Aqueous phase electrochemical hydrogenation (ECH) of benzaldehyde (BZ) on Cu/C in alkaline electrolytes (pH varying between 8.6 and 12.3) forms both benzyl alcohol (BA), the C=O hydrogenation product, and hydrobenzoin (HB), the carbon-carbon coupling product, with high selectivity towards C-C coupling (> 84 %) and high overall Faradaic efficiency. The rate-determining step for BA formation is the second H addition to the radical alpha-C of the surface hydroxy intermediate, while that for HB formation is the first H addition to the carbonyl O of an adsorbed BZ molecule. The subsequent C-C bond formation and second H addition (for HB formation) are fast. In the absence of BZ (i.e., in pure electrolyte), the rate-determining step for H-2 evolution on Cu/C in alkaline conditions is the dissociation of H2O on the electrode's surface to form surface H* (i.e., the Volmer step). The H addition occurs primarily via a proton-coupled electron-transfer (PCET)-type mechanism wherein H2O molecules act as the proton source, forming OH(-)in the process. The high selectivity towards C-C coupling in alkaline media, compared to acidic media, is attributed to the slow H addition kinetics caused by the weaker protonation ability of H2O compared to H3O(+). Increasing electrolyte pH, concentration of Na+ cations, or the applied external overpotential positively influences the BZ ECH rates while maintaining high C-C coupling selectivity.

Mislabeling and unnoticed component deviations remain persistent challenges in the quality control of cigarette feed liquids. This study develops an efficient machine learning approach based on Attenuated Total ReflectanceFourier Transform Infrared spectroscopy (ATR-FTIR) to address these issues. ATR-FTIR provides stable spectral acquisition while preserving complete compositional information, enabling reliable identification of characteristic absorption bands associated with glucose, malic acid, and water. A dataset comprising 423 spectra from 21 feed liquid formulations was used for model development. Linear discriminant analysis (LDA) achieved accurate classification of feed liquid types, with an accuracy of 93.0% +/- 2.9%, effectively resolving mislabeling. For outof-specification detection, a binary classification model based on a 95% confidence interval was established. The model evaluates sample qualification using parameters derived from out-of-limit spectral regions.The overall recognition rate out-of-specification samples reached 90.5%, while blind sample testing achieved 95.2%. These results indicate that integrating ATR-FTIR spectroscopy with interpretable machine learning offers a robust and efficient strategy for quality control of cigarette feed liquids.

Abstract Effective screening for colorectal cancer (CRC) remains limited by the invasiveness of colonoscopy and the insufficient sensitivity of conventional liquid biopsy approaches. Here, we present Machine Learning-Assisted, Ago-Mediated Droplet Fluorescence Analysis with a Knowledge-Enabled Agent (MADAK-Agent), an integrated analytical platform that combines an Argonaute-mediated cascade amplification module with a monodisperse droplet-assisted, object-resolved post-amplification fluorescence measurement workflow and a knowledge-enabled interpretation interface. We developed a dual-channel cascade system coupling the exponential amplification reaction (EXPAR) with Clostridium perfringens Argonaute (CpAgo)-mediated cleavage, enabling the conversion of trace circulating miR-21 and miR-92a into distinct fluorescent signals. Following EXPAR–CpAgo cascade amplification, the reaction products were compartmentalized into monodisperse microdroplets using a coaxial flow-focusing microfluidic chip. Droplet-level fluorescence quantification yielded calculated limits of detection (LODs) of 45.6 aM for miR-21 and 59.2 aM for miR-92a. Compared with bulk fluorescence readout, the LODs were improved by 5.20-fold for miR-21 and 3.09-fold for miR-92a. Automated droplet segmentation and feature extraction were achieved using a Cellpose-based analysis module, reducing operator-dependent variability. Based on the extracted droplet features, a support vector machine (SVM) classifier achieved area under the receiver operating characteristic curve (AUC) values of 0.901 for miR-21 and 0.848 for miR-92a in a cohort of 60 clinical serum samples. To enable evidence-grounded interpretation, we further implemented a retrieval-augmented generation (RAG)-based question-answering (QA) agent with dual operating modes. Both the QA agent and the image analysis module are deployable as web-based and local tools, supporting flexible and privacy-preserving use. MADAK-Agent establishes a unified and deployable framework for sensitive droplet-based miRNA detection with integrated analysis, interpretation, and reporting.

