All living organisms produce antiviral peptides (AVPs) as a part of their first line of defense against invading viruses. The Deep-AVPiden model proposed in this paper and its corresponding web app, deployed at https://deep-avpiden.anvil.app, is an effort toward discovering novel AVPs in proteomes of living organisms. Apart from Deep-AVPiden, a computationally efficient model called Deep-AVPiden (DS) has also been developed using the same underlying network but with point-wise separable convolutions. The Deep-AVPiden and Deep-AVPiden (DS) models show an accuracy of 90% and 88%, respectively, and both have a precision of 90%. Also, the proposed models were statistically compared using the Student’s t-test. On comparing the proposed models with the state-of-the-art classifiers, it was found that they are much better than them. To test the proposed model, we identified some AVPs in the natural defense proteins of plants, mammals, and fishes and found them to have appreciable sequence similarity with some experimentally validated antimicrobial peptides. These AVPs can be chemically synthesized and tested for their antiviral activity.An alarming number of fatalities caused by the COVID-19 pandemic has forced the scientific community to accelerate the process of therapeutic drug discovery. In this regard, the collaboration between biomedical scientists and experts in artificial intelligence (AI) has led to a number of  Deep learning-based sequence modeling techniques such as recurrent neural networks (RNNs), long-short term memory (LSTM) networks, temporal convolutional networks (TCNs)9, etc., can be effectively used to develop robust models to classify and discover novel therapeutic peptides like AMPs, anti-cancer peptides11, etc., in proteomes of various life-forms. Note that sequence modeling is a technique that inputs and outputs sequential data, which can be in the form of text, audio, video, etc. For this purpose, RNN was developed as a deep learning architecture for capturing dependencies between the units of a given sequence to make predictions. However, it fails to capture long-range dependencies between these units due to the vanishing gradient problem. LSTMs were proposed as an improvement over RNNs in that they overcome this problem by using a gating mechanism  to remember the correlation among the units over a long range. However, LSTMs require more memory than RNNs to store partial results. Additionally, RNN and LSTM-based models work sequentially, so the units of a given sequence  cannot be processed in parallel. However, such shortcomings are not present in TCNs. The computations performed by this deep learning architecture can be easily distributed and parallelized on multi-core processing systems, and it also does not consume much memory.The discovery of novel antimicrobial drugs that kill or inhibit life-threatening pathogens is attracting much attention due to the incapacity and inefficiency of conventional antibiotics. However, it is pertinent that the new class of therapeutics must have high efficacy, broad-spectrum activity, and few or no side effects on human health. In this direction, medications can be developed using antimicrobial peptides (AMPs), which form an integral part of living organisms’ natural first line of defense. Nowadays, analyzing and modeling AMPs using machine/deep learning has caught momentum12. In contrast, some others interfere with different stages of the viral life cycle, such as viral gene expression, replication, etc. Interestingly, numerous AVPs are present in the proteomes of mammals, plants, fishes, and other living organisms. A family of AVPs called cyclotides found in plants prevents a wide array of human viruses such as human immunodeficiency virus (HIV)13, H1N114, and dengue15 from binding to the host’s cell membrane. The Cecropin-A derived from a moth acts against HIV by suppressing its genetic expression. Similarly, a family of antimicrobial peptides (AMPs) known as dermaseptins found in the frogs of the Phyllomedusa genus have shown virucidal potential against HIV-116. Speaking of AVPs derived from marine organisms, a class of peptides known as clavanins inhibits the virulence of herpes simplex virus (HSV), rotavirus, and adenovirus17.Several models have been built to classify antiviral peptides (AVPs) using these deep learning algorithms. Note that AVPs are a sub-class of AMPs that target the host against invading viruses by targeting them or the host cells to inhibit viral replication. Some AVPs are virucidal because they either inhibit the viral protein outside the host cell or compete for the link-site on the host’s cell membrane18, while DeepAVP19 uses both bidirectional-LSTM and CNN for the same. In20, the authors performed multi-label classification for predicting several functional activities exhibited by a peptide  using bi-LSTM, CNN, and support vector machine (SVM). Lastly, the authors of21 trained various machine/ deep learning architectures like the Transformers, CNNs, bi-LSTM, Random Forests (RFs), and Support Vector Machine (SVM) on a set of AVPs and found RF with Word2Vec representations to be the highest-performing model (iACVP) to predict anti-coronavirus peptides. Deep learning architectures like CNN fail to capture long-range dependencies between the units of an AVP, i.e., the amino acids (AAs). The bi-LSTM-based models evade this drawback but not in the case of very long sequences. Also, training and tuning a bi-LSTM model takes considerable time  and consumes a lot of memory, too22. To sum up, a significant issue deep neural networks face is the computationally expensive mode of training and operation. In other words, deep learning models consume a lot of computational resources while getting trained. They are large in size, making their training and deployment very difficult in resource-constrained environments.Some of the deep learning-based tools built to classify AVPs are as follows. The Deep-AVPpred model uses convolutional neural networks (CNNs) for the prediction and discovery of AVPs23 used several hand-engineered features derived from peptide sequences, i.e., motifs, amino acid composition, and some physicochemical properties, to classify AVPs. The AntiVPP 1.0 model uses the RF algorithm that uses compositional and physicochemical features to predict antiviral peptides. Pang et al.24 proposed the AVPiden model based on RFs to perform a two-stage classification. In the first stage, it categorizes peptides as AVPs and non-AVPs, and in the second stage, it predicts the potential of AVPs against eight kinds of viruses and six virus families. In25, the authors employed four machine learning algorithms, namely, SVMs, RFs, Instance-based classifier, and K-star, to perform AVP classification using physicochemical properties with amino-acid composition, the binary profile of residues, etc. The ENNAVIA model26 uses physicochemical and compositional features on a deep neural network architecture for classifying AVPs and non-AVPs. In yet another study27, authors used six machine learning algorithms for this purpose. The PreTP-Stack model is built using ten features and four machine learning algorithms28. Lastly, in the FIRM-AVP model29, Chowdhury et al. used three machine learning techniques for building an AVP classifier and found that the SVM-based model performs the best. The biggest drawback of using machine learning-based models is the added burden of crafting, collecting, and refining hand-engineered features that serve as the input. Also, the machine learning models lag behind their deep learning counterparts in terms of performance when the dataset is large. Another shortcoming of these studies is that most of them do not have dedicated web servers to help wet lab researchers discover and classify AVPs30.Apart from the deep learning algorithms, researchers have been using some quantifiable properties of peptides  with machine learning algorithms like SVM, random forests (RFs), etc., to build AVP classifiers. The authors of22 named Deep-AVPiden for the classification and discovery of AVPs. The TCNs are abundantly employed for sequence modeling since they are faster than bi-LSTM networks and can also easily capture long-range dependencies, unlike CNNs. The proposed model is trained to identify AVPs in proteins of various organisms like mammals, plants, amphibians, fishes, arthropods, etc. The model’s performance has been compared with the existing state-of-the-art classifiers like AVPIden, ENNAVIA, iAMP-CA2L, Meta-iAVP, PreTP-Stack, iACVP, and DeepAVP, and the results indicate that it performs better than them. Moreover, we also proposed an alternate model using depth-wise separable convolutions that drastically reduces the number of training parameters as compared to standard convolutions. The model that is trained using this technique is named Deep-AVPiden (DS). A web app has been built using both models and deployed at https://deep-avpiden.anvil.app/. Apart from classifying AVPs, this app can also discover AVPs in proteins. To illustrate the working of this app, we found some AVPs in several antiviral proteins found in mammals, plants, and fishes. This paper’s major contributions are enumerated as follows. A novel deep learning model based on TCNs called Deep-AVPiden has been proposed to distinguish between AVPs and non-AVPs.An alternate model called Deep-AVPiden (DS), which is a less compute and memory-intensive version of Deep-AVPiden, has also been proposed using point-wise separable TCNs. This model can be easily deployed on resource-constrained devices for discovering AVPs.https://deep-avpiden.anvil.app/ with the ability to discover AVPs in protein sequences to aid wet-lab researchers.A web app based on these models has been built and deployed at Using the app, 15 AVPs were identified in proteins of plants, mammals, and fishes and proposed for chemical synthesis and experimental validation.The proposed models have been compared with the state-of-the-art classifiers and shown to perform better than them.The proposed models were statistically analyzed and compared using the Student’s t-test.The rest of the paper is depicted through Fig. To overcome most of the aforementioned shortcomings, we propose a model based on TCNsIn this section, we describe the dataset and the sequence modeling technique that was used to build the Deep-AVPiden model.31, HIPdb32, the starPep database35, DRAMP36, and the SATPdb37. The non-AVPs were taken from the Swiss-Prot database38 and AVPdb. After collecting 10,500 AVPs and 9000 non-AVPs, data cleansing was performed. Peptides that were composed of non-standard amino acids  and containing less than five or more than fifty amino acids were removed. Then we used CD-HIT41 program separately on the AVPs and