Diagnostic Purposes Research Articles

Major approaches of inflammatory processes and peri-implant infections in the cellular and molecular scenario of mesenchymal stem cells, exosomes, and microRNAs: a systematic review

Introduction: Bone diseases are denoted by fractures, osteoporosis, and osteoarthritis that affect a large number of individuals, with a rising prevalence of osteopenia for 64.3 million American individuals and osteoporosis for 11.9 million by the year 2030. Dental implants are not free from possible complications with consequent failure, the causes of which are still the subject of debate in the dental scientific community. In particular, peri-implant infections are multifactorial pathological conditions characterized by inflammation of the peri-implant mucosa with or without progressive loss of supporting bone. Specific expression profiles of microRNAs (miRNAs) extracted from peri-implant tissues are predictive of specific clinical outcomes of dental implants and can be used as biomarkers in implant dentistry for diagnostic and prognostic purposes. Objective: It was to address the main approaches to inflammatory processes and peri-implant infections and dental implants in the cellular and molecular scenarios of mesenchymal stem cells, exosomes, and microRNAs, emphasizing the main biomarkers in the therapeutic control of harmful dental implant processes. Methods: The PRISMA Platform systematic review rules were followed. The search was carried out from October 2023 to January 2024 in the Scopus, PubMed, Science Direct, Scielo, and Google Scholar databases. The quality of the studies was based on the GRADE instrument and the risk of bias was analyzed according to the Cochrane instrument. Results and Conclusion: A total of 138 articles were found, 44 articles were evaluated in full and 34 were included and developed in the present systematic review study. Considering the Cochrane tool for risk of bias, the overall assessment resulted in 26 studies with a high risk of bias and 24 studies that did not meet GRADE and AMSTAR-2. Most studies did not show homogeneity in their results, with X2=67.9%>50%. It was concluded that specific expression profiles of miRNAs extracted from peri-implant tissues are predictive of specific clinical outcomes of dental implants and can be used as biomarkers in implant dentistry for diagnostic and prognostic purposes. Studies have shown that many of the miRNAs extracted from the implant's peri-crevicular fluid were common to those detected in soft tissues taken from the same peri-implant sites. Evidence suggests that exosomes derived from adipose-derived stem cells exhibit similar functions to those cells, with low immunogenicity and no tumorization. Insufficient bone volume directly impacts the placement of dental implants. Adipose-derived stem cells can accelerate bone healing when combined with dental implants. An increase in the concentration of exosomes with negative expression of miRNA-21-3p and miRNA-150-5p may be related to the development of peri-implantitis.

Influence of Frequent Phlebothomy on Blood Iron Concentration, Haematological, Metabolic and Endocrine Parameters in Rams

Abstract Frequent phlebotomies, even when only a small volume of blood is taken for diagnostic purposes, can lead to the development of iron deficiency with hematological and metabolic changes. The study aimed to determine the influence of frequent blood loss by phlebotomy on blood iron concentration (Fe), hematology, metabolic and endocrine parameters and their relationships. Blood samples were collected from 30 blood donor rams for 6 consecutive weeks, with approximately 10% of blood collected weekly. Such chronic blood loss resulted in a decrease in Fe. Indicators of iron transport in the bloodstream changed, so the value of total iron-binding capacity (TIBC) and unsaturated iron-binding capacity (UIBC) increased, while the value of transferrin saturation percentage (TS%) decreased. Hematological changes included a decrease in red blood cells, hemoglobin, mean red blood cell volume and hematocrit and a tendency for reticulocyte count and red cell distribution width to increase. Chronic blood loss resulted in a specific metabolic response that included the increase in glucose, cholesterol, triglycerides, aspartate-aminotransferase, and insulin resistance, while thyroxine, triiodothyronine and cortisol decreased and there was a tendency for lactate to increase and BHB to decrease. The mentioned blood parameters correlated with Fe and additionally showed greater changes when Fe was extrapolated to the level of clinical deficit (Fe=9μmol/L). These correlations suggest the need to monitor the metabolic and endocrine status during chronic blood loss, in addition to Fe and erythrocyte indices. Compared with previous results in other animal species, Fe may have a direct influence on metabolic processes in rams.

