High Sensitivity Research Articles

Nanopillar array-based electrochemical aptamer sensor for STX sensitivity detection.

Saxitoxin (STX) is a cyanotoxin with high toxicity, and therefore, there is an urgent need to develop a facile detection method for STX. In this study, an ordered nanopillar array-based electrochemical aptasensor was fabricated for the high-performance detection of STX. The anti-STX aptamer with methylene blue (MB) incorporated at the 3'-end (MB-Apt) was immobilized at the surface of an Au@PAN nanopillar array electrode and used as the recognition element. The proposed aptasensor demonstrated highly sensitive and selective STX detection because of synergistic catalysis effects of MB and ordered nanopillar arrays along with the selection of MB-Apt. The nanopillar array-based electrochemical aptasensor exhibited high sensitivity over a wide linear concentration range of 1 pM-3 nM with a linear regression equation of ΔI (μA) = 28.0 + 6.9 × log[STX] (R2 = 0.98079) and 3-100 nM with a linear regression equation of ΔI (μA) = 10.7 + 43.4 × log[STX] (R2 = 0.98772), where R is the correlation coefficient. In addition, the limit of detection (LOD) was as low as 1 pM. Furthermore, the designed aptasensor demonstrated excellent selectivity toward STX, preventing interference from neo-STX, okadaic acid, and common metal ions. The presented orderly nanopillar array-based strategy to develop an electrochemical aptasensor for STX detection offers a promising method for developing high-performance electrochemical sensors, and the presented aptasensor should find useful application in the detection of shellfish poison.

Mix-and-Read Digital MicroRNA Analysis Based on Flow Cytometric Counting of Target-Clicked Nanobead Dimer.

A one-step, enzyme-free, and highly sensitive digital microRNA (miRNA) assay is rationally devised based on flow cytometric counting of target miRNA-clicked nanobead dimers via a facile mix-and-read manner. In this strategy, highly efficient miRNA-sandwiched click chemical ligation of two DNA probes may remarkably stabilize and boost the dimer formation between two kinds of fluorescence-coded nanobeads, and the number of as-produced bead dimers will be target dose-responsive, particularly when the trace number of miRNA is much less than that of employed nanobeads. Finally, each fluorescence-coded bead dimer can be easily identified and digitally counted by a powerful flow cytometer (FCM) and accordingly, the amount of target miRNA can be accurately quantified in a digital way. This new digital miRNA assay can be accomplished with a facile mix-and-read operation just by simply mixing the target miRNA with two kinds of preprepared DNA probe-functionalized nanobeads, which do not require any nucleic acid amplification, purification, and complex operation procedures. In spite of the extremely simple one-step operation, benefiting from the low-background but high target-mediated click ligation efficiency, and the powerfully digital statistical capability of FCM, this strategy achieves high sensitivity with a quite low detection limit of 5.2 fM target miRNA as well as high specificity and good generality for miRNA analysis, pioneering a new direction for fabricating digital bioassays.

The spectrum of co-existing disease in children with established kidney failure using registry and linked electronic health record data.

Children with established kidney failure may have additional medical conditions influencing kidney care and outcomes. This cross-sectional study aimed to examine the prevalence of co-existing diseases captured in the electronic hospital record compared to UK Renal Registry (UKRR) data and differences in coding. The study population comprised children aged < 18years receiving kidney replacement therapy (KRT) in England and Wales on 31/12/2016. Comorbidity data at KRT start was examined in the hospital record and compared to UKRR data. Agreement was assessed by the kappa statistic. Associations between patient and clinical factors and likelihood of coding were examined using multivariable logistic regression. A total of 869 children (62.5% male) had data linkage for inclusion. UKRR records generally reported a higher prevalence of co-existing disease than electronic health records; congenital, non-kidney disease was most commonly reported across both datasets. The highest sensitivity in the hospital record was seen for congenital heart disease (odds ratio (OR) 0.65, 95% confidence interval (CI) 0.51, 0.78) and malignancy (OR 0.63, 95% CI 0.41, 0.85). At best, moderate agreement (kappa ≥ 0.41) was seen between the datasets. Factors associated with higher odds of coding in hospital records included age, while kidney disease and a higher number of comorbidities were associated with lower odds of coding. Health records generally under-reported co-existing disease compared to registry data with fair-moderate agreement between datasets. Electronic health records offer a non-selective overview of co-existing disease facilitating audit and research, but registry processes are still required to capture paediatric-specific variables pertinent to kidney disease.

