Sensors & diagnostics最新文献

[This corrects the article DOI: 10.1039/D2SD00023G.].

[This corrects the article DOI: 10.1039/D2SD00028H.].

This work introduces a new electrochemical sensing approach, where the liquid sample containing nucleic acid targets can be blotted onto an electrode that is pre-functionalized with probe DNA. The post-hybridization signal and probe DNA signal (obtained by melting the hybrid) can be successively measured later, making the sensing scheme resilient to probe layer deterioration and circumventing the need to measure probe signal immediately before sample collection, ultimately mitigating the need for electrochemical sensing equipment at the sample collection site.

Unlike the polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP) lacks a consistent thermal cycle, making quantification particularly challenging. Previously, we demonstrated that LAMP can accurately diagnose Kaposi sarcoma (KS) from skin lesion biopsies at the point of care (receiver operating characteristic area under the curve (AUC) = 0.967). A common approach in LAMP analysis involves setting a minimum absorbance threshold and time cutoff for positivity, which can introduce bias. We present a less biased, automated signal processing approach involving the fitting of a signal curve to five, two-parameter algebraic function fits, and the training of an artificial intelligence (AI) model on those parameters and their variances. An extreme gradient boosting (XGB) model was trained and tested on a primary dataset consisting of 1317 LAMP curves (from 451 unique patient samples with replicates). Five-fold k-validation on the train/test set yielded an receiver operating curve (ROC) area under the curve (AUC) of 0.952 ± 0.029. Each of the five-fold models were then validated on a separate secondary dataset of 966 LAMP curves (from 414 unique patient samples with replicates) and achieved an AUC of 0.950 ± 0.005. While the traditional methodology (which did not implement k-validation or a test/train split) outperformed the AI model's train/test set performance, the AI model generalized better and achieved a higher accuracy on the validation set (0.950 ± 0.005 vs. 0.9347). It performed even better when the analysis was applied directly to the raw signal data without additional pre-processing steps such as artifact filtering. This suggests that the AI model is more generalizable to new data and is able to discriminate KS-present and KS-absent samples better than traditional methods.

Biofilms are intricate bacterial communities encased in a self-produced extracellular matrix (ECM) of DNA, lipids, proteins, and polysaccharides. The diverse ECM composition across bacterial species significantly influences the progression of biofilm-associated infections, making precise identification crucial for effective treatment. Traditional methods such as biochemical assays, MALDI-TOF mass spectrometry, DNA sequencing and culturing provide valuable insights but have notable drawbacks, including time-consuming procedures, high costs, and the need for specialized equipment and trained personnel. These limitations hinder the rapid and widespread adoption of biofilm identification in clinical settings, underscoring the need for more streamlined, accurate, and accessible methods. In this study, we employed a paper-based ratiometric sensor array with fluorescent dyes (3-hydroxyflavone derivatives) pre-adsorbed onto paper microzone plates to identify bacterial biofilms. The fluorescence signals from the sensor upon interaction with biofilms were analyzed using linear discriminant analysis and different machine learning algorithms, including neural networks, support vector machines, and naïve Bayes classifiers. Our results show that the sensor array accurately distinguishes between biofilms of eight species with 97.5% classification accuracy. It effectively identifies individual bacteria at OD₆₀₀ as low as 0.002 o.u. Additionally, using neural networks, the sensor array achieves more than 95% accuracy in distinguishing planktonic bacteria from biofilms and shows over 85% accuracy in identifying clinical bacterial species and biofilms. These findings highlight the sensor's potential for high-precision biofilm identification in laboratory and clinical settings, offering a valuable tool for advancing biofilm research and enhancing clinical diagnostics.

