Bernard D G Eenink, Josephin M Holstein, Magdalena Heberlein, Carina Dilkaute, Joachim Jose, Florian Hollfelder, Bert van Loo, Erich Bornberg-Bauer, Tomasz S Kaminski, Andreas Lange
Characterizing the dynamics and functional shifts during protein evolution is essential, both for understanding protein evolution and for rationalizing efficient strategies for e.g. enzymes with desired and effective functions. Most proteins organize in families, sets of divergent sequences which share a common ancestor and have a similar structural fold. Here, we study aryl sulfatases, a subfamily of the large and evolutionary old alkaline phosphatase superfamily. We demonstrate how ultrahigh-throughput droplet microfluidics can be used for studying aryl sulfatases and their computationally reconstructed putative common ancestors. We compare the evolvability and robustness of three ancestors and three extant aryl sulfatases which all exhibit catalytic promiscuity towards a range of substrate classes. Using varying mutations rates, eleven libraries were constructed and expressed in single-cell microdroplets. In general, higher mutation rates resulted in wider distribution of active variants but fewer improved variants overall. However, the impact of mutation rate differed between enzymes, with some benefiting from higher and others from lower mutation rate, underscoring the need to test diverse mutagenesis regimes.
{"title":"Studying the evolutionary potential of ancestral aryl sulfatases in the alkaline phosphatase family with droplet microfluidics.","authors":"Bernard D G Eenink, Josephin M Holstein, Magdalena Heberlein, Carina Dilkaute, Joachim Jose, Florian Hollfelder, Bert van Loo, Erich Bornberg-Bauer, Tomasz S Kaminski, Andreas Lange","doi":"10.1039/d5an00865d","DOIUrl":"10.1039/d5an00865d","url":null,"abstract":"<p><p>Characterizing the dynamics and functional shifts during protein evolution is essential, both for understanding protein evolution and for rationalizing efficient strategies for <i>e.g.</i> enzymes with desired and effective functions. Most proteins organize in families, sets of divergent sequences which share a common ancestor and have a similar structural fold. Here, we study aryl sulfatases, a subfamily of the large and evolutionary old alkaline phosphatase superfamily. We demonstrate how ultrahigh-throughput droplet microfluidics can be used for studying aryl sulfatases and their computationally reconstructed putative common ancestors. We compare the evolvability and robustness of three ancestors and three extant aryl sulfatases which all exhibit catalytic promiscuity towards a range of substrate classes. Using varying mutations rates, eleven libraries were constructed and expressed in single-cell microdroplets. In general, higher mutation rates resulted in wider distribution of active variants but fewer improved variants overall. However, the impact of mutation rate differed between enzymes, with some benefiting from higher and others from lower mutation rate, underscoring the need to test diverse mutagenesis regimes.</p>","PeriodicalId":63,"journal":{"name":"Analyst","volume":" ","pages":"1794-1807"},"PeriodicalIF":3.3,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12917725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146217860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nadine Nieste, Steven W. M. Olde Damink, Ron M.A. Heeren, E. Cuypers, Stefan Bouwense
Pancreatic cancer remains one of the deadliest malignancies, due to its highly aggressive tumour biology and often late diagnosis at advanced stages. Profound intratumour heterogeneity and a complex tumour microenvironment (TME) are critical characteristics of pancreatic cancer that require untargeted and spatially resolved molecular analysis for their in-depth investigation. Mass spectrometry imaging (MSI) can fulfil these requirements and has provided valuable insights into the molecular mechanisms underlying pancreatic cancer and its precursor lesions. Here, we present key MSI-based studies in pancreatic cancer research, along with other spatial biology methodologies, covering applications ranging from biomarker discovery and tumour classification to the characterisation of treatment response and metastatic progression. In addition, current technical limitations, challenges in clinical translation and future directions driven by ongoing advancements in spatial omics are discussed. This review summarises the contributions of MSI and other spatial biology technologies in elucidating TME heterogeneity in pancreatic cancer and highlights their potential to substantially advance clinical diagnostics and therapeutic strategies.