non-AVPs with a threshold of 0.9 for filtering out similar sequences from the AVPs and non-AVPs, respectively. To eliminate any bias in performance due to the imbalance in the number of instances belonging to each class, we randomly removed 699 non-AVPs. The final dataset consisted of 5414 peptides (comprising 2707 AVPs and 2707 non-AVPs) which were then sub-divided into training (70% of the data points), test (15% of the data points), and validation (15% of the data points) sets.The proposed models use peptides as data points which are basically alphabetical strings where each letter represents a standard amino acid. The AVPs were collected from various sources such as AVPdbThe data points, represented by alphabetical strings, were tokenized and converted into numerical strings using a one-to-one character-to-integer mapping. This was done to convert the input into a computer-understandable format. Since the dataset comprised numerical strings of varying lengths, to bring uniformity, the strings with lengths in the interval  were padded with extra zeroes until their lengths became equal to 50. This resulting set of numerical strings was trifurcated into training, validation, and test sets. Then, the training set was used to generate a word embedding matrix (thoroughly described in section “Word embedding techniques convert each word (the numbers representing amino acid residues) into a fixed-length vector. One-hot encoding (OHE), and word2vec are the most common methods used for this purpose. There are two popular word2vec algorithms: the skip-gram and continuous bag of words (CBoW), which convert each word (represented by a one-hot vector) into a fixed-length feature vector using its context (which refers to the words surrounding a given word in the data points of the training set). Thus, semantically similar words are given similar feature vector representations.d), which increases the receptive field .Temporal Convolutional Networks (TCNs) consist of one or more blocks of one-dimensional convolutional (1D-CONV) layers. In these layers, the filter taps may be applied on the input units or time steps in a non-consecutive manner. In other words, the dilated convolutions are used, in which case it is not necessary that in a given 1D-CONV layer, the filter taps must be applied on consecutive time steps. This is controlled by the size of the dilation factor (t-1) to calculate the output at a time step t, whereas in acausal TCNs, the past and future units are used for this purpose. In this work, causal TCNs have been used to build the models. The convolution operation (C(t)) at position t in a dilated causal 1D-CONV layer with a dilation factor of d is given by Eq.  is as per the given equation.F(x) is the output of the last layer of the TCN block, and activation is a non-linear function . Skip connections allow the residual block to learn an identity function of the input, which may help stabilize the learning process in deep neural networks.Temporal convolutional networks are of two types: acausal and causal. In causal TCNs, a CONV layer uses only the past time steps 1 to  to t-1 t43 and later successfully used in45 to train deep ConvNets. It factorizes a standard convolution operation into two parts, i.e., depth-wise and point-wise convolutions, described as follows. Depth-wise Convolutions: In this stage, a single filter is applied to every input channel separately. So, if in a standard convolution operation, we had to apply N filters of size 1 X M X Point-wise Convolutions: A 1 X 1 convolutional layer comprising N filters of size 1 X 1 X N.This kind of factorization reduces the number of training parameters used in the network, which leads to a considerable reduction in the number of computations while the model training phase. The resultant model gets trained in less time, consumes less space, and can be efficiently trained and deployed on resource-constrained platforms, including mobile devices.The concept of depth-wise separable convolutions (DwSCs) came into existence due to the rising interest of the research community in building small and efficient models. Before the conception of this idea, either the pre-trained models were compressed or the underlying networks were made shallow. Thus, as an alternative, DwSCs were introduced inAs shown in Fig. Embedding layer: In this work, the skip-gram algorithm has been used to construct a word embedding matrix for 20 standard amino acid residues using the data points contained in our training set. This layer converts the numerical string into a  feature matrix .Spatial Dropout layer: This layer performs regularization by dropping columns (frames) from the feature matrix instead of individual elements. This layer is preferred over the normal dropout layer when the correlation between the frames is high. The Deep-AVPiden model employs a 1D spatial dropout layer after the embedding layer( with a dropout rate of 0.5).TCN blocks: This model uses dilated causal TCN architecture. It contains two parallel TCN blocks consisting of 1D-CONV layers with one skip connection. The difference between the two blocks lies in the size of the filters used in them. These layers employed rectified linear unit (ReLU) as the activation function. We used batch normalization layers in between layers to stabilize the learning process. As far as the dilation factor d is concerned, it increases in the consecutive layers by a factor of 2 .Global Average Pooling (GAP) layer: A 1D-GAP layer is used after each TCN block. It computes the average of the feature map obtained from the TCN blocks.Concatenation layer: It simply combines the output of the two GAP layers for further processing.Dense layer with dropout: A dense layer has been incorporated after the concatenation layer. It contains 64 units and uses ReLU as its activation function. After this layer, a dropout layer is incorporated to prevent overfitting.Output layer: This layer consists of a neuron using the sigmoid function for activation. The output of this neuron is a real number lying in the interval . A peptide is predicted as an AVP if the output is greater than or equal to 0.5.Apart from training the model using standard convolutions, depth-wise separable convolutions were used for building a more efficient model (in terms of computation and storage space). In other words, two models have been proposed in which one model comprises residual TCN networks that use standard convolutions. In contrast, the other was built by replacing the 1D-CONV layers in the TCN blocks with depth-wise and point-wise convolutional layers. These models have been compared and discussed in section “This section presents the details about the setup used to train the models, followed by their comparison with the state-of-the-art models based on the specified performance metrics. Also, a pilot study of the free web app has been done and elaborated using some representative protein sequences found in various organisms.46 as the backend and Keras-TCN library47. These models were compared using a test set with various state-of-the-art classifiers like DeepAVP19, AVPIden24, iAMP-CA2L20, ENNAVIA26, Meta-iAVP27, PreTP-Stack28, and iACVP21. Note that we have compared with only those models that have removed identical and homologous sequences from their dataset, which is important to prevent any bias in a model’s performance. Furthermore, ENNAVIA and AVPIden, only classify sequences with lengths lying in the interval  and , respectively. Also, iACVP classifies sequences having more than 5 AA residues. So, when these models were executed, the test set was curated as per their specifications. Also, after obtaining the results of iAMP-CA2L, it was observed that this model sometimes does not label the functional type of an AMP . So, to prevent any ambiguity, we removed such instances from the test set while giving the results for iAMP-CA2L.The proposed models were trained on a compute node having 2.4 GHz Intel-Xeon Skylake 6148 CPU processors with RAM of 192 GB and NVIDIA V100 graphical processing units with 16 GB RAM. We used Python for coding and certain libraries such as Keras with TensorflowThe models were compared on certain performance metrics like accuracy, precision, and the area under the receiver operating characteristic curve (AUC-ROC). All of these metrics can be expressed in terms of True Positives , False Positives , True Negatives , False Negatives . It is evident that the Deep-AVPiden model outperforms other models by a significant margin.While building the model, both causal and acausal TCNs were considered. However, there was not much difference in their performance, as mentioned in Table Apart from this, an insight into the pros and cons of both models is also required. As is clear from Fig.  in Eqs. –9. Tablehttps://deep-avpiden.anvil.app. Using it, some AVPs have been discovered in the antiviral proteins of various mammals, plants, and fishes. These antiviral proteins belong to different families, including ribosome-inactivating protein (RIP), RNA-binding protein (RBP), and Dicer-like protein (DCL), among others. The RIPs have enzymatic activities  that can damage ribosomes and interfere with protein translation. The RBPs, as the name suggests, bind to their target RNA and inhibit the translation and replication of RNA viruses. The proteins belonging to the DCL family perform RNA-silencing by cleaving double-stranded RNA (dsRNA) region of single-stranded RNAs (ssRNAs)48. The plant antiviral proteins belonging to these families, such as pokeweed antiviral protein (PAP), Phytolacca insularis antiviral protein (PIP), DCL4, Arabidopsis Pumilio-5 (APUM5), trichosanthin, etc., display antiviral activity against plant viruses such as Potato virus Y (PVY), Potato virus X (PVX), Cucumber mosaic virus (CMV), Potato leafroll virus (PLRV), Turnip mosaic virus (TuMV), etc.A freely accessible web app based on the Deep-AVPiden (and Deep-AVPiden (DS)) has been deployed at 51. Furthermore, Tripartite motif (TRIM), ubiquitin-like (Ubl), Peroxiredoxin-1 (PRDX1), and Mx proteins found in fishes use distinct mechanisms to inhibit entry, replication, and dissemination of HCV, HIV-1, etc.54. Some protein sequences mentioned in Table Model: Deep-AVPiden (DS)Probability Score: 0.90Minimum length of desired AVPs : 10Maximum length of desired AVPs : 30After discovering AVPs in these proteins, the BLAST tool available at http://www.camp3.bicnirrh.res.in/ncbiBlast/55 was employed to confirm whether the discovered AVPs had any sequence similarity with existing experimentally validated AMPs present in public databases. Then, the AVPs with sufficient similarity with the annotated AMPs were selected and mentioned in Table https://heliquest.ipmc.cnrs.fr/56. The length of the arrow in the