Advances in nanopore sensing: Signal processing prospects for the future.

Biogenic amines (BAs) are basic, polar, or semi-polar compounds having low molecular weight present in various foods such as alcoholic beverages, cheese, fish, and meat products. Microbial decarboxylation of amino acids in food attributed to its improper storage can result in BAs’ formation. BAs exhibit their precursors’ characteristics, which are considered when analytical methods are being developed for their determination. BAs act as a nitrogen source and are precursors of many biochemical substances such as hormones, alkaloids, proteins, and nucleic acids. BAs are compounds of high polarity without intrinsic property. Moreover, these are present in low concentrations and more interfering compounds in complex matrices, making their determination and quantification difficult. The LC-MS system is one of the most powerful techniques used to determine different compounds in complex biological matrices with high quantitative accuracy. GC-MS and GC-MS/MS are commonly used techniques in clinical chemistry and allow the identification of analytes with even minute structural differences because of high resolution, high sensitivity.

Nucleic acid probes in living organisms play an essential role in therapeutics and diagnosis. Through the imaging and sensing of nucleic acid probes in complex biological matrices, a variety of diseases-related biological process, pathogenic process, or pharmacological responses to a therapeutic intervention have been discovered. However, a critical challenge of nucleic acid probes applied in complex matrices lies in enhancing the stability of nucleic acid probes, especially when it suffers from nuclease degradation and protein adsorption. In order to enhance the application of nucleic acid nanoprobes in complex matrices, great efforts have been devoted to improving the stability of probes operated in complex media, including construction of nucleic acid nanoprobes with nuclease resistance and protein adsorption resistance, sample pretreatment, anti-biofouling and signal correction. In this review, we aim to summarize recent advances in the stability of nucleic acid nanoprobes in complex matrices, including the methods of enhancing the stability of probes or signals, and the application of nucleic acid nanoprobes for disease diagnosis.

Nanopore-based sensing has emerged as a transformative approach for biomarker detection, offering label-free, single-molecule analysis with exceptional sensitivity and specificity. By monitoring ionic current modulations as individual molecules traverse biological or solid-state nanopores, these platforms provide direct insights into molecular size, structure, charge, and interactions. Over the past five years, advances in nanopore fabrication, surface engineering, and signal interpretation have expanded their analytical scope, from nucleic acids and proteins to peptides and small metabolites, while enabling operation in complex biological matrices. This review focuses on recent advancements (from last 5 years) that enhance detection performance and specificity through recognition strategies, engineered pore chemistries, and data analysis. Applications are discussed across key disease areas, including cancer, cardiovascular, neurological, metabolic, and infectious diseases, emphasizing early detection, multiplexed measurements, clinical adaptability and translational diagnostics, highlighting progress toward portable, point-of-care systems. Collectively, these developments underscore the potential of nanopore biosensing to bridge fundamental research and real-world diagnostics, paving the way for rapid, sensitive, and accessible health monitoring tools in precision medicine.Graphical Schematic illustration of sensing of various health biomarkers (protein, peptides, miRNA, small molecules etc.) through nanopores.

Reversed phase HPLC with electrochemical detection (HPLC-ED) was used for quantitative determination of adrenaline, noradrenaline, and dopamine in several complex biological matrices, including plasma, uremic plasma, and urine. Three different methods of sample preparation for use in this clinical chemistry were tested. These were adsorption of catecholamines on alumina, organic solvent extraction after complex formation with diphenylborate, and adsorption of catecholamines on a cation exchange gel followed by organic solvent extraction of the elute. The selectivity and precision of the three methods were evaluated. The organic solvent extraction proved to be more precise and selective than adsorption on alumina (adrenaline: cv=3.80% vs. 7.58%; noradrenaline: cv=1.70% vs. 4.26%); it also proved suitable for use in the routine quantitative determination of catecholamines in plasma from patients with normal renal function (creatinine <1.2 mg/dl). However when working with uremic plasma or urine, a more selective sample preparation was required. In this case the adsorption of catecholamines on a cation exchange gel followed by organic solvent extraction of the elute was sufficiently selective and precise and thus allowed a reliable quantitative determination of adrenaline and noradrenaline from rather complex biological matrices (adrenaline: cv=6.2%; noradrenaline: cv=2.8%). Use of this specific method showed that basal plasma catecholamine levels in dialysis patients are comparable to those found in patients with normal renal function (adrenaline: 47.7±22.2 pg/ml; noradrenaline: 310.3±121.4 pg/ml).

