Classes Of Analytes Research Articles

Direct Processing and Storage of Cell-Free Plasma Using Dried Plasma Spot Cards.

Plasma separation cards represent a viable approach for expanding testing capabilities away from clinical settings by generating cell-free plasma with minimal user intervention. These devices typically comprise a basic structure of the plasma separation membrane, unconstrained porous collection pad, and utilize either (i) lateral or (ii) vertical fluidic pathways for separating plasma. Unfortunately, these configurations are highly susceptible to (i) inconsistent sampling volume due to differences in the patient hematocrit or (ii) severe contamination due to leakage of red blood cells or release of hemoglobin (i.e., hemolysis). Herein, we combine the enhanced sampling of our previously reported patterned dried blood spot cards with an assembly of porous separation materials to produce a patterned dried plasma spot card for direct processing and storage of cell-free plasma. Linking both vertical separation and lateral distribution of plasma yields discrete plasma collection zones that are spatially protected from potential contamination due to hemolysis and an inlet zone enriched with blood cells for additional testing. We evaluate the versatility of this card by quantitation of three classes of analytes and techniques including (i) the soluble transferrin receptor by enzyme-linked immunosorbent assay, (ii) potassium by inductively coupled plasma atomic emission spectroscopy, and (iii) 18S rRNA by reverse transcriptase quantitative polymerase chain reaction. We achieve quantitative recovery of each class of analyte with no statistically significant difference between dried and liquid reference samples. We anticipate that this sampling approach can be applied broadly to improve access to critical blood testing in resource-limited settings or at the point-of-care.

Single‐Photon‐Induced Post‐Ionization to Boost Ion Yields in MALDI Mass Spectrometry Imaging**

AbstractMatrix‐assisted laser desorption/ionization mass spectrometry imaging (MALDI‐MSI) is a rapidly growing method in the life sciences. However, for many analyte classes, its sensitivity is limited due to poor ionization efficiencies. To mitigate this problem, we here introduce a novel post‐ionization scheme based on single‐photon induced chemical ionization using pulsed RF‐Kr lamps. The fine‐vacuum conditions of a dual ion‐funnel ion source effectively thermalize the evolving MALDI plume and enable ample gas‐phase reactions. Injected chemical dopants crucially support fragment‐less ionization to [M+H]+/[M−H]− species. Based on this interplay, numerous glycerophospho‐, sphingo‐, and further lipids, registered from mammalian tissue sections, were boosted by up to three orders of magnitude, similar to results obtained with laser‐based post‐ionization (MALDI‐2). Experiments with deuterated matrix and dopant, however, indicated complex chemical ionization pathways different from MALDI‐2.

Single-Photon-Induced Post-Ionization to Boost Ion Yields in MALDI Mass Spectrometry Imaging.

Matrix‐assisted laser desorption/ionization mass spectrometry imaging (MALDI‐MSI) is a rapidly growing method in the life sciences. However, for many analyte classes, its sensitivity is limited due to poor ionization efficiencies. To mitigate this problem, we here introduce a novel post‐ionization scheme based on single‐photon induced chemical ionization using pulsed RF‐Kr lamps. The fine‐vacuum conditions of a dual ion‐funnel ion source effectively thermalize the evolving MALDI plume and enable ample gas‐phase reactions. Injected chemical dopants crucially support fragment‐less ionization to [M+H]+/[M−H]− species. Based on this interplay, numerous glycerophospho‐, sphingo‐, and further lipids, registered from mammalian tissue sections, were boosted by up to three orders of magnitude, similar to results obtained with laser‐based post‐ionization (MALDI‐2). Experiments with deuterated matrix and dopant, however, indicated complex chemical ionization pathways different from MALDI‐2.

Two dimensional neighborhoods of elliptic curves: analytic classification in the torsion case

We investigate the analytic classification of two dimensional neighborhoods of an elliptic curve with torsion normal bundle. We provide the complete analytic classification for those neighborhoods in the simplest formal class and we indicate how to generalize this construction to general torsion case.

