Diagnostic Applications Research Articles

Controllable Self-Assembly of V═O Metalloradical Complex with Intramolecular Charge Transfer for Enhanced NIR-II Fluorescence Imaging-Guided Photothermal Therapy.

Near-infrared second region (NIR-II) fluorescence imaging provides enhanced tissue penetration, achieving efficient NIR-II fluorescence and photoacoustic imaging (PA)-guided photothermal therapy (PTT) all in one material remains a challenging yet promising approach in cancer treatment. Herein, open-shell V═O metalloradical complex (VONc) is self-assembled into VONc nanospheres (VONc NPs). VONc NPs exhibit light absorption from 300 to 1400 nm, fluorescence spectra ranging from 900 to 1400 nm, and a distinct fluorescence signal even at 1550 nm. Moreover, VONc NPs exhibit outstanding photostability and a higher photothermal conversion efficiency of 46.6% than that of closed-shell zinc naphthalocyanine nanorods (ZnNc NRs). V═O centered metalloradical serves as transient electron-withdrawing groups to facilitate charge transfer (CT), introducing additional nonradiative energy dissipation pathways and enhancing efficient heat generation. In vitro experiments of VONc NPs indicate that a highly effective photothermal action causes harm to both mitochondria and lysosomes, resulting in the death of tumor cells, closed-shell ZnNc NPs exhibit almost no cell killing as contrast. In vivo anti-tumor therapy results of VONc NPs demonstrate excellent NIR-II fluorescence imaging-guided PTT against tumors with a favorable biosafety profile. "Centered metalloradical boosting CT" toward open-shell metal complexes provides significant insight for developing single-material integrated nanosystems for diagnostic and therapeutic applications.

Machine learning reveals CAT gene as a novel potential diagnostic and prognostic biomarker in non-small cell lung cancer

BackgroundNon-small cell lung cancer (NSCLC) represents one of the most prevalent forms of lung cancer, with a five-year survival rate of 21.7%. There is an urgent need to identify pertinent biomarkers to inform the diagnosis and prognosis of tumors, particularly those that can be applied to different age groups. Herein, we would apply machine learning methods to specifically analyze the issue of biomarker applicability across different age groups in NSCLC.MethodsStudies have shown a higher incidence of NSCLC in people over 40 years of age, and due to the limitations of data set, studies of individuals under 40 years of age were not included in this study. To simulate the human aging model as closely as possible, we gathered corresponding non-small cell lung cancer (NSCLC) samples from the UCSC Xena database based on patient age information. These samples were then categorized into three groups: 40–60, 60–80, and over 80 years old. Subsequently, we employed four machine learning methods—Random Forest, LASSO regression analysis, XGBoost, and GBM—to identify gene sets with significant diagnostic value for each age group. By taking the intersection of these sets, we identified the optimal gene and assessed its prognostic significance in NSCLC. Then, the diagnostic value of CAT gene was validated using global public databases, including the GSE32863, GSE43458, GSE68571, GSE10072, and GSE63459 datasets from the Americas, the GSE30219 and GSE102511 datasets from Europe, and the GSE31210 and GSE19804 datasets from Asia. Furthermore, immunohistochemical staining was performed in an independent cohort from a tissue microarray. Additionally, cell culture and RT-qPCR were employed for external validation.ResultsThrough the implementation of machine learning methods, we successfully identified the catalase (CAT) gene. Our analysis revealed that individuals with high expression of the CAT gene experienced improved survival rates. Additionally, these individuals exhibited elevated immune scores. We further discovered that the CAT gene synergizes with multiple components of neutrophils, including TLRs, FcRn, and the selective GEF of Rho-family GTPases. In addition, we identified a potential immune checkpoint, TNFSF15, which is applicable to the human aging model. Finally, we validated the CAT gene's diagnostic value using databases encompassing the Americas, Europe, and Asia regions. Through external RT-qPCR validation, we verified that CAT expression in BEAS-2B was higher than that of A549. In an independent human cohort, we also verified that CAT is lowly expressed in lung cancer tissues. In addition, higher CAT levels were associated with improved survival in the 40–60 and 60–80 age groups.ConclusionsIn our analysis of the NSCLC database, we pinpointed the CAT gene, which holds promise for potential diagnostic and prognostic applications in the context of human aging. Furthermore, it may offer insights into addressing age-related heterogeneity of NSCLC.

