An allergic reaction is the immune system's overreacting to a previously encountered, typically benign molecule, frequently a protein. Allergy reactions can result in rashes, itching, mucous membrane swelling, asthma, coughing, and other bizarre symptoms. To anticipate allergies, a wide range of principles and methods have been applied in bioinformatics. The sequence similarity approach's positive predictive value is very low and ineffective for methods based on FAO/WHO criteria, making it difficult to predict possible allergens.This work advocated the use of a deep learning model LSTM (Long Short-Term Memory) to overcome the limitations of traditional approaches and machine learning lower performance models in predicting the allergenicity of dietary proteins. A total of 2,427 allergens and 2,427 non-allergens, from a variety of sources, including the Central Science Laboratory and the NCBI are used. The data was divided 80:20 for training and testing purposes. These techniques have all been implemented in Python. To describe the protein sequences of allergens and non-allergens, five E-descriptors were used. E1 (hydrophilic character of peptides), E2 (length), E3(propensity to form helices), E4(abundance and dispersion), and E5 (propensity of beta strands) are used to make the variable-length protein sequence to uniform length using ACC transformation. A total of eight machine learning techniques have been taken into consideration.The Gaussian Naive Bayes as accuracy of 64.14 %, Radius Neighbour's Classifier with 49.2 %, Bagging Classifier was 85.8 %, ADA Boost was 76.9 %, Linear Discriminant Analysis has 76.13 %, Quadratic Discriminant Analysis was 84.2 %, Extra Tree Classifier was 90%, and LSTM is 91.5 %.https://doi.org/10.17632/tjmt97xpjf.1 to access the ProAll-D server and data.As the LSTM, has an AUC value of 91.5 % is regarded best in predicting allergens. A web server called ProAll-D has been created that successfully identifies novel allergens using the LSTM approach. Users can use the link  Allergy, often described as an autoimmune disorder, is a clinical condition characterized by the immune system’s sensitivity to normally innocuous elements. The substance that causes allergy is known as an allergen. Allergens can be dust, pollen, cosmetics, and food. In food, allergy is usually caused by proteins. Proteins are an essential part of our diet, but some proteins can also be harmful to some individuals. One of the reasons behind this is that nowadays, the use of genetically modified crops that are transgenic food crops is increasing rapidly. Thus, it is necessary to assess them before they are introduced into the food chain. Allergy can be innate, acquired, predictable, and at times rapid. Allergic reactions are caused by an antibody called immunoglobulin E (IgE), which causes hyperactivity in white blood cells such as mast cells and basophils, resulting in the production of inflammatory chemicals like histamine. Apart from symptoms such as uneasiness, sneezing, wheezing, and swelling, allergic reactions can also lead to life-threatening situations. As a result, assessing them is critical to protect society's wellbeing.According to the Food and Agriculture Organization, a protein is a potential allergen if it has a homology of six successive amino acids or a sequence identity of more than 35 percent . Poms etAllerTop was developed as an alignment-free allergen prediction method. Protein properties were defined using Z-descriptors. The ACC transformation was used to convert the variable-length strings to uniform-length strings. The KNN (K-Nearest Neighbor) algorithm was applied for classification and consistently outperformed other algorithms . AllergeA study of GE  crops found they are as safe as conventional food crops. Screening the recombinant protein for predicting potential allergens is one of the assessment procedures for GE crops. It implies that there is currently no clear parameter that can be used to anticipate the pathogenicity of proteins . AllerCaThis paper comprises the report on the development methods of a set of novel allergen prediction models that use the knowledge gained via a publicly available server: AllerTOP v.2 . We propWe gathered a total of 2,427 allergens and 2,427 non-allergens from a variety of sources, including the Central Science Laboratory and the National Center for Biotechnology Information (NCBI). The redundancies were eliminated. A sample protein sequence is shown in Five E-descriptors were used to characterize the protein sequences of allergens and non-allergens . VenkataProteins are composed of amino acid sequences, each distinct and varies in length. So, ACC transformation was employed to convert the variable-length sequence to uniform length so that the classification algorithms could be applied to it. Here, the 5 E-Descriptors have been considered, which were derived from 237 physio-chemical properties of amino acids as used by Dimitrov et al. .Auto Cross Covariance includes Auto Covariance and Cross Covariance. The equation to calculate Auto covariance is as follows:The equation to calculate Cross covariance is as follows:j was used for the E-descriptors , n is the number of amino acids in a sequence, the index i is the amino acid position  and l is the lag, length of the minimum sequence . In order to investigate the influence of close amino acid proximity on protein allergenicity, a short range of lags  was used [Index was used .For classification, a few machine learning, ensemble learning, and deep learning algorithms have been considered, which include: The Gaussian Naive Bayes, Radius Neighbour's Classifier, Bagging Classifier, ADA Boost, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Extra Tree Classifier, and LSTM, which have been implemented in Python.Hochreiter and Schmidhuber proposed the term LSTM (Long Short-Term Memory) in 1997. LSTMs are a kind of RNN  that aid in the resolution of the long-term dependence problem. A conventional RNN encounters the difficulty of vanishing gradient problems, which makes learning extended sequences difficult. This is where LSTM comes in to address the challenge described above. The LSTM model is built iteratively. To construct the model, one must add different sorts of layers with varied parameters and experiment with dropout layers. The network constructed here consists of four layers with Three relu activation functions and one SoftMax function. "Categorical cross-entropy," was considered as the loss function with "rmsprop" as the optimizer.For training and testing purposes, the data was split in the ratio 80:20. The accuracy results of the model based on training and testing data has been represented in A web server, namely ProAll-D, has been developed to predict the potential allergens using the LSTM algorithm. It is developed using the Python Django framework, which is fast and user-friendly. The detailed functioning of the webserver has been described in the supplementary section.ACC includes both autocovariance and cross-covariance. Auto covariance is calculated between the same E Descriptors, that is between E1 and E1, along with the lag value. AC111 represents the autocovariance between E1 and E1 along with the lag value as shown in The classification methods considered in this research are: The Gaussian Naive Bayes, Radius Neighbour's Classifier, Bagging Classifier, ADA Boost, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Extra Tree Classifier, and LSTM, which have been implemented in Python.Gaussian Naive Bayes is a statistical predictive model for classification that is based on the Naive Bayes algorithm. For our dataset, this algorithm produced an accuracy of 64.14 percent . The RadLinear and Quadratic Discriminant Analysis resulted in an accuracy of 76.13 and 84.2 percent. A Bagging classifier is an ensemble classifier that works on random samples of the data and then combines various instances to obtain the final output. This method resulted in an accuracy of 85.8 percent. Extra tree classifier, an extended version of the random forest algorithm, resulted in an accuracy of 90 percent. LSTM model resulted in an accuracy of 91.5 percent.In A ROC curve (short for receiver operating characteristic) plots the rate of true positives vs. false positives to assess the effectiveness of a classification model. AUC measures how well a model distinguishes between positive and negative classes. AUC is not affected by the classification threshold value. Modifying the threshold value does not affect AUC because it is an aggregate measure of ROC.The LSTM model has the highest AUC, indicating that it has the largest area under the curve and is the best model for correctly classifying observations. The software for the server ProAll-D has been developed to predict potential allergens using the LSTM algorithm. It is developed using the Python Django framework, which is fast and user-friendly.There are three different sections, namely Home, Datasets, and Method Description as shown in The supplementary section describes the detailed functioning of the web application.This study builds on and expands earlier studies to develop the analysis of potential protein allergens. Our first aim has been to update the technique while keeping the previous factors in mind. We evaluated deep learning, ensemble learning, and machine learning models such as the Gaussian Naive Bayes, Radius Neighbour’s Classifier, Bagging Classifier, ADA Boost, Linear Discriminant Analysis, Quadratic Discriminant Analysis, and LSTM to predict the allergenicity of proteins. Extensive testing produced excellent results. They were superior and corroborated earlier research. Furthermore, the AUC value of LSTM (the best performance) was 0.9152. So far, this is the only study to apply the aforementioned methods to evaluate protein allergenicity, and it will serve as a paradigm for future protein allergen prediction.