On the value set of 1-forms for plane branches

The value semigroup \(\Gamma \) and the value set \(\Lambda \) of 1-forms are, respectively, a topological and an analytical invariant of a plane branch. Given a plane branch \({\mathcal {C}}\) with value semigroup \(\Gamma \) there are a finitely number of distinct possible sets \(\Lambda \) according to the analytic class of \({\mathcal {C}}\). In this work we show that the value set of 1-forms \(\Lambda \) determines the value semigroup \(\Gamma \) and we present an effective method to recover \(\Gamma \) by \(\Lambda \). In particular, this allows us to decide if a subset of \({\mathbb {N}}\) is a value set of 1-forms for a plane branch.

ОСУЧАСНЕННЯ НАПРЯМІВ НІВЕЛЮВАННЯ ФІНАНСОВОЇ НАПРУЖЕНОСТІ : ЗАГРОЗИ ВИНИКНЕННЯ ПРОДОВОЛЬЧОЇ КРИЗИ

Recent decades have been defined by the principles of development, liberal democracy, market economy and sophisticated competition. A new generation grew up on these claims, believing that economic growth, interdependence, freedom of thought and speech, and human progress were as predictable as they were inevitable. However, in 2022 the situation has changed radically, the consequences of which are the basis for the spread of the food crisis, both in Ukraine and in the world as a whole. The aim of the article is to identify obvious and inconspicuous factors of the food crisis in Ukraine and the world, characterize the risks of new segments of financial tensions, identify issues of segmentation of markets and production, the formation of relevant trade blocs. The subject of research - current trends of financial tensions, changes in financial, economic and food supply chains, new threats of food crises. The research hypothesis is the unconditional influence of the consequences of financial and economic deformations on the tendencies of modification of food chains, regional centers of food security, the emergence and spread of the food crisis on all continents and opportunities to prevent it. Methods used in the study: comparison, comparison, systematic and analytical approaches, generalization and classification, dialectical and formal logic. Presenting main material. It is established that currently Ukrainian society is at a critical stage of historical development, the existing economic landscapes are changing instantly. The global pandemic, new external positions, and climate change indicators have already made a negative contribution to the future of the economy and financial markets: too much external debt, too low projected growth and too fast-growing inflation are currently being noted. Neither the government nor civil society is able to meet these challenges alone, so domestic and global cooperation is becoming vital to us. Some countries are taking unilateral trade measures today, which is exacerbating the crisis. It is emphasized that the lack of food security is a catalyst for all crises, which directly leads to armed conflict. The originality and practical significance of the results is the possibility of applying the recommendations to improve accounting and analytical and financial control procedures, improving the efficiency of business processes of economic entities in modern conditions. Conclusions of the study. Thus, the current year becomes the starting point of the economic downturn, which causes the application of austerity measures, which in turn will lead to new manifestations of populism and new uncompromising positions. Regardless of whether discount rates will rise or oil prices fall, influential investors will continue to grow and determine the direction of the global financial capital market.

(Invited) Fast and Generalizable Electrochemical Sensing of Small Molecules, Peptides, and Proteins Using a Nucleic Acid Nanostructure with Analyte-DNA Conjugates