Partial Discharge Monitoring of Power Transformers by Calibrated UHF Measurements

Partial discharge (PD) measurement is an established technique to detect local defects in the oil/paper insulation. They can be measured by electrical PD measurements according to IEC 60270 or by electromagnetic measurements in the ultra-high frequency range (UHF: 300 MHz – 3 GHz). The electromagnetic emissions of PD are recorded using an UHF sensor which is inserted into the transformer tank. The importance of PD measurements is reflected in the standard for charge-based electrical measurements (IEC 60270). Because of the standard, apparent charge, QIEC, became an essential factor for determining insulation quality in transformers. To be an accepted factor for both, quality testing in factory acceptance tests (FAT) or site acceptance tests (SAT) and continuous PD monitoring, the UHF technology must be as reliable and reproducible as the electrical method. To achieve this, a standardized calibration procedure is needed: It makes UHF measurement results from different systems comparable. Because this calibration procedure was lacking in the past, the UHF technology does not provide comparability between measurement systems and different sensors, yet. However, it shows advantages for on-site acceptance tests like SAT, continuous monitoring, and diagnostic purposes. E.g., regarding signal to noise ratios, the possibility for localization of PD sources and also for practical reasons like preparation times and accessibility on-site. This contribution presents a PD monitoring system which can be calibrated according to the calibration process described recently in Cigré TB 861 and two different types of UHF PD sensors for power transformers. One sensor type is for new transformers using dielectric windows and the other is for retrofitting transformers using their DN50/80 drain valves. A recommendation or strategy on where to place window-type UHF sensors at the tank of new power transformers is also provided in accordance with TB 861. Additionally, this contribution provides a description of the calibration process for UHF PD measurement systems consisting of two steps: 1) Calibration of the measurement instrument analogous to the calibration of the electrical PD measurement circuit using a defined reference signal. 2) Calibration of a UHF sensor using its characteristic antenna factor (AF). Furthermore, it shows three case studies from different power transformers equipped with UHF sensors and UHF PD monitoring systems.

Real-time FPGA Integration for ECG Monitoring: Bidirectional Recurrent Chimp Search Model

The primary diagnostic tool for monitoring problems with the heart or related diseases is the electrocardiogram (ECG). The rising mortality rate caused by cardiac abnormalities leads to the development of more advanced techniques to detect heart anomalies. An ECG is used for multiple diagnostic purposes since it produces continuous tracings of the electrophysiological activity generated from the heart. This paper developed a Bidirectional recurrent chimp search (Bi-RCS) method for diagnosing abnormalities by collecting ECG signals via various phases such as the data acquisition phase, signal processing phase, feature extraction phase, abnormality detection phase, real-time FPGA integration phase, monitoring and reporting phase as well as feedback phase. Signal pre-processing removes contaminants like noise using the Adaptive filtering methodology which severely limits the utility of the recorded ECG for better clinical evaluation. Bi-RNN is utilized for extracting related features and detecting abnormalities and the Chimp search optimization is employed to tune the parameters of Bi-RNN. After detecting the abnormalities, the reports of patients are monitored and recorded to provide an alert signal to healthcare providers. Finally, the potency of the methodology is analyzed with the ECG signal dataset and metrics like precision, F1-score accuracy, sensitivity, and specificity values. The obtained outputs equalized with the existing methods like KNN, EBT, and SVM. The analysis showed convincing performance with an accuracy of 99% and less error rate of 0.05.

