and the associations of race, ethnicity, age, and sex with the presence of extended spectrum beta-lactamases (ESBLs).All urine cultures within the electronic medical record of Corewell Health East were screened for adult outpatients who live in the 3 counties within the main catchment area in southeast Michigan  with positive urine cultures for the year 2019. We identified 22,169 positive urine cultures, of which 1,571 were ESBLs. Demographics for each patient with an ESBL were analyzed including race, ethnicity, sex, age, and zip code. Zip codes were used to determine median annual household income using the 2020 United States Census records and 2019 Internal Revenue Service reports. Logistic regression estimated the association between race, ethnicity, sex, age, and income and presence of an ESBL in the urine. A stepwise regression was also performed to determine the strongest predictors of ESBL.Asian race , Arabic/Middle Eastern ethnicity  and male sex  were associated with ESBLs. Age showed an increase in the odds ratio of 1.014 for every year over the age of 18 (95% CI 1.012-1.017). No association was found with median annual household income and ESBL.These findings show a clear relationship between race, ethnicity, sex, and age and ESBL which appears to be independent of economic status. This serves as proof of concept that social determinants of health are associated with antibiotic resistance. Future work should expand on these relationships in other types of infections which can then be utilized in predictive models of antibiotic resistance geared at improving empiric antibiotic selection in diverse populations.Matthew Sims, MD, PhD, Astra-Zeneca: Investigator for company-sponsored studies|ContraFect: Investigator for company-sponsored studies|Crestone: Investigator for company-sponsored studies|Finch: Investigator for company-sponsored studies|Janssen: Investigator for company-sponsored studies|Leonard-Meron: Investigator for company-sponsored studies|Merck and Co: Investigator for company-sponsored studies|OpGen Inc: Advisor/Consultant|OpGen Inc: Investigator for company-sponsored studies|Pfizer: Investigator for company-sponsored studies|Prenosis: Advisor/Consultant|Prenosis: Investigator for company-sponsored studies|QIAGEN Sciences LLC: Investigator for company-sponsored studies|Roche: Investigator for company-sponsored studies|Seres Therapeutics: Investigator for company-sponsored studies


Extensive and long-term efforts on wheat breeding  have madA. The development of hypoallergenic wheat lines, which possess equivalent food processing abilities to that of conventional wheat lines. B. The replacement of wheat with rice, soybean, or other gluten-free ingredients.Dr. Morita’s group has thus far developed a non-transgenic, hypoallergenic line from wheat cultivar Hokushin . The HokRecently, a variety of approaches have been undertaken to develop hypoallergenic wheat products edible for patients with IgE-mediated wheat allergies. In a narrative review, Morita et al.  summarizOther approaches include producing gluten-free food products which have been made using wheat flour as a major ingredient. Yano and Fu  reviewedFoods, have various Special Issues featuring gluten-free products, as well as the pros and cons of a gluten-free lifestyle. Research on the development of gluten-free foods will surely expand in the future. While wheat products currently provide 20% of calories and protein for people worldwide [At present, copious scientific journals, including


The consumption and availability of ultra-processed foods (UPFs), which are associated with an increased risk of noncommunicable diseases, have increased in most countries. While many countries have or are planning to incorporate UPF recommendations in their national dietary guidelines, the classification of food processing levels relies on expertise-based manual categorization, which is labor-intensive and time-consuming. Our study utilized transformer-based language models to automate the classification of food processing levels according to the NOVA classification system in the Canada, Argentina, and US national food databases. We showed that fine-tuned language models using the ingredient list text found on food labels as inputs achieved a high overall accuracy (F1 score of 0.979) in predicting the food processing levels of Canadian food products, outperforming traditional machine learning models using structured nutrient data and bag-of-words. Most of the food categories reached a prediction accuracy of 0.98 using a fined-tuned language model, especially for predicting processed foods and ultra-processed foods. Our automation strategy was also effective and generalizable for classifying food products in the Argentina and US databases, providing a cost-effective approach for policymakers to monitor and regulate the UPFs in the global food supply. In recent decades, the availability of ultra-processed foods (UPFs) has increased in most countries, dominating the food supplies in high-income countries and rapidly