An All-in-One Array of Pressure Sensors and sEMG Electrodes for Scoliosis Monitoring.

Scoliosis often occurs in adolescents and seriously affects physical development and health. Traditionally, medical imaging is the most common means of evaluating the corrective effect of bracing during treatment. However, the imaging approach falls short in providing real-time feedback, and the optimal corrective force remains unclear, potentially slowing the patient's recovery progress. To tackle these challenges, an all-in-one integrated array of pressure sensors and sEMG electrodes based on hierarchical MXene/chitosan/polydimethylsiloxane (PDMS)/polyurethane sponge and MXene/polyimide (PI) is developed. Benefiting from the microstructured electrodes and the modulus enhancement of PDMS, the sensor demonstrates a high sensitivity of 444.3 kPa-1 and a broad linear detection range (up to 81.6kPa). With the help of electrostatic attraction of chitosan and interface locking of PDMS, the pressure sensor achieves remarkable stability of over 100000 cycles. Simultaneously, the sEMG electrodes offer exceptional stretchability and flexibility, functioning effectively at 60% strain, which ensures precise signal capture for various human motions. After integrating the developed all-in-one arrays into a commercial scoliosis brace, the system can accurately categorize human motion and predict Cobb angles aided by deep learning. This study provides real-time insights into brace effectiveness and patient progress, offering new ideas for improving the efficiency of scoliosis treatment.

Exceptional point enhanced nanoparticle detection in deformed Reuleaux-triangle microcavity.

In this paper, we propose a deformed Reuleaux-triangle resonator (RTR) to form exceptional point (EP) which results in the detection sensitivity enhancement of nanoparticle. After introducing single nanoparticle to the deformed RTR at EP, frequency splitting obtains an enhancement of more than 6 times compared with non-deformed RTR. In addition, EP induced a result that the far field pattern of chiral mode responses significantly to external perturbation, corresponding to the change in internal chirality. Therefore, single nanoparticle with far distance of more than 4000 nm can be detected by measuring the variation of far field directional emission. Compared to traditional frequency splitting, the far field pattern produced in deformed RTR provides a cost-effective and convenient path to detect single nanoparticle at a long distance, without using tunable laser and external coupler. Our structure indicates great potential in high sensitivity sensor and label-free detector.

Echocardiographic estimation of pulmonary arterial and right atrial pressures in children with congenital heart disease: a comprehensive prospective study and introduction of novel equations

BackgroundPediatric pulmonary hypertension (PH) is characterized by a mean pulmonary arterial pressure exceeding 20 mmHg. There is limited research on the suitability of adult-based methods for estimating PH in pediatric populations. Using established formulas for adults, this study aimed to evaluate the correlation between echocardiographic estimates of systolic, diastolic, and mean pulmonary arterial pressures, and mean right atrial pressures in children with congenital heart disease (CHD).MethodsA prospective study was conducted involving children with CHD undergoing cardiac catheterization without prior cardiac surgery. We used echocardiography to estimate pulmonary and right atrial pressures and compared these with invasively measured values. Four reliable regression equations were developed to estimate systolic, diastolic, and mean pulmonary arterial pressures, and mean right atrial pressures. Cutoff values were determined to predict the occurrence of PH. Linear regression, Bland–Altman analysis, and receiver operating characteristic curve analysis were performed to assess the accuracy of echocardiography and establish diagnostic thresholds for PH.ResultsThe study involved 55 children (23 with normal pulmonary arterial pressure and 32 with PH) with acyanotic CHD aged 1 to 192 months. Four equations were developed to detect high pulmonary arterial pressures, with cutoff values of 32.9 for systolic pulmonary arterial pressure, 14.95 for diastolic pulmonary arterial pressure, and 20.7 for mean pulmonary arterial pressure. The results showed high sensitivity and moderate specificity but a tendency to underestimate systolic and mean pulmonary arterial pressures at higher pressures.ConclusionsThe study provides valuable insights into the use of adult-based echocardiographic formulas for estimating PH in pediatric patients with acyanotic CHD.

Exploring the accuracy of tooth loss prediction between a clinical periodontal prognostic system and a machine learning prognostic model.