{"title":"Mass Spectrometry Imaging in Spatial Biology of Pancreatic Cancer","authors":"Nadine Nieste, Steven W. M. Olde Damink, Ron M.A. Heeren, E. Cuypers, Stefan Bouwense","doi":"10.1039/d5an01264c","DOIUrl":"https://doi.org/10.1039/d5an01264c","url":null,"abstract":"Pancreatic cancer remains one of the deadliest malignancies, due to its highly aggressive tumour biology and often late diagnosis at advanced stages. Profound intratumour heterogeneity and a complex tumour microenvironment (TME) are critical characteristics of pancreatic cancer that require untargeted and spatially resolved molecular analysis for their in-depth investigation. Mass spectrometry imaging (MSI) can fulfil these requirements and has provided valuable insights into the molecular mechanisms underlying pancreatic cancer and its precursor lesions. Here, we present key MSI-based studies in pancreatic cancer research, along with other spatial biology methodologies, covering applications ranging from biomarker discovery and tumour classification to the characterisation of treatment response and metastatic progression. In addition, current technical limitations, challenges in clinical translation and future directions driven by ongoing advancements in spatial omics are discussed. This review summarises the contributions of MSI and other spatial biology technologies in elucidating TME heterogeneity in pancreatic cancer and highlights their potential to substantially advance clinical diagnostics and therapeutic strategies.","PeriodicalId":63,"journal":{"name":"Analyst","volume":"57 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147471726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xuan Liu, Xuerui Song, Yusuf Sulub, David Zoller, Zhenyu (James) Kong, Blake N. Johnson
Accurate identification of post-consumer plastics is essential to establishing high-performance recycling processes and enabling a circular and sustainable economy and environment through effective recycling and remanufacturing. However, Fourier transform infrared (FTIR) spectra of recycled materials often exhibit noise, baseline shifts, and overlapping signatures from additives or contaminants, resulting in datasets that are both sparse and severely imbalanced. This data complexity, sparsity, and class imbalance can degrade conventional machine-learning classifiers, resulting in higher rates of misclassifying plastics. To address these challenges, we investigated if data augmentation using generative adversarial networks could enhance polymer classification performance. We implemented a Generative Adversarial Network (GAN) framework that integrates adversarial training with a classifier-guided feedback loop to synthesize realistic, class-discriminative FTIR spectra for six commonly recycled polymers, polyethylene (PE), polypropylene (PP), polystyrene (PS), polycarbonate (PC), polyethylene terephthalate (PET), and acrylonitrile butadiene styrene (ABS), and trained multilayer perceptron classifiers on datasets with varying ratios of synthetic data. The optimal balanced accuracy of 96.2% was achieved when synthetic spectra accounted for 50% of the training set, whereas including more than 90% synthetic data degraded generalization. Synthetic data augmentation using a GAN with the optimal augmentation ratio improved ABS classification accuracy, precision, and recall by 43%, 50%, and 33%, respectively, compared with no augmentation and replicate experimental measurements. These results demonstrate that GAN-based data augmentation can effectively mitigate data sparsity and class imbalance in spectral classification of common plastics, providing a practical foundation for creating robust online polymer classification systems.
{"title":"Classification of recycled plastics using sparse and imbalanced spectral data and data augmentation by the generative adversarial network","authors":"Xuan Liu, Xuerui Song, Yusuf Sulub, David Zoller, Zhenyu (James) Kong, Blake N. Johnson","doi":"10.1039/d5an01042j","DOIUrl":"https://doi.org/10.1039/d5an01042j","url":null,"abstract":"Accurate identification of post-consumer plastics is essential to establishing high-performance recycling processes and enabling a circular and sustainable economy and environment through effective recycling and remanufacturing. However, Fourier transform infrared (FTIR) spectra of recycled materials often exhibit noise, baseline shifts, and overlapping signatures from additives or contaminants, resulting in datasets that are both sparse and severely imbalanced. This data complexity, sparsity, and class imbalance can degrade conventional machine-learning classifiers, resulting in higher rates of misclassifying plastics. To address these challenges, we investigated if data augmentation