Clinical Urine Samples Research Articles

Urinary MicroRNA-21 for Prostate Cancer Detection Using a Silver Nanoparticle Sensor: A Promising Diagnostic Tool

In this study, we detected the expression levels of miR-21 in 38 clinical urine samples, obtained from 10 patients with PCa (with each sample obtained at three time points: before surgery, 1 month after surgery, and 3 months after surgery), 3 patients with benign prostatic hypertrophy (BPH), and 5 healthy subjects (as a control group). All of the samples were examined using a silver nanoparticle-based biosensor, and the sensitivity of the biosensor was simultaneously confirmed via qRT-PCR. The results were further analyzed together with clinical data such as PSA values and cancer stages. The sensitivity of the biosensor ranged up to 100 fM, and it presented a rather high selectivity rate. Our results indicated a significantly decreased expression level of miR-21 in the patient cases at 3 months post-surgery when compared with pre-surgery levels (p-value < 0.001). In addition, when distinguishing the differences in the expression level of miR-21 between healthy subjects and patients with PCa or BPH, both groups showed highly significant differences (p = 0.008 and p < 0.001, respectively). The results strongly suggest that the proposed miR-21 biosensor can be used as an auxiliary screening tool for the early detection of PCa and may effectively facilitate tracking of the progression of PCa, thus enabling more accurate healthcare decision making.

An amplification-free CRISPR/cas13a-powered dry chemistry-based electrochemiluminescence gene sensor for ultrasensitive RNA detection

In this work, a novel amplification-free CRISPR/cas13a-powered dry chemistry-based electrochemiluminescence (ECL) gene sensor was developed for ultrasensitive RNA detection, where the self-enhanced probe dried on the cloth-based closed bipolar electrode-ECL (C-BPE-ECL) lateral flow chip captured the trans-cleavage products of CRISPR/Cas13a. The poly-(L-lysine) (PLL) ability to covalently binding abundant Ru(II) luminescent substances was utilized to construct the intra-molecular self-enhanced ECL probes (S1 and S2), and as a result the ECL signals were greatly amplified and meanwhile no additional co-reactants were applied onto the lateral flow chip. In addition, the trans-cleavage of Cas13a served to amplify the signals, while CRISPR/Cas13a’s two-part recognition mechanism enhanced the detection specificity. Therefore, the proposed ECL method realized the amplification-free detection of RNA. With the optimized conditions, the proposed gene sensor had a wide linear range (10-105 fM) for RNA detection of Escherichia coli O157:H7, with a limit detection of 0.65 fM. Moreover, the sensor exhibited acceptable selectivity, reproducibility and storage stability, and had been successfully applied to detect Escherichia coli O157:H7 RNA in clinical human urine and blood samples. Importantly, the proposed method possessed good versatility for detection of different RNA targets, and provided the potential for rapid and sensitive detection in 20min. These results revealed that the ECL method possessed great prospects for RNA detection with wide applications.

Smartphone-assisted colorimetry and array test strip integrated platform for urine multi-index simultaneous precise quantification and personalized healthcare monitoring

As a non-invasive monitoring method, urine analysis is one of the “three routines” of medical examination, which is of great significance for the early diagnosis, therapeutic effect judgment and long-term fine monitoring of chronic diseases such as metabolic system diseases and kidney diseases. It is imperative to develop a home urine routine detection system. In this paper, Smart Urine Analyzer is innovatively developed by combining smartphones with array urine dipsticks, and equipped with the unique design of adjustable bracket suitable for camera position of different smartphones. The optimal colorimetric value was selected for analysis based on chromogenic response characteristics combined with digital image colorimetry, and the breakthrough from qualitative semi-quantification to precise quantification of 9 kinds of urine biomarkers was completed. And a smartphone App for automated image recognition and processing was developed, which was applied for at-home routine healthcare. 100 clinical urine samples were used to evaluate the reliability of the system. Among them, the ROC curve showed a high consistency between Smart Urine Analyzer and the Dirui H-800 urine analyzer in hospital, which further verified the practicability. As a result, the user-friendly Smart Urine Analyzer can share health data and condition with doctors in real time, which provides a new scheme and means for the home detection of urine biomarkers, and has great practical application value as a potential home medical service assistant.