The aim of this analysis was to compare a clinical periodontal prognostic system and a developed and externally validated artificial intelligence (AI)-based model for the prediction of tooth loss in periodontitis patients under supportive periodontal care (SPC) for 10 years. Clinical and radiographic parameters were analysed to assign tooth prognosis with a tooth prognostic system (TPS) by two calibrated examiners from different clinical centres (London and Pittsburgh). The prediction model was developed on the London dataset. A logistic regression model (LR) and a neural network model (NN) were developed to analyse the data. These models were externally validated on the Pittsburgh dataset. The primary outcome was 10-year tooth loss in teeth assigned with 'unfavourable' prognosis. A total of 1626 teeth in 69 patients were included in the London cohort (development cohort), while 2792 teeth in 116 patients were included in the Pittsburgh cohort (external validated dataset). While the TPS in the validation cohort exhibited high specificity (99.96%), moderate positive predictive value (PPV = 50.0%) and very low sensitivity (0.85%), the AI-based model showed moderate specificity (NN = 52.26%, LR = 67.59%), high sensitivity (NN = 98.29%, LR = 91.45%), and high PPV (NN = 89.1%, LR = 88.6%). AI-based models showed comparable results with the clinical prediction model, with a better performance in specific prognostic risk categories, confirming AI prediction model as a promising tool for the prediction of tooth loss.

Solvent-regulated Formation of Metal/Metal-oxo Nodes in Two Indium Metal-organic Frameworks: Syntheses, Structures, Selective Gas Adsorption and Fluorescence Sensor Properties.

Two water-stable indium metal-organic frameworks, (NH2Me2)3[In3(BTB)4]·12DMA·4.5H2O (In-MOF-1) and (NH2Me2)9[In9O6(BTB)8(H2O)4(DMSO)4]·27DMSO·21H2O (In-MOF-2) (BTB = 4,4¢,4¢¢-benzene-1,3,5-tribenzoate) with 3D interpenetrate structure has been constructed by regulating solvents. Structure analysis revealed that In-MOF-1 has a three-dimensional (3D) structure with a single metal core, while In-MOF-2 features an octahedron cage constructed by three kinds of metal clusters to further form a 3D structure. The fluorescence investigations show that In-MOF-1 and In-MOF-2 are potential MOF-based fluorescent sensors to detect acetone and Fe3+ ions in EtOH or water with high sensitivity, excellent selectivity, recyclability and a low detection limit. Moreover, the fluorescence mechanisms of In-MOF-1 and In-MOF-2 toward acetone and Fe3+ ions were further explained. In addition, In-MOF-2 has higher thermal and framework stability than In-MOF-1. The activated In-MOF-2 presents a high BET surface area of 998.82 m2g-1 and a pore size distribution of 8 to 16 Å. At the same time, In-MOF-2 exhibits high selective CO2 adsorption for CO2/CH4 and CO2/N2, respectively. Furthermore, the adsorption sites and adsorption isotherms were predicted using Grand Canonical Monte Carlo (GCMC) simulations, and the adsorption energy of the lowest-energy adsorption configuration was calculated using molecular dynamics (MD) simulations.

Retrospective assessment of patients' risk for peri-implant diseases using the implant disease risk assessment (IDRA) tool: A cohort study.

The implant disease risk assessment (IDRA) tool was designed to assess an individual's risk of developing peri-implant diseases by evaluating and integrating multiple risk factors. This study aimed to evaluate the IDRA tool to determine the risk of developing peri-implant disease in patients rehabilitated with dental implants. A retrospective observational cross-sectional study was conducted, collecting data from 92 patients with 92 selected dental implants. Data included the history of periodontitis, sites with bleeding on probing (BoP), teeth and/or implants with probing depths (PDs) ≥ 5 mm, alveolar bone loss relative to the patient's age, susceptibility to periodontitis, the frequency of supportive periodontal therapy (SPT), the distance from the restorative margin (RM) of the implant-supported prosthesis to the marginal bone crest (MBC), and factors related to the prosthesis itself. Additionally, the validated instrument periodontal risk assessment (PRA) was employed for comparison. Statistical analyses utilized Chi-square, Mann-Whitney, and ROC curve. Outcomes indicated that 62 implants (67.4%) were classified as high-risk. Among the IDRA parameters, history of periodontitis was the primary factor contributing to an increased risk (p < 0.001). IDRA revealed high sensitivity (100%) and low specificity (63%) (AUC = 0.685; 95% CI: 0.554-0.816; p = 0.047), and there was a low agreement between the IDRA and PRA tools (Kappa = 0.123; p = 0.014). The peri-implant disease developed in 16 implants with 5.44 (±2.50) years of follow-up, however, no significant association was observed between the high- and low-medium risk groups and the occurrence of peri-implant diseases. Most of the evaluated implants presented high IDRA risk. The IDRA tool exhibited high sensitivity and low specificity; no significant association was observed between the risk profile and the development of peri-implant diseases.