Continuous in vivo monitoring of biomarkers remains challenging due to limited sensitivity, integration, and biocompatibility. Here, we report an integrated microneedle-based electrochemiluminescence device (MN-ECLD) for real-time detection of protein biomarkers in interstitial fluid. Leveraging hydrogen-bonded organic frameworks with ultrabright, biocompatible electrochemiluminescence, the emitters were incorporated into porous gold-coated microneedle arrays and regulated via interface-specific Y-shaped probes, enabling efficient coreactant-free signal generation. The device achieved ultrasensitive protein detection in vitro with a linear range of 100 fg/mL to 10 ng/mL, a detection limit of 21.3 fg/mL, and stability over 12 days, delivering an 87-fold sensitivity enhancement over conventional emitters. In vivo, MN-ECLD enabled real-time monitoring of cardiac biomarkers, achieving early warning of acute myocardial infarction in rats and pigs, with biomarker trends consistent with serum ELISA. This work establishes a versatile platform for continuous in vivo diagnostics of acute cardiovascular and metabolic disorders.

E. coli O157:H7 and Staphylococcus aureus have emerged as significant foodborne pathogens, characterized by considerable incidence rates and mortality. Despite advancements, current detection methods are hindered by challenges in enhancing specificity and sensitivity. Herein, we introduced a cutting-edge biosensor that employs a novel CHA-coupled CRISPR multi-stage signal amplification technique for the rapid and ultra-sensitive detection of these two pathogens. This microfluidic device consisted of an upstream serpentine mixing channel and a downstream boat-shaped microcavity equipped with a microcolumn array, facilitating efficient reagent mixing, robust CHA amplification, and CRISPR reactions. Multiple signal amplification was achieved through bacterial competitive binding triggered by catalytic hairpin assembly (CHA) and crRNA-mediated CRISPR reactions. Based on this platform, the detection of target bacteria is transformed into nucleic acid detection, with a maximum detection range of 134 CFU/mL for E. coli O157:H7 and 181 CFU/mL for Staphylococcus aureus , which were better or comparable to previously reported biosensors. The entire assay was completed within approximately 1.5 h, with a minimal sample volume requirement of just 10 mu L. The biosensor exhibited a high recovery rate, ranging from 95% to 115%, and demonstrated excellent specificity towards the target bacteria. In summary, this biosensor offers a rapid, accurate, and highly sensitive tool for food safety and clinical diagnostics. (c) 2026 Published by Elsevier B.V. on behalf of Chinese Chemical Society and Institute of Materia Medica, Chinese Academy of Medical Sciences.

Aptamers are widely used in biosensing and targeted therapeutics, yet reported data remain fragmented across unstructured text, tables, and figures. Existing databases are limited in coverage and diversity, which constrains computational modeling. Here, we present AptaNexus (https://www.aptanexus.com/), a multitier aptamer database containing over 12,000 sequences targeting 1900 molecular entities, curated from literature published between 2005 and 2025. Its extraction pipeline, Dual-LLM Extraction with Reverse Grounding (Dual-LLMs+RG), achieves an F1 score of 0.970 at a fraction of the cost of the state-of-the-art model. Records are stratified into four quality tiers, supporting both experimental selection and machine learning applications. Beyond conventional keyword search, AptaNexus incorporates the Model Context Protocol (MCP) and an embedded conversational agent, Chat Aptamer, enabling natural-language queries that return structured, source-linked, and application-oriented recommendations. For example, users can request detection of a target in blood to receive a ranked list of aptamers with validated sensor platforms or query drug delivery targets to obtain functionally annotated candidates. By combining large-scale, evidence-grounded data with agent-accessible interfaces, AptaNexus makes aptamer information readily accessible across disciplines and, for the first time, provides native AI-agent interoperability.

Current therapeutic regimens for castration-resistant prostate cancer (CRPC) are plagued by multiple limitations. RhoB has emerged as a promising molecular target for prostate cancer (PCa) therapy. Icaritin, a bioactive prenylated flavonoid isolated from Epimedii Folium, exhibits potent antitumor and osteogenic properties. However, the anti-CRPC efficacy of icaritin and its underlying molecular mechanisms remain incompletely elucidated. Herein, we evaluated the anti-CRPC potential of icaritin and investigated the involvement of RhoB signaling. Results demonstrated that icaritin significantly suppressed the viability, proliferation, and clonogenic capacity of CRPC cells. Furthermore, in an RM-1 xenograft model, icaritin inhibited tumor growth and reduced serum levels of prostate-specific antigen (PSA) and testosterone. Integrated RNA-seq analysis and The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD) dataset identified RhoB as a candidate therapeutic target in CRPC. Subsequent mechanistic investigations revealed that icaritin inhibits CRPC progression by modulating RhoB-mediated Akt signaling. Collectively, these findings indicate that the anti-CRPC activity of icaritin is at least partially mediated through the RhoB/Akt signaling, providing a pharmacological basis for the further development of icaritin as a potential therapeutic agent against CRPC.