Shotgun metagenomics sequencing is a powerful tool for the characterization of complex biological matrices, enabling analysis of prokaryotic and eukaryotic organisms and viruses in a single experiment, with the possibility of reconstructing de novo the whole metagenome or a set of genes of interest. One of the main factors limiting the use of shotgun metagenomics on wide scale projects is the high cost associated with the approach. However, we demonstrate that—for some applications—it is possible to use shallow shotgun metagenomics to characterize complex biological matrices while reducing costs. We measured the variation of several summary statistics simulating a decrease in sequencing depth by randomly subsampling a number of reads. The main statistics that were compared are alpha diversity estimates, species abundance, detection threshold, and ability of reconstructing the metagenome in terms of length and completeness. Our results show that a classification of prokaryotic, eukaryotic and viral communities can be accurately performed even using very low number of reads, both in mock communities and in real complex matrices. With samples of 100,000 reads, the alpha diversity estimates were in most cases comparable to those obtained with the full sample, and the estimation of the abundance of all the present species was in excellent agreement with those obtained with the full sample. On the contrary, any task involving the reconstruction of the metagenome performed poorly, even with the largest simulated subsample (1M reads). The length of the reconstructed assembly was smaller than the length obtained with the full dataset, and the proportion of conserved genes that were identified in the meta-genome was drastically reduced compared to the full sample. Shallow shotgun metagenomics can be a useful tool to describe the structure of complex matrices, but it is not adequate to reconstruct—even partially—the metagenome.

Shotgun metagenomics sequencing is a powerful tool for the characterization of complex biological matrices, enabling analysis of prokaryotic and eukaryotic organisms in a single experiment, with the possibility of de novo reconstruction of the whole metagenome or a set of genes of interest. One of the main factors limiting the use of shotgun metagenomics on wide scale projects is the high cost associated with the approach. However, we demonstrate that-for some applications-it is possible to use shallow shotgun metagenomics to characterize complex biological matrices while reducing costs. Here we compared the results obtained on full size, real datasets with results obtained by randomly extracting a fixed number of reads. The main statistics that were compared are alpha diversity estimates, species abundance, and ability of reconstructing the metagenome in terms of length and completeness. Our results show that a classification of the communities present in a complex matrix can be accurately performed even using very low number of reads. With samples of 100,000 reads, the alpha diversity estimates were in most cases comparable to those obtained with the full sample, and the estimation of the abundance of all the present species was in excellent agreement with those obtained with the full sample. On the contrary, any task involving the reconstruction of the metagenome performed poorly, even with the largest simulated subsample (1M reads). The length of the reconstructed assembly was sensibly smaller than the length obtained with the full dataset, and the proportion of conserved genes that were identified in the meta-genome was drastically reduced compared to the full sample. Shallow shotgun metagenomics can be a useful tool to describe the structure of complex matrices, but it is not adequate to reconstruct de novo-even partially-the metagenome.