using generative adversarial networks could enhance polymer classification performance. We implemented a Generative Adversarial Network (GAN) framework that integrates adversarial training with a classifier-guided feedback loop to synthesize realistic, class-discriminative FTIR spectra for six commonly recycled polymers, polyethylene (PE), polypropylene (PP), polystyrene (PS), polycarbonate (PC), polyethylene terephthalate (PET), and acrylonitrile butadiene styrene (ABS), and trained multilayer perceptron classifiers on datasets with varying ratios of synthetic data. The optimal balanced accuracy of 96.2% was achieved when synthetic spectra accounted for 50% of the training set, whereas including more than 90% synthetic data degraded generalization. Synthetic data augmentation using a GAN with the optimal augmentation ratio improved ABS classification accuracy, precision, and recall by 43%, 50%, and 33%, respectively, compared with no augmentation and replicate experimental measurements. These results demonstrate that GAN-based data augmentation can effectively mitigate data sparsity and class imbalance in spectral classification of common plastics, providing a practical foundation for creating robust online polymer classification systems.","PeriodicalId":63,"journal":{"name":"Analyst","volume":"8 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147471725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bridget E. Murray, Roger C. Diehl, Moritz Pott, Daniel A. Holland-Moritz, Alison R. H. Narayan, Robert T. Kennedy
High-throughput screening is important in a diverse array of applications including drug discovery, synthetic reaction development, and enzyme engineering. Well plates are often used for sample preparation and containment in such applications, so analytical methods that are compatible with this format are required. Mass spectrometry (MS) is an attractive analytical technique for high-throughput analysis due to its potential for rapid, sensitive, selective, and label-free multiplexed measurements. Here, we present a method that uses the Venturi effect to withdraw droplet samples from a well plate and infuse them to the electrospray ionization (ESI) source of a mass spectrometer. The Venturi effect is generated by flow of nebulizing gas through a constriction at the outlet of the ESI source. The resulting negative pressure allows sample to be pulled to the ESI source via a sample transfer capillary that is coaxial with the ESI source at the outlet and can be dipped into sample at the inlet. To keep different samples from mixing, 380 nL sample plugs flowing at 330 µL min−1 are sipped into the source and separated by air gaps resulting in segmented flow to the source. The system requires no valves or connections for achieving sample pick-up and analysis. An x,y,z-positioner is used to move the sample inlet for automated sampling from different wells. Increasing capillary inner diameter and nebulizing gas pressure increased the throughput of Venturi droplet-MS by increasing sample flow rate. An interaction of sample plug size and number within the capillary on overall flow rate was observed and affected the possible throughput. When using perfluoroalkoxy alkane (PFA) tubing as the transfer capillary, carryover between samples was 0.88 ± 0.16%. The method is demonstrated by screening 283 whole cell reactions for enzyme engineering at 0.4 samples per s, while showing good agreement (R2 = 0.92) with liquid chromatography-mass spectrometry (LC-MS). This work improves upon previous uses of segmented flow for high-throughput MS by integrating sample generation and transfer in one step. Compared to other high-throughput MS methods this approach requires no custom MS sources or specialty sample introduction equipment.
高通量筛选在包括药物发现、合成反应开发和酶工程在内的各种应用中都很重要。孔板通常用于样品制备和此类应用中的容器，因此需要与此格式兼容的分析方法。质谱（MS）是一种具有快速、敏感、选择性和无标记的多路测量潜力的高通量分析技术。在这里，我们提出了一种利用文丘里效应从孔板中提取液滴样品并将其注入质谱仪的电喷雾电离源的方法。文丘里效应是由雾化气体流过ESI源出口处的收缩而产生的。由此产生的负压允许样品通过样品转移毛细管被拉到ESI源，该毛细管与出口处的ESI源同轴，并且可以在进口处浸入样品中。为了防止不同的样品混合，以330µL min - 1流速的380 nL样品塞被吸进源中，并通过气隙分离，导致流向源的分段流。该系统不需要阀门或连接来实现样品采集和分析。x,y，z定位器用于移动样品入口，以便从不同的井中自动采样。增大毛细管内径和雾化气体压力可通过增大样品流量提高文丘里液滴-质谱的通量。观察到毛细管内样品塞的大小和数量对总流速的相互作用，并影响可能的吞吐量。采用全氟烷氧基烷烃（PFA）管作为转移毛细管时，样品间携带率为0.88±0.16%。该方法以0.4个样品/ s的速度对283个全细胞反应进行酶工程筛选，与液相色谱-质谱分析（LC-MS）结果吻合良好（R2 = 0.92）。这项工作改进了以前使用分段流的高通量质谱，将样本生成和传输集成在一个步骤中。与其他高通量质谱方法相比，该方法不需要自定义质谱源或专用样品导入设备。
