Prostate cancer (PCa) is one of the most commonly diagnosed cancers among men worldwide. Despite the presence of accumulated clinical strategies for PCa management, limited prognostic/sensitive biomarkers are available to follow up on disease occurrence and progression. MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression through post-transcriptional regulation of their complementary target messenger RNA (mRNA). MiRNAs modulate fundamental biological processes and play crucial roles in the pathology of various diseases, including PCa. Multiple evidence proved an aberrant miRNA expression profile in PCa, which is actively involved in the carcinogenic process. The robust and pleiotropic impact of miRNAs on PCa suggests them as potential candidates to help more understand the molecular landscape of the disease, which is likely to provide tools for early diagnosis and prognosis as well as additional therapeutic strategies to manage prostate tumors. Here, we emphasize the most consistently reported dysregulated miRNAs and highlight the contribution of their altered downstream targets with PCa hallmarks. Also, we report the potential effectiveness of using miRNAs as diagnostic/prognostic biomarkers in PCa and the high-throughput profiling technologies that are being used in their detection. Another key aspect to be discussed in this review is the promising implication of miRNAs molecules as therapeutic tools and targets for fighting PCa.  Prostate cancer (PCa) is a multifactorial disease with several factors contributing to its pathophysiology The classification of PCa is widely determined using the American Joint Committee TNM system, which takes into consideration both clinical and pathological staging Here we overview the implication of miRNAs in PCa tumorigenesis and the significance of their usage as biomarkers. Also, we discuss the high-throughput screening technologies and the promising therapeutic approaches that can be potentially used as tools against PCa.MiRNA molecules serve as guiding molecules for regulating gene expression. A single miRNA can target and regulate hundreds of mRNAs, which implicate them in almost all physiological and cellular processes The first step in miRNA biogenesis involves the transcription of miRNA gene by RNA polymerase II, which generates a 5′ capped and 3′ polyadenylated pri-miRNA transcript ,23. The Next, the pre-miRNA is exported to the cytoplasm by Exportin-5 (EXP5), a RAN-GTP dependent nucleo/cytoplasmic cargo transporter Given the importance of miRNAs in regulating gene expression and modulating a plethora of cellular processes, it is not surprising that their dysregulation is associated with various pathological diseases. Aberrant expression patterns of miRNAs are reported in a wide spectrum of human cancers including PCa ,34. ThisAlterations in miRNA-containing genomic loci and their associated genes have been proved to be partly responsible for miRNA dysregulation in cancer As elaborated in the previous section, different components participate in miRNA biogenesis cascade and ensure the correct maturation of pri-miRNA. The level of expression and activity of the core processing enzymes, along with other regulatory factors, are often found dysregulated in cancers. This dysregulation is accompanied by aberrant miRNA biogenesis and accordingly an altered miRNA expression profile ,44. As eLike other coding and non-coding genes, miRNAs are subjected to epigenetic regulations which control the DNA methylation and histone modification patterns at their genomic loci Dysregulated miRNAs have been shown to contribute to prostate tumorigenesis via the loss of tumor-suppressing miRNAs or amplification of tumor-promoting/oncogenic miRNAs . NumerouIt is well-established that alterations in AR signaling critically derives the growth and progression of both localized and advanced metastatic PCa. Several miRNAs are proved to interact with AR mRNA and regulate its expression, either by binding directly to the mRNA of AR itself or AR-associated factors, or indirectly by affecting the expression of AR co-regulators .MiR-21 is one of the most widely studied miRNAs in cancer ,75. It iBesides, a study on PCa cells identified binding sites for miR-185 in the 3′UTR of AR mRNA. Remarkably, the blockade of AR transcripts by miR-185 showed to compromise the interaction between AR and androgen response element (ARE) and decrease the level of AR target genes' expression, hence suggesting miR-185 as a negative regulator of AR signaling . MiR-letAmong promising miRNAs explored, miR-18a which belongs to the miR-17–92 cluster is upregulated in PCa and functions as a tumor promoter . MiR-18aMoreover, miR-122 serves as a tumor suppressor miRNA in PCa pathogenesis. Its downregulation is linked with increased proliferation and upregulation of Rho Associated Coiled-Coil Containing Protein Kinase 2 (ROCK2) . In anotZhiping et al. reported the expression of miR-181a to be at higher levels in metastatic prostate tumor samples compared with primary prostate tumors. The overexpression of miR-181a showed to contribute to EMT phenotype by downregulating the expression of the epithelial marker E-cadherin and upregulating that of the mesenchymal markers N-cadherin, vimentin, and snail. MiR-181 overexpression was also shown to promote PCa cell migration and invasion by directly targeting transforming growth factor beta-induced factor 2 protein (TGIF2) . As wellBesides, the tumor suppressor miRNAs of miR-200 family are critical regulators of EMT. This family includes miR-200a/b/c, miR-429, and miR-141. In a study done to investigate the role of miR-200c-3p in PCa invasiveness, miR-200c-3p was found to be significantly down-regulated in human PCa cell lines (PC3 and DU145) compared with the normal prostatic epithelial cell line (RWPE1). MiR-200c-3p is proposed to inhibit cell migration and invasion via targeting the E-cadherin repressor and EMT inducer; Zinc Finger E-Box Binding Homeobox 2 (ZEB2) . In lineAs defined by the National Cancer Institute (NCI), a biomarker is a molecule detected in the body fluids or tissues indicating a normal or abnormal process, condition, disease, or even treatment response. A number of established biomarkers exist in the literature of which miRNAs emerged as potential candidates for cancer diagnosis, prognosis, and therapy selection and assessment . MiRNAs These facts, among others, made miRNAs attractive molecules to explore as biomarkers and employ in routine clinical practice. To fulfill this purpose, miRNAs are being profiled using suitable and powerful technologies that were developed and enhanced over the years to overcome the challenges inherited by the nature of these molecules . Here, wqRT-PCR is considered a gold standard technique for the quantification of miRNAs expression . In factMicroarray-based technique is widely used for the profiling of identified miRNAs and is commonly employed in PCa studies . It is ahttps://www.mirbase.org/). Accordingly, bioinformatics analysis allows the identification and quantification of established and novel miRNAs and their variants such as isomers [Next-generation sequencing (NGS)-based methods are powerful tools to screen whole-genome miRNAs and are being extensively used in various systems including PCa . These a isomers . The mai isomers . Deep se isomers . Neverth isomers ,170,171. isomers .m) of all targets where subsequently the hybridization is performed at the same temperature regardless of miRNAs sequence. Following these steps, capture and reporter probes are hybridized to the corresponding miRNAs of interest forming unique target-probe complexes or codesets. The capture probes immobilize the codesets on the imaging surface in the nCounter Cartridge, whereas the reporter probes allow the identification of the miRNAs of interest through specific barcodes (color codes) of 6 molecules formed by the combination of 4 fluorophores. Subsequently, the sample is scanned by a digital analyzer and the targets are directly counted and identified by their unique color codes [nCounter Analysis Systems by Nanostring are newly developed, digital-quantification, amplification-free methods that are being increasingly used in PCa . These por codes ,169. nCoor codes ,176.Third-generation sequencing platforms such as nanopore single-molecule technology and its variants are novel, rapidly evolving strategies that are reported in few studies working on PCa. Based on recent work that validated the efficiency of nanopore-based techniques to detect particular miRNAs, these approaches seem promising in screening these molecules noninvasively while avoiding any sample processing and amplification steps. Interestingly, these can be engineered allowing the detection of multiple miRNAs targets at the same time. Consequently, future studies may validate the feasibility of implementing these approaches for high-throughput screening, particularly in clinical practices .In a conclusion, no definitive technique for miRNA detection that is suitable for routine clinical practices exists yet. All current, as well as new emerging approaches, have advantages and limitations that need to be considered for the study design. Obtained results require meticulous analysis and assessment since conclusions may differ depending on the platform used.MiRNA translation into clinical practice can be a convenient tool in the determination of PCa diagnosis, prognosis, and therapeutic response. PSA remains the primary diagnostic test for PCa, however, its specificity is limited which often leads to over-diagnosis . Its diaNumerous miRNAs have been