Speech perception in adverse listening situations can be exhausting. Hearing loss particularly affects processing demands, as it requires increased effort for successful speech perception in background noise. Signal processing in hearing aids and noise reduction (NR) schemes aim to counteract the effect of noise and reduce the effort required for speech recognition in adverse listening situations. The present study examined the benefit of NR schemes, applying a combination of a digital NR and directional microphones, for reducing the processing effort during speech recognition. The effect of noise (intelligibility level) and different NR schemes on effort were evaluated by measuring the pupil dilation of listeners. In 2 different experiments, performance accuracy and peak pupil dilation (PPD) were measured in 24 listeners with hearing impairment while they performed a speech recognition task. The listeners were tested at 2 different signal to noise ratios corresponding to either the individual 50% correct (L50) or the 95% correct (L95) performance level in a 4-talker babble condition with and without the use of a NR scheme. In experiment 1, the PPD differed in response to both changes in the speech intelligibility level (L50 versus L95) and NR scheme. The PPD increased with decreasing intelligibility, indicating higher processing effort under the L50 condition compared with the L95 condition. Moreover, the PPD decreased when the NR scheme was applied, suggesting that the processing effort was reduced. In experiment 2, 2 hearing aids using different NR schemes (fast-acting and slow-acting) were compared. Processing effort changed as indicated by the PPD depending on the hearing aids and therefore on the NR scheme. Larger PPDs were measured for the slow-acting NR scheme. The benefit of applying an NR scheme was demonstrated for both L50 and L95, that is, a situation at which the performance level was at a ceiling. This opens the opportunity for new means of evaluating hearing aids in situations in which traditional speech reception measures are shown not to be sensitive.

Bacterial infections remain a global threat, particularly in low-resource settings, where access to accurate and timely diagnosis is limited. Point-of-care nucleic acid amplification tests have shown great promise in addressing this challenge. However, their dependence on complex traditional sample preparation methods remains a major challenge. To address this limitation, we present a paper-based sample preparation device that integrates bacterial cell lysis, DNA purification, and concentration using an electrokinetic technique called isotachophoresis (ITP). This is the first device that (i) integrates electrochemical bacterial lysis with ITP and (ii) demonstrates the focusing of whole bacterial genomic DNA (gDNA) in paper. Characterization with buffers showed that the paper-based ITP sample preparation module (p-ITPrep) concentrated bacterial gDNA with an average concentration factor of 12×, and DNA could be extracted from a sample containing as few as 102 CFU mL-1Mycobacterium smegmatis (Msm). From complex biological matrices - human saliva, human blood serum, and artificial urine - p-ITPrep extracted DNA from samples containing 102 CFU Msm per mL saliva or artificial urine and 103 CFU Msm per mL serum within 20 min. The extraction procedure involved only 3 user steps, in contrast to conventional solid phase extraction kits that require more than 10 user steps. p-ITPrep may provide a simple, inexpensive, and versatile alternative to conventional multi-step nucleic acid extraction protocols for point-of-care diagnostics.

The outstanding properties of metal nanoclusters, stabilized with different scaffolds, i.e., proteins, nucleic acids, polymers and dendrimers, enable their application in a wide range of fields. The recent advances in the fabrication and synthesis of nanoclusters have revolutionized the design of biosensors, leading to significant improvements in the selective and sensitive determination of several targets. In particular, in recent years, copper nanoclusters (CuNCs) have attracted more attention mainly for their unique fluorescent properties, as well as their large Stokes shifts, low toxicity, and high biocompatibility. The high-photoluminescent features of CuNCs facilitate highly sensitive target detection even in complex biological matrices. For these reasons, in this work, we exploited the specific template-targeted CuNCs’ growth for the sensitive and accurate determination of human serum albumin (HSA) in urine and human serum. HSA is the most abundant protein in plasma, acting as a carrier for many key biological molecules such as hormones, fatty acids and steroids, and it contributes to the maintenance of the oncotic blood pressure. The concentration of HSA in body fluids greatly influences the state of health of the patients. Taking into account these considerations, the quantitative detection of human serum albumin plays a key role in the early diagnosis of serious pathological conditions such as albuminuria and albuminemia. Here, we present a CuNCs-based assay in which copper nanoclusters were used as fluorescent signal indicators to detect serum albumin in a complex biological matrix.

