Mycobacterium tuberculosis (M. tb) which causes the world’s dominant fatal bacterial contagious disease. Increasing studies have indicated that exosomes may be a novel option for the diagnosis and treatment of TB. Exosomes are nanovesicles (30-150 nm) containing lipids, proteins and non-coding RNAs (ncRNAs) released from various cells, and can transfer their cargos and communicate between cells. Furthermore, exosomal ncRNAs exhibit diagnosis potential in bacterial infections, including TB. Additionally, differential exosomal ncRNAs regulate the physiological and pathological functions of M. tb-infected cells and act as diagnostic markers for TB. This current review explored the potential biological roles and the diagnostic application prospects of exosomal ncRNAs, and included recent information on their pathogenic and therapeutic functions in TB.Tuberculosis (TB) is a major infectious disease induced by  Receper year , 4. Pathman host . Macrophactivity .M. tb as sensitively as microbial cultures  are critical regulators involved in the immune defense of  process . With th process . These eM. tb infection.The present study comprehensively reviews the function and molecular mechanisms of exosomal ncRNAs in the physiological and pathological process of 2Exosomes are 30-150 nm microvesicles, have the same topology as their origin cells and exhibit enrichment of selected proteins, lipids and nucleic acids , 17. ExoM. tb infection.Although exosomes involved in cell-to-cell communication and immune regulation are a topic of intense research, most reports divert to finding aberrations in exosomal protein and RNA contents related to disease occurrence and advancement, which are initially associated with cancer or infection . In partM. tb could induce macrophages to secrete exosomes with pathogen-associated molecular patterns, and these exosomes are in turn transferred into uninfected macrophages to be activated through the Toll-like receptor and myeloid differentiation factor 88-dependent pathway  may be a perfect platform to generate diagnostic biomarkers because the exosomes-regulated resistance against RNA degradation is durable .3NcRNAs are non-protein coding molecules with <200 nucleotides, and comprise a myriad of RNA classes, including miRNAs, circular RNAs  and long non-coding RNAs (lncRNAs), with diverse functions, including numerous small transcripts without related functions , 42. ExoExpression assays demonstrate that ncRNAs are accurately modulated in space and time, and a disturbance in gene expression modes can lead to pathological phenotypes, including various cancers and infectious diseases . Among nAccumulating studies have demonstrated that exosomal ncRNAs are associated with various diseases, including diabetes, coronary artery disease, cancers and infectious diseases –49. For Exosomes transport ncRNAs to communicate phenotypic characteristics between cells . Some po4Exosomes mediate the exchange of intricate intercellular messages such as miRNAs . miRNAs M. tb infection  and most of these miRNAs were decreased  in peripheral blood mononuclear cells (PBMCs) in patients with ATB, patients with LTBI and healthy controls, respectively, and these exosomal miRNAs were involved in functions in hematopoietic cell differentiation and the transition from LTB to ATB  of tuberculosis and other benign lesions using RNA-seq and reverse transcription-quantitative PCR (RT-qPCR). These different miRNA profiles may support the use as biomarkers for differential diagnosis of PEs with more verification based on larger cohorts . As repoquencing . A bioinquencing . The miRquencing .Alipoor et al. identified miR-484, miR-425 and miR-96 in serum-derived exosomes of patients with TB by RT-qPCR, and these three miRNAs were markedly increased in the serum, suggesting that these exosomal miRNAs have diagnostic potential in ATB, and their diagnostic value could be improved through combination with conventional diagnostic markers . Hu et aM. tb infection, and promote the generation of potential molecular targets for the diagnosis of LTB or ATB  and one upregulated miRNA (miR-20a-5p) for LTB, and three decreased miRNAs  and one increased miRNA (miR-584) for ATB using small RNA-seq, and only two miRNAs were shared by the two types of TB (miR-125a-5p and miR-203a) . These eB or ATB . In addiB or ATB . AdditioCarranza et al. found three upregulated miRNAs  to be sensitive separators between controls and patients with TB for both drug-resistant-TB and multidrug-resistant (MDR)-TB groups using a multivariate analysis . MiR-letThese exosomal miRNA sequencing profiles reveal the molecular mechanism of regulating target genes, and of regulation of host physiology and pathology in the process of TB infection by differential miRNAs. These findings provide a comprehensive understanding of miRNAs and repetitive region-derived small RNAs in exosomes during the M. tb infectious process and facilitate the development of potential molecular targets for the detection/diagnosis of TB.5CircRNAs are new classes of internal ncRNAs with tissue- and cell-specific expression profiles, and are covalently shut down and extensively expressed in eukaryotes . CircRNAM. tb H37Ra- and M. bovis BCG-infected macrophages, circ_0129477, circ_0082641, circ_0072892, circ_0104568 and circ_0036372 were detected and the possible downstream regulatory pathway of these differential circRNAs was revealed  in LTBI and 3 RNA genes  in ATB samples , mycobacteria components lipoarabinomannan and the 19-kDa lipoprotein  help detect subjects with a higher risk of developing ATB, or at early stages of disease , 106. ToM. tb infection induces the abnormal expression of genes (including ncRNAs) involved in immune, inflammation, autophagy and apoptosis pathways that can be potential diagnostic or prognostic biomarkers of disease and therapy response. Pedersen et al. have attempted to find known ncRNA signatures that can be assayed in accessible specimens, focusing on circulating ncRNAs, which were measured in blood (PBMCs or serum/plasma)  . The expThe amount of exosomal biomolecules in the blood is difficult to detect. Compared with proteins and lipids, exosomal ncRNAs can be amplified by RT-qPCR, and thus, is more frequently used in the clinic and assayed . Due to 7M. tb infection, exosomes from the infected immune cells exert inherent immune regulation effects on anti-TB immunity. Therefore, exploring potential specific components and biological functions of exosomes is helpful in developing novel diagnosis and therapy strategies. Different ncRNA molecules have been found in exosomes after M. tb infection, which shed new light on the potential role of exosomal ncRNAs as novel TB biomarkers for developing the next generation of TB diagnostic strategies. Exosomes have shown strong potential in delivering vaccine components  in different infectious diseases, showing the potential to provide a more effective vaccine strategy for TB.In YL, JWu, and NW conceived the work and wrote the manuscript. JWa and LT designed and revised the manuscript. XY and YJ discussed and edited the manuscript. All authors contributed to the article and approved the submitted version.


This approach requires isolation and identification of single cells starting from an entire population. A cell suspension obtained by tumor tissue dissociation or hematological material can be manipulated using different techniques to separate individual cells, employed for single-cell downstream analysis. Single-cell data can then be used to analyze cell–cell diversity, thus mapping evolving cancer biological processes. Despite its unquestionable advantages, single-cell analysis produces massive amounts of data with several potential biases, stemming from cell manipulation and pre-amplification steps. To overcome these limitations, several bioinformatic approaches have been developed and explored. In this work, we provide an overview of this entire process while discussing the most recent advances in the field of functional omics at single-cell resolution.Cancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processes behind clonal evolution. In the last few years, functional single-cell  Over the last few decades, major steps forward have been made in the understanding of cancer biology, including disease pathogenesis, progression and dissemination to distant sites. This deeper insight has allowed several advances in cancer diagnosis and treatment, leading to a paradigm shift from a “one size fits all” to a “tailored” approach . DespiteStandard cell-based assays measure the average response in a bulk population, assuming that this average value is representative of each neoplastic cell, or at least the majority of cancer cells. This assumption excludes the possibility that a small but crucial cell subpopulation can be lost. The study of individual cell heterogeneity is critical to detect these subpopulations, and analysis at the single-cell level can disclose molecular mechanisms which cannot be detected in the bulk population. Single-cell analysis also makes it possible to generate a molecular fingerprint representing the specific functional state of any given cell . Moreovemulti-omics level, different technologies have been developed for single-cell isolation and omics analysis, there is also an increasing need for appropriate bioinformatic tools that will store, analyze and interpret the impressive amount of data deriving from a single cell [While at a gle cell ,9,10,11.gle cell ,13.omics.In this review, we will initially illustrate the consolidated cutting-edge technologies required for single-cell isolation and analysis and will then focus on the necessary bio-informatic tools needed for data storage and interpretation in the field of single-cell The identification of specific subtypes of selected cells followed by their individual isolation are mandatory steps in the context of single-cell analysis. In the following sections, we will discuss methods employed for single-cell isolation , Table 1omics analyses [Laser microdissection (LMD)-based systems are high-resolution methods that allow the recovery of selected cells from their surrounding tissues using a microscope, without cell-labeling or genetic manipulation. These cells can then be used for a variety of downstream applications, including analyses .To date, several systems are available, including laser capture microdissection (LCM), laser-assisted mechanical tissue microdissection (LAM), laser pressure catapult (LPC) microdissection, gravity-assisted microdissection (GAM) and mechanical microdissection (MMD). Employing these systems, the cells of interest are manually selected on a screen and a laser is then used to precisely cut tissue for subsequent cell isolation .The LCM system a is the Fluorescence-activated cell sorter (FACS) is a high-throughput and sensitive technique that allows fast sorting and quantitative detection of cells, achieving a purity higher than 98% . Each ceMicrofluidics is a technology that sorts cells by employing several approaches, all based on physical and chemical cellular characteristics .Recently, droplet microfluidic methodology has emerged as an extremely promising technology. This approach combines the flow rates of one cell with a cell partitioning system by chemical particles (barcoding), resulting in a barcoded single-cell capture c. BarcodUsing microwell-based single-cell isolation methodologies, the bulk cell population is dispersed into a microarray containing micropores capable of trapping one cell e. Once tMagnetic-activated cell sorting (MACS) uses a passive separation technique, ensuring high-throughput separation of magnetically labeled cells through antibody-coated magnetic beads .The cells are separated by magnetic beads through an antibody interaction with specific surface markers on the target cells f. Two tyomics era  and