alpha-helical representations is directly proportional to the hydrophobic moment. It was found in57 that if the hydrophobic moment is high, it denotes that the peptide has high penetration efficiency (it can easily kill/ inhibit its target). On a general note, it can be seen that the discovered AVPs have a high hydrophobic moment. It is very likely that these AVPs have good antiviral potential, and the same can be verified by chemically synthesizing them in laboratories. It can be observed that the length of the discovered AVPs are much smaller than their parent proteins. Hence, this tool efficiently identifies the core antiviral region of a given protein that is responsible for its antiviral activity. Moreover, we performed CD-HIT with a threshold of 0.9 on the AVPs found in each protein separately and tried to visualize their distribution with respect to the AVPs present in our training set. For this purpose, the isometric mapping technique has been used58. The 2D visualization of these data points is presented in Fig. The interferons (IFNs) are antiviral glycoproteins that can be classified (based on the structure of receptors on the cell surface) into three families: type I, II, and III IFNs. Proteins coded by the IFN-alpha genes have known antiviral activities, and they are actively used in the treatment of hepatitis C virus (HCV), hepatitis B virus (HBV), and human immunodeficiency virus-1 (HIV-1) infectionsIn this work, the Deep-AVPiden model has been proposed for identifying AVPs in several protein sequences to accelerate the task of antiviral drug discovery. It is a deep learning model based on TCNs that predicts whether a given peptide is antiviral or not. It takes peptides  as input, converts them into feature matrices, and outputs a probability score for them, which is used to interpret their antiviral potential. In addition to this, we used depth-wise separable convolutions to build another computationally and space-efficient model called Deep-AVPiden (DS), which can be deployed on resource-constrained devices. The Deep-AVPiden and Deep-AVPiden (DS) models have an accuracy of 90% and 89%, respectively, which is much better than the existing classifiers’ performance.https://deep-avpiden.anvil.app/ where users can enter different proteins and find AVPs with good antiviral potential. After choosing fifteen antiviral proteins found in various mammals, plants, and fishes, our app discovered and presented some AVPs which are purported to have a good antiviral potential . In the future, we would like to use other state-of-the-art sequence modeling techniques like transformers to build classification models. Also, designing a two-level multi-label classifier for AVPs can be considered. Such a classifier would predict whether a peptide is antiviral or not in the first stage and then classify it according to its target virus family in the second stage.Furthermore, a web app has been deployed at


Mycobacterium tuberculosis strains obtained from Kazakhstani patients with pre-extensively drug-resistant tuberculosis diagnosed in Kazakhstan. Methods: Whole-genome sequencing was performed on 10 pre-extensively drug-resistant M. tuberculosis strains from different regions of Kazakhstan. All strains had high-confidence resistance mutations according to the resistance grading system previously established by the World Health Organization. The genome analysis was performed using TB-Profiler, Mykrobe, CASTB, and ResFinder. Results: Valuable information for understanding the genetic diversity of tuberculosis in Kazakhstan can also be obtained from whole-genome sequencing. The results from the Phenotypic Drug Susceptibility Testing (DST) of bacterial strains were found to be consistent with the drug resistance information obtained from genomic data that characterized all isolates as pre-XDR. This information can help in developing targeted prevention and control strategies based on the local epidemiology of tuberculosis. Furthermore, the data obtained from whole-genome sequencing can help in tracing the transmission pathways of tuberculosis and facilitating early detection of outbreaks. Conclusions: The results from whole-genome sequencing of tuberculosis clinical samples in Kazakhstan provide important insights into the drug resistance patterns and genetic diversity of tuberculosis in the country. These results can contribute to the improvement of tuberculosis control and management programs in Kazakhstan.Background: Kazakhstan has a high burden of multidrug-resistant tuberculosis in the Central Asian region. This study aimed to perform genomic characterization of  According to the World Health Organization’s (WHO) data in 2021, tuberculosis (TB) affected approximately 10.6 million people worldwide, with 6.4 million new diagnoses. TB claimed 1.6 million lives in the same year, including 187,000 people with HIV. TB is currently the 13th leading cause of death and the second leading infectious killer after COVID-19, surpassing HIV/AIDS [In Kazakhstan, the Ministry of Health reports that between 2000 to 3000 people contract multidrug-resistant tuberculosis (MDR-TB) each year, with a 7–8% mortality rate. MDR-TB patients comprise 25% of all TB cases in the country, and the primary cause of drug resistance is the irregular intake of anti-tuberculosis drugs. Despite these challenges, the effectiveness of TB treatment in Kazakhstan was 79.5% at the end of 2018, one of the highest in the world. The number of MDR-TB patients has decreased by 30% over five years, from 10,455 in 2013 to 7282 in 2018. Over the last decade, TB incidence in Kazakhstan has declined by 8–10% annually, with a 24.6% decrease in 2020 compared to 2019. Effective diagnosis and treatment have saved over 53,000 people from TB over the last three years.M. tuberculosis strains that meet the MDR-TB definition and are resistant to rifampicin and any fluoroquinolone. XDR-TB is now defined as TB caused by M. tuberculosis strains that are resistant to isoniazid, rifampicin, any fluoroquinolone, and either bedaquiline or linezolid, or both. The emergence of pre-XDR-TB strains represents a critical stage in the progression toward XDR-TB strains. In this study, we utilized whole-genome sequencing (WGS) to predict pre-XDR drug resistance in a selected subset of M. tuberculosis isolates from Kazakhstan.In January 2021, the WHO introduced new definitions for extensively drug-resistant tuberculosis (XDR-TB) and pre-extensively drug-resistant tuberculosis (pre-XDR-TB). Pre-XDR-TB refers to Mtb was performed by the absolute concentration method on a solid LJ medium according to WHO recommendations [The pre-XDR-TB strains from hospitals and public health laboratories throughout Kazakhstan were sent to the National Scientific Center of Phthisiopulmonology (NSCP) of the Ministry of Health of the Republic of Kazakhstan for TB confirmation and evaluation of antimicrobial susceptibility. Drug Susceptibility Testing (DST) of ndations  and by undations  from visThe studies involving human participants were reviewed and approved by the ethical committee of the Center for Life Sciences, National Laboratory Astana, Nazarbayev University .Libraries for WGS were prepared from DNA samples with the Nextera XT kits and run on Illumina next-generation sequencing platforms (MiSeq) as instructed by the manufacturer . Base calling was performed with bcl2fastq2, version 2.20 software was used to demultiplex samples and generate paired-end fastq reads.https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) (1 March 2022). The generated raw sequencing data in FASTQ format were filtered with Trim Galore  (1 March 2022) using default values and a minimum Phred score of 20 to remove adapters and poor-quality bases. The sequence reads were aligned to reference strain M. tuberculosis H37Rv (NC_000962.3) and variants were called using Samtools mpileup v1.2 [Quality evaluation of paired-end reads was performed using FastQC v0.11.9 . CASTB Phylogenetic analysis was performed on ten clinical isolates of MTB and nine reference strains using the CSI Phylogeny tool with the FastTree method. The tree was constructed based on the identification of single-nucleotide polymorphisms (SNPs) from completely sequenced whole-genome sequences of these isolates and strains obtained from the NCBI .rpoB, rpoC), isoniazid , ethambutol , streptomycin (rpsL), pyrazinamide (pncA), ethionamide , fluoroquinolones (gyrA), amikacin (rrs), kanamycin (eis), para-aminosalicylic acid (folC), and other genes .Resistance-associated genes were analyzed to evaluate phenotypic resistance to rifampicin  with complex MTB infections were recruited in 2022. Among these patients, seven were diagnosed with infiltrative pulmonary TB with or without bacterial effusion, one with fibrous cavernous pulmonary TB, one with extrapulmonary tuberculosis , and the last patient with long-term sequelae of respiratory and unspecified tuberculosis . The diaThe sequence reads from 10 samples were successfully mapped to the H37Rv reference genome with an average read depth of 160X and mean genome coverage of 98.95% . The tabM. tuberculosis. The results of the NGS reading were compared to the reference strain of M. tuberculosis H37Rv, which is displayed in M. tuberculosis in the Kazakhstan strain was 4,357,379 base pairs, and the smallest was 4,288,471 base pairs.Mapping of the raw MTB sequence data led to high average genome-wide coverage across the clinical isolates . A total of 10 samples were sequenced, and all of them showed good-quality data. WGS was used to determine the genotype of the 10 samples of M. tuberculosis isolates showed drug resistance to first-line anti-TB basic drugs INH, RMP and to one of the tested second-line anti-TB drugs fluoroquinolones (FLQ) (Moxifloxacin or Levofloxacin). All isolates were identified as drug-susceptible for Linezolid (Lzd) and Bedaquiline (Bdq). Therefore, all ten isolates regarding the WHO’s new definitions were classified as pre-extensively drug-resistant tuberculosis (pre-XDR-TB). Nine clinical isolates showed drug resistance to first-line anti-TB drugs PZA, while other anti-TB drugs showed resistance to one or more of the M. tuberculosis clinical isolates. Additionally, several isolates are resistant to ethambutol (EMB) and streptomycin (SM). The second-line antibiotics levofloxacin (Lfx), moxifloxacin (Mfx), and prothionamide/ethionamide (Pro/Eto) show the highest level of resistance across the sample group. Some isolates are also resistant to kanamycin (Km), capreomycin (Cm), amikacin (Am), and clofazimine (Cfz) , isoniazid (INH), and pyrazinamide (PZA). All ten ne (Cfz) . The resrpsL and katG had a missense variant of Lys43Arg and Ser315Thr, respectively, making them the most commonly mutated genes associated with drug