Adipose Tissue as a Major Launch Spot for Circulating Extracellular Vesicle-Carried MicroRNAs Coordinating Tissue and Systemic Metabolism

Circulating microRNAs (miRNAs), especially transported by extracellular vesicles (EVs), have recently emerged as major new participants in interorgan communication, playing an important role in the metabolic coordination of our tissues. Among these, adipose tissue displays an extraordinary ability to secrete a vast list of EV-carried miRNAs into the circulation, representing new hormone-like factors. Despite the limitations of current methodologies for the unequivocal identification of the origin and destination of EV-carried miRNAs in vivo, recent investigations clearly support the important regulatory role of adipose-derived circulating miRNAs in shaping the metabolism and function of other tissues including the liver, muscle, endocrine pancreas, cardiovascular system, gastrointestinal tract, and brain. Here, we review the most recent findings regarding miRNAs transported by adipose-derived EVs (AdEVs) targeting other major metabolic organs and the implications of this dialog for physiology and pathology. We also review here the current and potential future diagnostic and therapeutic applications of AdEV-carried miRNAs.

A Hybrid Model for Freight Train Air Brake Condition Monitoring

The Digital Freight Train is expected to revolutionise the rail freight industry. A critical aspect of this transformation is real-time condition monitoring of air brake systems, which are among the leading causes of train malfunctions. To achieve this goal, advanced algorithms for air brake modelling are required. This paper introduces a computationally efficient air brake model tailored for real-time diagnostic applications. A hybrid approach, integrating both empirical data and simplified fluid-dynamic equations, has been adopted. Compared to other air brake models found in the literature, the innovative contributions of the presented model are the reduction of the number of required parameters and the estimation of the brake cylinder pressure directly from the main brake pipe pressure using a feed-forward approach. Moreover, a new approach in the evaluation of the first braking phase and the brake cylinder pressure build-up as the saturation of the brake mode is presented. The model input includes the main brake pipe pressure, the weighing valve pressure, and the brake mode, and the output includes the pressure at the brake cylinder. The air brake model has been validated using data from a previous experimental campaign. The model’s accuracy in replicating the air brake system mechanism makes it well-suited for future development of model-based algorithms designed for air brake fault detection.

Etched CoFe Prussian blue analog nanozymes with superior peroxidase activity for colorimetric biosensing of hydrogen peroxide and dopamine.

Nanozymes represent a compelling alternative to natural enzymes due to their exceptional stability and cost-efficiency in diagnostic and therapeutic applications. In this work, a nanozyme etched CoFe Prussian blue analog (etched PBA) was designed and fabricated by a simple approach involving first synthesizing CoFe PBA and then chemical etching. The fabricated etched PBA acts as a peroxidase mimic catalyst. Compared to the naturally occurring enzyme (horseradish peroxidase), the etched PBA exhibited improved peroxidase-like catalytic efficiency for oxidizing 3,3',5,5'-tetramethylbnmenzidine (TMB). Particularly, the exceptional peroxidase-like activity of the etched PBA is attributed to modifications in its electronic and structural properties, which improved affinity for hydrogen peroxide (H2O2) and TMB. Hence, these findings highlight etched PBA as a promising peroxidase mimic, enabling a colorimetric assay for real-time H2O2 and dopamine (DA) detection. H2O2 was detected via PBA-catalyzed TMB oxidation (bluish-green color), while DA monitoring occurred through ox-TMB reduction. The detection thresholds for H2O2 and DA were determined to be 0.083 μM and 0.05 μM, respectively, with broad linear ranges of 0.1 μM - 4.0 mM for H2O2 and 0.1-240 μM for DA. In addition, the etched PBA was validated for real sample analysis, showing potential for enzyme catalysis, biosensing, and colorimetric analysis of pharmaceuticals and biomolecules.

