Real-time Quantitative Monitoring Research Articles

Ion coordination and chelation in Eu-MOFs matrices: Ultrafast fluorescence visual quantification monitoring of antibiotic residues

Rapid monitoring of trace antibiotics in the field in real time is essential for environment forewarning and human health. High sensitivity and real-time on-site quantitative monitoring of antibiotic residues can be accomplished by integrating portable sensors alongside fluorescent optics to construct an intelligent sensing platform that smoothly eliminates the instability of conventional detection methods. In this study, a ratiometric fluorescence sensor for the ultrasensitive detection of pefloxacin was built employing the photoinduced electron transfer (PET) mechanism from red Eu-MOFs to Mn2+-PEF complex. A visual color change results from the photoinduced electron transfer process from manganese ions to pefloxacin weakening the ligand metal charge transfer (LMCT) process in Eu-MOFs. This enables the ultrafast visible detection of pefloxacin and produces a transient shift in visual color with a detection limit as low as 15.4 nM. For the detection of pefloxacin in water, tomato, and raw pork samples, various sensing devices based on the developed fluorescent probes exhibit good practicability and accuracy. With the development of the ratiometric fluorescence sensing probe, it is now possible to quickly and quantitatively identify pefloxacin residues in the environment, offering a new method for ensuring the safety of food and people's health.

Quantitative Detection of Vertical Track Irregularities under Non-Stationary Conditions with Variable Vehicle Speed.

Track irregularities directly affect the quality and safety of railway vehicle operations. Quantitative detection and real-time monitoring of track irregularities are of great importance. However, due to the frequent variable vehicle speed, vehicle operation is a typical non-stationary process. The traditional signal analysis methods are unsuitable for non-stationary processes, making the quantitative detection of the wavelength and amplitude of track irregularities difficult. To solve the above problems, this paper proposes a quantitative detection method of track irregularities under non-stationary conditions with variable vehicle speed by order tracking analysis for the first time. Firstly, a simplified wheel-rail dynamic model is established to derive the quantitative relationship between the axle-box vertical vibration and the track vertical irregularities. Secondly, the Simpson double integration method is proposed to calculate the axle-box vertical displacement based on the axle-box vertical acceleration, and the process error is optimized. Thirdly, based on the order tracking analysis theory, the angular domain resampling is performed on the axle-box vertical displacement time-domain signal in combination with the wheel rotation speed signals, and the quantitative detection of the track irregularities is achieved. Finally, the proposed method is validated based on simulation and field test analysis cases. We provide theoretical support and method reference for the quantitative detection method of track irregularities.

Live Microscopy of Multicellular Spheroids with the Multimodal Near-Infrared Nanoparticles Reveals Differences in Oxygenation Gradients.

Assessment of hypoxia, nutrients, metabolite gradients, and other hallmarks of the tumor microenvironment within 3D multicellular spheroid and organoid models represents a challenging analytical task. Here, we report red/near-infrared (NIR) emitting cell staining with O2-sensitive nanoparticles, which enable measurements of spheroid oxygenation on a conventional fluorescence microscope. Nanosensor probes, termed "MMIR" (multimodal infrared), incorporate an NIR O2-sensitive metalloporphyrin (PtTPTBPF) and deep red aza-BODIPY reference dyes within a biocompatible polymer shell, allowing for oxygen gradient quantification via fluorescence ratio and phosphorescence lifetime readouts. We optimized staining techniques and evaluated the nanosensor probe characteristics and cytotoxicity. Subsequently, we applied nanosensors to the live spheroid models based on HCT116, DPSCs, and SKOV3 cells, at rest, and treated with drugs affecting cell respiration. We found that the growth medium viscosity, spheroid size, and formation method influenced spheroid oxygenation. Some spheroids produced from HCT116 and dental pulp stem cells exhibited "inverted" oxygenation gradients, with higher core oxygen levels than the periphery. This contrasted with the frequently encountered "normal" gradient of hypoxia toward the core caused by diffusion. Further microscopy analysis of spheroids with an "inverted" gradient demonstrated metabolic stratification of cells within spheroids: thus, autofluorescence FLIM of NAD(P)H indicated the formation of a glycolytic core and localization of OxPhos-active cells at the periphery. Collectively, we demonstrate a strong potential of NIR-emitting ratiometric nanosensors for advanced microscopy studies targeting live and quantitative real-time monitoring of cell metabolism and hypoxia in complex 3D tissue models.