increasing in middle-income countries ,2. EvideMachine learning (ML) is rapidly gaining popularity as a valuable tool among researchers in nutrition and public health policy. Previous studies have implemented machine learning and used inputs such as representing food ingredient appearance by binary numbers of 0 and 1 or the structured data of nutrient values for estimating label nutrients and food processing levels ,20. HoweRecent advances in large language models (LLMs) for natural language processing (NLP) have provided a new possibility for extracting information from unstructured data. While evidence regarding the application of large language models in food- and nutrition-related tasks is relatively limited, these models have consistently demonstrated an expectational performance in tasks such as text classification, document summarization, question and answering, and generating interpretable explanations in a variety of domains ,22,23. BHowever, to the best of our knowledge, no study has applied a pre-trained language model and fine-tuned method to automate the food processing level classification in the global food supply. Therefore, this study aims to utilize a fine-tuning transformer-based language model to automate the classification of foods under the NOVA classification system for the foods available in the food supplies of Canada, Argentina, and the US.n = 118,985), the FLIP-Latin America and the Caribbean countries (FLIP-LAC) Database 2018–2022 (n = 8465), and the United States Department of Agriculture-Branded Food Products Database (USDA-BFPD) 2013–2022  as inputs [This study used the University of Toronto Food Label Information and Price Canada (FLIP-Canada) Database 2010–2020 n = 1,985, thes inputs . Brieflys inputs . The latThe processing levels of packaged foods and beverages are categorized under the NOVA food classification system, which includes four groups. NOVA 1: unprocessed or minimally processed foods; NOVA 2: processed culinary ingredients; NOVA 3: processed foods; and NOVA 4: ultra-processed foods . The NOVn = 74,445, >50% manually validated for NOVA by a trained nutrition researcher) to validate the machine learning algorithms developed from FLIP-Canada 2017. Furthermore, we applied the algorithm to the prediction datasets FLIP-LAC 2018–2022 for Argentina  and USDA-BFPD .A pre-trained language model  was used to convert the ingredient text lists on food labels into low-dimensional dense vector representations. In addition, a bag-of-words (BoW) representation  and structured nutrient fact data  were used as inputs . All theWe utilized pre-trained language models and added a classification layer on top. The entire model was then fine-tuned end-to-end using specified datasets. We used the BERT-Base, DistilBERT-Base, MPNet-Base, MiniLM-L6, and multi-qa-MiniLM-L6-cos classification algorithms . These pThe performance of the model on the given NOVA classification tasks was measured using different parameters, including accuracy, balanced accuracy, F1 score, confusion matrix, normalized confusion matrix, receiver operating characteristic (ROC) curve, and area under the curve. Accuracy was the ratio of correctly predicted observations to the total observations, which mainly depended on the performance that the algorithm achieved on the biggest classes. Balanced accuracy is useful for multi-class classification when classes are imbalanced, and each class will have an equal weight in the final calculation . F1 scorThe fine-tuned language model performed the best among the different machine learning algorithms , with anDifferent food categories have shown an overall NOVA prediction accuracy range from 0.954 to 1, except for foods for children under four years of age (0.889) . Salads,We applied the fine-tuned language model  to the FLIP-Canada 2020, FLIP-LAC (Argentina), and USDA-BFPD datasets. The results indicated that the fine-tuned language model maintained a high accuracy depending on the source of data, which outperformed the structured nutrient facts, bag-of-words, and pre-trained language models . Using sThe fine-tuned language model predicted a prevalence of ultra-processed foods (NOVA 4) between 76.3% (FLIP-Canada 2013), 72.3% (FLIP-Canada 2017t), and 71.3% (FLIP-Canada 2020) in the FLIP Canadian databases . There wMoreover, between 11.2%, 2.7%, 14.9% (FLIP-Canada 2020), 14.4%, 2.1%, and 10.8% (FLIP-LAC 2022) and 10.7%, 2.0%, and 10.4% (USDA-BFPD 2021) of foods were classified as processed foods (NOVA 3), processed culinary ingredients (NOVA 2), and unprocessed or minimally processed foods (NOVA 1), respectively .The findings from this study first demonstrated that the fine-tuned language model using ingredient lists as inputs performed well in predicting NOVA food processing categories. Our results showed that the fine-tuned language model reached a 0.979 F1 score for NOVA food classification, and this model is generalizable and presents a moderately high accuracy (F1 scores of 0.889–0.947) when applied to different food composition datasets from Canada, the