Early Detection of Parkinson Disease Throughbiomedical Speech and Voice Analysis

Parkinson's disease, widely recognized as a neurodegenerative condition characterized by subtle changes in voice, has spurred an investigation into voice analysis for diagnostic purposes. This study is dedicated to the early detection of Parkinson's disease through a comprehensive examination of biomedical speech attributes. Parameters such as fundamental frequency range, jitter, shimmer, noise-to-harmonics ratio, and features derived from nonlinear analysis are considered, alongside variables like status, indicating the presence of neurological disorders, and class for classification purposes. Together, these attributes provide a detailed representation of voice signals, offering valuable insights into both neurological and voice disorders for research purposes. The dataset exhibits promising potential for applications in medical diagnostics and voice analysis. In the pursuit of accurate disease detection, various machine learning methodologies are employed, including Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), Neural Networks (NN), and state- of-the-art Convolutional Neural Networks (CNNs). The incorporation of CNNs is pivotal, signifying a significantleap in accuracy of 100%for disease detection. The results showcase a model adept at discerning subtle changesassociated with Parkinson's disease, with SVM achieving 96%, Decision Tree demonstrating a perfect 100%, Neural Network attaining 98%, and Random Forest showcasing an accuracy of 99%. This innovative approach not only transforms early Parkinson's disease identification through voice analysis, setting a precision benchmark, but also underscores the transformative potential of cutting-edge technologies in healthcare practices. The study positions the model as a reliable diagnostic tool, capable of advancing medical diagnosticsthrough the seamless integration of biomedical research and machine learning, contributing to the broader fieldof neurodegenerative disease diagnostics.

Future advances of artificial biosensor technology in biomedical applications

Recent advancements in synthetic biology have facilitated the concept of a cell-based and cell-free biosensing platform, which enables the identification of molecular signals encompassing metal/chemical to disease biomarkers. The artificial sensing incorporates the concept of both whole-cell and cell-free biosensing strategies, which include highly regulated natural and synthetic components to exhibit genetically encoded molecular sensing properties. These sensors utilize protein expression to release signalling molecules as the result of received input to facilitate the detection of analytes. Intending to use modified living cells or artificial cells in biosensing, the proposed study highlights the importance of cell-based and cell-free sensors in biomedical and diagnostics. The article's first section will explain the biosensing types including cell-free, cell-based, vesicle-based, and paper-based sensing where sensing relies on cell, cellular components, and cell-free systems which mostly involve transcriptional or translational machinery. It highlights the advantages, disadvantages, and challenges of advancing approaches. The second section of the article elaborates on the principle of sensing and the strategies involved. Though very few studies have been reported on this topic, therefore, the current article focuses on the artificial sensors that have been designed for medical and diagnostic purposes. The review also marks the current and future advancements in the field including artificial intelligence, nanotechnology, stem cells, and omics. Sensing recently has a big impact on disease diagnosis as well as drug development and targeted therapies. While newly developed biology-based diagnostics technologies still have high costs, require highly trained personnel, suffer stability issues and reduce sensor performance. Therefore, this review brings readers’ attention to advances and challenges in the following field and promotes the resolution of medical and diagnostics issues in the future.

Material characterization of human middle ear using machine-learning-based surrogate models

This study aims to introduce a novel non-invasive method for rapid material characterization of middle-ear structures, taking into consideration the invaluable insights provided by the mechanical properties of ear tissues. Valuable insights into various ear pathologies can be gleaned from the mechanical properties of ear tissues, yet conventional techniques for assessing these properties often entail invasive procedures that preclude their use on living patients. In this study, in the first step, we developed machine-learning models of the middle ear to predict its responses with a significantly lower computational cost in comparison to finite-element models. Leveraging findings from prior research, we focused on the most influential model parameters: the Young's modulus and thickness of the tympanic membrane and the Young's modulus of the stapedial annular ligament. The eXtreme Gradient Boosting (XGBoost) method was implemented for creating the machine-learning models. Subsequently, we combined the created machine-learning models with Bayesian optimization (BoTorch) for fast and efficient estimation of the Young's moduli of the tympanic membrane and the stapedial annular ligament. We demonstrate that the resultant surrogate models can fairly represent the vibrational responses of the umbo, stapes footplate, and vibration patterns of the tympanic membrane at most frequencies. Also, our proposed material characterization approach successfully estimated the Young's moduli of the tympanic membrane and stapedial annular ligament (separately and simultaneously) with values of mean absolute percentage error of less than 7%. The remarkable accuracy achieved through the proposed material characterization method underscores its potential for eventual clinical applications of estimating mechanical properties of the middle-ear structures for diagnostic purposes.

A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors.

