Logarithmic Data Processing Can Be Used Justifiably in the Plotting of a Calibration Curve

A sustainable, eco-friendly Tb/Eu-modified HOFs for ultrasensitive detection and efficient adsorption of carcinogens in complex water environments

Developing a multifunctional material that can detect and remove carcinogens in water environments, simultaneously monitor their toxic metabolites in living organisms is significant for environmental remediation and human health. However, most research only focused on detection or adsorption carcinogens due to the difficulty of integrating multiple functions into one material, let alone monitoring their toxic metabolites. Here, a multifunctional Tb/Eu@TATB-HOF (1) is first developed to monitor two carcinogens, malachite green (MG) and its metabolites leucomalachite green (LMG), and simultaneously remove MG from the contaminated water. 1, as the dual-emission fluorescence sensor, can achieve ultrasensitive and highly visualized sensing for MG and LMG with different response modes. Even in actual samples, 1 still exhibits satisfactory sensing performances. As the adsorbent, 1 displays good recyclability and high adsorption capacity for MG. The sensing and adsorption mechanisms are explored through experiments and theoretical calculations. This work not only provides a novel insight for environmental remediation and human health through detection and removal of carcinogens, simultaneously monitoring their toxic metabolites, but first reveals the enormous potential of HOFs as multifunctional materials simultaneously for fluorescence sensing and adsorption.

Ultrasensitive electrochemical immunosensor for the detection of C-reactive protein antigen

Cardiovascular disease is one of the leading causes of premature death worldwide, and the determination of C-reactive protein (CRP) from human serum is of vital importance for the diagnosis of the disease. For this study, we have developed an electrochemical immunosensor based on onion-like carbon@polyacrylonitrile (OLC-PAN) for the detection of CRP antigens. This was accomplished by immobilizing CRP antibodies on a modified glassy carbon electrode (GCE). Several electrochemical techniques such as cyclic voltammetry (CV), square wave voltammetry (SWV), and electrochemical impedance spectroscopy (EIS) were employed to evaluate the electrochemical detection of the CRP antigen. This ultrasensitive method for CRP antigen detection exhibited a very good logarithmic plot from -4.52 to -12.05 g mL-1 and a limit of detection (LOD) of 0.9 fg mL-1. The high selectivity, sensitivity, and stability of the developed electrochemical immunosensor would facilitate miniaturization for point-of-care applications and the efficient diagnosis of cardiovascular diseases.

ZIF-67 and Biomass-Derived N, S-Codoped Activated Carbon Composite Derivative for High-Effective Removal of Hydroquinone from Water

Hydroquinone (HQ) in wastewater is of great concern, as it is harmful to human health and threatens the ecological environment. However, the existing adsorbents have low adsorption capacity for HQ. To improve the removal of HQ, N,S-codoped activated carbon-ZIF-67 (NSAC-ZIF-67@C) was synthesized in this study by in situ growth of ZIF-67 on N,S-codoped activated carbon (NSAC) and carbonization. The influence of pH, contact time, and initial concentration on the adsorption behaviors of NSAC-ZIF-67@C on HQ were investigated. Owing to the synergistic effect of abundant active sites and well-developed pore structure, the NSAC-ZIF-67@C achieved a prominent adsorption capacity of 962 mg·g-1 and can still retain high adsorption performance after 5 cycles for HQ, which is superior to that of reported other adsorbents. HQ adsorption follows the pseudo-second-order kinetics model (R2 = 0.99999) and the Freundlich isotherm model. X-ray photoelectron spectroscopy (XPS) analysis before and after adsorption as well as density functional theory (DFT) calculation results showed that pyridinic-N-termini were conducive to the π-π interactions and hydrogen-bonding interactions. Therefore, the adsorption mechanisms of NSAC-ZIF-67@C on HQ involve pore filling, electrostatic attraction, π-π interaction, and hydrogen bonding. This study is expected to provide a reference for designing highly effective adsorbents for wastewater treatment.

In situ construction of covalent-organic framework on hydrogen-bond organic framework: Fluorescence detection and removal of 4-nitrophenol and metamitron in aqueous media

A multifunctional COF@HOF (ETTA-DFP@TCBP-HOF) composite is prepared by adding red-fluorescent ETTA-DFP COF to the blue-fluorescent TCBP-HOF preparation system through molecular hydrogen bonding or π - π stacking interactions in situ one-pot synthesis. ETTA-DFP@TCBP-HOF is a multifunctional material for the quantitative detection and simultaneous adsorption of 4-nitrophenol (4-NP) and metamitron (MET) in aqueous solution. As a dual-emission fluorescent sensor, the ETTA-DFP@TCBP-HOF has both fluorescence of TCBP-HOF at 474 nm and ETTA-DFP COF at 592 nm, which shows a ratiometric response to 4-NP and MET with high selectivity, good sensitivity, good anti-interference performance and fast response. As a adsorbent, ETTA-DFP@TCBP-HOF displays rapid adsorption kinetics, and acceptable adsorption capacity for 4-NP and MET. In conclusion, this work constructs a novel multifunctional hybrid material with dual-emission center of HOF and COF, which can not only be used as a ratiometric fluorescent probe for detection, but also for removal of hazardous pollutants, suggesting a new strategy for environmental remediation and human health.

Combining recombinase polymerase amplification with tyrosine modified 2'-deoxyuridine-5'-triphosphate for direct voltammetric detection of double-stranded DNA: Application to potato pathogen Dickeya solani