nucleotide variations (NVs) a. One ceSingle-cell epigenomics is a powerful new method to better understand gene expression regulation and cell–cell diversity in the context of tumor heterogeneity. Epigenetic events involve chemical modifications of both DNA and histone proteins, modulating chromatin access and transcription factors activity. DNA modifications mostly include the chemical addition of a methyl group on cytosine residues (metC), which can be mapped by sequencing of the bisulfite-converted DNA, generating genome methylation level data ,45 Figub. For hiSingle-cell transcriptomics is an advanced technology that is currently employed to investigate the gene expression profile of an individual cancer cell. Unlike DNA, RNA presents multiple copies for each expressed gene, representing an advantage for gene profiling analysis. However, RNA is an unstable molecule, and its manipulation requires larger amounts of starting material. At the single-cell level, the amount of RNA is very low, therefore, after cDNA generation, a cDNA pre-amplification step is mandatory to perform single-cell transcriptomics c 20,47,,47,48. TAmong them, MARS-seq, SMART-seq2 and 10× Genomics are often used. MARS-seq represents an automatic method where single cells are sorted by FACS, then rapidly lysed, and a 3′ end-counting mRNA sequencing is applied for partial cDNA generation. Indeed, this method does not generate a full-length cDNA. At this time, cDNA is barcoded by a unique molecular identifier (UMI), which ensures the correct quantification of gene expression ,54. SmarMore recently, single-cell spatial transcriptomics (scST) is emerging as an interesting tool for gene expression profiling analysis, including FFPE tissues. Spatial transcriptomics can measure gene expression according to coordinates of the cells within a tissue, representing a promising approach for biological investigation of cells’ transcriptomes in their native context . This meRecently, a new methodology called RNA velocity has been proposed to predict the future transcriptional state of individual cells based on the time derivative of their gene expression . RNA velomics. The first issue in single-cell bioinformatic analysis concerns the normalization of the observed coverage due to the errors introduced by pre-amplification steps in both DNA and RNA sequencing [Data analysis represents the most challenging step in the interpretation of single-cell quencing .Genome sequencing enables the analysis of the entire genome  or only of the exon regions . Data concerning SNVs and insertions/deletions (INDELs) are filtered to calculate the mutation rate, while CNAs are organized as a matrix derived from the read counts of each analyzed DNA region ,72,73. FTranscriptomics experiments generate a large number of reads from each examined gene, making interpretation extremely complex. Usually, transcriptomics data are normalized as transcripts per million bases (RPMK), and several computational approaches have been proposed to detect variations in gene expression ,77,78,79For epigenomic events, methylome alterations are the most studied phenomenon in cancer. The analyses of promoter methylated regions ,87,88,89Overall, these observations highlight the crucial role of computational pipelines in single-cell data interpretation. To improve single-cell data analysis integration, bioinformatic methods based on individual cell machine learning (ML) have been generated and are likely to further improve the correct interpretation of these findings.multi-omics data and identify pathways involved in cancer at the single-cell level [Machine learning is defined as a computational method that generates predictive models integrating and analyzing multiple datasets . In othell level ,95,96,97multi-omics data without mapping the input data to output data. The most used applications are (i) principal component analysis (PCA), which transforms high-dimension data into lower-dimension data while retaining as much information as possible [Unsupervised learning analyzes possible ; (ii) capossible ,100; andpossible .multi-omics data with drug response [Supervised learning is used to predict a specific event. Usually, given a specific input and output label, supervised learning can map the input data to the labeled information. In cancer, labeled information can be phenotype, disease-stage or cancer type. The most common supervised learning models are (i) elastic net, which is used to integrate response ; and  and the single-cell autoencoder tool (scAEspy) [A bioinformatic workflow for scRNA-seq is normally performed by three steps concerning (i) the scRNA-seq data assembly, (ii) data processing and (iii) downstream analysis. Then, the obtained results can answer several biological questions. However, the interpolation of these complex data, in order to generate a predictive model, required an enhancement of artificial intelligence approaches. To this end, neural networks, an improvement to ML, have been developed as a root of deep learning (DL) algorithms, providing better predictive methods ,104. DeescAEspy) ,109. AddscAEspy) , variabiscAEspy) , single-scAEspy)  and neurscAEspy) , impute scAEspy) ,115,116.In conclusion, compared to classical ML, DL has demonstrated a high capacity for computing massive amounts of data derived from scRNA-seq, and also for removing technical noise and artifacts derived from single-cell manipulation procedures. Although the results derived from this bioinformatic triumphing process can be used to answer relevant biological questions linked to transcriptomics, additional improvements are needed as a result of the high complexity of the data. Therefore, potential future directions in the field are oriented toward developing DL algorithms better able to compute the scRNA-seq-derived data to determine the crucial steps of transcriptome analysis and to create a more advanced pipeline.omics data are organized and classified by online portals  [https://singlecell.broadinstitute.org/single_cell accessed on 21 May 2023). CellxGene is a collection of tools useful for downloading and analyzing single-cell data. The Broad Institute portal contains around 500 studies, with data derived from more than 35 million cells. Using both of these online portals, scientists can explore a large single-cell dataset, including scRNA-seq, WGA and WGS associated with specific diseases and cell types. Moreover, it is possible to interpolate a specific gene with a given disease to identify a potential relationship.Another important repository for multi-omics single-cell data is represented by the CellxGene platform (ay 2023)  and singOverall, multi-omics portals represent a suite of web-based platform tools that assists researchers in understanding and gaining a more comprehensive picture of the field of single-cell omics data.omics approaches. The WGS method is the gold standard in the assessment of chromosome imbalances and CNVs, while whole-transcriptome analysis is a powerful approach to investigate cell-to-cell diversity in the context of adaptative stimuli such as microenvironment, drug exposure or tumor dissemination [Here, we exposed single-cell analysis as an advanced approach to the study of several biological questions concerning single-cell resolution, as it is able to dissect intracellular heterogeneity, which is masked when a bulk analysis is performed. The most popular methods used to study the biology of one cell consist of analyzing the entire genome and transcriptome via mination .omics data [its spatial context”. To address this issue, spatial transcriptomics was developed, which aimed to analyze the transcription state in a specific location of one given cell [Despite these indisputable rewards, several limitations occur, caused by single-cell isolation procedures, the low amount of genetic material, the massive amount of data and technical-derived noise. Concerning single-cell isolation procedures, various methods have been discussed in the present work, and each of them aimed to isolate one cell from a tissue or circulating system. In all cases, the cell manipulation associated with each procedure might influence the cell viability and transcriptomic profiling, affecting both the number of viable cells and ics data . Furtherven cell . Howeverven cell . On the ven cell ,132. Lasven cell .An important limitation concerning the scRNA-seq at single-cell resolution is represented by the cell cycle state of a given cell in a given cell cycle phase. It has been well established that in moving between cell cycle phases, the transcriptomics arrangement is affected, thus impacting gene expression profiling. This cellular event leads to additional noise in subsequent data analysis and should be removed. A previous published study, Cyclum, used a DL approach to data obtained by Hoechst-stained cells to distinguish the cell cycle phases .Overall, single-cell analysis remains an intriguing and fascinating approach to study tumor evolution, but a series of limitations make it subject to further improvement.omics applications in different cancer types.Single-cell sequencing has been widely applied in cancer research due to its ability to identify specific alterations in one cell, with relevant implications for cancer diagnosis and treatment . In thisS100A4 as an immunoregulatory gene for suppressive T-cells, potentially representing an immunotherapeutic target in glioma patients [Primitive brain tumors classically derive from neoplastic transformation of glial cells, and their molecular profiling is mandatory for diagnosis . Throughpatients .Breast cancer can be classified in different intrinsic variants according to its molecular profiling ,141,142.Colorectal cancer (CRC) is classified in different subtypes according to its molecular profile, and each subtype shows differences in both clinical and biological properties that suggest high heterogeneity for this cancer type ,148. ConLung cancer often displays a poor prognosis due to its rapid growth, invasive and early metastatic properties. A high heterogeneity is emerging in non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) with the application of single-cell analysis. Using RNA-seq in individual NSCLC-derived single cells, Maynard and colleagues reported that cells surviving to treatment display a molecular signature associated with an alveolar cell pathway . FurtherProstate cancer is a common tumor in men worldwide. From a genomic standpoint, prostate cells have been classified as basal-like, luminal and neuroendocrine ,158, higThe most common thyroid cancer variant is the papillary subtype (PTC), which, although it often has an indolent course, may progress to an undifferentiated or anaplastic state, determining a dismal prognosis. Therefore, the identification of specific alterations that can predict disease progression or identify effective therapeutic targets can play a crucial role in the clinical management of these patients ,162. Pu JAK2-negative myeloproliferative disorders, documenting a monoclonal evolution of the tumor [Hematological tumors comprise a group of malignancies characterized by the clonal expansion of cells derived from the hematopoietic system . Severalhe tumor . In chrohe tumor .omics analysis can be performed in several tumor subtypes [CTCs display both spatial and temporal heterogeneity when compared to the primary tumor. At the single-cell level, CTC subtypes , includisubtypes ,174,175.subtypes .In the era of personalized medicine, tumor molecular profiling is critical to define the peculiar features of each cancer type ,178,179.Despite the outstanding potential of this innovative approach, single-cell methodologies still pose many challenges. Indeed, they are complex techniques due to the low amount of starting material, ingle-cell manipulation, as well as gathering enough isolated cells to generate representative data. Nucleic acid amplification steps are mandatory, but they may introduce biases that must be considered during single-cell data interpretation. Hence, this technical noise can affect single-cell data analysis. To improve the overall performance of this approach, several bioinformatic pipelines have been proposed and implemented, including ML and more advanced neuronal networks or DL.omics data collected at the single-cell resolution likely represent the next frontier of molecular cancer research, further investigations are needed to improve these approaches and incorporate them into the clinical management of cancer patients.While