Gait disorder is common among people with neurological disease and musculoskeletal disorders. The detection of gait disorders plays an integral role in designing appropriate rehabilitation protocols. This study presents a clinical gait analysis of patients with polymyalgia rheumatica to determine impaired gait patterns using machine learning models. A clinical gait assessment was conducted at KATH hospital between August and September 2022, and the 25 recruited participants comprised 18 patients and 7 control subjects. The demographics of the participants follow: age 56 years ± 7, height 175 cm ± 8, and weight 82 kg ± 10. Electromyography data were collected from four strained hip muscles of patients, which were the rectus femoris, vastus lateralis, biceps femoris, and semitendinosus. Four classification models were used-namely, support vector machine (SVM), rotation forest (RF), k-nearest neighbors (KNN), and decision tree (DT)-to distinguish the gait patterns for the two groups. SVM recorded the highest accuracy of 85% among the classifiers, while KNN had 75%, RF had 80%, and DT had the lowest accuracy of 70%. Furthermore, the SVM classifier had the highest sensitivity of 92%, while RF had 86%, DT had 90%, and KNN had the lowest sensitivity of 84%. The classifiers achieved significant results in discriminating between the impaired gait pattern of patients with polymyalgia rheumatica and control subjects. This information could be useful for clinicians designing therapeutic exercises and may be used for developing a decision support system for diagnostic purposes.

Measuring Pathological and Nonpathological Orthorexic Behavior: Validation of the Teruel Orthorexia Scale (TOS) among Polish Adults.

Measuring orthorexia nervosa is challenging due to the use of various existing tools and problems with sample representativeness. Another challenge for the Polish population is the adaptation of existing research tools and the evaluation of their relevance and research reliability. Our research aimed to adapt the TOS to the Polish language and measure pathological and nonpathological orthorexic behavior among the Polish population. The adaptation of the PL-TOS has high psychometric value and allows us to assess healthy and nervous orthorexia levels. This scale can be used not only for further research but also for diagnostic purposes in the daily work of clinicians and psychologists. Our results obtained in the present study indicate a correlation between TOS and both the use of supplements and diet. Higher TOS, ORTO_R and KZZJ_Diet Restrictions scores were obtained for individuals using dietary supplements than for those not using dietary supplements. In the future, it is worth conducting research aimed at various risk groups of individuals with orthorexia to confirm the psychometric properties of this adaptation of the TOS.

Application of electrical impedance tomography imaging technology combined with generative adversarial network in pulmonary ventilation monitoring

Electrical impedance tomography (EIT) plays a crucial role in the monitoring of pulmonary ventilation and regional pulmonary function test. However, the inherent ill-posed nature of EIT algorithms results in significant deviations in the reconstructed conductivity obtained from voltage data contaminated with noise, making it challenging to obtain accurate distribution images of conductivity change as well as clear boundary contours. In order to enhance the image quality of EIT in lung ventilation monitoring, a novel approach integrating the EIT with deep learning algorithm was proposed. Firstly, an optimized operator was introduced to enhance the Kalman filter algorithm, and Tikhonov regularization was incorporated into the state-space expression of the algorithm to obtain the initial lung image reconstructed. Following that, the imaging outcomes were fed into a generative adversarial network model in order to reconstruct accurate lung contours. The simulation experiment results indicate that the proposed method produces pulmonary images with clear boundaries, demonstrating increased robustness against noise interference. This methodology effectively achieves a satisfactory level of visualization and holds potential significance as a reference for the diagnostic purposes of imaging modalities such as computed tomography.

Spontaneous, persistent, T cell-dependent IFN-γ release in patients who progress to Long Covid.

After acute infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a proportion of patients experience persistent symptoms beyond 12 weeks, termed Long Covid. Understanding the mechanisms that cause this debilitating disease and identifying biomarkers for diagnostic, therapeutic, and monitoring purposes are urgently required. We detected persistently high levels of interferon-γ (IFN-γ) from peripheral blood mononuclear cells of patients with Long Covid using highly sensitive FluoroSpot assays. This IFN-γ release was seen in the absence of ex vivo peptide stimulation and remains persistently elevated in patients with Long Covid, unlike the resolution seen in patients recovering from acute SARS-CoV-2 infection. The IFN-γ release was CD8+ T cell-mediated and dependent on antigen presentation by CD14+ cells. Longitudinal follow-up of our study cohort showed that symptom improvement and resolution correlated with a decrease in IFN-γ production to baseline levels. Our study highlights a potential mechanism underlying Long Covid, enabling the search for biomarkers and therapeutics in patients with Long Covid.