Progress in Procalcitonin Detection Based on Immunoassay.

Procalcitonin (PCT) serves as a crucial biomarker utilized in diverse clinical contexts, including sepsis diagnosis and emergency departments. Its applications extend to identifying pathogens, assessing infection severity, guiding drug administration, and implementing theranostic strategies. However, current clinical deployed methods cannot meet the needs for accurate or real-time quantitative monitoring of PCT. This review aims to introduce these emerging PCT immunoassay technologies, focusing on analyzing their advantages in improving detection performances, such as easy operation and high precision. The fundamental principles and characteristics of state-of-the-art methods are first introduced, including chemiluminescence, immunofluorescence, latex-enhanced turbidity, enzyme-linked immunosorbent, colloidal gold immunochromatography, and radioimmunoassay. Then, improved methods using new materials and new technologies are briefly described, for instance, the combination with responsive nanomaterials, Raman spectroscopy, and digital microfluidics. Finally, the detection performance parameters of these methods and the clinical importance of PCT detection are also discussed.

Progress of near-infrared spectroscopy in cerebral blood oxygenation detection: A mini review

In contrast to conventional oximeters, near-infrared spectroscopy-based brain tissue oximetry monitoring devices are capable of non-invasive, continuous, and real-time quantitative monitoring of cerebral oximetry parameters. Initially, these devices were utilized for intensive care or surgical monitoring of oxygen saturation. Due to the rapid advancement of optoelectronic sensing and measurement technologies over the past decade, the derived functional near-infrared brain imaging devices have been widely used in a variety of fields. This paper first introduces the basic principles of near-infrared spectroscopy-based cerebral oxygenation parameter detection, then focuses on the most recent developments in this field of study. Finally, a prospect on their future application in practical settings is also provided.

In vivo visualization of tumor-associated macrophages re-education by photoacoustic/fluorescence dual-modal imaging with a metal-organic frames-based caspase-1 nanoreporter

Tumor-associated macrophages (TAMs) are vital in the tumor microenvironment, contributing to immunosuppression and therapy tolerance. Despite their importance, the precise re-education of TAMs in vivo continues to present a formidable challenge. Moreover, the lack of real-time and efficient methods to comprehend the spatiotemporal kinetics of TAMs repolarization remains a significant hurdle, severely hampering the accurate assessment of treatment efficacy and prognosis. Herein, we designed a metal–organic frameworks (MOFs) based Caspase-1 nanoreporter (MCNR) that can deliver a TLR7/8 agonist to the TAMs and track time-sensitive Caspase-1 activity as a direct method to monitor the initiation of immune reprogramming. This nanosystem exhibits excellent TAMs targeting ability, enhanced tumor accumulation, and stimuli-responsive behavior. By inducing the reprogramming of TAMs, they were able to enhance T-cell infiltration in tumor tissue, resulting in inhibited tumor growth and improved survival in mice model. Moreover, MCNR also serves as an activatable photoacoustic and fluorescent dual-mode imaging agent through Caspase-1-mediated specific enzyme digestion. This feature enables non-invasive and real-time antitumor immune activation monitoring. Overall, our findings indicate that MCNR has the potential to be a valuable tool for tumor immune microenvironment remodeling and noninvasive quantitative detection and real-time monitoring of TAMs repolarization to immunotherapy in the early stage.