Rapid Dynamic Surface-Enhanced Raman Spectroscopy Detection of Amoxicillin-Mediated Morphological Changes in a Pathogen for Diagnosis of Clinical Urine Samples.

The swift and stable detection of pathogens in urine samples holds significant implications for the immediate clinical diagnosis and treatment of urinary tract infections (UTIs). In this study, we propose a detection strategy utilizing a hybrid substrate composed of graphene oxide (GO) and silver nanoparticles (Ag NPs) for the detection of pathogens subjected to amoxicillin-mediated (amo-mediated) treatment. This strategy employs dynamic surface-enhanced Raman spectroscopy (D-SERS) for stable and rapid detection, capturing signal variations induced by amo-mediated changes in pathogen morphology. During the 5 min D-SERS detection time window, stable SERS signals were detected for three types of pathogens and four types of pathogens were successfully distinguished using principal component analysis (PCA). In comparison to conventional nanosubstrates, the GO/Ag NP hybrid substrate exhibits outstanding stability and enhancement effects. This approach enables the dual detection of the pathogen cell structure and metabolites, facilitating specific identification of pathogens in the urinary tract, with a detection limit for Escherichia coli reaching 1 × 104 colony-forming units (CFU)/mL, meeting the clinical microbiology laboratory diagnostic standards for UTIs (105 CFU/mL). Testing of 188 clinically collected urine samples using this strategy yielded a sensitivity (SENS) of 86.4% and a specificity (SPC) of 89.7%. This introduces a novel method for diagnosing UTIs, offering broad applications in the field of clinical pathogen detection.

UHPLC-QTOFMS Urine Drug Screening With Dilute-and-Shoot Sample Preparation and Vacuum-Insulated Probe-Heated Electrospray Ionization.

We developed a method for comprehensive urine drug screening by applying dilute-and-shoot extraction and vacuum-insulated probe-heated electrospray ionization with ultra-high performance liquid chromatography high-resolution quadrupole time-of-flight mass spectrometry (DS-UHPLC-VIP-HESI-QTOFMS). The method involved five-fold post-hydrolysis dilution of urine samples and chromatography on a C18 UHPLC column prior to QTOFMS analysis. The recently introduced VIP-HESI ion source was chosen due to its enhanced ionization efficiency and compatibility with UHPLC-QTOFMS. Extensive data was acquired in positive ion mode with a low collision energy (7 eV) and an elevated collision energy (30 eV), using the broadband collision-induced dissociation data acquisition scan mode that continuously generated high-resolution and accurate mass for parent and fragment qualifier ions, and parent ion isotopic patterns. Compound identification was performed against an in-house database with 1263 compound entries, using an automated post-run reverse target database search with preset identification criteria. Method validation with 56 different drugs showed acceptable results for the limit of identification (median 5 ng/mL), matrix effects (70-130%), repeatability of retention times (< 1%), mass accuracy (< 1 mDa), as well as for specificity and stability. As compared with an established UHPLC-QTOFMS method relying on solid-phase extraction and conventional electrospray ionization, DS-UHPLC-VIP-HESI-QTOFMS produced comparable results from authentic clinical urine samples for most drugs, but showed clearly improved detectability for pregabalin, gabapentin, and ritalinic acid. We anticipate that the new method will be a step forward for laboratories performing routine urine drug screening due to its fast turnaround time, reduced manual workload, cost efficiency, and broad substance coverage.

Intelligent Screening of Prostate Cancer Individuals Using an Enzyme-Assisted Multicolor Visualization Platform.

