Neutrophil-derived migrasomes are an essential part of the coagulation system.

Migrasomes are organelles that are generated by migrating cells. Here we report the key role of neutrophil-derived migrasomes in haemostasis. We found that a large number of neutrophil-derived migrasomes exist in the blood of mice and humans. Compared with neutrophil cell bodies and platelets, these migrasomes adsorb and enrich coagulation factors on the surface. Moreover, they are highly enriched with adhesion molecules, which enable them to preferentially accumulate at sites of injury, where they trigger platelet activation and clot formation. Depletion of neutrophils, or genetic reduction of the number of these migrasomes, significantly decreases platelet plug formation and impairs coagulation. These defects can be rescued by intravenous injection of purified neutrophil-derived migrasomes. Our study reveals neutrophil-derived migrasomes as a previously unrecognized essential component of the haemostasis system, which may shed light on the cause of various coagulation disorders and open therapeutic possibilities.

Exosomal mRNA in plasma serves as a predictive marker for microvascular invasion in hepatocellular carcinoma.

BACKGROUND AND AIM: There is a pressing need for non-invasive preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). This study investigates the potential of exosome-derived mRNA in plasma as a biomarker for diagnosing MVI. METHODS: Patients with suspected HCC undergoing hepatectomy were prospectively recruited for preoperative peripheral blood collection. Exosomal RNA profiling was conducted using RNA sequencing in the discovery cohort, followed by differential expression analysis to identify candidate targets. We employed multiplexed droplet digital PCR technology to efficiently validate them in a larger sample size cohort. RESULTS: A total of 131 HCC patients were ultimately enrolled, with 37 in the discovery cohort and 94 in the validation cohort. In the validation cohort, the expression levels of RSAD2, PRPSAP1, and HOXA2 were slightly elevated while CHMP4A showed a slight decrease in patients with MVI compared with those without MVI. These trends were consistent with the findings in the discovery cohort, although they did not reach statistical significance (P > 0.05). Notably, the expression level of exosomal PRPSAP1 in plasma was significantly higher in patients with more than 5 MVI than in those without MVI (0.147 vs 0.070, P = 0.035). CONCLUSION: This study unveils the potential of exosome-derived PRPSAP1 in plasma as a promising indicator for predicting MVI status preoperatively.

Emerging concepts of migrasome: An up-and-coming organelle from biology to the clinic.

Since the migrasome concept was first proposed in 2015, extensive research has been conducted on these novel organelles, which grow on retracted fibers at the posterior end of migrating cells. Recently, molecular markers, biological functions, and clinical values based on the initial formation mechanism of migrasomes have emerged. Additionally, researchers are recognizing the significant role that migrasomes play in the pathological and diagnostic processes of clinical diseases. In this review, we summarize recent advances in the biology and clinical application of migrasomes and provide a comprehensive view of the prospective challenges surrounding their clinical application.

An integrated platform for decoding hydrophilic peptide fingerprints of hepatocellular carcinoma using artificial intelligence and two-dimensional nanosheets.

Hydrophilic peptides (HPs) play a critical role in the pathogenesis of hepatocellular carcinoma (HCC). However, the comprehensive and in-depth high-throughput analysis of specific changes in HPs associated with HCC remains unrealized, due to the complex nature of biological fluids and the challenges of mining complex patterns in large data sets. The clinical diagnosis of HCC still lacks a non-destructive and accurate classification method, given the limited specificity of widely used biomarkers. To address these challenges, we have established a multifunctional platform that integrates artificial intelligence computation, hydrophilic interaction extraction of HPs, and MALDI-MS testing. This platform aims to achieve highly sensitive HP fingerprinting for accurate diagnosis of HCC. The method not only facilitates efficient detection of HPs, but also achieves a remarkable 100.00% diagnostic accuracy for HCC in a test cohort, supported by machine learning algorithms. By constructing a panel of HPs with 10 characteristic features, we achieved 98% accuracy in the test cohort for rapid diagnosis and identified 62 HPs deeply involved in pathways related to liver diseases. This integrated strategy provides new research directions for future biomarker studies as well as early diagnosis and individualized treatment of HCC.

Hydrogen therapy: recent advances and emerging materials.