Dementia is generally caused by neurodegenerative diseases affecting the brain, which leads to a progressive neurocognitive decline characterized by inability to perform major higher functioning tasks. Fluorodeoxyglucose-positron emission tomography (FDG-PET) scan is one of the main imaging tests performed for diagnostic purposes. However, with FDG-PET being quite expensive and not widely available, an attempt to find an alternative is set. Arterial-spin-labelling magnetic resonance imaging (ASL-MRI) is an increasingly investigated substitute to FDG-PET for the diagnosis of dementia. Thereby, the main purpose of this systematic review and meta-analysis is to compare the diagnostic ability of FDG-PET and ASL-MRI in detecting dementia.PRISMA checklist for diagnostic test accuracy was employed in outlining this paper. A literature search was done using several search engines including PubMed, Core, and Cochrane. Two researchers (HH and SH) extracted the essential information from all included articles. Risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies tool, version 2 (QUADAS-2). A qualitative analysis and summary of studies’ results were provided. In addition, a meta-analysis was executed based on the studies which involved sensitivity and specificity measures of diagnostic accuracy.Fourteen total studies were included in the given review. Qualitative analysis of the articles showed that nine studies demonstrated an overlap between metabolic and perfused brain maps as derived by FDG-PET and ASL-MRI respectively, while the remaining five studies registered significant differences across both modalities, with superiority to FDG-PET. As for the meta-analysis implemented, summary ROC-curve analysis revealed that FDG-PET performed better than ASL-MRI, with pooled sensitivity being significantly higher for FDG-PET.Comparing the diagnostic value of FDG-PET and ASL-MRI, the results of this systematic review and meta-analysis indicate that FDG-PET still has an advantage over ASL-MRI. Such implication could be related to the technical differences relating to both modalities, with ASL-MRI having lower temporal resolution. It’s worth mentioning that specificity was rather quite similar among both modalities and some studies found an overridden metabolic and perfused images. These findings call for future research to focus their scope of investigation while exploring the diagnostic value of ASL-MRI. Dementia is derived from the Latin word “de mens” which describes the deteriorating aspect of mental capabilities . AccordiAlzheimer’s disease (AD) is the most common neurodegenerative disease affected by and it constitutes 75% of all dementia cases . VasculaIn an attempt to diagnose patients with dementia, a number of clinical and cognitive tests are prone to take place to confirm the designated disease. The earlier the diagnosis is made, the better the prognosis . NeuroimFluorodeoxyglucose-PET (FDG-PET) scan is employed in the early assessment and differential diagnosis of dementia . NeuroimSeveral studies in the literature have explored and compared the ability of FDG-PET and ASL-MRI to detect dementia. Ceccarini et al. compared the diagnostic ability of ASL-MRI and FDG-PET in differentiating dementia patients and controls . EquivalHenceforth, the former findings derived from literature suggest that FDG-PET and ASL-MRI are comparable, and that both can offer important information about the diagnosis of demented patients. With that kept in mind, our main purpose in this systematic review and meta-analysis is to summarize the results relating to the diagnostic values of ASL-MRI and FDG-PET in correctly distinguishing dementia cases. Our review will combine a qualitative assessment of all studies, and a meta-analysis summarizing sensitivity and specificity measures.The following systematic review follows the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses- Diagnostic Test Accuracy (PRISMA-DTA)  for repoStudies were considered eligible for recruitment if they involved dementia patients of any disease, where these patients were examined using both ASL-MRI and FDG-PET during the same interval of time. There was no restriction on time nor gender. Studies were excluded if recruited patients in the retrieved studies were younger than 18 years of age, had an accompanying history of diseases like epilepsy, have received corticosteroid treatment, or underwent radiotherapy or other treatments before imaging studies were attained.An electronic database search was employed to extract the eligible articles from literature. Electronic databases searched included PubMed, CORE, Cochrane, and additional academic journals being biomedical central journal, Neurology journal, Journal of Neuroimaging, Radiology Journal, Annals of Neurology, and Journal of MRI.Literature was searched through online electronic databases, from March till May 2022, using MeSH terms that involved “dementia”, “FDG-PET”, “PET-scan”, “ASL-MRI”, and “arterial spin labeling”. The former MeSH terms were combined by Boolean operators “OR” and “AND”. The adjusted search overall was in the following form: (“dementia” AND (“FDG-PET” OR “PET-scan”) AND ).Two reviewers (HH and SH) have separately and independently navigated the literature and assessed the articles for inclusion. If there was any disagreement among the reviewers, a discussion was set to resolve it.Information extracted from all included articles is as follows: study’s citation, study design, target condition, sample size of healthy and diseased patients, index tests used, assessment method for analyzing the data received from the employed imaging modalities , the diagnostic accuracy measures reported, and the main findings of the study.In addition to the previous process of data extraction, studies that provided diagnostic accuracy measures in the form of sensitivity and specificity had an additional separate process of data extraction. Data extracted or derived from these latter articles involved the study’s citation, target condition, assessment method of data acquired from index tests, sensitivity, specificity, positive predictive value, and negative predictive value.Studies’ risk of bias was evaluated by the Quality Assessment of Diagnostic Accuracy Studies tool, version 2 (QUADAS-2) .Eligible articles were qualitatively assessed and interpreted. Imaging-based findings were separately and qualitatively summarized for all articles. On the other side, studies that reported sensitivity and specificity measures were part of the meta-analysis executed.Qualitative analysis of the articles consisted mainly of providing a narrative summary of the findings related to metabolized versus perfused brain patterns, while categorizing them based on the nature of the dementia-related disease.As for the meta-analysis, sensitivity and specificity diagnostic measures were extracted from selected studies and converted into true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values to be able to undertake the necessary statistical analyses. Pooled sensitivity and specificity values were estimated and compared while being clearly demonstrated using Forest plots per imaging modality.A summary receiver-operator characteristic curve (SROC-curve) was constructed based on a bivariate model provided by Reitsma . The appAll statistical analyses, summary curves and forest plots were performed using MetaDTA , RStudioThe overall literature search yielded a total of 373 articles, of which 112 articles were screened for eligibility after reading through their title and abstract only. Of these 112 articles, only 16 articles were sought for retrieval. Fourteen articles were overall included , or the method of assessing data .As it is demonstrated in Table Risk of bias was significantly high in 2 out of the 14 articles in the patient selection domain, whereas 8 articles had unclear risk of bias, and 4 articles showed low risk. In the index test domain, only one study showed unclear risk, but the rest of the articles had low risk of bias. All articles had low risk in the remaining domains along with the applicability concerns  Table . On the A ROC-curve was created to further illustrate the results of the meta-analysis conducted as seen in Fig. To investigate the heterogeneity of the studies included in the meta-analysis and understand whether there are factors affecting the variability of both diagnostic measures, sensitivity and specificity, I2 index based on the approach described by Zhou and Dendukuri  was extrThe heterogeneity I2 index, as described by Zhou and Dendukuri , equals Dementia comes in many forms, and it is portrayed through various diseases including Alzheimer’s disease, fronto-temporal dementia, and dementia with Lewy bodies. FDG-PET is a commonly used imaging modality in the diagnosis of the varying dementia-related diseases. However, FDG-PET is known for being expensive and selectively available. Thus, seeking an alternative to FDG-PET is a common target for researchers and clinicians. ASL-MRI is a technique navigated by MRI. It is far cheaper than FDG-PET and is rather widely secured for people. Thereby, the following systematic review and meta-analysis was performed to compare literature findings related to the diagnostic ability of FDG-PET and ASL-MRI, and to conclude whether ASL-MRI is an eligible efficient alternative for FDG-PET.A search of the available electronic literature review yielded a total of fourteen articles to be reviewed and assessed. All articles were qualitatively evaluated and systematically reviewed. Nonetheless, a meta-analysis was performed combining six out of the fourteen articles, for they reported sensitivity and specificity measures of diagnostic accuracy. Our results have shown, from a qualitative perspective, that studies are on opposite sides of the spectrum. Some studies have identified metabolism and perfusion brain maps to overlap in various brain regions depending on the disease investigated. However, other studies couldn’t find overridden brain regions among metabolism and perfusion brain maps, emphasizing that FDG-PET performed better at the diagnostic level. From a quantitative perspective, our meta-analysis revealed, as per the SROC-curve and AUC measures, that FDG-PET with an AUC of 86.7%, displays a better diagnostic performance than ASL-MRI with an AUC of 84.2%. The pooled sensitivity of FDG-PET is 0.858, significantly higher than that of ASL-MRI (0.71). Specificity was rather similar among both techniques (0.863 (FDG-PET) versus 0.834 (ASL-MRI)).Generally speaking, our results indicate that FDG-PET still holds an advantage over ASL-MRI in diagnosing dementia. This could be explained by the fact that FDG-PET measures glucose metabolism which is highly sensitive to neuronal and synaptic activity changes reflecting back directly any degeneration or alteration . On the Our findings somewhat coincide with a previous review comparing the diagnostic performance of ASL-MRI to FDG-PET and other neuroimaging modalities in the diagnosis of various neurological diseases including dementia. Quantitative summary of results derived from literature have indicated that there’s a regional overlap between hypometabolism and hypoperfusion data when considering patients