resistance  and is shown in M. tuberculosis were identified by WGS (katG (n = 10) and fabG1 (n = 1). All samples have missense variants in katG , rpsL (Lys43Arg), gyrA , mshA , gid (Glu92Asp) and upstream gene variant in rpoC (-339T > C), embA (-590C > T), rpsL (-165T > C), rrs (-187C > T), ald (-32T > C)  .The resistance profiles obtained from the phenotypic testing  and the In both approaches, all isolates were found to be resistant to first-line antibiotics, including RMP and INH, as indicated by the results obtained from the four databases . Moreover, the TB-Profiler database, known for its specialized TB genome analysis, identified resistance to pyrazinamide (PZA) for all 10 isolates. The phenotypic testing identified resistance to fluoroquinolones (Mfx or Lfx) in all isolates, whereas the genetic analysis showed resistance to FLQ in all isolates, specifically highlighting resistance to moxifloxacin (Mfx) in all samples. However, in contrast, the phenotypic analysis indicated that samples 304 and 1483 were found to be susceptible to levofloxacin (Lfx). This difference may be due to variations in the genetic markers used for detecting resistance. Phenotypic testing identified some isolates’ resistance to EMB, SM, Km, Cm, Am, and Cfz. Both the phenotypic and genomic DST methods provided a perfect match in detecting susceptibility to amikacin. The genetic analysis confirmed resistance to these antibiotics in some isolates but did not provide information for others. Phenotypic testing classified all isolates as drug-susceptible to Bdq and Lzd. However, the genetic analysis currently lacks specific information regarding resistance to Bdq and Lzd. Phenotypic testing revealed that samples 304, 711, 1483, and 1748 were resistant to ethionamide (Eto). In contrast, the genetic analysis showed that samples 232,1853 and 304 exhibited resistance to Eto by TB-Profiler database. However, samples 163, 260, 711, 1155, 1483, 1748, and 2325 were found to be susceptible to Eto based on the genetic analysis.M. tuberculosis.Overall, the phenotypic resistance profile obtained from the phenotypic testing provides more comprehensive information about resistance to specific antibiotics, including second-line drugs and additional antibiotics. However, the genetic analysis using TB-Profiler, Mykrobe, CASTB, and ResFinder provides insights into the genetic variants associated with drug resistance and can complement the results obtained from phenotypic testing. It is important to note that the differences between the two approaches could be due to variations in the methods used, the specific genetic markers analyzed, and the limitations of each approach. Both phenotypic testing and genetic analysis are valuable for understanding the resistance profiles of the isolates, and a combined approach can provide a more complete characterization of drug resistance in M. tuberculosis genomes. The study utilized the MTBseq tool to identify the lineage and outbreak clade of the ten pre-XDR-TB samples under investigation at the time.We obtained a list of SNPs in the All the isolates were classified as belonging to the Beijing genotype, which is known for its high virulence and association with drug resistance. Five isolates were classified as that belonged to the Central Asia Outbreak (CAO) clade, while three isolates were classified as belonging to the Central Asian sublineage, and two were classified as belonging to the European/Russian strain W148 Outbreak .The CAO clade is a distinct branch of the Beijing genotype that was known for its association with multidrug resistance and increased transmissibility. The Central Asian sublineage was a heterogeneous group, but its most virulent component was similarly named the CAO clade .Phylogenetic analysis was performed on ten clinical isolates of MTB and nine reference strains .M. tuberculosis H37Rv, CDC1551, and H37Ra reference strains.The resulting tree consists of several major clades, each representing a group of closely related strains. The largest clade, located towards the center of the tree, includes commonly studied strains such as M. tuberculosis strain RUS B0 are also closely related, indicating a possible transmission event.The tree reveals that strains from Kazakhstan and Russia form a monophyletic group that is genetically distinct from the other strains. Within this group, MTB-pXDR-KZ (1853) and MTB-pXDR-KZ (1748) are closely related, suggesting a recent common ancestor. Additionally, MTB-pXDR-KZ (304) and M. tuberculosis CDC1551, M. tuberculosis PanR1006, and several clinical isolates from different geographic regions, are more distantly related. Notably, M. tuberculosis BeijingNITR203 forms a separate branch, indicating it is genetically distinct from all other strains.The remaining strains, including M. tuberculosis K strain appears to be closely related to two MTB-pXDR-KZ samples , forming a clade with them, suggesting a possible genetic similarity or relationship between the M. tuberculosis K strain and these MTB-pXDR-KZ samples.The All of the examined MTB isolates in this study were found to belong to the Beijing lineage , which iM. tuberculosis. Other molecular assays identified a limited set of resistance mutations through the hybridization of probes to specific genetic sequences. The scientific work of many researchers using whole genomic sequencing of M. tuberculosis is oriented to studying the TB disease dynamics, the transmission of infection and treatment, the study of small TB outbreaks, and the study of the unique processes of evolutionary dynamics for the spread TB infection [Next-generation sequencing (NGS) nowadays gives us good opportunities for drug resistance detection, and various NGS platforms become more widely available for TB clinical services in many countries. NGS technologies can provide us with complete sequence information for different well-known genes associated with drug resistance for first/second anti-TB drug lines or whole-genomes of clinical isolates of nfection ,15,16.M. tuberculosis. For example, some research work conducted in Kazakhstan was carried out investigating the mutations in genes of M. tuberculosis responsible for drug resistance to various anti-TB drugs, especially for rifampicin and isoniazid [M. tuberculosis [M. tuberculosis genotypes (non-Beijing strains) from Kazakhstan in the early 2000s. In the article by Kozhamkulov et al., mutations associated with drug resistance to rifampicin and isoniazid among Kazakh isolates of M. tuberculosis were characterized. A study by Akhmetova et al. [pncA and rpsA genes in pyrazinamide-resistant and pyrazinamide-susceptible M. tuberculosis isolates in Kazakhstan, and 12MIRU-VNTR typing was performed to assess the potential use of genotyping in the determination of pyrazinamide resistance. In the work of Ibrayeva et al. [M. tuberculosis isolates distributed in the penitentiary system of Kazakhstan and the civil sector was studied. In the works of Kubica et al. [M. tuberculosis collected in Kazakhstan in 2001 and 2008 was carried out by spoligotyping and IS6110 RFLP methods and using 24 MIRU-VNTR methods and spoligotyping, respectively. Klotoe et al. [M. tuberculosis using high-throughput hybridization methods, TB-SPRINT and TB-SNPID typing. According to genotyping data, the Beijing genotype was the predominant genotype in all Kazakhstani studies [Previous studies in Kazakhstan were reported about the mutations associated with drug resistance in genes of soniazid ,18,19, mrculosis ,21,22,23rculosis ,25,26,27rculosis  in theira et al.  and SkibM. tuberculosis isolated in Kazakhstan performed by using a next-generation sequencing platform [Mycobacterium tuberculosis induced by a Nanomolecular Iodine-Containing Complex FS-1 [Only some publications relate to preliminary data and reported several genome sequences of DR  clinical isolates of platform ,27,28,29platform  and one platform ,27 were lex FS-1 . The genM. tuberculosis isolates from Kazakhstan, demonstrating its utility in managing and controlling tuberculosis in the country. This study illustrated the potential for the identification of drug resistance by using four available analyzing programs  as a method for rapidly diagnosing drug-resistant.Therefore, we conducted the first WGS study in the country that reports the application of WGS to clinical pre-XDR-TB M. tuberculosis clinical isolates and found that the concordance rate of the phenotypic DST and WGS DR data were similar for isoniazid, rifampicin, amikacin, fluoroquinolones  among all 10 isolates. It should be noted similarities of both methods for the identification of MDR, pre-XDR clinical isolates in our study. Resistance to other drugs varied among the isolates, with some showing complete resistance and others showing partial resistance, emphasizing the importance of performing Drug Susceptibility Testing and additional genomic analysis. Some mismatches between phenotypic DST and WGS could be explained by the involvement of other genes in the development of any drug resistance, lack of some mutations in databases, false results of phenotypic DST, laboratory cross-contaminations, etc.In this study, we investigated the drug resistance patterns of Importantly, para-aminosalicylic acid exhibited efficacy against all isolates except one, suggesting that it could be a promising treatment option. Our results emphasize the urgent need for developing new drugs to combat drug-resistant tuberculosis and highlight the importance of continued monitoring of drug-resistance patterns in clinical isolates. The NGS is a good instrument for finding rare and novel mutations and possible new target genes. These findings are significant as they provide insight into the distribution of MTB genotypes in Kazakhstan and suggest that the Central Asia outbreak clade may be responsible for the high prevalence of pre-XDR-TB and XDR-TB in the region. Furthermore, the identification of specific MTB genotypes can inform the development of effective treatment and control strategies to combat the spread of drug-resistant TB in Kazakhstan and beyond.M. tuberculosis, sequencing-based diagnostics for TB drug resistance present an attractive option for the surveillance of TB drug resistance in various settings [M. tuberculosis on drug resistance TB diagnostics.The WHO emphasizes the ability of whole-genome sequencing technologies to generate a complete picture of the drug resistance profile of a clinical sample and the flexibility of sequencing applications to accommodate a growing knowledge base surrounding the association between specific mutations and phenotypic drug resistance settings . The res