Diabetic macular edema (DME) is a highly common cause of vision loss in patients with diabetes. Optical coherence tomography (OCT) is crucial in classifying DME and tracking the results of DME treatment. The presence of intraretinal cystoid fluid (IRC) and subretinal fluid (SRF) and the disruption of the ellipsoid zone (EZ), which is part of the photoreceptor layer, are three crucial factors affecting the best corrected visual acuity (BCVA). However, the manual segmentation of retinal fluid and the EZ from retinal OCT images is laborious and time-consuming. Current methods focus only on the segmentation of retinal features, lacking a correlation with visual acuity. Therefore, we proposed a modified U-net, a deep learning algorithm, to segment these features from OCT images of patients with DME. We also correlated these features with visual acuity. The IRC, SRF, and EZ of the OCT retinal images were manually labeled and checked by doctors. We trained the modified U-net model on these labeled images. Our model achieved Sørensen–Dice coefficients of 0.80 and 0.89 for IRC and SRF, respectively. The area under the receiver operating characteristic curve (ROC) for EZ disruption was 0.88. Linear regression indicated that EZ disruption was the factor most strongly correlated with BCVA. This finding agrees with that of previous studies on OCT images. Thus, we demonstrate that our segmentation network can be feasibly applied to OCT image segmentation and assist physicians in assessing the severity of the disease. Diabetic retinopathy (DR) is a common cause of vision loss in patients with diabetes . Some paResearchers have developed various algorithms for the segmentation of normal retinal layers from OCT images . HoweverWith the increasing power of computers and sophistication of deep learning algorithms, researchers have attempted to develop artificial intelligence (AI) models that can process OCT images, color fundus photographs and optical coherence tomography angiography (OCTA) for classifying diseases, staging diseases, and extracting features ,8,9,10. Nevertheless, most papers focused mainly on segmentation methods, lacking a correlation with visual acuity. In this study, we developed a deep learning-based automatic segmentation algorithm to segment the IRC, SRF, and EZ. We evaluated the correlations of these segmented factors and CST, which are used in current clinical practice, with BCVA. We demonstrated that the results of our fully automatic segmentation were consistent with the previous manual segmentation research on OCT features that correlated with BCVA .This study proceeded in two stages. First, we constructed our AI segmentation model; second, we evaluated the correlation of each segment with BCVA . All imaPatients with DME who visited the Department of Ophthalmology of Taipei Veterans General Hospital (TVGH) for OCT imaging at any time between February 2011 and August 2019 were enrolled. This study was approved by the Institutional Review Board of TVGH in Taipei, Taiwan. OCT images , CST measurements, and subjective, objective, assessment, and plan (SOAP) notes were retrieved from the big data center of TVGH. Images with an insufficiently high resolution were excluded to ensure data quality. Participants were enrolled only if they had (1) a diagnosis of DME ; (2) macular edema, defined by a CST of >250 μm; (3) SRF or IRC, as indicated by spectral domain OCT; (4) BCVA between 0.05 and 1.5  measured using a Snellen chart; and (5) an age of >20 years. Prospective participants were excluded if they had a history of any of the following ophthalmological diseases: (1) choroidal neovascularization due to AMD, RVO, uveitis, or any other inflammatory disease; (2) cataracts or clouded lenses; (3) glaucoma or any other neuropathy; (4) epiretinal membrane, vitreomacular traction disease, or any other maculopathy; or (5) corneal disease or degeneration. The decimal BCVA data of patients who received OCT imaging were retrieved from the SOAP notes on the closest date. These data were converted to the logarithm of the minimum angle of resolution (logMAR-BCVA), where a higher logMAR-BCVA indicates poorer VA.After the images were collected and subjected to initial quality control, the BM data set was labeled by three experts, and an experienced ophthalmologist performed a final check to ensure that the label was correct. Using the image segmentation function of MATLAB , the experts labeled each pixel as one of the following five retinal features, which served as biomarkers in this study: the neurosensory retina, EZ, retinal pigment epithelium (RPE), IRC, or SRF . SpecifiThe structure of our fully convolutional segmentation network is similar to that of U-net , but we Our decoder is identical to that used by U-net. It comprises four decoder blocks, and each block is composed of one upsampling layer and two convolutional layers. The features extracted by the encoder blocks  were concatenated to the decoder blocks to propagate high-resolution features. In the final part of the model, the sigmoid function is used to produce the segmentation result. The trainable parameters in the proposed model total 37 million. The architecture of the model is illustrated in The OCT images in the BM data set were grouped into training (127 scans from 50 patients), validation (38 scans from 16 patients), and testing (38 scans from 17 patients) data sets. No single patient had images in two or more of these data sets. The segmentation network took OCT scans as inputs and output a probability map for each class. All the images were cropped to ensure uniform size. The images were augmented through slicing and flipping. Every image in the training data set was vertically sliced into eight slices and then flipped horizontally; this augmentation method is similar to that used by Roy et al. .The segmentation network was trained, validated, and tested using a desktop computer with a central processing unit, an 8-core CPU , 16 GB memory, and a graphics processing unit (GPU)  with 12 GB graphics memory and 4352 CUDA cores. The operating system was Microsoft Windows 10. The loss function was the averaged Dice loss, which was calculated as follows:Since the output of the segmentation network is a binary image, we can calculate the Sørensen–Dice coefficient using Equation (3). However, different numbers of pixels in different classes would affect the training results. If we do not compute the Dice coefficient for each class separately, but use Equation (4) to calculate the loss of Dice coefficient of the whole image, the model would tend to focus on classes with large numbers of pixels and ignore classes with fewer pixels. To alleviate this problem, the adopted averaged Dice coefficient (Equation (2)) is the average of the Dice coefficients across different classes, with equal contributions from each class regardless of the pixel number in each class. Therefore, the use of the loss of averaged Dice coefficient allowed us to deal with the class imbalance problem. https://github.com/karolzak/keras-unet, accessed on 21 May, 2022), was used as the conventional U-net. The hyperparameters of the conventional U-net and proposed U-net are shown in The ablation study was conducted to compare the performance between the conventional U-net and the proposed modified U-net based on the Dice coefficient. The keras-unet package, proposed by Karol Zak on github , and automatically segmented images (segmentation images) from the testing data set are displayed in The ROC curve was used to evaluate the performance of the algorithm in detecting EZ disruption, IRC, and SRF. We also calculated the correlations of these features with logMAR-BCVA. The associations between the chosen retinal features and BCVA were assessed using linear regression and correlational analyses. An OCT image was defined as indicating EZ disruption if the minimum thickness of the EZ was 0. Pixels with a probability >0.9 were summed to compute the volumes of IRC and SRF. Before linear regression, all the features were normalized through subtraction of the mean from each data point value and division of this difference by the standard deviation.The correlations of the retinal features with BCVA are shown in AI is a promising tool for processing imaging findings and clinical data. The application of AI to the treatment and diagnosis of DME, a leading cause of vision loss, helps clinicians to better assess the severity of any given case of DME. In this study, we used a small data set to train a segmentation model, which was then applied to a large data set. The proposed model could automatically detect and quantify EZ disruption, IRC, and SRF, with accuracies of AUC = 0.88, 0.96, and 1.0, respectively, and Sørensen–Dice coefficients of 0.81, 0.80, and 0.89, respectively. The correlations between the features extracted by the model and BCVA were determined; segmented EZ disruption and IRC were significantly correlated with logMAR-BCVA. These results demonstrate the practicability of deep learning neural networks for segmenting key DME-associated retinal features from OCT images for determining DME severity and treatment progress.Our method performed better than others in the literature. For example, Lu et al. achieved a mean Sørensen–Dice coefficient of 0.73 and mean AUC of 0.99 in the detection of retinal fluid when they applied their method to the RETOUCH database . Lee et Previous studies have reported that EZ integrity is the most strongly correlated feature with VA in patients with DME ,5. Our sIn the current state of the art, physicians and researchers use CST to assess DME severity and track treatment progress. Nevertheless, we found that EZ disruption and IRC were more strongly correlated with BCVA than was CST. Increases in CST are primarily caused by IRC and SRF. This may explain why CST was more strongly correlated with BCVA than IRC was in the multivariable regression.Some studies have revealed that the presence of SRF did not necessarily worsen BCVA and that tolerance toward SRF may preserve a patient’s BCVA, unlike total dryness of macula ,35. Our Our study has some limitations. First, our data sets were limited in scope; this limited the performance of our model, which nonetheless had similar accuracy as its counterparts in the literature ,20,33,36We formulated and validated a fully automatic segmentation model that can effectively quantify the OCT biomarkers of EZ disruption, IRC, and SRF in patients with DME. These OCT biomarkers are more correlated with VA than is CST, which is presently analyzed by clinicians and researchers from retinal OCT images. Our novel deep learning model aids in the clinical determination of DME severity and treatment progress. Future applications to three-dimensional OCT images are warranted to improve the model.