BACKGROUND AND AIMS:The succinate receptor GPR91 is highly expressed in HSCs, with its expression further elevated during metabolic dysfunction-associated steatohepatitis (MASH)-induced fibrotic progression. However, convincing in vivo data on whether blocking GPR91 signaling leads to fibrotic regression in MASH are lacking. APPROACH AND RESULTS:MASH models were induced by choline-deficient amino acid-defined diet feeding along with lipopolysaccharide injection (CDAA-LPS) plus i.p. injection of succinate or by high-fat and high-calorie diet plus high fructose and glucose in drinking water (HFCD-HF/G) in wild-type (WT) mice and HSC-specific GPR91 knockout (HSC-GPR91-KO) mice. Our findings demonstrate that administration of succinate significantly exacerbated fibrosis in CDAA-fed WT mice, as evidenced by increased collagen deposition and hydroxyproline levels along with an increased GPR91 expression in activated HSCs. Both WT and HSC-GPR91-KO mice exhibited substantial elevation in hepatic succinate levels upon HFCD-HF/G diet feeding. However, in comparison to HFCD+HF/G-fed WT mice, hepatic fibrosis was markedly ameliorated in HSC-GPR91-KO mice, as evidenced by diminished hepatic hydroxyproline content with downregulated fibrogenic markers. Succinate stimulation led to an increase in α-SMA, GPR91, phosphorylated ERK1/2, c-jun, and Smad3 protein levels and enhanced the molecular interaction between c-jun and Smad3, inhibited forskolin-induced cAMP production in human HSCs, and increased p-NF-κB transcriptional activity, thereby suppressing HSC apoptosis. CONCLUSIONS:HSC-specific GPR91 receptor deficiency effectively halted hepatic fibrosis, probably through 2 distinct signaling pathways: suppressing the succinate-GPR91-Gβγ-ERK/c-jun-Smad3 axis, which positively regulates HSC activation, and abrogating the GPR91-Gαi-cAMP-NF-κB pathway, which hinders their apoptosis. These findings confer GPR91 as a promising target for molecular interventions in blocking MASH-fibrotic progression.

Large language models (LLMs) hold significant promise in the field of medical diagnosis. There are still many challenges in the direct diagnosis of hepatocellular carcinoma (HCC). α-Fetoprotein (AFP) is a commonly used tumor marker for liver cancer. However, relying on AFP can result in missed diagnoses of HCC. We developed an artificial intelligence (AI) agent centered on LLMs, named ChatExosome, which created an interactive and convenient system for clinical spectroscopic analysis and diagnosis. ChatExosome consists of two main components: the first is the deep learning of the Raman fingerprinting of exosomes derived from HCC. Based on a patch-based 1D self-attention mechanism and downsampling, the feature fusion transformer (FFT) was designed to process the Raman spectra of exosomes. It achieved accuracies of 95.8% for cell-derived exosomes and 94.1% for 165 clinical samples, respectively. The second component is the interactive chat agent based on LLM. The retrieval-augmented generation (RAG) method was utilized to enhance the knowledge related to exosomes. Overall, LLM serves as the core of this interactive system, which is capable of identifying users' intentions and invoking the appropriate plugins to process the Raman data of exosomes. This is the first AI agent focusing on exosome spectroscopy and diagnosis, enhancing the interpretability of classification results, enabling physicians to leverage cutting-edge medical research and artificial intelligence techniques to optimize medical decision-making processes, and it shows great potential in intelligent diagnosis.