Current understanding of therapeutic and diagnostic applications of exosomes in pancreatic cancer.

Unusual symptoms, rapid progression, lack of reliable early diagnostic biomarkers, and lack of efficient treatment choices are the ongoing challenges of pancreatic cancer. Numerous research studies have demonstrated the correlation between exosomes and various aspects of pancreatic cancer. In light of these facts, exosomes possess the potential to play functional roles in the treatment, prognosis, and diagnosis of the pancreatic cancer. In the present study, we reviewed the most recent developments in approaches for exosome separation, modification, monitoring, and communication. Moreover, we discussed the clinical uses of exosomes as less invasive liquid biopsies and drug carriers and their contribution to the control of angiogenic activity of pancreatic cancer. Better investigation of exosome biology would help to effectively engineer therapeutic exosomes with certain nucleic acids, proteins, and even exogenous drugs as their cargo. Circulating exosomes have shown promise as reliable candidates for pancreatic cancer early diagnosis and monitoring in high-risk people without clinical cancer manifestation. Although we have tried to reflect the status of exosome applications in the treatment and detection of pancreatic cancer, it is evident that further studies and clinical trials are required before exosomes may be employed as a routine therapeutic and diagnostic tools for pancreatic cancer.

SMOC2, OGN, FCN3, and SERPINA3 could be biomarkers for the evaluation of acute decompensated heart failure caused by venous congestion

BackgroundVenous congestion (VC) sets in weeks before visible clinical decompensation, progressively increasing cardiac strain and leading to acute heart failure (HF) decompensation. Currently, the field lacks a universally acknowledged gold standard and early detection methods for VC.MethodsUsing data from the GEO database, we identified VC's impact on HF through key genes using Limma and STRING databases. The potential mechanisms of HF exacerbation were explored via GO and KEGG enrichment analyses. Diagnostic genes for acute decompensated HF were discovered using LASSO, RF, and SVM-REF machine learning algorithms, complemented by single-gene GSEA analysis. A nomogram tool was developed for the diagnostic model's evaluation and application, with validation conducted on external datasets.ResultsOur findings reveal that VC influences 37 genes impacting HF via 8 genes, primarily affecting oxygen transport, binding, and extracellular matrix stability. Four diagnostic genes for HF's pre-decompensation phase were identified: SMOC2, OGN, FCN3, and SERPINA3. These genes showed high diagnostic potential, with AUCs for each gene exceeding 0.9 and a genomic AUC of 0.942.ConclusionsOur study identifies four critical diagnostic genes for HF's pre-decompensated phase using bioinformatics and machine learning, shedding light on the molecular mechanisms through which VC worsens HF. It offers a novel approach for clinical evaluation of acute decompensated HF patient congestion status, presenting fresh insights into its pathogenesis, diagnosis, and treatment.

Interdigitated Gear-Shaped Screen-Printed Electrode Using G-PANI Ink for Sensitive Electrochemical Detection of Dopamine

In this research, a novel interdigitated gear-shaped, graphene-based electrochemical biosensor was developed for the detection of dopamine (DA). The sensor’s innovative design improves the active surface area by 94.52% and 57% compared to commercially available Metrohm DropSens 110 screen-printed sensors and printed circular sensors, respectively. The screen-printed electrode was fabricated using laser processing and modified with graphene polyaniline conductive ink (G-PANI) to enhance its electrochemical properties. Fourier Transform Infrared (FTIR) Spectroscopy and X-ray diffraction (XRD) were employed to characterize the physiochemical properties of the sensor. Dopamine, a neurotransmitter crucial for several body functions, was detected within a linear range of 0.1–100 µM, with a Limit of Detection (LOD) of 0.043 µM (coefficient of determination, R2 = 0.98) in phosphate-buffer saline (PBS) with ferri/ferrocyanide as the redox probe. The performance of the sensor was evaluated using cyclic voltammetry (CV) and Chronoamperometry, demonstrating high sensitivity and selectivity. The interdigitated gear-shaped design exhibited excellent repeatability, with a relative standard deviation (RSD) of 1.2% (n = 4) and reproducibility, with an RSD of 2.3% (n = 4). In addition to detecting dopamine in human serum, the sensor effectively distinguished dopamine in a ternary mixture containing uric acid (UA) and ascorbic acid (AA). Overall, this novel sensor design offers a reliable, disposable, and cost-effective solution for dopamine detection, with potential applications in medical diagnostics and neurological research.