correlated with PCa diagnosis, however, only a few have been identified in more than one study. In this regard, Porzycki et al. reported that miR-106b, miR-141-3p, miR-21, and miR-357 were significantly overexpressed in the serum levels of PCa patients when compared to healthy control patients with 93% sensitivity and 63% specificity in the prediction of PCa . In anotAs mentioned previously, miRNA dysregulation can also describe disease progression. They have been shown to possess the potential to differentiate between localized and advanced disease, determine Gleason score, cancer metastasis, and biochemical recurrence post-radical prostatectomy. A study by Brase et al. identified miR-141 and miR-375 as the most distinct markers for tumor progression after analysis of sera from individuals with metastatic PCa and localized tumors . The diaMiRNA levels have been shown to describe PCa patients' response to therapy. The serum levels of miR-21 in docetaxel chemotherapy-resistant hormone-refractory PCa patients were found to be elevated . AnotherClinical efforts are aiming at evaluating the diagnostic and prognostic powers of miRNAs in PCa patients. Two ongoing clinical trials NCT04662996 and aim to determine the predictive capacity of miRNAs. NCT04662996 is expected to enroll a cohort of 50 castration-resistant PCa patients anticipated to undergo chemotherapy or novel hormonal therapy, and assess miRNA expression in blood samples before and after therapy. While NCT02366494 intends to enroll 60 PCa patients and validate the capacity of 200–300 exosomal miRNAs, identified by RNA sequencing, in predicting the response to ADT. Subsequently, the top-five most prevalent miRNAs will be reported ,203. MorIn summary, determining miRNAs that could be used as biomarkers for PCa diagnosis and prognosis is still a necessity and an ongoing work. The latter is instrumental in the therapeutic route of PCa deliberated to each patient  and 3.TaMiRNAs can be used not only for diagnosing PCa but also for developing therapeutics against it. MiRNA-based therapeutics aim to reverse the pathological miRNA alterations that enhance the proliferation, invasion, and metastasis of tumors. Since oncogenic miRNAs repress the translation of mRNA of tumor suppressor genes and tumor suppressor miRNAs repress those of oncogenes, inhibiting upregulated oncogenic miRNAs and restoring downregulated tumor suppressor miRNAs can be effective for treating PCa refer to .Fig. 4Scin vitro and in vivo, chemical modifications can be introduced mainly in at the C2’ position of the sugar ring and/or in the backbone to increase their potency [in vivo [in vivo in PCa. In these AMOs, the 3’ end is conjugated to cholesterol, thiol moieties replace the non-bridging oxygen of few phosphate groups at the 3’ and 5’ ends, and O-methyl modification on the C2’ of ribose sugar (2’OMe) is applied to the whole strand. For example, the intratumoral injection of anti-miRNA-221 and anti-miRNA-222 antagomirs resulted in the knockdown of the corresponding oncogenic miRNAs and the restoration of tumor suppressor p27, reducing the growth of PC3 cells-derived subcutaneous xenografts in mouse [in vitro and reduced tumor proliferation in the mouse subcutaneous tumor xenograft pre-transfected with this inhibitor [The loss-of-function modulation of miRNA can be achieved through different methods. The most common one is using anti-miRNA oligonucleotides (AMOs) which are synthetic, about 17–22 nucleotide-long, anti-sense oligonucleotides that are designed to be complementary to their target miRNA especially at the seed region ,214 refA. They a potency . One exa potency . These w[in vivo , and thein mouse . 2’OMe min mouse . Locked in mouse . PNA is in mouse . In a stnhibitor . In addiex vivo can be achieved by expressing this circRNA in PCa cells through lentiviral expression plasmids which leads to the sponging and inhibition of miRNA-767-5p and hence increases the expression of - the target gene of this miRNA- TET1 [Another strategy to silence oncogenic miRNA is using miRNA sponges refer to A. MiRNA NA- TET1 , a tumorNA- TET1 . ZhendonNA- TET1 .Several other promising strategies of inhibiting oncogenic miRNAs have been employed for other types of cancers. MiRNA masks are chemically modified antisense oligonucleotides refer to A. Opposiin vivo [MiRNA replacement therapy aims to restore the normal physiological levels of downregulated tumor suppressor miRNAs. The main strategy is using miRNA mimics which are chemically modified synthetic miRNA-like RNA duplexes refer to B. They sin vivo . MiRNA rin vivo .in vivo.MiRNA inhibition and replacement strategies have shown a promising therapeutic potential to treat tumors, yet there are some serious problems that need to be addressed. Unmodified miRNA mimics and inhibitors are prone to rapid degradation by serum nucleases and rapid clearance by renal excretion, which causes a very short systemic circulation time . MoreoveMany viral and non-viral delivery systems that enhance the stability, decrease the side effects, and increase the accuracy and cellular uptake of miRNA-based therapeutics have been reviewed . Viral vin vivo. One of these is the cationic polymer nanoparticle polyethyleneimine (PEI), yet it exerts cytotoxic effects [in-vivo prostate carcinoma and melanoma mouse models [Many recent studies show the efficiency of polymeric nanoparticle-based vectors in delivering miRNA therapeutics  effects . Conte e effects . PHB is  effects . The res effects . Kunz ete models .In addition to PEI, Chitosan is a natural biodegradable polysaccharide with no immunogenicity and toxicity that can easily complex with miRNA due to its cationic character . SystemiLipid nanoparticle-based vectors are used to deliver miRNA-based therapeutics too. Among these are cationic niosomes which are biodegrabale vectors made up of non-ionic surfactants for improved stability, neutral lipids for improved cellular uptake, and cationic lipids for charging the nucleic acid . GhaffarDespite the advancements in miRNA-based therapeutics and their delivery strategies, further preclinical studies are needed to verify the efficacy and safety of these methods. Currently, there are no miRNA treatments being tested clinically for PCa .It is well established that the expression of some miRNAs is dysregulated in diverse types of cancer including PCa. Several mechanisms may lead to an aberrant profile of miRNAs that may range from chromosomal modifications, altered epigenetic regulations, and abnormal miRNA biogenesis. As such, prostate cells with dysregulated miRNA expression may have a shifted balance between tumor-suppressing and tumor-promoting/oncogenic miRNAs. Consequently, crucial cellular processes may be affected such as AR signaling, proliferation, apoptosis, EMT, and invasion; all leading to PCa development and progression.Interestingly, the dysregulated profiles of miRNAs form signatures that have been exploited to determine specific biomarkers for early diagnosis, prognosis, and prediction of treatment efficacy against several cancer types together with PCa. The latter is unique in the sense that blood, urine, and semen may be used as sources for biomarkers detection. This is of particular interest for clinical use since miRNAs are present in these samples which may be extracted non-invasively and repeatedly from the patients. To detect and determine specific miRNA biomarkers, different high-throughput tools evolved and were employed over the years. Mainly, qRT-PCR, microarray, NGS, and nCounter-based methods are being used. Each technique involves advantages and pitfalls whereby employing one over the other depends on many factors such as the research question/targets, sample types, and resources. To date, an ideal miRNA high-throughput detection technique that combines the following characteristics does not yet exist. In summary, this method should have high sensitivity to detect low levels of miRNAs, high specificity to confidently discriminate one nucleotide difference between the targets, the ability to provide quantitative outputs of miRNAs expression levels, steps that are easy/simple to perform, and equipment/reagents that are readily available Despite all advances, management of mCRPC still presents challenges emphasizing the need for novel treatment and drug delivery approaches. miRNA-based therapeutics have been considered as potential strategies to target PCa. However, till now, no miRNA treatments have been clinically tested for PCa, as miRNA-based therapeutic approaches are still naïve lacking further preclinical studies to validate their efficacy and safety.Not applicable.None.Not applicable.Not applicable.© The Author(s).Fatima Ghamlouche: Conceptualization, Project administration, Supervision, Investigation, Methodology, Writing – original draft, Writing – review & editing, Visualization, Validation. Amani Yehya: Conceptualization, Project administration, Supervision, Investigation, Methodology, Writing – original draft, Writing – review & editing, Visualization, Validation. Yousef Zeid: Methodology, Writing – original draft, Validation. Hiam Fakhereddine: Methodology, Writing – original draft, Validation. Jhonny Fawaz: Methodology, Writing – original draft, Validation. Yen-Nien Liu: Conceptualization, Writing – review & editing, Visualization, Validation. Mohamed Al-Sayegh: Conceptualization, Writing – review & editing, Visualization, Validation. Wassim Abou-Kheir: Conceptualization, Project administration, Supervision, Writing – review & editing, Visualization, Validation.All authors declared that there are no conflicts of interest.