Young breast cancer survivors with body image disturbance have poor quality of life. Self-compassion and different coping styles can influence their body image. The purpose of the study was to investigate the relationship between self-compassion, coping styles, and body image disturbance, and examined the mediation role of coping styles between self-compassion and body image disturbance among young breast cancer survivors in China.In the cross-sectional study, a total of 310 young women with breast cancer were assessed on self-compassion, coping styles, and body image disturbance by self-reported questionnaires in China. Spearman’s correlation was used to test the links between variables and to verify the indirect effects between variables by constructing a structural equation model.There were correlations between self-compassion, different coping styles, and body image disturbance. Confrontation, avoidance, and acceptance-resignation coping had significant mediation effects on the association between self-compassion and body image disturbance. The mediation effects of confrontation coping were greater than avoidance, and acceptance-resignation coping.In this study, different coping styles acted as mediators between self-compassion and body image disturbance, providing support for further understanding the mechanism between self-compassion and body image disturbance, and developing comprehensive interventions for body image disturbance. Oncology nurses should pay attention to the breast cancer survivors’ self-compassion and coping styles and encourage them to adopt adaptive coping strategies, which can reduce body image disturbance. The global incidence of breast cancer among younger adults is increasing annually , and morSelf-compassion is a valuable psychological resource for improving BID , 13. UsiStudies have shown that BID manifested as chronic stress can persist in BCS and affect their quality of life , 18. CopCoping is the process through which individuals attempt to manage the needs arising from stressful events . The maiAdaptive coping is negatively correlated with BID , 25. As Self-compassion is viewed as a key element for coping with stress . Self-coPressure-Cognitive Interaction Theory suggests that cognitive appraisal  can indirectly influence mental health outcomes through coping styles . A recenOverall, BID is a complex and multidimensional concept, and young BCS’ pursuit of beauty in appearance makes their BID more special. BID in young BCS are receiving increasing research attention. Although self-compassion has a positive effect on reducing BID, the potential mechanism is unclear . Coping The study aimed to investigate the relationship between self-compassion, coping styles, and BID, and assess the different mediating roles of coping styles in the connection between self-compassion and BID among young BCS in China. Furthermore, we proposed the following hypotheses: H1) In young BCS, the BID is negatively related to self-compassion; (H2) a greater degree of BID is related to less use of confrontation coping and more use of avoidance and acceptance-resignation coping; (H3) greater self-compassion is associated with more use of confrontation coping and less use of avoidance and acceptance-resignation coping; (H4) self-compassion has an indirect effect on BID, which is mediated by this study’s three discussed coping types  diagnosed with breast cancer first; (b) aged from18 to 50 years; (c) received conventional surgical treatment for breast cancer. Furthermore, participants who had co-morbidity with other cancers or were incapable of completing the survey were excluded.At least 200 cases are deemed necessary to successfully implement the structural equation model . MoreoveThe study obtained the approval from the college Ethics Committee. The researcher provided all participants with the primary study purpose and followed the principles of voluntariness and confidentiality throughout the study. All the participants completed the paper-and-pencil questionnaires after providing written informed consent.The general information questionnaire included two parts: demographic characteristics  and clinical characteristics .The Body Image Self-rating Questionnaire for Breast Cancer (BISQ-BC) was used to measure BID in Chinese BCS. Zhou et al. , 33 deveThe Self-Compassion Scale (SCS) developed by Neff  is used The Medical Coping Modes Questionnaire (MCMQ) assessed coping styles. Feifel et al.  develope2/df) was < 3, the comparative fit index (CFI) and the Tucker–Lewis index (TLI) were ≥ 0.95, the root-mean‐square error of approximation (RMSEA) was < 0.08, and the standardized root mean square residual (SRMR) was < 0.09 [Data analysis was performed using SPSS (version 21.0). The variables were described using percentage, mean, and standard deviation (SD). The direction and degree of the relationships among the variances were discussed using Spearman’s correlation analysis. To test the hypothesized mediation model, path analysis was conducted using Mplus 8.0. The Model fit was acceptable if the chi-square/degree of freedom  of the participants were disturbed by their body image (median score > 78).Table r = -.865, P < .01). Higher degrees of self-compassion were correlated with higher use of confrontation coping, and lower use of avoidance and acceptance-resignation coping . Additionally, confrontation coping was negatively correlated with BID in young BCS , and avoidance and acceptance-resignation coping were positively correlated with BID in young BCS . The study results thus supported our hypotheses H1, H2, and H3.Table 2/df = 3.437, CFI = 0.968, TFI = 0.958, NFI = 0.955, RMSEA = 0.089, SRMR = 0.026), as shown in Fig. P < .01), confrontation , avoidance , and acceptance-resignation coping . Confrontation coping directly negatively predicted BID , and avoidance , and acceptance-resignation coping  directly positively predicted BID.The fitting index of the mediation model was good (χP < 0.001), avoidance , and acceptance-resignation coping  (Table Moreover, 95% CI in the mediation model did not include 0, and all the indirect effects were significant. Self-compassion indirectly affected BID through confrontation (β = -0.248, The study results demonstrated that 50.8% of young BCS had poor body image. In this study only 8.1% of patients underwent breast-conserving surgery and 1.9% of patients underwent reconstructive surgery, and most patients suffered from breast asymmetry, which may be related to the fact that more than half of the participants had BID. Self-compassion, coping styles, and BID were correlated in participants. Additionally, confrontation, avoidance, and acceptance-resignation coping played a role of mediation in the association between self-compassion and BID. This study further identified the key role of coping styles between self-compassion and BID.Our correlation analysis found that the self-compassion of young BCS was directly and negatively associated with BID, which supported the hypothesis H1. This result confirms previous studies’ findings regarding the association of increasing self-compassion with less BID , 14, 39.This study confirmed that confrontation coping was positively associated with self-compassion, and negatively associated with BID, thus partially supporting hypotheses H2 and H3. Based on the correlation between variables we established a hypothetical model mediated by confrontation coping. The results demonstrated that confrontation coping as a mediator influences the relation between self-compassion and BID, which is consistent with hypotheses H4. The results provided a new basis for formulating comprehensive measures to improve the BID. Young BCS with higher levels of self-compassion are more likely to adopt confrontation coping, resulting in less BID. The mediating effect of confrontation coping was found to be the most significant, which suggested that self-compassion is primarily influenced BID by confronting coping. It is further suggested that confrontation coping can be used as a necessary intervention factor to improve BID in young BCS.Self-compassion can help BCS reappraise stressful events with a more balanced perspective and make them accepting of their own flaws and inadequacies under the influence of the common humanity style of thinking, thus further reducing the threat of stressors . Self-coThis result suggested that young BCS with a low level of self-compassion are more likely to use avoidance and acceptance-resignation coping; this could lead them to develop greater BID, confirming the part of hypotheses H2 and H3. Using the hypothesis model, this study verified the mediating role of avoidance and acceptance-resignation coping in self-compassion and BID, thus supporting the part of hypotheses H4.Under the same stress conditions, BCS with a lower level of self-compassion were likely to engage in excessive self-criticism and self-isolation, indulge in painful events, ignore or avoid the influence of stress events on themselves . Thus, tIn the mediation model of this study, nearly two-thirds of the association between self-compassion and BID was explained by coping styles, suggesting that coping styles are important in self-compassion measures to improve BID. Therefore, it is suggested that oncology nurses can develop and implement comprehensive interventions guided by the above pathways. Building self-compassionate for young BCS in coping with stressful events can promote adaptive coping, reduce maladaptive coping, and effectively improve BID.Although the findings are meaningful, there are several limitations to this study that should be further addressed. First, the cross-sectional design restricted our ability to validate causal relationships for variables. Moreover, the BID was affected by the individuals and their environment; this is a dynamic process, and individual coping styles could also change. Longitudinal research designs are necessary for clarifying the path relationship between self-compassion, coping styles, and BID in young BCS in a dynamic context. Second, the study was conducted in only one hospital, and most of the participants had been diagnosed one year earlier; therefore, the representativeness of the sample was limited. It is suggested that future researchers increase the diversity of their samples and conduct multi-center studies.This study validated the mediation effects of different coping styles between self-compassion and BID among young BCS in China, thus providing support for further understanding the mechanism between self-compassion and BID, and implementing comprehensive interventions for BID. We identified confrontation coping as the most significant mediation effect on self-compassion and BID in young BCS. Therefore, in order to improve the intervention effects of BID, oncology nurses should focus on the coping styles of young BCS and promote adaptive coping.