This work reports on some methodological aspects of an off-line combination of preparative ITP and HPLC with mass spectrometric detection (pITP-HPLC-MS) and its potential applications to the analysis of high molecular mass compounds present in complex biological matrices from the analytical chemistry perspective. Lysozyme served as the model analyte and human saliva as the complex biological matrix in this study. A mixture of five low-molecular mass compounds was found and successfully used in the pITP experiments as discrete spacers to isolate the analyte from the interferents present in the complex biological matrix and to minimize their disturbance effect on the final MS analysis. The experiments at the pITP stage were performed in the cationic mode. On-column conductivity detectors were used for the detection of ITP zones. Lysozyme was found in the human saliva samples using just deconvolution of the MS data after background correction. The MS data obtained from HPLC-MS analysis of pITP fractions exhibited the great analytical potential of the combination of pITP-HPLC-MS resulting from the ITP clean-up effect as well as the ITP preconcentration of the analyte present at low concentration levels in complex biological matrices.

Cardiovascular problems have become a major source of concern for people all over the world. The significance of disease necessitates proper diagnosis as well as right and early treatment. ECG is the most extensively utilized important signal nowadays, providing precise information regarding the function of the cardiovascular. A novel illness diagnostic algorithm created on the forward searching for ECG signal processing techniques is implemented in an Application Specific Integrated Circuit (ASIC) for the cardiovascular diagnosis of diseases on a feature extraction method. The CMOS small leakage research strategies are used to create an ASIC. The PQRST ASIC has a surface area of with a supply voltage of the PQRST ASIC dissipates of energy. ECGs can provide a great deal of information on the normal and anomalous functioning of the heartbeat. The form of the ECG is similar to the irregularities of a heart. One cardiovascular phase of an ECG signal is made up of the feature facts P-QRS-T. The magnitude and frequency principles of a P-QRS-T section define how a human's heart pumps. An anomaly in ECG signals occurs when the electrical impulses of a heart are unpredictable, quicker, or less than usual. The ASIC output has been sent to a feature extraction method to diagnose the ECG signal, which provides a show that the design can be emailed to a cardiologist. This research looks at numerous strategies provided by researchers for extracting features from ECG signals. Using the Manually curated PTB diagnostics ECG database, the ASIC and feature extraction are validated for the diagnosis of bundle branch blockage, hypertrophic, arrhythmias, and myocardial infarction. The proposed ASIC, in conjunction with the feature extraction method, is best suited for an energy-effective peripheral cardiovascular disease recognition technique.

Exposure to respirable fractions of crystalline silica quartz dust particles is associated with silicosis, cancer and the development of autoimmune conditions. Early cellular interactions are not well understood, partly due to a lack of suitable technological methods. Improved techniques are needed to better quantify and study high-level respirable crystalline silica exposure in human populations. Techniques that can be applied to complex biological matrices are pivotal to understanding particle-cell interactions and the impact of particles within real, biologically complex environments. In this study, we investigated whether imaging flow cytometry could be used to assess the interactions between cells and crystalline silica when present within complex biological matrices. Using the respirable-size fine quartz crystalline silica dust Min-u-sil® 5, we first validated previous reports that, whilst associating with cells, crystalline silica particles can be detected solely through their differential light scattering profile using conventional flow cytometry. This same property reliably identified crystalline silica in association with primary monocytic cells in vitro using an imaging flow cytometry assay, where darkfield intensity measurements were able to detect crystalline silica concentrations as low as 2.5 μg/mL. Finally, we ultilised fresh whole blood as an exemplary complex biological matrix to test the technique. Even after the increased sample processing required to analyse cells within whole blood, imaging flow cytometry was capable of detecting and assessing silica-association to cells. As expected, in fresh whole blood exposed to crystalline silica, neutrophils and cells of the monocyte/macrophage lineage phagocytosed the particles. In addition to the use of this technique in in vitro exposure models, this method has the potential to be applied directly to ex vivo diagnostic studies and research models, where the identification of crystalline silica association with cells in complex biological matrices such as bronchial lavage fluids, alongside additional functional and phenotypic cellular readouts, is required.

