Conventional Detection Methods Research Articles

Is it there?—Estimating the invasion of armored sailfin catfish (Pterygoplichthys sp.) in the water bodies of Eastern Ghats, India using the eDNA approach

AbstractEarly detection of invasive species is crucial for effectively controlling the potential damage they can inflict on the ecosystems. In contrast to the many limitations that conventional detection methods like visual surveys and netting hold, environmental DNA assay provides a powerful alternative. This non‐invasive, highly sensitive, and user‐friendly technique offers the advantage of detecting invasive species even in areas where direct observation is challenging, thus addressing the shortcomings of traditional techniques and enhancing overall accuracy in estimating distribution. The spread of invasive Pterygoplichthys sp. has become a cause for concern in biodiversity‐rich countries like India. Despite this, comprehensive studies on the prevalence of this invasive species are limited. The Eastern Ghats of India remain under‐explored with a high potential for supporting diverse lifeforms. Studying the extent of biological invasions in the Eastern Ghats is essential for effective management and to mitigate the ecological and socioeconomic impacts of invasive species. In this study, we have designed and optimized an eDNA‐based quantitative PCR assay to map the presence and spread of invasive Pterygoplichthys sp. in selected freshwater ecosystems of the Eastern Ghats. This assay detected invasive Pterygoplichthys sp. in almost 65% of the total locations sampled. This study can be further extended to larger geographical areas, which can contribute to formulating necessary measures by the authorities to manage invasion and conserve the diversity of the freshwater ecosystem.

Heterogeneous Feature Fusion for Improving Performance of Action Detection

We present a novel framework aimed at improving video action detection through the integration of heterogeneous features. Conventional action detection methods which focus on modeling the relationships between person/object instances rely exclusively on video features and do not exploit valuable intra-instance heterogeneous features, such as person pose, positional information or object category, that can support action recognition. Our proposed framework, termed Heterogeneous Feature Fusion (HFF) framework, addresses this limitation by integrating such intra-instance heterogeneous features for person/object instances, and can improve existing action detection methods. To efficiently exploit each heterogeneous feature, which vary in importance depending on actions and/or scenes, we introduce an attention mechanism to dynamically enhance important heterogeneous features within an instance. Experiments on JHMDB and AVA v2.2 datasets show that our HFF significantly enhances the action detection performance of two existing methods.

An adaptive frequency-hopping detection for slowly-varying fading dispersive channels.

Frequency hopping (FH) signals have been widely used to improve performance against frequency selective fading phenomenon of underwater channels. However, the channel is slowly varying in regard to changes in weather conditions, and thus the conventional FH detection transmitting signals with fixed frequency cannot guarantee good detection performance in the dynamic underwater environment. To overcome the performance degradation in slowly-varying fading dispersive channels, this paper proposes an adaptive frequency-hopping (AFH) target detection method. Compared with conventional FH detection methods, the AFH can adaptively select the optimal detection frequency based on premeasured background noise and channel frequency response measured from previous experiments. Numerical simulations and lake trials are conducted to verify the effectiveness of the AFH. The simulation results show that the AFH has better detection performance than the conventional FH. The lake trial results have also verified the validity and feasibility of AFH. Importantly, AFH also achieves a better output signal-to-noise ratio under actual noise interference.

Exploring new frontiers: cell surface vimentin as an emerging marker for circulating tumor cells and a promising therapeutic target in advanced gastric Cancer