Hydrogen therapy, leveraging its selective attenuation of hydroxyl radicals (˙OH) and ONOO-, has emerged as a pivotal pathophysiological modulator with antioxidant, anti-inflammatory, and antiapoptotic attributes. Hydrogen therapy has been extensively studied both preclinically and clinically, especially in diseases with an inflammatory nature. Despite the substantial progress, challenges persist in achieving high hydrogen concentrations in target lesions, especially in cancer treatment. A notable breakthrough lies in water/acid reactive materials, offering enhanced hydrogen generation and sustained release potential. However, limitations include hydrogen termination upon material depletion and reduced bioavailability at targeted lesions. To overcome these challenges, catalytic materials like photocatalytic and sonocatalytic materials have surfaced as promising solutions. With enhanced permeability and retention effects, these materials exhibit targeted delivery and sustained stimuli-reactive hydrogen release. The future of hydrogen therapy hinges on continuous exploration and modification of catalytic materials. Researchers are urged to prioritize improved catalytic efficiency, enhanced lesion targeting effects, and heightened biosafety and biocompatibility in future development.

Longitudinal metabolomics of human plasma reveal metabolic dynamics and predictive markers of antituberculosis drug-induced liver injury.

Tuberculosis (TB) remains the second leading cause of death from a single infectious agent and long-term medication could lead to antituberculosis drug-induced liver injury (ATB-DILI). We established a prospective longitudinal cohort of ATB-DILI with multiple timepoint blood sampling and used untargeted metabolomics to analyze the metabolic profiles of 107 plasma samples from healthy controls and newly diagnosed TB patients who either developed ATB-DILI within 2 months of anti-TB treatment (ATB-DILI subjects) or completed their treatment without any adverse drug reaction (ATB-Ctrl subjects). The untargeted metabolome revealed that 77 metabolites (of 895 total) were significantly changed with ATB-DILI progression. Among them, levels of multiple fatty acids and bile acids significantly increased over time in ATB-DILI subjects. Meanwhile, metabolites of the same class were highly correlated with each other and pathway analysis indicated both fatty acids metabolism and bile acids metabolism were up-regulated with ATB-DILI progression. The targeted metabolome further validated that 5 fatty acids had prediction capability at the early stage of the disease and 6 bile acids had a better diagnostic performance when ATB-DILI occurred. These findings provide evidence indicating that fatty acids metabolism and bile acids metabolism play a vital role during ATB-DILI progression. Our report adds a dynamic perspective better to understand the pathological process of ATB-DILI in clinical settings.

Fructose@histone synergistically improve the performance of DNA-templated Cu NPs: rapid analysis of LAM in tuberculosis urine samples using a handheld fluorometer and a smartphone RGB camera.

This study presented a nanoparticle-enhanced aptamer-recognizing homogeneous detection system combined with a portable instrument (NASPI) to quantify lipoarabinomannan (LAM). This system leveraged the high binding affinity of aptamers, the high sensitivity of nanoparticle cascade amplification, and the stabilization effect of dual stabilizers (fructose and histone), and used probe-Cu2+ to achieve LAM detection at concentrations ranging from 10 ag mL-1 to 100 fg mL-1, with a limit of detection of 3 ag mL-1 using a fluorometer. It can also be detected using an independently developed handheld fluorometer or the red-green-blue (RGB) camera of a smartphone, with a minimum detection concentration of 10 ag mL-1. We validated the clinical utility of the biosensor by testing the LAM in the urine of patients. Forty urine samples were tested, with positive LAM results in the urine of 18/20 tuberculosis (TB) cases and negative results in the urine of 6/10 latent tuberculosis infection cases and 10/10 non-TB cases. The assay results revealed a 100% specificity and a 90% sensitivity, with an area under the curve of 0.9. We believe that the NASPI biosensor can be a promising clinical tool with great potential to convert LAM into clinical indicators for TB patients.

Steric Hindrance-Mediated Enzymatic Reaction Enable Homogeneous Dual Fluorescence Indicators Aptasensing of Hepatocellular Carcinoma CTCs.