Six anaplastic lymphoma kinase-tyrosine kinase inhibitors (ALK-TKIs), including one domestic (ensartinib) and five imported ALK-TKIs , have been recommended as first-line treatments for advanced ALK-positive NSCLC in China. This study sought to examine the cost-effectiveness of these six novel therapies in Chinese patients.via deterministic sensitivity analyses and probabilistic sensitivity analyses.We constructed a Markov model to compare the cost-effectiveness of the six ALK-TKIs as a first-line treatment for patients with advanced ALK-positive NSCLC from the perspective of the Chinese healthcare system. Transition probabilities were estimated by synthesizing data from the PROFILE 1,029 trial and a network meta-analysis. Health state utilities and costs were sourced from published literature, publicly available national databases, and local general hospitals. The robustness of model was assessed Compared with crizotinib, ensartinib achieved additional 0.12 quality-adjusted life-year (QALY) with marginal costs of $3,249, resulting in an incremental cost-effectiveness ratio (ICER) of $27,553/ QALY. When compared with ceritinib and brigatinib, ensartinib achieved additional 0.06 and 0.03 QALYs with substantially reduced costs. When compared with lorlatinib and alectinib, ensartinib was associated with a lower QALY and decreased total costs; the ICERs for lorlatinib and alectinib were $934,101/ QALY and $164,888/ QALY, respectively.For Chinese patients with advanced ALK-positive NSCLC, ensartinib was a cost-effective option compared with crizotinib, and was a dominant alternative to ceritinib and brigatinib. Although lorlatinib and alectinib were associated with prolonged survival compared with ensartinib, they were less cost-effective than ensartinib due to the overwhelming total costs. Lung cancer is an aggressive malignancy responsible for nearly one fifth of cancer-related deaths in the world . In 2020Crizotinib is an imported ALK-TKI that was firstly approved by the Chinese National Medical Products Administration (NMPA) in 2013 due to its superior clinical efficacy to conventional chemotherapies . HoweverRecently, the eXalt3 phase 3 clinical trial  investigated the efficacy of China-developed ALK-TKI ensartinib (second-generation) vs. crizotinib in the first-line setting for advanced ALK-positive NSCLC. They found that ensartinib has superior systemic and intracranial efficacy to crizotinib, as well as an overall favorable safety profile . More imTherefore, in the current study, we compared the cost-effectiveness of domestic ensartinib vs. all imported ALK-TKIs  that are recommended by the CSCO Guidelines as a first-line treatment for advanced ALK-positive NSCLC.https://www.treeage.com/) for mathematical modeling and R software  for survival fitting, we established a cost-effectiveness model to compared first-line ensartinib with crizotinib, ceritinib, alectinib, brigatinib, and lorlatinib.Using TreeAge Pro software , progressive disease (PD) and death. All patients entered the model in the PFS health state and were randomized to receive first-line ensartinib , crizotiThe primary model outputs were quality-adjusted life-year (QALY) and cost in 2021 US dollars  . We usedIndirect comparisons of the six ALK-TKIs were conducted using the survival data of crizotinib from the PROFILE 1,029 clinical trial, because crizotinib is a commonly used comparator in the clinical trials of next-generation ALK-TKIs –12, 14. other−ALK−TKIs = λcrizotinib × HR and shape γother−ALK−TKIs = γcrizotinib parameters.Given the absence of head-to-head clinical trials that directly compared these six ALK-TIKs, we used the HRs of OS and PFS for ensartinib, ceritinib, alectinib, brigatinib, and lorlatinib vs. crizotinib extracted from a published network meta-analysis (NMA)  to estimUtility assigned to each health state was obtained from a previous study that applied a Chinese-specific value algorithm to the EuroQol five-dimension (EQ-5D) questionnaire data . In addiDrugs acquisition, AEs management, routine follow-ups, subsequent anticancer therapies, BSC and palliative care costs were included in the model. The daily costs of these six items were converted by their prices according to daily dose. The prices of ensartinib, crizotinib, ceritinib, alectinib and lorlatinib were obtained from the latest available bid-winning price in the public national databases ; the priCosts of routine follow-up costs  were also estimated using data from local general hospitals. In addition, subsequent anticancer treatment, BSC and palliative care costs were extracted from previous literature .Two sensitivity analyses were carried out to test the robustness of our conclusion. Deterministic sensitivity analyses (DSA) were performed by varying individual parameters to identify sensitive factors. HRs and health state utilities varied across the published 95% confidence intervals (CIs), ALK-TKIs costs varied within specific ranges based on the Chinese national condition, other costs and disutility varied between the plus and minus 20% of the baseline values, and discounted rate varied from 0 to 8%. Probabilistic sensitivity analyses (PSA) were performed by varying multiple parameters simultaneously. Each parameter was matched with an appropriate statistical distribution, and 10,000 Monte Carlo simulations were used to generate 10,000 model outputs. For Chinese patients with advanced ALK-positive NSCLC, first-line ensartinib achieved additional 0.12, 0.06 and 0.03 QALYs in comparison with crizotinib, ceritinib, and brigatinib, respectively. The incurred total cost associated with ensartinib was marginally greater than crizotinib , but slightly lower than ceritinib  and substantially lower than brigatinib  . These rMoreover, ensartinib was associated with inferior effectiveness and lower total costs compared with lorlatinib and alectinib. The ICER between lorlatinib and ensartinib was $934,101/ QALY and between alectinib and ensartinib was $164,888/ QALY. These results indicated that lorlatinib and alectinib were not cost-effective compared with ensartinib.OS for alectinib vs. crizotinib, the daily cost of alectinib and the HROS for ensartinib vs. crizotinib. To be more specific, decreasing the HROS for alectinib vs. crizotinib from 0.67 to 0.42, decreasing the daily cost of alectinib from $84.33 to $48.74 and increasing the HROS for ensartinib vs crizotinib from 0.88 to 1.68, would allow the ICERs under the WTP threshold of $37,654 per QALY. Other model parameters had marginal influence on the model as illustrated by In DSA for alectinib vs. ensartinib, the model was particularly sensitive to the HROS for lorlatinib vs. crizotinib and the HROS for ensartinib vs. crizotinib. All other parameters varied but did not yield substantial changes in the ICER  recommended by the CSCO Guidelines for treating advanced ALK-positive NSCLC in Chinese patients.Our results demonstrated favorable effectiveness associated with alectinib and lorlatinib, which were consistent with previous clinical trials , 21–24. Sensitivity analyses revealed that HR OS plays a decisive role in determining whether the studied ALK-TKI is cost-effective or not. Due to the lack of clinical trial that directly compares these six ALK-TKIs, HRs from a systematic NMA were used for transition probabilities estimation in this economic evaluation. This NMA comparatively summarized the effectiveness and safety of several first-line ALK-TKIs by synthesizing the clinical data from all randomized controlled trials completed to date . Apart fSince the price of brigatinib is not available in Chinese mainland market, we used the price from the Hong Kong Hospital Authority to inform the cost estimation. Results from this cost-effectiveness model suggested that brigatinib was dominated by ensartinib due to a lower QALY and a greater total costs. Moreover, given the price advantage of domestic drugs and the government support, the listing price of brigatinib in Chinese mainland would unlikely be lower than that of ensartinib. Therefore, we could conservatively conclude that brigatinib does not represent as a cost-effective strategy compared with ensartinib. As for loratinib approved on April 27th, considering its superior efficacy in prolonging survival over ensartinib, we performed separate sensitivity analyses to explore the sensitive factors related to its cost-effectiveness. We found that compared with ensartinib, loratinib was not a preferred strategy regardless of its daily cost.This study has some limitations. First, although the indirect cost-effectiveness comparisons of these six ALK-TKIs were based on a well-designed comprehensive NMA analysis, further validation of our findings is necessary when head-to-head clinical trial data are available. Second, owing to the dearth of quality-of-life data specifically applicable to Chinese patients with advanced ALK-positive NSCLC, we used the health state utilities measured for advanced NSCLC patients from a Chinese-based study ; howeverIn conclusion, in this economic evaluation comparing the domestic ALK-TKI ensartinib with five imported ALK-TKIs for advanced ALK-positive NSCLC in Chinese patients, ensartinib was a cost-effective option compared with crizotinib, but a dominant alternative to ceritinib and brigatinib. Moreover, despite lorlatinib and alectinib showing superior efficacy in prolonging survival over ensartinib, their overwhelming costs made them less cost-effective than ensartinib.The original contributions presented in the study are included in the article/QL: had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. XL, XZ, and LP: concept and design. XL and ZZ: drafting of the manuscript. QL: statistical analysis. XZ and XL: obtained funding. All authors: acquisition, analysis, or interpretation of data and critical revision of the manuscript for important intellectual content. All authors contributed to the article and approved the submitted version.This work was supported by the Hunan Provincial Natural Science Foundation [grant number 2021JJ40817]; Hunan Provincial Natural Science Foundation [grant number 2021JJ80080].The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Quantitative proteomics has become an increasingly prominent tool in the study of life sciences. A substantial hurdle for many biologists are, however, the intricacies involved in the associated high throughput data analysis.https://github.com/tbaccata/amica and is also incorporated in the web application.In order to facilitate this task for users with limited background knowledge, we have developed amica, a freely available open-source web-based software that accepts proteomic input files from different sources. amica provides quality control, differential expression, biological network and over-representation analysis on the basis of minimal user input. Scientists can use amica’s query interface interactively to compare multiple conditions and rapidly identify enriched or depleted proteins. They can visualize their results using customized output graphics, and ultimately export the results in a tab-separated format that can be shared with collaborators. The code for the application, input data and documentation can be accessed online at https://bioapps.maxperutzlabs.ac.at/app/amica.The strong emphasis on dynamic user interactions, the integration of various databases and the option to download processed data, facilitate the analysis of complex proteomic data for both first-time users and experienced bioinformaticians. A freely available version of amica is available at The online version contains supplementary material available at 10.1186/s12864-022-09058-7. Mass spectrometry (MS)-based proteomics enables deep qualitative and quantitative characterization of any organism’s proteome which is crucial for the understanding of the underlying cell biology, physiology and biochemistry. The constant technological advancement of instruments and data-acquisition techniques, as well as the parallel development of a broad variety of methods such as proximity-dependent labeling for the study of weak or transient protein-protein interactions (PPIs), has expanded the scope and relevance of MS-based approaches in tackling specific biological questions. As a result, proteomic approaches have become increasingly popular, but the complex analysis of MS-based proteomics data is an obstacle for many novices in the field, complicating and delaying the interpretation of experimental outcomes.Moreover, the many software platforms available for processing proteomics raw data and their different output formats require advanced knowledge for obtaining final interpretable results. Several attempts to solve this problem have been made in the past. The software Perseus , for exahttps://github.com/broadinstitute/protigy) permits the upload of generic user input, none of these tools allow the systematic comparison of proteins across multiple groups or the integration and comparison of multiple proteomics experiments.Recently developed applications integrate their software into R-Shiny apps, allowing for interactive user interactions and visualizations of the output from the MaxQuant package, one of the most widely used software platforms in MS-based proteomics . LFQ-AnaAs a solution to these issues, we have developed amica, a user-friendly web-based platform for comprehensive quantitative proteomics data analysis that can automatically handle multiple database search tool outputs such as for instance data from FragPipe - a recent but increasingly popular open-source software package , 9 - as A comparison of amica with currently available R-Shiny apps for proteomic data analysis  is shown in Table amica is implemented as an open-source, interactive R-Shiny app that provides generic user input, quality control and differential abundance analysis for quantitative proteomics data.amica uses established software tools and libraries and integrates various biological databases. The front page of amica and Table amica can read in common database search tool output formats, custom formats and its own tab-separated format. It is able to achieve this by mapping file specific column names into common features present in proteomics data. These include a unique protein id, a gene name, different type of processed intensities, peptide counts, spectral counts and other common columns in proteomics output formats. A description of column names from analyzed data mapped to amica’s format is shown in Table Three different data upload options are available in amica  was used to analyze the raw files. As search database, UniProt UP000005640 (downloaded on 10th September 2021) was used, with Trypsin/P as proteolytic enzyme allowing for two missed cleavages. The match between runs (MBR) feature was not used. Oxidation on methionine and protein N-terminal acetylation were set as variable modifications, and Carbamidomethylation of Cysteine was set as fixed modification. Label-free quantification and normalization was performed with the MaxLFQ algorithm .Additionally, FragPipe version 16) with MSFragger   tab, users can generate and inspect plots at different processing levels of the data, such as raw intensities, LFQ intensities, iBAQ intensities and normalized and imputed intensities. This makes it easy to examine the impact of common pre-processing steps . An automatically generated report containing user selected analysis parameters, and plots comparing intensities before and after normalization and imputation can be downloaded, allowing for reproducible output and analysis.The available visualizations for the intensity distributions in density - and box plots for different samples are particularly useful in this respect  of significance for all user-selected comparisons. In addition to the filters applicable to each column, users can use the gene name column to further subset the data table by specifying search patterns using regular expressions. amica’s unique query interface allows to immediately inspect differentially abundant proteins that are part of a specific process or pathway : Platform independent.Programming language: R.Other requirements: none for the webserver, R version 4 or higher for local installation.License: GNU GPL version 3.0.Any restrictions to use by non-academics: none.https://github.com/tbaccata/amica.amica’s source code, online documentation and a formatted example data set are available on Additional file 1:Supplementary Material — File 1. Link to a user manual demonstrating every available function and output graphics, as well as extensive tutorials.