Targeting APE1: Advancements in the Diagnosis and Treatment of Tumors.

With the emergence of the precision medicine era, targeting specific proteins has emerged as a pivotal breakthrough in tumor diagnosis and treatment. Apurinic/apyrimidinic Endonuclease 1 (APE1) is a multifunctional protein that plays a crucial role in DNA repair and cellular redox regulation. This article comprehensively explores the fundamental mechanisms of APE1 as a multifunctional enzyme in biology, with particular emphasis on its potential significance in disease diagnosis and strategies for tumor treatment. Firstly, this article meticulously analyzes the intricate biological functions of APE1 at a molecular level, establishing a solid theoretical foundation for subsequent research endeavors. In terms of diagnostic applications, the presence of APE1 can be detected in patient serum samples, biopsy tissues, and through cellular in situ testing. The precise detection methods enable changes in APE1 levels to serve as reliable biomarkers for predicting tumor occurrence, progression, and patient prognosis. Moreover, this article focuses on elucidating the potential role of APE1 in tumor treatment by exploring various inhibitors, including nucleic acid-based inhibitors and small molecule drug inhibitors categories, and revealing their unique advantages in disrupting DNA repair function and modulating oxidative-reduction activity. Finally, the article provides an outlook on future research directions for APE1 while acknowledging major technical difficulties and clinical challenges that need to be overcome despite its immense potential as a target for tumor therapy.

What Is the Accuracy of 16S PCR Followed by Sanger Sequencing or Next-generation Sequencing in Native Vertebral Osteomyelitis? A Systematic Review and Meta-analysis.

Identifying a microorganism in patients with native vertebral osteomyelitis presents diagnostic challenges. Microorganism identification through culture-based methods is constrained by prolonged processing times and sensitivity limitations. Despite the availability of molecular diagnostic techniques for identifying microorganisms in native vertebral osteomyelitis, there is considerable variability in reported sensitivity and specificity across studies, leading to uncertainty in their clinical utility. What are the sensitivity, specificity, and diagnostic odds ratios for 16S broad-range PCR followed by Sanger sequencing (16S) and metagenomic next-generation sequencing (NGS) for detecting bacteria in native vertebral osteomyelitis? On June 29, 2023, we searched Cochrane, Embase, Medline, and Scopus for results from January 1970 to June 2023. Included studies involved adult patients with suspected native vertebral osteomyelitis undergoing molecular diagnostics-16S bacterial broad-range PCR followed by Sanger sequencing and shotgun or targeted metagenomic NGS-for bacteria detection. Studies involving nonnative vertebral osteomyelitis and cases of brucellar, tubercular, or fungal etiology were excluded. The reference standard for the diagnosis of native vertebral osteomyelitis was a composite clinical- and investigator-defined native vertebral osteomyelitis diagnosis. Diagnostic performance was assessed using a bivariate random-effects model. Risk of bias and diagnostic applicability were evaluated using the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. After a manual screening of 3403 studies, 10 studies (5 on 16S, 5 on NGS) were included in the present analysis, from which 391 patients were included from a total of 958 patients overall. Quality assessment via QUADAS-2 criteria showed moderate risk of bias and good applicability. 16S showed 78% (95% confidence interval [CI] 95% CI 31% to 96%) sensitivity and 94% (95% CI 73% to 99%) specificity, whereas NGS demonstrated 82% (95% CI 63% to 93%) sensitivity and 71% (95% CI 37% to 91%) specificity. In addition, the diagnostic ORs were 59 (95% CI 9 to 388) and 11 (95% CI 4 to 35) for 16S and NGS, respectively. Summary receiver operating characteristic curves showed high test performance for 16S (area under the curve for 16S 95% [95% CI 93% to 97%] and for NGS 89% [95% CI 86% to 92%]). Certainty in estimates was moderate because of sample size limitations. This meta-analysis found moderate-to-high diagnostic performance of molecular methods on direct patient specimens for the diagnosis of native vertebral osteomyelitis. When used as a complementary test to microbiological analyses, a positive 16S result rules in the diagnosis of native vertebral osteomyelitis, while further studies are needed to understand the role of NGS in the diagnosis of native vertebral osteomyelitis. When available, these tests should be used in addition to conventional microbiology, especially in complex cases with extensively negative standard microbiological test results, to detect fastidious bacteria or to confirm the causative bacteria when their isolation and pathogenicity are unclear. A large sample size is needed in future research to understand the use of these techniques as standalone tests for diagnosis. Level III, diagnostic study.