Sensors based on electrochemical (EC) readout offer low cost, miniaturization, and adaptability to the point-of-care (POC). Nonetheless, most EC sensors are specialized to a particular target, and there remains a need for a robust EC biosensor platform for the multitude of biomarkers that are not EC-active, do not undergo enzymatic conversion, or are not suited for potentiometry [1,2]. Impressively, aptamer-based EC sensors have been proven for sensing in living animals with temporal resolution as low as a few seconds [3], yet most method development has been target-focused, lacking generalizability [4]. Presently, the clinical EC toolbox is a conglomerate of targeted methods, and there is a pressing need to develop an EC platform amenable to rapid, generalizable, quantitative readout of multiple classes of clinically relevant targets.Direct EC sensing without added reagents or amplification steps should be ideal for this purpose. For example, the Kelley group recently developed a reagentless “molecular pendulum” EC sensor for a broad range of protein analytes [5]. Our group has been working to address this need and expand to more analyte classes for several years, and in 2019 we designed a versatile DNA-nanostructure architecture attached to gold electrode surfaces [6]. Initially, our sensors were validated with biotechnology controls, antibodies, and with a small molecule immunomodulatory drug in human serum. In this presentation, we discuss the expansion of the generalizability of our sensor platform, chiefly through custom synthesis of varied DNA-analyte bioconjugates to incorporate within the DNA-nanostructure.For peptide sensing, DNA-peptide conjugates were synthesized, purified, then ligated to the DNA-nanostructure. Sensors were validated for quantifying exendin-4 (4.2 kDa)—a human glucagon-like peptide-1 receptor agonist important in diabetes therapy—for the first time using direct EC methods, with an LOD of 6 nM [7]. Sensors for larger proteins were made using DNA-epitope conjugates. The antibody-binding epitope of creatine kinase MM (CK-MM) was conjugated into the nanostructure, allowing CK-MM sensing in the 10 to 100 nM range. Finally, DNA-steroid bioconjugates have been incorporated into the sensors. Sensing of testosterone throughout the clinically relevant range (for males) was accomplished from 1 to 50 nM (LOD of 0.9 nM), and cortisol could be easily detected in the 1 to 100 nM range, well below the 90 – 550 nM range in blood and nicely encompassing the 6 – 75 nM range in saliva.All of these sensors were functional in 98% human serum, and several detection ranges overlap with the clinical/therapeutic ranges, boding well for future applications in biosensing or therapeutic drug monitoring. Overall, this new DNA nanostructure platform provides a generalizable sensor with minimal workflow, direct-readout, and the capability to expand EC sensing to a wide variety of clinically important analytes.

The Case of the Limit of Detection

Students learn early on in science courses that reliable results can only be achieved from accurate measurements. In some of the first general chemistry classes, but even more intensively in analytical chemistry classes, students are introduced to the concept of significant figures. They learn that a measurement is only as true and precise as the instrument used to carry it out, and that the level of skill of the measurer (and how they interpret and report the measurement) is also important. A classic example to introduce significant figures involves the measurement of an object’s length using a ruler. Let us say that the object is a string, and one has two rulers available to measure it. The first ruler has 1-cm markings, while the second one presents 0.1-cm markings. When performing any measurement, students learn that more accurate results will be achieved if one estimates and includes an additional digit to the measurement. Thus, as an example, let us say that the string’s length is determined to be between 3 and 4 cm using the first ruler. The measurer will then estimate the tenths decimal place and report the string’s length as 3.4 cm, for example. Although considered significant, the tenths place (i.e., 4) is an estimate and, therefore, it is uncertain. The other digit (i.e., 3) is both significant and certain. Using the second ruler, the string’s length falls between 3.4 and 3.5 cm. The measurer will again estimate an additional decimal place and report the length as 3.48 cm. In this example, the digits 3 and 4 are both significant and certain, while the hundredths place (i.e., 8) is significant but uncertain. Now, how does the concept of significant figures relate to limit of detection (LOD)? There are different interpretations for LOD, but it simply is the lowest concentration of analyte generating an instrument response that is statistically different from (and higher than) that recorded for the blank. According to the International Union of Pure and Applied Chemistry (IUPAC), the LOD is calculated as three times the standard deviation of the instrument response for the blank (Sb), divided by the calibration curve slope (m), i.e., LOD = 3Sb/m. The value Sb is calculated from repeated measurements of the blank solution, usually 10 - 20 replicates (n = 10 -20). Considering a normal distribution, 99.86% of the data is < (x ̅+3S) (i.e., < than the value corresponding to three standard deviations above the mean). Therefore, the LOD calculated at 3Sb is statistically different from, and has a 99.86% chance of being larger than the blank.4 At the LOD, the instrument response recorded for the analyte is certainly not due to the random variation of the blank signal (almost 100% certain). However, there is no certainty associated with the analyte concentration. In other words, one knows the analyte is present, but cannot quantify it with an adequate level of confidence. Considering Sb as noise, the analyte’s signal-to-noise ratio (S/N) at the LOD is 3 (i.e., the instrument response recorded for the analyte is three times higher than the noise). Because S/N is the reciprocal of the relative standard deviation (RSD), analyte signal variations higher than 33% (RSD = 1/3) are expected at the LOD. Therefore, the concentration corresponding to the LOD is, by definition, uncertain. Going back to the concept of significant figures, the LOD must be reported with a single digit, which is significant but uncertain. Many researchers fail to follow these fundamental concepts of statistics and analytical chemistry, as LODs are often reported with several significant figures (!). Perhaps, it is due to a natural difficulty to connect concepts learned at different points in one’s education. Nevertheless, “the case of the LOD” may foster an awareness that could facilitate the identification of instances in which the connection between different concepts is essential: from adequate reporting of analytical results to new ideas and discoveries.