Childhood systemic lupus erythematosus (cSLE) is a complex multisystem autoimmune disease. In 2019, belimumab was approved for the clinical treatment for cSLE, making it the only biological agent approved for cSLE children aged 5 and older in 60 years.To review emerging evidence on belimumab in cSLE published up to April 2022, so as to provide information for clinical decision-making.A comprehensive search of relevant publications up to the date of April 2022 in PUBMED, EMBASE, WOS, COCHRANE, ClinicalTrials.gov, CBM, CNKI and WANFANG was performed using the following criteria: (a) English and Chinese language studies; (b) RCT studies, cohort studies, or case-control studies; (c) patients with age <18; (d) Observational studies or case series studies contain more than 5 patients. All relevant literature was independently screened and reviewed by at least two reviewers and the obtained literature data were extracted and reviewed by two authors.Five publications met the inclusion/exclusion criteria for cSLE: one randomized controlled trial, one retrospective cohort study, and three case series. There was a high degree of heterogeneity among several studies, and the availability of baseline and outcome data provided was uneven.At present, there is a lack of high-quality clinical trials of belimumab in the treatment of cSLE. Based on the current research, it is believed that the use of belimumab can inhibit cSLE activity, reduce the dose of corticosteroids and immunosuppressants, and delay kidney damage. Also it shows clinical benefit in alleviating symptoms of monogenic cSLE refractory to standard therapy. More studies are urgently needed to validate the clinical efficacy of belimumab in cSLE and to evaluate its long-term safety in pediatric populations to promote evidence-based practice. Systemic lupus erythematosus (SLE) is an autoimmune disease caused by immune dysfunction. As the disease progresses, it can manifest as multisystem involvement . Most paCompared with adult SLE, cSLE shows a more aggressive course, and is more active with the progression of the disease. Meanwhile, due to age factors, the drug burden of children is relatively heavy, and cSLE patients have a higher use rate of immunosuppressants , 7, 8. SWith the subject terms of “systemic lupus erythematosus” and “belimumab”, a comprehensive search of relevant publications up to the date of April 2022 was conducted in PUBMED, EMBASE, WOS, COCHRANE, ClinicalTrials.gov, CBM, CNKI and WANFANG. Studies were eligible for inclusion if they met the following criteria: (a) English and Chinese language studies; (b) RCT studies, cohort studies, or case-control studies; (c) patients with age <18; (d) Observational studies or case series studies contain more than 5 patients.Publications that met the following criteria were excluded: (a) studies on other diseases ; (b) systematic reviews, guidelines, comments and clinical trials with unavailable data; (c) case reports with the number of cases < 5; (d) meeting abstracts only or incomplete text; (e) studies with a focus on other interventions than belimumab; (f) adult studies that did not meet age criteria; (g) Animal experiments; (h) actually duplicated documents with different titles.FC and YZ) independently screened the titles and abstracts of all publications according to the predefined inclusion/exclusion criteria. Full texts of the articles were read if it could not be excluded by reading the abstracts. All relevant publications were independently reviewed by two reviewers.Two reviewers , after exclusion of duplicates. After screening titles and abstracts, 79 studies that did not meet the inclusion criteria were excluded, the remaining 14 articles were read in full. After evaluating the publications according to the inclusion/exclusion criteria, 5 studies were finally determined to meet the complete criteria , mononuclear macrophages, tumor necrosis factor (TNF) and other factors are involved in the autoantigen immune response and inflammatory response of cSLE . In addiB cells, as antigen-presenting cells, which secretes pro-inflammatory cytokines and produce protective antigens, plays an important role in humoral immunity in healthy individuals; For SLE patients, B cell activation caused by various factors is the main pathogenesis, in which B cells are damaged and fail to recognize self-antigens. Thereby it produces antibodies against the self-antigens and triggers an overactive inflammatory response, resulting in extensive tissue damage , 16. In Glucocorticoids are still the most commonly used basic drugs in the treatment of cSLE. Studies have shown that cSLE has higher frequency of immunosuppressive use and dosage of glucocorticoids than adults SLE . HoweverIn recent years, a variety of targeted drugs have been developed based on the pathogenesis of SLE, such as atacicept and telitacicept, targeted drugs for B cells. Besides, there are targeted drugs for the type I interferon pathway, anifrolumab and BIIB059, and baricitinib in the signal transducer and activator of transcription pathway of the Janus kinase, ustekinumab in regulating inflammatory factors, etc. . As an eBEL is a recombinant human monoclonal antibody that combines and neutralizes BAFF, which is developed as a new biological treatment for SLE. B cells are an important factor in connecting the two. According to studies, the level of BAFF in patients with SLE is much higher than that of healthy people, and BAFF level is correlated to SLE disease activities. BEL can bind and antagonize soluble BAFF to prevent it from combining with other receptors –24.The two large-scale multi-centered RCTs among adults, BLISS-52 and BLISS-76, showed that BEL group’s SELENA-SLEDAI score, PGA, BILAG, glucocorticoids use and peripheral blood B cells, anti-dsDNA and complement were significantly improved. Besides, most outcomes have reached statistical significance compared with the comfort group. RTX is another candidate therapy targeted at B -cells, in addition to BEL. Although the ability of RTX to consume B -cells is far better than BEL, neither its primary nor secondary outcomes in clinical trials have reached statistical significance. Based on these, BEL was approved as the first new therapy for SLE in 2011 , 22, 25.Pharmacokinetic models were established in 53 children treated with BEL in the PLUTO study, and samples were provided for PK assessment and exposure response analysis at each treatment. The parameters included in the analysis were maximum serum drug concentration (Cmax), minimum serum drug concentration (Cmin), stable distribution concentration (Cavg_ss), first phase elimination half-life (T1/2α), second  phase elimination half-life (T1/2β), steady-state volume of distribution (Vss), final phase volume of distribution (Vz), clearance (CL) and area under serum drug concentration-time curve (AUCss). After the recording and analysis of the above values, in the group of children treated with 10 mg/kg BEL every 4 weeks, the Cmin was 56.2ug/ml, the Cmax was 325ug/ml, the CL was 158ml/day, and the terminal half-life was 16.3 days with a distribution half-life of 0.8 days. In the comparison of serum drug concentrations, it was found that children were consistent with adults in the study ; the steady-state PK curve simulated from children’s data was consistent with adult’s curve. The exposure distribution of all age groups in children shows a high degree of overlap in the charts and is consistent with that of adults , 26.However, since the data on the pharmacokinetics of BEL were derived from Chinese adults, children from the same ethnic group were not included. Therefore, in order to prove the applicability of the medication regimen in cSLE population, Chinese scholars used the previous research data of BEL to compare the pharmacokinetics and exposure distribution between adult and pediatric patients of Chinese and non-Chinese ethnic groups. Thus, steady-state BEL exposure was predicted in cSLE children receiving 10 mg/kg every 4 weeks. In examining the effects of covariates such as age and race on BEL exposure, no significant differences were found in exposure between races. After the treatment of adult and pediatric patients, the first and third quartiles of Cavg_ss in the pediatric patient population were 78.2 and 105.8 mg/L. The adult distribution interval was calculated with 80-125%. The first quartile of the total adult population and Northeast Asian adults was 64.8-101.3 mg/L, 60.6-94.8 mg/L, and the third quartile was 89.0-139.0 mg/L and 78.4-122.5 mg/L . In termDue to the complexity of clinical symptoms and serological manifestations of cSLE, it is a highly heterogeneous disease. The severity of the disease also varies over time, with periods of inactive alternating with periods of relapse. In addition to such heterogeneity, complications and infections further complicate the clinical presentation of the disease. Therefore, to facilitate the assessment of disease activity in clinic, different disease scoring tools have emerged –33. The Zeng et al. conducted a cohort analysis of 256 children with cSLE from 37 centers and found that the SLEDAI score of children in BEL group and the traditional drug group both decreased gradually with extension of time, however the mean SLEDAI scores at week 28 were similar between the two treatment regimens and the difference was not statistically significant  is the first double-blind, placebo-controlled randomized trial in children with active SLE. In this study, a total of 93 children (aged 5-17 years old) were assigned to the intervention group and the control group with 53 and 40 children respectively. The intervention group was administered at a dose/frequency of 10mg/kg every 4 weeks for 52 weeks. Compared with the control group, the intervention group had an average advantage of 5.3% higher in the improvement of SLEDAI  in BEL treatment group (40.5%) was higher than that in traditional drug treatment group (8.8%) (According to p (8.8%) . In termp (8.8%) ; As for p (8.8%) . In addiOverall, BEL appears to have a positive effect on improving disease scores in children with cSLE, but in higher-quality evidence-based study, the combination of BEL does not show an impressive advantage compared with conventional medicine. Still, it showed potential for faster control of lupus activity to a lower state.Based on the B-cell targeting mechanism of BEL and the immune overregulation and organ damage of SLE, we mainly focus on peripheral blood B cells, SLE-related antibodies such as ANA, anti-dsDNA, and complement. In terms of organ damage, kidney involvement is common, and we also pay attention to the situation of proteinuria.It is mentioned above that B cells are the main target of BEL in the treatment of SLE. At week 52 of the PLUTO study, compared with the placebo group, the total number of peripheral blood B cells and immature B cells in the treatment group were significantly reduced. However, the study was based on measures developed for the treatment of adults, there was a lack of sufficient samples amount to support the statistical testing .SLE is an autoimmune disease that forms immune complexes and antibodies, and it involves multiple systems. Therefore, serum antibodies are of great significance to its diagnosis and judgment of disease progression. ANA has high sensitivity and is the first choice for SLE screening, but its specificity is poor, and the sensitivity of it is easily affected by the quality of detection, which cause a risk of misdiagnosis and missed diagnosis. Since anti-dsDNA has higher specificity, combined detection of antibodies is considered to be more beneficial for the diagnosis of SLE , 36.Zeng mentioned above. After 28 weeks of treatment, the study mainly observed the changes of ANA and anti-dsDNA before and after treatment in the serological aspect. At week 0 of enrollment, the positive rates of antibodies in the two groups were similar. After 28 weeks of treatment, the positive rates in both groups decreased. Compared with before, the difference was statistically significant, but there was no statistical difference between the two groups.The PLUTO study showed that anti-dsDNA in the treatment group was lower than that in the placebo group, but the article did not provide detailed relevant data, thus the study did not support statistical testing . At presTao, 8 children with active cSLE were selected as the observation objects. The SLE antibody in 4 children turned negative or decreased in positive rate after treatment, but limitations such as small sample size, short study duration, and lack of bias control   deficiency. BEL was added after the failure of intensive treatment with standard therapy. The dosage of BEL was 10 mg/kg every 2 weeks for 3 doses, then every 4 weeks. After BEL treatment, the skin and mucosal manifestations of all patients were significantly improved. As for arthritis, 2 patients completely resolved while another one with DNaseII deficiency has significant improvement from joint pain despite deformed joints. In terms of corticosteroid dosage, except for 1 patient had disease recurrence and corticosteroid supplementation due to irregular use of BEL, 1 patient achieved corticosteroid withdrawal, and during the follow-up, the average daily glucocorticoid dosage of 3 patients decreased significantly. After using BEL, 4 patients showed sustained improvement in SLEDAI score within a short period of time, but their PGA scores increased to varying degrees. As for parameters such as related antibodies and complement levels, except for the missing part of follow-up data, only a small number of patients achieved improvement, and most of them have no obvious changes or even worsened. For cSLE patients with nephritis, the combination of BEL and MMF had a positive effect on the urine protein-to-creatinine ratio, and this combination was also shown to be beneficial for inducing remission in lupus nephritis in adult studies. According to the above research results, it is suggested that the effectiveness of BEL in the treatment of monogenic cSLE is basically the same as that of ordinary cSLE. As for safety, except for 1 patient who lost follow-up after treatment and died of septic shock, the remaining patients had good tolerance to BEL, and no obvious adverse events occurred . In the placebo group, the main adverse events were infections. Yet the serious AEs haven’t been proved to be associated with the use of BEL. It is worth noting that the 3 cases of serious psychiatric adverse events  reported in the study all occurred in the placebo group. For other adverse events, there was no significant difference between the BEL group (67.92%) and the placebo group (67.5%) .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.