Advances in High-Performance Liquid Chromatography (HPLC) and Ultra-Performance Liquid Chromatography (UPLC)

High-Performance Liquid Chromatography (HPLC) and Ultra-Performance Liquid Chromatography (UPLC) are fundamental analytical techniques that have revolutionized chemical analysis across various fields. These chromatographic methods offer exceptional separation capabilities, high resolution, and precise quantification of complex mixtures. HPLC has evolved significantly since its inception, with improvements in column technology, detection systems, and automation. UPLC, introduced as an advancement to conventional HPLC, operates at significantly higher pressures using sub-2-μm particle sizes, resulting in enhanced resolution, speed, and sensitivity. Both techniques have found extensive applications in pharmaceutical analysis, environmental monitoring, food safety, clinical diagnostics, and biomedical research. The implementation of various detection methods, including UV-visible, fluorescence, mass spectrometry, and electrochemical detection, has expanded their analytical capabilities. Recent developments in column technology, such as core-shell particles and monolithic columns, have further improved separation efficiency. The integration of artificial intelligence and machine learning has enhanced method development and data analysis. This review discusses the fundamental principles, technological advancements, and diverse applications of HPLC and UPLC. Additionally, it discusses current challenges, emerging trends, and future prospects in chromatographic science, including green chromatography initiatives and miniaturization efforts. The continuous evolution of these techniques contributes significantly to analytical chemistry's advancement, promising even more sophisticated applications in various scientific disciplines.

Impact of Nuclear Peripheral Chromatin Lamin LMNB1 Gene in the Proliferation and Migration of Glioma Cells.

The goal of this study is to explore the role of the LMNB1 gene in glioma. A cohort of 160 patients who underwent glioma surgery were randomly selected of this study. The LMNB1 expression was assessed employing immunohistochemical and real-time quantitative polymerase chain reaction methods. Initially, RNA interference technology was applied to suppress gene expression, followed by the evaluation of tumor cell proliferation, apoptosis, cell cycle dynamics, and migration. The underlying molecular mechanisms of LMNB1 function were examined by a human phospho-kinase array and immunoblotting. And we established the xenograft models to determine the effect of tumor growth as well as the degree of invasion in shLMNB1 mice. Elevated LMNB1 expression correlated with unfavorable overall survival and disease-free survival. A substantial inhibition in cell growth was observed subsequent to LMNB1 knockdown in SHG-44 and U251 glioma cells. SHG-44-shLMNB1 cells exhibited a reduction in the S phase population, along with an increase in cells in G1 and G2 phases. Similarly, shLMNB1 U251 cells showed fewer cells in the S phase and an elevation in cells in G1 phase. Notably, increased apoptosis was observed in U251-shLMNB1 cells and SHG-44-shLMNB1 cells. Wound healing and Transwell migration assays demonstrated a significant decrease in the migration rate of both SHG-44-shLMNB1 and U251-shLMNB1 cells. The phosphorylation levels of Akt1/2/3, as well as the expressions of PI3K, AKT, and p-AKT proteins, were reduced in the shLMNB1 group. Downregulation of LMNB1 repressed tumor progress in vivo. The silencing of LMNB1 was found to significantly reduce the proliferation of human glioma cells, induce apoptosis in tumor cells, impede the progression of the cell cycle, and inhibit the migration of tumor cells. Consequently, we hypothesize that LMNB1 promotes glioma cell proliferation through mechanisms involving the inhibition of tumor cell apoptosis, acceleration of the cell cycle, and enhancement of tumor cell migration. We found that LMNB1 exert critical roles in glioma progression may via regulation of PI3K/Akt signaling pathway. These observations suggest that LMNB1 holds clinical potential for diagnostic and prognostic applications in glioma, presenting novel targets for drug development.