suffering from dementia like AD and FTD patients . The latThe given systematic review and meta-analysis is the first review to summarize the findings comparing the diagnostic accuracy of FDG-PET and ASL-MRI in solely differentiating demented patients from healthy ones. In addition, qualitative and quantitative analyses were both employed. Using qualitative analysis, a summary of the results extracted from studies was outlaid. Quantitatively, a meta-analysis of sensitivity and specificity measures of diagnostic accuracy was undertaken to compare the diagnostic value of both modalities.Despite the significance of this systematic review, it still holds several shortcomings to be highlighted. The number of articles included is low, and the type of our review is a diagnostic test accuracy (DTA) review. This type of reviews is known to be characterized with several limitations. The majority of studies included for the review followed a case-control design, and such design might exaggerate the diagnostic value of tests . MoreoveHeterogeneity pertaining results variation is another limitation in a diagnostic accuracy test review. As different studies have used different cut-off values in determining the sensitivity and specificity measures of diagnostic accuracy, results recorded would substantially vary from one study to another. However, our meta-analysis revealed a low heterogeneity index, which could be attributed to having several sets of sensitivity and specificity in the same study, decreasing the variations across studies, which is counted as a great advantage to the systematic review.With the overall results suggesting that FDG-PET performs better at the diagnostic level for distinguishing dementia patients, still an undeniable part of the results shows that ASL-MRI somewhat performs in a similar way to FDG-PET. Qualitative findings did mention that metabolism and perfusion brain maps override each other in specific regions. In addition, while comparing the diagnostic accuracy between FDG-PET and ASL-MRI, specificity was somehow close in value which indicates a similar performance at that level. This definitely hints on the possibility that ASL-MRI could replace FDG-PET for the many advantages it possesses, if researchers can prove that this technique can guarantee an undistinguishable sensitivity from that of FDG-PET. Thus, it is recommended for future research to explore the effect of some variations, like the disease investigated or the method of assessing the imaging data, on the studies’ heterogeneity in order to decrease the impact of the heterogeneity-evoking factors, and to settle for more accurate comparisons between the two imaging modalities.In spite of the former limitations, our systematic review has successfully met the main research objective. With the systematic review and meta-analysis undertaken, a well-established comparison between FDG-PET and ASL-MRI in diagnosing dementia patients was maintained. While identifying any overlap in metabolism and perfusion brain regions, qualitative findings showed either a similar diagnostic ability of ASL-MRI to FDG-PET, or a diagnostic advantage of FDG-PET over ASL-MRI. On the other hand, the meta-analysis implemented, which summarized sensitivity and specificity measures of diagnostic accuracy, revealed a higher performance associated with FDG-PET. Henceforth, our results although favor FDG-PET in the diagnosis of dementia, still some evidence shedding light on an equivalent performance by ASL-MRI can certainly be further investigated.


Neurofilament-light chain (Nf-L) is a non-specific early-stage biomarker widely studied in the context of neurodegenerative diseases (NDD) and traumatic brain injuries (TBI), which can be measured in biofluids after axonal damage. Originally measured by enzyme-linked immunosorbent assay (ELISA) in cerebrospinal fluid (CSF), Nf-L can now be quantified in blood with the emergence of ultrasensitive assays. However, to ensure successful clinical implementation, reliable clinical thresholds and reference measurement procedures (RMP) should be developed. This includes establishing and distributing certified reference materials (CRM). As a result of the complexity of Nf-L and the number of circulating forms, a clear definition of what is measured when immunoassays are used is also critical to achieving standardization to ensure the long-term success of those assays. The use of powerful tools such as mass spectrometry for developing RMP and defining the measurand is ongoing. Here, we summarize the current methods in use for quantification of Nf-L in biofluid showing potential for clinical implementation. The progress and challenges in developing RMP and defining the measurand for Nf-L standardization of diagnostic tests are addressed. Finally, we discuss the impact of pathophysiological factors on Nf-L levels and the establishment of a clinical cut-off. This protein is found principally in large-caliber myelinated axons and is part of a complex called neurofilament (Nf). Nf-L is associated with other intermediate filaments (IF) subunits: neurofilament-heavy chain (Nf-H), medium chain (Nf-M) and alpha-internexin in the central nervous system (CNS) or peripherin in the peripheral nervous system (PNS) ,2. They ke blood . While tHere, we will summarize the current context of Nf-L, highlighting the different associated diseases, biofluids and diagnostic purposes as well as the current quantification techniques showing great potential for clinical implementation. Then, we will focus on the remaining challenges around this biomarker. We will first describe the measurement variability and the pre-analytical component affecting Nf-L levels in biofluids. Furthermore, we will focus on the metrological challenges such as developing RMP and CRM and on characterizing the measurand, and how mass spectrometry could help to address them. These considerations are critical to achieving standardization of the assays and the development of assay calibrants in order to ensure long-term success. Finally, we will also describe the remaining clinical challenges, including the different pathophysiological factors affecting Nf-L measurements and the need for defining reliable clinical thresholds prior to implementation in clinical routines.Nf-L is a well-established biomarker in the clinic for assessing axonal damage. However, it is not specific to a disease as its level increases in several pathologies such as multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), dementias including Alzheimer’s disease (AD), fronto-temporal dementia (FTD) and other diseases ,6,7,8,9.Nevertheless, in some instances, Nf-L can differentiate one disease from another and help make differential diagnoses. This is the case with Parkinson’s disease (PD) and FTD. Indeed, a higher level of Nf-L has been shown to correlate with atypical Parkinsonian disorder than with PD. A study demonstrated a good diagnostic accuracy to differentiate both with a receiver operating characteristic (ROC) curve between 0.81 and 0.91 . SimilarAdditionally, its utility has been highlighted not only for diagnostic but also for prognostic purposes and for evaluation of treatment efficiency. Indeed, several studies have shown that an increase in Nf-L levels correlates with disease evolution. For instance, in patients suffering from MS, a rise in Nf-L concentration can predict relapses. It is also the case for AD patients. Indeed, different values in Nf-L measurements were observed between cognitively impaired and AD dementia patients ,9. In adMoreover, as with many other NDD biomarkers, Nf-L levels increase before any clinical manifestations. A rise of Nf-L can be seen as early as 10 years in sporadic AD before any clinical symptoms appear . In famiFinally, monitoring Nf-L in clinical trials showed an enormous potential to determine treatment efficiency. Raket et al.  supporteNf-L quantitation in body fluids is performed currently by immunoassays using various technologies  but somePre-analytical variations including the type of matrix or the sampling procedure and sample storage are important considerations, which will be discussed below in greater detail.2 of 0.88 between CSF and serum Nf-L measurements using the Simoa platform and 0.78 with the MSD. Similar results were obtained for Nf-L in CSF and blood samples from two cohorts using the Simoa platform [Initially measured using semi-quantitative methods such as immunoblotting, Nf-L concentrations are now measurable in blood and its derivatives owing to the advent of ultrasensitive assays. Nf-Lin human CSF was first measured with ELISA developed by Rosengren et al. in 1996 . Subsequplatform . Despite the emergence of sensitive techniques and a better understanding of Nf-L as a biomarker, some challenges remain to be addressed before fully implementing Nf-L in clinical practice. First of all, there is a lack of standardization for this marker. In the meta-analysis published by Forgrave et al. , whilst Nf-L values may vary depending on the type of body fluid in which it is measured. Nf-L in CSF is often more concentrated than in blood due to its proximity to the brain. This difference is also present between plasma Nf-L and serum Nf-L. For plasma, the type of collection tube used for sampling has an impact on the total Nf-L levels measured. For example, Ashton et al.  measuredSample treatment is also a source of variability as storage temperature, delayed freezing and multiple freeze–thawing cycles can alter the sample and affect the measurement. Nonetheless, good stability for this marker can be achieved after 3–4 freeze–thawing cycles and where freezing is delayed to 24 h at 2–8 °C or room temperature ,28,29.To ensure clinical implementation, an RMP and CRM should be developed to allow standardization and traceability of results over time and space. Efforts are ongoing to standardize the measurement of Nf-L through various initiatives, e.g., the CSF working group of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). Powerful techniques such as mass spectrometry have been employed to develop both CRMs and RMP but also to define the measurand.Achieving traceability is essential to reduce the inter-laboratory and inter-method variability stemming from different methods and/or choices of calibrants. The ultimate aims are to ensure patient safety when diagnosis is established and to underpin the regulatory approval of the assays . For theBy definition, metrological traceability is the property of a measurement result associated with a reference . This is®-DA480/IFCC, ERM®-DA481/IFCC and ERM®-DA482/IFCC). Their values were assigned through the development of an RMP and they have been used to successfully recalibrate three commercial tests [CRMs are stable and sufficiently homogeneous materials which are fit for their intended use. They should reflect the properties and the quantities of the analyte in clinical samples. For these materials, providers must state a value that is assigned by a metrologically valid method and associated with a defined uncertainty value . In geneal tests . Howeveral tests . Commutaal tests . This pral tests .RMPs enable assessment of the measurement trueness of measured quantity values obtained from other measurement procedures which are measuring quantities of the same kind for calibration or characterization of reference materials . Those mIn the field of the NDD biomarker, two were successfully developed, approved and published for Aβ 1-42 on the JCTLM (Joint Committee for Traceability in Laboratory Medicine) databases as C12RMP1  and C11RSeveral studies, in CSF and in blood, have demonstrated that circulating forms of Nf-L are different from the full-length protein localized in neurons . Some apIn 2017, Brureau et al.  observedAnother publication presented an approach based on immunoprecipitation-mass spectrometry (IP-MS) for the identification