{"title":"Utilizing Venturi effect for automated high-throughput droplet-MS from well plates","authors":"Bridget E. Murray, Roger C. Diehl, Moritz Pott, Daniel A. Holland-Moritz, Alison R. H. Narayan, Robert T. Kennedy","doi":"10.1039/d6an00065g","DOIUrl":"https://doi.org/10.1039/d6an00065g","url":null,"abstract":"High-throughput screening is important in a diverse array of applications including drug discovery, synthetic reaction development, and enzyme engineering. Well plates are often used for sample preparation and containment in such applications, so analytical methods that are compatible with this format are required. Mass spectrometry (MS) is an attractive analytical technique for high-throughput analysis due to its potential for rapid, sensitive, selective, and label-free multiplexed measurements. Here, we present a method that uses the Venturi effect to withdraw droplet samples from a well plate and infuse them to the electrospray ionization (ESI) source of a mass spectrometer. The Venturi effect is generated by flow of nebulizing gas through a constriction at the outlet of the ESI source. The resulting negative pressure allows sample to be pulled to the ESI source <em>via</em> a sample transfer capillary that is coaxial with the ESI source at the outlet and can be dipped into sample at the inlet. To keep different samples from mixing, 380 nL sample plugs flowing at 330 µL min<small><sup>−1</sup></small> are sipped into the source and separated by air gaps resulting in segmented flow to the source. The system requires no valves or connections for achieving sample pick-up and analysis. An <em>x</em>,<em>y</em>,<em>z</em>-positioner is used to move the sample inlet for automated sampling from different wells. Increasing capillary inner diameter and nebulizing gas pressure increased the throughput of Venturi droplet-MS by increasing sample flow rate. An interaction of sample plug size and number within the capillary on overall flow rate was observed and affected the possible throughput. When using perfluoroalkoxy alkane (PFA) tubing as the transfer capillary, carryover between samples was 0.88 ± 0.16%. The method is demonstrated by screening 283 whole cell reactions for enzyme engineering at 0.4 samples per s, while showing good agreement (<em>R</em><small><sup>2</sup></small> = 0.92) with liquid chromatography-mass spectrometry (LC-MS). This work improves upon previous uses of segmented flow for high-throughput MS by integrating sample generation and transfer in one step. Compared to other high-throughput MS methods this approach requires no custom MS sources or specialty sample introduction equipment.","PeriodicalId":63,"journal":{"name":"Analyst","volume":"60 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147461832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Farjana Haque, Kiran Shrestha, Fatema Farhana, Moutoshi Chakraborty, Md. Akeruzzaman Shaon, Milkiyas Toru Tantu, Omar Hamza Bin Manjur, Sharmin Aktar, Kevin M. Koo, Tanveer Hussain, Geoffrey M. Gurr, Muhammad J. A. Shiddiky
Plant diseases pose a growing threat to global food security, with invasive bacterial pathogens presenting particular challenges for early detection and containment. Xylella fastidiosa is among the most destructive of these pathogens, infecting hundreds of plant species and posing a severe biosecurity risk to agricultural systems, including those in Australia. Despite its significance, routine detection still relies on laboratory-based molecular amplification methods that are slow, costly, and poorly suited to field deployment. Here, we present an amplification-free, proof-of-concept electrochemical method for detecting X. fastidiosa DNA based on potential-induced DNA adsorption onto a screen-printed gold (SPG) electrode. Target DNA is first isolated using magnetic beads and then rapidly adsorbed onto an SPG electrode through a 30 s cathodic potential step, enabling direct differential pulse voltammetric (DPV) readout without enzymatic amplification. The method clearly discriminates the X. fastidiosa 9a5c isolate from non-specific bacterial DNA (Xanthomonas albilineans), delivering a sensitive and selective signal within 2 minutes (30 s for adsorption plus 75 s for DPV measurement). The entire assay is completed in under 30 minutes, offering approximately fourfold faster analysis than conventional molecular amplification. When applied to spiked buffer and xylem sap (i.e., a complex biological matrix) samples, the assay maintains high analytical performance, achieving a detection limit of 100 aM without compromising specificity or sensitivity. To support on-site testing, we also introduce a low-cost, 3D-printed device for rapid xylem sap extraction, allowing direct analysis with minimal handling and seamless integration into the detection workflow. Overall, the method provides a simple, rapid, and portable diagnostic strategy that advances plant pathogen detection beyond the laboratory. With further field validation, it could support earlier intervention and strengthen biosecurity surveillance for X. fastidiosa and other high-priority pathogens.