Nicotinamide adenine dinucleotide (NAD+) metabolism is involved in a series of cancer pathogenesis processes, and is considered a promising therapeutic target for cancer treatment. However, a comprehensive analysis of NAD+ metabolism events on immune regulation and cancer survival has not yet been conducted. Here, we constructed a prognostic NAD+ metabolism-related gene signature (NMRGS) associated with immune checkpoint inhibitor (ICI) efficacy in glioma.40 NAD+ metabolism-related genes (NMRGs) were obtained from the Reactome database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Glioma cases with transcriptome data and clinical information were obtained from Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). NMRGS was constructed based on the calculated risk score using univariate analysis, Kaplan–Meier analysis, multivariate Cox regression, and nomogram. This NMRGS was verified in training (CGGA693) and validation (TCGA and CGGA325) cohorts. The immune characteristics, mutation profile, and response to ICI therapy were subsequently analyzed for different NMRGS subgroups.Six NAD+ metabolism-related genes, including CD38, nicotinamide adenine dinucleotide kinase (NADK), nicotinate phosphoribosyltransferase (NAPRT), nicotinamide/nicotinic acid mononucleotide adenylyltransferase 3 (NMNAT3), poly(ADP-Ribose) polymerase family member 6 (PARP6), and poly(ADP-Ribose) polymerase family member 9 (PARP9), were ultimately used to construct a comprehensive risk model for glioma patients. Patients in the NMRGS-high group showed a poorer survival outcome than those in the NMRGS-low group. The area under curve (AUC) indicated that NMRGS has good potential in glioma prognostic prediction. A nomogram with improved accuracy was established based on independent prognostic factors . Furthermore, patients in the NMRGS-high group showed a more immunosuppressive microenvironment, higher tumor mutation burden (TMB), higher human leucocyte antigen (HLA) expression and a more therapeutic response to ICI therapy.This study constructed a prognostic NAD+ metabolism-related signature associated with the immune landscape in glioma, which can be used for guiding individualized ICI therapy. Glioma is the most common primary brain tumor originating from neuroglial progenitor cells, accounting for 80% of malignant tumors in the central nervous system . AccordiMetabolic reprogramming is recognized to occur in all cellular components of the tumor microenvironment. NAD+ is an essential redox cofactor in cellular metabolism, affecting gene expression, energy production, glycolysis, DNA repair, and cell cycle progression . SeveralIn this study, we systematically profiled the NAD+ Metabolism-Related genes of glioma patients and then developed a prognostic signature for glioma. We then investigated the potential value of this signature in predicting prognosis, evaluating the immune microenvironment, and guiding clinical treatment in glioma patients. Our findings demonstrated that NMRGS score was a promising prognostic index associated with the therapeutic effect of ICI therapy in glioma.https://portal.gdc.cancer.gov/) database and the Chinese Glioma Genome Atlas  database were employed for downloading RNA-seq transcriptome data and clinical information of glioma patients. The criteria for including patients were as follows: (a) patients with survival data and overall survival (OS) ≥ 30 days; (b) patients with mRNA sequencing data; and (c) patients with definitive histopathological diagnosis. With these inclusion criteria, 1486 patients with gliomas were included in the follow-up analysis. Specifically, the CGGA693 dataset (n=638) served as a training cohort. At the same time, the TCGA dataset (n=550) and the CGGA325 dataset (n=298) served as a validation cohort. The detailed clinical information of glioma patients from the training and validation cohort was shown in The Cancer Genome Atlas  database. The “clusterProfiler” software package of R (version 4.1.3) was used to analyze the gene ontology (GO) and KEGG pathway for functional annotation of these NAD+ metabolism-related genes with a significant p-value (<0.05).40 NAD+ Metabolism-Related genes were obtained from the Reactome database (R-HAS-196807) and Kyoto Encyclopedia of Genes and Genomes database (has00760). The detail of the NAD+ Metabolism-Related genes is shown in . Kaplan-Meier (K-M) method was used further prognostic identification (p < 0.05). Subsequently, the multivariate Cox regression analysis was then used to filter the independent OS-associated NMRGs. The Lasso regression analysis was conducted to ensure that overfitting was avoided. Finally, the selected NMRGs were applied to establish the NMRGS based on the multivariate Cox regression coefficient. The risk score for each patient was determined using Equation: NMRGS score = expression level of genePatients were then divided into two groups according to the median NMRGS score, including the NMRGS-low group and the NMRGS-high group. The difference in clinicopathologic factors and NMRGs expression between the NMRGS-low group and the NMRGS-high group was exhibited with a heatmap. The comparison of OS between the NMRGS-low group and the NMRGS-high group was conducted using K-M survival curves with the “survminer” package in R. The receiver operating characteristic (ROC) curves were plotted to evaluate the accuracy of NMRGS with the “timeROC” package in R. Univariate and multivariate Cox regression analyses were conducted to identify the NMRGS score as an independent predictor for OS with the “survival” package in R. Similar validation analyses were performed simultaneously in both training and validation cohorts.To predict the one-, three-, and five-year survival rates of glioma patients, we constructed a nomogram based on NMRGS score and the independent prognostic clinicopathologic parameters in the training cohort by employing the R package “rms,” “regplot,” and “Hmisc. The availability of this nomogram was evaluated by the C-indices and calibration curves.via the “Maftools” package in R. Relevant mutation data were downloaded from the TCGA database. The correlation between TMB value and NMRGS score was analyzed with the spearman method. The influence of TMB value on glioma patient survival was evaluated among the NMRGS-low group and the NMRGS-high group with K-M method.TMB is defined as the total number of somatic mutations and has emerged as a quantitative biological marker of the immune response. We analyzed the TMB value and visualized the mutation profiles in the NMRGS-low group and the NMRGS-high group https://tide.dfci.harvar.edu) algorithm were used to assess the difference in sensitivity to immunotherapy between the NMRGS-low group and NMRGS-high group.Enrichment analysis to understand the immune-associated signal transduction pathways in which the NMRGS were involved was conducted using Gene set enrichment analysis (GSEA) between the NMRGS-low group and the NMRGS-high group. Those signaling pathways meeting the screening criterion  < 0.25) were considered significantly enriched. Single-sample GSEA (ssGSEA) analysis was conducted to identify immune cell-related infiltrating scores and immune-related pathways using the “gsva” package in R. In addition, CIBERSORT was used to determine the relative proportions of 22 types of immune cells between the NMRGS-low group and the NMRGS-high group. ImmuneScore, StromalScore, ESTIMATEScore, and TumorPurity were calculated by ESTIMATE  algorithm utilizing the R-package “estimate.” The relationships between NMRGS score and the recognized immune checkpoint genes were conducted. In addition, Tumor immune dysfunction and exclusion  and reverse transcribed into cDNA with RT Supermix Reagent Kit (Vazyme R323). The detection and amplification of the respective genes were conducted using SYBR qPCR Master Mix (Q311-02/03) according to the manufacturer instruction. Correlation data was calculated based on the ΔΔCT of the target gene, and Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) was kept as an endogenous control. The primer sequences are shown in R version 4.1.3, Perl language, and GraphPad Prism 8.3.0  were used to analyze the data or visualized pictures. Mann–Whitney U-test was used to detect the differences in the continuous variables between the NMRGS-low group and the NMRGS-high group. Comparisons of categorical variables between the NMRGS-low group and the NMRGS-high group were executed with the Chi-square test. Spearman’s test was used to evaluate the correlation coefficient. P value <0.05 was regarded as statistically significant .The research workflow diagram for NMRGS construction and corresponding analyses is presented in Univariate Cox regression analysis was performed to identify the NMRGs correlated with OS based on the data of the training cohort  found in Single-sample GSEA analysis showed a remarkable positive correlation between NMRGS score and immune-related functions. Immune cell-related infiltrating scores and immune-related pathways were significantly different between the NMRGS-low group and the NMRGS-high group  samples in the NMRGS-high group and 265/272 (97.43%) samples in the NMRGS-low group. The top 20 genes with the highest mutation rates in NMRGS subgroups were identified. The current understanding of the pathways leading to the restoration of HLA expression could be utilized to conceive immunotherapies. Tumor cells evade immune detection by developing deficiencies in their HLA presentation pathways, allowing important tumor antigens to persist undetected by the immune system. We compared the expression of various HLA molecules between the NMRGS-high group and the NMRGS-low group. The correlation between multiple HLA molecules and NMRGS score has also been further studied. Our study showed that HLA molecules were uniformly under-expressed in the NMRGS-high group compared with the NMRGS-low group S17A. SiThe expression level of immune checkpoints is directly related to the therapeutic effect of ICI. Representative immune checkpoints were investigated in both CGGA693, TCGA and CGGA325 cohorts. Significantly higher expression levels were detected in all these represent immune checkpoints between NMRGS-high group and NMRGS-low group . According to the results of qRT-PCR, the mRNA expressions of CD38, NADK, PARP9 were significantly elevated in glioma tissues compared with NBT, while the mRNA expressions of NAPRT and PARP6 showed a significant downward trend in glioma tissues  or the validation cohorts (TCGA and CGGA325), the NMRGS showed robust capacity in predicting the survival outcomes of glioma patients, which is demonstrated by survival analysis, ROC curve, and Cox regression analysis. A nomogram with improved predictive capacity was developed by combining the prognostic NMRGS with other independent prognostic factors (1p19q codeletion status and WHO