High throughput and high confidence sequence variant analysis in therapeutic antibodies using Evosep One liquid chromatography tandem mass spectrometry with synthetic heavy peptides

Sequence variants are anomalous misincorporations of amino acids into the primary structure of therapeutic antibodies during DNA replication and protein biosynthesis. As these low abundance variants contribute to molecular heterogeneity and could negatively impact the safety and efficacy of a protein therapeutic, analytical methods like liquid chromatography tandem mass spectrometry (LC-MS2) are used to monitor them with the goal of establishing control strategies that limit their occurrence. Current LC-MS2 strategies depend on relatively long gradients that minimize coelution between abundant non-variant peptide peaks and trace-level variants to limit ion suppression that can potentially conceal the latter. However, lengthy LC gradients reduce the number of samples that can be analyzed per day, limiting the practicality of LC-MS2 when analyzing large sample sets. Furthermore, confident variant identification partly depends on capturing rich MS2 spectra that localize any amino acid misincorporations, which can be challenging due to the low abundance of this class of analyte. This work drastically reduces the cycle time to run each therapeutic antibody sample with roughly the same or even more variant identifications, compared to traditional LC-MS2 analysis, by integrating an Evosep One LC platform with an Orbitrap Fusion Lumos mass spectrometer. It also introduces a novel strategy using synthetic peptides that contain heavy isotopes placed near both termini to validate lower confidence variants in one targeted LC-MS2 run according to retention time, precursor mass signal, and MS2 fragment patterns shared with the heavy peptide variant. Taken together, this approach enables high-throughput sequence variant analysis at 30 samples per day as well as validation for lower confidence variants that can be integrated into therapeutic antibody process development and characterization.

Yamaguchi -Noshiro Type Bi-Univalent Functions Associated with Sălăgean-Erdély–Kober Operator

We defined two new subclasses of analytic bi-univalent function class Σ, in the open unit disk related with the Sălăgean–Erdély–Kober operator. The bounds on initial coefficients |a2|,|a3| and |a4| for the functions in these new subclasses of Σ are investigated. Using the estimates of coefficients a2,a3, we also discuss the Fekete-Szegö inequality results for the function classes defined in this paper. Relevant connections of these results, presented here as corollaries, are new and not studied in association with Sălăgean-Erdély–Kober operator for the subclasses defined earlier.

A Gold Nanoparticle-Based Molecular Self-Assembled Colorimetric Chemosensor Array for Monitoring Multiple Organic Oxyanions

Determination of oxyanions is of paramount importance because of the essential role they play in metabolic processes involved in various aquatic environmental problems. In this investigation, a novel chemical sensor array has been developed by using gold nanoparticles modified with different chain lengths of aminothiols (AET-AuNPs) as sensing elements. The proposed sensor array provides a fingerprint-like response pattern originating from cross-reactive binding events and capable of targeting various anions, including the herbicide glyphosate. In addition, chemometric techniques, linear discrimination analysis (LDA) and the support vector machine (SVM) algorithm were employed for analyte classification and regression/prediction. The obtained sensor array demonstrates a remarkable ability to determine multiple oxyanions in both qualitative and quantitative analysis. The described methodology could be used as a simple, sensitive and fast routine analysis for oxyanions in both laboratory and field settings.