Application specific integrated circuits (ASICs) are commonly used to implement high-performance signal-processing systems for high-volume applications, but their high development costs and inflexible nature make ASICs inappropriate for algorithm development and low-volume DoD applications. In addition, the intellectual property (IP) embedded in the ASIC is at risk when fabricated in an untrusted foundry. Lincoln Laboratory has developed a flexible signal-processing architecture to implement a wide range of algorithms within one application domain, for example radar signal processing. In this design methodology, common signal processing kernels such as digital filters, fast Fourier transforms (FFTs), and matrix transformations are implemented as optimized modules, which are interconnected by a programmable wiring fabric that is similar to the interconnect in a field programmable gate array (FPGA). One or more programmable microcontrollers are also embedded in the fabric to sequence the operations. This design methodology, which has been termed a coarse-grained FPGA, has been shown to achieve a near ASIC level of performance. In addition, since the signal processing algorithms are expressed in firmware that is loaded at runtime, the important application details are protected from an unscrupulous foundry.

Nucleic acids play a pivotal role in the diagnosis of diseases. However, rapid, cost-efficient, and ultrasensitive identification of nucleic acid targets still represents a significant challenge. Herein, we describe an enzyme-free DNA amplification method capable of achieving accurate and ultrasensitive nucleic acid detection via DNA-templated click ligation chain reaction (DT-CLCR) catalyzed by a heterogeneous nanocatalyst made of Cu2O (hnCu2O). This hnCu2O-DT-CLCR method is built on two cross-amplifying hnCu2O-catalyzed DNA-templated azide-alkyne cycloaddition-driven DNA ligation reactions that boast a fast reaction rate and a high DNA ligation yield in minutes, enabling rapid exponential amplification of specific DNA targets. This newly developed hnCu2O-DT-CLCR-enabled DNA amplification strategy is further integrated with two signal reporting mechanisms to achieve low-cost and easy-to-use biosensors: an electrochemical sensor through the conjugation of a methylene blue redox reporter to a DNA probe used in hnCu2O-DT-CLCR and a colorimetric sensor through the incorporation of the split-to-intact G-quadruplex DNAzyme encoded into hnCu2O-DT-CLCR. Both sensors are able to achieve specific detection of the intended DNA target with a limit of detection at aM ranges, even when challenged in complex biological matrices. The combined hnCu2O-DT-CLCR and sensing strategies offer attractive universal platforms for enzyme-free and yet efficient detection of specific nucleic acid targets.

A novel algorithm based on forward search is developed for real-time electrocardiogram(ECG) signal processing and implemented in application specific integrated circuit (ASIC)for QRS complex related cardiovascular disease diagnosis. The authors have evaluated theiralgorithm using MIT-BIH database and achieve sensitivity of 99.86% and specificity of99.93% for QRS complex peak detection. In this Letter, Physionet PTB diagnostic ECGdatabase is used for QRS complex related disease detection. An ASIC for cardiovasculardisease detection is fabricated using 130-nm CMOS high-speed process technology. The areaof the ASIC is 0.5 mm2. The power dissipation is 1.73 μW at the operatingfrequency of 1 kHz with a supply voltage of 0.6 V. The output from the ASIC is fed totheir Android application that generates diagnostic report and can be sent to acardiologist through email. Their ASIC result shows average failed detection rate of 0.16%for six leads data of 290 patients in PTB diagnostic ECG database. They also haveimplemented a low-leakage version of their ASIC. The ASIC dissipates only 45 pJ with asupply voltage of 0.9 V. Their proposed ASIC is most suitable for energy efficienttelemetry cardiovascular disease detection system.