Rapid and intelligent identification of prostate cancer (PCa) is critical for early diagnosis. Herein, a convenient, reliable, and intelligent strategy is proposed to screen PCa individuals through indirectly quantifying sarcosine (Sar), an early indicator of PCa, in clinical urine samples. Success is achieved by integrating sarcosine oxidase (SOX) as a specific recognition unit; nanozyme-assisted multicolor intelligent visualization platform as a signal reporter. With the Fe-MOFs and peroxidase, the synergetic action of SOX and response gold nanorods (Au NRs) is controlled etched to exhibit a multicolored signal. The sensor exhibits excellent linearity with Sar within 1-60 × 10-6 m, boasting a remarkable detection limit of 0.12 × 10-6 m. The RGB value of the display color can be directly extracted using a mobile phone camera. PCa diagnosis can be swiftly made (within 15 min) and directly by identifying two RGB colors (R < 175 or B > 135). The enzyme-assisted multicolor intelligent visualization platform is adept at detecting minute differences in Sar concentration in urine samples between PCa patients and healthy individuals. The concept of enzyme-assisted multicolor sensing can be further expanded by modifying the type of immobilized enzymes, providing a valuable guideline for the rational design of multiple probes to measure specific biomarkers in biological samples.

Rapid and non-invasive renal injury diagnosis unlocked by a glimpse into urinary protein particle size and charge

Urinary protein, an important marker for early detection of kidney injury, would change in type and content dynamically with the degree of kidney injury due to the particle size and charge selectivity of the glomerular filtration system, making it significantly valuable for accurate classification and early diagnosis. In this study, we developed a fluorescence sensor (Ami-AuNP/DNAs) based on charge interaction to rapidly identify the progression of kidney injury. When the positively charged Ami-AuNP combines with negatively charged DNAs, fluorescence quenching occurs, and urine proteins that appear compete with the DNAs, leading to fluorescence recovery. Based on these signal changes, PCA and PSO-BP neural network analysis were used to successfully identified kidney injury progression in 197 animal kidney injury and 62 clinical chronic kidney disease urine samples through a simple urine sample drop. Additionally, the sensor could also evaluate the effect of Huangkui capsule on kidney injury in adriamycin nephropathy model mice. Accordingly, this method transforms complex biological signals in vivo into macroscopic visual optical signals, amplifying differences of urinary protein, making up for the deficiency of the traditional method in hysteresis and low accuracy, and promoting urinary protein as the potential noninvasive biomarker for evaluating kidney injury.

Raspberry-Like Plasmonic Nanoaggregates with Programmable Hierarchical Structures for Reproducible SERS Detection of Wastewater Pollutants and Biomarkers.

Conventional solid-based SERS substrates often face challenges with inconsistent sample distribution, while liquid-based SERS substrates are prone to aggregation and precipitation, resulting in irreproducible signals in both cases. In this study, we tackled this dilemma by designing and synthesizing raspberry-like plasmonic nanoaggregates that exhibit a high density of hotspots and are colloidally stable at the same time. In particular, the nanoaggregates consist of a core made of functionalized polystyrene (PS) microspheres, which act as a template for rapid self-assembly of Au@Ag core-shell nanoparticles to form raspberry-like hierarchical nanoaggregates within 5 min of mixing. The optimized nanoaggregates can be used as reproducible and stable SERS substrates for a range of wastewater pollutants (e.g., rhodamine 6G (R6G) and malachite green (MG)) and nucleobases (e.g., adenine and uracil), with the detection limits as low as 1 × 10-10, 1 × 10-16, 3 × 10-8, and 3 × 10-7 M, respectively. Additionally, the trace detection of adenine in clinical urine samples has been successfully demonstrated. Our modular assembly approach opens up new possibilities in SERS substrate design and advanced trace-chemical detection technologies.

A novel machine-learning aided platform for rapid detection of urine ESBLs and carbapenemases: URECA-LAMP.