Optical Genome Mapping for Comprehensive Cytogenetic Analysis of Soft-Tissue and Bone Tumors for Diagnostic Purposes

Soft tissue and bone tumours represent a heterogeneous group of tumours encompassing more than 100 histological subtypes today. Identifying genetic aberrations is increasingly important in these tumours for accurate diagnosis. While gene mutations are typically detected by second generation sequencing, the identification of structural variants (SVs) and copy number alterations (CNAs) remains challenging and requires various cytogenetic techniques including karyotyping, fluorescent in situ hybridisation and arrays, each having important limitations.We applied Optical Genome Mapping (OGM), a non-sequencing-based technique for high-resolution detection of SVs and CNAs in a retrospective series of diagnostic soft tissue and bone tumour samples. Sample preparation was successful in 38 out of 53 cases, with the highest success rate in non-adipocytic soft tissue tumours (24/27 cases, 89%). In 32 out of 35 cases carrying a diagnostic SV or CNA, OGM identified the aberration (91%) including a POU2AF3::EWSR1 fusion in a round cell sarcoma and a translocation t(1;5)(p22;p15) in a myxoinflammatory fibroblastic sarcoma. Interestingly, OGM shed light on the genomic complexity underlying the various aberrations. In five samples OGM showed that chains of rearrangements generated the diagnostic fusion, three of which involved chromoplexy. Additionally, in nine samples chromothripsis was causal to the formation of giant marker/ring/double minute chromosomes. Finally, compared to standard-of-care cytogenetics, OGM revealed additional aberrations, requiring further investigation of their potential clinical relevance.

HeLa cell segmentation using digital image processing

Computational cell segmentation is a vital area of research, particularly in the analysis of images of cancer cells. The use of cell lines, such as the widely utilized HeLa cell line, is crucial for studying cancer. While deep learning algorithms have been commonly employed for cell segmentation, their resource and data requirements can be impractical for many laboratories. In contrast, image processing algorithms provide a promising alternative due to their effectiveness and minimal resource demands. This article presents the development of an algorithm utilizing digital image processing to segment the nucleus and shape of HeLa cells. The research aims to segment the cell shape in the image center and accurately identify the nucleus. The study uses and processes 300 images obtained from Serial Block-Face Scanning Electron Microscopy (SBF-SEM). For cell segmentation, the morphological operation of erosion was used to separate the cells, and through distance calculation, the cell located at the center of the image was selected. Subsequently, the eroded shape was employed to restore the original cell shape. The nucleus segmentation uses parameters such as distances and sizes, along with the implementation of verification stages to ensure accurate detection. The accuracy of the algorithm is demonstrated by comparing it with another algorithm meeting the same conditions, using four segmentation similarity metrics. The evaluation results rank the proposed algorithm as the superior choice, highlighting significant outcomes. The algorithm developed represents a crucial initial step towards more accurate disease analysis. In addition, it enables the measurement of shapes and the identification of morphological alterations, damages, and changes in organelles within the cell, which can be vital for diagnostic purposes.

Impact of Protein Phosphatase Expressions on the Prognosis of Hepatocellular Carcinoma Patients.

The study was conducted to unveil the significance of protein phosphatases in the prognosis of hepatocellular carcinoma (HCC) patients and its related molecular biological attributes as well as to discover novel potential biomarkers for therapeutic significance and diagnostic purposes that may benefit clinical practice. Analyzing a data set from 159 HCC patients using high-throughput phosphoproteomics, we examined the dysregulated expression of protein phosphatases. Employing bioinformatic and pathway analyses, we explored differentially expressed genes linked to protein phosphatases. A protein-protein interaction network was constructed using the search tool for the retrieval of interacting genes/proteins database. We quantified a total of 11,547 phosphorylation sites associated with 4043 phosphoproteins from HCC patients. Within this data set, we identified 105 identified phosphorylation sites associated with protein phosphatases; 28 genes were upregulated and 3 were downregulated in HCC. Enriched pathways using Gene Set Enrichment Analysis encompassed oocyte meiosis, proteoglycans in cancer, the oxytocin signaling pathway, the cGMP-PKG signaling pathway, the vascular smooth muscle, and the cAMP signaling pathway. The Kyoto encyclopedia of genes and genomes (KEGG) analysis highlighted pathways like mitogen-activated protein kinase, AMPK, and PI3K-Akt, indicating potential involvement in HCC progression. Notably, the PPI network identified hub genes, emphasizing their interconnections and potential roles in HCC. In our study, we found significantly upregulated levels of CDC25C, PPP1R13L, and PPP1CA, which emerge as promising avenues. This significant expression could serve as potent diagnostic and prognostic markers to enhance the effectiveness of HCC cancer treatment, offering efficiency and accuracy in patient assessment. The findings regarding protein phosphatases reveal their elevated expression in HCC, correlating with unfavorable prognosis. Moreover, the outcomes of gene ontology and KEGG pathway analyses suggest that protein phosphatases may influence liver cancer by engaging diverse targets and pathways, ultimately fostering the progression of HCC. These results underscore the substantial potential of protein phosphatases as key contributors to HCC's development and advancement. This insight holds promise for identifying therapeutic targets and charting research avenues to enhance the comprehension of the intricate molecular mechanisms underpinning HCC.