The misuse of animal-derived stimulants in food is becoming increasingly common, and mass spectrometry (MS) is used extensively for their detection and analysis. There is a growing demand for abused-substances detection, highlighting the need for systematic studies on the advantages of MS-based methods in detecting animal-derived stimulants.We reviewed the application of chromatography-mass spectrometry to the screening and detection of food stimulants of animal origin. Specifically, we analyzed four common animal sources of synthetic steroids, β-receptor agonists, zearalenol (ZAL), and glucocorticoids. We also explored the potential of using chromatography-mass spectrometry to detect and analyze animal-derived foods.We searched and screened the Web of Science and Google Scholar databases until April 2023. Our inclusion criteria included a publication year within the last 5 years, publication language of English, and the research fields of food analysis, environmental chemistry, and polymer science. Our keywords were “mass spectrometry,” “anabolic androgenic steroids,” “β-2agonists,” “glucocorticoids,” “zearalenone,” and “doping.”Although traditional techniques such as thin-layer chromatography and enzyme-linked immunoassays are simple, fast, and suitable for the initial screening of bulk products, they are limited by their relatively high detection limits. Among the methods based on MS, gas chromatography–mass spectrometry and liquid chromatography–tandem mass spectrometry are the most widely used for detecting food doping agents of animal origin. However, a sensitive method with high repeatability and a short analysis time for a large number of samples is still required. Advances in MS have enabled the detection of extremely low concentrations of these substances. Combining different techniques, such as high-resolution mass spectrometry, ultra-high performance liquid chromatography–tandem mass spectrometry, gas chromatography-combustion-isotope ratio mass spectrometry, ultra-high performance liquid chromatography-high resolution mass spectrometry, and two-dimensional chromatography, offers significant advantages for detecting trace illicit drugs in animal-derived foods. Due to advances in assay technology and sample preparation methods, sample collection and storage methods such as dried blood spots, dried urine spots, and volumetric absorptive microsampling are increasingly accepted because of their increased stability and cost-effectiveness.MS significantly improves the efficiency of detecting doping agents of animal origin. With the continuous development of MS technology, its application in the fields of doping detection and the analysis of doping agents of animal origin is expected to become more extensive. In an eTraditional doping-detection methods suffer from various issues, such as low accuracy, low efficiency, and the requirement for a large number of samples . The conThis study reviews the research progress in MS technology applied to food and summarizes the latest developments in the classification, contamination sources, and detection of several common food stimulants. The aim of this review is to raise athletes’ vigilance toward high-risk foods and avoid inadvertently violating anti-doping rules, while providing technical support for food-safety assurance in sports events and theoretical support for the further development of food-borne doping-detection technology.2.Chromatography-mass spectrometry is the most widely used and powerful analytical method in food detection and analysis . Althoug2.1.Triple-quadrupole mass spectrometry (TQMS) is currently the most widely used quantitative-analysis instrument in MS owing to its short analysis time, ability to analyze multiple components, and accurate quantification. However, it is also limited by poor qualitative capabilities in high-resolution and multistage tandem mass spectrometry. TQMS is a low-resolution mass spectrometry technique that includes gas chromatography triple quadrupole mass spectrometry (GC-QQQ-MS), liquid chromatography triple quadrupole mass spectrometry (LC-QQQ-MS), and liquid chromatography quadrupole linear ion trap mass spectrometry (LC-Qtrap-MS). GC-QQQ-MS is primarily suitable for analyzing highly polar and volatile compounds . In cont2.2.High-resolution mass spectrometry (HRMS) is a powerful technique for collecting full-scan data with high mass accuracy and resolution. It enables the screening and identification of known and unknown compounds with high selectivity and sensitivity . Current2.2.1.TOF-MS is an MS-based technique developed in the 1990s for substance analysis. The main principle of this technology is that ions with different mass-to-charge ratios, but the same kinetic energy, require different times to pass the same distance in a constant magnetic field . This te2.2.2.In 2000, Russian scientist Makarov introduced a novel mass spectrometer known as the “Orbitrap” or electrostatic-field Orbitrap mass spectrometer . The OrbCurrently, electrostatic field Orbitrap high-resolution mass spectrometry includes liquid chromatography-linear ion trap electrostatic-field Orbitrap high-resolution mass spectrometry and liquid chromatography-quadrupole electrostatic-field Orbitrap high-resolution mass spectrometry. During the screening process of electrostatic-field Orbitrap high-resolution mass spectrometry, the accurate mass number of the primary mass spectrometry is used for qualitative and quantitative analysis, and fragment ion secondary mass spectrometry is used for further confirmation. Electrostaticfield Orbitrap high-resolution mass spectrometry is used to screen pharmaceuticals, cosmetics, and environmental samples. Liquid chromatography-quadrupole/electrostatic-field Orbitrap high-resolution mass spectrometry has the advantages of high sensitivity, high selectivity, and high mass accuracy, and it is widely used in the fields of pharmaceutical analysis and environmental monitoring , 29. The2.2.3.MAG-MS is a highly sensitive and stable technique that can achieve resolutions of up to 100,000 pixels . Recent FT-ICR-MS offers ultra-high resolution and high mass accuracy, but is expensive, bulky, and complicated to operate. Currently, it is only used by a few research laboratories for macromolecular analysis, gas-phase ion reaction kinetics, and complex system analysis.Low- and high-resolution MS have their own characteristics for sample analysis. High-resolution MS has excellent qualitative advantages and can accurately determine the mass-to-charge ratio of the parent ions. However, when the mass-to-charge ratios of two target substances are similar, accurately characterizing them using LRMS is challenging. Nevertheless, LRMS is highly selective and can easily distinguish between target objects. However, high-resolution MS is easily affected by impurities, and the sample-purification requirements are relatively high. Q-TOF-MS and Orbitrap-MS have been developed for HRMS to detect various drugs. The combination of high-resolution MS and chromatographic technology may lead to the development of high-throughput and high-sensitivity analyses of veterinary-drug residues. TOF-MS can perform qualitative and quantitative analyses and is an effective method for detecting known targets in food samples and screening unknown compounds. The combination of Orbitrap-MS and UPLC is more conducive for the rapid and sensitive screening and detection of unknown compounds in samples. Its ultrahigh resolution ensures the high sensitivity and specificity required for the analysis of complex samples.3.The use of doping in animal husbandry and drug residues in animal-derived foods pose a significant risk to athletes who may consume these products and test positive for doping. The International Olympic Committee (IOC) prohibits doping, and the WADA updates its list of banned substances annually. However, criminals in the animal-husbandry industry continue to use various feed additives and hormonal compounds, such as anabolic steroids, β-receptor agonists, zearalanol, and glucocorticoids, to promote protein synthesis and increase muscle content, even though these substances are banned or restricted in many countries. Positive cases of doping tests mainly arise from drug residues or banned drugs used in animal breeding.Anabolic steroids, β-2 agonists, glucocorticoids, and zearalenone were the four most commonly detected animal-derived stimulant types . Detecti3.1.Anabolic androgenic steroids (AASs) are synthetic chemical substances that are structurally and biologically similar to the endogenous male hormone testosterone. AASs are the most common stimulants abused by athletes based on doping testing and are listed under category S1 of the WADA 2020 Prohibited List . During Over the past few decades, sports drug-monitoring laboratories have made significant progress in detecting AAS residues in various matrices , 35 Fig. MethodsUHPLC allows for higher flow rates and a better separation of compounds of similar or equal mass and retention time (such as the α- and β-isomers of testosterone) while reducing run times. Recent studies have demonstrated , 40 that+ or [M + H-2TMSOH]+ ions, which can be used as precursor ions for ion pairs in the selective ion-monitoring mode, thereby improving the selection of specific substances. Therefore, it can be explained that atmospheric pressure chemical ionization has the potential to replace traditional chemical-ionization sources in the detection of AASs. In addition, the accuracy and sensitivity of the detection instrument significantly improved. Compared with the triple quadrupole, the HRMS has a retrospective full-scan high-resolution data-acquisition mode, which can directly and easily add and verify new unknown compounds. Doping samples stored for 10 years can be used to re-evaluate previously obtained data. Urine was analyzed using gas chromatography-quadrupole-time-of-flight mass spectrometry (GC/Q-TOF) and gas chromatography-tandem quadrupole-electrostatic field orbital ion-trap mass spectrometry (GC/Q-Orbitrap), and the measurement of 46 AASs in human urine was compared using these two instrument types. The analytical performance of the two instruments complied with the WADA specifications, their mass accuracy was excellent, and their detection limits were considerably lower than 50% of the WADA’s minimum required performance level. Both GC–HRMS detection platforms can be used for anti-doping screening. Compared to GC/Q-TOF, the GC/Q-Orbitrap has a higher resolution, sensitivity, and noise-reduction ability  methods capable of distinguishing internal and external sources of some anabolic androgenic steroids and methods for the detection of testosterone esters using a dried blood spot (DBS) have also become a research hotspot in recent years. The use of anabolic steroids, especially the exogenous administration of endogenous steroids such as testosterone, is a significant obstacle for anti-doping regulators. Therefore, doping-control laboratories require analytical methods that can distinguish endogenous steroids from their synthetic analogs in urine. To this end, GC has been used as a highly specialized instrumental-validation technique combined with combustion IRMS to measure the carbon isotope ratio (∆13C) of urinary steroids and determine their synthetic origin based on abnormal  content . Over th content . Abuse o content . The useTesting blood samples is one of the most effective strategies for detecting EAAS, and the WADA has approved the analysis of steroid esters in blood matrices. However, whole blood samples have some disadvantages, such as the need for venipuncture and well-trained medical personnel as well as the high cost of transportation and storage (samples need to be stored in a long-term freezer to ensure the stability of serum and plasma) . DBS is 3.2.