A paper-based ratiometric fluorescent sensor for NH3 detection in gaseous phase: Real-time monitoring of chilled chicken freshness

A ratiometric fluorescence sensor platform with easy-to-use and accurate is nanoengineered for NH3 quantitative detection and visual real-time monitoring of chicken freshness using smartphones. The ratiometric fluorescent probe formed by combining the zinc ion complex and carbon dots has a double-emitted fluorescence peak. The fluorescence intensity of the complex changed can be clearly observed with the increase of the concentration of ammonia solution under 365 nm wavelength excitation. In order to detect NH3 concentration in gaseous phase, a portable paper-based sensor was designed. The sensor had a good linear relationship with NH3 concentration ranging from 10.0 to 90.0 μmol/L and the LOD value was 288 nM. This fluorescent paper-based sensor was used to check the freshness of chicken breast refrigerated at 4 °C, revealed observable shifts from blue to green. The fluorescent paper-based sensor can detect NH3 concentration in real time and simplify the monitoring process of meat freshness while ensuring accuracy and stability.

Letermovir Prophylaxis Reduced Cytomegalovirus Reactivation and Resistance Post Umbilical Cord Blood Transplantation

Objective: Umbilical cord blood transplantation (UCBT) has proved to be an effective treatment with its own unique advantages nowadays, while long-lasting immunosuppression and delayed immune recovery make the UCBT recipients susceptible to a range of infections. Human cytomegalovirus (HCMV) is the main cause of viral complications post transplantation, which is associated with poor prognosis. Although the implementation of pre-emptive therapy (PET) has significantly reduced the incidence of CMV disease, the long-time antigenemia and the toxicity of antiviral drug remains challenging and risky. Letermovir (LET), a kind of CMV DNA terminase complex inhibitor, is a relatively new antiviral drug for prophylaxis against CMV and has received positive opinion in adult CMV-seropositive recipients of allogeneic haematopoietic stem cell transplant (allo-HSCT). Nevertheless, the clinical efficacy of LET prophylaxis on CMV reactivation and resistance post UCBT remained unknown. Methods: We retrospectively analyzed 43 UCBT patients with hematological diseases at the First Affiliated Hospital of University of Science and Technology of China between August 2022 and May 2023, who received LET prophylaxis since the first day post transplantation until 100d or CMV reactivation occurred, whichever came first. A historical cohort of 208 UCBT patients also from our transplant center between 2021 and 2022 without LET prophylaxis was compared as control group. The CMV DNA testing was performed using the quantitative real-time PCR (RT-PCR) method and CMV monitoring was examined twice per week for at least 100 days and once a week for at least half a year after UCBT. The 180-day cumulative incidences of CMV reactivation (defined as DNA achieved 500 copies/ml or 1000 copies/ml) and refractory CMV infection (defined as CMV viral load remains the same level or increases after at least 2 weeks of appropriately dosed antiviral therapy) were compared between the two groups. To explore independent predictors of the development of CMV reactivation and resistance, univariate and multivariate analyses were performed by using Fine-Gray proportional hazard regression. Results: The two groups were similar in demographic and transplant characteristics, and in the LET group, patients received LET for a median of 100 days (range, 16-100). The 180-day cumulative incidence of CMV DNA achieved 500 copies/ml and 1000 copies/ml and refractory CMV infection in the LET group were significantly lower than that in the control group [500 copies/ml: 33.8% vs. 78.4%, P < 0.001; 1000 copies/ml: 30.2% vs. 74.0%, P < 0.001; refractory CMV: 4.7% vs. 31.7%, P < 0.001]. Furthermore, multivariate competing risk analysis identified LET prophylaxis as an independent protective factor for CMV reactivation (adjusted hazard ratio [HR] 0.27, 95% CI 0.15 to 0.48, P < 0.001) and refractory CMV infection ([HR] 0.16, 95% CI 0.04 to 0.62, P = 0.004). However no significantly lower incidences of CMV disease (4.7% vs. 8.8%, P = 0.542) and recurrent CMV infection (3.7% vs. 9.6%, P = 0.386) were found in LET group. Among the patients who developed CMV reactivation, there was no significant difference in the time from UCBT to CMV reactivation between two groups (39 days vs. 41 days, P = 0.367). LET prophylaxis was associated with a shorter duration of CMV DNAemia (median 27 days vs. 42 days, P = 0.014), and there was a trend towards lower peak CMV viral load in the LET group (median 2620 copies/ml vs. 5380 copies/ml, P = 0.165). Conclusion: We demonstrated that LET is highly effective at preventing CMV reactivation and resistance after UCBT, and its benefit for UCBT patients perhaps will be observed in a longer-time following time.