Root and canal morphology of the mandibular first molar: A micro-computed tomography-focused observation of literature with illustrative cases. Part 2: Internal root morphology

The endodontic intervention of the mandibular first molar can be challenging. Once root canals or any portion of them remain undiscovered and untreated, the risk of treatment failure greatly increases. The consensus is that mandibular first molars may have three or four main root canals. However, variations have been noted between populations, which include the mid-mesial canal (MM) and the mid-distal canal (MD). Authors have also attempted to classify root canal configurations to identify common patterns for diagnostic and treatment planning purposes. The introduction of micro-computed tomography (micro-CT) to root and canal morphological studies revolutionised observation of complex root canal anatomy in three dimensions and high definition. This paper is the second of two providing an overview of literature on various aspect of the external and internal root and canal morphology of the mandibular first permanent molar. The aim is to provide an overview of relevant aspects of the internal root morphology of the mandibular first molar in different populations. The content is supported by illustrative micro-CT images and a report on clinical cases where anomalies have been treated

Oncogenic miR-146a-PBX2 axis, a novel leukemic target in xenograft acute lymphoblastic leukemia model

Acute lymphoblastic leukemia (ALL) is one of the major hematological malignancies both in adults and children. Bone marrow biopsy is the main requirement for its accurate diagnostic and prognostic purpose till now that is a very painful process especially for children. The reluctance to go for bone marrow biopsy leads to delayed diagnosis treatment of ALL. MicroRNAs are emerging as diagnostic and therapeutic targets for cancer including ALL using less painful methods. The current study was conducted to evaluate the potential of miR-146a-PBX2 axis as a leukemic target of ALL using ALL-induced rabbit model. A total of 8 rabbits were divided into two groups: ALL-induced (diseased) and normal (control). The whole blood was collected from both groups and plasma isolation was performed. The levels of miR-146a and PBX2 gene in plasma of diseased and control samples were quantified by the real-time PCR (RT-qPCR). The normalized fold expression was calculated using miR-16 and the reference gene GAPDH. The results showed that there was a significant increase in miR-146a expression in diseased (ALL-induced) samples as compared to the control. Moreover, a significant upregulation of PBX2 gene was found in the diseased model as compared to that in the control. The results implied that miR-146a-PBX2 axis may be utilized as a potential target for ALL.

Harnessing Catalytic RNA Circuits for Construction of Artificial Signaling Pathways in Mammalian Cells.

Engineering of genetic networks with artificial signaling pathways (ASPs) can reprogram cellular responses and phenotypes under different circumstances for a variety of diagnostic and therapeutic purposes. However, construction of ASPs between originally independent endogenous genes in mammalian cells is highly challenging. Here we report an amplifiable RNA circuit that can theoretically build regulatory connections between any endogenous genes in mammalian cells. We harness the system of catalytic hairpin assembly with combination of controllable CRISPR-Cas9 function to transduce the signals from distinct messenger RNA expression of trigger genes into manipulation of target genes. Through introduction of these RNA-based genetic circuits, mammalian cells are endowed with autonomous capabilities to sense the changes of RNA expression either induced by ligand stimuli or from various cell types and control the cellular responses and fates via apoptosis-related ASPs. Our design provides a generalized platform for construction of ASPs inside the genetic networks of mammalian cells based on differentiated RNA expression.