Abstract 3924: Deep, unbiased multi-omics approach for identification of pancreatic cancer biomarkers from blood

Abstract Pancreatic cancer is the seventh leading cause of cancer-related death worldwide and the third leading cause of cancer-related death in the USA. The low survival rate of pancreatic cancer is due to the challenges in early detection of disease, highlighting the need for early diagnostic test development. While cancer signatures are less challenging to identify at the localized pancreatic tumor via biopsy, cancer signals found in the bloodstream due to cellular leakage, metastasis, signaling, innate immune response, remain of key interest due to reduced invasive sampling. The key challenges encountered in liquid biopsy cancer biomarker discovery studies are analyte degradation and dilution in a complex biological matrix, which limits high specificity and sensitivity measurements. To overcome these challenges, PrognomiQ has developed a comprehensive multi-omics platform that facilitates uncovering previously untapped information to gain a more holistic biological perspective at unprecedented depths and integrate molecular signatures across complex levels of biology. Implementation of this approach has led to the discovery of new pancreatic cancer-specific biomarkers and a deeper understanding of the integrated pathways of pancreatic cancer. In this case-control study, the plasma proteome and metabolome data were collected from 193 human plasma samples comprising 92 pancreatic cancer and 104 healthy subjects utilizing liquid chromatography-mass spectrometry. Subject samples were collected post-diagnosis, but pre-treatment for cancer subjects versus non-cancer controls. Sample collection and handling were the same for all samples. In our initial analysis, we detected 3,381 proteins in all samples (minimum of 3 samples per class), and utilizing a Bonferroni correction (FDR = 0.05) we showed 124 proteins to be statistically significant. We also determined ~200 lipids out of 678 total lipids and 49 of 299 metabolites present in all samples (minimum of 3 samples per class) to be statistically significant with a Bonferroni correction (FDR = 0.05). The detected analytes (proteins, lipids, and metabolites) are both known, and unknown, to have an association with pancreatic cancer. Analysis of the data collected from the described cohort will continue to determine Analysis is ongoing to integrate the multi-omics datasets and determine multivariate statistical performance to detect pancreatic cancer. Conclusion: The intent of this cohort and study was to detect biological signal for pancreatic cancer, and this preliminary analysis suggests there are significant differences between classes in the samples as collected. It remains to be seen if combining features within and across analyte classes improve detection. This is a case-control study, not an intent to test study but shows promise for the detection of pancreatic cancer across a multitude of analyte classes. Citation Format: Bruce Wilcox, John Blume, Kavya Swaminathan, Preston Williams, Manoj Khadka, Jared Deyarmin, Saividya Ramaswamy, Yuya Kodama, Brian Young, Chinmay Belthangady, Manway Liu, Mi Yang, Philip Ma. Deep, unbiased multi-omics approach for identification of pancreatic cancer biomarkers from blood [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3924.

Exponential ReLU Neural Network Approximation Rates for Point and Edge Singularities

In certain polytopal domains varOmega , in space dimension d=2,3, we prove exponential expressivity with stable ReLU Neural Networks (ReLU NNs) in H^1(varOmega ) for weighted analytic function classes. These classes comprise in particular solution sets of source and eigenvalue problems for elliptic PDEs with analytic data. Functions in these classes are locally analytic on open subdomains Dsubset varOmega , but may exhibit isolated point singularities in the interior of varOmega or corner and edge singularities at the boundary partial varOmega . The exponential approximation rates are shown to hold in space dimension d = 2 on Lipschitz polygons with straight sides, and in space dimension d=3 on Fichera-type polyhedral domains with plane faces. The constructive proofs indicate that NN depth and size increase poly-logarithmically with respect to the target NN approximation accuracy varepsilon >0 in H^1(varOmega ). The results cover solution sets of linear, second-order elliptic PDEs with analytic data and certain nonlinear elliptic eigenvalue problems with analytic nonlinearities and singular, weighted analytic potentials as arise in electron structure models. Here, the functions correspond to electron densities that exhibit isolated point singularities at the nuclei.