of Nf-L fragments . SeveralFinally, fragments of Nf-L were also identified in blood in two other studies ,43. One These studies suggest the presence of matrix-specific fragments which are highly dependent on the proteases present in the different biofluids. Thus, the characterization of the different proteoforms measured by immunoassays in biological fluids is also essential for the standardization and development of assay calibrants and CRMs . As showIdentifying reliable clinical thresholds would be invaluable from the clinical point-of-view to better stratify the different patient groups. It is known that several pathophysiological conditions result in different Nf-L concentrations in body fluids and a better understanding of the clinical background is essential prior to the establishment of cut-off values. Nf-L levels are influenced by several factors like gender, age, comorbidities, or even body mass index (BMI). Here we summarize their effects on Nf-L levels.Numerous studies have tried to better understand the link between Nf-L levels and the sex of the patients. However, no correlation has been established yet. Some have reported higher levels of Nf-L among men than women. It was suggested that the higher proportion of white matter in men’s brains contributes to these results ,45. OtheIt is established that Nf-L concentration correlates and varies with the age in healthy controls and patients. Indeed, during the aging process, it was shown that Nf-L levels were rising. This is because, during the brain aging process, neuronal loss occurs and the blood–brain barrier becomes more permeable ,45,46. ISeveral studies have reported a strong inverse correlation between BMI and Nf-L levels. Individuals with higher BMI and therefore larger blood volume have in general a lower Nf-L concentration when measured in the blood due to dilution effects ,49. InteIt was shown that plasma and serum Nf-L are positively correlated with the creatinine concentration and negatively correlated with the estimated glomerular filtration rate as described by Akamine et al.  and PolyComorbidities such as cardiovascular risk or diabetes also have an impact on Nf-L concentrations, especially in the blood. Patients with a history of stroke, cardiovascular lesion or diabetes show higher Nf-L concentrations . As described above, Nf-L is a dynamic biomarker. Its concentration varies significantly depending on physiological and pathological factors. Therefore, defining reliable predictive cut-off values is paramount for better diagnosis. Cut-offs are normally defined in healthy controls; however as Nf-L also increases with age, it must be defined taking patient age into account. The first study was published in 2020 by Hviid and co-workers  and deteIn a recent publication, Simrén et al.  also rephttps://mybiomarkers.shinyapps.io/Neurofilament/, accessed on 26 June 2023) in which the measured values of Nf-L and patient age can be visualized and interpreted among several reference group values using different percentile intervals and for different types of neurodegenerative diseases.Currently, some online tools are available for physicians, allowing them to take a decision based on cut-offs. For example, Amsterdam UMC developed an interface (This review describes the current limitations of bioassays for Nf-L and their implementation in clinical practice. The major untackled challenges relate to the variability of results and to the numerous clinical and analytical factors affecting the measurement of this biomarker. Despite the use of sensitive and robust instruments for the quantification of Nf-L in biological fluids, the obtained values vary considerably which can lead to misinterpretation. Standardization of Nf-L measurement would be invaluable to ensure the harmonization of values globally. The development of CRM and RMP, not only in CSF but also in blood, is crucial in the near future to support the diagnostic use of Nf-L in clinics. Moreover, the factors influencing Nf-L levels outlined in this review are important and must be considered carefully. Besides the urgent need for measurement standardization, the establishment of cut-off values will be key for accurate use of Nf-L for diagnosis, prognosis and patient follow-up.


A systematic appraisal and statistical aggregation of primary studies in humans.The researchers utilized PubMed (Medline) and Scopus databases as the primary data sources for this study. They performed a comprehensive literature search based on free keywords and Medical Subject Heading (MeSH) terms to enhance the search accuracy. The database search was concluded on November 13, 2022. Furthermore, a meticulous examination of the references cited in the selected studies was conducted to identify additional relevant articles that could be incorporated into the analysis.The systematic review focused on partially or fully edentulous patients receiving dental implants and aimed to determine if the lack of keratinized mucosa at the implant site increased the risk of peri-implantitis compared to patients with adequate keratinized mucosa. Human studies with a minimum of 100 implants, cross-sectional, cohort, or case-control designs, and a follow-up period of at least one year were included. Studies lacking a clear case definition or information on peri-implantitis and those that did not investigate keratinized mucosa as a risk indicator were excluded.2 test and I2 index, determining whether a random-effects or fixed-effects model should be applied. Subgroup and cluster analyses were conducted based on specific criteria, and forest plots and funnel plots were generated to visualize results and identify potential study bias. Sensitivity analysis was performed to verify the robustness of the meta-analysis, with statistical significance set at p < 0.05. The Review Manager (RevMan) software facilitated data analysis.Two reviewers independently utilized a systematic review screening website  to select potential articles, and conflicts were resolved through discussion or consultation with a third reviewer. The data extraction process involved recording information from the included articles, such as study design, patient and implant numbers, prosthesis type (fixed or removable), follow-up duration, peri-implantitis case definition, prevalence at patient and implant levels, keratinized mucosa cutoff value, odds ratio (OR) of peri-implantitis considering keratinized mucosa, and conclusions on the potential effect of keratinized mucosa from each study. The Newcastle Ottawa scale (NOS) and a modified version of NOS were used, respectively, to assess the quality of cohort and cross-sectional studies. Studies scoring below 6 out of 9 points were classified as low quality. For the meta-analysis, the relationship between peri-implantitis and keratinized mucosa was evaluated using the odds ratio (OR) and standard error (SE). Heterogeneity was assessed through the ChiThe GRADE rating system was used to determine the level of evidence, considering factors such as bias risk, imprecision, inconsistency, indirectness, and publication bias. The certainty of the evidence was evaluated based on the overall outcomes of analyzed subgroups.p < 0.00001). Subgroup analyses with a consistent case definition of peri-implantitis (MBL ≥ 2 mm) showed similar results . Studies focusing on fixed prostheses only demonstrated that the lack of keratinized mucosa was associated with an increased prevalence of peri-implantitis . Among patients under regular implant maintenance, the absence of keratinized mucosa significantly raised the occurrence of peri-implantitis . Studies adjusting for other variables also confirmed a higher risk of peri-implantitis with inadequate keratinized mucosa . Although some publication bias was observed, the certainty of evidence based on the GRADE system was judged to be "moderate."Twenty-two primary studies were identified, and a meta-analysis was conducted on 16 cross-sectional studies. The prevalence of peri-implantitis ranged from 6.68% to 62.3% at the patient level and from 4.5% to 58.1% at the implant level. The overall analysis revealed a significant association between the lack of keratinized mucosa and a higher prevalence of peri-implantitis . Future studies with consistent methodologies shall confirm these findings and identify additional risk indicators to improve implant dentistry practices. Mahardawi B, Jiaranuchart S, Damrongsirirat N et al.Sci Rep 2023; 13: 3778.The lack of keratinized mucosa as a risk factor for peri-implantitis: a systematic review and meta-analysis. GRADE Rating:2. However, complications such as peri-implant mucositis and peri-implantitis can arise, posing risks to implant success4. Peri-implant mucositis involves inflammation without bone loss, while peri-implantitis entails progressive bone loss and mucosal inflammation5. These conditions can significantly impact peri-implant tissue health and compromise implant longevity3. Preventive measures and early risk assessment are crucial for managing peri-implant inflammations7 .Among various risk factors, the width of keratinized mucosa (KM) has been suggested as a potential indicator of peri-implantitis9. Studies have reported conflicting findings regarding the association between KM and peri-implantitis prevalence12, making it an area requiring further investigation. The reviewed meta-analysis by Mahardawi et al. aimed to analyze the available evidence and determine whether the absence of KM increased the risk of peri-implantitis. The synthesis also considered specific case definitions, restorative protocols, and maintenance conditions to mitigate potential confounding factors. In other words, the study intended to contribute to improving implant outcomes and patient care by clarifying the relationship between KM and peri-implantitis.Dental implants are widely used to replace missing teeth and restore oral function and aesthetics13. did not find a significant difference in peri-implant disease outcomes between sites with less than 2 mm or greater than or equal to 2 mm of KM width.The review effectively addressed the research question, examining the impact of the absence of KM on the risk of peri-implantitis while considering relevant confounding factors. A thorough literature search identified 22 relevant articles, with 16 cross-sectional studies included in the meta-analysis. The overall analysis revealed a significant association between the lack of KM and a higher prevalence of peri-implantitis. Subgroup analyses consistently supported this finding, including case definition, fixed prostheses, regular maintenance, and adjustment for other variables. The quality assessment of the included studies indicated moderate to high quality. Although there might be some publication bias, the results overall suggest that the absence of KM is a risk factor that increases the prevalence of peri-implantitis. It is essential to consider this factor when placing dental implants. However, the review did not explicitly discuss the applicability of the findings or elaborate on potential biases in the review process. Moreover, a recent review and meta-analysis by Ravidà et al. This systematic review possesses several notable strengths. Firstly, it includes a substantial number of studies, enhancing the reliability and robustness of the findings. Additionally, the review conducted multiple meta-analyses based on specific study features, such as case definition, prosthesis type, and maintenance frequency, thereby minimizing the impact of potential confounding factors and increasing the homogeneity of the results. Moreover, the review avoids fixating on a specific cut-off point for KM and instead considers various values, acknowledging the arbitrary nature of determining an optimal width.Several limitations should be acknowledged. The predominant inclusion of cross-sectional studies limits