Colorectal cancer (CRC) is a common malignancy requiring early screening to improve patient outcomes. Current screening methods such as colonoscopy and fecal occult blood tests have several limitations including high cost, poor specificity, invasiveness, and inconvenience. Recent research has identified specific bacterial communities associated with CRC, notably Parvimonas micra (P. micra), which serves as a biomarker for early screening and diagnosis owing to its accumulation in the malignant tissues and feces of CRC patients. Herein, we employed the whole-bacterium systematic evolution of ligands by the exponential enrichment (SELEX) method to isolate high-affinity aptamers against P. micra using 17 selection cycles. These aptamers were subsequently bound to Au@Fe3O4 nanoparticles, and the interaction of P. micra and aptamers inhibited the peroxidase-like activity of Au@Fe3O4 nanoparticles, thereby blocking the 3,3',5,5'-tetramethylbenzidine (TMB) chromogenic reaction and resulting in a measurable reduction in absorbance. This colorimetric detection strategy demonstrated a linear response across a range of 100-108 CFU/mL for P. micra with a limit of detection of 11 CFU/mL. Using a colorimetric aptasensor, we assessed the abundance of P. micra in clinical fecal samples and found significantly higher levels in the feces of CRC patients as compared to that of healthy individuals, which was consistent with the quantitative polymerase chain reaction results. This study therefore represents the first successful identification of an aptamer with high affinity and specificity for P. micra, leading to the development of a highly specific and sensitive aptasensor for its detection. The presented approach has a significant potential for CRC screening and diagnosis.

Colorectal cancer (CRC) is the third most common cancer and leading cause of cancer-related deaths worldwide. However, current CRC screening methods are complex, invasive, and tend to exhibit low sensitivity. Recent evidence has highlighted gut microbiota dysbiosis, especially elevated Fusobacterium nucleatum levels, as a promising biomarker for CRC. In this study, a sensitive and specific detection platform was developed for F. nucleatum by combining a highly specific aptamer with rolling circle amplification (RCA) and the CRISPR/Cas12a technology. The aptamer enables specific target recognition, while RCA amplifies the target signal, and the Cas12a-mediated cleavage of a fluorescence-quenching substrate generates a quantifiable fluorescence or grayscale signal. Using a microplate reader, this assay achieved a limit of detection (LOD) of 3.68 CFU/mL; furthermore, by incorporating smartphone-assisted ImageJ grayscale analysis, it elevated the LOD to 4.30 CFU/mL, thereby enabling a dual-mode output along with on-site applicability. Additionally, the strong correlation between the two signals allowed for mutual validation. Upon application to clinical fecal samples, the developed method sensitively distinguished CRC patients from healthy controls, and its results correlated with the quantitative polymerase chain reaction results. This triple-synergistic platform, integrating aptamer specificity, RCA amplification, and CRISPR/Cas12a sensitivity, enables the noninvasive, ultrasensitive detection of F. nucleatum, supporting early CRC screening, prognosis monitoring, and microbiome-targeted therapy. Moreover, this approach overcomes the challenges of detecting low-abundance bacteria in early stage CRC and advances the precision of microbiome-based diagnostics for CRC.

Obesity is a significant risk factor for diabetes, cardiovascular diseases, and certain cancers, and manifests as excessive fat accumulation. The browning of white adipose tissue (WAT) represents one of the most promising strategies for preventing and treating obesity and metabolic diseases. To date, an increasing number of studies have focused on key molecular mechanisms regulating fat thermogenesis, laying the foundation for effective intervention strategies. Here, REGγ expression is shown to be significantly upregulated in adipose tissue of obese individuals and in inguinal WAT (iWAT) of obese mice. Deficiency in REGγ expression reduces fat deposition, increases energy expenditure in adipose tissue, and protects mice from HFD-induced obesity and insulin resistance. Mechanistically, REGγ expression regulates browning of WAT by modulating ACADM and KLF15-UCP1 signaling in a ubiquitin-independent degradation manner. Overactivation of the NRF2-REGγ axis facilitates adipose tissue function to cause obesity. Notably, inhibition of REGγ in the iWAT alleviates HFD-induced obesity, thereby identifying REGγ as a latent target for obesity treatment. Together, the findings provide new targets for intervening in obesity and might ultimately offer new options for treating obesity.

Rapid and sensitive detection of Epstein-Barr virus cell-free DNA (EBV cfDNA) is crucial for early diagnosis and monitoring of nasopharyngeal carcinoma (NPC), but accessibility to screening is limited by complicated and costly conventional DNA isolation and purification approaches. Here, a fully integrated ion concentration polarization (ICP)-enriched and nanozyme-catalyzed lateral flow assay (ICP-cLFA) is developed, enabling total analysis of EBV cfDNA in whole blood samples, with DNA isolation, pre-concentration, and amplification performed on a microfluidic chip, consequently providing the signal readout within 75 min. Specifically, ICP preconcentration and amplification steps, together with target recognition catalyzed by a platinum-decorated mesoporous gold nanosphere (MGNS@Pt) nanozyme, result in an ultralow detection limit of 4 aM in standard cfDNA samples and 100 aM in whole blood from NPC-bearing rats. The high sensitivity and specificity of the ICP-cLFA suggest strong potential for cfDNA screening in resource-limited clinical and field applications.