Toxicokinetic (TK) models have been used for decades to estimate concentrations of per-and polyfluoroalkyl substances (PFAS) in serum. However, model complexity has varied across studies depending on the application and the state of the science. This scoping effort seeks to systematically map the current landscape of PFAS TK models by categorizing different trends and similarities across model type, PFAS, and use scenario. A literature review using Web of Science and SWIFT-Review was used to identify TK models used for PFAS. The assessment covered publications from 2005–2020. PFOA, the PFAS for which most models were designed, was included in 69 of the 92 papers, followed by PFOS with 60, PFHxS with 22, and PFNA with 15. Only 4 of the 92 papers did not include analysis of PFOA, PFOS, PFNA, or PFHxS. Within the corpus, 50 papers contained a one-compartment model, 17 two-compartment models were found, and 33 used physiologically based pharmacokinetic (PBTK) models. The scoping assessment suggests that scientific interest has centered around two chemicals—PFOA and PFOS—and most analyses use one-compartment models in human exposure scenarios. Poly- and perfluoroalkyl substances (PFAS) are a family of more than 4730 highly fluorinated aliphatic compounds manufactured for a variety of diverse applications . The lishttps://pfastoxdatabase.org/, accessed on 15 December 2022). Epidemiological studies suggest an association between these legacy PFAS chemicals and immune suppression, thyroid disease, high cholesterol, ulcerative colitis, kidney cancer, testicular cancer, breast cancer, hypertension during pregnancy, decreased weight at birth, and increased risk of miscarriage [Currently, much of the human health effect data available for PFAS are for a handful of chemicals, primarily legacy PFAS such as perfluorooctanoic acid  and perfluorooctane sulfonate . As of this writing, the most up to date list of PFAS with peer reviewed health information on the EPA CompTox Dashboard is the PFAS-Tox Database, containing 44 different chemicals, only 29 of which are unique   were imported into an EndNote library, screened for duplicates, and then imported into the SWIFT Review tool (n = 688) [Papers for the systematic mapping were collected using the Web of Science (WOS) search tool with a search string based on a systematic review protocol developed by DeLuca et al.  and provn = 688) . Machinen = 688) . In the n = 688) . We emplBased on the behavior of our training set, we estimate that by setting the required inclusion probability to 0.5 we captured about 95% of relevant papers . This ren = 58). Due to the relatively small size of this additional search, it was subjected to only a manual abstract review and in the inclusion of 6 additional relevant papers. Combined, these search and screening activities resulted in a total corpus of 92 papers. The total literature search and screening effort is described in Finally, an additional literature search in WOS was conducted in July 2021 for studies published in the first half of 2020–2021 to identify any recently published studies that were not included in the previous literature search  basic bibliographic details such as title, authors, journal, publication year, location of publication, and number of references; (2) the type of model , (3) the source of model (novel model or from a previously published source), (4) organism , (5) the chemical(s) included, and (6) the analytical instrumentation used. Papers were subset by implementation of a one compartment model. Included papers develop new toxicokinetic models for PFAS, calibrate parameters, or leverage toxicokinetics to estimate dose or exposure (reverse dosimetry). DV), a theoretical volume needed to contain the total mass of drug administered  at the concentration observed in the plasma. A value of DV comparable to the blood volume suggests that the drug/toxicant is confined to the blood, whereas a value comparable to the body volume indicates that the drug/toxicant distributes throughout the body. In contrast, PBTK models consider physiological volumes  explicitly and comprise more than two compartments. Additional information is available in the Model type is determined by the number of compartments. A one compartment model will consider the body as a single ‘compartment’, whereas a two-compartment model typically consists of a central and peripheral compartment, or it depicts the relationship between two organisms (i.e mother-child). Both one and two compartment models employ the concept of volume of distribution (sC is concentration in the serum (ng/mL), D is the daily intake dose (ng/kg-bw/day), DV is the volume of distribution (mL/kg), and ek is the elimination rate (1/day). At steady-state d(Cs)/dt = 0, giving the following:Equation (1) describes a simple one-compartment toxicokinetic model. This model was used by East et al.  to prediDV would result in an increase in the steady state concentration, other parameters held constant. Since the drug/toxicant has less theoretical volume within which to distribute, it is more concentrated. Similarly, a lower elimination rate constant signifies less substance is eliminated over time, resulting in a higher steady state concentration. Equation (1) is specified with zero-order input D which is often practical for modeling human exposure scenarios. Alternatively, Equation (1) can also be specified with first order absorption to interpret animal studies by gavage as described by Wambaugh et al. [In Equation (2), a lower value of h et al. . Additioh et al.  describeExtracted data from the included studies were then analyzed using the systematic mapping methodology outlined in James et al. , which dA summary analysis regarding similarities in model structure, the prevalence of animal versus human studies, and frequency of the most common PFAS species studied are presented in the results. These descriptions of the corpus provide some description of the field. Model structures are classified as one compartment (OC), two compartment (TC), or physiologically based toxicokinetic (PBTK). Because some models leverage multiple model structures, flagging multiple structures occurs in this analysis. Two compartment models are inclusive of both physiological compartment models (kidney to liver) and transfers between unique receptors, such as mother to child. The most commonly modeled PFAS across publication year is also included. Again, because some manuscripts evaluate multiple PFAS, duplicate assignments are recorded. As such, the counts of model type and PFAS evaluated exceed the number of papers in the corpus. Bibliometrix [Biblioshiny [Bibliometrix. This package allowed for the construction of networks of citations between papers in the corpus, with papers represented by nodes and citations between papers represented as edges between the papers. In this way, metrics such as centrality could be used to describe the relative influence that papers and/or TK models have had on the overall field of study. To analyze the bibliometric relationships between the authors of the included studies, the R Package iometrix ,39 and tlioshiny  were useBibliometrix network plots were used in two ways. First, after having gathered basic information on TK structures from the corpus, a co-citation network was leveraged to identify important papers outside the corpus. For this we examined the network of references co-cited by the corpus [Bibliometrix function networkPlot, with clustering set to “louvain”, and n = 20 nodes for interpretability [e corpus . Articletability . The Loutability  has beentability . A ‘betwtability ,44. In ttability .Bibliometrix [Secondly, given our focus on one compartment models for humans (OCH), we generated a visualization of publication activity over the years. This was accomplished with a ‘historiograph’, a chronological network map of the most relevant direct citations resulting from a bibliographic collection ,46. The iometrix .Bibliometrix for further extraction to characterize the important applications of OCH models. The GSC, the basis for determining which models were selected, was recorded in the initial WOS search performed in December 2019, and several commonly cited publications have exceeded this threshold since the WOS search. The purpose of this subset is to evaluate OCH PFAS TK models that have greater influence on other research communities. In addition to the graphical summaries, manuscripts employing OCH models with over 50 global citations were pulled from Bibliometrix package are shown in Descriptive statistics on the corpus generated by the The most common chemical modeled was PFOA, which was included in 69 of the 92 analyses, followed by PFOS being included in 60 analyses. Perfluorohexanesulfonic acid  was reported in 22 manuscripts and perfluorononanoic acid ) was present in 15. Only 4 of the 92 papers did not include analysis of PFOA, PFOS, PFNA, or PFHxS a. Most pIn the included corpus, 55 of the 92 manuscripts recorded use of PFAS TK models for humans and 43 used models for animal studies. A breakdown of TK model types by organism and common PFAS chemicals is presented in The co-citation network in Measures of the network centrality of the 20 publications depicted in None of the yellow or red node publications depicted in The chronology and relationships between publications containing one-compartment models for humans are captured in the historiography depicted in −1 for PFOA and 0.0003 day−1 for PFOS, and values of 170 and 230 mL/kg-bw for the volume of distribution, respectively. We note that a one-compartment TK model is fully specified with biological half-life (DV. Note that the elimination rate constant (ke) is inversely related to the biological half-life, ln(2)/ek. The isolated cluster at the top of the plot is a collection of papers on the C8 Health project, an epidemiological study with 69,030 individuals enrolled over a 13-month period from 2005–2006 [With an LCS-to-GCS ratio of 25%, Thompson et al.  was the 005–2006 . We iden005–2006 , which wAdditional evaluation of influential publications of OCH PFAS TK was conducted on a subset of models with greater than 50 global citations . These pDerived from the manuscripts in mbl/VDG, where mblG is rate of blood serum loss by menstruation and calculated at 6.1 mL/kg-bw/year for women age 12.5 to 50 [DV of PFOS (230 mL/kg-bw). Thus, units of mbl/VDG (year−1) are the same as the elimination rate constant, ek (year−1).The one compartment models presented in the manuscripts listed in .5 to 50 . The mod.5 to 50  for VD oWe applied an evidence-mapping approach to characterize the use of TK models of PFAS, with emphasis on simple one compartment models. In our approach to evidence-mapping we applied methods to contextualize the papers retrieved from the scheme in As the bibliometric analysis was largely restricted to the corpus of 92 papers, inclusion criteria of PFAS TK models within the collected body impacted the results. The WOS searches and SWIFT review identified a set of potentially relevant papers, shown as the circle in Bibliometrix is identification of manuscripts that were missed in the initial search, as 2 of the 14 papers identified outside of the corpus implemented TK models. This ‘second screen’ approach may enhance repositories of papers in future searches. In addition, some models leveraged for TK of PFAS may have originated from other sources not targeted in the search strategy used here. For example, the ‘Ritter Model’  is citedDe Silva et al.  found thAdapted from East et al. , Figure However, modeling approaches and subsequent risk assessment may vary across PFAS given variation in half-lives. A short-chain PFAS, PFBS, has a published half-life as low as one month . In addiIn citations per year , Gomis eThis scoping identified several “full” PBTK structures ,108,109 This scoping assessment identified influential models, generic one compartment model structures, and the breadth of existing publications which apply TK models for PFAS. In addition, a collection of 13 papers which have informed the community of research were identified. Models were classified as one-compartment, two-compartment, PBTK, human or animal, and steady state or dynamic for all 92 papers in the corpus. The complete corpus of papers is available in the Characterization of outside influence on model selection for each study is nearly impossible. However, dynamics of TK models for PFAS have been evolving since modeling began. As such, conventional models have been frequently sourced from several authors for toxicokinetics. As modeling challenges increase in complexity, including accounting for PFAS-specific toxicokinetics (long-chain and short-chain), and variations in human populations , a commensurate effort is required in model selection and implementation. Within the corpus, one compartment models are the most common, and feature heavily among the most referenced articles. PFOA and PFOS feature most frequently in TK models. However, calls and efforts to transition to more complex models are growing. This scoping assessment has identified influential papers and has detected trends in the literature which arc towards more granular TK modeling approaches.