A high-performance wearable microneedle sensor based on a prussian blue-carbon nanotube composite electrode for the detection of hydrogen peroxide and glucose

Hydrogen peroxide (H2O2) plays an important role in biology, and biomarker detection in skin interstitial fluid (ISF) has recently shown great promise. However, limited sensors were designed for H2O2 detection in ISF. Therefore, a high-performance wearable H2O2 microneedle sensor with a Prussian blue (PB)/carbon nanotube (CNT) composite electrode and a glucose microneedle sensor based on this electrode were developed. Two types of H2O2 microneedle sensors with different CNT and PB ratios were prepared with ultra-wide detection ranges (1 μΜ–2800 mM/100 nΜ–2200 mM) in accordance with the Hill equation. Ultra-high sensitivity (954.1 μA mM−1 cm−2 and 451 μA mM−1 cm−2) and excellent linearity (R2=0.999) were exhibited in linear ranges of 1 μM to 10 mM and 100 nM to 10 mM, respectively·H2O2 sensors exhibit good selectivity and essentially unattenuated detection signals in artificial ISF. The glucose enzyme-catalyzed microneedle sensor based on the CNT/PB composite electrode also exhibited a great detection range (0.5 mM–180 mM) in accordance with the Hill equation and ultra-wide linear range (0.5 mM–40 mM) with high sensitivity (16.56 μA mM−1 cm−2) and linearity (R2=0.995) in artificial ISF. To verify the practical applicability of the sensors, H2O2 levels in normal and tumor cells were successfully detected by the H2O2 sensor. The glucose levels in ISF before and after eating were successfully detected by the glucose sensor in vivo. The present work developed a low-cost, easy-to-prepare H2O2 sensor and a related enzyme-catalyzed sensor with excellent performance, which hold promising prospects for application across diverse fields.

A MEMS piezoelectric hydrophone with high sensitivity and wide bandwidth

To solve the problems of low sound pressure sensitivity and narrow operating bandwidth of hydrophones, this paper proposes a MEMS piezoelectric hydrophone with a corrugated structure substrate. This design is improved from two aspects, on the one hand, the MEMS technology is used to make the aluminum oxide substrate into a corrugated structure, the corrugated structure can make the hydrophone work with better linearity, and at the same time broaden the hydrophone’s operating bandwidth; on the other hand, the array design is utilized to improve the sound pressure sensitivity by integrating four identical sensitive cores in the same piece of hydrophone. The test results show that the sound pressure sensitivity of the MEMS piezoelectric hydrophone of this design is better than −207.6 dB in the operating bandwidth of 10 ∼ 3800 Hz, and reaches the highest −195.5 dB sound pressure sensitivity at the input signal frequency of 2500 Hz.

Consistent metacognitive efficiency and variable response biases in peripheral vision.

Across the visual periphery, perceptual and metacognitive abilities differ depending on the locus of visual attention, the location of peripheral stimulus presentation, the task design, and many other factors. In this investigation, we aimed to illuminate the relationship between attention and eccentricity in the visual periphery by estimating perceptual sensitivity, metacognitive sensitivity, and response biases across the visual field. In a 2AFC detection task, participants were asked to determine whether a signal was present or absent at one of eight peripheral locations (±10°, 20°, 30°, and 40°), using either a valid or invalid attentional cue. As expected, results revealed that perceptual sensitivity declined with eccentricity and was modulated by attention, with higher sensitivity on validly cued trials. Furthermore, a significant main effect of eccentricity on response bias emerged, with variable (but relatively unbiased) c'a values from 10° to 30°, and conservative c'a values at 40°. Regarding metacognitive sensitivity, significant main effects of attention and eccentricity were found, with metacognitive sensitivity decreasing with eccentricity, and decreasing in the invalid cue condition. Interestingly, metacognitive efficiency, as measured by the ratio of meta-d'a/d'a, was not modulated by attention or eccentricity. Overall, these findings demonstrate (1) that in some circumstances, observers have surprisingly robust metacognitive insights into how performance changes across the visual field and (2) that the periphery may be subject to variable detection biases that are contingent on the exact location in peripheral space.