β-receptor agonists are a class of drugs that have a core with a β-phenylethanolamine structure, and they can relax smooth muscles and relieve bronchospasms. β-2 agonists are widely used clinically to treat bronchial asthma; athletes with asthma or exercise-induced asthma must confirm the existence of this condition with the medical committee of their national or international governing body before using β-2 agonist drugs and obtain recognition for therapeutic use exemptions. Non-asthmatic athletes are prohibited from using β-2 agonists as performance-enhancing drugs, which are listed as S3 drugs by WADA 2020. Some athletes and bodybuilders abuse β-agonists to enhance athletic performance and build their body shape. This type of drug can bind to the β-receptor in the cell membrane, thereby producing a series of physiological effects, reducing fat deposition, and increasing the lean-meat percentage . TherefoThe detection methods of β-receptor agonists vary according to the sample-matrix pretreatment methods and detection instruments, and they currently include an enzyme-linked immunoassay, radioimmunoassay (RIA), LC, GC–MS/MS, liquid analytical methods such as phase chromatography tandem mass spectrometry, initially ELISA and RIA were used as control methods for β-agonists. These methods use specific antibodies to detect the presence of β-receptor agonists. The advantages of these methods include their rapidity, accuracy, and high sensitivity. However, immunological cross-reactivity can occur, and the quality of the reagents produced by the manufacturer can affect the consistency of the results. Liquid chromatography is only suitable for single-component detection and has disadvantages such as low sensitivity, susceptibility to interference from impurities, and the inability to detect multiple stimulants simultaneously. Consequently, its applications have been limited in recent years.In comparison, detection institutions tend to use chromatography-mass spectrometry technology with high sensitivity, good repeatability, and strong specificity. The qualitative characteristics of a compound structure can yield better results. Chromatography-mass spectrometry is also a mainstream method for analyzing β-receptor agonists, including gas chromatography–mass spectrometry and liquid chromatography-mass spectrometry. GC–MS analysis was primarily used before the 1980s. Because β-receptor agonists are polar compounds with a high boiling point and low volatility, they are unsuitable for direct GC or GC/MS analysis. First, the sample must be derivatized to increase its volatility and stability. The polarity of the sample is then reduced to improve the analytical effect and increase the sensitivity. This method takes a long time to derivate, the conditions are not well controlled, and there are few receptor agonists that can be detected; thus, the use of this method is decreasing annually. In 1989, Blanchflower and Kennedy used LC–MS for the biological analysis of β-receptor agonists for the first time. As mentioned earlier, the pretreatment of LC–MS/MS is straightforward and does not require derivatization, therefore, results could be obtained quickly. This method is widely used to detect animal-derived β-receptor agonists.In recent years, research on the LC–MS detection of β-receptor agonists has focused on the development and application of sample pretreatment and instrumental multi-residue detection technologies. Pretreatment research has mainly focused on the selection of sample hydrolysis and optimization of purification methods. Liu et al.  used a 1With the development of detection instruments, the application of ion trap-time-of-flight tandem mass spectrometry has shown obvious advantages, such as high sensitivity, high mass precision analysis of samples, and detection of target compounds in complex samples. Liu et al.  establis3.3.Zearalenone (ZEN) is a type of weak estrogenic mycotoxin that is mainly produced by Fusarium fungi. It belongs to the group of resorcylic acid lactone nonsteroidal assimilation hormones. Taking ZEN can promote muscle contraction, fat breakdown, and metabolism, improve cardiopulmonary function, and enhance the excitability of the nervous system, thereby improving the reaction, endurance, and athletic performance of athletes. This hormone is classified as S1.2, other hormone-like substances, under the WADA’s prohibition list. It is also widely used in animal husbandry to promote animal growth. ZEN can enter the human body through biological accumulation, and it can affect sexual function and the normal development of secondary sexual characteristics. It has certain toxic effects on organs, such as the liver and kidneys, and even exhibits some carcinogenicity. Several countries explicitly prohibit the use of these substances in poultry and livestock farming.Detection methods for ZEN include TLC, GC, ELISA, fluorescence immunoassay, GC–MS, LC–MS, and LC–MS/MS. TLC and immunoassays are simple, fast, and cost-effective methods; however, they are prone to false positives, making them suitable for the preliminary screening of bulk products. GC requires derivatization, particularly phenolic hydroxyl derivatization, which is complex and rarely used alone. GC–MS can also simultaneously detect multiple substances, but pretreatment requires derivatization, the operation is complex, and personnel quality requirements are high. Owing to the high fluorescence of ZEN, HPLC is widely used for detection owing to its sensitivity, selectivity, and high degree of separation. LC–MS can simultaneously detect multiple components and is reliable for qualitative and quantitative analyses; however, it is susceptible to interference from sample matrices, making quantitative analysis difficult. LC–MS/MS is better suited for the detection of complex matrices, owing to the characteristics of tandem mass spectrometry. It also has the advantages of high throughput and rapid detection, making it the most commonly used method for ZEN detection. Qi et al.  used theHPLC-tandem mass spectrometry (HPLC–MS/MS) is the preferred method for analyzing ZENs owing to its strong separation ability and high sensitivity. However, cumbersome sample-preparation procedures and time-consuming column separations render it unsuitable for rapid detection. Wang et al.  proposedGC and HPLC typically require high-purity solvents, which may limit their application. Therefore, high-performance thin-layer chromatography (HPTLC) is an effective method. It can simultaneously separate and identify multiple samples in a short time and is an inexpensive, simple, reliable, and efficient method. HPTLC can be combined with MS, which enables the rapid confirmation of analytes in samples. HPTLC methods for determining ZEN in grains are available worldwide . Jorquer3.4.Glucocorticoids are a class of endogenous steroid hormones secreted by the adrenal cortex that have anti-inflammatory and anti-allergic pharmacological effects. This type of drug belongs to the S9 category of the WADA’s 2020 prohibition list. Its use in animal husbandry can cause animal protein deposition, which improves the feed-conversion rate and increases economic benefits. Glucocorticoids also play an important role in competition. They promote the synthesis and decomposition of glucose and provide energy to the muscles. However, long-term use of high doses of glucocorticoids may lead to adverse reactions such as immune-system suppression, bone loss, and muscle atrophy.Detection methods for glucocorticoids in biological samples mainly include the following: TLC, HPLC, GC, GC–MS, LC–MS, and LC–MS/MS. TLC, GC, and HPLC have poor specificity and low sensitivity; thus, their use makes it difficult to meet the requirements for trace veterinary-drug residue analyses . GC–MS hHPLC-Q-TOF-MS has high mass accuracy and resolution and can provide a full-scan analysis of samples with high sensitivity and high mass precision. Liu et al.  used HPL4.Currently, MS analysis is a widely utilized technique for doping detection due to its exceptional accuracy and sensitivity in detecting minute quantities of stimulants. However, MS-based detection techniques face limitations due to the complexity of the sample pretreatment and separation process, the requirement for a substantial number of samples, the cost of detection equipment, and the time sensitivity of the samples. In this regard, HRMS has effectively addressed these issues and is extensively employed for rapid screening of veterinary-drug residues. At the same time, HRMS has also played a certain driving role in the development of sample pre-treatment technology. The sample processing stage is no longer limited to traditional SPE and liquid–liquid extraction. The pre-treatment process is developing towards simple, fast, and low-cost operations. For example, the pretreatment of GC samples can be cumbersome due to the need for derivatization, while LC–MS is increasingly becoming the preferred method for qualitative and quantitative analysis; the QuEChERS method, as a relatively environment-friendly treatment technology, reduces sample operations, avoids loss of target analytes, improves recovery rates, and has been applied to the extraction of different compounds. Nanofiber-based SPE is another promising technique because of its simple operation, high speed, and low organic-solvent consumption. Currently, urine, whole blood, and serum samples are the primary matrices for detection; however, new supplementary matrices, such as DBS , DUS 7272, and VDetection technology for veterinary-drug residues in animal-derived foods is advancing toward high through put and automation; however, no universal methods for sample pretreatment, chromatographic separation, and MS detection exist. Research has focused on the development of better and faster methods to detect a more comprehensive range of veterinary-drugs in meat products. The establishment of fast, simple, and environment-friendly detection methods will promote the widespread use of detection technology in daily laboratory testing, thereby protecting meat-product safety and human health. Given the similarity between metabolites of certain stimulants and natural metabolites, the selection and optimization of detection methods have become highly challenging. Future research should focus on improving the accuracy and sensitivity of detection methods for animal-derived stimulants, including the development of novel technologies and methodologies. In addition, it is crucial to pay attention to positive drug cases both domestically and internationally, improve the control list for animal-derived stimulants, and promptly adjust the prohibited substances list to prevent the emergence of new stimulants, thereby ensuring food safety standards and the well-being of athletes. Countries should strengthen collaboration and communication to collectively address the challenges faced in stimulant detection.QZ and YZ wrote the original draft of the manuscript. QZ and HD conducted the search processes. HD reviewed and edited the manuscript. YZ supervised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted without 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.https://www.frontiersin.org/articles/10.3389/fnut.2023.1226530/full#supplementary-materialThe Supplementary material for this article can be found online at: Click here for additional data file.