The approach based on a combination of isothermal recombinase polymerase amplification (RPA), 2'-deoxyuridine-5'-triphosphate modified with tyrosine aromatic group (dUTP-Y1), and direct voltammetric detection of RPA product carrying electroactive labels was successfully applied to the potato pathogen Dickeya solani. The artificial nucleotide dUTP-Y1 demonstrated a good compatibility with RPA, enabling by targeting a section of D. solani genome with a unique sequence to produce the full-size modified products at high levels of substitution of dTTP by dUTP-Y1 (up to 80-90 %) in the reaction mixture. The optimized procedure of square wave voltammetry allowed to reliably detect the product generated by RPA at 80 % substitution of dTTP by dUTP-Y1 (dsDNA-Y1) in microliter sample volumes on the surface of disposable carbon screen printed electrodes at the potential of about 0.6 V. The calibration curve for the amplicon detection was linear in coordinates 'Ip, A vs. Log (c, M)' within the 0.05-1 μM concentration range. The limit of detection for dsDNA-Y1 was estimated as 8 nM. The sensitivity of the established electrochemical approach allowed to detect amplicons generated in a single standard 50 μL RPA reaction after their purification with silica-coated magnetic beads. The overall detectability of D. solani with the suggested combination of RPA and voltammetric registration of dsDNA-Y1 can be as low as a few copies of bacterial genome per standard reaction. In total, amplification, purification, and electrochemical detection take about 120-150 min. Considering the potential of direct electrochemical analysis for miniaturization, as well as compliance with low-cost and low-power requirements, the findings provide grounds for future development of microfluidic devices integrating isothermal amplification, amplicon purification and detection based on the tyrosine modified nucleotide for the purpose of 'on-site' detection of various pathogens.

Food and environmental safety monitoring platform based on Tb(III) functionalized HOF hybrids for ultrafast detection of thiabendazole and 2-chlorophenol

Development of efficient and intelligent method for detecting harmful agrochemicals in resource-limited settings remains an urgent need to ensure food and environmental safety. Herein, a novel dual-emitting Tb3+-modified hydrogen-bonded organic framework (Tb@TBTC, TBTC is the ligand of HOF-TBTC.) with visible green fluorescence has been prepared through coordination post-synthetic modification. Tb@TBTC can be designed as a fluorescence sensor for the identification of two harmful carcinogenic pesticides, thiabendazole (TBZ) and 2-chlorophenol (2-CP) with high sensitivity, high efficiency and excellent selectivity. Tb@TBTC can also adsorb 2-CP with high adsorption rate. In realistic fruit juice and river water samples, the detection limits of Tb@TBTC toward TBZ and 2-CP are as low as 2.73 μM and 2.18 μM, respectively, demonstrating the feasibility in practical application. Furthermore, an intelligent real-time and on-site monitoring platform for 2-CP detection is constructed based on Tb@TBTC-agarose hydrogel films with the assistance of back propagation neural network, which can efficiently and accurately determine the concentration of 2-CP from fluorescence images through human-machine interaction. This work presents a facile pathway to prepare Tb@HOF fluorescent sensor for food and ecological environment safety, which is highly promising for preventing human disease and improving global public health.

Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19

The death of coronavirus disease 2019 (COVID-19) is primarily due to from critically ill patients, especially from ARDS complications caused by SARS-CoV-2. Therefore, it is essential to contribute an in-depth understanding of the pathogenesis of the disease and to identify biomarkers for predicting critically ill patients at the molecular level. Immunogenic cell death (ICD), as a specific variant of regulatory cell death driven by stress, can induce adaptive immune responses against cell death antigens in the host. Studies have confirmed that both innate and adaptive immune pathways are involved in the pathogenesis of SARS-CoV-2 infection. However, the role of ICD in the pathogenesis of severe COVID-19 has rarely been explored. In this study, we systematically evaluated the role of ICD-related genes in COVID-19. We conducted consensus clustering, immune infiltration analysis, and functional enrichment analysis based on ICD differentially expressed genes. The results showed that immune infiltration characteristics were altered in severe and non-severe COVID-19. In addition, we used multiple machine learning methods to screen for five risk genes (KLF5, NSUN7, APH1B, GRB10 and CD4), which are used to predict COVID-19 severity. Finally, we constructed a nomogram to predict the risk of severe COVID-19 based on the classification and recognition model, and validated the model with external data sets. This study provides a valuable direction for the exploration of the pathogenesis and progress of COVID-19, and helps in the early identification of severe cases of COVID-19 to reduce mortality.

Efficient Multi-stimulus-Responsive Luminescent Eu(III)-Modified HOFs Materials: Detecting Thiram and Caffeic Acid and Constructing a Flexible Substrate Anti-counterfeiting Platform

The powerful capability of multi-stimulus-responsive luminescent hydrogen-bonded organic frameworks (HOFs) to respond to external chemical or physical stimuli in various manners makes them appealing in the luminescence anti-counterfeiting field. Herein, a novel Eu3+-functionalized HOF (Eu@GC-2) that combines the emission of HOFs with the characteristic emission of Eu3+ ions has been successfully synthesized, which can generate various fluorescence at different excitation wavelengths. Eu@GC-2 has enormous potential as a raw material for a paper-based sensor that is designed for detecting the pesticides thiram and caffeic acid in crops with favorable selectivity, anti-interference, and high efficiency. Based on the above excellent properties, Ln3+-functionalized HOFs (Ln@GC-2) were then employed to produce four luminescent anti-counterfeiting inks. With the incorporation of back-propagation neural network and Gray code conversion functions, a multi-stimulus-responsive luminescent anti-counterfeiting platform, coregulated by the excitation light and the chemical reagent, has been constructed. This approach can not only achieve multiple encryptions and fast information identification but also enhance the code-breaking complexity, making it an efficient strategy for information encryption and decryption.

The construction and validation of prognostic prediction model for sudden sensorineural hearing loss in middle-aged and elderly people

OBJECTIVE

Idiopathic sudden sensorineural hearing loss (ISSNHL), as an otologic emergency, is commonly encountered and its prevalence has been climbing every year recently. To our knowledge, the prognosis of middle-aged and elderly patients is worse than that of young patients. Previous researches mainly focused on the adult population, which was considered as prognostic models who performed hearing recovery in ISSNHL. However, few studies regarding the middle-aged and elderly population who are regarded as prognostic models have been reported. Therefore, we aim to construct and validate a nomogram-based prognostic prediction model, which can provide a reference for the prognostic assessment in the middle-aged and elderly patients with ISSNHL.

METHOD

A total of 371 middle-aged and elderly ISSNHL patients who were admitted to the Department of Otolaryngology-Head and Neck Surgery, Yanbian Hospital, Yanbian University, from April 2018 to April 2023 were enrolled in the study. All subjects were randomly divided into two groups including training group (n = 263) and validation group (n = 108). Lasso regression and multi-factor logistic regression were jointly utilized to screen out prognosis-related independent risk factors and establish a nomogram-based risk prediction model. The accuracy and clinical application value of the model were evaluated by combining the Bootstrapping method and k-fold cross-validation, plotting the receiver operating characteristic  (ROC)  curve, calculating the area under the ROC curve (AUC), plotting the decision curve analysis (DCA), and the calibrating curve.