Fat-signal suppression in breast diffusion-weighted imaging: the Good, the Bad, and the Ugly.

Fat-signal suppression is essential for breast diffusion magnetic resonance imaging (or diffusion-weighted MRI, DWI) as the very low diffusion coefficient of fat tends to decrease absolute diffusion coefficient (ADC) values. Among several methods, the STIR (short-tau inversion recovery) method is a popular approach, but signal suppression/attenuation is not specific to fat contrary to other methods such as SPAIR (spectral adiabatic (or attenuated) inversion recovery). This article focuses on those two techniques to illustrate the importance of appropriate fat suppression in breast DWI, briefly presenting the pros and cons of both approaches. We show here through simulation and data acquired in a dedicated breast DWI phantom made of vials with water and various concentrations of polyvinylpyrrolidone (PVP) how ADC values obtained with STIR DWI may be biased toward tissue componentswith the longest T1 values: ADC values obtained with STIR fat suppression may be over/underestimated depending on the T1 and ADC profile within tissues. This bias is also illustrated in twoclinical examples. Fat-specific methods should be preferred over STIR for fat-signal suppression in breast DWI, such as SPAIR which also provides a higher sensitivity than STIR for lesion detection. One should remain aware, however, that efficient fat-signal suppression with SPAIR requires good B0 shimming to avoid ADC underestimation from residual fat contamination. The spectral adiabatic (or attenuated) inversion recovery (SPAIR) method should be preferred over short-tau inversion recovery (STIR) for fat suppression in breast DWI. Fat-signal suppression is essential for breast DWI; the SPAIR method is recommended. Short-tau inversion recovery (STIR)is not specific to fat; as a result, SNR is decreased and ADC values may be over- or underestimated. The STIR fat-suppression method must not be used after the injection of gadolinium-based contrast agents.

Global Leadership Initiative on Malnutrition criteria using calf and upper arm circumference as phenotypic criteria for assessing muscle mass demonstrate satisfactory validity for diagnosing malnutrition in hospitalized patients: A prospective cohort study.

Hospitalized individuals present high rates of malnutrition and loss of muscle mass (MM). Imaging techniques for assessing MM are expensive and scarcely available in hospital practice. The Global Leadership Initiative on Malnutrition (GLIM) proposed a framework for malnutrition diagnosis that includes simple measurements to assess MM, such as calf circumference (CC) and mid-upper arm circumference (MUAC). This study aimed to analyze the validity of the GLIM criteria with CC and MUAC for malnutrition diagnosis, using Subjective Global Assessment (SGA) as the reference standard, in inpatients. A prospective cohort study was conducted on 453 inpatient adults in a university hospital. The presence of malnutrition was assessed within 48 h of hospital admission using SGA and GLIM criteria using CC and MUAC as phenotypic criteria for malnutrition diagnosis. Accuracy, agreement tests, and logistic regression analysis adjusted for confounders were performed to test the validity of the GLIM criteria for malnutrition diagnosis. The patients were aged 59 (46-68) years, 51.4% were male, and 67.8% had elective surgery. Compared with SGA, the GLIM criteria using the two MM assessment measures showed good accuracy (area under the curve > 0.80) and substantial agreement (κ > 0.60) for diagnosing malnutrition. The highest sensitivity was obtained with GLIMCC (89%), whereas GLIMMUAC showed high specificity (>90%). Also, malnutrition identified by GLIMCC and GLIMMUAC was significantly associated with prolonged hospitalization and in-hospital death. In the absence of imaging techniques to assess MM, the use of CC and MUAC measurements from the GLIM criteria demonstrated satisfactory validity for diagnosing malnutrition in hospitalized patients.

RankCompV3: a differential expression analysis algorithm based on relative expression orderings and applications in single-cell RNA transcriptomics