grade). Biological processes and pathways associated with immunity were identified in functional enrichment analyses. We further uncovered the differential immune landscape between the two risk subgroups by comparing the abundance of immune cells, immune and stromal scores, and expression levels of immunoregulatory molecules. Additionally, the NMRGS score may be associated with differences in ICI therapy efficacy.The NMRGS comprised six crucial genes, including CD38, NADK, NAPRT, NMNAT3, PARP6, and PARP9. The first key gene, CD38, is the major NAD-hydrolyzing ectoenzyme in most mammals and has recently been implicated in regulating metabolism and the pathogenesis of cancers . HoweverRecent discoveries have revealed that the glioma microenvironment affects tumorigenesis and its response to immunotherapeutic treatment. A deep understanding of the complex heterogeneity of glioma immune microenvironment may be the key to unleashing the full potential of immunotherapy strategies. In our study, we found significant differences in TME among NMRGS subgroups, especially in the tumor immune microenvironment. GSEA and ssGSEA showed different immune functional statuses between the two groups, including Interferon response, NK cell-mediated cytotoxicity, cytolytic activity, MHC class I expression, and so on. Glioma cells deprived nutrients and secreted interferential metabolites to remold the immune microenvironment, which activated anti-inflammatory and tolerant mechanisms and hindered anti-tumor responses. Metabolic remodeling injured the tumor recognition, phagocytosis and lysis of cytotoxic T lymphocytes, natural killer cells, glioma-associated macrophages, and dendritic cells, conferred immune silencing phenotypes on dendritic cells and macrophages, and promoted the expansion and infiltration of immunosuppressive regulatory T cells and myeloid-derived suppressor cells. Tumor-associated macrophages recruited to the glioma microenvironment can release growth factors and cytokines in response to the activity of cancer cells, exhibiting an immunosuppressive M2 phenotype and functional behavior . Our finInhibitory and costimulatory receptors, known as “immune checkpoints,” are one of the mechanisms by which cancer cells evade immune surveillance, induce immune tolerance and escape immune destruction . In receIn this study, we developed an NMRGS to predict glioma prognosis and ICI treatment. However, it is worth noting that there are still certain limitations in the present study. Firstly, further experiments are needed to elucidate the roles and specific mechanisms of the six hub genes in glioma. Secondly, retrospective data were used to construct and validate NMRGS. Multi-center prospective studies are needed to validate its clinical value and make it more convincing. Finally, we indirectly assessed the potential of NMRGS immunotherapeutic response with TIDE, more studies are needed to confirm this conclusion.http://www.cgga.org.cn/) and TCGA  websites.Publicly available datasets were analyzed in this study. This data can be found here: CGGA (The studies involving human participants were reviewed and approved by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. The patients/participants provided their written informed consent to participate in this study.CJ, YZ, and LY conducted this study. JZ drew the figures and tables. XJ, JL, and XW made some revisions of the manuscript. All authors contributed to the article and approved the submitted version.


However, the DTI identification process often requires expensive and time-consuming tasks, including biological experiments involving large numbers of candidate compounds. Thus, a variety of computation approaches have been developed. Of the many approaches available, chemo-genomics feature-based methods have attracted considerable attention. These methods compute the feature descriptors of drugs and proteins as the input data to train machine and deep learning models to enable accurate prediction of unknown DTIs. In addition, attention-based learning methods have been proposed to identify and interpret DTI mechanisms. However, improvements are needed for enhancing prediction performance and DTI mechanism elucidation. To address these problems, we developed an attention-based method designated the interpretable cross-attention network (ICAN), which predicts DTIs using the Simplified Molecular Input Line Entry System of drugs and amino acid sequences of target proteins. We optimized the attention mechanism architecture by exploring the cross-attention or self-attention, attention layer depth, and selection of the context matrixes from the attention mechanism. We found that a plain attention mechanism that decodes drug-related protein context features without any protein-related drug context features effectively achieved high performance. The ICAN outperformed state-of-the-art methods in several metrics on the DAVIS dataset and first revealed with statistical significance that some weighted sites in the cross-attention weight matrix represent experimental binding sites, thus demonstrating the high interpretability of the results. The program is freely available at  Drug–target protein interaction (DTI) identification is fundamental for drug discovery and drug repositioning, because therapeutic drugs act on disease-causing proteins. However, the DTI identification process often requires expensive and time-consuming tasks, including biological experiments involving large numbers of candidate compounds . To addrRecent studies have proposed various end-to-end, deep learning frameworks that integrated sequence representation and model training in a unified architecture ,25. DeepSuch interactions are widely considered by exploiting the attention mechanisms of Transformer, an encoder-decoder model consisting of multi-headed attention layers and pairwise feed-forward to extract sequence-to-sequence features. TransformerCPI  implemenTo overcome these problems, we developed a new attention-based method that predicts DTIs based on SMILES of drugs and AA sequences of proteins; this method was designated the interpretable cross-attention network (ICAN). We optimized the attention mechanism architectures in terms of cross-attention or self-attention, attention layer depth, and selection of the context matrixes from the attention networks. ICAN outperformed state-of-the-art methods in several respects and first revealed with statistical significance that some weighted attention sites represent experimental binding sites, thus demonstrating the distinct interpretability of the method.The proposed cross-attention model consists of three networks: an embedding layer, an attention layer, and an output layer, as shown in The three widely used datasets were prepared: DAVIS, BindingDB, and BIOSNAP, as shown in Drugs are represented by the SMILES , which iThe frequent consecutive sub-sequence (FCS) mining method  is used We paired each token of SMILES FCS, AA FCS, SMILES, SELFIES, and AAs with its corresponding label integer to generate a label-encoding vector. The resulting label-encoding vectors were embedded into token-embedding matrixes by two encoding methods, nn.Embedding (PyTorch) and one-hot encoding. As drug and protein sequences have varying lengths, the maximum sizes of the label-encoding vectors for drugs and proteins were set to 50 and 545, respectively. nn.Embedding embeds the label-encoding vectors of a drug and protein into 50 × 384 and 545 × 384 token-embedding matrixes, respectively. The one-hot encoding method encodes a drug and a protein into 50× (length of the SMILES character list or SELFIES word list) and 545× (length of AA letter list) token-embedding matrixes, respectively.We also added a positional embedding method to the token-embedding, because the position of words generally plays an essential role in any language grammar, defining the semantics of a sentence. As the token-embedding matrixes do not have any information regarding position of the sub-sequences or tokens, we added position information for the sub-sequences to their features. The position label–encoding vector is embedded by nn.Embedding into the position-embedding matrix, which is added to the token-embedding matrix. The resulting matrix was designated the embedding matrix.h [YP by:YD by:We built multi-head cross-attention networks with an attention head number of h . The qued represents the column dimension of We calculated the dot product of The multi-head attention is expressed by:CP is given bySubsequently, context matrix of protein Next to the context matrix of a protein, we computed the following context matrix of a drug, and vice versa.CD, CP), context matrix of a drug alone CD, and context matrix of a protein alone CP.The final context matrixes from the cross-attention networks have three types: consecutively concatenated context matrixes of a drug and protein  (SN), specificity (SP), precision (PR), F1, area under the receiver operating characteristic curve (ROCAUC), and area under the precision and recall curve (PRAUC) . SN, SP,To demonstrate the interpretability of the attention mechanisms, we investigated the relationship between a high value in the attention weight matrix and the experimental drug-binding sites of target proteins or whether the attention sites correspond to the experimental DTI binding sites. First, we added experimental data regarding drug-binding sites, as derived from sc-PDB , to the All the programs were written in Python. Programming regarding SMILES and SELFIES was written using RDKit . The deeThere are a number of network architectures in the attention-based methods . We desiFirst, we defined CA_DP , Fig 1 aTo select the optimal context matrixes resulting from the attention mechanisms, we compared CA_DP, CA_D, and CA_P . CA_P prTo demonstrate the superiority of the nn.Embedding of FCS, five CA_Ps with different encoding methods  were tesWe compared the optimal architecture of ICAN with 7 state-of-the-art methods on DAVIS, as shown in To characterize the robustness of the optimal network architecture of ICAN, we compared it with 7 state-of-the-art methods on the BindingDB and BIOSNAP datasets, as shown in Figs The performance of the deep learning models including ICAN depended on the datasets. No method is ideal or perfect for all three datasets. ICAN provided the highest PRAUC value on the DAVIS and BindingDB test datasets  but not on the BIOSNAP test dataset . This suggests that ICNA has an advantage in predicting imbalanced test datasets. It is notable that the DAVIS and BindingDB test datasets contain a much greater abundance of negative samples than positive samples. In contrast, the BIOSNAP test dataset contains an equal number of negative and positive samples . Both thIt is essential that DTI predictors exhibit generalization capability and robustness for external tests and practical