Pathogenic gram-negative bacteria frequently carry genes encoding extended-spectrum beta-lactamases (ESBL) and/or carbapenemases. Of great concern are carbapenem resistant Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii. Despite the need for rapid AMR diagnostics globally, current molecular detection methods often require expensive equipment and trained personnel. Here, we present a novel machine-learning-aided platform for the rapid detection of ESBLs and carbapenemases using Loop-mediated isothermal Amplification (LAMP). The platform consists of (i) an affordable device for sample lysis, LAMP amplification, and visual fluorometric detection; (ii) a LAMP screening panel to detect the most common ESBL and carbapenemase genes; and (iii) a smartphone application for automated interpretation of results. Validation studies on clinical isolates and urine samples demonstrated percent positive and negative agreements above 95% for all targets. Accuracy, precision, and recall values of the machine learning model deployed in the smartphone application were all above 92%. Providing a simplified workflow, minimal operation training, and results in less than an hour, this study demonstrated the platform's feasibility for near-patient testing in resource-limited settings.IMPORTANCEExtended-spectrum beta-lactamases (ESBL) and carbapenemases confer resistance to third-generation cephalosporins and carbapenems in pathogenic Gram-negative bacteria such as Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii. Conventional antimicrobial susceptibility testing is based on phenotypic methods, and results can take several days to be obtained. Current genotypic detection methods can be rapid but require expensive equipment and trained personnel. In this study, we present a novel machine learning-aided platform for the rapid detection of ESBLs and carbapenemases using Loop-mediated isothermal Amplification (LAMP). The validation of the platform demonstrated percent positive and negative agreements above 95% for all targets. The newly developed platform provided a simplified workflow, minimal technical training, and results in less than an hour. This study demonstrated the platform's feasibility for rapid testing of ESBL and carbapenemases in bacteria and urine specimens.

Target-Triggered Ultrafast Chondroitin Gelation Enabled Power-Free and Point-of-Care Bioassays.

Point-of-care (POC) tests increasingly highlight the importance of portable, cost-effective, and visually quantitative detection of biomarkers. Herein, we developed a power-free and visual signal-readout POC sensor based on the target-triggered ultrafast gelation process. In the gelation process, the target triggered the cascade reaction catalyzed by oxidase and ferrous glycinate to produce carbon radicals that immediately initiated the rapid polymerization and cross-linking of acryloylated chondroitin sulfate and dimethylacrylamide. This highly efficient enzymatic polymerization process contributed to the ultrafast generation of chondroitin hydrogel within 1 min at 25 °C. The increase in viscosity of aqueous solution resulted from hydrogel formation was then visually measured according to the distance of solution migration on a tick-labeled pH test strip, which thus realized the quantification of a target. By utilizing glucose oxidase as an oxidase model during the gelation process, this POC sensor was successfully employed in the rapid quantitative detection of glucose without the need for any auxiliary instruments. Benefiting from the specificity and stability of the enzymatic polymerization reaction, the sensor exhibited excellent performance in the detection of glucose in clinical blood samples. Moreover, the sensor was further extended to uric acid detection and enabled accurate assay in clinical urine samples, which indicated the versatility and practicability of this sensor in the POC test.

Simultaneous Determination of Sulfamethoxazole and Trimethoprim from Clinical Urine and Blood Serum Samples by the Application of Poly(Cu2P4BCL4)/GCE.