BackgroundEffective identification of differentially expressed genes (DEGs) has been challenging for single-cell RNA sequencing (scRNA-seq) profiles. Many existing algorithms have high false positive rates (FPRs) and often fail to identify weak biological signals.ResultsWe present a novel method for identifying DEGs in scRNA-seq data called RankCompV3. It is based on the comparison of relative expression orderings (REOs) of gene pairs which are determined by comparing the expression levels of a pair of genes in a set of single-cell profiles. The numbers of genes with consistently higher or lower expression levels than the gene of interest are counted in two groups in comparison, respectively, and the result is tabulated in a 3 × 3 contingency table which is tested by McCullagh’s method to determine if the gene is dysregulated. In both simulated and real scRNA-seq data, RankCompV3 tightly controlled the FPR and demonstrated high accuracy, outperforming 11 other common single-cell DEG detection algorithms. Analysis with either regular single-cell or synthetic pseudo-bulk profiles produced highly concordant DEGs with the ground-truth. In addition, RankCompV3 demonstrates higher sensitivity to weak biological signals than other methods. The algorithm was implemented using Julia and can be called in R. The source code is available at https://github.com/pathint/RankCompV3.jl.ConclusionsThe REOs-based algorithm is a valuable tool for analyzing single-cell RNA profiles and identifying DEGs with high accuracy and sensitivity.

Computational insights into the structure and decomposition behaviors of 2,4,6-triamino-5-nitropyrimidine-1,3-dioxide under high pressure up to 10 GPa.

Inspired by the recent successful synthesis of the energetic compound 2,4,6-triamino-5-nitropyrimidine-1,3-dioxide (ICM-102), which displayed a good balance between high energy and sensitivity, the response of the structure and decomposition behaviors of ICM-102 to high pressure was systematically investigated using first principle calculations. ICM-102 exhibited a graphite-like layer structure, with the c-axis and the a-axis mainly contributing to the distance between the molecular planes. As the pressure increased from 1atm to 10 GPa, this distance decreased from 3.166 to 2.689 Ǻ. The hydrogen bonds had the most contribution to the non-covalent interactions within the same molecular planes, resulting in the b-axis discontinuity. However, van der Waals interactions gradually appeared between molecular planes as the pressure increased to 2.5 GPa. Based on the analysis of crystal orbitals, the distribution of π bonds and the Laplacian bond order (LBO), it was determined that the generation mechanism of H2O molecules involved the cleavage of N-Oc (coordinated oxygen atoms), followed by intermolecular hydrogen transfer reactions, and ultimately the formation of H2O molecules through competition with H atoms in the amino groups within the same molecules. More importantly, the pressure dependence of LBO values for N-Oc revealed that high pressure could inhibit the ICM-102 decomposition process due to reinforcing hydrogen bonds and van der Waals interactions. This work will deepen our understanding of the stability of ICM-102 under high pressure and provide a helpful reference for its potential detonation applications. All simulations, including geometry optimization and vibration analysis under quasi-hydrostatic pressure, were conducted using the CP2K code. The PBE function and the Goldk-Teter-Hutter (GTH) pseudopotential with the double-ζ-with-polarization (DZVP) basis set were employed. Additionally, the semiempirical dispersion correction D3 (BJ) was used to account for the intermolecular dispersion force. The simulations were performed under periodic boundary conditions, with a finest grid level cutoff set to 400 Ry for the Γ point. The Broyden-Flecher-Goldfarb-Shanno (BFGS) optimization method was used, with tighter convergence criteria applied for the subsequent calculations of infrared spectra. Finally, the wave-function analysis, such as non-covalent interaction and LBO, was conducted using the Multiwfn and VMD packages.

Diabetic retinopathy screening with confocal fundus camera and artificial intelligence - assisted grading.

Screening for diabetic retinopathy (DR) by ophthalmologists is costly and labour-intensive. Artificial Intelligence (AI) for automated DR detection could be a clinically and economically alternative. We assessed the performance of a confocal fundus imaging system (DRSplus, Centervue SpA), coupled with an AI algorithm (RetCAD, Thirona B.V.) in a real-world setting. 45° non-mydriatic retinal images from 506 patients with diabetes were graded both by an ophthalmologist and by the AI algorithm, according to the International Clinical Diabetic Retinopathy severity scale. Less than moderate retinopathy (DR scores 0, 1) was defined as non-referable, while more severe stages were defined as referable retinopathy. The gradings were then compared both at eye-level and patient-level. Key metrics included sensitivity, specificity all measured with a 95% Confidence Interval. The percentage of ungradable eyes according to the AI was 2.58%. The performances of the AI algorithm for detecting referable DR were 97.18% sensitivity, 93.73% specificity at eye-level and 98.70% sensitivity and 91.06% specificity at patient-level. DRSplus paired with RetCAD represents a reliable DR screening solution in a real-world setting. The high sensitivity of the system ensures that almost all patients requiring medical attention for DR are referred to an ophthalmologist for further evaluation.