applications. To this end, we must understand what deep learning models learn that can affect the generalization capability and use not only publically available data for drugs and proteins but also unlabeled biochemical and biophysical data, including structural information. In this regard, structural information emerging from the AlphaFold Protein Structure Database  would beTo demonstrate the interpretability of the attention mechanisms, we fed the 160 DTIs with their binding sites into ICAN to generate corresponding attention weight matrixes  and calcTo determine why it is necessary to use the top 30 attention sites, we investigated how a change in the maximum number of attention sites affects the experimental consistency numbers and their corresponding z-scores, as shown in To further validate the relationship between attention sites and experimental binding sites, we shifted the attention sites by −2, −1, 0, 1, and 2 to calculate the experimental consistency numbers and corresponding z-scores. Zero indicates no shift of the attention sites, whereas a value of 1 indicates that the attention site was up-shifted by one amino acid. A shown in Recently developed attention-based deep learning models have suggested that attention can be interpreted by investigating the relationship between highly weighted attention sites and experimental binding sites. Data from TransformerCPI suggested that weighted attention sites are related to binding sites of transforming growth factor-beta type 1 receptor and C-X-C chemokine receptor type 4 . MolTranWe designed an attention-based DTI method designated ICAN, which is composed of an embedding layer, attention mechanism, and output layer. Analyses of the attention mechanism factors  revealed that the plain cross-attention mechanism with CNN and CA_P (ICAN) provided the highest prediction performance. The cross-attention mechanism considers sub-sequence interactions between a drug and a protein to produce context matrixes; the subsequent CNN extracts local sub-sequence patterns within the context matrixes using different filters. ICAN successfully decodes drug-related protein context features without the need for any protein-related drug context features. This is similar to TransformerCPI, which decodes protein features based on encoded drug features, but it is not consistent with MolTrans, which uses two self-attention mechanisms. MolTrans implements an interaction map that integrates the drug and protein context features from the self-attention mechanism to complement the lack of a cross-attention mechanism between drugs and proteins. We then selected nn.Embedding of FCS as the best of the five encoding methods: nn.Embedding of FCS, nn.Embedding of SMILES, nn.Embedding of SELFIES, one-hot encoding of SMILES, and one-hot encoding of SELFIES. Furthermore, we found that SELFIES was more effective than SMILES in terms of DTI prediction, because SELFIES decomposes chemical formulas into a chemically meaningful character list, but SMILES does not.We also characterized the optimal architecture of ICAN in comparison with 7 state-of-the-art methods: a classical LR-based method, CNN-based models , and the latest attention mechanism–based methods . ICAN provided the highest PRAUC value on the imbalanced datasets  but not on the balanced dataset (BIOSNAP). These results suggest that ICAN has an advantage in predicting imbalanced test datasets. The attention-based methods provided higher ROCAUC and PRAUC values than CNN-based models on DAVIS. Both the attention-based methods (ICAN and MolTrans) and CNN-based methods (DeepDTA and DeepConv-DTI) showed very competitive performance on BindingDB. DeepDTA and DeepConv-DTI exhibited better performance than the attention-based models on BIOSNAP. No best method was identified for all three datasets. These results indicate that the performance of deep learning models depends on the dataset.The important task is to first conduct a statistical analysis to clearly determine the relationships between attention sites and experimental binding sites. ICAN was shown to enable the interpretation of various DTI mechanisms. We consider that such interpretability results from the plain structure of the cross-attention mechanism between a drug and protein. On the other hand, our analyses also suggested that the attention mechanism captures critical factors other than binding sites, as it was shown to explain only a few of the 30 attention sites considered. To further increase the interpretability and robustness in examining independent datasets, it is necessary to determine exactly what the attention mechanism really captures and how this affects the generalization capability. Additional DTI predictors remain to be developed using different chemo-genomics approaches.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.


Despite decades of interventions, 240 million people have schistosomiasis. Infections cannot be directly observed, and egg-based Kato-Katz thick smears lack sensitivity, affected treatment efficacy and reinfection rate estimates. The point-of-care circulating cathodic antigen (referred to from here as POC-CCA+) test is advocated as an improvement on the Kato-Katz method, but improved estimates are limited by ambiguities in the interpretation of trace results.We collected repeated Kato-Katz egg counts from 210 school-aged children and scored POC-CCA tests according to the manufacturer’s guidelines (referred to from here as POC-CCA+) and the externally developed G score. We used hidden Markov models parameterized with Kato-Katz; Kato-Katz and POC-CCA+; and Kato-Katz and G-Scores, inferring latent clearance and reinfection probabilities at four timepoints over six-months through a more formal statistical reconciliation of these diagnostics than previously conducted. Our approach required minimal but robust assumptions regarding trace interpretations.Antigen-based models estimated higher infection prevalence across all timepoints compared with the Kato-Katz model, corresponding to lower clearance and higher reinfection estimates. Specifically, pre-treatment prevalence estimates were 85% , 99%  and 98% . Post-treatment, 93% , 72%  and 65%  of those infected were estimated to clear infection. Of those who cleared infection, 35% , 51%  and 44%  were estimated to have been reinfected by 9-weeks.Treatment impact was shorter-lived than Kato-Katz–based estimates alone suggested, with lower clearance and rapid reinfection. At 3 weeks after treatment, longer-term clearance dynamics are captured. At 9 weeks after treatment, reinfection was captured, but failed clearance could not be distinguished from rapid reinfection. Therefore, frequent sampling is required to understand these important epidemiological dynamics. The impact of treatment for schistosomiasis in high-endemicity settings is short-lived because treatment is far less efficacious than the World Health Organization–recommended Kato-Katz method would suggest, and reinfection occurs more rapidly than is detected by current surveillance time frames. Schistosoma mansoni reside in the mesentery venules, forming sexually reproducing dioecious pairs and remaining directly unobservable for up to 40 years, producing hundreds of eggs daily [More than 240 million people live with schistosomiasis, disproportionately affecting those in low- and middle-income countries . Humans gs daily , cancer gs daily .The 2021–2030 neglected tropical disease (NTD) road map from the World Health Organization (WHO) renews the commitment to eliminating schistosomiasis as a public health problem . Key to S. mansoni. Despite widespread use, measuring only eggs, this has proved to be an imperfect method for estimating total worm burden and is plagued by low sensitivity and high variability within individuals [Historically, the WHO recommended Kato-Katz thick smears for diagnosis and mapping of ividuals . The uriividuals .The POC-CCA test has traditionally been scored as negative, trace, +, ++, or +++ (hereafter POC-CCA+) providing a semiquantitative indication of infection intensity. However, ongoing debate about the interpretation of trace results as negative or positive , 13–15 hLatent class analyses are a heterogeneous set of statistical models, of which hidden Markov models (HMMs) are a subset dealing with categorical latent (unobserved) variables. Valuable when there is no reference standard, the “true” infection status of an individual is a latent variable probabilistically estimated with the diagnostics considered imperfect estimators of this status, such that each additional diagnostic adds information. This is beneficial, for example, when a diagnostic lacks sensitivity or specificity but can be complemented by an alternative imperfect diagnostic—as is the case for the Kato-Katz and POC-CCA diagnostics. Such analyses have been used to investigate the relative accuracy of Kato-Katz thick smears, POC-CCA tests, and other diagnostics , 13–15 aPrevious modeling efforts have also failed to overcome 2 distinct challenges. First, previous studies using latent class analyses have made governing assumptions regarding trace score interpretation, considering trace scores as positive or negative from the outset, presenting both versions of the results separately , 14, or Here, we present an HMM that identifies the relationship between the quantitative Kato-Katz thick smears and the semiquantitative POC-CCA tests without making governing assumptions about trace score interpretation. We quantify the rates of unobservable clearance and reinfection after treatment by tracking the probability of infection at the individual level over 6 months after treatment. We incorporate 3 diagnostic methods, including the new G-Score method, assessing the suitability of different diagnostic methods and quantifying the impact of treatment.Data were collected as part of a longitudinal study at Bugoto Lake View Primary School, Mayuge District, Uganda, from 210 children with even sex distribution , aged 6–S. mansoni after treatment, we developed a discrete time stochastic HMM, using the diagnostics data from our 4 time points. We fit 3 versions of the model, using (1) Kato-Katz data alone, (2) Kato-Katz and POC-CCA+ data, and (3) Kato-Katz and G-Score data (To estimate the unobservable clearance and reinfection dynamics of ore data . The Bayore data .runjags package [HMMs consist of unobservable (“hidden”) states, here the infection status of each individual (either infected or uninfected), and observable processes, here the outcomes of the different diagnostics, which are driven by the hidden states. Individuals with undetectable infections  were considered uninfected in the model, because status was estimated based on the diagnostic data at each time point. Between each time step, an individual could remain in the same state  or could switch to another state, for