{"title":"On-Site Amplification-Free Electrochemical Detection of Plant Pathogen Xylella fastidiosa via Cathodic Potential-Induced DNA Adsorption","authors":"Farjana Haque, Kiran Shrestha, Fatema Farhana, Moutoshi Chakraborty, Md. Akeruzzaman Shaon, Milkiyas Toru Tantu, Omar Hamza Bin Manjur, Sharmin Aktar, Kevin M. Koo, Tanveer Hussain, Geoffrey M. Gurr, Muhammad J. A. Shiddiky","doi":"10.1039/d6an00096g","DOIUrl":"https://doi.org/10.1039/d6an00096g","url":null,"abstract":"Plant diseases pose a growing threat to global food security, with invasive bacterial pathogens presenting particular challenges for early detection and containment. Xylella fastidiosa is among the most destructive of these pathogens, infecting hundreds of plant species and posing a severe biosecurity risk to agricultural systems, including those in Australia. Despite its significance, routine detection still relies on laboratory-based molecular amplification methods that are slow, costly, and poorly suited to field deployment. Here, we present an amplification-free, proof-of-concept electrochemical method for detecting X. fastidiosa DNA based on potential-induced DNA adsorption onto a screen-printed gold (SPG) electrode. Target DNA is first isolated using magnetic beads and then rapidly adsorbed onto an SPG electrode through a 30 s cathodic potential step, enabling direct differential pulse voltammetric (DPV) readout without enzymatic amplification. The method clearly discriminates the X. fastidiosa 9a5c isolate from non-specific bacterial DNA (Xanthomonas albilineans), delivering a sensitive and selective signal within 2 minutes (30 s for adsorption plus 75 s for DPV measurement). The entire assay is completed in under 30 minutes, offering approximately fourfold faster analysis than conventional molecular amplification. When applied to spiked buffer and xylem sap (i.e., a complex biological matrix) samples, the assay maintains high analytical performance, achieving a detection limit of 100 aM without compromising specificity or sensitivity. To support on-site testing, we also introduce a low-cost, 3D-printed device for rapid xylem sap extraction, allowing direct analysis with minimal handling and seamless integration into the detection workflow. Overall, the method provides a simple, rapid, and portable diagnostic strategy that advances plant pathogen detection beyond the laboratory. With further field validation, it could support earlier intervention and strengthen biosecurity surveillance for X. fastidiosa and other high-priority pathogens.","PeriodicalId":63,"journal":{"name":"Analyst","volume":"234 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147471727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lirong Nie,Rongwei Sun,Zixin Xing,Ziyin Tang,Zihan Jiang,Xiang He,Shun Yao,Jilu Hong
Aqueous biphasic systems (ABSs), known for their mild operating conditions, have attracted considerable attention in the field of separation science. The advent of ionic liquids (ILs) and deep eutectic solvents (DESs) as tunable and sustainable solvents has introduced breakthrough alternatives to conventional polymer/salt-based ABSs. This review provides a comprehensive analysis of the physicochemical properties of ILs and DESs, and the applications of IL/DES-based ABSs, with a focus on their phase behavior, phase separation mechanisms, and inherent sustainability advantages. Furthermore, future perspectives are proposed to advance the field, including the rational design of task-specific ILs/DESs, and integration with emerging hybrid separation technologies. These insights aim to accelerate the development of high-performance, eco-friendly separation platforms for industrial applications.