the establishment of causality and may introduce bias. Thus, future longitudinal studies would provide more robust evidence in this regard. Furthermore, due to insufficient data, the review could not account for certain patient-related and site-specific factors, such as implant location, oral hygiene, time-in-function, and bone augmentation. The reliance on cross-sectional designs also introduces the possibility of deviation in outcomes based on the enrolled sample, potentially reducing the quality of evidence. Finally, although sensitivity analysis did not indicate significant changes, there is a slight asymmetry in the funnel plot, suggesting the presence of publication bias that should be considered when interpreting the results.Future research on peri-implantitis should focus on longitudinal studies with standardized case definitions and analysis methods to establish stronger causal relationships. Additionally, exploring the impact of patient-related and site-specific factors, such as implant location, oral hygiene practices, time-in-function, and the influence of bone augmentation, will provide a more comprehensive understanding of peri-implantitis risk factors. Determining optimal cutoff points for KM width and its association with peri-implant tissue health could inform improved clinical practices in dental implantology.To sum up, the lack of KM increased the risk of peri-implantitis. The review used a rigorous study selection process, data extraction, and quality assessment but could have addressed applicability and potential biases more explicitly. Additionally, the review did not reach high-quality evidence due to insufficient data on patient-specific factors and the predominance of cross-sectional studies. Future research should confirm these findings and identify additional risk indicators.Practice pointsAdequate keratinized mucosa is essential for peri-implant tissue health. Clinicians should consider its presence and width during dental implant placement.Lack of keratinized mucosa increases the prevalence of peri-implantitis. Clinicians should be aware of this risk factor and consider it when assessing patients for implant placement.


Auditory salience is a fundamental property of a sound that allows it to grab a listener's attention regardless of their attentional state or behavioral goals. While previous research has shed light on acoustic factors influencing auditory salience, the semantic dimensions of this phenomenon have remained relatively unexplored owing both to the complexity of measuring salience in audition as well as limited focus on complex natural scenes. In this study, we examine the relationship between acoustic, contextual, and semantic attributes and their impact on the auditory salience of natural audio scenes using a dichotic listening paradigm. The experiments present acoustic scenes in forward and backward directions; the latter allows to diminish semantic effects, providing a counterpoint to the effects observed in forward scenes. The behavioral data collected from a crowd-sourced platform reveal a striking convergence in temporal salience maps for certain sound events, while marked disparities emerge in others. Our main hypothesis posits that differences in the perceptual salience of events are predominantly driven by semantic and contextual cues, particularly evident in those cases displaying substantial disparities between forward and backward presentations. Conversely, events exhibiting a high degree of alignment can largely be attributed to low-level acoustic attributes. To evaluate this hypothesis, we employ analytical techniques that combine rich low-level mappings from acoustic profiles with high-level embeddings extracted from a deep neural network. This integrated approach captures both acoustic and semantic attributes of acoustic scenes along with their temporal trajectories. The results demonstrate that perceptual salience is a careful interplay between low-level and high-level attributes that shapes which moments stand out in a natural soundscape. Furthermore, our findings underscore the important role of longer-term context as a critical component of auditory salience, enabling us to discern and adapt to temporal regularities within an acoustic scene. The experimental and model-based validation of semantic factors of salience paves the way for a complete understanding of auditory salience. Ultimately, the empirical and computational analyses have implications for developing large-scale models for auditory salience and audio analytics. In its most general definition, attention can be described as an information selection process that facilitates the brain's ability to select the most relevant information in the sensory space while filtering out less relevant information  to quantify the effect of images, scenes, and videos during free-viewing or complex task execution by observers, making it a n Treue, . Numeroun Treue, .By tapping into the natural behavior of eye movement during free viewing, visual salience studies have revealed a complex interplay of factors guiding attention to the most salient regions in a scene  unfold over time. It is an important element of auditory scene analysis and perception as natural sounds are characterized by their temporal dynamics which can span multiple timescales from few milliseconds  to hundreds of milliseconds  or longer . Such multiscale temporal integration is a key computational principle for auditory function , where the scenes were presented in their original format, and a backward experiment (referred to hereafter as bwd), where the scenes were time-reversed , as stimuli. These scenes were downloaded from reversed . Table 1reversed .fwd experiment, and the remaining 136 participated in the bwd experiment. The median age of participants was 29 years [standard deviation (std) = 8 years], with 170 male, 83 female, and 15 non-binary or unspecified gender. While 116 participants reported having no musical experience, 76 participants reported having at least 2 years of musical training. Participants were compensated for taking part in the study after completion. All experimental procedures were approved by the Johns Hopkins University Institutional Review Board (IRB).A total of 268 participants were recruited for this study from the Amazon Mechanical Turk (MTurk) platform for a web-based experiment. Among this pool, 132 listeners participated in the The dichotic listening paradigm, used in earlier auditory salience studies  for all subsequent analyses.Responses from each trial represented attentional switches toward one of the scenes in the dichotic listening paradigm. Responses were assigned a value of 1 when participants listened to a given scene and 0 otherwise. The resulting behavioral salience for each scene was derived by averaging responses from all participants for that scene (and different opposing scenes). All individual responses were adjusted by 1 s to account for an average participant reaction time delay, based on estimates from earlier findings . For each participant, the individual reaction time was defined as the time elapsed from the event onset to when the participant switched their attention toward the scene. If the participant did not change their attention 1 s before or after the event onset, they were considered non-responsive to the event. The choice of 1s before the onset reflected the reaction time adjustment. Other reasonable choices around 1 s resulted in quantitatively and qualitatively similar results. For each participant, reaction times were averaged across responsive events resulting in individual reaction times per participant.fwd and bwd experiments was analyzed using a rank-based correlation metric. For each scene, fwd salience responses were correlated with the corresponding time-reversed bwd salience response, using Spearman correlation. This correlation served as a measure of agreement between responses from playing a scene in forward and backward directions.The agreement between continuous responses from fwd and bwd discrete salient events was also analyzed. bwd event boundaries were reversed in time by subtracting the onsets and offsets from the scene duration to match with fwd time stamps as shown in fwd event, the maximum overlap with bwd event was computed by comparing with all bwd events for the corresponding scene. An overlap score for each fwd event was calculated by normalizing the maximum overlap with the event duration. fwd events with more than 50% normalized overlap were called forward-matched events (referred to hereafter as fwd-match ), and the remaining events were called forward-mismatched events (referred to hereafter as fwd-mismatch ) as shown in bwd events, resulting in bwd-match and bwd-mismatch events. While the 50% threshold was chosen heuristically, similar results were obtained for different reasonable thresholds around 50%.In addition to continuous salience curves, agreement in timing between z-score normalized per scene. Changes in these features were analyzed around salient event onsets to examine the relation between acoustic changes in the scene and attentional switches. Changes in features were defined as the difference between the average feature in the interval 0.5–1.0 s before and after the event. Feature changes were analyzed for matched and mismatched events from the fwd and bwd experiments to explore differences between these two classes of events.Sixteen acoustic cues were used to analyze the effect of low-level acoustic dimensions on salience, which are outlined in For semantic analysis of the scenes, the open-source EfficientAT audio tagging model , a layer-wise surprisal was computed for each layer to compress the information to 1-dimension and capture differences between the present and immediate past representations , Things , Music , Animals , and Background . Source-ambiguous sounds (ex: impact sounds) and Channel, environment, and background sounds in the ontology were merged into the Background category. For finding the dominant class, the top 10 classes with maximum average posterior probabilities within the segment were considered candidate classes. Posterior probabilities of these classes were added to the top-level root node to propagate the beliefs. The top-level class with maximum probability after the belief propagation was considered the dominant class within the segment. This labeling method was carried out for the segments from fwd and bwd scenes. The evaluation extracted the dominant 10 classes for each scene presented in fwd and bwd fashion based on posterior values; then, compared the predictions of bwd scenes with fwd and identified hits and misses. A recall score of 80% was noted across all scenes.In order to ensure that the backwards scenes were not disadvantageously processed using the semantic neural network model, we evaluated the model separately on the Regression models predicting average behavioral salience were developed using acoustic and semantic features. All three representations  were resampled at a sampling period of 64 ms. The resampled features were smoothed with moving-average smoothing, with different averaging lengths for acoustic and semantic features, which were treated as hyperparameters.rt on the salience curve as a linear mapping from acoustic features at and semantic features st. The features were sampled to include past context and future context to incorporate long-term context and were flattened into vectors of dimension da for acoustic features and ds for semantic features . The lengths of past and future contexts were considered hyperparameters. The model can be mathematically written asThe regression model predicted each point Wa, Wcandb were estimated to minimize the Euclidean distance between behavioral salience rt and predicted salience 2-regularization on the parameters was utilized for parameter estimation