Spatial confinement growth of high-performance persistent luminescence nanoparticles for image-guided sonodynamic therapy.

Near-infrared (NIR) persistent luminescence nanoparticles (PLNPs) have significant potential in diagnostic and therapeutic applications owing to their unique persistent luminescence (PersL). However, obtaining high-performance NIR PLNPs remains challenging because of the limitations of current synthesis methods. Herein, we introduce a spatial confinement growth strategy for synthesizing high-performance NIR PLNPs using hollow mesoporous silica (hmSiO2). By calcining precursor ions in the hollow cavity, the yolk size of NIR PLNPs was regulated, yielding well-dispersed Zn1.3Ga1.4Sn0.3O4: Cr0.005, Y0.003@hmSiO2 (ZS) with a yolk-shell structure. Compared to the conventional template method, ZS synthesized via the spatial confinement growth strategy exhibited a 7.7-fold increase in PersL intensity and a threefold increase in specific surface area. As a proof of concept, ZS@PpIX@CaP-AMD (ZPSC-AMD) nanoparticles, with potential for sonodynamic therapy (SDT), were synthesized by loading the sonosensitizer protoporphyrin IX (PpIX) into ZS, coating it with a calcium phosphate (CaP) shell, and modifying it with a tumor-targeting molecule plerixafor (AMD-3100). The tumor enrichment behavior of ZPSC-AMD was monitored by sensitive NIR PersL to guide SDT. Simultaneously, ZPSC-AMD enabled the precise monitoring of tumor accumulation, thereby guiding effective SDT. In addition, Ca2+ released from CaP degradation increased the level of reactive oxygen species during SDT, promoting tumor cell apoptosis. This study outlines a reliable design and synthesis approach for high-performance NIR PLNPs and promotes their development in biomedical applications. STATEMENT OF SIGNIFICANCE: The potential of near infrared (NIR) persistent luminescence nanoparticles (PLNPs) in bio applications is hindered by limitations in the synthesis method. In this article, we proposed a spatial confinement growth strategy of high-performance NIR PLNPs. The obtained PLNPs with yolk-shell structure showed a 7.7-fold increase in PersL intensity and a 3-fold increase in specific surface area, compared with the commonly used template method. Due to the advantages, sonodynamic therapeutic nanoparticles were constructed based on the above PLNPs, where persistent luminescence was used for ultrasensitive imaging to determine the optimal timing in sonodynamic therapy. In addition, the multifunctional calcium phosphate shell elevated the intracellular reactive oxygen species level to promote tumor cell apoptosis.

Biology and applications of CRISPR-Cas12 and transposon-associated homologs.

CRISPR-associated Cas12 proteins are a highly variable collection of nucleic acid-targeting proteins. All Cas12 variants use RNA guides and a single nuclease domain to target complementary DNA or, in rare cases, RNA. The high variability of Cas12 effectors can be explained by a series of independent evolution events from different transposon-associated TnpB-like ancestors. Despite basic structural and functional similarities, this has resulted in unprecedented variation of the Cas12 effector proteins in terms of size, domain composition, guide structure, target identity and interference strategy. In this Review, we compare the unique molecular features of natural and engineered Cas12 and TnpB variants. Furthermore, we provide an overview of established genome editing and diagnostic applications and discuss potential future directions.