BackgroundCirculating tumor cells (CTCs) hold immense promise in guiding treatment strategies for advanced gastric cancer (GC). However, their clinical impact has been limited due to challenges in identifying epithelial-mesenchymal transition (EMT)-CTCs using conventional methods.MethodsTo bridge this knowledge gap, we established a detection platform for CTCs based on the distinctive biomarker cell surface vimentin (CSV). A prospective study involving 127 GC patients was conducted, comparing CTCs enumeration using both EpCAM and CSV. This approach enabled the detection of both regular and EMT-CTCs, providing a comprehensive analysis. Spiking assays and WES were employed to verify the reliability of this marker and technique. To explore the potential inducer of CSV+CTCs formation, a combination of Tandem Mass Tag (TMT) quantitative proteomics, m6A RNA immunoprecipitation–qPCR (MeRIP–qPCR), single-base elongation- and ligation-based qPCR amplification method (SELECT) and RNA sequencing (RNA-seq) were utilized to screen and confirm the potential target gene. Both in vitro and in vivo experiments were performed to explore the molecular mechanism of CSV expression regulation and its role in GC metastasis.ResultsOur findings revealed the potential of CSV in predicting therapeutic responses and long-term prognosis for advanced GC patients. Additionally, compared to the conventional EpCAM-based CTCs detection method, the CSV-specific positive selection CTCs assay was significantly better for evaluating the therapeutic response and prognosis in advanced GC patients and successfully predicted disease progression 14.25 months earlier than radiology evaluation. Apart from its excellent role as a detection marker, CSV emerges as a promising therapeutic target for attenuating GC metastasis. It was found that fat mass and obesity associated protein (FTO) could act as a potential catalyst for CSV+CTCs formation, and its impact on the insulin-like growth factor-I receptor (IGF-IR) mRNA decay through m6A modification. The activation of IGF-I/IGF-IR signaling enhanced the translocation of vimentin from the cytoplasm to the cell surface through phosphorylation of vimentin at serine 39 (S39). In a GC mouse model, the simultaneous inhibition of CSV and blockade of the IGF-IR pathway yielded promising outcomes.ConclusionIn summary, leveraging CSV as a universal CTCs marker represents a significant breakthrough in advancing personalized medicine for patients with advanced GC. This research not only paves the way for tailored therapeutic strategies but also underscores the pivotal role of CSV in enhancing GC management, opening new frontiers for precision medicine.

Magnetic focusing sensor and its characterizations of defect non-destructive testing for ferromagnetic steel plate

The fast development in precision manufacturing and product quality inspection of ferromagnetic material desperately demands that the defect determining the performance, service life and reliability of the mechanical part should be accurately sensed, detected and evaluated. However, conventional defect detection methods and related magnetic induction sensors are usually characterized with low accuracy in small defect detection, which makes the non-destructive testing (NDT) for ferromagnetic material full of challenges. Thus, a new magnetic focusing sensor is proposed and designed for the precision defect detection of ferromagnetic steel plate here. First, the theoretical principle analysis of the magnetic focusing sensor is introduced, and the preliminary finite element modeling (FEM) is conducted to demonstrate the feasibility of the magnetic focusing sensor in tiny defect inspection. Then, both the static and coupling dynamic characterizations of the magnetic focusing sensor are investigated using the FEM method. The optimal structural parameters of the magnetic focusing (MF) sensor in static conditions are summarized. Furthermore, the effects of the dynamic movement of the MF sensor on the multi-physical fields of the magnetization field, electric field, electromagnetic attraction force field and thermal field are all revealed. Finally, experiments regarding the performance evaluation and comparison between the MF sensor and other two magnetic flux leakage (MFL) sensors are conducted, and the prominent capabilities of accurate and precise defect detection for the MF sensor is demonstrated through the defect detection with different sizes. The effectiveness and feasibility of the MF sensor in detecting tiny defects on ferromagnetic object, as well as its great potential for practical application is therefore validated. Additionally, the main conclusions, advantages of the MF sensor are summarized and the future work is discussed.

Design and fabrication of a molecularly imprinted electrochemical sensor with high sensitivity for zearalenone assessment in maize

A highly sensitive and selective molecularly imprinted polymer-based electrochemical sensor was developed for rapid detection of zearalenone (ZEA) in maize samples. The sensor exhibited a wide linear dynamic range from 1 × 10−4 to 1 × 102 ng/mL with an impressive limit of detection of 3.4 × 10−5 ng/mL. It demonstrated excellent selectivity towards ZEA over structurally similar mycotoxins and provided accurate quantification in spiked maize extracts with recoveries ranging from 92 % to 108 % and relative standard deviations under 10 %. Comparative analysis with a conventional HPLC-fluorescence detection method revealed strong correlation (R2 = 0.982) between the two datasets. The proposed imprinted electrochemical sensing platform offers distinct advantages of simplicity, portability, cost-effectiveness, and high analytical performance, making it a promising tool for rapid on-site screening of ZEA contamination in food commodities.