Harnessing Catalytic RNA Circuits for Construction of Artificial Signaling Pathways in Mammalian Cells

AbstractEngineering of genetic networks with artificial signaling pathways (ASPs) can reprogram cellular responses and phenotypes under different circumstances for a variety of diagnostic and therapeutic purposes. However, construction of ASPs between originally independent endogenous genes in mammalian cells is highly challenging. Here we report an amplifiable RNA circuit that can theoretically build regulatory connections between any endogenous genes in mammalian cells. We harness the system of catalytic hairpin assembly with combination of controllable CRISPR‐Cas9 function to transduce the signals from distinct messenger RNA expression of trigger genes into manipulation of target genes. Through introduction of these RNA‐based genetic circuits, mammalian cells are endowed with autonomous capabilities to sense the changes of RNA expression either induced by ligand stimuli or from various cell types and control the cellular responses and fates via apoptosis‐related ASPs. Our design provides a generalized platform for construction of ASPs inside the genetic networks of mammalian cells based on differentiated RNA expression.

The unique monoclonal antibodies and immunochemical assay for comprehensive determination of the cell-bound and soluble HER2 in different biological samples

The expression of the HER2 (human epidermal growth factor receptor 2) protein in cancer cells is a well-established cancer marker used for diagnostic and therapeutic purposes in modern treatment protocols, especially in breast cancer. The gold-standard immunohistochemical diagnostic methods with the specific anti-HER2 antibodies are utilized in the clinic to measure expression level of the membrane-bound receptor. However, a soluble extracellular domain (ECD) of HER2 is released to the extracellular matrix, thus the blood assays for HER2 measurements present an attractive way for HER2 level determination. There is a need for accurate and validated assays that can be used to correlate the concentration of the circulating HER2 protein with disease clinical manifestations. Here we describe two monoclonal antibodies binding HER2 with a unique sequence of the complementarity-determining regions that recognize HER2 ECD. Development and validation of the sandwich enzyme-linked immunosorbent assay (ELISA) for quantification of the soluble HER2 in a variety of biological samples is also presented. The assay provides HER2 quantitation within a concentrations range from 1.56 to 100 ng/ml with sensitivity at the level of 0.5 ng/ml that meets the expectations for measurements of HER2 in the blood and tumor tissue samples. The method presents satisfactory intra- and inter-assay precision and accuracy for immunochemical quantification of biomarkers in biological samples. The utility of the generated monoclonal anti-HER2 antibodies has been confirmed for use in the precise measurement of HER2 (both cell-bound and soluble) in several types of biological material, including serum, solid tumor tissue, and cell culture medium. Additionally, the developed immunochemical tools have a potential for HER2 detection, not only in a wide range of sample types but also independently of the sample storage/pre-processing, allowing for comprehensive HER2 analysis in tissue (IHC), cultured cells (immunofluorescence) and blood (ELISA).

Targeted Quantification of Proteoforms in Complex Samples by Proteoform Reaction Monitoring.

Existing mass spectrometric assays used for sensitive and specific measurements of target proteins across multiple samples, such as selected/multiple reaction monitoring (SRM/MRM) or parallel reaction monitoring (PRM), are peptide-based methods for bottom-up proteomics. Here, we describe an approach based on the principle of PRM for the measurement of intact proteoforms by targeted top-down proteomics, termed proteoform reaction monitoring (PfRM). We explore the ability of our method to circumvent traditional limitations of top-down proteomics, such as sensitivity and reproducibility. We also introduce a new software program, Proteoform Finder (part of ProSight Native), specifically designed for the easy analysis of PfRM data. PfRM was initially benchmarked by quantifying three standard proteins. The linearity of the assay was shown over almost 3 orders of magnitude in the femtomole range, with limits of detection and quantification in the low femtomolar range. We later applied our multiplexed PfRM assay to complex samples to quantify biomarker candidates in peripheral blood mononuclear cells (PBMCs) from liver-transplanted patients, suggesting their possible translational applications. These results demonstrate that PfRM has the potential to contribute to the accurate quantification of protein biomarkers for diagnostic purposes and to improve our understanding of disease etiology at the proteoform level.