US, and Argentina.Our model predicted that the Canadian food supply primarily comprises UPFs (71.3% in the FLIP-Canada 2020 database). Previous work by our research group manually classified packaged food and beverage products in FLIP-Canada 2017 under the NOVA classification system, and 73.5% were classified as UPFs . It is wThe dietary and health outcomes due to a high consumption of UPFs, as defined by the NOVA system, have been widely studied . EmerginClassifying foods under the NOVA classification system primarily relies on manual categorization and validation by trained nutrition researchers. This process is labor-intensive and time-consuming. For instance, traditional approaches to identifying UPFs involve assessing the ingredients list, especially looking for food substances rarely used in kitchens  or other food additives that make the final product palatable or hyper-palatable , which are usually present in UPFs .Machine learning provides a powerful tool for food classification and nutrition quality prediction tasks. An earlier study indicated that a pre-trained language model and supervised machine learning accurately predicted packaged food category and nutrition quality using the text information found on food labels . Since tCompared to existing methods, the strength of our strategy is that it provides a direct and powerful machine learning tool to utilize the text information available on the food labels displayed on food packages  for the prediction of NOVA food processing levels. Our model was trained on about 20,000 food products that were manually assessed and validated under the NOVA food classification system. This algorithm directly utilized the single text of ingredient lists for the fine-tuned language model and achieved a higher accuracy, which made more products available for training, including food products with missing nutritional information. When the ingredients list was missing, using the remaining nutrient information displayed in the nutrition facts table  and the traditional machine learning algorithm still provided a moderate accuracy. Thus, our algorithm provided a fast and accurate assessment of the NOVA food processing classification, which is necessary for monitoring the dynamic and ever-changing food supply and could facilitate cross-country comparisons, where extensive nutrition information may be lacking. Coupled with recent e-grocery trends and methodologies used to collect food label information through web-scrapping and OCR, our strategy largely reduced the manual work of food processing level classification under NOVA and other systems. In addition, our algorithm is generalizable and can be applied to food composition databases with varied information collected from different countries, especially countries without extensive food composition information in their food database.However, our method is not perfect and its performance on new datasets could decrease, which indicates that further training on new datasets is necessary and some manual validation on the appropriate proportion of predicted data is still needed. The performance of our model was also limited by different local languages and the accuracy of the optical character recognition text recognition. For example, for the FLIP-LAC Argentina database, the algorithm was based on translated ingredient lists from Spanish to English; therefore, the model accuracy slightly decreased when applying our algorithm to other countries. Future research could explore the incorporation of stemming techniques and the development of food-specific corpora to improve the recognition of food ingredients and enhance the overall model performance across diverse linguistic and cultural contexts. Another limitation of this study is that our data sample in FLIP and USDA-BFPD databases did not cover 100% of the food products available in the food supply. Although the databases used in this analysis still have a very good coverage rate of the food supply , future work is needed to improve the challenge of the timely collection of food label information in a country.This study demonstrated that using large language models is an effective and generalizable automation strategy for classifying the NOVA food processing levels of packaged foods. Our automation strategy can be applied to different countries to expedite the food categorization process under the NOVA classification system globally, given that the ingredient text lists on food packages are the easiest accessible data compared to more complex nutrient composition data. Our approach could have profound policy implications. By facilitating a faster and more efficient NOVA ultra-processed food categorization process, we can assist researchers and policymakers in monitoring the changes of UPFs in food supply. Moreover, it could enhance our understanding of the correlations between NOVA food processing levels and health outcomes, which can inform future policy decisions aiming to improve food environments, diet quality, and public health on a global scale.