RESULT

We used the method of lasso regression combined with multivariate logistic regression and finally screened out eight predictors (including age, number of affected ears, degree of hearing loss, type of hearing curve, duration of disease, presence of vertigo, diabetes, and lacunar cerebral infarction) that were included into the nomogram. The C-index were 0.823 [95% CI (0.725, 0.921)] and 0.851 [95% CI (0.701, 1.000)], and the AUC values were 0.812 and 0.823 for the training and validation groups, respectively. The calibration curve for the validation group was approximately conformed to that for the modeling group, indicating favorable model calibration. The DCA results revealed the modeling group (3%-86%) and the validation group (2%-92%) showed significant net clinical benefit under the majority of thresholds.

CONCLUSION

This study developed and validated a nomogram-based prognostic prediction model which based on the eight independent risk factors mentioned above. The predictors are conveniently accessible and may assist clinicians in formulating individualized treatment strategies.

On topological indices and entropy measures of beryllonitrene network via logarithmic regression model

Chemical graph theory, a subfield of graph theory, is used to investigate chemical substances and their characteristics. Chemical graph analysis sheds light on the connection, symmetry, and reactivity of molecules. It supports chemical property prediction, research of molecular reactions, drug development, and understanding of molecular networks. A crucial part of computational chemistry is chemical graph theory, which helps researchers analyze and manipulate chemical structures using graph algorithms and mathematical models. Beryllonitrene , a compound of interest due to its potential applications in various fields, is examined through the lens of graph theory and mathematical modeling. The study involves the calculation and interpretation of topological indices and graph entropy measures, which provide valuable insights into the structural and energetic properties of Beryllonitrene's molecular graph. Logarithmic regression models are employed to establish correlations between these indices, entropy, and other relevant molecular attributes. The results contribute to a deeper understanding of Beryllonitrene's complex characteristics, facilitating its potential applications in diverse scientific and technological domains. In this study, degree-based topological indices TI are determined, as well as the entropy of graphs based on these TI .

From a glimpse into the key aspects of calibration and correlation to their practical considerations in chemical analysis

In this tutorial review, we provide a guiding reference on the good practice in building calibration and correlation experiments, and we explain how the results should be evaluated and interpreted. The review centers on calibration experiments where the relationship between response and concentration is expected to be linear, although certain of the described principles of good practice can be applied to non-linear systems, as well. Furthermore, it gives prominence to the meaning and correct interpretation of some of the statistical terms commonly associated with calibration and regression. To reach a mutual understanding in this significant field, we present, through a practical example, a step-by-step procedure, which deals with typical challenges related to linearity and outlier assessment, calculation of the associated error of the predicted concentration, and limits of detection. The utilization of regression lines to compare analytical methods is also elaborated. The results of regression and correlation data are acquired by implementing the Excel spreadsheet of Microsoft, being perhaps one of the most widely used user-friendly software in education and research.

Deep Learning-Assisted Intelligent Artificial Vision Platform Based on Dual-Luminescence Eu(III)-Functionalized HOF for the Diagnosis of Breast and Ovarian Cancer

Developing an advanced analytical method to detect spermine (Spm) and N-acetylneuraminic acid (NANA), the biomarkers of breast and ovarian cancers, respectively, is critical for the early diagnosis of the two cancers, which is very meaningful for women's health. Here, a deep learning-assisted artificial vision platform based on a dual-emission ratiometric fluorescence sensor is first constructed to monitor Spm and NANA. The ratiometric fluorescence sensor (Eu@TCBP-HOF, 1) can selectively detect Spm with high sensitivity based on "Turn-on" mode. After adding Spm, the new ratiometric fluorescence sensor (1-Spm, named 2) shows high sensitivity for NANA with "Turn-off" mode. Moreover, the fluorescence sensors can achieve an obvious fluorescence color response to Spm and NANA. Even in real saliva and serum samples, 1 and 2 still show high sensitivity and color responsiveness with limit of detection (LODs) of 0.5 μM for Spm and 0.96 μM for NANA. In virtue of different fluorescence responses, the DenseNet algorithm of deep learning assists the fluorescence sensors, which can simulate the human visual systems to identify fluorescence images and distinguish the concentration of Spm and NANA within 1 s with over 99% recognition accuracy. The intelligent artificial vision platform developed in this work may provide a prospective analytical method for the early diagnosis of female malignant tumors.

Harnessing Small-Molecule Analyte Detection in Complex Media: Combining Molecularly Imprinted Polymers, Electrolytic Transistors, and Machine Learning

Small-molecule analyte detection is key for improving quality of life, particularly in health monitoring through the early detection of diseases. However, detecting specific markers in complex multicomponent media using devices compatible with point-of-care (PoC) technologies is still a major challenge. Here, we introduce a novel approach that combines molecularly imprinted polymers (MIPs), electrolyte-gated transistors (EGTs) based on 2D materials, and machine learning (ML) to detect hippuric acid (HA) in artificial urine, being a critical marker for toluene intoxication, parasitic infections, and kidney and bowel inflammation. Reduced graphene oxide (rGO) was used as the sensory material and molecularly imprinted polymer (MIP) as supramolecular receptors. Employing supervised ML techniques based on symbolic regression and compressive sensing enabled us to comprehensively analyze the EGT transfer curves, eliminating the need for arbitrary signal selection and allowing a multivariate analysis during HA detection. The resulting device displayed simultaneously low operating voltages (<0.5 V), rapid response times (≤10 s), operation across a wide range of HA concentrations (from 0.05 to 200 nmol L-1), and a low limit of detection (LoD) of 39 pmol L-1. Thanks to the ML multivariate analysis, we achieved a 2.5-fold increase in the device sensitivity (1.007 μA/nmol L-1) with respect to the human data analysis (0.388 μA/nmol L-1). Our method represents a major advance in PoC technologies, by enabling the accurate determination of small-molecule markers in complex media via the combination of ML analysis, supramolecular analyte recognition, and electrolytic transistors.