Synthetic antibiotics known as sulfonamides suppress the synthesis of tetrahydrofolic acid, which cures respiratory tract infections and protozoal infections by preventing the creation of dihydrofolic acid. Electrochemical sensors based on tetrakis(1,10-phenanthroline)-μ-(4,4'-bipyridine) dicopper(II) chloride monohydrate ([P2Cu-Bip-CuP2]Cl4·H2O or simply Cu2P4BCl4) have been successfully applied for the determination of sulfamethoxazole (SMX) and trimethoprim (TMP) from samples. The experimental conditions and parameters were optimized to achieve the best electrode performances for simultaneous quantification of SMX and TMP. Based on the analysis of the effect of scan rate on the peak parameters, the R 2 for the peak current vs square root of the scan rate was greater than that of the peak current vs scan rate, indicating diffusion-controlled behavior of both species. The current intensities of both SMX and TMP were highly improved due to surface activation of the electrodes by electropolymerization. For SMX, the limit of detection was determined to be 27.94 nM, while for TMP, it was 21.56 nM, and the limit of quantifications was 71.88 nM, and the corresponding relative standard deviation for each was 0.74% and 0.11%. The constructed electrode was stored for varying durations ranging from two h to 2 days, and it was found to be above 97% stable after storing for 15 days. The real applicability of the suggested sensor for the simultaneous determination of SMX and TMP was verified by sensing clinical serum and urine samples and their spike recovery studies.

Highly Specific Non-Enzymatic Electrochemical Sensor for the Detection of Uric Acid Using Carboxylated Multiwalled Carbon Nanotubes Intertwined with GdS-Gd2O3 Nanoplates in Human Urine and Serum.

Herein, the electrochemical sensing efficacy of carboxylic acid functionalized multiwalled carbon nanotubes (C-MWCNT) intertwined with coexisting phases of gadolinium monosulfide (GdS) and gadolinium oxide (Gd2O3) nanosheets is explored for the first time. The nanocomposite demonstrated splendid specificity for nonenzymatic electrochemical detection of uric acid (UA) in biological samples. It was synthesized using the coprecipitation method and thoroughly characterized. The presence of functional groups and disorder in the as-synthesized nanocomposite are confirmed using Fourier transform infrared spectroscopy and Raman spectroscopy. Furthermore, field emission scanning electron microscopy, high-resolution transmission electron microscope, X-ray powder diffraction, and X-ray photoelectron spectroscopy provides a clear understanding of the morphology, coexisting phases, and elemental composition of the as-synthesized nanocomposites. The differential pulse voltammetry technique was utilized to elaborate the electrochemical sensing of UA using a GdS-Gd2O3/C-MWCNT modified glassy carbon electrode (GCE), The sensor showed an enhanced current response by more than 2-fold compared to bare GCE. Also, the sensor's performance was further improved by dispersing the nanocomposite in an ionic liquid with the exceptional reproducibility (SD = 0.0025, n = 3). The fabricated UA sensor GdS-Gd2O3/C-MWCNT/IL/GCE demonstrated a wide linear detection range from 0.5-30 μM and 30-2000 μM, effectively covering the entire physiological range of UA in biological fluids with a limit of detection (LOD) of 0.380 μM (+3SD of blank) and a sensitivity of 356.125 μA mM-1 cm-2. Moreover, the electrodes exhibited storage stability for 2 weeks with decrease in zero-day current by only 4.5%. The sensor was validated by quantifying UA in 12 unprocessed clinical human urine and serum samples, and its comparison with the gold standard test yielded remarkable results (p < 0.05). Hence, the proposed nonenzymatic electrochemical UA sensor is selective, sensitive, reproducible, and stable, making it reliable for point-of-care diagnostics.

Field-Deployable Colorimetric Array for On-Site Diagnosis of Urinary Tract Infection and Identification of Causative Pathogens.

Urinary tract infection (UTI) is a common and prevalent disease caused by a spectrum of pathogens. Lack of access to rapid, portable, and high-quality diagnostics in resource-limited settings aggravates the improper treatment of UTIs, which is also a major driver of antibiotic misuse worldwide. Here, we describe a custom-made portable colorimetric array (PoCA) for reading out polymerase chain reaction (PCR) amplicons, the rationale of which is to transfer the previously developed dsDNA-based photosensitization colorimetric assay (solution) onto paper discs for detection. By integrating mini-LED irradiation and paper discs, the PoCA can read out 96 PCR tests in one pot, thus allowing diagnosis and identification of 12 prevailing UTI pathogens in less than 2 h, coupled with a portable thermal cycler for PCR. After analyzing 200 clinical urine samples, the pathogen profiling accuracy of the PoCA was demonstrated to be higher than the standard urine culture (confirmed with metagenomic next-generation sequencing). The PoCA platform could be used in primary care for rapid UTI diagnosis and pathogen identification.