IEDs were recorded simultaneously with 4 tri-axial Rb- and 4 tri-axial He-OPMs in a child with RFE. IEDs were identified visually, isolated from magnetic background noise using independent component analysis (ICA) and were studied following their optimal magnetic field orientation thanks to virtual sensors. Most IEDs  were detectable by both He- and Rb-OPM recordings. IEDs were isolated by ICA and the resulting magnetic field oriented mostly tangential to the scalp in Rb-OPMs and radial in He-OPMs. Likely due to differences in sensor locations, the IED amplitude was higher with Rb-OPMs. This case study shows comparable ability of Rb-OPMs and He-OPMs to detect IEDs and the substantial benefits of triaxial OPMs to detect IEDs from different sensor locations. Tri-axial OPMs allow to maximize spatial brain sampling for IEDs detection with a limited number of sensors.Cryogenic magnetoencephalography (MEG) enhances the presurgical assessment of refractory focal epilepsy (RFE). Optically pumped magnetometers (OPMs) are cryogen-free sensors that enable on-scalp MEG recordings. Here, we investigate the application of tri-axial OPMs [ Magnetoencephalography (MEG) records the magnetic fields generated by electrical brain activity . Its maia fortiori even worse for subjects with a small head circumference such as children   requirinchildren .87Rb vapor heated to ~150°C (Rb-OPMs)  OPMs with noise levels  close to SQUIDs , a bandwidth limited to below 130 Hz, a dynamic range limited to a few nT (<5 nT), single- to tri-axial magnetic field measurement  are cryogen-free sensors that measure minute magnetic field variations  . Currentsurement , and heasurement . Tri-axisurement . Neverthmponents . Thanks mponents  as well mponents ,b,d and mponents . In the mponents . The advmponents , and migmponents .87Rb vapor may constrain the number of sensors that can be applied on the scalp to ensure sufficient heat dissipation  than Rb-OPMs, which therefore require a specific adaptable helmet to place them on the individuals’ head and limit the number of sensors that can be used. They have a higher noise level  but a larger dynamic range (beyond 200 nT) and bandwidth  that offers the opportunity to investigate high frequency brain oscillations, three axes of magnetic field measurement , and they dissipate only ~0.01 W per sensor as 4He needs no heating  . In the  heating . He-OPMs heating  and phys heating  activitiBoth Rb- and He-OPMs benefit from three axes of magnetic field measurement at a single location that may prove particularly useful to increase spatial brain sampling and maximize the sensitivity of IEDs detection in epileptic patients. The number of OPMs that can be placed on the scalp is indeed restricted by technical  or practical  constraints.This study therefore aims at demonstrating the practical application of the three axes of measurements of Rb- and He-OPMs to maximize the detection of IEDS in one school-aged epileptic girl. It also aims at comparing the amplitude and SNR of IEDs simultaneously recorded with Rb- and He-OPMs. For that purpose, the patient underwent a multimodal electrophysiological recording comprising simultaneous scalp He-OPM-MEG (4 sensors) and Rb-OPM-MEG (4 sensors) alongside simultaneous scalp electroencephalography .We studied an 11-year-old girl suffering from RFE, with an epilepsy onset at the age of 7 years [Patient 5 in This study was approved by the institutional Ethics Committee (Reference: P2019/426). The patient and her legal representative gave written informed consent prior to the inclusion.2) on the helmet were not based on conventional EEG montages , five He-OPM prototypes [as described in montages , left bumontages , right, EEG signals were recorded using a commercially available EEG amplifier . OPM signals were fed to distinct digital acquisition units . A 1-Hz square-wave trigger signal was generated from the Rb-OPM acquisition system and sent to both He-OPM acquisition electronics and EEG amplifier to enable re-synchronization of the simultaneous recordings.Recordings took place inside a compact MSR optimized for OPM recordings  was performed on both Rb-OPM and He-OPM band-filtered signals separately (FastICA with nonlinearity t tests (significance at p < 0.05), so as to identify the most prominent field component at each sensor. Second, the best magnetic orientation was estimated at each sensor as the principal component of its tri-axial signals and then used to replace each tri-axial sensor by a virtual sensor projected along this orientation (t tests (significance at p < 0.05).Statistics were performed on a sub-selection of 102 IEDs simultaneously observed in artifact-free periods of Rb-OPM and He-OPM data, with IED amplitude being estimated at the peak of these IEDs. These analyses focused on the Rb- and He-OPM showing the highest IED amplitude . Given that the tri-axial measurements of two OPMs may vary just because of differences in their scalp location relative to