N/S co-doped carbon dots-based solution-gated graphene transistors (SGGT) for ultra-sensitive and selective determination of Hg (II) ions in real water and serum samples

The conventional methods for detecting Hg2+ ions are generally impaired by low sensitivity, a high limit of detection (LOD), and the long detection time required. In this work, we develop a novel solution gated graphene transistor (SGGT) based on carbon dots doped with N and S elements (N/S CDs) as gate electrode probes. The prepared N/S CDs, which are 3–5 nm in diameter, are first modified at the gate of the SGGT through covalent bonding that specifically recognizes Hg2+ ions. The SGGT is then used as a Hg2+ sensor, exhibiting good selectivity, ultrahigh sensitivity, good reproducibility, and a short detection time. The SGGT sensor has an excellent linear detection range, from 0.1 fM to 1 nM. The LOD can reach 28 aM, which is far lower than that of conventional detection methods. Moreover, the SGGT sensor also has a rapid response time of ∼ 40 s. Finally, the ability to analyze real water samples and serum samples is demonstrated.

Detection of Campylobacter jejuni based on CRISPR/Cas12a with novel target yclQ obtained by pan-genome analysis

Campylobacter jejuni (C. jejuni) is one of the most common causes of bacterial gastrointestinal foodborne infections worldwide. Conventional detection methods based on bacterial culture are time-consuming, therefore, rapid detection methods are urgently needed to be developed. The objective of this study was to screen a novel species-specific target and to develop a rapid and accurate method for the detection of C. jejuni in food. Through pan-genome analysis of 298 Campylobacter whole-genome sequence data, a new species-specific target yclQ was screened. The yclQ gene was verified through PCR to be unique to C. jejuni. Subsequently, based on the target yclQ, we developed of a CRISPR/Cas12a-based platform for fluorescence visualization and nucleic acid test strips. The RAA-CRISPR/Cas12a system demonstrates the ability to detect C. jejuni at 1.8 CFU/mL within 50 min. Compared to PCR (1.8 × 101 CFU/mL), the RAA and RAA-CRISPR/Cas12a assay showed higher sensitivity. The detection results of the actual samples were consistent with those of the isolation and culture procedures. In conclusion, the RAA-CRISPR/Cas12a system, based on the target yclQ, demonstrates significant potential for the rapid and accurate detection of C. jejuni in food.

Design of liquid level detection circuit based on sampling probe structure capacitance.

Liquid level detection system is an essential core functional component of automatic clinical medical testing instrument. The conventional liquid level detection method has low detection accuracy and sensitivity, and may have the problem of false detection, which may lead to the inaccurate test results. This paper presents a high sensitivity liquid level detection system based on the principle of variable capacitance. When the sampling probe contacts the liquid level, the probe capacitance will change. The liquid level detection circuit board judges whether the probe contacts the liquid level by sensing the change of probe capacitance. When judging the liquid level signal, the combination of slope detection and amplitude detection is used. The liquid level detection circuit board takes the phase-locked loop(PLL) circuit as the center to detect the change of the capacitance. The reference signal of the PLL is set as a square wave of 375kHz. The double tube probe is used as a part of the tuning capacitor of the voltage controlled oscillator to control the frequency of the output signal, which can realize the rapid phase locking. The experimental results show that the system has accurate detection results, high sensitivity, stable and reliable operation, good dynamic response performance in the case of large and small liquid volume. Compared with other liquid level detection methods based on machine vision, ultrasonic, optics and so on, the proposed liquid level detection system has simpler structure and lower cost, it can avoid the problems of collision, carryover contamination and empty suction by controlling the depth of sampling needle inserted into liquid.