{"title":"Tunable solvents for separation: a comprehensive review of ILs and DESs in aqueous biphasic systems.","authors":"Lirong Nie,Rongwei Sun,Zixin Xing,Ziyin Tang,Zihan Jiang,Xiang He,Shun Yao,Jilu Hong","doi":"10.1039/d5an01175b","DOIUrl":"https://doi.org/10.1039/d5an01175b","url":null,"abstract":"Aqueous biphasic systems (ABSs), known for their mild operating conditions, have attracted considerable attention in the field of separation science. The advent of ionic liquids (ILs) and deep eutectic solvents (DESs) as tunable and sustainable solvents has introduced breakthrough alternatives to conventional polymer/salt-based ABSs. This review provides a comprehensive analysis of the physicochemical properties of ILs and DESs, and the applications of IL/DES-based ABSs, with a focus on their phase behavior, phase separation mechanisms, and inherent sustainability advantages. Furthermore, future perspectives are proposed to advance the field, including the rational design of task-specific ILs/DESs, and integration with emerging hybrid separation technologies. These insights aim to accelerate the development of high-performance, eco-friendly separation platforms for industrial applications.","PeriodicalId":63,"journal":{"name":"Analyst","volume":"52 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147461674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the design, modeling, and experimental validation of a Fringing Field Sensing Capacitor (FFSC)-based sensor for non-invasive real-time glucose monitoring. The proposed sensor is integrated with a notch filter operating within the 0.5 to 1 GHz frequency range, where glucose concentration-dependent dielectric variations influence the fringing electric fields. These variations induce measurable shifts in the magnitude of the transmission coefficient (|S21|), which are detected using a Vector Network Analyzer (VNA) and processed in real time using a dedicated system. The sensor enables both in vitro testing using glucose samples in a beaker and non-invasive detection by finger placement on the sensing area. An Equivalent Circuit Model (ECM) is developed to accurately characterize the sensor's electrical behavior. Compared to conventional invasive methods, the FFSC approach offers a contact-based, skin-friendly alternative that avoids penetration, enhancing user comfort and enabling continuous monitoring. Simulation and experimental results confirm the sensor's high sensitivity, linearity, and reliability, establishing it as a strong candidate for wearable and battery-efficient glucose monitoring systems in diabetic care.
{"title":"FFSC-based sensors for non-invasive real-time glucose monitoring.","authors":"Muthukumara Rajaguru Kattiakara Muni Samy,Abhishek Gudipalli","doi":"10.1039/d5an01287b","DOIUrl":"https://doi.org/10.1039/d5an01287b","url":null,"abstract":"This paper presents the design, modeling, and experimental validation of a Fringing Field Sensing Capacitor (FFSC)-based sensor for non-invasive real-time glucose monitoring. The proposed sensor is integrated with a notch filter operating within the 0.5 to 1 GHz frequency range, where glucose concentration-dependent dielectric variations influence the fringing electric fields. These variations induce measurable shifts in the magnitude of the transmission coefficient (|S21|), which are detected using a Vector Network Analyzer (VNA) and processed in real time using a dedicated system. The sensor enables both in vitro testing using glucose samples in a beaker and non-invasive detection by finger placement on the sensing area. An Equivalent Circuit Model (ECM) is developed to accurately characterize the sensor's electrical behavior. Compared to conventional invasive methods, the FFSC approach offers a contact-based, skin-friendly alternative that avoids penetration, enhancing user comfort and enabling continuous monitoring. Simulation and experimental results confirm the sensor's high sensitivity, linearity, and reliability, establishing it as a strong candidate for wearable and battery-efficient glucose monitoring systems in diabetic care.","PeriodicalId":63,"journal":{"name":"Analyst","volume":"212 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147461673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A background signal, or baseline, is typical a low frequency signal that is composited with the target signal and commonly occurs in electrochemical biosensing data. Square wave voltammetry (SWV) has been widely used to acquire data for electrochemical aptamer-based (EAB) biosensors. However, one challenge with SWV is that the true baseline is unable to be assessed, but can only be estimated. The background signal of SWV usually contains various features, such as levels, trends, and shapes. These features are usually uninformative and, if unaccounted for, they may confuse the results of the analysis. Consequently, standardizing the signal by correcting the baseline is an essential step processing electrochemical sensing results. In this research, we present an adaptive polynomial baseline correction method for the baseline correction of SWV data from real E-AB biosensors. This method can automatically identify the uninformative regions in the signal and provide a robust mathematical equation to estimate the baseline. Employing real world sensing data, We compared our method with other published methods and showed that our method performs more reliably within acceptable errors. We also used the baseline-corrected E-AB biosensing data to develop a statistical model for predicting the concentration of cocaine and THC in saliva samples and developed a friendly user interface that enables front-end users to analyse the data without code interaction. This work shows potential to facilitate data automation to detect specific analytes for point-of-care (POC) applications.