Dual-Function Platform Based on Postsynthetic Functionalization of a Water-Stable Hydrogen-Bonded Organic Framework: Ratiometric Sensing of Nicotine and Cotinine and Dynamic Anticounterfeiting for Information Encryption

Nicotine and its major metabolite cotinine are widely used as markers of tobacco smoke abstinence as well as indicators of active smoking levels and the assessment of passive inhalation of tobacco smoke in nonsmokers. Therefore, using an easy-to-prepare sensing platform that can provide a rapid, highly sensitive response for the simultaneous detection of salivary nicotine levels and urinary cotinine levels is especially crucial for helping heavy cigarette smokers quit smoking and protecting public health. Hydrogen-bonded organic frameworks, as a novel class of porous crystalline materials, show immense potential for functional modification and optical sensing. Herein, a new HOF was prepared by a simple solvent evaporation method, and a dual-emitting material Eu(bpy)@HOF-215(1) was obtained by the postsynthetic modification of HOF by lanthanide luminescent complexes, which maintains favorable structural stability and introduces the characteristic emitting of Eu, allowing use as a ratiometric fluorescent sensor for salivary nicotine and urinary cotinine, with a limit of detection of nicotine of 0.045 μM in saliva and a limit of detection of cotinine of 0.591 μM in urine. Furthermore, luminescent inks based on HOF-215 have been fabricated based on the photoresponse variations of 1 to NIC and COT, which enables the multilevel encryption and decryption of information, in a dynamic and recyclable process. This work not only synthesizes a novel blue HOF but also provides a representative successful case of a dual-function platform for simultaneous application to ratiometric sensing and dynamic anticounterfeiting.

Aptamer-based kinetically controlled DNA reactions coupled with metal-organic framework nanoprobes for sensitive detection of SARS-CoV-2 spike protein

Since the outbreak in 2019, COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become the deadliest infectious disease worldwide for people of all ages, from children to older adults. As a main structural protein of SARS-CoV-2, spike protein is reported to play a key role in the entry of the virus into host cells and is considered as an effective antigenic marker for COVID-19 diagnosis. Herein, we develop a new aptamer-based fluorescence method for SARS-CoV-2 spike protein detection based on using kinetically controlled DNA reactions and metal-organic framework nanoprobes. Specifically, the binding of SARS-CoV-2 spike protein to its aptamer is designed to precisely control the kinetics of a DNA displacement reaction, leading to the release of free signaling probes. By reasonable integration of magnetic enrichment and exonuclease-fuelled recycling, the released probes efficiently disrupt the interaction within metal-organic framework nanoprobes, thereby generating a remarkable fluorescent response. Experimental results show that the method not only exhibits a wide linear range and a low detection limit of 7.8 fg mL-1 for SARS-CoV-2 spike protein detection but also demonstrates desirable specificity and utility in complex samples. Therefore, the method may provide a valuable tool for the detection of SARS-CoV-2 spike protein, and has bright prospects in the rapid diagnosis of COVID-19, which is of great significance for guiding rational treatment during a pandemic of respiratory infectious diseases and reducing the occurrence of severe disease in children.

Multifunctional Eu(III)-modified HOFs: roxarsone and aristolochic acid carcinogen monitoring and latent fingerprint identification based on artificial intelligence

The exploration of multifunctional materials and intelligent technologies used for fluorescence sensing and latent fingerprint (LFP) identification is a research hotspot of material science. In this study, an emerging crystalline luminescent material, Eu3+-functionalized hydrogen-bonded organic framework (Eu@HOF-BTB, Eu@1), is fabricated successfully. Eu@1 can emit purple red fluorescence with a high photoluminescence quantum yield of 36.82%. Combined with artificial intelligence (AI) algorithms including support vector machine, principal component analysis, and hierarchical clustering analysis, Eu@1 as a sensor can concurrently distinguish two carcinogens, roxarsone and aristolochic acid, based on different mechanisms. The sensing process exhibits high selectivity, high efficiency, and excellent anti-interference. Meanwhile, Eu@1 is also an excellent eikonogen for LFP identification with high-resolution and high-contrast. Based on an automatic fingerprint identification system, the simultaneous differentiation of two fingerprint images is achieved. Moreover, a simulation experiment of criminal arrest is conducted. By virtue of the Alexnet-based fingerprint analysis platform of AI, unknown LFPs can be compared with a database to identify the criminal within one second with over 90% recognition accuracy. With AI technology, HOFs are applied for the first time in the LFP identification field, which provides a new material and solution for investigators to track criminal clues and handle cases efficiently.

Portable, Intelligent Fluorescence Sensing Platform for Dense Convolutional Network-Capable Detection of Indophenol Sulfate and Methylmalonic Acid Using a Luminescent Eu@HOF Film

Indophenol sulfate (IS) and methylmalonic acid (MMA) are biomarkers of chronic kidney disease (CKD) and diabetes polyneuropathy (DPN), respectively. Portable and accurate monitoring of IS and MMA is very important to ensuring human health. The dense convolutional network (DenseNet) with image recognition has great potential in fluorescence sensing, but developing a platform with high precision and portability to diagnose the disease still faces huge challenges. Herein, we developed a high-sensitivity platform with a fluorescence material, a smartphone, and the DenseNet to monitor IS and MMA. A red-emitting Eu@PFC-13 (1) is prepared, and 1 shows high selectivity and low detection limits (DLs) to detect IS and MMA. The sensing mechanism of 1 toward IS and MMA is investigated by experiments and theoretical calculation. For detecting IS and MMA in serum and urine, 1 is fabricated into an Eu@PFC-13/AG (2) film with DLs of 1.4 and 1.6 μM, respectively. In addition, a portable smartphone platform is designed to monitor IS and MMA with high precision. Moreover, the DenseNet is constructed by Python, which can output the concentration of analytes by identifying fluorescence images and judge whether any is in a dangerous range. This work not only proposes a novel method that integrates a fluorescence material, a smartphone, and deep learning to detect analytes but also opens a new way for the diagnosis of CKD and DPN.