In light of the COVID-19 pandemic, the distribution of social support for mental health problems has likely become unequal. Family- and community-based social support has been recognized as a promising approach for mental disorders; however, limited global frameworks have been applied to developing countries such as Vietnam.The aim of this study was to evaluate the quality of life and social support among patients with mental health disorders in Vietnam and to investigate the factors associated with quality of life among these patients.A cross-sectional study was conducted on 222 psychiatric patients in Hanoi from 2020 to 2022. A structured questionnaire was developed based on four standardized scales: Mental Well-Being-5 scale, Multidimensional Scale of Perceived Social Support, EuroQoL-visual analog scale (EQ-VAS), and EuroQoL-5 dimensions-5 levels (EQ-5D-5L) scale. Tobit regression was used to identify factors associated with the EQ-5D-5L and EQ-VAS scores. Structural equation modeling was applied to verify the relationship between quality of life and social support.P<.001). The hypothetical model indicated statistically significant correlations between EQ-VAS score and social support (P=.004), EQ-5D-5L and mental well-being (P<.001), and social support and mental well-being (P<.001). Critical deterioration of quality of life and inconsistency in social support for patients with mental illness were also recorded.The results showed that perceived support from family scored the highest compared to support from friends and significant others. Patients with depression reported the lowest quality of life and perceived social support. Structural equation modeling showed a root mean square error of approximation of 0.055 (90% CI 0.006-0.090), comparative fit index of 0.954, Tucker-Lewis index of 0.892, and standardized root mean squared error of 0.036 (There is a need to enhance social support and service delivery in Vietnam, focusing on occupation and quality of life. The correlations between social support, quality of life, and mental health issues suggest the potential of a clinical-social integrated intervention model of care. Awareness of the burden posed by mental health disorders is steadily growing on a global scale, particularly in low- and middle-income countries . AccordiAn increasing body of research has been dedicated to examining the influence of social determinants on health care outcomes. The WHO has officially acknowledged the significant impact of social factors on the prevalence and duration of mental disorders, attributing them to be responsible for shaping, sustaining, and enhancing overall health quality ,12. The As COVID-19 presented unique challenges to individuals with mental health disorders, it is critical to understand the role of social support in their quality of life (QoL). The standardized social support frameworks have been found to be limited in their applicability to the context of resource-scarce settings. While previous studies have identified unfavorable social outcomes for patients with mental health disorders, including social dysfunction, lack of social networks, severe stigma, and interpersonal challenges, contextualized social support models have neither been implemented nor studied for this population in Vietnam ,18. To cAfter reviewing the existing literature, we identified demographic, behavioral, illness-related, mental health–related, and social support factors that contribute to influencing the QoL of individuals with mental health conditions -21. The From January 2020 to June 2022, a cross-sectional study was conducted in Hanoi, Vietnam. The eligibility criteria included patients who were 18 years old or above, currently being treated at the Mai Huong Daycare Psychiatric, willing to participate in the study through providing informed consent, and had the physical and mental capacity to answer the interviewer’s questions. Patients who had serious cognitive impairment or were unable to answer the data collectors’ questions were excluded from the recruitment process. A day psychiatric hospital model is currently being applied by Mai Huong Daycare Psychiatric, making the participants outpatients. The day-hospital model allows patients to spend less time at the hospital, reducing the potential negative effects of prolonged hospital stays, while facilitating faster recovery and improved QoL [There were five investigators who were members of the research team and well-trained medical staff from Hanoi Medical University. The interview was conducted in a separate room where patient privacy was guaranteed. The patient was interviewed in a face-to-face manner using the questionnaire. The questionnaire required approximately 10-15 minutes to complete.A structured questionnaire was used consisting of five main components: (1) demographic characteristics, (2) clinical history , (3) mental well-being , (4) social support , and (5) QoL . The EQ-5D-5L scale was used to measure the QoL of participants through five domains: Mobility, Self-care, Usual Activities, Pain/Discomfort, and Anxiety/Depression. Each question had a score from 1 (“extreme problems”) to 5 (“no problems”), which resulted in 3125 possible health states from 1111  to 55,555  . One sinThe MSPSS was used with 12 items to measure the social support from the following subscales: family (4 items), friends (4 items), and significant others (4 items) . ParticiRespondents were asked about their sociodemographic background, including age, gender, marital status, and occupation.We asked participants one question each about their current smoking and alcohol consumption behavior.Participants were asked about their clinical characteristics, including comorbidities, chronic diseases, acute symptoms, family history of mood disorders, type of disorders , family history of mental health disorders, the number of outpatient medical examinations, and the number of mental health disorders examinations.The MHI-5 scale was used to measure the general mental health status of participants. There was a total of five items with each item scored from 1 to 5. The total score was measured by adding the total score of 5 questions and multiplying by 4. The total score ranged from 20 to 100 . Higher Total population sampling was used in this study. The sample size was calculated using the formula to estimate the mean score of EQ-VAS among participants according to EQ-VAS scores reported from another study conducted in Vietnam . This waThe data analysis was conducted using Stata version 16 and missing data were managed using the listwise deletion method. Descriptive statistics are presented as the mean (SD) for continuous variables and frequencies and proportions for categorical variables. The Wilcoxon-Mann-Whitney test and Kruskal-Wallis test were used to test for differences in perceived social support and QoL scores across individual characteristics.P<.20 as the inclusion threshold. Statistical significance was set at P<.05. In contrast, the inferential statistics applied in this study utilized structural equation modeling, which involved employing maximum-likelihood estimation and path analysis to establish the implicit connection between the variables as originally hypothesized in Individual characteristics, clinical history, mental well-being, and social support were considered potential covariates for the full models’ QoL. Multivariate Tobit regression was utilized to identify factors related to the EQ-VAS and EQ-5D-5L scores. Stepwise forward techniques were employed to create reduced models, with a significance level of This analysis used a part of the data set of a longitudinal study on individuals with and without mental health disorders in Vietnam, Vietnam’s Brain and Behavior Cohort, led by authors BXT and RCMH. The study monitors changes in functional near-infrared data and self-reported health outcomes using portable, functional near-infrared spectroscopy and clinical-behavioral assessments among Vietnamese patients, in an effort toward developing an artificial intelligence–based diagnosis system for psychiatric disorders in Vietnam.The protocol of this study was approved by the Institutional Review Board of Hanoi Medical University (58/GCN-HĐĐNCYSH-ĐHYHN). We confirmed that all procedures were performed in accordance with relevant guidelines and regulations. Written informed consent was required from participants before participating in the study. Patients invited to participate were fully explained the content, purpose, and benefits of the study. The collected information was kept confidential and only used for research purposes. The data were encrypted to ensure confidentiality of the information.P=.03). Unemployed participants had the lowest perceived social support total score, while blue-collar workers had the highest perceived social support total score.P=.01). Patients who were white-collar workers had the highest EQ-VAS and EQ-5D-5L scores, retired patients had the lowest EQ-VAS scores, and students had the lowest EQ-5D-5L scores. No significant differences were found between groups in EQ-5D-5L scores.P=.03). Patients with sleep disorders had the highest total perceived social support score, followed by those with anxiety disorders (P=.01). In addition, patients with anxiety disorders had the highest family support scores (P=.06), whereas patients with sleep disorders had the highest support scores for the friend group (P=.04) and significant others group (P=.02).P<.001). The structural equation model showed that mental well-being was significantly related (P<.001) to the QoL score  and with social support . Additionally, the model revealed a bidirectional relationship between social support and EQ-VAS . This study revealed a decline in QoL and unequal distribution of social support among patients with mental health disorders, as students experienced the lowest social support and workers perceived the highest levels of social support. Depression, sleeping disorders, and bipolar-related problems were the most significant mental health concerns recorded. The current resource allocation scheme was problematic, as all common mental health issues had critically lower levels of social support than other disorders. This study found that QoL decreased with age, among women, and for those with alcohol consumption. The most prevalent source of support across all patient groups was family support, followed by friend groups, implying a potential design of integrated community- and family-based interventions for mental health issues in resource-scarce settings.This study did not measure any COVID-19–related variables; however, since the study was conducted during the severe COVID-19 epidemic, the results can provide a comprehensive picture of mental health disorders in Vietnam during COVID-19. Overall, the QoL among patients with mental health disorders was lower than that of the general Vietnamese population . PatientIn this study, participants only received outpatient treatments. Therefore, in addition to attending medical appointments, many patients still work and carry out daily tasks. Our study revealed that patients with mental health disorders had the lowest scores related to perceived support from friend groups. Amal et al  conducteOur study revealed inconsistent patterns in QoL and support availability among different occupations and mental disorders. Students experienced the lowest social support and EQ-5D-5L scores. Blue-collar workers received the highest levels of social support but experienced the lowest QoL compared to other occupation groups. Understandably, stable employment tends to provide certain benefits such as secure incomes, health insurance , and support systems . The worAccording to several studies, depression, a mix of anxiety-depression, and sleeping disorders are common determinants of low QoL, not only among mental health patients but across all population groups -42. PatiPatients with mental disorders have lower QoL and lack social support. The structural equation model analysis confirmed the bidirectional relationship between social support and EQ-VAS scores. Previous research has linked the lack of social support to social isolation, loneliness, and higher risks of physical health problems , which cThis study offers several implications for mental health treatment and interventions. First, given the outpatient nature of treatment where patients continue to work and carry out daily tasks alongside their therapy appointments, and considering the long-term and often lifelong nature of mental health treatment, it is crucial to provide stable and lifelong sources of social support such as family-based support, which has been shown to have a substantial impact on treatment outcomes ,54. FamiThe main strength of this study is that the majority of participants only received outpatient treatments, allowing for the investigation of variables in general settings such as daily life and the workplace.However, our study had several limitations. First, we were unable to analyze causal relationships due to the cross-sectional study design. As a result, we cannot be certain whether the capacity of the patients was impacted by social support or other reasons. Second, self-reported data may suffer from a limitation of recall bias, and social desirability bias may cause certain answers to be underestimated or overestimated. Furthermore, because the time of data collection varied, recall bias might be a concern that leads to inaccuracies, especially for patients with mental health disorders. The population sampling technique may have limited the generalizability of the results for Vietnamese patients. Finally, our study was conducted during the COVID-19 pandemic, limiting the generalizability of the study results in a postpandemic period. Despite these limitations, the study identified significant determinants and trends, enabling a proposal of multilevel interventions for mental health issues and social support.Our study sheds light on the challenges faced by individuals with mental health disorders in Vietnam, including a decrease in QoL and barriers to accessing social support. The QoL model was used to fit a structural equation model to systematically verify and analyze the relationship between QoL and other variables with social support as a mediator. We found that mental well-being, QoL, and social support are interconnected in a complementary manner, suggesting that improving one factor can positively impact the others. However, further research is needed to generalize these findings to other populations before implementing a standardized framework. Our study identified several issues within the current social support system, including inadequate support for labor workers with mental health issues and a severity-based approach to resource allocation rather than one that prioritizes QoL. Finally, our study highlights the need for a shift in the mental health intervention approach, and we propose an integrated model of family-based and therapeutic interventions to address mental health disorders.