Detection of Serrated Adenoma in NBI Based on Multi-Scale Sub-Pixel Convolution

Colorectal cancer ranks third in global malignancy incidence, and serrated adenoma is a precursor to colon cancer. However, current studies primarily focus on polyp detection, neglecting the crucial discrimination of polyp nature, hindering effective cancer prevention. This study established a static image dataset for serrated adenoma (SA) and developed a deep learning SA detection model. The proposed MSSDet (Multi-Scale Sub-pixel Detection) innovatively modifies each layer of the original feature pyramid’s structure to retain high-resolution polyp features. Additionally, feature fusion and optimization modules were incorporated to enhance multi-scale information utilization, leveraging the narrow-band imaging endoscope’s ability to provide clearer colonoscopy capillary and texture images. This paper utilized 639 cases of colonic NBI endoscopic images to construct the model, achieving a mean average precision (mAP) of 86.3% for SA in the test set. The SA detection rate via this approach has significantly surpassed conventional object detection methods.

On-site detection of methicillin-resistant Staphylococcus aureus (MRSA) utilizing G-quadruplex based isothermal exponential amplification reaction (GQ-EXPAR)

Methicillin-resistant Staphylococcus aureus (MRSA) has a high incidence in infectious hospitals and communities, highlighting the need for early on-site detection due to its resistance to methicillin antibiotics. The present study introduces a highly sensitive detection system for mecA, a crucial methicillin marker, utilizing an RCA-based isothermal exponential amplification reaction. The G-quadruplex-based isothermal exponential amplification reaction (GQ-EXPAR) method designs probes to establish G-quadruplex secondary structures incorporating thioflavin T for fluorescence. The system, unlike conventional genetic detection methods, works with portable isothermal PCR devices (isoQuark), facilitating on-site detection. A detection limit of 0.1 fmol was demonstrated using synthetic DNA, and effective detection was proven using thermal lysis. The study also validated the detection of targets swabbed from surfaces within bacterial 3D nanostructures using the GQ-EXPAR method. After applying complementary sequences to the padlock probe for the target, the GQ-EXPAR method can be used on various targets. The developed method could facilitate rapid and accurate diagnostics within MRSA strains.

CeO2 nanocages with tetra-enzyme mimetic activities for dual-channel ratiometric colorimetric detection of microcystins-LR

BackgroundMicrocystin-leucine-arginine (MC-LR) produced by various cyanobacteria during harmful algal bloom poses serious threats to drinking water safety and human health. Conventional chromatography-based detection methods require expensive instruments and complicated sample pretreatment, limiting their application for on-site detection. Colorimetric aptasensors are simple and rapid, and are amenable to fast detection. However, they provide only one output signal, resulting in poor sensitivity and accuracy. Dual-channel ratiometric colorimetric method based on the peroxidase-like activity of nanozyme can achieve self-calibration by recording two reverse signals, providing significantly enhanced sensitivity and accuracy. ResultsCeO2 nanocages (CeO2 NCs) with tetra-enzyme mimetic activities (oxidase-, peroxidase-, catalase- and superoxide dismutase-like activities) were facilely synthesized using zeolitic imidazolate framework-67 (ZIF-67) as sacrificial template. The peroxidase-like activity of CeO2 NCs can be regulated by DNA, and it showed opposite response to two chromogenic substrates (2,2′-azino-bis-(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) and 3,3′,5,5′-tetramethylbenzidine (TMB)), which was mainly attributed to the changed affinity. On the basis of MC-LR aptamer-tunable peroxidase-like activity of CeO2 NCs in TMB and ABTS channel, a dual-channel ratiometric colorimetric aptasensor was constructed for detection of MC-LR. Compared with conventional single-signal colorimetric assays, the proposed method showed lower limit of detection (0.66 pg mL−1) and significantly enhanced sensitivity. Moreover, the practicability of the ratiometric colorimetric assay was demonstrated by detecting MC-LR in real water samples, and satisfactory recoveries (94.9–101.9 %) and low relative standard deviations (1.6–6.3 %) were obtained. SignificanceThis work presents a nanozyme-based ratiometric colorimetric aptasensor for MC-LR detection by recording the reverse responses of two chromogenic reactions. Benefiting from the self-calibration function, the method can achieve higher sensitivity and accuracy. The short detection time and practical application in real water samples show great potential for environmental monitoring.