Traditional enzyme-linked immunosorbent assays (ELISAs) rely on horseradish peroxidase (HRP)-conjugated antibodies to generate a colorimetric response proportional to target antibody concentration. However, spectrophotometric quantification requires expensive benchtop equipment, limiting its usability for frequent, population-scale immunity screening. To overcome this barrier, we previously developed LC15, an antibody-invertase fusion protein that catalyzes sucrose-to-glucose conversion in proportion to antibody levels. This fusion protein enabled antibody quantification using handheld glucometers - affordable, widely available devices already integrated with telehealth infrastructure. Unlike commercial ELISAs, which report relative antibody titers, LC15 facilitates absolute antibody quantification (μg mL^-1), enhancing applications such as epidemiological monitoring and convalescent plasma dosing. To increase the number of clinical samples processed in a single run of the assay, in this study we transitioned from poly(methyl methacrylate) strips to microwell plates, optimizing pH conditions and reagent concentrations. This adaptation yielded similar sensitivity to the original strip-based assay, but with a 5-fold reduction in reagent consumption and in plasma, as opposed to serum used for the previous study. Using the SARS-CoV-2 receptor binding domain (RBD) as the antigen, we applied LC15 in a 96-well plate format to screen 72 clinical samples in triplicate for anti-RBD antibodies. A blinded comparison with commercial ELISAs demonstrated strong linear correlation (R ² = 0.85) over four orders of magnitude in concentration. By combining accuracy with accessibility, this approach has the potential to facilitate population-level immunity assessments, supporting rapid public health responses in future outbreaks.

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MYCN gene amplification critically drives neuroblastoma aggressiveness and poor outcomes, necessitating precise preoperative identification to guide risk-adapted therapies. Current invasive detection methods present substantial challenges for pediatric patients. To address this unmet need, we developed a noninvasive MRI-based radiomic signature for predicting MYCN amplification status in childhood abdominal neuroblastoma. In this prospective study, 99 patients with pathologically confirmed abdominal neuroblastoma underwent preoperative MRI between April 2019 and September 2021. From T2-weighted images, 1409 radiomic features were extracted per subject. Through two-sample statistical testing and least absolute shrinkage and selection operator (LASSO) regression, we constructed an optimized radiomic signature incorporating six highly discriminative features. The signature achieved exceptional performance (AUC = 0.91) in predicting MYCN amplification, significantly outperforming neuron-specific enolase levels (AUC = 0.68, p-value < 0.001) and all individual radiomic features. When integrated with neuron-specific enolase via multivariate logistic regression, the model achieved comparable performance (AUC = 0.91) to the signature only. Our findings establish the clinical viability of this MRI-based approach for noninvasively stratifying MYCN amplification status, offering significant potential to optimize surgical planning and therapeutic strategies for pediatric neuroblastoma.

Artificial intelligence (AI) is increasingly shaping modern healthcare by improving the accuracy and efficiency of disease diagnosis. This review summarises the modern advancements in AI-driven diagnostic technologies, with a focus on machine learning (ML) and deep learning (DL) applications for the detection and characterization of cancer, cardiovascular diseases, diabetes, neurodegenerative disorders, and bone diseases. AI models, particularly those employing convolutional neural networks, have demonstrated expert-level performances in interpreting medical images, genomic profiles, and electronic health records, often surpassing traditional diagnostic methods in terms of sensitivity, specificity, and overall accuracy. Using advanced methods like machine learning and deep learning, AI systems can analyze large and complex medical datasets—including images, electronic health records, and laboratory results—to detect patterns linked to various diseases. While integration of AI into clinical practice has shown significant benefits, challenges remain in ensuring the reliability, interpretability, and broad adoption of these systems. Thus, continued research and careful implementation are needed to maximize the potential of AI in transforming diagnostic processes and improving patient outcomes.

Cortisol is a hormone which regulates the body's response to stressors. Detection and monitoring of cortisol levels can provide information about physical and psychological health, thus it is essential to develop a sensor that can detect it in a sensitive manner. This study presents a biocompatible near-infrared fluorescent sensor, wherein single-walled carbon nanotubes (SWCNT) are functionalized with a cortisol-specific aptamer. We found this sensor was capable of detecting cortisol from 37.5 μg mL⁻¹ to 300 μg mL⁻¹ and that it was selective for cortisol compared to the similar molecule estrogen. Moreover, SWCNT functionalized with non-specific oligonucleotides did not exhibit a concentration-dependent response to cortisol, demonstrating the specificity provided by the aptamer sequence. The sensor also demonstrated the ability to detect cortisol in artificial cerebrospinal fluid. We anticipate that future optimization of this sensor will enable potential point-of-care or implantable device-based rapid detection of cortisol, with the potential for improving overall patient health and stress.