The diagnostic accuracy of urinary neutrophil gelatinase-associated lipocalin in assessing kidney function in severe hydronephrosis.

Up to now, there is no perfect indicator to evaluate the renal function of severe hydronephrosis, which poses difficulties in the selection of clinical treatment decisions. This study investigates the role of neutrophil gelatinase-associated lipocalin (NGAL) in urine drained from the nephrostomy tube shortly after nephrostomy to evaluate the renal function of patients with severe hydronephrosis caused by ureteral obstruction. The clinical data, and blood and urine samples of 24 patients with severe hydronephrosis due to ureteral obstruction were retrospectively collected. The NGAL in the urine drained from the nephrostomy tube on the morning of the first day after the procedure was measured. The glomerular filtration rate (GFR) was determined using a nuclear scan, and the clearance rate of creatinine was calculated based on nephrostomy drainage. The correlation between the NGAL level, urine volume post-nephrostomy, affected side GFR, and creatinine clearance rate (Ccr) was assessed. Moreover, the relationship between the urinary NGAL levels and prognosis was analyzed based on whether the patients underwent nephrectomy. There was a significant correlation between the urine NGAL from the nephrostomy of the affected side and the Ccr and urine volume post-nephrostomy (both P<0.05). Compared with the patients in the kidney preservation group, those who underwent nephrectomy had significantly increased NGAL levels, and significantly reduced Ccrs and nephrostomy drainage urine output. Through the receiver operating characteristic (ROC) curve evaluation, the efficacy of NGAL in predicting nephrectomy was found to be superior to both the Ccr and urine output, with an area under the curve (AUC) of 0.845. The NGAL in the urine shortly after nephrostomy may indicate severe renal functional deterioration.

Smart Fabrics with Integrated Pathogen Detection, Repellency, and Antimicrobial Properties for Healthcare Applications

AbstractHealthcare textiles serve as key reservoirs for pathogen proliferation, demanding an urgent call for innovative interventions. Here, a new class of Smart Fabrics (SF) is introduced with integrated “Repel, Kill, and Detect” functionalities, achieved through a blend of hierarchically structured microparticles, modified nanoparticles, and an acidity‐responsive sensor. SF exhibit remarkable resilience against aerosol and droplet‐based pathogen transmission, showcasing a reduction exceeding 99.90% compared to uncoated fabrics across various drug‐resistant bacteria, Candida albicans, and Phi6 virus. Experiments involving bodily fluids from healthy and infected individuals reveal a significant reduction of 99.88% and 99.79% in clinical urine and feces samples, respectively, compared to uncoated fabrics. The SF's colorimetric detection capability coupled with machine learning (96.67% accuracy) ensures reliable pathogen identification, facilitating accurate differentiation between healthy and infected urine and fecal contaminated samples. SF holds promise for revolutionizing infection prevention and control in healthcare facilities, providing protection through early contamination detection.

Market-ready U-AST kit: simple, fast, cost-effective solution for concurrently detecting urinary tract infection and antibiotic resistance.