Real-time visualization the pH fluctuations of living cells with a ratiometric near-infrared fluorescent probe

In situ real-time quantitative monitoring pH fluctuation in complex living systems is vitally significant. In the current work, a ratiometric near-infrared (NIR) probe (MCyOH) was developed to confront this challenge. MCyOH exhibited good sensitivity, photostability, reversibility, and an ideal pKa (pKa = 6.65). Ratiometric character of MCyOH is beneficial to accuracy detect the pH fluctuations in living cells under different stimulation. The observations showed that intracellular pH was decreased when HepG2 cells under oxidative stress or starvation conditions. In particular, HepG2 cells was acidulated after addition of ethanol, however, the acidification phenomenon was attenuated or disappeared when HepG2 cells preincubated with disulfiram or fomepizole. Finally, MCyOH was successfully applied to observe the increasement of intracellular pH when HepG2 cells treated with fomepizole individually. Overall, MCyOH would be a practical candidate to explore pH-associated physiological and pathological varieties.

Ratiometric upconversion luminescence sensing platform for visual monitoring of antiarrhythmic drug level in serum

Arrhythmia seriously threatens life and health, it requires long-term use of antiarrhythmic drugs such as mexiletine and needs to monitor the dosage of drugs in the body to ensure efficacy and avoid serious side effects. Here, we designed a sensing platform for real-time visual monitoring of drug efficacy using a ratiometric upconversion probe, which overcomes the fluorescence background interference in the serum, and improves the resolution and accuracy of visual detection. By combining lanthanide-doped upconversion nanoparticles (UCNPs) with readily available organic dyes, an efficient upconversion nanoprobe was constructed under the reaction mechanism of internal filtration effect (IFE) to realize the quantitative detection of mexiletine in serum. Due to the addition of mexiletine, the characteristic emission peak of the probe at 540 nm changes, while the fluorescence intensity at 655 nm is unchanged. Therefore, the visual detection of mexiletine in serum is realized according to the fluorescence intensity changes of I540/I655. In addition, the designed sensing platform is combined with a smartphone to realize real-time visualization and quantitative monitoring of mexiletine, and the detection limits in fluorescence and colorimetric modes are as low as 55.6 nM and 469.75 nM respectively, consequently resulting in sensitive monitoring the effective dosage of drugs in the serum. The proposed detection platform provides a new sensing strategy for the visual determination of antiarrhythmic drugs and an instant device to warn the treatment process of the disease immediately.

A real-time quantitative acceleration monitoring method based on triboelectric nanogenerator for bridge cable vibration

Real-time monitoring of vibration acceleration of civil infrastructure is imperative for effective management and safe operation of structures. Although the triboelectric nanogenerator (TENG) shows potential for self-powered sensing, it faces challenges in correlating limited experimental electrical amplitudes to structural responses, thereby hindering comprehensive performance analysis of civil infrastructure. Herein, a self-driven acceleration TENG sensor (A-TENG) is designed and manufactured, comprising an outer shell and an inner mass-spring-damper system. By establishing a sensing model based on vibration theory and the TENG mechanism, the electrical signals generated by the sensors can be correlated with structural acceleration, enabling self-driven real-time quantitative characterization. Indoor and on-site experiments demonstrate that the A-TENG sensor is capable of continuously monitoring the acceleration profile with excellent consistency compared to commercial sensors. The non-contact free-standing sliding mode design and ultra-lightweight construction of the A-TENG sensor (∼8 g) enhance its start-up sensitivity (∼0.1 m/s2), long-term stability (∼30,000 loading cycles) while minimizing mass interference. The proposed sensing theory renders a novel approach to offering complete time-domain information, which is vital for the precise analysis of structural behavior. This work facilitates understanding of self-driven sensors utilizing TENG technology and provides a useful tool for long-term real-time quantitative structural health monitoring.