example, an infected child becoming uninfected . From th package , 20), thThe relationship between the observation process (the diagnostic results) and the hidden infection states was modeled as follows. For the Kato-Katz method, truly uninfected individuals must have zero counts (assuming 100% specificity). Infected individuals have a true infection intensity drawn from a gamma-distributed population-level mean at baseline, with an autoregressive random walk component used to estimate true intensity at subsequent time points. We assumed an individual’s true infection intensity to be linearly correlated with this population mean, with the overdispersion of the Kato-Katz counts modeled with a negative-binomial distribution, accounting for interday and intraday variation. The variance in each step of the autoregressive component was scaled to account for the length of the time step and the effect of treatment .When modeling the POC-CCA+ and G Scores, we assumed the true antigen value to be related to true infection intensity following a logistic function. The true antigen values range from 0 to 4 when using POC-CCA+ (representing results from negative to +++), while they range from 0 to 9 when using G Scores (representing scores from 1 to 10). The estimated values of these 2 diagnostics were allowed to vary around the true antigen value following a gaussian  distribution. The variance was calculated from the observed data and fixed in the model.The funding providers had no role in study design, data collection, analysis, interpretation, or report writing. The corresponding authors had full access to all study data and had final responsibility for the decision to submit for publication.Ethical approval for data collection was granted from the Vector Control Division Research Ethics Committee (no. VCDREC/062), Uganda National Council of Science and Technology (no. UNCST-HS 2193), and the University of Glasgow Medical, Veterinary and Life Sciences Research Ethics Committee (no. 200160068). Signed or thumb-printed informed consent was obtained before data and sample collection from the parent or legal guardian for all recruited children, and informed assent was obtained from all children aged ≥8 years.S. mansoni to be 85%   . The Kat79%–92%) .We estimated the proportion of infected individuals who cleared infection  and the The above-described dynamics result in the infection prevalence at each time point shown in S. mansoni clearance and reinfection over 6 months after praziquantel treatment. Our model framework improves on existing models by making no assumptions regarding the interpretation of trace POC-CCA scores. Our model was also suitable for the inclusion of G-Score data through a simple modification. A benefit of this approach is the flexibility imparted by the logistic function. If there were a linear relationship between the POC-CCA scores and infection intensity, the logistic function would take on a linear form. Furthermore, because the parameters of the logistic function estimated by the model are done so with regard to the data the model is fitted to, this framework can readily be applied to data from low-prevalence/low-intensity locations. We quantified the temporal dynamics of Latent class analyses are often used in diagnostic sensitivity and specificity analyses but are limited in their practicality because of the artificial positivity thresholds implemented for trace results . Our impIt is notable that these POC-CCA tests were conducted in accordance with the manufacturer’s guidelines at the time, with 2 drops of urine rather than the single drop and chase buffer, as was historically used. There is evidence that this new method is less specific than the original method and can lead to a significant number of false-positives . HoweverTreatment efficacy is historically quantified by comparing egg counts before and after treatment. Our models indicate that this approach overestimates treatment efficacy and inadequately identifies and quantifies reinfection, with major implications for the timing of MDA. The WHO guidelines recommend annual praziquantel in high-risk communities . Having S. mansoni (re)infection are being missed. Sampling 3 weeks after treatment is sufficient for the characterization of treatment efficacy (Our study was performed 4 months after MDA, with only the recruited cohort being treated at this time, and therefore it is unlikely this would affect community-wide force of infection. However, coverage in this community is known to be low , suggestefficacy , and samOnchocerca and Ascaris [S. mansoni [The differences in epidemiological dynamics estimated between the Kato-Katz–only model and the Kato-Katz and POC-CCA models can be explained by several hypotheses. The reduced rate of clearance in the Kato-Katz and POC-CCA models is likely explained by non–egg-producing worms or low levels of egg production. Surviving adults still produce detectable antigens; however, treatment-induced reproductive quiescence has been seen in  Ascaris , 25 and  mansoni . In addi mansoni . The difRapid reinfection also shows that improving treatment accessibility and equitability  to demogA secondary treatment within weeks of the primary treatment to target the surviving juveniles has been suggested as a solution to rapid resurgence. Though shown to be effective , innate S. mansoni after treatment in a high-endemicity area. Our results raise questions regarding treatment efficacy, the timing of surveillance, and what type of infection the POC-CCA test is detecting. Quantifying the probability of infection for those who receive POC-CCA+ trace or G2/G3 scores will be integral to answering these questions.To conclude, we present an HMM framework that makes no assumptions about the interpretation of trace scores. Using Kato-Katz and POC-CCA data, including the recently developed G-Score method for POC-CCA tests, we quantified the temporal dynamics of clearance and reinfection of Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.Supplementary materials are available at ciab679_suppl_Supplementary_MaterialsClick here for additional data file.


The standard of care for ovarian cancer chemotherapy is paclitaxel-carboplatin. In Stage III and Stage IV patients, the addition of bevacizumab has been reported to be effective, and bevacizumab combined with paclitaxel-carboplatin and bevacizumab combined with docetaxel-carboplatin are used. Patients who received bevacizumab combined with docetaxel-carboplatin experienced a high incidence of skin hardening followed by peeling. In patients treated with bevacizumab combined with docetaxel-carboplatin, we experienced a high incidence of post-sclerotic peeling of the skin, a symptom that is rarely seen with paclitaxel-carboplatin (TC), docetaxel-carboplatin (DC), or bevacizumab combined with paclitaxel-carboplatin, and has been reported in a few cases. Therefore, we investigated the actual situation of skin desquamation caused by bevacizumab combined with docetaxel-carboplatin. Thirty-one patients were included in the study, and their age (mean ± SD) was 62.9 ± 9.0. The breakdown of treatment was as follows: TC in nine patients, bevacizumab combined with paclitaxel-carboplatin in ten patients, DC in six patients, and bevacizumab combined with docetaxel-carboplatin in six patients. No number of patients with TC or bevacizumab combined with paclitaxel-carboplatin showed skin desquamation. One for DC, and five for bevacizumab combined with docetaxel-carboplatin. The five patients treated with bevacizumab combined with docetaxel-carboplatin improved with topical steroids and moisturizers, but symptoms repeatedly appeared after each course. Skin desquamation was more frequent in bevacizumab combined with docetaxel-carboplatin. Neuropathic pain may also occur. In advanced cases, autonomic symptoms such as burning sensation with distal extremity predominance, sensory disturbance involving all senses, sensory-motor disturbance, and bradyarrhythmia may also occur [The standard for ovarian cancer chemotherapy is paclitaxel-carboplatin (TC) , 2. Howeso occur . Since tBevacizumab (BV) is an antibody–drug against vascular endothelial growth factors. In the GOG218 study , a repreThirty-one patients who underwent TC, bevacizumab combined with paclitaxel-carboplatin, DC, or bevacizumab combined with docetaxel-carboplatin for ovarian cancer at our institution from January 2019 to December 2019 were included in the study. The evaluation item was the presence or absence of dermabrasion, and the medical records were examined retrospectively. Ethical approval for the present study was obtained from the local ethics committee .The age (mean ± SD) of the 31 patients was 62.9 ± 9.0. Postoperative adjuvant therapy was used in 23 patients, unsuitable for surgery in three patients and recurrence in five patients. The breakdown of regimens was TC in nine patients, bevacizumab combined with paclitaxel-carboplatin in ten patients, DC in six patients, and bevacizumab combined with docetaxel-carboplatin in six patients. The regimens included TC in nine, bevacizumab combined with paclitaxel-carboplatin in ten, DC in six, and bevacizumab combined with docetaxel-carboplatin in six. The number of patients with skin desquamation was zero for TC, 0 for bevacizumab combined with paclitaxel-carboplatin, one for DC, and five for bevacizumab combined with docetaxel-carboplatin. A summary of the five six cases that presented with dermabrasion is shown in Table In Table Sixty-five years old, female. Stage IVB ovarian cancer. History of bilateral hip osteoarthritis. History of allergy to alcohol. After four courses of DC as preoperative chemotherapy, the patient underwent tumor reduction surgery. After surgery, bevacizumab combined with docetaxel-carboplatin was administered, and after one course, intense skin desquamation occurred on the palms. The swelling was seen, and the skin hardened and then peeled. The pain was mild, with no redness. After four courses of bevacizumab combined with docetaxel-carboplatin, the symptoms improved with the switch to BV monotherapy.Bevacizumab combined with docetaxel-carboplatin was associated with a higher frequency of skin desquamation than TC, bevacizumab combined with paclitaxel-carboplatin, or DC.BV binds explicitly to human vascular endothelial growth factor (VEGF), thereby inhibiting the binding of VEGF to the VEGF receptor expressed on vascular endothelial cells and blocking the VEGF signaling pathway. The blockade of VEGF signaling inhibits angiogenesis in tumor tissue, which VEGF plays a role in, and thus inhibits tumor growth. The package insert for Avastin 2005 for intravenous infusion states that skin desquamation of BV is less than 1%. When used in combination with chemotherapy, skin disorders such as exfoliative dermatitis and rash have been reported to occur in 19–46% of patients and delayed wound healing in 13% of patients . All of We reported the characteristic skin desquamation of bevacizumab combined with docetaxel-carboplatin. The mechanism by which bevacizumab combined with docetaxel-carboplatin causes a high frequency of skin peeling is unknown, but it is suggested that the skin symptoms of DTX may be a synergistic manifestation of the dermatitis of BV. Vascular and endothelial cadherin repeats are relaxed by VEGF, which also controls vascular permeability . This ad