Progress in the Genetic Detection of Thalassemia Based on Third-Generation Gene Sequencing --Review

Thalassemia is most widely distributed single gene autosome recessive genetic disease in the world, whose clinical manifestation was changed from asymptomatic anemia to severe anemia requiring continous blood transfusion to maintain life, thus resulting in a serious economic burden to society and families. Therefore, it is necessary to carry out the corresponding prentatal screening and diagnosis. Most of the conventional detection methods can only detect the common thalassemia genotype, it can easy to cause misdiagnosis or missed diagnosis for those rate genetic variantions. The third-generation sequecing (TGS) has been applied to the detection of thalassemia genes, which is more accurate, reliable and superior to the converntional detection methods. This article reviews the latest research progress of the TGS technology in genetic testing of thalassemia.

The application of targeted RNA sequencing for the analysis of fusion genes, gene mutations, IKZF1 intragenic deletion, and CRLF2 overexpression in acute lymphoblastic leukemia.

Acute lymphoblastic leukemia (ALL) is characterized by highly genetic heterogeneity, owing to recurrent fusion genes, gene mutations, intragenic deletion, and gene overexpression, which poses significant challenges in clinical detection. RNA sequencing (RNA-seq) is a powerful tool for detecting multiple genetic abnormalities, especially cryptic gene rearrangements, in a single test. Sixty samples (B-ALL, n = 49; T-ALL, n = 9; mixed phenotype acute leukemia (MPAL), n = 2) and 20 controls were analyzed by targeted RNA-seq panel of 507 genes developed by our lab. Of these, 16 patients were simultaneously analyzed for gene mutations at the DNA level using a next-generation sequencing panel of 51 genes. Fusion genes, CRLF2 expression, and IKZF1 intragenic deletion were also detected by reverse transcription-polymerase chain reaction (RT-PCR). Karyotype analysis was performed using the R-banding and G-banding technique on bone marrow cells after 24 hours of culture. Partial fusion genes were analyzed using fluorescence in situ hybridization (FISH). Compared with the results of Karyotype analysis, FISH, and RT-PCR, the detection rate of fusion genes by targeted RNA-seq increased from 48.3% to 58.3%, and six unexpected fusion genes were discovered, along with one rare isoform of IKZF1 intragenic deletion (IK10). The DNA sequencing analysis of 16 ALL patients revealed that 96.2% (25/26) of gene mutations identified at the DNA level were also detectable at the RNA level, except for one mutation with a low variant allele fraction. The detection of CRLF2 overexpression exhibited complete concordance between RT-PCR and RNA-seq. The utilization of RNA-seq enables the identification of clinically significant genetic abnormalities that may go undetected through conventional detection methods. Its robust analytical performance might bring great application value for clinical diagnosis, prognosis, and therapy in ALL.

Development of an Immunocapture-Based Polymeric Optical Fiber Sensor for Bacterial Detection in Water.

Conventional methods for pathogen detection in water rely on time-consuming enrichment steps followed by biochemical identification strategies, which require assay times ranging from 24 hours to a week. However, in recent years, significant efforts have been made to develop biosensing technologies enabling rapid and close-to-real-time detection of waterborne pathogens. In previous studies, we developed a plastic optical fiber (POF) immunosensor using an optoelectronic configuration consisting of a U-Shape probe connected to an LED and a photodetector. Bacterial detection was evaluated with the immunosensor immersed in a bacterial suspension in water with a known concentration. Here, we report on the sensitivity of a new optoelectronic configuration consisting of two POF U-shaped probes, one as the reference and the other as the immunosensor, for the detection of Escherichia coli. In addition, another methos of detection was tested where the sensors were calibrated in the air, before being immersed in a bacterial suspension and then read in the air. This modification improved sensor sensitivity and resulted in a faster detection time. After the immunocapture, the sensors were DAPI-stained and submitted to confocal microscopy. The histograms obtained confirmed that the responses of the immunosensors were due to the bacteria. This new sensor detected the presence of E. coli at 104 CFU/mL in less than 20 min. Currently, sub-20 min is faster than previous studies using fiber-optic based biosensors. We report on an inexpensive and faster detection technology when compared with conventional methods.