The results of a pilot study in Ukraine are compared to other studies . Approbation of the method on DNA samples of children with ataxia-telangiectasia and Nijmegen syndrome showed a high sensitivity of TRECs  for the detection of these diseases. Thus, the tested method has shown its effectiveness for the detection of T- and B-lymphopenia and can be used for implementation of newborn screening for SCID in Ukraine.Severe combined immunodeficiency (SCID) is a group of inborn errors of immunity (IEI) characterized by severe T- and/or B-lymphopenia. At birth, there are usually no clinical signs of the disease, but in the first year of life, often in the first months the disease manifests with severe infections. Timely diagnosis and treatment play a crucial role in patient survival. In Ukraine, the expansion of hemostatic stem cell transplantation and the development of a registry of bone marrow donors in the last few years have created opportunities for early correction of IEI and improving the quality and life expectancy of children with SCID. For the first time in Ukraine, we initiated a pilot study on newborn screening for severe combined immunodeficiency and T-cell lymphopenia by determining T cell receptor excision circles (TRECs) and kappa-deleting recombination excision circles (KRECs). The analysis of TREC and KREC was performed by real-time polymerase chain reaction (RT-PCR) followed by analysis of melting curves in neonatal dry blood spots (DBS). The DBS samples were collected between May 2020 and January 2022. In total, 10,350 newborns were screened. Sixty-five blood DNA samples were used for control: 25 from patients with ataxia-telangiectasia, 37 - from patients with Nijmegen breakage syndrome, 1 – with X-linked agammaglobulinemia, 2 – with SCID (JAK3 deficiency and DCLRE1C deficiency). Retest from the first DBS was provided in 5.8% of patients. New sample test was needed in 73 (0.7%) of newborns. Referral to confirm or rule out the diagnosis was used in 3 cases, including one urgent abnormal value. CID (T Severe Combined Immunodeficiency (SCID) is a genetically heterogeneous group of diseases that are accompanied by impaired T- and B-cell immune responses, leading to a combined dysregulation of cellular and humoral immunity . PatientSCID affects approximately 1: 50,000-1: 100,000 newborns and only up to a third of the newly diagnosed cases have a family history . CertainSince SCID is a rare disease which is accompanied by life-threatening health problems, is not determined by standard clinical examinations, and has cutarive treatments, the effectiveness of which depends on the time of diagnosis, the need and appropriateness of screening has become evident , 9.Neonatal screening, which was first introduced in the 1960s, now covers dozens of diseases and has reached great progress in Europe and worldwide in recent years . In 2008Of methods proposed for screening diagnosis of SCID, a DNA based technique has received prominence . It is aThe TREC assay can only detect T-lymphopenia. However, there is another group of severe IEI associated with B-lymphocyte deficiency, including X-linked agammaglobulinemia  and autosomal recessive hypogammaglobulinemia, which also lead to life-threatening conditions. In 2007, a method was developed based on the detection of kappa-deleting recombination excision circles (KRECs), DNA fragments formed during the maturation of B-cells in the bone marrow, using polimerase chain reaction (PCR) . CombiniHowever, as the results of screening programs shown, low levels of TRECs/KRECs are detected not only in SCID, but also in other immunodeficiencies and diseases that are characterized by low levels of T- and/or B-cells –25.Newborn screening for severe T- and/or B-cell deficiencies is an important tool for the timely diagnosis of IEI , 27. DiaIn 2020, we initiated the project “A pilot study on newborn screening for primary immunodeficiencies using TREC/KREC assay to identify T- and B-lymphopenia”, which received support from the Ministry of Health of Ukraine. The study was performed according to the 1975 Declaration of Helsinki (as revised in 2000), and approved by the I.Horbachevsky Ternopil National Medical University Ethics Committee .In addition to the 4 diseases  of the newborn screening panel that are already being tested for in Ukraine, we included a screening for primary immunodeficiencies using TREC/KREC assay. The additional dried blood spot (DBS) for the SCID screening was collected between May 2020 and January 2022 . The pilot study covered the Ternopil region in western Ukraine. A total of 15 maternity hospitals in the region were involved in the study, including two maternity hospitals in the region capital, which had the largest numbers of births (5591 DBS). In addition, patients from the neonatal intensive care units of the neighboring Ivano-Frankivsk and Lviv regions were included the study. As of 01.01.2021, the population of Ternopil region was 1,030,600 inhabitants.The TREC/KREC determination was performed at the Scientific Medical Genetic Center LeoGENE, LTD, Lviv, Ukraine. The other project partners were the Institute of Hereditary Pathology of the Ukrainian National Academy of Medical Sciences, Lviv, Ukraine; Western-Ukrainian Specialized Children’s Medical Center, Lviv, Ukraine; and Ivano-Frankivsk Regional Children’s Hospital, Ivano-Frankivsk, Ukraine.We organized several information meetings for pediatricians, neonatologists, nurses, during which we provided information about the screening, its purpose and features. Neonatologists and nurses informed parents about the project and parental consent was obtained for each newborn.Heel prick blood spot tests were performed mainly on the third day post-partum as part of the national NBS program. Blood samples were blotted on a blank disc of filter paper, which carries a unique number and information about the newborn. Blood spots were dried at a room temperature for at least 3 hours, protected from direct sunlight and stored at a temperature of +2 to +8°C.Data on all DBS were entered into a computer database shared with the center where the analysis was performed. The database included key characteristics of the newborns, such as birth date, date of sample collection, sex, birth weight (BW), and gestational age (GA). According to the WHO definition, newborns with GA ≥38 weeks were defined as born at term; GA ≥32–37 weeks – as moderate preterm, GA ≥28–32 weeks - very preterm; <28 weeks - extremely preterm .The analysis of TREC and KREC was performed by real-time polymerase chain reaction (RT-PCR) followed by analysis of melting curves in neonatal DBS. Due to the disruptions caused by the COVID-19 pandemic, it was not feasible to obtain commercial kits for screening. This compelled the Scientific Medical Genetic Center LeoGENE to adapt their methodology and develop a proprietary testing approach.At the first stage of the pilot study, when logistical issues were agreed upon and the methodology was worked out, the time between the blood sampling and obtaining the newborn screening results was 3-5 weeks. At the second stage, we managed to reduce this time to 10 days.Isolation and purification of DNA from DBS on filter paper was carried out using a kit for the isolation of nucleic acids DNA-SorbB . The DNA sample was dissolved in 75 μl of TE buffer and the concentration and optical characteristics were determined using the DENOVIX instrument.The quantity of TREC and KREC molecules was analyzed by RT-PCR. Primer sequences used to amplify the sequences of TREC were , KREC  and the albumin gene as an internal control  according to the protocol . RT-PCR Plasmids with a known number of TREC and KREC copies were used as standards in RT-PCR. Deionized water was used as a no template control. DNA sample of patient with NBS and low number of TRECs and KRECs was used as a positive control. Real-time PCR was performed with the following parameters: polymerase activation , initial denaturation  and 50 cycles of following: denaturation , annealing and extension . The RT-PCR was carried out on CFX96 Touch Real-Time PCR Detection System, Bio-Rad, USA. The Ct of positive control is 3-5 cycles above the majority of DNA samples we analyzed (Ct = 30 - 33). The RT-PCR amplification curves of DNA samples with low TRECs and/or KRECs were similar to NTC curves.Due to the low copy number of TRECs and KRECs, compared to nuclear DNA genes, an additional analysis of PCR products by the melting method was performed to increase the specificity of the method. Analysis of melting curves was performed at a temperature from 50°C to 90°C with a step of 0.5°C . This technique is based on the dependence between the melting temperature of DNA fragments to their primary structure. This additional step was implemented to differentiate negative results from unspecific PCR product.We used as a comparative method RT-PCR with fluorescent probes for three targets: albumin gene, TREC and KREC. RT-PCR reaction was prepared for three different primer pairs and contained 2.5 µl of dd water, 5µl of 2xMaxima Probe qPCR Master Mix (no ROX) , 0.5 µl of primer-probe mix , 1 µl of DNA samples. This method was used as an additional check of the tested patients with low levels of TRECs and/or KRECs. DNA sample with NBS was used as a positive control, DNA samples with normal level of TRECs/KRECs were used as a negative control, deionized water was used as a no template control. The results of 2 methods were quite similar.6 cells was calculated using the following formula , one sample from a patient with XLA and 2 samples from the patients with SCID (JAK3 deficiency and DCLRE1C (Artemis) deficiency).In cases where new sample test was needed, we collected additional information about the mother’s history and treatment during pregnancy, the newborn’s clinical condition, medicines taken , and laboratoty tests results.