Polar germs, Jacobian ideal and analytic classification of irreducible plane curve singularities

We examine the relationship between the analytic type of an irreducible plane curve singularity with a single characteristic exponent and the germs of curve defined by the elements of its Jacobian ideal, in particular its polar germs.

Analysis of sentiments on the onset of Covid-19 using Machine Learning Techniques

The novel coronavirus (Covid-19) pandemic has struck the whole world and is one of the most striking topics on social media platforms. Sentiment outbreak on social media enduring various thoughts, opinions, and emotions about the Covid-19 disease, expressing views they are feeling presently. Analyzing sentiments helps to yield better results. Gathering data from different blogging sites like Facebook, Twitter, Weibo, YouTube, Instagram, etc., and Twitter is the largest repository. Videos, text, and audio were also collected from repositories. Sentiment analysis uses opinion mining to acquire the sentiments of its users and categorizes them accordingly as positive, negative, and neutral. Analytical and machine learning classification is implemented to 3586 tweets collected in different time frames. In this paper, sentiment analysis was performed on tweets accumulated during the Covid-19 pandemic, Coronavirus disease. Tweets are collected from the Twitter database using Hydrator a web-based application. Data-preprocessing removes all the noise, outliers from the raw data. With Natural Language Toolkit (NLTK), text classification for sentiment analysis and calculate the score subjective polarity, counts, and sentiment distribution. N-gram is used in textual mining -and Natural Language Processing for a continuous sequence of words in a text or document applying uni-gram, bi-gram, and tri-gram for statistical computation. Term frequency and Inverse document frequency (TF-IDF) is a feature extraction technique that converts textual data into numeric form. Vectorize data feed to our model to obtain insights from linguistic data. Linear SVC, MultinomialNB, GBM, and Random Forest classifier with Tfidf classification model applied to our proposed model. Linear Support Vector classification performs better than the other two classifiers. Results depict that RF performs better.

Preparation and chromatographic performance of cardanol-bonded silica stationary phase

天然产物作为一种绿色低毒、来源广泛、功能位点丰富的单体,已被广泛应用于色谱固定相的研制与开发。该文以天然可再生资源腰果酚为配体,通过一步法开环反应将其接枝到由γ-缩水甘油醚氧丙基三甲氧基硅烷(KH-560)修饰的硅胶上,制备得到腰果酚键合硅胶固定相。利用傅里叶红外光谱、元素分析、热失重分析和N2吸附脱附实验对固定相进行表征,结果表明成功制备了腰果酚键合硅胶色谱固定相。采用Tanaka实验试剂、烷基苯、多环芳香烃、苯酚类化合物和芳香族位置异构体为探针评价其分离性能和保留机制,并与C18柱进行对比。研究发现,腰果酚键合固定相除疏水作用外,还具有π-π和氢键作用。基于上述保留作用,腰果酚键合硅胶固定相对测试探针表现出良好的分离性能。重复进样10次,各探针保留时间的RSD为0.052%~0.079%,峰面积的RSD为0.104%~0.847%,峰高的RSD为0.081%~0.272%,表明该色谱柱具有良好的重复性和稳定性。此外,腰果酚键合硅胶色谱柱对中药喜树果和吴茱萸果的粗提物具有良好的分离性能,验证了其在实际样品分析中的巨大潜力。将天然产物腰果酚用于色谱固定相的制备,为分离纯化喜树碱和吴茱萸提供了新的方法,同时拓展了腰果酚在色谱分离材料方面的应用。