IED source, steps were taken to assess and partly mitigate the effects of sensor positioning. First, the three magnetic components of each of the 102 IEDs considered for the analyses were compared using a one-way ANOVA and post-hoc entation . Comparit test at p < 0.05) with a similar estimate of noise level extracted from OPM data devoid from IED activity . IED noise also enabled the comparison of peak IED SNR  across modalities (two-sided paired t tests at p < 0.05).To illustrate the denoising efficiency of ICA on IED recordings in both OPM modalities, the noise level of the ICA components that include IED activity was estimated from the background signal  and compared statistically . It was significantly higher on one tangential axis for the Rb-OPM  and on the radial axis for the He-OPM  with the Rb-OPM sensor. Given the strong difference in IED field orientations at the Rb- and He-OPMs, the higher IED amplitude in the Rb-OPM probably reflected a better positioning relative to the IED neural source for the Rb-OPM compared to the He-OPM, rather than a difference in performance per se. In fact, background noise in the IED-specific ICA component was similar in the two modalities . This contrasted with the case of the other, IED-free, ICA components which exhibited higher background noise in the He-OPM .This revealed a higher IED amplitude and likewise a higher SNR .The lower IED amplitude and SNR reported for He-OPM-MEG is likely due to differences in sensor positioning rather than an intrinsically lower sensitivity. Indeed, Rb- and He-OPMs were placed at different scalp positions, with ~2 cm distance between OPMs . It is tThese data illustrate clearly, in a clinical setting, the benefits of tri-axial OPMs to maximize spatial brain sampling with a limited number of sensors . Apart fIEDs were successfully isolated within a single ICA component with both Rb-OPMs and He-OPMs. The efficiency of ICA in this context has been shown previously in MEG . Our datThis study suffers from several limitations. First, it deserves confirmation in a larger population of epileptic patients with different forms of temporal and extra-temporal epilepsies. Second, it was limited by the low number of He- and Rb-OPMs that were placed at different locations to allow simultaneous recordings. This prevented proper spatial coverage of the brain area generating IEDs to enable IED source reconstruction, which would ultimately be the way to provide a comparison of Rb- and He-OPM-MEG free from the effect of relative positioning. Alternatives could be to swap sensor locations between He- and Rb-OPMs for a second recording or to perform consecutive recordings with He- and Rb-OPMs placed at similar locations. Still, these alternatives also suffer from some limitations such as increasing the recording duration that may impact patients’ cooperation (swapping and consecutive recordings) or that differences in IEDs amplitude/SNR may be due to variation in IEDs across time (consecutive recordings). Finally, the impact of simultaneously recording EEG electrodes on OPM SNR is also difficult to estimate.Overall, this study highlights the added value of multi-axial OPMs when spatial sampling is limited. It also shows that Rb- and He-OPMs are both able to detect IEDs with similar noise levels on IED activity properly isolated with ICA, opening the door for the automatization of OPM-MEG data analyses in epileptic patients. Future clinical on-scalp OPM-MEG users should consider selecting the type of OPM to use depending on the balance between the benefits and disadvantages of each OPM technology.Data are available upon reasonable request to the corresponding author and after approval of institutional  authorities.The studies involving humans were approved by Comité d’éthique – Hôpital Erasme – ULB. 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. We also obtained the written informed consent from the child. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.OF: Conceptualization, Formal analysis, Investigation, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing. PC: Investigation, Software, Writing – review & editing. EL: Conceptualization, Funding acquisition, Project administration, Writing – review & editing. MM: Investigation, Writing – review & editing. LR: Data curation, Writing – review & editing. CS: Resources, Writing – review & editing. NH: Software, Writing – review & editing. MB: Software, Writing – review & editing. SG: Supervision, Writing – review & editing. RR: Software, Writing – review & editing. SM: Resources, Writing – review & editing. AP-L: Resources, Writing – review & editing. DS: Methodology, Writing – review & editing. NB: Investigation, Writing – review & editing. PD: Supervision, Writing – review & editing. FW: Supervision, Writing – review & editing. VW: Data curation, Formal analysis, Methodology, Software, Validation, Writing – review & editing. XDT: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing.