Circulating tumor cells (CTCs) serve as important biomarkers in the liquid biopsy of hepatocellular carcinoma (HCC). Herein, a homogeneous dual fluorescence indicators aptasensing strategy is described for CTCs in HCC, with the core assistance of a steric hindrance-mediated enzymatic reaction. CTCs in the sample could specifically bind to a 5'-biotin-modified glypican-3 (GPC3) aptamer and remove the steric hindrance formed by the biotin-streptavidin system. This influences the efficiency of the terminal deoxynucleotidyl transferase enzymatic reaction. Then, methylene blue (MB) was introduced to react with the main product poly cytosine (polyC) chain, and trivalent cerium ion (Ce3+) was added to react with the byproduct pyrophosphate to form fluorescent pyrophosphate cerium coordination polymeric nanoparticles. Finally, the CTCs were quantified by dual fluorescence indicators analysis. Under optimized conditions, the linear range was 5 to 104 cells/mL, and the limits of detection reached 2 cells/mL. Then, 40 clinical samples (15 healthy and 25 HCC patients) were analyzed. The receiver operating characteristic curve analysis revealed an area under the curve of 0.96, a sensitivity of 92%, and a specificity of 100%. Therefore, this study established a sensitive and accurate CTCs sensing system for clinical HCC patients, promoting early tumor diagnosis.

Extraordinary Structural Reconstruction of Nanolaminated Ta2FeC MAX Phase for Enhanced Oxygen Evolution Performance.

Renewable energy technologies, such as water splitting, heavily depend on the oxygen evolution reaction (OER). Nanolaminated ternary compounds, referred to as MAX phases, show great promise for creating efficient electrocatalysts for OER. However, their limited intrinsic oxidative resistance hinders the utilization of conductivity in Mn+1Xn layers, leading to reduced activity. In this study, a method is proposed to improve the poor inoxidizability of MAX phases by carefully adjusting the elemental composition between Mn+1Xn layers and single-atom-thick A layers. The resulting Ta2FeC catalyst demonstrates superior performance compared to conventional Fe/C-based catalysts with a remarkable record-low overpotential of 247 mV (@10 mA cm-2) and sustained activity for over 240 h. Notably, during OER processing, the single-atom-thick Fe layer undergoes self-reconstruction and enrichment from the interior of the Ta2FeC MAX phase toward its surface, forming a Ta2FeC@Ta2C@FeOOH heterostructure. Through density functional theory (DFT) calculations, this study has found that the incorporation of Ta2FeC@Ta2C not only enhances the conductivity of FeOOH but also reduces the covalency of Fe─O bonds, thus alleviating the oxidation of Fe3+ and O2-. This implies that the Ta2FeC@Ta2C@FeOOH heterostructure experiences less lattice oxygen loss during the OER process compared to pure FeOOH, leading to significantly improved stability. These results highlight promising avenues for further exploration of MAX phases by strategically engineering M- and A-site engineering through multi-metal substitution, to develop M2AX@M2X@AOOH-based catalysts for oxygen evolution.

Climate change, its impact on emerging infectious diseases and new technologies to combat the challenge.

ABSTRACTImproved sanitation, increased access to health care, and advances in preventive and clinical medicine have reduced the mortality and morbidity rates of several infectious diseases. However, recent outbreaks of several emerging infectious diseases (EIDs) have caused substantial mortality and morbidity, and the frequency of these outbreaks is likely to increase due to pathogen, environmental, and population effects driven by climate change. Extreme or persistent changes in temperature, precipitation, humidity, and air pollution associated with climate change can, for example, expand the size of EID reservoirs, increase host-pathogen and cross-species host contacts to promote transmission or spillover events, and degrade the overall health of susceptible host populations leading to new EID outbreaks. It is therefore vital to establish global strategies to track and model potential responses of candidate EIDs to project their future behaviour and guide research efforts on early detection and diagnosis technologies and vaccine development efforts for these targets. Multi-disciplinary collaborations are demanding to develop effective inter-continental surveillance and modelling platforms that employ artificial intelligence to mitigate climate change effects on EID outbreaks. In this review, we discuss how climate change has increased the risk of EIDs and describe novel approaches to improve surveillance of emerging pathogens that pose the risk for EID outbreaks, new and existing measures that could be used to contain or reduce the risk of future EID outbreaks, and new methods to improve EID tracking during further outbreaks to limit disease transmission.

Visual and colorimetric detection of microRNA in clinical samples based on strand displacement amplification and nanozyme-mediated CRISPR-Cas12a system.