Active Doppler monitoring of de novo cavitation induced by a dual-mode pulsed high-intensity focused ultrasound array

Pulsed high-intensity focused ultrasound (pHIFU) is capable of inducing inertial cavitation in tissues without the need for microbubbles or other ultrasound contrast agents, thereby permeabilizing the tissue to enhance passive diffusion of systemically administered therapeutic agents. Here, we report on the use of ultrafast, synchronous power Doppler imaging to spatially map cavitation activity and resulting tissue disruption in ex vivo and invivo porcine tissues using a dual-mode imaging-therapy linear pHIFU array. The array (64 elements, aperture 51.2 mm × 14 mm, frequency 1.0MHz, 40% bandwidth) was driven by a power-enhanced Verasonics V1 system to deliver a series of 1 ms pulses at a 1% duty cycle while electronically scanning the pHIFU focus over an azimuthal range of ±1 cm. Plane wave Doppler ensembles were captured immediately after each pHIFU pulse to map the distribution and characteristics of the remnants of cavitation bubbles. The degree of resulting tissue disruption was assessed by histology and was correlated with cumulative Doppler power contours computed over the exposure region. The results show that ultrafast synchronous power Doppler is a promising approach for real-time quantitative monitoring of cavitational therapies. [Work supported by NIH R01EB023910 and T32DK007742.]

Chemometrics-assisted excitation-emission matrix fluorescence spectroscopy for real-time migration monitoring of multiple polycyclic aromatic hydrocarbons from plastic products to food simulants

Polycyclic aromatic hydrocarbons (PAHs), as a class of organic pollutants that have attracted much attention, are likely to be formed with the production and processing of plastic products, and they may migrate from contaminated plastic products to food, causing the risk of poisoning or cancer. In this study, migration tests were carried out on disposable plastic products for food contact, and a novel strategy that combines excitation-emission matrix (EEM) fluorescence spectroscopy with the advanced second-order calibration method based on the three-direction resection alternating trilinear decomposition (TDR-ATLD) algorithm was used to monitor the migration of three PAHs anthracene (ANT), pyrene (PYR), and phenanthrene (PHE) from the plastic products to food simulants in real-time. With the “second-order advantage”, even if the fluorescence spectra of the target analytes overlapped seriously, and other unknown substances migrated from the plastic products to food simulants, accurate qualitative and quantitative results were still obtained by the proposed method. In the static system, the coefficient of determination (R2) of the three PAHs within the calibration range were all greater than 0.99, and the average spiked recoveries were 99.5-107.1%, with the standard deviation lower than 8.9%. The figures of merit (FOMs) and intra- or inter-day precision also showed good feasibility and reliability of the method. In the simulation study of the migration kinetic process, three PAHs can be quantified in real-time in complex matrix, then the related migration equations were established. The results indicate that the proposed method can be used for real-time migration quantitative monitoring of PAHs, providing a potential and available method for the study of the migration kinetics of hazardous substances from food contact materials to food or food simulants.

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion.

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images.

Nonlinear ultrasonic concrete crack identification with deep learning based on time-frequency image