In situ vibrational spectroscopy provides a unique tool for decoding molecular-level factors involved in electrocatalytic reactions. Typically, spectra are recorded when the system reaches steady states under set potentials, known as steady-state measurements, providing static pictures of electrode properties at specific potentials. However, transient information that is crucial for understanding the dynamic of electrocatalytic reactions remains elusive. Thus, time-resolved in situ vibrational spectroscopies are developed. This mini review summarizes time-resolved in situ infrared and Raman techniques and discusses their application in electrocatalytic research. With different time resolutions, these time-resolved techniques can capture unique dynamic processes of electrocatalytic reactions, short-lived intermediates, and the surface structure revolution that would be missed in steady-state measurements alone. Therefore, they are essential for understanding complex reaction mechanisms and can help unravel important molecular-level information hidden in steady states. Additionally, improving spectral time resolution, exploring low/ultralow frequency detection, and developing operando time-resolved devices are proposed as areas for advancing time-resolved techniques and their further applications in electrocatalytic research.Understanding the structure-activity relationship of catalysts and the reaction pathway is crucial for designing efficient, selective, and stable electrocatalytic systems.  Electron-transfer reactions at the electrode surface can be categorized as inner- or outer-sphere reactions. For an outer-sphere reaction, the electrons transfer tunnelling through a solvent layer, thus, no direct chemical interaction between the electrode and active species occurs. Therefore, the electron-transfer rate is exponentially increased with the applied overpotential. In contrast, electrocatalysis involves typical inner-sphere reactions where the electron-transfer rate is highly rated to the surface structure of the catalyst and reaction pathway, in addition to the applied overpotential making it far more complicated than an outer-sphere reaction.in situ and ex situ to monitor the structure evolution, coordination environment, valence state, electronic property, etc., , and sum frequency generation (SFG) spectroscopy able to capture the molecular fingerprint information are necessary  can provide some cues of the critical adsorption sites and interface structures. Morphology and structure detection techniques such as scanning electron microscopy, transmission electron microscopy, atomic force microscopy, X-ray absorption, X-ray diffraction, X-ray photoelectron spectroscopy, electron paramagnetic resonance, Mössbauer spectroscopy, etc., have been applied both y, etc., . Howeverecessary . SFG, a ecessary . But theecessary . This miin situ” refers to the measurements performed during the reaction under relevant reaction conditions. In comparison, “operando” measurements are taken under reaction conditions similar to those of realistic reactors.  coupled with ATR detection mode is widely used in spectroelectrochemistry  and bridge-bonded CO (COB) under −0.17 V and −0.54 V vs. SCE respectively. Furthermore, the difference in potential between COL and COB indicates that the activation energy of methanol oxidation via COB is lower than COL, revealing a dual reaction mechanism.High temporal-resolution detection methods with ms and μs resolution based on the FT-IR had been developed. One is rapid-scan time-resolved FT-IR which employs moving mirrors to reduce the scan time to the ms scale and is useful for monitoring processes with a half-life time 100 ms . An examin situ step-scan time-resolved FTIR with a sub-millisecond resolution to monitor a one-electron reduction of heptylviologen  (CO)3Cl as the catalyst based on the transmission mode. The reaction intermediate, Re-Re dimer, was directly observed from the difference spectrum  . In this2 reduction reaction on Cu (oxide) electrode during the cyclic voltammetry. They used a potential sweep rate of 10 mV/s and spectral sampling time of 1 s to obtain vibration modes of copper oxides, carbonate/bicarbonate, and CO. , gated detectors are generally employed for high time-resolved experiments. , 1997. P(1) Improve spectral time resolution.In this mini review, we provide a summary of the current instrumental methods of time-resolved vibrational spectroscopies and their applications in electrocatalysis research. With the rapid development of photoelectric detectors, there are immense opportunities to improve our understanding of electrocatalytic energy conversion. Herein, three main opportunities and challenges are addressed.(2) Explore low/ultralow frequency detection.To investigate short-lived intermediates in electrocatalytic reactions, it is necessary to employ time-resolved techniques with a temporal resolution of milliseconds to microseconds. Distinguishing between active and poisoned intermediates is important for understanding the pathway of electrocatalytic reaction. Time-resolved techniques can monitor intermediate generation, decay, and conversion processes, enabling determination of reaction kinetics and intermediate lifetimes, thus, revealing reaction kinetics. Microsecond time-resolved vibrational spectroscopy techniques still remain challenging. At this time scale, electrochemical process of the system needs to be carefully cheeked. For example, the charging process of the electric double layer controls the RC time constant that determines the test time zone. The mass transport process affects the thickness of the diffusion layer and kinetic behaviour according to Fick’s law. Thus, flow cells and ultramicroelectrodes are particularly designed and used to reduce the RC time constant. Besides, charge transfer is another important process in electrocatalytic reactions. To probe such ultrafast processes under the ns-fs scale, ultrafast techniques are required such as the pump-probe, and pump-push-probe strategies that pulse laser acts as the trigger instead of the potential. The limitation lies in the potentiostat, as the response time of commercial potentiostat is much longer than the laser that customized potentiostat is needed .