There is an increasing demand for an inexpensive, quick, accessible, and simple method for the detection of urinary tract infection (UTI) together with the antibiotic-resistance profile of the infection-causing bacteria. Our primary goal is to assist doctors in prescribing antibiotics that will quickly treat infections and reduce the likelihood of antibiotic resistance spreading throughout the community. To this end, a urinary tract infection antibiotic-sensitivity test (U-AST) kit was developed for the validation of bacterial infection in the urinary tract and determination of the antibiotic-resistance profile of the bacteria in a short time. The U-AST kit was standardized using standard strains of bacteria, specifically Escherichia coli, Enterococcus faecalis, Pseudomonas aeruginosa, Vibrio cholerae, and Pseudomonas species. Further, the kit was validated using 50 clinical urine samples with variation in their physical and chemical parameters, and the resistance pattern against five therapeutically important antibiotics were tested. The results acquired using the U-AST kit showed a 100% similarity to those acquired using the laboratory-based gold standard method. Interestingly, the U-AST kit required a maximum of 9 h to understand the bacterial contamination and resistance profile of the bacterial community, which was observed by a simple color change. The same result can be obtained using the gold standard method but requires 36-72 h, a sophisticated microbiology method, and skilled microbiologists. Other methods can also predict infection quickly with the aid of sophisticated instrumentation; however, understanding the antibiotic-resistance pattern is not possible. To the best of our understanding, this is a unique technique for the quick, easy, and inexpensive detection of UTI with antibiotic sensitivity testing and does not require a special laboratory set-up or expert personnel. The commercialization of the developed clinically validated U-AST kit is currently underway.

Upconverting Nanoparticle-based Enhanced Luminescence Lateral-Flow Assay for Urinary Biomarker Monitoring.

Development of efficient portable sensors for accurately detecting biomarkers is crucial for early disease diagnosis, yet remains a significant challenge. To address this need, we introduce the enhanced luminescence lateral-flow assay, which leverages highly luminescent upconverting nanoparticles (UCNPs) alongside a portable reader and a smartphone app. The sensor's efficiency and versatility were shown for kidney health monitoring as a proof of concept. We engineered Er3+- and Tm3+-doped UCNPs coated with multiple layers, including an undoped inert matrix shell, a mesoporous silica shell, and an outer layer of gold (UCNP@mSiO2@Au). These coatings synergistically enhance emission by over 40-fold and facilitate biomolecule conjugation, rendering UCNP@mSiO2@Au easy to use and suitable for a broad range of bioapplications. Employing these optimized nanoparticles in lateral-flow assays, we successfully detected two acute kidney injury-related biomarkers─kidney injury molecule-1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL)─in urine samples. Using our sensor platform, KIM-1 and NGAL can be accurately detected and quantified within the range of 0.1 to 20 ng/mL, boasting impressively low limits of detection at 0.28 and 0.23 ng/mL, respectively. Validating our approach, we analyzed clinical urine samples, achieving biomarker concentrations that closely correlated with results obtained via ELISA. Importantly, our system enables biomarker quantification in less than 15 min, underscoring the performance of our novel UCNP-based approach and its potential as reliable, rapid, and user-friendly diagnostics.

Biomarker-guided detection of acute kidney injury in abdominal aortic surgery: the new and the old.