An Overview of Microfluidic‐Assisted Strategies for Synthesis and Applications of Molecularly Imprinted Polymers

AbstractThis review gives an overview of using microfluidics in conjunction with molecularly imprinted polymers (MIP), which covers two aspects: on the one hand, on‐chip synthesis of polymer and MIP particles on the nano and the micro scale. This comprises both approaches using two different immiscible solvents and homogeneous matrices to obtain the desired particle morphologies. On the other hand, especially paper‐based microfluidic systems have attracted increasing interest as low‐cost analytical tools that are inherently useful for applying at the point of care. By now, there have been several successful attempts to combine them with MIP (instead of biological recognition systems) and to successfully apply them in environmental samples, food matrices, and for diagnostic applications.

Nanoarchitectonics in immobilization of DNT/TNT specific binding peptides on laser-induced graphene

Abstract Bio/chemical sensors require high selectivity for specific targets. Carbon nanomaterials, especially graphene-based materials, are preferred in sensors due to their high surface area, superior electrical conductivity, mechanical strength, and flexibility. In this study, DNT-bp (Dinitrotoluene binding peptide) and TNT-bp (Trinitrotoluene binding peptide) were immobilized on LIG (Laser-induced graphene) films through covalent and non-covalent interactions. EDC (1-ethyl-3-3-dimethylaminopropyl carbodiimide hydrochloride) is used to activate carboxylic acid groups, which then react with NHS (N-hydroxysuccinimide) to form NHS esters, facilitating the binding of amine-containing peptides to the LIG surface. XPS analysis of LIG films functionalized with EDC+NHS shows a decrease in COOH (carboxyl) and an increase in C-N/C=N/C-O groups. N1s peak at high-resolution XPS spectrum indicates that DNT-bp immobilization leads to higher elimination of NHS-esters, resulting in fewer N-C=O (amide) functional groups and more protonated nitrogen, which suggests that DNT-bp immobilization is more effective than TNT-bp immobilization. The findings suggest that LIG-based sensors provide a cost-effective platform for the detection of DNT (2,4-dinitrotoluene) and 2,4,6-trinitrotoluene (TNT). Further advancements in LIG-based biosensors may enable their broader application in security, environmental monitoring, and health diagnostics.

Modulating Biomimetic Peroxidase Activity of CuMOFs Through Ligand Engineering: Sensitive Colorimetric Detection of H2O2 and Glutathione

AbstractNatural peroxidase enzymes are widely utilized for various diagnostic applications, yet their inherent instability and susceptibility to external factors pose challenges. This study introduces a ligand engineering approach to fine‐tune the peroxidase enzyme‐like activity of copper‐based metal‐organic frameworks (MOFs). We have rationally modulated the enzyme‐mimicking activity of a series of Cu‐BDC‐X based MOFs via employment of various ligands (BDC = 1,4‐benzene dicarboxylic acid; CuMOF‐1, X═H2O; CuMOF‐2, X═4,4′‐bipyridine and CuMOF‐3, X═N‐methylimidazole). As compared to its analogues, CuMOF‐3 exhibited an enhanced catalytic activity, most likely due to N‐methylimidazole‐induced structural distortion that fine‐tunes the metal's electronic environment and improves substrate interactions. Additionally, this modification allowed CuMOF‐3 to be miniaturized to the nanoscale, enhancing its catalytic performance. Mechanistic investigations revealed the generation of •OH as reactive oxygen species (ROS) to accelerate tetramethylbenzidine oxidation, promoting peroxidase‐like activity. The observed catalytic behavior of CuMOF‐3 followed Michaelis–Menten kinetics, exhibiting lower Km value compared to those of natural peroxidase enzymes. Under optimized conditions, CuMOF‐3 facilitated the development of a colorimetric assay for sensitive detection of H2O2 and glutathione (GSH) in various spiked food samples. This study uniquely demonstrates the impact of tailored ligand combinations and MOF geometry on optimizing peroxidase‐mimicking activity, providing a new direction for the design of high‐performance colorimetric diagnostic tools in the food processing industry.