MSNovelist: de novo structure generation from mass spectra

Current methods for structure elucidation of small molecules rely on finding similarity with spectra of known compounds, but do not predict structures de novo for unknown compound classes. We present MSNovelist, which combines fingerprint prediction with an encoder–decoder neural network to generate structures de novo solely from tandem mass spectrometry (MS2) spectra. In an evaluation with 3,863 MS2 spectra from the Global Natural Product Social Molecular Networking site, MSNovelist predicted 25% of structures correctly on first rank, retrieved 45% of structures overall and reproduced 61% of correct database annotations, without having ever seen the structure in the training phase. Similarly, for the CASMI 2016 challenge, MSNovelist correctly predicted 26% and retrieved 57% of structures, recovering 64% of correct database annotations. Finally, we illustrate the application of MSNovelist in a bryophyte MS2 dataset, in which de novo structure prediction substantially outscored the best database candidate for seven spectra. MSNovelist is ideally suited to complement library-based annotation in the case of poorly represented analyte classes and novel compounds.

The effects of FDI on economic growth in Africa

This paper investigates the growth effects of FDI in Africa for the period 1990–2016 using a dynamically common correlated effect approach for an error-correction model. It uses an analytical classification of African economies, with each being fragile, factor-driven or investment-driven. It also accounts for interaction effects and the problem of cross-sectional dependence that previous studies overlooked. While the long-run effect of FDI on growth is significantly positive in investment- and factor-driven economies, its short-run effect is insignificant in the latter type of economies. The effect of FDI on growth is insignificant in the fragile category both in the short-run and long-run, however.

Laser-induced graphene-based electrochemical biosensors for environmental applications: a perspective.

Biosensors are miniaturized devices that provide the advantage of in situ and point-of-care monitoring of analytes of interest. Electrochemical biosensors use the mechanism of oxidation-reduction reactions and measurement of corresponding electron transfer as changes in current, voltage, or other parameters using different electrochemical techniques. The use of electrochemically active materials is critical for the effective functioning of electrochemical biosensors. Laser-induced graphene (LIG) has garnered increasing interest in biosensor development and improvement due to its high electrical conductivity, specific surface area, and simple and scalable fabrication process. The effort of this perspective is to understand the existing classes of analytes and the mechanisms of their detection using LIG-based biosensors. The manuscript has highlighted the potential use of LIG, its modifications, and its use with various receptors for sensing various environmental pollutants. Although the conventional graphene-based sensors effectively detect trace levels for many analytes in different applications, the chemical and energy-intensive fabrication and time-consuming processes make it imperative to explore a low-cost and scalable option such as LIG for biosensors production. The focus of these potential biosensors has been kept on detection analytes of environmental significance such as heavy metals ions, organic and inorganic compounds, fertilizers, pesticides, pathogens, and antibiotics. The use of LIG directly as an electrode, its modifications with nanomaterials and polymers, and its combination with bioreceptors such as aptamers and polymers has been summarized. The strengths, weaknesses, opportunities, and threats analysis has also been done to understand the viability of incorporating LIG-based electrochemical biosensors for environmental applications.

New Approaches to the Diagnosis of Rejection and Prediction of Tolerance in Liver Transplantation.

Immunosuppression after liver transplantation is essential for preventing allograft rejection. However, long-term drug toxicity and associated complications necessitate investigation of immunosuppression minimization and withdrawal protocols. Development of such protocols is hindered by reliance on current paradigms for monitoring allograft function and rejection status. The current standard of care for diagnosis of rejection is histopathologic assessment and grading of liver biopsies in accordance with the Banff Rejection Activity Index. However, this method is limited by cost, sampling variability, and interobserver variation. Moreover, the invasive nature of biopsy increases the risk of patient complications. Incorporating noninvasive techniques may supplement existing methods through improved understanding of rejection causes, hepatic spatial architecture, and the role of idiopathic fibroinflammatory regions. These techniques may also aid in quantification and help integrate emerging -omics analyses with current assessments. Alternatively, emerging noninvasive methods show potential to detect and distinguish between different types of rejection while minimizing risk of adverse advents. Although biomarkers have yet to replace biopsy, preliminary studies suggest that several classes of analytes may be used to detect rejection with greater sensitivity and in earlier stages than traditional methods, possibly when coupled with artificial intelligence. Here, we provide an overview of the latest efforts in optimizing the diagnosis of rejection in liver transplantation.