Electrochemiluminescence Aptasensor with Dual Signal Amplification by Silica Nanochannel-Based Confinement Effect on Nanocatalyst and Efficient Emitter Enrichment for Highly Sensitive Detection of C-Reactive Protein

The rapid and sensitive detection of the important biomarker C-reactive protein (CRP) is of great significance for monitoring inflammation and tissue damage. In this work, an electrochemiluminescence (ECL) aptasensor was fabricated based on dual signal amplification for the sensitive detection of CRP in serum samples. The sensor was constructed by modifying a silica nanochannel array film (SNF) on a cost-effective indium tin oxide (ITO) electrode using the Stöber solution growth method. Gold nanoparticles (AuNPs) were grown in situ within the nanochannels using a simple electrodeposition method as a nanocatalyst to enhance the active electrode area as well as the ECL signal. The negatively charged nanochannels also significantly enriched the positively charged ECL emitters, further amplifying the signal. The recognition aptamer was covalently immobilized on the outer surface of SNF after modification with epoxy groups, constructing the aptasensor. In the presence of CRP, the formation of complexes on the recognitive interface led to a decrease in the diffusion of ECL emitters and co-reactants to the supporting electrode, resulting in a reduction in the ECL signal. Based on this mechanism, ECL detection of CRP was achieved with a linear range of 10 pg/mL to 1 μg/mL and a low limit of detection (7.4 pg/mL). The ECL aptasensor developed in this study offers advantages such as simple fabrication and high sensitivity, making promising applications in biomarker detection.

Ultrasensitive Monitoring of Cyanide Concentrations in Water Using a Aucore-Agshell Hybrid-Coating-Based Fiber Optical Sensor

Cyanides, which are extremely toxic chemicals that are rapidly absorbed into the human body and interact with cytochrome oxidase, strongly inhibit cellular respiration to body death with convulsions. Cyanide ions that exist in many forms in nature such as those found in apricot kernels, cassava roots, and bamboo shoots as cyanogenic glycosides are inevitably used in various industries, including gold and silver mining as well as in dyes and plastic industries. In this study, for the sake of developing ultrahigh-sensitive sensors for cyanide monitoring in a simple manner, we chemically synthesize Aucore-Agshell hybrid nanomaterials of different core/shell thicknesses for colorimetric sensors and fiber optical sensors. Their sensing principle relies on the formation of the Ag/Au cyanocomplex upon cyanide injection. The generated metal cyanocomplex induced changes in refractive indices, causing changes in properties of localized surface plasmon resonance (LSPR), i.e., optical absorbance change for the colorimetric sensors. For fiber optical sensors, the hybrid metal nanoparticles were immobilized on the fiber core surface and the metal cyanocomplex formation induced changes in the fiber cladding refractive index, enabling quantitative cyanide detection with ultrahigh sensitivity. The LSPR-based colorimetric sensor provided the lowest detectable cyanide concentration of 5 × 10-6 M, whereas the value for the fiber-based sensor was 8 × 10-11 M.

Proximity Amplification-Enabled Electrochemical Analysis of Tumor-Associated Glycoprotein Biomarkers

Glycoproteins produced and secreted from specific cells and tissues are associated with several diseases and emerge as typical biomarkers to provide useful information in cancer diagnosis considering their abnormal expression levels. In this work, we design a universal method to achieve the accurate and sensitive analysis of tumor-associated glycoprotein biomarkers based on both carbohydrate recognition and protein recognition at the same protein surface. The byproduct of dual recognition-induced proximity amplification, pyrophosphate, triggers the disassembly of methylene blue-encapsulated metal-organic frameworks, MB@ZIF-90. As a result, methylene blue molecules are released to arouse amplified electrochemical responses for glycoprotein analysis. Experimental results demonstrate the high-accuracy analysis of carcinoembryonic antigen, a typical glycoprotein biomarker in cancer diagnosis, in a linear range of 0.001-100 ng mL-1 with a low limit of detection of 0.419 pg mL-1. The method also displays satisfactory specificity and recoveries in complex serum samples and proves good versatility by adopting two other tumor-associated glycoprotein biomarkers, α-fetoprotein and mucin-1, as the targets. Therefore, this work provides a valuable tool for the analysis of glycoprotein biomarkers, which may be of great potential in early warning of malignant tumors in clinical applications.

Intrinsic SERS Fingerprints of Aptamer-Peptide Conjugates for Direct High-Specific Profiling Abnormal Protein Levels in Cancer Patients

Surface-enhanced Raman spectroscopy (SERS) with ultrasensitive vibrational fingerprints enables quick identification and trace detection of various kinds of molecules. But proteins usually have low Raman cross sections and are difficult to generate recognizable signals in direct SERS detection. Recently, nucleic acid-peptide conjugates are emerging with great potential in structuring, assembling, catalyzing, sensing, etc., and the coupling of aptamers further enables superior biological recognition and programmability. Here, we develop the aptamer-peptide conjugates as a new kind of SERS probe for direct high-specific profiling abnormal protein levels in cancer patients. The aptamer conjugated with glutathione (GSH) functions as both the recognition element and the SERS reporters that can simultaneously generate SERS fingerprints of both peptides and nucleic acids. This kind of biocompatible probe appears to have excellent performance in high-salt environments and realizes rapid, simple, and multisignal detection of thrombin (TB). Data-driven soft independent modeling of class analogy (DD-SIMCA) is used to distinguish SERS profiles of actual blood samples and realize the identification and classification of cancer patients. Furthermore, the effect of low-temperature storage time on blood samples is analyzed by tracking the changes of SERS profiles; the results hint that plasma samples stored under 4 °C for more than 2 days could generate false negative results due to TB hydrolysis, which has important implications for clinical sample analysis. This kind of nucleic acid-peptide conjugate provides new ideas for SERS sensing strategy in the future.

A high-performance electrochemical biosensor using an engineered urate oxidase

We constructed a high-performance biosensor for detecting uric acid by immobilizing an engineered urate oxidase on gold nanoparticles deposited on a carbon-glass electrode. This biosensor showed a low limit-of-detection (9.16 nM), a high sensitivity (14 μA/μM), a wide range of linearity (50 nM-1 mM), and more than 28 days lifetime.