While massive studies are focused on platinum (Pt)-based nanozyme for antitumor therapies, their therapeutic efficiency is deficient due to the weak catalytic activity in the highly complex tumor microenvironment. Herein, mesoporous gold nanospheres confined platinum nanoclusters (MGNSs@Pt) as robust hydroxyl radical and oxygen nanogenerators are achieved for multimodal therapies. Benefiting from the confinement effect of the mesopores in the MGNSs, the Pt nanoclusters (Pt NCs) demonstrate enhanced stability and catalytic activity, with a catalytic constant (Kcat) of 1.42 × 106 s-1, which is 2 and 5 orders magnitude higher than Kcat values of Pt-decorated non-porous gold nanoparticles and pure Pt NCs respectively. Density functional theory (DFT) calculations reveal the proper interaction of intermediates contributes to the ultra-high catalytic activity of MGNSs@Pt. Meanwhile, owing to the local surface plasmon resonance (LSPR) effect in the second near-infrared (NIR-II) bio-window of MGNSs, the nanozymes exhibited high photothermal conversion efficiency up to 43.4%, which enhanced the nanocatalytic damage on cancer cells. This process can induce robust oxidative stress and oxygenation within the tumor, thereby activating the apoptosis pathway for tumor eradication by mitochondrial dysfunction, cell membrane disruption, HIF-1α downregulation as well as caspase 3 activation, which pave the way for multimodal and effective cancer treatment.

Metabolic dysfunction-associated steatotic liver disease (MASLD) covers a broad spectrum of profile from simple fatty liver, evolving to metabolic dysfunction-associated steatohepatitis (MASH), to hepatic fibrosis, further progressing to cirrhosis and hepatocellular carcinoma (HCC). MASLD has become a prevalent disease with 25% in average over the world. MASH is an active stage, and requires pharmacological intervention when there is necroptotic damage with fibrotic progression. Although there is an increased understanding of MASH pathogenesis and newly approved resmetirom, given its complexity and heterogeneous pathophysiology, there is a strong necessity to develop more drug candidates with better therapeutic efficacy and well-tolerated safety profile. With an increased list of pharmaceutical candidates in the pipeline, it is anticipated to witness successful approval of more potential candidates in this fast-evolving field, thereby offering different categories of medications for selective patient populations. In this review, we update the advances in MASH pharmacotherapeutics that have completed phase II or III clinical trials with potential application in clinical practice during the latest 2 years, focusing on effectiveness and safety issues. The overview of fast-evolving status of pharmacotherapeutic candidates for MASH treatment confers deep insights into the key issues, such as molecular targets, endpoint selection and validation, clinical trial design and execution, interaction with drug administration authority, real-world data feedback and further adjustment in clinical application.

Surface-enhanced fluorescence can occur when fluorophores approach a rough or nanoscopic metallic surface, owing to the enhanced local electric field. However, revealing the surface fluorescence enhancement mechanism remains an important concern, primarily due to the lack of a nanoruler for precisely tuning the distance between the fluorophores and the substrate. Importantly, the precise distance is also critical as the heterogeneous size of spacers can lead to differentiated enhancement, resulting in low reproducibility. Herein, we propose a tetrahedral DNA framework (TDF) nanoruler strategy. Three vertexes of TDFs are thiolated for their immobilization on the Au matrix, while the fourth vertex is labeled with fluorophores. Therefore, the distance between the fluorophores and the Au substrate is governed by the size of TDFs. Various TDFs are combined as the nanoruler, and thus, we reveal the distance-dependent emission efficiency. We clearly observed the gradually enhanced and subsequently decreased emission along with the increased distance with the nanoruler. The maximum fluorescence enhancement was achieved at a critical distance of 5-7 nm with a narrow range for the labeled fluorophores on the Au substrate. Moreover, their rigid structure also realizes the uniform size of TDFs for homogeneous signal enhancement. Thus, sensitive detection of microRNA biomarkers of prostate cancer (PCa) was realized with a detection limit of 1 aM and used for early diagnosis of PCa. Furthermore, the TDF nanoruler strategy can be easily extended to other distance-dependent systems, enabling the optimization of their performance.