The sensitive and accurate detection of target microRNA is especially important for the diagnosis, staging, and treatment of hepatocellular carcinoma (HCC). Herein, we report a simple strand displacement and CRISPR-Cas12a amplification strategy with nanozymes as a signal reporter for the binary visual and colorimetric detection of the HCC related microRNA. Pt@Au nanozymes with excellent peroxidase enzyme activity were prepared and linked to magnetic beads via a single-stranded DNA (ssDNA) linker. The target microRNA was designed to trigger strand displacement amplification and release a DNA promoter to activate the CRISPR-Cas12a system. The activated CRISPR-Cas12a system efficiently cleaved the linker ssDNA and released Pt@Au nanozymes from magnetic beads to induce the colorimetric reaction of 3,3',5,5'-tetramethylbenzidine. The strand displacement amplification converted the single microRNA input into abundant DNA promoter output, which improved the detection sensitivity by over two orders of magnitude. Through integration of strand displacement amplification and the nanozyme-mediated CRISPR-Cas12a system, limits of detection of 0.5 pM and 10 pM for miRNA-21 were achieved with colorimetric and visual readouts, respectively. The proposed strategy can achieve accurate quantitative detection of miRNA-21 in the range from 1 pM to 500 pM. The detection results for miRNA-21 using both colorimetric and visual readouts were validated in 40 clinical serum samples. Significantly, the proposed strategy achieved visual HCC diagnosis with the naked eye and could distinguish distinct Barcelona clinical HCC stages by colorimetric detection, showing good application prospects for sensitive and facile point-of-care testing for HCC.

Crosstalk between metabolism and cell death in tumorigenesis.

It is generally recognized that tumor cells proliferate more rapidly than normal cells. Due to such an abnormally rapid proliferation rate, cancer cells constantly encounter the limits of insufficient oxygen and nutrient supplies. To satisfy their growth needs and resist adverse environmental events, tumor cells modify the metabolic pathways to produce both extra energies and substances required for rapid growth. Realizing the metabolic characters special for tumor cells will be helpful for eliminating them during therapy. Cell death is a hot topic of long-term study and targeting cell death is one of the most effective ways to repress tumor growth. Many studies have successfully demonstrated that metabolism is inextricably linked to cell death of cancer cells. Here we summarize the recently identified metabolic characters that specifically impact on different types of cell deaths and discuss their roles in tumorigenesis.

Current clinical findings of acute neurological syndromes after SARS-CoV-2 infection.

Neuro-COVID, a condition marked by persistent symptoms post-COVID-19 infection, notably affects various organs, with a particular focus on the central nervous system (CNS). Despite scant evidence of SARS-CoV-2 invasion in the CNS, the increasing incidence of Neuro-COVID cases indicates the onset of acute neurological symptoms early in infection. The Omicron variant, distinguished by heightened neurotropism, penetrates the CNS via the olfactory bulb. This direct invasion induces inflammation and neuronal damage, emphasizing the need for vigilance regarding potential neurological complications. Our multicenter study represents a groundbreaking revelation, documenting the definite presence of SARS-CoV-2 in the cerebrospinal fluid (CSF) of a significant proportion of Neuro-COVID patients. Furthermore, notable differences emerged between RNA-CSF-positive and negative patients, encompassing aspects such as blood-brain barrier integrity, extent of neuronal damage, and the activation status of inflammation. Despite inherent limitations, this research provides pivotal insights into the intricate interplay between SARS-CoV-2 and the CNS, underscoring the necessity for ongoing research to fully comprehend the virus's enduring effects on the CNS. The findings underscore the urgency of continuous investigation Neuro-COVID to unravel the complexities of this relationship, and pivotal in addressing the long-term consequences of COVID-19 on neurological health.

Integrating Paper-Based Microfluidics and Lateral Flow Strip into Nucleic Acid Amplification Device toward Rapid, Low-Cost, and Visual Diagnosis of Multiple Mycobacteria.

Efficient diagnosis of mycobacterial infections can effectively manage and prevent the transmission of infectious diseases. Unfortunately, existing diagnostic strategies are challenged by long assay times, high costs, and highly specialized expertise to distinguish between pulmonary tuberculosis (PTB) and nontuberculous mycobacterial pulmonary diseases (NTM-PDs). Herein, in this study, an optimized 3D paper-based analytical device (µPAD) is incorporated with a closed lateral flow (LF) strip into a loop-mediated isothermal amplification (LAMP) device (3D-µPAD-LF-LAMP) for rapid, low-cost, and visual detection of pathogenic mycobacteria. The platform's microfluidic feature enhanced the nucleic acid amplification, thereby reducing the costs and time as compared to boiling, easyMAG, and QIAGEN techniques. Moreover, the LF unit is specifically designed to minimize aerosol contamination for a user-friendly and visual readout. 3D-µPAD-LF-LAMP is optimized and assessed using standard strains, demonstrating a limit of detection (LOD) down to 10 fg reaction-1 . In a cohort of 815 patients, 3D-µPAD-LF-LAMP displays significantly better sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and diagnostic accuracy than conventional bacterial culture and Xpert techniques. Collectively, 3D-µPAD-LF-LAMP demonstrates enhanced accessibility, efficiency, and practicality for the diagnosis of multiple pathogenic mycobacteria, which can be applied across diverse clinical settings, thereby ultimately improving public health outcomes.