{"title":"An Adaptive Weighted Polynomial Baseline Correction Method for Electrochemical Aptamer-based Sensor","authors":"Zhijian Wen, Yasmin Liu, Onyekachi Raymond, Emeka Jude Itumoh, Janet Stacey, James Curran","doi":"10.1039/d5an01347j","DOIUrl":"https://doi.org/10.1039/d5an01347j","url":null,"abstract":"A background signal, or baseline, is typical a low frequency signal that is composited with the target signal and commonly occurs in electrochemical biosensing data. Square wave voltammetry (SWV) has been widely used to acquire data for electrochemical aptamer-based (EAB) biosensors. However, one challenge with SWV is that the true baseline is unable to be assessed, but can only be estimated. The background signal of SWV usually contains various features, such as levels, trends, and shapes. These features are usually uninformative and, if unaccounted for, they may confuse the results of the analysis. Consequently, standardizing the signal by correcting the baseline is an essential step processing electrochemical sensing results. In this research, we present an adaptive polynomial baseline correction method for the baseline correction of SWV data from real E-AB biosensors. This method can automatically identify the uninformative regions in the signal and provide a robust mathematical equation to estimate the baseline. Employing real world sensing data, We compared our method with other published methods and showed that our method performs more reliably within acceptable errors. We also used the baseline-corrected E-AB biosensing data to develop a statistical model for predicting the concentration of cocaine and THC in saliva samples and developed a friendly user interface that enables front-end users to analyse the data without code interaction. This work shows potential to facilitate data automation to detect specific analytes for point-of-care (POC) applications.","PeriodicalId":63,"journal":{"name":"Analyst","volume":"33 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yinghao Cao, Yuting Tan, Chang Li, Erping Long, Lin Wang
Achieving high spatial resolution is critical for revealing tissue-specific metabolite distributions in mass spectrometry imaging (MSI), yet practical constraints often limit achievable resolution. While deep learning offers promising post-acquisition enhancement, the relative efficacy of different generative architectures for MSI data remains inadequately explored. This study establishes a comparative evaluation of three advanced deep learning architectures (SwinIR, MambaIR, and ResShift) against the established GAN-based model MOSR. Evaluated across three MSI datasets and six image quality metrics, MOSR and a bicubic pre-trained ResShift model demonstrated superior capacity in reconstructing complex textural details. Capitalizing on this, we developed a focused transfer learning strategy to adapt the pretrained ResShift model using only ten mouse brain sagittal section images. The fine-tuned model achieved a 41.5% improvement in a composite performance score over its pre-trained state and a 14.0% improvement over MOSR. Remarkably, this model generalized effectively to distinct anatomical planes (horizontal brain sections) and entirely different tissue types (mouse kidney), as validated using multiple metabolites. Our work provides a benchmark for generative models in MSI super-resolution and proposes a practical, data-efficient fine-tuning framework that enhances image fidelity across diverse biological samples, offering a computational tool for spatially resolved metabolomics.
{"title":"A Practical Framework for Super-resolution of Mass Spectrometry Images via Adaptation of Deep Learning Models","authors":"Yinghao Cao, Yuting Tan, Chang Li, Erping Long, Lin Wang","doi":"10.1039/d6an00012f","DOIUrl":"https://doi.org/10.1039/d6an00012f","url":null,"abstract":"Achieving high spatial resolution is critical for revealing tissue-specific metabolite distributions in mass spectrometry imaging (MSI), yet practical constraints often limit achievable resolution. While deep learning offers promising post-acquisition enhancement, the relative efficacy of different generative architectures for MSI data remains inadequately explored. This study establishes a comparative evaluation of three advanced deep learning architectures (SwinIR, MambaIR, and ResShift) against the established GAN-based model MOSR. Evaluated across three MSI datasets and six image quality metrics, MOSR and a bicubic pre-trained ResShift model demonstrated superior capacity in reconstructing complex textural details. Capitalizing on this, we developed a focused transfer learning strategy to adapt the pretrained ResShift model using only ten mouse brain sagittal section images. The fine-tuned model achieved a 41.5% improvement in a composite performance score over its pre-trained state and a 14.0% improvement over MOSR. Remarkably, this model generalized effectively to distinct anatomical planes (horizontal brain sections) and entirely different tissue types (mouse kidney), as validated using multiple metabolites. Our work provides a benchmark for generative models in MSI super-resolution and proposes a practical, data-efficient fine-tuning framework that enhances image fidelity across diverse biological samples, offering a computational tool for spatially resolved metabolomics.","PeriodicalId":63,"journal":{"name":"Analyst","volume":"100 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147393631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A Dawson McLachlan,Rashne Vakharia,Emir Nazdrajić,Diana M Cárdenas-Soracá,Leslie M Bragg,Mark R Servos,W Scott Hopkins
Tandem mass spectrometry relies on unique parent-to-product transitions for selective analysis. For sets of isomers or isobars that have identical behaviors in multiple separation dimensions (e.g., LC retention, m/z), quantitation is challenging owing to feature convolution. For example, recent environmental analysis of the enantiomers of O-desmethylvenlafaxine (ODV), an anti-depressant manufactured in a racemic mixture, identified tramadol (TRA, a racemic painkiller) as a co-eluting interference. Here, we demonstrate that differential ion mobility spectrometry (DMS) coupled with chiral LC-MS2 can be used to separate and quantify the enantiomers of ODV and TRA. This method was applied to six wastewater influent samples from an Ontario municipal wastewater plant, where the sum of the enantiomeric concentrations was statistically identical to the racemic concentrations observed on reverse-phase LC-MS2 (t-test, α = 0.05, p-value = 0.26 for ODV and p-value = 0.47 for TRA). We also identify low-intensity product ions specific to ODV that enable isolation and quantitation via chiral LC-MS2 alone, albeit at a relatively high limit of quantitation (LOQ) in comparison to the most intense MRM transition (m/z 264 → 58). Using our chiral (LC × DMS)-MS2 method, the instrumental LOQ of each enantiomer of TRA was determined to be 0.67 ng mL-1 and 5.0 ng mL-1 for the enantiomers of ODV.