The identification of latent atrial fibrillation (AF) in patients with ischemic stroke (IS) attributed to noncardioembolic etiology may have therapeutic implications. An artificial intelligence (AI) model identifying the electrocardiographic signature of AF present during normal sinus rhythm  can identify individuals with a high likelihood of paroxysmal AF (PAF) with NSR electrocardiogram (ECG).Using AI-ECG-AF, we aimed to compare the PAF risk between noncardioembolic IS subgroups and general patients of a university hospital after controlling for confounders. Further, we sought to compare the risk of PAF among noncardioembolic IS subgroups.After training AI-ECG-AF with ECG data of university hospital patients, model inference outputs were obtained for the control group  and NSRs of noncardioembolic IS patients. We conducted multiple linear regression (MLiR) and multiple logistic regression (MLoR) analyses with inference outputs (for MLiR) or their binary form (set at threshold = 0.5 for MLoR) used as dependent variables and patient subgroups and potential confounders (age and sex) set as independent variables.The number of NSRs inferenced for the control group, cryptogenic, large artery atherosclerosis (LAA), and small artery occlusion (SAO) strokes were 133,340, 133, 276, and 290, respectively. The regression analyses indicated that patients with noncardioembolic IS had a higher PAF risk based on AI-ECG-AF relative to the control group, after controlling for confounders with the “cryptogenic” subgroup having the highest risk  followed by the “LAA”  and “SAO” subgroups . Subsequent regression analyses failed to illustrate the differences in PAF risk based on AI-ECG-AF among noncardioembolic IS subgroups.Using AI-ECG-AF, we found that noncardioembolic IS patients had a higher PAF risk relative to the general patient population. The results from our study imply the need for more vigorous cardiac monitoring in noncardioembolic IS patients. AI-ECG-AF can be a cost-effective screening tool to identify high-risk noncardioembolic IS patients of PAF on-the-spot to be candidates for receiving additional prolonged cardiac monitoring. Our study highlights the potential of AI in clinical practice. Atrial fibrillation (AF) is a well-known risk factor for ischemic stroke (IS) and is associated with a five-fold increase in stroke incidence –3. IS paA recent study demonstrated that an artificial intelligence (AI) model could identify individuals with a high likelihood of paroxysmal AF (PAF) using their normal sinus rhythm (NSR) electrocardiogram (ECG) . This siThus, in this study, we aimed to retrospectively compare the risk of PAF between noncardioembolic IS patients and general patients of Ajou University Medical Center (AUMC) using AI-ECG-AF. Further, we sought to compare the risk of PAF among noncardioembolic IS subgroups.Noncardioembolic IS patients were identified from AUMC's institutional stroke registry, which prospectively collects relevant data, including stroke etiologies of all hospitalized stroke patients. For all stroke patients, 48-h ECG monitoring in the stroke unit, 24-h Holter monitoring, and transthoracic echocardiography were routinely performed to screen for cardiac sources of embolism. From this database, we identified noncardioembolic IS patients admitted between March 2008 and August 2018. Specifically, IS attributed to large artery atherosclerosis (LAA), and small artery occlusion (SAO), or cryptogenic (“cryptogenic embolism” or “other cryptogenic”) IS according to the Stop Stroke Study Trial of Org 10172 in Acute Stroke Treatment (SSS-TOAST) classification were included . In thesTM system , output probability was obtained for each of the five chosen models and the average probability was used as the final inference output.A receiver operating characteristics (ROC) curve of the AI-ECG-AF was created for the control group and the area under the ROC curve was calculated. We computed the accuracy, sensitivity, specificity, positive and negative predictive values, and F1 score of the AI-ECG-AF when the threshold was set at 0.5 by convention. The aforementioned performance metrics were calculated not only for the first recorded NSR ECG per patient in the control group to mimic a real screening scenario but also for all the NSR ECGs in the control group.After the AI-ECG-AF development, model inference outputs were obtained for the NSR ECGs of the control group and those that were measured within seven days before or after the admission date of noncardioembolic IS patients. The inference outputs could be considered to represent the relative probability of PAF presence in each NSR. The time window of seven days before or after the admission date of noncardioembolic IS patients was selected to accurately reflect the state of the patient at the time of stroke occurrence.P-value < 0.05 was considered significant in all tests.We conducted MLiR and MLoR analyses with the AI-ECG-AF's inference outputs (for MLiR) or their binary form  set as the dependent variables and patient subgroups  and potential confounders (age and sex) set as independent variables. First, to compare PAF risk based on AI-ECG-AF between noncardioembolic IS patients and the control group, noncardioembolic IS patients' data and the control group data were included in the regression analyses. Second, to compare PAF risk based on AI-ECG-AF among noncardioembolic IS subgroups, data for only patients with noncardioembolic IS were included in the analyses. The methods and results for checking the overall significance or goodness of fit of these regression models are specified in p-value between 0.05 and 0.1), all other independent variables were statistically significant at a significance level of 0.05 at the threshold of 0.60 for the inference output; for the patient subgroups, “cryptogenic” subgroup had the highest β-coefficient values at all the thresholds.In this study, we trained and validated AI-ECG-AF and used it to compare the risk of PAF between noncardioembolic IS patients and general patients. The regression analyses demonstrated that based on AI-ECG-AF, noncardioembolic IS patients had a higher PAF risk relative to the control group, after controlling for confounders: For the SSS-TOAST subgroups, the “cryptogenic” subgroup had the highest OR followed by the “LAA” and “SAO” subgroups. However, subsequent regression analyses failed to illustrate the differences in PAF risk based on AI-ECG-AF among noncardioembolic IS subgroups.Using AI-ECG-AF, the increased PAF risk in noncardioembolic IS demonstrated in our study supports the recent shifts in IS guidelines that encourage vigorous cardiac monitoring. Patients with embolic stroke of undetermined source (ESUS) carry a substantial annual stroke recurrence rate of 3–6% despite antithrombotic therapy , 17. HowOur study results did not indicate a difference in risk among noncardioembolic IS subgroups, which is aligned with the findings in another recent study that compared PAF risk between the embolic stroke of undetermined source and other mechanisms of stroke (excluding AF) using AI-ECG-AF and illustrated no association . AlthougWhile AI-ECG-AF cannot be considered a confirmatory test because its accuracy is not yet close to 100%, it is an efficient screening tool that can non-invasively assess PAF risk on-the-spot using only ECG as input. Previous studies have been conducted, or are being conducted, using AI-ECG-AF or a similar AI model. Raghunath et al. , have trIf AI-ECG-AF is well validated, it can be a cost-effective screening tool to identify high-risk noncardioembolic IS patients of PAF on-the-spot as candidates for receiving additional prolonged cardiac monitoring. For AF identification, short- to medium-term cardiac monitoring might not be long enough to sufficiently reveal previously undetected AF , 8, 23, Our study has some limitations. First, we did not add potential confounders other than age and sex to the regression analyses. Including more confounders that can be risk factors for PAF and possibly have varying distributions across different patient subgroups, such as those with hypertension or diabetes mellitus, would be ideal. However, incompleteness is a frequently occurring problem in EMRs . For exaIn conclusion, using the AI-ECG-AF, we found that noncardioembolic IS patients had a higher PAF risk relative to the general patient population. The results from our study imply the need for more vigorous cardiac monitoring in noncardioembolic IS patients. AI-ECG-AF can be a cost-effective screening tool to identify high-risk noncardioembolic IS patients of PAF on-the-spot to be candidates for receiving additional prolonged cardiac monitoring. Our study highlights the potential of AI in clinical practice.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Institutional Review Board of Ajou University Medical Center (protocol AJIRB-MED-MDB-21-618). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.CH, S-JL, and DY conceived and designed the overall study. CH extracted the ECG data, carried out the statistical analyses, and drafted the manuscript. S-JL and DY made critical revisions to the manuscript. JL, JH, and S-JL generated and extracted the data of noncardioembolic IS patients from the institutional stroke registry. OK preprocessed the data and developed the neural network model. MC reviewed the raw waveforms of all the AF ECGs of patients classified as positive for AF to improve the accuracy of data labeling. OK, MC, SJ, YL, JL, and JH improved the conception and design of the study. All authors revised the manuscript and approved the final version of the manuscript.This work was supported by the Korea Medical Device Development Fund grant funded by the Korean government  (Project Number 1711138152 and KMDF_PR_20200901_0095). This study was also supported by a new faculty research seed money grant of Yonsei University College of Medicine for 2021 (2021-32-0044). This work was also partly supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education .OK, MC, SJ, and YL are employees of VUNO Inc. DY is an employee of BUD.on Inc. VUNO Inc. and BUD.on Inc. did not have any role in the study design, analysis, decision to publish, or the preparation of the manuscript. We have no patents, products in development, or marketed products to declare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.