Programmable DNA Circuit-Facilitated Determination of Circulating Extracellular Vesicle PD-L1 for Lung Cancer Diagnosis and Immunotherapy Response Prediction

Circulating extracellular vesicle (EV) PD-L1 is correlated with the occurrence and progression of lung cancer and has great potential as a valuable diagnostic and immunotherapy predictive biomarker. In this work, we propose a fluorescent biosensing method for the sensitive and accurate determination of circulating EV PD-L1. Specifically, after the phosphatidylserine-targeting peptide-assisted magnetic enrichment, a programmable DNA circuit is designed to translate the presence of PD-L1 to the appearance of numerous duplex DNA probes on the circulating EV surface. Upon fructose treatment, these newly formed duplex DNA probes are released from the EV surface to activate the trans-cleavage activity of CRISPR/Cas12a system, which finally produces a significant fluorescence signal. Experimental results reveal that the method not only enables sensitive determination of EV PD-L1 with a detection limit of 67 particles/mL but also demonstrates the potential use in the diagnosis and immunotherapy response prediction of lung cancer in a principle-of-proof study. Therefore, the method may provide a useful tool for EV PD-L1 determination, which may provide valuable information for the precise diagnosis and personalized treatment of lung cancer patients.

Flumequine-mediated fluorescent zeolitic imidazolate framework functionalized by Eu3+ for sensitive and selective detection of UO22+, Ni2+ and Cu2+ in nuclear wastewater

Currently, antibiotics and heavy metal contaminants have posed a great threat for ecological security and human health. Herein, the lanthanide functionalized ZIF (named ZIF-90-PABA-Eu) is constructed by coordinating with Eu³⁺ via p-aminobenzoic acid intermediate. Due to the excellent fluorescence properties, the novel fluorescent probe can selectively monitor flumequine based on "turn on" mode. Furthermore, the obtained new material (named ZIF-90-PABA-Eu-Flu) can be used as "turn off" sensor for selective detection of both radioactive and nonradioactive heavy metal ions (UO₂²⁺, Ni²⁺ and Cu²⁺) which are the main component of nuclear industrial wastewater. ZIF-90-PABA-Eu-Flu shows ultra-short fluorescence response time (3 s) and ultra-low limit of detection (9.0 × 10^-3, 1.3 × 10^-2 and 6.1 × 10^-4 ppm) for three metal ions, which may be attributed to its good affinity with UO₂²⁺, Ni²⁺ and Cu²⁺. Moreover, principal component analysis (PCA) is applied to distinguish the three metal ions. Additionally, the possible sensing mechanism is investigated by the UV-vis spectra, luminescence lifetimes and theoretical calculation analysis. Based on these results, ZIF-90-PABA-Eu possesses promising potential in practical application and provides insight for the design of novel probes to continuously monitor flumequine, radioactive and nonradioactive heavy metal ions.

Linear Fitting of Time-Varying Signals in Static Noble Gas Mass Spectrometry Should Be Avoided

This Perspective highlights a malpractice during data processing in static noble gas mass spectrometry, viz. retrodicting the noble gas signals at "time zero" based on the linear fitting results of the time-varying analytical responses. Linear fitting is the most commonly used by noble gas analysts mainly on the consideration of high coefficient of determination (R²), while it poses risks of inaccurate estimation of required information. Here, we appeal to re-examining the signal variation mechanisms before deciding the way of fitting instead of merely choosing one with good R². The primary processes during the static mass spectrometry measurement are considered, and a mechanism-based exponential fitting is recommended for the relevant data processing. For the same given data set, exponential fitting is a more robust processing strategy than linear fitting because not only does the former show perfect goodness of fitting but also it contributes to better consistence of retrodicted signals. For the sake of maintaining a high level of data quality in analytical science, we propose to apply the mechanism-based exponential fitting to retrodict the "time zero" signal in static noble gas mass spectrometry, and the commonly used linear fitting should be avoided.

Ratio Fluorescent Detecting of Tryptophan and Its Metabolite 5-Hydroxyindole-3-acetic Acid Relevant with Depression via Tb(III) Modified HOFs Hybrids: Further Designing Recyclable Molecular Logic Gate Connected by Back Propagation Neural Network

Exploring intelligent fluorescent materials with high reliability and precision to diagnose diseases is significant but remains a great challenge. Herein, based on coordination post-synthetic modification, a Tb³⁺ functionalized ME-PA (Tb@1) is prepared, which can emit brilliant green fluorescence through ligand-to-mental charge transfer-assisted energy transfer (LMCT-ET) process from ME-PA to Tb³⁺ ions. Tb@1 can simultaneously distinguish Tryptophan (Try) and its metabolite 5-hydroxyindole-3-acetic acid (5-HIAA), two effective indicators for depression, in ratio and colorimetric mode. And this sensor behaves the advantages of high efficiency and sensitivity, as well as excellent reusability and anti-interference. The PET process from ME to Try and 5-HIAA, and the competitive absorption between analytes and Tb@1 may be relevant to sensing mechanism. In realistic serum or urine environment, the detection limits of Tb@1 for Try and 5-HIAA are 0.0183 and 0.0149 mg L^-1 respectively. Moreover, in conjunction with back propagation neural network (BPNN), two dual-output molecular logic gates that can be calculated circularly are further designed, which realizes intelligent control of the electronic component to identify the existence of two biomarkers and judge their concentrations from fluorescence images. This work offers a novel approach to modulate logic circuits based on ML-assisted HOF fluorescent sensor, with promising application for a precise and pictorial depression diagnosis.

A luminescent Eu@SOF film fabricated by electrophoretic deposition as ultrasensitive platform for styrene gas quantitative monitoring through fluorescence sensing and ANNs model

Styrene is a harmful gas widely existing in the air, which can damage human organs. Therefore, it is very crucial to develop a sensitive, portable and simple sensor for monitoring styrene. Herein, we design and fabricate a luminescent Eu@TMA-ME/FTO film (F) through EPD method. F emits bright red light of Eu(III) ions and shows superior fluorescence response to styrene gas as a sensor, which enable real-time and quantitative monitoring for styrene gas. More importantly, F exhibits a linear response to styrene gas in a wide concentration range of 10^-7 to 10^-2 M and a low DL with 0.20 ppm. The efficient PET process to styrene induced by ME and the competitive absorption between styrene and F are responsible for the sensing mechanism. Besides, the detection of styrene solution is also investigated in deionized water, tap water and river water. For the further application, an intelligent ANNs model has been constructed to process the fluorescence sensing results, which can convert fluorescence sensing images to the concentration of styrene gas. The data demonstrates that ANNs model can accurately monitor the concentration of styrene gas via deep ML without tedious data processing.