串联质谱法依靠独特的母体到产品的过渡进行选择性分析。对于在多个分离维度（例如，LC保留，m/z）具有相同行为的同分异构体或等压条集，由于特征卷积，定量具有挑战性。例如，最近对o-去甲基文拉法辛（ODV）对映异构体的环境分析发现，曲马多（TRA，一种消旋止痛药）是一种共洗脱干扰物。本研究证明差分离子迁移谱法（DMS）与手性LC-MS2联用可以分离和定量ODV和TRA的对映体。该方法应用于安大略省某市政污水厂的6个污水进水样本，其中对映体浓度总和与反相LC-MS2观察到的外消旋体浓度在统计学上相同（t检验，α = 0.05， ODV的p值= 0.26，TRA的p值= 0.47）。我们还鉴定了ODV特异性的低强度产物离子，可以通过手性LC-MS2单独进行分离和定量，尽管与最强烈的MRM转变（m/z 264→58）相比，其定量限（LOQ）相对较高。采用手性（LC × DMS）-MS2方法测定了TRA各对映体的定量限为0.67 ng mL-1， ODV各对映体的定量限为5.0 ng mL-1。
{"title":"Separating O-desmethylvenlafaxine and tramadol enantiomers using two-dimensional chiral LC × DMS mass spectrometry.","authors":"A Dawson McLachlan,Rashne Vakharia,Emir Nazdrajić,Diana M Cárdenas-Soracá,Leslie M Bragg,Mark R Servos,W Scott Hopkins","doi":"10.1039/d6an00119j","DOIUrl":"https://doi.org/10.1039/d6an00119j","url":null,"abstract":"Tandem mass spectrometry relies on unique parent-to-product transitions for selective analysis. For sets of isomers or isobars that have identical behaviors in multiple separation dimensions (e.g., LC retention, m/z), quantitation is challenging owing to feature convolution. For example, recent environmental analysis of the enantiomers of O-desmethylvenlafaxine (ODV), an anti-depressant manufactured in a racemic mixture, identified tramadol (TRA, a racemic painkiller) as a co-eluting interference. Here, we demonstrate that differential ion mobility spectrometry (DMS) coupled with chiral LC-MS2 can be used to separate and quantify the enantiomers of ODV and TRA. This method was applied to six wastewater influent samples from an Ontario municipal wastewater plant, where the sum of the enantiomeric concentrations was statistically identical to the racemic concentrations observed on reverse-phase LC-MS2 (t-test, α = 0.05, p-value = 0.26 for ODV and p-value = 0.47 for TRA). We also identify low-intensity product ions specific to ODV that enable isolation and quantitation via chiral LC-MS2 alone, albeit at a relatively high limit of quantitation (LOQ) in comparison to the most intense MRM transition (m/z 264 → 58). Using our chiral (LC × DMS)-MS2 method, the instrumental LOQ of each enantiomer of TRA was determined to be 0.67 ng mL-1 and 5.0 ng mL-1 for the enantiomers of ODV.","PeriodicalId":63,"journal":{"name":"Analyst","volume":"45 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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