Nucleic Acid and Nanomaterial Synergistic Amplification Enables Dual Targets of Ultrasensitive Fluorescence Quantification to Improve the Efficacy of Clinical Tuberculosis Diagnosis.

Interferon-γ (IFN-γ) release assays (IGRAs) are constrained by the limited diagnostic performance of a single indicator and the excessive Mycobacterium tuberculosis (Mtb) antigen stimulation time. This study presents a simultaneous, homogeneous, rapid, and ultrasensitive fluorescence quantification strategy for IFN-γ and IFN-γ-induced protein 10 (IP-10). This method relies on the high-affinity binding of aptamers to IFN-γ and IP-10, the enzyme-free catalytic hairpin assembly reaction, and the heightened sensitivity of CdTe quantum dots to Ag+ and hairpin structure C-Ag+-C and carbon dots to Hg2+ and hairpin structure T-Hg2+-T. Under optimized conditions, the selectivity of IFN-γ and IP-10 was excellent, with a linear range spanning from 1 to 100 ag/mL and low limits of detection of 0.3 and 0.5 ag/mL, respectively. Clinical practicality was confirmed through testing of 57 clinical samples. The dual-indicator combination detection showed 92.8% specificity and 93.1% sensitivity, with an area under the curve of 0.899, representing an improvement over the single-indicator approach. The Mtb antigen stimulation time was reduced to 8 h for 6/7 clinical samples. These findings underscore the potential of our approach to enhance the efficiency and performance of a tuberculosis (TB) clinical diagnosis.

Ag@TiO2 nanoribbon array: a high-performance sensor for electrochemical non-enzymatic glucose detection in beverage sample.

Developing an accurate, cost-effective, reliable, and stable glucose detection sensor for the food industry poses a significant yet challenging endeavor. Herein, we present a silver nanoparticle-decorated titanium dioxide nanoribbon array on titanium plate (Ag@TiO2/TP) as an efficient electrode for non-enzymatic glucose detection in alkaline environments. Electrochemical evaluations of the Ag@TiO2/TP electrode reveal a broad linear response range (0.001 mM - 4 mM), high sensitivity (19,106 and 4264 µA mM-1 cm-2), rapid response time (6 s), and a notably low detection limit (0.18 µM, S/N = 3). Moreover, its efficacy in measuring glucose in beverage samples shows its practical applicability. The impressive performance and structural benefits of the Ag@TiO2/TP electrode highlight its potential in advancing electrochemical sensors for small molecule detection.

Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography.

Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning-based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR-TB and classify crucial subtypes, including rifampicin-resistant tuberculosis, multidrug-resistant tuberculosis, and extensively drug-resistant tuberculosis. Moreover, chest lesions were manually annotated to endow the model with robust power to assist radiologists in image interpretation and the Circos revealed the relationship between chest abnormalities and specific types of DR-TB. Finally, DeepTB achieved an area under the curve (AUC) up to 0.930 for thoracic abnormality detection and 0.943 for DR-TB diagnosis. Notably, the system demonstrated instructive value in DR-TB subtype classification with AUCs ranging from 0.880 to 0.928. Meanwhile, class activation maps were generated to express a human-understandable visual concept. Together, showing a prominent performance, DeepTB would be impactful in clinical decision-making for DR-TB.

Smartphone-Based Free-to-Total Prostate Specific Antigen Ratio Detection System Using a Colorimetric Reaction Integrated with Proximity-Induced Bio-Barcode and CRISPR/Cas12a Assay.