–Whitney test and the Kruskal–Wallis test. Qualitative variables are shown as absolute frequencies and percentages. Quantitative variables were tested using Kolmogorov–Smirnov test or Shapiro–Wilk test for normal distribution and are expressed as median and interquartile range (IQR), when appropriate. For quantitative variables the Mann–Whitney test were performed. P-values of <0.05 were considered as statistically significant.Statistical analysis were performed using STATISTICA 10. To compare continuous variables between the groups we used the Mannnormal value. Concordantly, TRECs below cut-off and without DNA ampflification failure were designated as an abnormal value. In the latter category, we distinguish an urgent abnormal value when TRECs were absent or very low (<100 copies per 106 cells). In the case of DNA amplification failure the test result was considered as incomplete. DNA amplification failure was defined when albumin gene amplification curve was the same as NTC or in the case of PCR inhibition . Usually, number of amplification cycles for albumin was in range of 24-28 in all samples. Repeated RT-PCR-assay from the same newborn screening card was defined as a retest. If a new sample was requested for TREC analysis, it was defined as a new sample test. In the case of abnormal TRECs value after new sample testing, the newborn was recall for exmination to confirm or to rule out the diagnosis. We define this event as a referral. The same was applied to the evaluation of thr results of the KRECs analysis.To avoid misunderstanding in terminology, we used published recommendations for uniform definitions in newborn screening for SCID . TREC coSCID newborn screening diagnostic decision algorithm is demonstrated in The main goal of this screening program was to identify newborns with SCID (defined as CD3 below 300 cells/µL). The second target was to identify newborns with non-SCID T- lympopenia and B-lympopenia, including combined immunodeficiency (CID), XLA, secondary immunodeficiencies, and other syndroms with T-cell impairment , 34, 35.False-positive results were defined as abnormal TRECs and/or KRECs value in absence of SCID, XLA or other explanation of T- and/or B-lymphopenia.We analyzed 10,350 blood samples. The baseline characteristics of screened newborns are presented in The Mann–Whitney test did not reveal significant differences between the median values of TREC levels in groups of children with different GA (p>0.05), whereas the median value of KRECs in extremely preterm newborn was significantly lower than in other groups with different GA . Meanwhile, the levels of both TRECs and KRECs was significantly lower in newborns weighing less than 1,000 grams compared to those weighing 1,500-2,499 grams (p=0.04). The level of KRECs was also significantly lower in the neonates weighing less than 1,000 grams compared to neonates weighing 1,000-1,499 grams and more than 2,500 grams .One of the challenges of a pilot screening program is determination of cut-off values . Since w6 cells for TREC and 5,000 copies per 106 cells for KREC was applied considering referral values proposed in the published protocol  , as wellficiency Table 2.lowB+NK+). Unfortunately, the patient died at the age of 2 months due to COVID-19, complicated by pneumonia, venous thrombosis and progressive multiple organ failure. The amount of available biological material did not allow a further in-depth genetic research to determine the presence of specific mutations.Characteristics of the patients with the urgent abnormal value and abnormal value in a new sample test results are presented in low). The average age of patients with AT at the time of examination was 8.45 ± 2.74 years, ranging from 3 to 14 years. The average age of patients with NBS was 4.5 years, ranging from 1 month to 13 years. TREC levels in all patients with AT and NBS were below 5,000 copies per 106 cells, whereas KRECs levels were below 10,000 copies per 106 cells in patients with AT  deficiency. Albumin level was normal in all controls included to the study.To assess the quality and sensitivity of TREC/KREC assay, we performed it on blood samples of the patients with confirmed immunodeficiencies. The control group consisted of 25 patients with AT, 37 patients with NBS, 1 patient with XLA and 2 patients with SCID, i.e. one with JAK3 deficiency (T-B+) and one with DCLRE1C (Artemis) deficiency  and commercial kits such as EnLite™ TREC kit for newborns from PerkinElmer (USA) and SCREEN newborn screening kit ID ImmunoIVD (Sweden) Table 5.6 cells in order not to miss cases of other immunodeficiency conditions that present with T- and B-lymphopenia. At this stage, we detected one case of CID with urgent ubnormal value of TRECs (twise 0) that was confirmed by immunological examination (TlowB+NK+). However, with this cut-off value, the retest proportion was high (7.6%) in comparison to other studies , covering only one region of Ukraine, which did not make it possible to detect a larger number of SCID and establish the prevalence of the disease, since its average frequency is 1 per 50-100 thousand newborns 0, coveri. The TREThe recognition of patient with CID in this pilot study with involving of 10,350 newborns and the results of 65 tested patients of control group with confirmed inborn errors of immunity has shown that the tested method for determination of TRECs and KRECs by RT-PCR followed by analysis of melting curves in neonatal dry blood spots is effective for the detection of severe T- and B-lymphopenia and can be used in newborn screening for SCID in Ukraine. Although determination of TREC in newborn screening for SCID has proven high sensitivity, it should be stressed that in order not to miss any other types of PID as agammaglobulinemia, Nijmegen breakage syndrome, AT or DiGeorge syndrome, as well as late-onset ADA SCID, newborn screening for inborn errors of immunity in the form of triple assay (including TREC and KREC) is optimal.The data analyzed in this study is subject to the following licenses/restrictions: The datasets of this study are available on request from the corresponding author. Requests to access these datasets should be directed to boyarchuk@tdmu.edu.ua.The study was approved by the I.Horbachevsky Ternopil National Medical University Ethics Committee . Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.OB and HM designed and concept of the manuscript. OB, NY, and TH designed the concept of the pilot study project. NY, OS, IC, TH, LV, and MK were responsible for sample collection and logistic. VK, IS, and HM did the laboratory work. NY provided statistical analysis. OB, NY, VK, and HM analyzed and interpreted the data. OB, NY, and VK prepared figures and tables. OB, OS, and LV collected the relevant information and references. OB, OS, LV, and HM wrote the manuscript with contribution from all co-authors. All authors read, critically reviewed and approved the final version.The study was financed by the Ministry of Health of Ukraine with funds from the state budget, project title “Pilot study on neonatal screening of primary immunodeficiencies by the method of TRECs and KRECs for the determination of T- and B-lymphopenias”, state registration number 0120U104282, implementation period - 2020-2022.We would like to thank doctors of the Western-Ukrainian Specialized Children’s Medical Center, Lviv, Ukraine: Larysa Kostyuchenko, Yaryna Romanyshyn and of the Ivano-Frankivsk Regional Children’s Hospital, Ivano-Frankivsk, Ukraine: Olha Fedynska, Monika Makyan for their help in the enrollment of the control group. We wish to express our gratitude to all neonatologists of Ternopil region for their help in sample collection. We appreciate for providing the training on TRECs and KRECs standards by Ekaterina Polyakova from the Belarusian Research Center for Pediatric Oncology, Hematology and Immunology, Minsk Region, Belarus; to Dr. Linda Gailite from Scientific Laboratory of Molecular Genetics, Riga Stradiņs University, Latvia for reagents supply on the last stage of the study.Authors VK, IS, and HM were employed by Scientific Medical Genetic Center LeoGENE, LTD, Lviv, Ukraine.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.