(2) Expl−1 which was applied to probe the surface structure and additives  detection of Raman is much easier to implement as there are commercial BragGate notch filters to choose from. Taking advantage of the ultralow-frequency Raman, direct observation of structural changes (photon mode) of the metal clusters became possible ((3) Develop new methods and devices under operando conditions.The low and ultralow frequency range is crucial to understanding the structural and interactional information of catalysis, such as adsorption bonds, surface lattice species, and local strain, but has been seldom explored. Low frequency detection is quite difficult for ATR-SEIRAS due to the strong optical absorption of the conventional Si prism used in the electrolytic cell. Recently, a micromachined Si wafer window was developed that extends the detection window of ATR-SEIRAS down to 650 cmdditives . Howeverresearch . Both sypossible . It can possible .(3) Devein situ experiment, the mostly used spectral electrolytic cell and electrode such as a thin layer electrolytic cell and metal electrode, are typically designed for lab-scale research, which is far from industrial usage. Operando experiment is required to bridge the gap between lab research and realistic scenarios. One approach is to design special electrolytic cells that meet the optical detection requirements and also provide similar working conditions as realistic devices. For example, systematic operando studies of membrane electrodes are still lacking to correlate liquid-gas-solid multiphase information (During the ormation . Anotherormation , such asormation  and chalormation . These p


Purpose: The Cognitive Symptom Checklist-Work (CSC-W) is a self-report measure to assess cognitive symptoms  in working adults with cancer. To date, general working population norm data are lacking worldwide. We established CSC-W norm values in the general working population, and assessed associations of CSC-W scores with work and health-related factors. Methods: This cross-sectional study consisted of 1,000 Dutch working adults, of whom data was collected through an online respondent panel. The sample was stratified for sex and age, and data were weighted. Summary scores of the CSC-W total scale, and memory and executive function symptoms subscales, were determined . Z- and T-scores were calculated, and analysis of (co)variance has been applied. Results: Cognitive symptom scores were relatively stable across age groups, but 18-39-year-old respondents reported lower memory and executive function than respondents in other age groups. Symptom scores of memory function  were higher for all age groups and in both sexes compared to executive function . No sex differences in memory and executive function were observed. Higher symptom scores were associated with performing non-manual work only, manual work only, self-reported long-term illness, and higher levels of depressive symptoms and fatigue. Conclusion: The CSC-W norms may enhance the interpretation and facilitate the analysis of self-reported cognitive symptoms in patients with cancer at work. Our findings may support health care professionals in identifying working adults with cancer with cognitive symptoms and in developing personalized treatment. Working adults with cancer often have to cope with the effects of cancer and its treatment on physical, psychological and psychosocial health, and with limited understanding by their colleagues and employer , 2. One Cognitive symptoms among patients with cancer can be associated with the cancer itself , 6, cancThe CSC-W is a reliable and valid 19-item self-report measure of work-related cognitive symptoms in occupationally active adults with cancer . DorlandTo date, general working population norms for cognitive symptoms in working adults with cancer are lacking, but needed to interpret the prevalence levels of work-related cognitive complaints of patients with cancer compared to non-patients. Ultimately, normative data is needed by health care professionals to make informed treatment decisions. Further, an increased understanding of cognitive symptoms and associated factors may help health care professionals to identify patients at risk for cognitive symptoms. Therefore, this study aimed to develop general working population CSC-W norm scores to facilitate CSC-W interpretation among working adults with cancer, and to assess associations of CSC-W scores with work and health-related factors, known to be related to cognitive symptoms . We hypothesised that adults with cancer with self-reported long-term illness, and higher levels of depressive symptoms and fatigue report higher levels of cognitive symptoms than those with no health problems, and low levels of depressive symptoms and fatigue.https://www.motivaction.nl/panel-stempunt). Data were collected in December 2020, stratified for sex and age  with approximately 125 individuals per stratum. Ethical approval was granted by the non-Medical-Scientific Research with People Act (nWMO) applicable committee of the University Medical Center Groningen (METc 2020/343). Informed consent to participate was obtained from all participants prior to the study.We systematically collected data of participants aged 18–69 years from the general working population  through Motivaction, a panel research company , age (in years), marital status , and level of education. Education was classified according to the definition of Statistics Netherlands (CBS) as: (1) low, i.e., primary, lower vocational, and lower secondary education; (2) medium, i.e., intermediate vocational and intermediate secondary, and (3) high, i.e., higher secondary, higher vocational, and university.Clinical factors and psychological symptoms. Clinical factors included long-term illnesses or disabilities  and medication use . Psychological symptoms included depressive symptoms and fatigue. Depressive symptoms were assessed using the self-report Patient Health Questionnaire-9  [α = .89) . Responsα = .89) , 13. Fatα = .89) . The CISα = .89) . FatigueWork-related factors. Work-related factors included the type of