Circular RNA (circRNA), a subtype of noncoding RNA, has emerged as a significant focus in RNA research due to its distinctive covalently closed loop structure. CircRNAs play pivotal roles in diverse physiological and pathological processes, functioning through mechanisms such as miRNAs or proteins sponging, regulation of splicing and gene expression, and serving as translation templates, particularly in the context of various cancers. The hallmarks of cancer comprise functional capabilities acquired during carcinogenesis and tumor progression, providing a conceptual framework that elucidates the nature of the malignant transformation. Although numerous studies have elucidated the role of circRNAs in the hallmarks of cancers, their functions in the development of chemoradiotherapy resistance remain unexplored and the clinical applications of circRNA-based translational therapeutics are still in their infancy. This review provides a comprehensive overview of circRNAs, covering their biogenesis, unique characteristics, functions, and turnover mechanisms. We also summarize the involvement of circRNAs in cancer hallmarks and their clinical relevance as biomarkers and therapeutic targets, especially in thyroid cancer (TC). Considering the potential of circRNAs as biomarkers and the fascination of circRNA-based therapeutics, the "Ying-Yang" dynamic regulations of circRNAs in TC warrant vastly dedicated investigations.

Protein-small molecule interactions (PSMI) play critical biological roles by regulating protein function and are deeply involved in many major human diseases. In this research, we introduce an ultra-fast and simple platform for the characterization of protein-small molecule interactions, called the Ago-PSMI platform, which is based on Argonaute proteins. This innovative platform employs small molecule markers and 5 '-phosphorylated DNA as a probe for targeted protein recognition, facilitating signal detection via the Argonaute sensing system. The interaction between the target protein and the small molecule locally impedes the hybridization of the guide strand with the F-Q reporter, leading to a quenched fluorescence signal. By employing the biotin-streptavidin interaction and the digoxin/anti-digoxin antibody interaction as models, the Ago-PSMI platform adeptly translates the strength of PSMIs into fluorescence intensity with exceptional sensitivity and selectivity, effectively discriminating against other protein analogs. Moreover, the platform demonstrates strong performance in fetal bovine serum, exhibiting strong resistance to interference, making it suitable for detection in complex biological samples. In addition, the platform features a straightforward and expeditious operational process, with an overall assay time of 6 min. The Ago-PSMI platform not only holds promise for a wide range of drug discovery applications by simplifying and accelerating the elucidation of drug molecules and target proteins, but also expands the application of the Argonaute biosensing system by extending the use of the TtAgo system to PSMI detection.

The early diagnosis of lung cancer is crucial for improving patient prognosis, and liquid biopsy plays an important role in early lung cancer screening. In the field of liquid biopsy, label-free Surface-Enhanced Raman Scattering (SERS) possesses its unique advantages as it can provide comprehensive information about the insights into the chemical makeup of serum. However, the nonuniform SERS substrates poses challenges for reliable clinical diagnosis. We design a Surface Controlled SERS with Improved Ensemble Learning (SCSIEL) for lung cancer screening and diagnosis. This finely controlled SERS substrate ensures the uniformity of the surface and the high reproducibility of SERS spectra. The improved ensemble learning used in SCSIEL consists of a multi- layer structure which is inspired from the residual connection of deep learning networks. Although the framework is lightweight and integrates only a few simple base models, it achieves impressive results under the carefully constructed network. This SCSIEL system is also validated by direct analysis of clinical serum samples from 168 lung cancer patients and 100 healthy controls and the excellent performance is obtained with an area under the curve (AUC) of 97.0 % and accuracy of 93.4 %, which outperforms that of the clinical biomarkers for lung cancer. This SCSIEL is also explainable, which indicates the enhanced protein degradation in lung cancer. The SCSIEL method is a reliable and cost-effective method in the screen of lung cancer, shows great promise in clinical implementation.

The cutting-edge CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat)/Cas (CRISPR-associated proteins) system, as an emerging molecular diagnostic technique, is driving revolutionary developments in the detection field due to its high specificity and efficiency. However, the CRISPR-based assays typically require the combination with an additional pre-amplification step based on isothermal nucleic acid amplification to meet the requirements of clinical diagnosis, which brings issues including complicated operation and the risk of aerosol contamination. To address these challenges, one-pot CRISPR platforms are emerging as an attractive solution to streamline workflows, enabling rapid, cost-effective, and high-sensitivity diagnostics. This review outlines the current status, challenges, and three key strategies to realize highly efficient one-pot CRISPR-based detection. In addition, further perspectives are outlined that will inspire new exploration and promote one-pot CRISPR/Cas detection as the next generation of diagnostic tools.