An rolling circle amplification-assisted CRISPR/Cas12a-based biosensor for protein detection

Glycated hemoglobin (HbA1c) has been extensively studied as a pivotal marker for monitoring and diagnosing diabetes. In this study, we introduce a highly sensitive and rapid enzyme-catalyzed rolling circle amplification (RCA)-assisted nicked-PAM CRISPR/Cas12a assay (termed “RANPC”). The RANPC system initially recognizes and captures the target using immunomagnetic beads and antibodies, forming a sandwich structure. The antibodies, upon ligating the primers, trigger the RCA reaction, resulting in the generation of single-stranded DNA with numerous repetitive sequences. The product strand of RCA then initiates the trans-cutting activity of nicked-PAM CRISPR/Cas12a, ultimately reporting the target concentration through fluorescent signaling. This study combines dual antibody capture, signal amplification through RCA, and signal output by Cas12a, resulting in high sensitivity, high specificity, and high signal-to-noise ratio. Moreover, the strategic incorporation of an incomplete PAM design in the CRISPR/Cas12a system substantially reduces background interference and enhances assay specificity relative to previous conventional Cas12a detection methods. RANPC possesses advantages such as ultra-sensitivity (with the lowest detection limit up to 0.129 pg/mL and a linear range of 12.8 pg/mL to 1 µg/mL), high specificity, rapid reaction time (approximately 50 min), and straightforward operation. Under optimized experimental conditions, we successfully detected HbA1c in eight real-world blood samples, and the results were in good agreement with the theoretical values. This suggests that this method holds promise as a new approach for the analysis of HbA1c levels in the clinical setting.

Simple closed-system combining direct PCR and fluorescence for all types of milk and dairy product authentication

Cow's milk is a popular food choice due to its nutritional value. However, some people have cow milk protein allergy. Alternative milk and dairy products are available, but they may be adulterated or fraudulently substituted with cow's milk to lower the production cost. Current conventional methods for cow DNA detection suffer from laboratory-based instruments and settings, which can be expensive, limit on-site usage, lack sensitivity, or cannot be used with highly degraded samples. Here, a simple closed-system based on direct PCR, which eliminates the need to extract DNA from samples, and SYBR Green I detection of PCR amplicons using the naked eye was developed and validated. Fluorescence detection was enhanced with the use of a self-designed portable LED lightbox, which mitigated color interference, enabling detection in products with diverse colorations without any sample preparation. Validation studies revealed 100% specificity when tested with 13 other species, over 99% accuracy with 208 cow’s milk and dairy products in both liquid and solid forms, 1×10−5 ng limit of detection, and 100% accuracy with blind testing. Market testing of 156 samples showed a fraud ratio of 0.0% in cow’s milk and dairy products, while non-cow’s milk and dairy products showed a fraud ratio of 10.7%, emphasizing the assay's capacity to identify fraudulent substitutions. Overall, this is the first time a cow-specific simple closed-system direct PCR with fluorescence has been used to authenticate fresh milk and dairy products, with one of the quickest turn-around time from sampling to detection of 50 min. It could be useful in a semi-laboratory setting or in the field with a portable PCR machine.