Tuberculosis (TB) is one of the major public health issues in every country. Alcohol consumption is one of the reasons associated with the severity of symptoms and death among TB patients. The impact of alcohol use on TB relapse outcomes is still debatable. This study aimed to conduct a systematic review and meta-analysis (SR/MA) to find the link between alcohol use and TB relapse outcomes.Data collection was performed from December 2021 to March 2022; and was obtained from electronic databases including CINAHL, PubMed, and Scopus. The researcher carefully searched and reviewed all the relevant research concerning drinking alcohol and relapse outcomes among TB patients. A set of inclusion and exclusion criteria was used to assess research publications. The methodological quality of eligible publications was assessed using the Newcastle–Ottawa Scale. Random meta-analysis was used to determine odds ratios (ORs) with a 95% confidence interval (CIs). The funnel plot, Begg's test, and Egger's test were employed to investigate publication bias.p < 0.001) and mortality . The funnel plot, Begg's test, and Egger's test all revealed that there was no indication of publication bias.There were a total of 2,113 studies found and reviewed, and eight publications were chosen for the analysis. It was found that among TB patients with a moderate appearance of heterogeneity, drinking alcohol increases the probability of relapse (OR = 3.64; 95% CI: 2.26–5.88, Relapses and mortality among tuberculosis patients are considerably increased by alcohol drinking. More research into the causality of this link between the degree of alcohol use and the underlying processes is required.PROSPERO [CRD 42022295865]. Tuberculosis (TB) is a major public health concern in every country . TB is aAt present, the global TB incidence rate is slightly declining, but the relapse rate has increased . BetweenMycobacterium tuberculosis, a bacillus, causes TB, which is disseminated when persons with TB exhale germs into the air . The illness mainly affects the lungs (pulmonary TB), but it can affect other organs as well . Due to Exogenous reinfections cause TB relapses, which contribute to the prevalence rate of up to 27%. Their significance stems from the possibility of the emergence of multidrug-resistant Mycobacterium TB strains . The relFactors affecting the development of TB depend on the infection concentration at the time of infection, nutrition, poverty, and disease or exposure to certain substances or drugs that suppress the immune system, that is, HIV infection; all these factors are to be considered. There are various recognized risk factors for TB relapse, including treatment methods, treatment length, treatment consistency, alcohol usage, smoking, increasing age, and HIV infection –8. The sThe effects of alcohol consumption on TB relapse have been studied, but the results have been inconsistent due to a variety of factors. It was not concluded that alcohol use was identified as a risk factor or correlated. Many studies suggest that alcohol has an effect on TB recurrence. Drinking alcohol has been assumed to be the cause of TB relapse. Alcohol reduces the activity of many types of white blood cells, which are the body's mechanisms used to rid the body of TB before spreading to the point of being sick; and white blood cells function less if we drink alcohol, such as macrophage, CD 4+ lymphocyte, and CD 8+ lymphocyte in T cells . People TB relapse affects all aspects of the health system, family, community, and nation. In current studies, it was found that many TB patients had alcohol consumption behaviors. This behavior may be a contributing factor to TB relapse. However, the correlations between TB relapse and alcohol consumption are unclear and have potential public-health implications. Therefore, the researcher is interested in applying systematic review and meta-analysis methods to analyze correlations between alcohol use and the relapse of TB. The research aims to determine the effect of alcohol consumption on tuberculosis relapse outcomes.The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement was used to conduct this systematic review and meta-analysis . This re*] AND [relapse OR reinfection OR recurrence OR retreatment OR failure *] with slight adjustments depending on the database. There was no requirement for a study design or a language constraint. To prevent missing any papers, further searches were conducted in the reference lists of included research.CINAHL, PubMed, and Scopus databases were extensively searched to find relevant papers. Whenever possible, MeSH  was employed. We looked through bibliographic databases for relevant literature. The following keywords were used in the keyword search strategy: [Tuberculosis] AND [alcohol consumption OR alcohol drinking This study included all relevant studies that presented clinical features and epidemiological information on drinking among TB patients. We applied the PICO approach to generate precise questions and searching terms which included, P: population (patient with Tuberculosis), I: intervention/exposure group , C: comparison group , and O: outcomes . We considered all publications of any kind . Exclusions included animal studies, reviews, comments, editorials, expert opinions, letters, conferences, meeting abstracts, case reports, case series, systematic reviews, and meta-analyses. Studies that did not contain impact estimates or had inadequate data to determine effect estimates were also excluded. Papers with a clear connection to the alcohol industry were excluded.The primary outcome was TB patients' illness recurrence. Death among TB patients was a secondary result. The medical records as diagnosed and described by physicians were included in the phrase “disease relapse.” Any of the following criteria were used to determine relapse :(I) Tuberculosis patients who are diagnosed and described by physicians.(II) Relapse case, defined as a patient who has previously been treated for TB and has relapsed due to the same TB infection that caused the original TB disease, or reinfection: due to infection with a new strain after successful TB therapy.(III) Death case, as patient with TB; patient death from Tuberculosis disease.Each title, abstract, and full-text publication was separately screened for potentially suitable research by two investigators (The first and the fifth). Disagreements were settled after consulting with a third investigator (The third). All of the data that had been retrieved was examined independently by two scientists (The first and the fifth). The same investigators extracted information from all possibly relevant publications. Setting, geography, design, sample size, participant characteristics (such as age and sex), details of intervention/exposure , details of outcomes , and the number of TB patients were all retrieved from each research. The Newcastle–Ottawa Scale was used to assess the quality of individual research (NOS) . The NOSQ-statistic was used to study heterogeneity. To indicate heterogeneity among trials for each analysis, an alpha value of 0.10 was selected. I2 (I square) values were used to depict the degree of heterogeneity. High, moderate, and low heterogeneity were indicated by I2 values of more than 75%, 25–75%, and <25%, respectively  and 95% confidence interval (CI) using the number of drinkers and non-drinkers and pre-specified outcomes (relapse and death). A random-effects model was used to integrate the pool effects. Cochran's ectively . When thectively –16. In pion bias .When studies without adjusted OR were not reported, sensitivity analysis was used to assess the robustness of our analysis. Subgroup analyses were undertaken based on age, current and non-drinking status, and study quality.A total of 2,113 items were found in all databases during the first search. Duplicates were detected in 144 of these, which were removed. The title and abstract were used to filter all articles. After reviewing the abstracts, 1,940 papers were ruled out since their findings were unrelated to our goal. A total of eight papers comprising 17,128 TB patients were included in the meta-analysis after the complete text was evaluated . The impI2 was high (73.2%), according to the difference in research method, setting, and age. The results showed that drinking alcohol significantly increased the risk of relapse  and also significantly increased the risk of death , drinking alcohol in men TB patients significantly increased the risk of death , and drinking alcohol in new TB patients significantly increased the risk of death   .p < 0.001). For relapse among drinkers, the OR for the random-effects model in the different age groups was 3.66 . The OR for the random-effects model in the MDR TB for death was 1.53 . While the death OR from the random-effects model in the stars ≥5 group  was 1.90   .p < 0.001). Men had a higher risk with an OR of 1.90  than those in women. In addition, new TB patients were associated with death with OR of 1.90  (p < 0.001)  . A sensi< 0.001) .A review of publication bias was carried out. The symmetric funnel plot demonstrated no apparent publication bias; and Begg's and Egger's tests found no significant differences in all age groups and outcomes , 2.p < 0.001), as well as the risk of mortality .To the best of our knowledge, this is the first study to find the relationship between drinking alcohol and relapse in TB patients. We discovered that consuming alcohol substantially increases the risk of relapse in TB patients  , 22, 23,Mechanisms of alcohol causing TB relapse can be explained as follows: alcohol reduces the activity of many types of white blood cells in the group of T cells, such as macrophages, CD4+ lymphocytes, and CD8+ lymphocytes. Drinkers have a poorer immune system than the general population. The efficiency of the major medicine used to treat TB, isoniazid, is reduced when alcohol is consumed . RelapseThe death of TB patients is a major reason why TB treatment initiatives have failed to reach their objectives. The use of alcohol was linked to a significantly increased risk of death . PreviouSelassie et al.  found a Tuberculosis type and severity of TB patient drinkers are two factors to be considered. The study discovered variables of AUD among patients with MDR-TB compared to their non-TB controls, as well as its connection with disease patterns and associated medical comorbidities. AUD was common in MDR-TB patients and was linked to several medical comorbidities as well as a worsening of the illness's severity . DrinkinThis study offers various advantages. First, we conducted a thorough search of major databases including , as is customary when doing a systematic review. Second, we used a broad search approach with no constraints on language or research design. Third, this meta-analysis follows the PRISMA checklist's recommended technique for systematic reviews and meta-analyses. Fourth, our investigation included up-to-date evidence and was carried out utilizing proper statistical procedures for analysis. Finally, the robustness analysis, which included sensitivity analysis and subgroup analysis, demonstrated that the results remained unaltered. The limitations of the study are as follows: First, we searched through four large databases, which may or may not have included all relevant research. Despite this, we discovered no indication of publication bias after using Begg's test, Egger's test, and a funnel plot. Second, this study did not include data on the amount of alcohol consumed, despite the fact that studies have shown that this is an effective method of drinking after being diagnosed.As a result, the