Identification of copper death-associated molecular clusters and immunological profiles in rheumatoid arthritis

Objective

An analysis of the relationship between rheumatoid arthritis (RA) and copper death-related genes (CRG) was explored based on the GEO dataset.

Methods

Based on the differential gene expression profiles in the GSE93272 dataset, their relationship to CRG and immune signature were analysed. Using 232 RA samples, molecular clusters with CRG were delineated and analysed for expression and immune infiltration. Genes specific to the CRGcluster were identified by the WGCNA algorithm. Four machine learning models were then built and validated after selecting the optimal model to obtain the significant predicted genes, and validated by constructing RA rat models.

Results

The location of the 13 CRGs on the chromosome was determined and, except for GCSH. LIPT1, FDX1, DLD, DBT, LIAS and ATP7A were expressed at significantly higher levels in RA samples than in non-RA, and DLST was significantly lower. RA samples were significantly expressed in immune cells such as B cells memory and differentially expressed genes such as LIPT1 were also strongly associated with the presence of immune infiltration. Two copper death-related molecular clusters were identified in RA samples. A higher level of immune infiltration and expression of CRGcluster C2 was found in the RA population. There were 314 crossover genes between the 2 molecular clusters, which were further divided into two molecular clusters. A significant difference in immune infiltration and expression levels was found between the two. Based on the five genes obtained from the RF model (AUC = 0.843), the Nomogram model, calibration curve and DCA also demonstrated their accuracy in predicting RA subtypes. The expression levels of the five genes were significantly higher in RA samples than in non-RA, and the ROC curves demonstrated their better predictive effect. Identification of predictive genes by RA animal model experiments was also confirmed.

Conclusion

This study provides some insight into the correlation between rheumatoid arthritis and copper mortality, as well as a predictive model that is expected to support the development of targeted treatment options in the future.

Identification of Unique Genetic Biomarkers of Various Subtypes of Glomerulonephritis Using Machine Learning and Deep Learning

(1) Objective: Identification of potential genetic biomarkers for various glomerulonephritis (GN) subtypes and discovering the molecular mechanisms of GN. (2) Methods: four microarray datasets of GN were downloaded from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression profiles of eight GN subtypes. Then, differentially expressed immune-related genes (DIRGs) were identified to explore the molecular mechanisms of GN, and single-sample gene set enrichment analysis (ssGSEA) was performed to discover the abnormal inflammation in GN. In addition, a nomogram model was generated using the R package "glmnet", and the calibration curve was plotted to evaluate the predictive power of the nomogram model. Finally, deep learning (DL) based on a multilayer perceptron (MLP) network was performed to explore the characteristic genes for GN. (3) Results: we screened out 274 common up-regulated or down-regulated DIRGs in the glomeruli and tubulointerstitium. These DIRGs are mainly involved in T-cell differentiation, the RAS signaling pathway, and the MAPK signaling pathway. ssGSEA indicates that there is a significant increase in DC (dendritic cells) and macrophages, and a significant decrease in neutrophils and NKT cells in glomeruli, while monocytes and NK cells are increased in tubulointerstitium. A nomogram model was constructed to predict GN based on 7 DIRGs, and 20 DIRGs of each subtype of GN in glomeruli and tubulointerstitium were selected as characteristic genes. (4) Conclusions: this study reveals that the DIRGs are closely related to the pathogenesis of GN and could serve as genetic biomarkers in GN. DL further identified the characteristic genes that are essential to define the pathogenesis of GN and develop targeted therapies for eight GN subtypes.

Rapid and ultrasensitive electrochemical detection of DNA methylation for ovarian cancer diagnosis

Alterations in DNA methylation, a stable epigenetic marker, are important components in the development of cancer. It is vital to develop diagnostic systems with the ability to rapidly quantify DNA methylation with high sensitivity and selectivity. However, the analysis of DNA methylation must address two main challenges: (i) ultralow abundance and (ii) differentiating methylated cytosine from normal cytosine on target DNA sequence in the presence of an overwhelming background of circulating cell-free DNA. Here we report the development of an ultrasensitive and highly-selective electrochemical biosensor for the rapid detection of DNA methylation in blood. The sensing of DNA methylation involves the hybridization on a network of probe DNA modified gold-coated magnetic nanoparticles (DNA-Au@MNPs) complementary to target DNA, and subsequently enzymatic cleavage to differentiate methylated DNA strands from corresponding unmethylated DNA strands. The biosensor presents a dynamic range from 2 aM to 20 nM for 110 nucleotide DNA sequences containing a single-site methylation with the lowest detected concentration of 2 aM. This DNA-Au@MNPs based sensor provides a promising method to achieve 35 min response time and minimally invasive diagnosis of ovarian cancer.

Novel nanocomposite of spiky-shaped gold nanourchins/ titanium dioxide/nafion for amplified signal and efficient electrochemiluminescence detection of ovomucoid

This work reports on the first electrochemiluminescence (ECL) immunosensor employing a novel nanostructured composite of titanium dioxide (TiO₂) and gold nanourchins (AuNU) to detect protein allergen Ovomucoid (Ovm) found in eggs. TiO₂ and AuNU were dispersed in Nafion and drop-casted onto SPGE with Tris(2,2'-bipyridyl)-ruthenium (II) ([Ru(bpy)₃]²⁺) and tri-n-propylamine (TPrA) served as strong luminophore/co-reactant pairs as a source of ECL signals. The linear range, limit of detection, reproducibility and practical applications of the sensor were assessed. The fabricated ECL immuosensor produced a promising limit of detection of as low as 0.01 pg/mL. Two linear ranges of 0.01-50 pg/mL and 100-750 pg/mL with corresponding correlation coefficients of R² = 0.99136 and R² = 0.97829) respectively, were determined. Despite its simple fabrication method, this label-free immunosensor also showcased excellent selectivity, reproducibility, interference-resistance and yielded outstanding recoveries between 97.42 and 104.05% of Ovm analysis in spiked real food samples.