to avoid model overfitting  event overlapped with a segment, the segment was labeled matched (or mismatched). When a segment overlapped with both matched and mismatched events, the category with higher overlap was assigned to the segment. Segments with no overlap with any events were placed in a non-event category. Assigning event categories to fwd segments gave 59% fwd-match , 34% fwd-mismatch , and 7% no-event segments. Similarly, bwd segments were split into 60% bwd-match , 33% bwd-mismatch , and 7% no-event segments.Regression models were evaluated on 8 s segments of the stimulus/behavioral response with 4 s overlap (ignoring the first and last 1 s to avoid edge effects), resulting in 547 segments for both a, andλc were chosen based on the correlation on the left-out fold. Once the hyper-parameters were fixed, the model parameters were estimated using the data from 55 scenes. We also evaluated the model using a different approach. Two sets of regression models for fwd and bwd data were trained using a 10-fold validation. By training on nine folds and testing on the left out fold, predictions were computed for the fwd and bwd data with their respective models. This second evaluation method yielded statistically similar effects as the first evaluation using the independent dataset.The regression model parameters were estimated using the 55 scenes from the DNSS-Ext dataset . In this analysis, the goal is not to produce a continuous estimate of salience as the scene unfolds but to derive binary outcomes about the onset timing of sound events. This approach is consistent with other works from the literature and allows us to directly gauge how the proposed acoustic and semantic features compare to other conceptual accounts of salience and sound event detection. We adopted the regression pipeline above with acoustic and/or semantic features to detect the events. Given the changed scope and limited data relative to degrees of freedom for the detection model, the event detection model differed from the regression model in a few ways: (i) a first-difference was applied to the features as the first step, (ii) the temporal weights were shared across the acoustic/semantic features, (iii) a sigmoid non-linearity was applied as the output function to convert the detection signal to a Bernoulli variable in the range ∈. The detection model predicted if an event onset occurred within an input segment of 1 s. Two variations of this model were considered for evaluation. The first model, referred to as the acoustic model, used only the acoustic features within the 1 s duration and was referred to as the Acoustic model. The second model (A-S model) utilized acoustic and semantic features within the segment with a past context of 3s appended to the 1 s segment. For both these models, a stochastic gradient descent algorithm using a cross-entropy loss function on the 55 scenes from the DNSS-Ext dataset estimated model parameters. The model was trained using a mini-batch size of 200 segments, with a learning rate of 0.01 for 100 epochs through the whole training data. The model with the lowest training loss was used as the final model.In addition to predicting continuous salience, the linear filter framework was used to train a model to predict the The performance of the A-S and A-only models was compared with other models from the literature. EfficientAT is primarily an audio tagging system and provides outputs that reflect changes in sound events. EfficientAT posteriors were thresholded at 0.5 and were smoothed using a median filter of width 1 s to provide smoothed binary labels indicating the presence of the audio classes. A first-order difference followed by summing across the classes resulted in a detection signal indicating changes in the class distribution. A second comparison was performed using the salient event detection model from Kim et al. . The modReceiver operating characteristic (ROC) and area under ROC (AUROC) were used for evaluating performance. A range of thresholds was applied to the detection signals from each model to produce onset estimates. By comparing the estimates with the reference, the fraction of hits and false alarms (FA) were computed for each threshold.fwd and bwd scenes. Individual reaction times characterized the delay between a salient event and when each participant responded to that event. For the fwd experiment, the median participant reaction time was 0.91 s (std = 0.14 s), and for the bwd experiment, the median participant reaction time was 0.89 s (std = 0.15 s). The distributions of individual reaction times for fwd and bwd scenes were not significantly different .Overall, participants responded with a similar reaction time to both fwd and corresponding bwd scenes was variable, with an average Spearman correlation ρ = 0.540 (std = 0.23) over 20 scenes. Randomly pairing fwd and bwd salience responses provided a noise floor for this correlation with an average ρ = 0.06 (std = 0.05) over 100 random permutations. Across the 20 scenes, correlations were widely variable (as low as ρ = 0.041 (p = 4e-10) for scene 10 and as high as ρ = 0.869 (p < 1e-100) for scene 20 (see Table 5). fwd and bwd responses, yielding a correlation ρ = 0.587 (p < 1e-100). While there was no clear factor explaining this variability, the range of correlations suggested a strong interplay of scene acoustics, sparsity, context, and semantics, as will be explored in subsequent analyses.The congruence of behavioral salience responses between fwd and bwd scenes were comparable in number and duration of events. There were 451 events in fwd and 460 events in bwd responses, with an average of 23 events per scene and one event approximately every 5s. The average event duration of fwd events was 2.75 s (std =1.46 s) and 2.63 s (std = 1.28 s) for bwd events, with no statistically significant differences in duration between fwd and bwd events . The average strengths of fwd events  and bwd events  were also not statistically significant .Salience responses were also analyzed anchoring on events representing attentional switches toward (onset) and away from (offset) from the scene. On average, salient events in fwd and bwd scenes. For fwd events, there were 63% matched and 37% mismatched events, whereas, for bwd events, there were 61% matched events and 39% mismatched events. With that, divergent patterns emerged between matched and mismatched categories. The matched and mismatched categories were analyzed for differences in duration and strengths as shown in fwd and bwd events, matched events were shorter than mismatched events. Salience strengths for matched events were higher than mismatched events for both fwd and bwd events. In addition, slopes of matched events were higher than mismatched events, indicating lower temporal agreement for mismatched events compared to matched events. The absolute consensus for matched events was not significantly different from mismatched events, indicating that a similar percentage of participants reacted to matched and mismatched events. Thus, mismatched events showed variability when participants responded to an event onset, but on average captured the attention of the same number of participants as matched events.Looking closer at event types, we broke down events into matched and mismatched categories for both experiments (see Methods). We noted that the ratio of matched/mismatched events was similar for fwd scenes as well as bwd scenes. We first performed an ANOVA to evaluate the differences in acoustic feature changes when grouped by the effects of scene presentation (fwd /bwd ), event type (matched/mismatched), and feature type. The ANOVA reveals that event type , feature type  groups showed significant effects and presentation type  did not show significant effects. The interactions among event and feature types significantly affected the acoustic changes . Interactions between presentation and event types  and presentation and feature types  were not significant. Next, we evaluated changes in individual acoustic features near event onsets, as shown in Changes in the acoustic profile of the scene were likely a factor in driving switches in attentional focus when listening to natural scenes. Changes in acoustic features before and after a salient event were quantified and contrasted for matched and mismatched events for fwd-match, fwd-mismatch, bwd-match, and bwd-mismatch events, indicating that subjects reacted to sudden increases in sound energy. When compared across groups, for all energy features, fwd-match and bwd-match events had a significantly higher magnitude of changes compared to fwd-mismatch and bwd-mismatch events, respectively. This indicated that changes around mismatched events were more subtle in intensity when compared to matched events. No significant differences were observed between fwd-match and bwd-match events for energy features.Energy features analyzed the signal strength (or intensity) captured across different levels, including loudness (LD), spectral energy (SE), and spectrotemporal energy (RSE). These features were found to have significant changes at event onsets for fwd-match and bwd-match events, with brightness and irregularity increasing at event onsets and bandwidth and flatness decreasing around events. For fwd-mismatch and bwd-mismatch events, while changes in BR, FL, and IR were significant, BW did not have significant changes. fwd-match events had a higher magnitude of changes compared to fwd-mismatch events only in FL, IR. In contrast, bwd-match events had a higher magnitude of changes compared to bwd-mismatch only for BR and FL features. No significant differences were observed between fwd-match and bwd-match events for the spectral features.Spectral features captured the timbral profiles of the scenes and included brightness (BR), bandwidth (BW), flatness (FL), and irregularity (IR). All four features considered showed significant changes for fwd-match and bwd-match events had significantly higher changes compared to fwd-mismatch and bwd-mismatch events for H, only bwd-match showed significant differences from bwd-mismatch for P. No significant differences were observed between fwd-match and bwd-match events for pitch and harmonicity.Pitch (P) and harmonicity (H) features captured harmonicity and voicing information in the scenes. Both features increased near event onsets for all event groups. While, fwd-match and bwd-match events had significantly larger changes compared to fwd-mismatch and bwd-mismatch events only for MS. No significant differences were observed between fwd-match and bwd-match events for the scale-related features.Spectral modulation features captured changes in spectral variations in the scenes. Max-scale energy (MS) increased at event onsets, and scale centroid (CS) decreased around event onsets. fwd-match , bwd-match , and bwd-mismatch events and no significant changes for fwd-mismatch events. Absolute rate centroid (CAR), which ignored the direction of modulations, had significant negative changes for all categories of events. As with other energy features, MR, HR, and LR features showed significant differences between fwd-mismatch and fwd-match events and between bwd-mismatch and bwd-match events. Both CR and CAR showed no significant differences between fwd-match and fwd-mismatch as well as bwd-match and bwd-mismatch groups. No significant differences were observed between fwd-match and bwd-match events for the temporal features.Temporal modulations were captured using features reflecting average and maximum changes in temporal rates or variations in the scenes. Maximum rate energy (MR), high-rate energy (HR), and low-rate energy (LR) increased at event onsets. Rate centroid (CR) had significant negative changes for fwd and bwd events. There were no significant differences between fwd-match and bwd-match events.Overall, pairwise comparisons