The free-to-total prostate-specific antigen (f/t-PSA) ratio is of great significance in the accurate diagnosis of prostate cancer. Herein, a smartphone-based detection system is reported using a colorimetric reaction integrated with proximity-induced bio-barcode and the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas12a assay for f/t-PSA ratio detection. DNA/antibody recognition probes are designed to bind f-PSA or t-PSA and induce the release of the DNA bio-barcode. The CRISPR/Cas12a system is activated by the DNA bio-barcode to release Ag+ from the C-Ag+-C structure of the hairpin DNA. The released Ag+ is used to affect the tetramethylbenzidine (TMB)-H2O2-based colorimetric reaction catalyzed by Pt nanoparticles (NPs), as the peroxidase-like activity of the Pt NPs can be efficiently inhibited by Ag+. A smartphone with a self-developed app is used as an image reader and analyzer to analyze the colorimetric reaction and provide the results. A limit of detection of 0.06 and 0.04 ng mL-1 is achieved for t-PSA and f-PSA, respectively. The smartphone-based method showed a linear response between 0.1 and 100 ng mL-1 of t-PSA or f-PSA. In tests with clinical samples, the smartphone-based method successfully diagnosed prostate cancer patients from benign prostatic hyperplasia patients and healthy cases with high sensitivity and specificity.

Single-Cell Liquid Biopsy of Lung Cancer: Ultra-Simplified Efficient Enrichment of Circulating Tumor Cells and Hand-Held Fluorometer Portable Testing.

Herein, we propose a paper-based laboratory via enzyme-free nucleic acid amplification and nanomaterial-assisted cation exchange reactions (CERs) assisted single-cell-level analysis (PLACS). This method allowed for the rapid detection of mucin 1 and trace circulating tumor cells (CTCs) in the peripheral blood of lung cancer patients. Initially, an independently developed method requiring one centrifuge, two reagents (lymphocyte separation solution and erythrocyte lysate), and a three-step, 45 min sample pretreatment was employed. The core of the detection approach consisted of two competitive selective identifications: copper sulfide nanoparticles (CuS NPs) to C-Ag+-C and Ag+, and dual quantum dots (QDs) to Cu2+ and CuS NPs. To facilitate multimodal point-of-care testing (POCT), we integrated solution visualization, test strip length reading, and a self-developed hand-held fluorometer readout. These methods were detectable down to ag/mL of mucin 1 concentration and the single-cell level. Forty-seven clinical samples were assayed by fluorometer, yielding 94% (30/32) sensitivity and 100% (15/15) specificity with an area under the curve (AUC) of 0.945. Nine and 15 samples were retested by a test strip and hand-held fluorometer, respectively, with an AUC of 0.95. All test results were consistent with the clinical imaging and the folate receptor (FR)-PCR kit findings, supporting its potential in early diagnosis and postoperative monitoring.

Large-scale analysis reveals splicing biomarkers for tuberculosis progression and prognosis.

BACKGROUND: Emerging evidence suggests that aberrant alternative splicing (AS) may play an important role in tuberculosis (TB). However, current knowledge regarding the value of AS in TB progression and prognosis remains unclear. METHOD: Public RNA-seq datasets related to TB progression and prognosis were searched and AS analyses were conducted based on SUPPA2. Percent spliced in (PSI) was used for quantifying AS events and multiple machine learning (ML) methods were employed to construct predictive models. Area under curve (AUC), sensitivity and specificity were calculated to evaluate the model performance. RESULTS: A total of 1587 samples from 7 datasets were included. Among 923 TB-progression related differential AS events (DASEs), 3 events (GET1-skipping exon (SE), TPD52-alternative first exons (AF) and TIMM10-alternative 5' splice site (A5)) were selected as candidate biomarkers; however, their predictive performance was limited. For TB prognosis, 5 events (PHF23-AF, KIF1B-SE, MACROD2-alternative 3' splice site (A3), CD55-retained intron (RI) and GALNT11-AF) were selected as candidates from the 1282 DASEs. Six ML methods were used to integrate these 5 events and XGBoost outperformed than others. AUC, sensitivity and specificity of XGBoost model were 0.875, 81.1% and 83.5% in training set, while they were 0.805, 68.4% and 73.2% in test set. CONCLUSION: GET1-SE, TPD52-AF and TIMM10-A5 showed limited role in predicting TB progression, while PHF23-AF, KIF1B-SE, MACROD2-A3, CD55-RI and GALNT11-AF could well predict TB prognosis and work as candidate biomarkers. This work preliminarily explored the value of AS in predicting TB progression and prognosis and offered potential targets for further research.