findings should be regarded with care. However, the effects of drinking alcohol in our research were constant across trials, which might imply that the findings are highly generalizable to any situation. Second, the degree of alcohol use was an essential component that may have an impact on our findings. Nonetheless, our sensitivity analysis revealed that drinking alcohol had a detrimental impact on the outcomes.To answer issues concerning the likelihood of recurrence and TB patients, well-designed prospective cohort studies are required. There is a need for more data from drinking alcohol that is routinely documented and examined among TB patients. Some characteristics, such as alcohol consumption level, kind and degree of consumption, length of drinking, alcohol addiction, size, amount, and duration of drinking, determine the outcome and determine when to stop drinking. These have an influence on the development of the condition.Alcohol use has been identified as a risk factor for TB recurrence and mortality in TB patients. People who consume alcohol have a greater risk of relapse and mortality from TB than those who do not consume alcohol. As a result, initiatives to minimize alcohol use among TB patients should be conducted to reduce the detrimental impacts that may occur to people, families, and communities. The results of this study can be used to inform even clinical practitioners or the policy makers on the control of alcohol consumption in TB patients and the intervention to prevent the recurrence of TB.The original contributions presented in the study are included in the article/DW and SS: conceptualization. DW, JP, and AU: methodology, formal analysis, and investigation. DW and SP: writing—original draft preparation. JP, SS, and AU: writing—review and editing and supervision. SS: funding acquisition. SP, DW, JP, and AU: resources. All authors contributed to the article and approved the submitted version.For the research, authoring, and/or publishing of this article, the author(s) received a grant from the University of Phayao's School of Pharmaceutical Sciences' Unit of Excellence on Clinical Outcomes Research and Integration (UNICORN).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 association between miRNAs and AF showed an OR of 2.51 . Our meta-analysis demonstrated that circulating miRNAs are potential biomarkers of AF, as they exhibit stable expression post–sample collection. In addition to regulating cellular processes, such as proliferation, differentiation, development, and cell death, miRNAs were found to be linked to arrhythmia development.Atrial fibrillation (AF) is the most frequently occurring supraventricular arrhythmia. Although microRNAs (miRNAs) have been associated with AF pathogenesis, standard protocols for quantifying and selecting specific miRNAs for clinical use as biomarkers should be optimized. In this study, we evaluated the clinical application of miRNAs as biomarkers for the prognosis and diagnosis of AF. Literature searches were conducted on PubMed, Cochrane Library, and EMBASE. We included prospective or retrospective observational studies that had been published as of 14 February 2022; our main objective was to analyze the relationship between circulating miRNAs and AF. The data were extracted using the descriptors “Atrial fibrillation AND miRNA”, “Atrial fibrillation AND diagnostic AND miRNA”, and “Atrial fibrillation AND prognosis AND miRNA”. No filters were applied for period delimitation, type of publication, or language. Studies using samples isolated from blood plasma and TaqMan and RT-qPCR for detecting and quantifying miRNAs were selected, and those that used atrial tissue samples were excluded. We identified 272 articles and excluded 102 duplicated articles. Two authors independently read the titles and abstracts of 170 out of 272 articles and selected 56 potential articles, 6 of which were selected for final review. Our analysis revealed a significant association between AF and miR-4798 [OR = 1.90 (95% CI 1.45–2.47)], AF and miRNA-133a [2.77 (2.73–2.82)], AF and miRNA-150 [3.77 (1.50–9.46); I Atrial fibrillation (AF) is the most frequently occurring supraventricular arrhythmia and is associated with high morbidity and mortality rates, substantially increasing the risk of heart failure, stroke, and systemic thromboembolism . AF affeThe two main pathophysiological mechanisms involved in AF development are re-entry and ectopic activity . These rAF presents numerous associated risk factors, such as hypertension, cardiomyopathies, obstructive sleep apnea, valve dysfunction, obesity, and sexual dysfunction . In addimiRNAs are small noncoding RNAs encoded by nuclear DNA and transcribed by RNA polymerase II. Their primary function is to regulate post-transcriptional gene expression by binding to complementary target sequences within the messenger RNA (mRNA), inhibiting translation or degradation of the transcribed target . The bioF 21 miRNAs can mix with microparticles and are frequently bound to high-density proteins and lipoproteins, which protects them against RNase activity, thereby maintaining their stability in plasma and still allowing for high specificity and sensitivity . TherefoThe quantification processes of specific miRNAs are faced with several challenging factors such as dosage method, gender, age, and associated comorbidities. Therefore, it is essential to develop a standard protocol for the quantification and selection of specific miRNAs to facilitate their clinical application to determine the correlation between changes in specific miRNAs and disease conditions. We performed a meta-analysis of studies reporting the associations between miRNAs and AF pathogenesis in an effort to identify the most promising AF biomarkers.This meta-analysis followed the PRISMA 2020 protocol and is registered on the PROSPERO platform (Registry CRD42021260109).We included prospective or retrospective observational studies published before 14 February 2022, in which the main objective was to analyze the relationship between circulating miRNAs and AF. Studies were considered eligible for the present review if they met the following criterion: patients with AF and blood plasma were used as the study sample.Studies that did not include miRNA analysis; animal studies; reviews; pharmacological studies; studies with incomplete statistical data; studies that did not present control case analysis; studies with patients with comorbidities associated with AF such as neoplasms, depression, and stroke; and studies with patients with other cardiovascular involvements were excluded.The literature searches were performed on PubMed, the Cochrane Library, and EMBASE using the descriptors “Atrial fibrillation AND miRNA AND biomarkers”, “Atrial fibrillation AND diagnostic AND miRNA”, and “Atrial fibrillation AND prognosis AND miRNA”, and no filters were applied for period delimitation, type of publication, or language. Two of the contributing authors (LCF and LJVB) of the present study conducted the literature search and perused all the selected papers.The following information was extracted from the selected studies: (1) details of the publication (surname of the first author and year of publication), (2) study design and study period, (3) characteristics of the studied population , and (4) evaluation of the result (specific relationship of the proposed miRNA with AF).To obtain the results, the effect size test was applied to combine the odds ratio (OR) and hazard ratio (HR) effect estimates. The effect size test has the potential to combine central creditors and their variances. In the present study, both effect estimates were considered: OR, which assesses a possible association between dependent and independent variables; and HR, which assesses the risk of unaffected participants to present the event. The exponential transformation was considered in the analysis; thus, it was possible to estimate how many times the study participants presented chances or risks of developing the outcome. The random effect was considered according to the I² test developed by Higgins and Thompson , which sBy applying the proposed methodology of our study, we conducted the last search on 14 February 2022 on all three platforms, yielding 272 articles, of which 102 duplicates were excluded through Zotero 6.0. . Two authors independently read the remaining 170 articles. After reading the abstracts, the authors included the studies that met the inclusion criteria. Reviews and papers without case analyses and controls were excluded. Subsequently, two authors (L.J.V.B. and L.C.F.) read the abstracts of the selected articles and selected 56 articles on the basis of the inclusion criteria to be read in full by the mediator reviewer (A.d.S.M.J.). After complete reading, six articles were selected for this study. These studies were performed on patients with AF and control groups associated with specific plasma miRNAs as positive or negative predictors of AF. To eliminate selection bias, we included studies using samples isolated from blood plasma and those using a specific methodology for detecting and quantifying similar miRNAs, such as TaqMan and RT-qPCR, and excluded those using atrial tissue samples .In the study by Galenko et al. , which lIn the case group, 50% of the patients were male and the mean age was 63.3 years. In the control group, 44% were male and the mean age was 57.9 years. Multivariate logistic regression determined the association between miRNAs and the comparison groups, and a logistic regression model was constructed for each miRNA . The autp < 0.001). Additionally, compared to the control group, patients with persistent AF showed an increase in the expression of miRNA-21, which plays an essential role in regulating fibrotic remodeling.In a study by Liu et al. , the autp = 0.009] as a predictor of the presence of AF and concluded that a decrease in miR-21 levels correlated with the presence of AF.Sieweke et al. , throughIn a study by Xiao et al. , 123 patChen et al.  selectedGeurts et al.  selected2 = 70%], AF and miRNA-21 , AF and hsa-miRNA4443 [OR = 2.32 (2.20–2.44)], and AF and miR-20a-5p [OR = 3.67 (1.42–9.49)]. In general, the association between miRNAs and AF showed an OR of 2.51, 95% CI  . Cardiac arrhythmias are monitored by outpatient follow-up and ECG, which are often flawed in the early detection of arrhythmias, especially in asymptomatic patients, and may be associated with poor prognosis .miRNAs have shown significant advantages in their use as biomarkers because they show stable expression after blood sample collection. In addition to being implicated in regulating cellular processes, such as proliferation, differentiation, development, and cell death, they are linked to arrhythmia development processes of the cardiovascular system . StudiesAmong the articles selected in this review, that by Sieweke et al.  is the oGalenko et al.  showed tGalenko et al.  also shoThe study by Geurts et al.  was the The present meta-analysis was limited by the diversity of the methods of sample collection, quantification, qualification, and miRNA adjustment tools. Moreover, the scarcity of studies that elucidate the mechanism of action of specific miRNAs was a limiting factor in the discussion.Using circulating miRNAs as biomarkers is a noninvasive method with high sensitivity and specificity, as shown in our study, and is of great importance in clinical use for identifying AF. Despite the limitations regarding the lack of studies conducted on specific miRNAs and the diversity of methodologies of the studies already elaborated, miRNAs still have great relevance in the diagnosis and early detection of AF.