showed that mismatched events had lower acoustic changes compared to matched events for both fwd (avg = 0.557) was significantly lower  than bwd segments (avg = 0.618). When the segments were broken down by the matched-mismatched categories, performance for fwd-match segments (avg = 0.581) segments was significantly higher than fwd-mismatch segments (avg = 0.473) . Similarly, performance on bwd-match segments (avg = 0.631) was significantly higher than bwd-mismatch segments(avg = 0.577) . No significant differences were observed between fwd-match and bwd-match segment correlations . Thus, instantaneous acoustic features predicted behavioral salience better around matched events when compared to mismatched events for both fwd and bwd scenes.The feature change analysis was limited in showing driving factors of salience as several features presented were highly correlated  and bwd (avg = 0.642) segments. Pairwise sign-rank tests indicated the improvements were significant for both fwd  and bwd  segments. Comparison between fwd and bwd segments indicated a significant difference in correlations . Breaking down by the events, adding long-term context significantly improved performance for fwd-match , fwd-mismatch , bwd-match  bwd-mismatch  segments. Despite the improvements in salience prediction with the long-term context, the differences between fwd-match and fwd-mismatch segments  and between bwd-match and bwd-mismatch segments  remained statistically significant. Significant differences were observed between fwd-match and bwd-match segment correlations . The relative improvements in average correlations from using the long-term context were higher for fwd-mismatch (9%) in comparison to fwd-match (2%), bwd-match (3%), and bwd-mismatch (4%). Thus, acoustic features with long-term context predicted salience better than those without context across all the segments and categories. The effect was strongest for fwd-mismatch segments, as indicated by the improvement in correlation. The addition of long-term context reduced the differences between fwd-match and fwd-mismatch events.Adding long-term context to acoustic features  and bwd segments , with only the improvements on fwd segments being significant. For this model, segment correlations for fwd were not significantly different from bwd . The further breakdown of fwd segment correlations revealed that the gains from the A-S model (compared to the A-C model) were significant only for fwd-mismatch segments . For other event groups, the difference in correlations between the A-S and A-C models was not significant , bwd-match , bwd-mismatch ). With the contribution from the semantic features, segment correlations for fwd-mismatch segments were not statistically different from fwd-match correlations , while bwd-mismatch segments were predicted significantly worse than bwd-match segments . Thus, the semantic features were only effective in improving prediction for fwd-mismatch segments, supporting our hypothesis that the mismatched events from fwd scenes were strongly driven by semantic attributes of the scene.The acoustic-semantic model (A-S model), which used semantic features in addition to acoustic features with long context, provided further gains in prediction. Adding semantic features improved prediction for fwd-mismatch segments. To account for the added parameters with the addition of more layers, we trained randomized baseline models by incrementally adding random values for surprisals. Performance of the randomized baseline models stayed constant for all the layers, which further cements the benefits of the layer-wise surprisals. When only the semantic surprisal was used for predicting the behavioral salience (S model), the performance across all segments was lower than the A-S model.To analyze the gains from the semantic surprisals, we tested regression models which incrementally included surprisals from different layers, from input layers to classification layers. To further understand the gains from the long-term context, we analyzed temporal correlations for individual features, focusing on segments around events. In fwd-match and fwd-mismatch segments, respectively. The segment correlations were analyzed for A-only and A-S models. A wide range of correlations was observed across different classes, and the improvement from the semantic models was not uniform across classes. In fwd-match segments, there were no significant differences in performance between A-only and A-S models . In fwd-mismatch segments, Human, Music, and Animal classes showed significant improvements with the A-S model  and the remaining classes showed no significant differences . These differences in class-wise performance indicated a higher-order dependence on semantics that is class-dependent.Further characterization of the salience prediction examined the effects of sound classes. Examining the matched-mismatched segmentation and class labels revealed that mismatched segments had a higher percentage of Human, Things, and Music classes when compared to matched . Figuresfwd data with AUROC of 0.761. The A-S model showed a significant improvement over the acoustic (A-only) model (AUROC = 0.723), validating the benefits of semantics and acoustic context. In comparison, a detection model using loudness from bark filters  should have a high agreement, as the acoustics remain similar when scenes are reversed. The lack of agreement points to additional factors other than acoustics playing a role. At the same time, various behavioral metrics such as reaction times, number of events, event durations, and event strengths were not significantly different, indicating a similar participation profile across the two experiments. The high variability in the correlations  across the scenes, which had a variety of objects, hints at a complex interplay of scene context and semantics along with acoustics.To establish the contextual and semantic effects, we first demonstrated that the fwd and bwd events with a threshold on the amount of overlap (chosen as 50%). This analysis provides mismatched events, which are moments in time where the fwd and bwd events did not have a corresponding shift in attention in the opposite direction. The proposed temporal alignment process is a novel approach that can theoretically separate contextually or semantically driven events from purely acoustically driven events. The similarity in absolute consensus for matched and mismatched events across fwd and bwd data points to a consistent attention switch within the event. This separation of events provided an anchor to explore the role of acoustics and semantics.In addition to the salience curves, we analyzed the temporal alignment between fwd-mismatch and bwd-mismatch events were driven by lower acoustic changes when compared to fwd-match and bwd-match events, respectively. While this effect was most evident in energy features, several other spectral and temporal modulation features showed similar trends. Features such as pitch, brightness, and roughness were observed to affect salience directly  reflect implicit features representations that generally reflect the semantic content of the acoustic scene and ultimately facilitate the tagging task for which the model is trained (LeCun et al., Prediction models such as linear regression take cross-correlations into account and provide a method to evaluate the relevance of individual features with the predicted variable. In this study, we used a linear model to predict salience from different features. By using a model with temporal weights and semantic features, we validated the effect of context and semantics in driving salience. This approach parallels several visual salience models that incorporated contextual and semantic factors using linear combinations of low-level and object-level salience (Cerf et al., The role of temporal context in salience prediction has been demonstrated in a number of studies. The present analysis shows that such context operates over larger time scales than originally reported in previous work. Earlier studies employed temporal context as a smoothing operation (Huang and Elhilali, fwd-mismatch segments than other segments. The context effect was stronger for fwd-mismatch, supporting our hypothesis that these segments were influenced by long-term temporal context. Secondly, adding semantic information offered a more striking result, with the improvement only observed for fwd-mismatch segments. While the acoustic feature change suggested both fwd-mismatch and bwd-mismatch events had lower acoustic changes, the semantic information only helped fwd-mismatch segments. These two observations provide strong evidence supporting our hypothesis that fwd-mismatch events were driven by high-level factors. Exploring the prediction performance for different event categories in the fwd-mismatch data indicated Human, Music, and Animal categories benefit from contextual and semantic information.While including long temporal context improved performance for different segment categories, as shown in bwd data served as a counterpoint to fwd events. While bwd-mismatch events show lower acoustic changes than bwd-match , similar to fwd-mismatch events, they do not benefit from the semantic information. Although the results shown in bwd data from this study. Thus, the fwd-mismatch events are distinct from bwd-mismatch events in that they are driven by semantics.In this study, events from The event detection paradigm provides an alternative framework to evaluate salience models, where the focus is detecting the moments when subjects switched their attention. In addition, we used this framework to compare the salience models proposed in this study with some notable computational frameworks of salience. As noted previously by Huang and Elhilali , the linA natural question arising from the event detection framework is whether a model trained with a sound event detection objective is suitable for detecting salient events. A change detection analysis of the posteriors from the audio tagging model detected salient events better than chance but worse than the proposed model. The acoustics and intermediate representations from the tagging model are essential for salient event detection. Thus, the high-level information from the tagging model useful for salience constitutes more than the object categories. We note that the detection framework focuses only on the onsets as opposed to sound event detection paradigms, which consider event offsets as well (Mesaros et al., The models proposed in this study for salience prediction and event detection use simple parametric formulations, which make them flexible to incorporate different types of semantic information with varied context lengths. While such simplicity can be limiting, it also enables easy incorporation of additional semantic abstractions. There is a possibility that more granular measures such as phonetic features and lexical surprisals could explain the salience of human sounds. The goal of building a comprehensive picture of salience has important technological consequences in addition to scientific benefits. Salience measures can be useful in associating a degree of perceptual confidence to audio events for applications such as audio event detection and audio captioning. More ready usage of salience in real-world applications requires further efforts to improve the computational models. The experimental data collected in this data can serve as a benchmark to evaluate and improve salience models such that they can closely match human experience in real-world scenarios.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving humans were approved by Johns Hopkins University Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.SK: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. ME: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.