work  and psychosocial work environment factors. Psychosocial work environment factors included quantitative job demands , work pace , and job control  measured with the Copenhagen Psychosocial Questionnaire (COPSOQ) [(COPSOQ) . ResponsSome degree of underrepresentation of individuals with low educational attainment levels across all age groups was observed in our study sample. Therefore, the distribution on age, sex, and educational attainment level has been weighted according to the distribution from the gold standard 2020 from CBS  to ensurWeighted and unweighted descriptive data analyses were performed to outline the baseline characteristics of the total study sample. Mean (M) scores and standard deviations (SD) of the CSC-W total scale and two subscales were determined by age group and sex. In addition to means, we also estimated median scores and the percentile distribution . We calculated norm-based scale Z-scores  and norm-based scale T-scores . Analyses of Variance were used to assess group differences between the strata. Analyses of Covariance were used to determine the associations between the CSC-W and work and health-related factors . All analyses were adjusted for age, sex, and level of education. A two-sided alpha of 0.05 is used for significance. Statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS) version 26.The mean age of the total weighted sample was 43.3 years SD = 12.2), and 53.9% was male. 68% of the working adults were living with a partner .Working adults reported an average CSC-W total score of 25.1 (SD = 15.8) Tables . Memory 3). No sex differences were found in cognitive symptoms total and subscale scores.Cognitive symptoms total scores, and memory and executive function symptoms scores, were relatively stable across age groups, but 18-39-year-old respondents scored significantly higher in cognitive symptoms total and subscale scores compared to respondents in the other age groups Tables  and 3. N3Respondents with a self-reported long-term illness or disability had higher CSC-W scores (indicating a higher level of cognitive symptoms at work) than those who reported no health problems Table . WorkingThe purpose of this study was to provide normative data from the general working population regarding cognitive symptoms, using the CSC-W. Cognitive symptoms scores were relatively stable across age and sex groups, suggesting overall norms may be used. Memory function symptom scores were higher for all age/sex groups compared to executive function. Higher symptoms scores were associated with performing non-manual work only, manual work only, self-reported long-term illness, and higher levels of depressive symptoms and fatigue.Stable patterns of cognitive symptoms scores were observed across age groups, except for some consistently higher scores reported by 18–39-year-old respondents compared to respondents in the other age groups. Our results are similar to those reported in recent general population normative studies for the European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire (EORTC QLQ-C30). For instance, Nolte et al. 2019 and 2020) [ and 2020The results of the current study also indicated that females and males do not rate their cognitive function at work differently. In earlier general population research, it has been shown that men tended to rate themselves lower  than women on the overall cognitive failure score, encompassing memory, attention, action, and perception, but these differences were negligibly small, and no differences were found in this specific study on the memory subscale .Higher symptom scores were associated with performing non-manual work only, and manual work only. This is in line with previous research in working adults with cancer by Dorland and colleagues (2018) , which sA strength of our study was the use of an internet panel, which enabled access to a large and diverse sample. This resulted in no missing items for the CSC-W. Also, previous research showed that employing panel data for patient-reported outcomes is generally comparable to those of national norms . FurtherThe general working population CSC-W norms provide crucial information for health care professionals to enhance the interpretation and facilitate the analysis of self-reported cognitive symptoms in adults with cancer at work. CSC-W assessments can increase symptom awareness, help timely intervention, and can be used as a basis for communication. The interpretation of work-related self-reported cognitive symptoms, using CSC-W, depends on definitions of normal and abnormal, the context for the examination, the relationship to prior levels of function, and whether diagnostic and therapeutic interventions are implied and anticipated. As with all patient-reported outcome data, for which reference standards are available, clinical judgment is required to weigh the possibilities of error in the individual test score and consider explanations of results substantially higher than expected values. A comprehensive way for a healthcare professional to use these results is to locate an individual patient’s CSC-W score within the percentile distribution shown for that patient’s sex and age. In case of higher cognitive symptom scores, the health care professional could, for example, consider referral to a neuropsychologist for an objective assessment of neuropsychological functioning. Specific work-related support could also be arranged, such as individual guidance, psycho-education, cognitive strategy training, and/or fatigue management  . PotentiIn future studies, normative data should be established in other countries to account for different work contexts, labor markets, and social security systems. Further, these normative data should be applied to other groups, with expected differences in cognitive symptom scores. Also, establishing cut-off points for the CSC-W would further facilitate interpretation of its scores.In this study, general working population normative data for cognitive symptoms for use in working adults with cancer on the CSC-W were established. The results provide a valuable resource for anyone assessing and detecting cognitive symptoms in working adults with cancer.