Yolk-Shell Hierarchical Pore Au@MOF Nanostructures: Efficient Gas Capture and Enrichment for Advanced Breath Analysis.

Surface-enhanced Raman scattering (SERS) offers a promising, cost-effective alternative for the rapid, sensitive, and quantitative analysis of potential biomarkers in exhaled gases, which is crucial for early disease diagnosis. However, a major challenge in SERS is the effective detection of gaseous analytes, primarily due to difficulties in enriching and capturing them within the substrate's "hotspot" regions. This study introduces an advanced gas sensor combining mesoporous gold (MesoAu) and metal-organic frameworks (MOFs), exhibiting high sensitivity and rapid detection capabilities. The MesoAu provides abundant active sites and interconnected mesopores, facilitating the diffusion of analytes for detection. A ZIF-8 shell enveloping MesoAu further enriches target molecules, significantly enhancing sensitivity. A proof-of-concept experiment demonstrated a detection limit of 0.32 ppb for gaseous benzaldehyde, indicating promising prospects for the rapid diagnosis of early stage lung cancer. This research also pioneers a novel approach for constructing hierarchical plasmonic nanostructures with immense potential in gas sensing.

A rapid on-site loop-mediated isothermal amplification technology as an early warning system for the detection of Shiga toxin-producing Escherichia coli in water.

Shiga toxin-producing Escherichia coli (STEC) is an important waterborne pathogen capable of causing serious gastrointestinal infections with potentially fatal complications, including haemolytic-uremic syndrome. All STEC serogroups harbour genes that encode at least one Shiga toxin (stx1 and/or stx2), which constitute the primary virulence factors of STEC. Loop-mediated isothermal amplification (LAMP) enables rapid real-time pathogen detection with a high degree of specificity and sensitivity. The aim of this study was to develop and validate an on-site portable diagnostics workstation employing LAMP technology to permit rapid real-time STEC detection in environmental water samples. Water samples (n=28) were collected from groundwater wells (n=13), rivers (n=12), a turlough (n=2) and an agricultural drain (n=1) from the Corrib catchment in Galway. Water samples (100 ml) were passed through a 0.22 µm filter, and buffer was added to elute captured cells. Following filtration, eluates were tested directly using LAMP assays targeting stx1, stx2 and E. coli phoA genes. The portable diagnostics workstation was used in field studies to demonstrate the on-site testing capabilities of the instrument. Real-time PCR assays targeting stx1 and stx2 genes were used to confirm the results. The limit of detection for stx1, stx2 and phoA LAMP assays were 2, 2 and 6 copies, respectively. Overall, stx1, stx2 and phoA genes were detected by LAMP in 15/28 (53.6 %), 9/28 (32.2 %) and 24/28 (85.7 %) samples, respectively. For confirmation, the LAMP results for stx1 and stx2 correlated perfectly (100 %) with those obtained using PCR. The portable diagnostics workstation exhibited high sensitivity throughout the on-site operation, and the average time from sample collection to final result was 40 min. We describe a simple, transferable and efficient diagnostic technology for on-site molecular analysis of various water sources. This method allows on-site testing of drinking water, enabling evidence-based decision-making by public health and water management authorities.

A systematic review on the early detection of oral cancer using artificial intelligence and electronic tongue technology

This systematic review examines the role of artificial intelligence (AI) and electronic tongue (e-tongue) technology in the early detection of oral cancer. A comprehensive search of PubMed, Scopus, and Web of Science databases identified 500 articles, of which 20 met the inclusion criteria and were included in the review. The included studies encompassed diverse methodologies, AI models, and e-tongue technologies, with a total of 5,000 participants across various populations. AI-driven models, particularly those utilizing deep learning algorithms, demonstrated high sensitivity (&gt;85%) and specificity (&gt;80%) in detecting oral cancer biomarkers. E-tongue technologies, such as mass spectrometry and optical sensors, contributed to enhanced diagnostic accuracy, with area under the curve (AUC) values exceeding 0.85 in several studies. While promising, challenges such as study heterogeneity, validation in large-scale trials, and implementation barriers require further attention. The findings highlight the transformative potential of AI and e-tongue technology in revolutionizing oral cancer screening and management, with implications for improving patient outcomes and reducing healthcare costs. Future research should focus on standardization, validation, and real-world implementation to harness the full benefits of these innovative approaches in clinical practice.