Unearth the Luminescence Potential of Metal-Organic Frameworks: Adopting a Feasible Strategy to Fabricate One Ratiometric Fluorescence Sensor for Monitoring Both 1-Hydroxypyrene and Cu2

In this work, a novel luminescent hybrid material with double emission centers (Eu(TTA)_0.2@9-1-UMOF) is successfully prepared, adopting a feasible design strategy. Initially, the second ligand 1,2,4-benzenetricarboxylic acid (H₃BTC) is encapsulated based on a solid solution approach, which effectively improves the ligand-based emission intensity of the original LMOF and provides functional sites for introducing the second luminescent center; then, Eu³⁺ as the red emission source is loaded into the frameworks through a coordination post-synthetic modification method; finally, to balance the emission intensity at 613 nm (Eu³⁺) and 465 nm (1,4-naphthalenedicarboxylic acid (H₂NDC)), 2-thenoyltrifluoroacetone (TTA) as a powerful antenna is introduced. Given the outstanding luminescence properties and structural stability of Eu(TTA)_0.2@9-1-UMOF, it is further developed as a ratiometric sensor for detecting 1-hydroxypyrene (1-HP, the biomarker of polycyclic aromatic hydrocarbons (PAHs)) and Cu²⁺, which promotes the pre-diagnosis of human health. Notably, Eu(TTA)_0.2@9-1-UMOF exhibits excellent selective recognition ability for both 1-HP and Cu²⁺ with high sensitivity (LOD = 4.06 × 10^-6 mg/mL, 3.85 × 10^-7 mol/L, respectively) and fast response speed. In addition, Eu(TTA)_0.2@9-1-UMOF as a fluorescent probe shows great potential for the determination of 1-HP and Cu²⁺ in actual samples. More importantly, this work widens the road for the development of dual/multiple LMOF-based sensors for analytical applications.

A double responsive fluorescent platform for sensing heavy metal ions based on a dual-emitting fluorescent covalent organic framework hydrogel film

A new luminescent hybrid material with dual-emission centers (Eu@Dye@TpDq hydrogel) based on covalent organic framework (COF) has been successfully prepared. First, we designed a dye (coumarin) modified COF (TpDq) hybrid...

Plasmon-Enhanced Nitrogen Vacancy-Rich Carbon Nitride Electrochemiluminescence Aptasensor for Highly Sensitive Detection of miRNA

The development of biosensors for biologically important substances with ultralow content such as microRNA is of great significance. Herein, a novel surface plasmon-enhanced electrogenerated chemiluminescence-based aptasensor was developed for ultrasensitive sensing of microRNA by using nitrogen vacancy-rich carbon nitride nanosheets as effective luminophores and gold nanoparticles as plasmonic sources. The introduction of nitrogen vacancies improved the electrochemiluminescence behavior due to improved conductance and electrogenerated chemiluminescence activity. The introduction of plasmonic gold nanoparticles increased the electrochemiluminescence signal intensity by more than eightfold. The developed surface plasmon-enhanced electrogenerated chemiluminescence aptasensor exhibited good selectivity, ultrasensitivity, excellent stability, and reproducibility for the determination of microRNA-133a, with a dynamic linear range of 1 aM to 100 pM and a limit of detection about 0.87 aM. Moreover, the surface plasmon-enhanced electrogenerated chemiluminescence sensor obtained a good recovery when detecting the content of microRNA in actual serum.

A novel cloth-based supersandwich electrochemical aptasensor for direct, sensitive detection of pathogens

Traditional detection methods for food-borne pathogens are usually expensive and laborious, so there is an urgent need for an economical, facile and sensitive method. In this work, a novel cloth-based supersandwich electrochemical aptasensor (CSEA) is firstly developed for direct detection of pathogens. Carbon ink- and wax-based screen-printing is used to make cloth-based electrodes and hydrophilic/hydrophobic regions respectively to fabricate the sensing devices. Two well-designed, specific single-stranded DNA sequences arise a cascade hybridization reaction to form the DNA supersandwich (DSS) whose grooves can be inserted by methylene blue (MB), which effectively amplifies the current signal to greatly improve the detection sensitivity. Taking the detection of Salmonella typhimurium (S. typhimurium) as an example, the aptamers bind to S. typhimurium to form the target-aptamers complex, which can simultaneously bind to the capture probe and DSS, resulting in detection of S. typhimurium. Moreover, the addition of tail sequences of aptamer makes the proposed CSEA versatile. Under optimized conditions, the electrochemical signal increases linearly with the logarithm of S. typhimurium concentration over the range from 10² to 10⁸ CFU mL^-1, with a limit of detection of 16 CFU mL^-1. Additionally, the CSEA efficiently determined the levels of S. typhimurium in milk samples. Experimental results illustrate that the fabricated CSEA is sensitive, specific, reproducible and stable. Moreover, when Ru(bpy)₃²⁺ replaces MB, the electrochemiluminescence (ECL) can be performed. Thus, for the proposed sensing strategy, the dual-mode detection of electrochemistry and ECL is easily realized.

Being Uncertain in Chromatographic Calibration-Some Unobvious Details in Experimental Design

This is an introductory tutorial and review about the uncertainty problem in chromatographic calibration. It emphasizes some unobvious, but important details influencing errors in the calibration curve estimation, uncertainty in prediction, as well as the connections and dependences between them, all from various perspectives of uncertainty measurement. Nonuniform D-optimal designs coming from Fedorov theorem are computed and presented. As an example, all possible designs of 24 calibration samples (3–8, 4–6, 6–4, 8–3 and 12–2, both uniform and D-optimal) are compared in context of many optimality criteria. It can be concluded that there are only two independent (orthogonal, but slightly complex) trends in optimality of these designs. The conclusions are important, as the uniform designs with many concentrations are not the best choices, contrary to some intuitive perception. Nonuniform designs are visibly better alternative in most calibration cases.