Acute kidney injury (AKI) is a common complication in patients undergoing major vascular surgery. Despite significant research efforts in this area, the incidence of AKI remains high, posing a significant challenge to healthcare systems, especially in situations where resources are limited. Early prediction of AKI severity and individualized postoperative care is therefore essential. The primary objective of this exploratory study was to assess the diagnostic value of urine cell-cycle arrest biomarkers [(TIMP-2) × (IGFBP7)] and soluble urokinase plasminogen activator receptor (suPAR) for predicting moderate or severe AKI within 24 h after open aortic surgery, and compared to routine kidney biomarkers. Seventy-five patients undergoing elective aortic surgery were included. Clinical parameters, urine and blood samples were collected preoperatively, immediately postoperatively, and 24 h later. AKI was defined using KDIGO criteria. Individual and combined diagnostic performance of biomarkers were evaluated. Of the 75 patients, 61% developed AKI, of which 28% developed moderate or severe AKI within 24 h of surgery. Baseline demographics, comorbidities and kidney parameters did not differ between patients with moderate or severe AKI (AKI II/III) and none or mild AKI (AKI 0/I), except for higher preoperative suPAR levels in later AKI II/III patients. Urine osmolality, Cystatin C and serum creatinine had the highest predictive power for AKI II/III with AUCs of 0.75-0.72. (TIMP-2) × (IGFBP7), and neither (TIMP-2) × (IGFBP7) nor suPAR individually showed superior diagnostic value. Combining CysC or SCr with urine osmolality and 6 h urine output gave the best performance with AUCs of 0.86 (95% CI, 0.74-0.96) and 0.85 (95% CI, 0.75-0.95) respectively. Our study suggests that routine parameters like urine osmolality, CysC, SCr and 6 h urine output perform best in predicting postoperative AKI after aortic surgery compared to the new biomarkers (TIMP-2) × (IGFBP7) and suPAR. Combining biomarkers, particularly CysC or SCr with urine output, urine osmolality, may enhance diagnostic accuracy. Further validation in larger cohorts and clinical settings is warranted to establish their clinical utility.

CRISPR/Cas12a-Powered Amplification-Free RNA Diagnostics by Integrating T7 Exonuclease-Assisted Target Recycling and Split G-Quadruplex Catalytic Signal Output.

Rapid and sensitive RNA detection is of great value in diverse areas, ranging from biomedical research to clinical diagnostics. Existing methods for RNA detection often rely on reverse transcription (RT) and DNA amplification or involve a time-consuming procedure and poor sensitivity. Herein, we proposed a CRISPR/Cas12a-enabled amplification-free assay for rapid, specific, and sensitive RNA diagnostics. This assay, which we termed T7/G4-CRISPR, involved the use of a T7-powered nucleic acid circuit to convert a single RNA target into numerous DNA activators via toehold-mediated strand displacement reaction and T7 exonuclease-mediated target recycling amplification, followed by activating Cas12a trans-cleavage of the linker strands inhibiting split G-Quadruplex (G4) assembly, thereby inducing fluorescence attenuation proportion to the input RNA target. We first performed step-by-step validation of the entire assay process and optimized the reaction parameters. Using the optimal conditions, T7/G4-CRISPR was capable of detecting as low as 3.6 pM target RNA, obtaining ∼100-fold improvement in sensitivity compared with the most direct Cas12a assays. Meanwhile, its excellent specificity could discriminate single nucleotide variants adjacent to the toehold region and allow species-specific pathogen identification. Furthermore, we applied it for analyzing bacterial 16S rRNA in 40 clinical urine samples, exhibiting a sensitivity of 90% and a specificity of 100% when validated by RT-quantitative PCR. Therefore, we envision that T7/G4-CRISPR will serve as a promising RNA sensing approach to expand the toolbox of CRISPR-based diagnostics.

NMR Precision Metabolomics: Dynamic Peak Sum Thresholding and Navigators for Highly Standardized and Reproducible Metabolite Profiling of Clinical Urine Samples.

Metabolomics, especially urine-based studies, offers incredible promise for the discovery and development of clinically impactful biomarkers. However, due to the unique challenges of urine, a highly precise and reproducible workflow for NMR-based urine metabolomics is lacking. Using 1D and 2D non-uniform sampled (NUS) 1H-13C NMR spectroscopy, we systematically explored how changes in hydration or specific gravity (SG) and pH can impact biomarker discovery. Further, we examined additional sources of error in metabolomics studies and identified Navigator molecules that could monitor for those biases. Adjustment of SG to 1.002-1.02 coupled with a dynamic sum-based peak thresholding eliminates false positives associated with urine hydration and reduces variation in chemical shift. We identified Navigator molecules that can effectively monitor for inconsistencies in sample processing, SG, protein contamination, and pH. The workflow described provides quality assurance and quality control tools to generate high-quality urine metabolomics data, which is the first step in biomarker discovery.