Label‐free surface‐enhanced Raman spectroscopy coupled with machine learning algorithms in pathogenic microbial identification: Current trends, challenges, and perspectives

AbstractInfectious diseases caused by microbial pathogens remain a primary contributor to global health burdens. Prompt control and effective prevention of these pathogens are critical for public health and medical diagnostics. Conventional microbial detection methods suffer from high complexity, low sensitivity, and poor selectivity. Therefore, developing rapid and reliable methods for microbial pathogen detection has become imperative. Surface‐enhanced Raman Spectroscopy (SERS), as an innovative non‐invasive diagnostic technique, holds significant promise in pathogenic microorganism detection due to its rapid, reliable, and cost‐effective advantages. This review comprehensively outlines the fundamental theories of Raman Spectroscopy (RS) with a focus on label‐free SERS strategy, reporting on the latest advancements of SERS technique in detecting bacteria, viruses, and fungi in clinical settings. Furthermore, we emphasize the application of machine learning algorithms in SERS spectral analysis. Finally, challenges faced by SERS application are probed, and the prospective development is discussed.

Comparison of Optical Genome Mapping With Conventional Diagnostic Methods for Structural Variant Detection in Hematologic Malignancies.

Structural variants (SVs) are currently analyzed using a combination of conventional methods; however, this approach has limitations. Optical genome mapping (OGM), an emerging technology for detecting SVs using a single-molecule strategy, has the potential to replace conventional methods. We compared OGM with conventional diagnostic methods for detecting SVs in various hematologic malignancies. Residual bone marrow aspirates from 27 patients with hematologic malignancies in whom SVs were observed using conventional methods (chromosomal banding analysis, FISH, an RNA fusion panel, and reverse transcription PCR) were analyzed using OGM. The concordance between the OGM and conventional method results was evaluated. OGM showed concordance in 63% (17/27) and partial concordance in 37% (10/27) of samples. OGM detected 76% (52/68) of the total SVs correctly (concordance rate for each type of SVs: aneuploidies, 83% [15/18]; balanced translocation, 80% [12/15] unbalanced translocation, 54% [7/13] deletions, 81% [13/16]; duplications, 100% [2/2] inversion 100% [1/1]; insertion, 100% [1/1]; marker chromosome, 0% [0/1]; isochromosome, 100% [1/1]). Sixteen discordant results were attributed to the involvement of centromeric/telomeric regions, detection sensitivity, and a low mapping rate and coverage. OGM identified additional SVs, including submicroscopic SVs and novel fusions, in five cases. OGM shows a high level of concordance with conventional diagnostic methods for the detection of SVs and can identify novel variants, suggesting its potential utility in enabling more comprehensive SV analysis in routine diagnostics of hematologic malignancies, although further studies and improvements are required.

Research on Blockchain-Enabled Smart Grid for Anti-Theft Electricity Securing Peer-to-Peer Transactions in Modern Grids.

Electricity theft presents a significant financial burden to utility companies globally, amounting to trillions of dollars annually. This pressing issue underscores the need for transformative measures within the electrical grid. Accordingly, our study explores the integration of block chain technology into smart grids to combat electricity theft, improve grid efficiency, and facilitate renewable energy integration. Block chain's core principles of decentralization, transparency, and immutability align seamlessly with the objectives of modernizing power systems and securing transactions within the electricity grid. However, as smart grids advance, they also become more vulnerable to attacks, particularly from smart meters, compared to traditional mechanical meters. Our research aims to introduce an advanced approach to identifying energy theft while prioritizing user privacy, a critical aspect often neglected in existing methodologies that mandate the disclosure of sensitive user data. To achieve this goal, we introduce three distributed algorithms: lower-upper decomposition (LUD), lower-upper decomposition with partial pivoting (LUDP), and optimized LUD composition (OLUD), tailored specifically for peer-to-peer (P2P) computing in smart grids. These algorithms are meticulously crafted to solve linear systems of equations and calculate users' "honesty coefficients," providing a robust mechanism for detecting fraudulent activities. Through extensive simulations, we showcase the efficiency and accuracy of our algorithms in identifying deceitful users while safeguarding data confidentiality. This innovative approach not only bolsters the security of smart grids against energy theft, but also addresses privacy and security concerns inherent in conventional energy-theft detection methods.