Smart Microneedles in Biomedical Engineering: Harnessing Stimuli‐Responsive Polymers for Novel Applications

ABSTRACTThis review aims to provide a comprehensive analysis of recent advancements in smart microneedles (MNs) within the biomedical field, focusing on the integration of stimuli‐responsive polymers for enhanced therapeutic and diagnostic applications. Conventional drug delivery and diagnostic methods are known to face limitations in precision, safety, and patient compliance, which can be addressed by the innovative features of smart MNs. Through the use of various stimuli‐responsive polymers, these MNs have been designed to react to environmental or physiological cues, allowing for on‐demand drug release, biomarker sensing, and localized therapeutic interventions. Fundamental materials used in the fabrication of these MNs, including metals, polymers, and composite hydrogels, are reviewed, and different categories of stimuli‐responsiveness, such as photo, electro, thermal, mechanical, and biochemical, are explored. Application‐specific designs of MNs in areas such as drug delivery, cancer therapy, diabetes management, and skin disease treatments are also examined. Through this discussion, it is highlighted that smart MNs are poised to play a significant role in advancing personalized and noninvasive medical treatments.

Enhanced ePAD-DNA biosensor incorporating ZnO-NRs: Rapid and reliable electrochemical detection of field-isolated Pasteurella multocida

An electrochemical sensor has received much attention due to its importance for early infection identification, hinting at its critical relevance in diagnostic applications. For the detection of field-isolated strains of Pasteurella multocida, this paper reports the development and fabrication of a DNA-based electrochemical biosensor by integrating zinc oxide (ZnO) nanorods (NRs) into an electrochemical paper-based analytical device (ePAD). One significant improvement over the state-of-the-art features of the sensor is the using paper, an economically viable substrate that can be manufactured in large numbers. These sensors, being categorized under precision instruments with an ecologically friendly design, are ideal for mass production of eco-sensitive substrates. The biosensor is more sensitive and specific as compared to the conventional PCR-based sensing techniques. Moreover, the biosensor exploits the inherent properties of ZnO-NRs to amplify the signal output, whereas ePAD limits detection cost. In addition, a probe targeting the KMT gene of P. multocida was first characterized using conventional PCR and then immobilized onto the ZnO nanorods-modified ePAD surface, which improved the biosensor's ability for the rapid and selective genetic material of the pathogen detection. In addition, the sensor was validated using the methods of Cyclic Voltammetry (CV) and Linear Sweep Voltammetry (LSV). The reported biosensor it provides Linear Response of 0.001-10 μg/ml with LOD 0.001 μg/ml within a response time of 30 s. Subsequently, the biosensor reveals fast kinetics of the reaction as a powerful tool for timely and accurate diagnostics, pointing to its contribution to further development in biosensing technologies in the diagnostic area of infectious diseases at hospitals and clinics.

Breast Cancer Prediction Using Transfer Learning-Based Classification Model

Breast cancer is currently the most prevalent type of cancer in women, with a growing number of fatalities worldwide. Different imaging methods like mammography, computed tomography, Magnetic Resonance Imaging, ultrasound, and biopsies assist in detecting breast cancer. Recent developments in deep learning have revolutionized breast cancer pathology by facilitating accurate image categorization. This study introduces a novel approach to enhance detection and classification using the Convolutional Neural Network Deep Learning method and Transfer Learning to create a high-speed, accurate image classification model. The model is trained on pre-processed data subjected to thorough analysis and augmentation to ensure the quality of inputs. The experimental results from the Breast Ultrasound Image dataset indicate that our model, with a 0.1 test size ratio, outperforms its counterparts. It achieved an accuracy of 90.12%, with a loss of 0.2641, validation accuracy of 90.15%, and validation loss of 0.31, evidencing its superior classification capability. This research introduces an innovative approach to the automated diagnosis of breast cancer. By combining CNN, Transfer Learning, and data augmentation, we have developed a desktop application that expedites the classification process and significantly improves accuracy. This advancement represents a key development in machine learning applications for breast cancer prognostics and diagnostics. Doi: 10.28991/ESJ-2024-08-06-014 Full Text: PDF