ABSTRACT By combining time-frequency images and deep learning models, the nonlinear ultrasound signals can be classified, detected, and predicted, using the nonlinear coefficient as a fundamental label for training deep learning models. This integrated approach enables quantitative identification and real-time monitoring of concrete damage, promoting the widespread adoption of nonlinear ultrasonic techniques in engineering applications. As a basis, the relationship between damage variations and nonlinear coefficients is discussed by performing nonlinear ultrasonic damage testing on concrete specimens with different crack lengths and angles. The testing signals are converted into time-frequency images using the short-time Fourier transform and the continuous wavelet transform, and both types of images are combined for data augmentation and input into the deep learning model for training, with nonlinear coefficients serving as labels for the time-frequency images. The MobileNetV2, VGG16, and ResNet18 deep learning models are trained separately on time-frequency image datasets for the length specimens, the angle specimens, and the length-angle specimens, and the performance of the different models is evaluated and compared. The results show that all three models have accuracy rates above 94%, indicating good identification performance. Finally, with the example, the nonlinear coefficients of the testing signals are compared with the labels of the nonlinear coefficients in the time-frequency images identified by the deep learning model, which confirms the high accuracy of damage identification by the deep learning model.

Paper-based fluorescent materials containing on-demand nanostructured brain-cells-inspired AIE self-assembles for real-time visual monitoring of seafood spoilage

Biogenic amines containing NH3 are important indicators for conducting full-scale appraisal of food spoilage and disease diagnosis. However, the currently-used detection methods of NH3 have several limitations such as time-consuming high cost, and inability to provide visual real-time monitoring. Therefore, researchers have attempted to explore strategies for quantitative real-time monitoring of NH3 for food spoilage has attracted widespread attentions. Herein, we developed sustainable, fast response, hypersensitized, user-friendly and molecular-level light-emitting biomass-based materials (AFP-FP) containing on-demand nanostructured brain-cells-inspired aggregation-induced-emission (AIE) self-assembles for real-time visual monitoring of seafood spoilage. The 2-hydroxy-5-methyl-isophthalaldehyde-based AIE probe (AFP) was synthesized using a simple “one-step” route. AFP-FP exhibited high selectivity, sensitivity, repeatable and quantitative recognition (y = 7.292×103x + 7.621×104, R = 0.990) of NH3 with a low detection limit (246 ppb) and fast response (<1 s). Furthermore, we integrated AFP-FP into a user-friendly smartphone color recognition app, enabling its practical application in visual, real-time daylight monitoring of food spoilage.

Femtosecond laser drilling of film cooling holes: Quantitative analysis and real-time monitoring

The damage to opposite wall caused by excessive ablation will severely affect the performance of the turbine blade in femtosecond laser drilling of film cooling holes. Real-time monitoring and control of drilling stage is regarded as one of the most effective methods for solving the excessive ablation problem. However, due to the complexity of femtosecond laser drilling process, it is difficult to model the relationship between drilling state and machining phenomenon physically. In this paper, the quantitative analysis of femtosecond laser drilling and an intelligent methodology for real-time monitoring and control of drilling stage were developed. The laser drilling was comprehensively analyzed and four drilling stage was firstly divided automatically based on the hole evolution process and the optical emission signal detected in drilling process. And the correlation between signal and drilling stages was established through the analysis of the optical emission signal. Furthermore, this paper proposed a data-driven method to build the classification model of different drilling stages. The periodicity and pulse characteristics of signal and corresponding time-domain features were extracted for further modelling. Finally, the classification model was established via AdaBoost method, which achieves a classification accuracy of 95.22 %. And the generalization performance for real-monitoring of drilling stage was further verified. This study presented a set of processes for real-time monitoring and process control in laser drilling process, which provides a new insight to solve the excessive ablation problem.

Detection of resistance mutations in patients with anaplastic lymphoma kinase-rearranged lung cancer through liquid biopsy.

Tyrosine kinase inhibitors (TKIs) significantly improve clinical outcomes in patients with non-small cell lung cancer due to anaplastic lymphoma kinase (ALK) gene rearrangement. However, the rate of relapse with TKIs is high owing to the development of resistance mutations during treatment. Repeated biopsies during disease progression are crucial for elucidating the molecular mechanisms underlying the development of resistance to ALK inhibitors. Analysis of cell-free DNA (cfDNA) obtained from plasma is a novel approach for tumor genotyping. In this mixed prospective and retrospective observational cohort study, we investigated the clinical feasibility of continuous quantitative monitoring of ALK-acquired mutations in plasma obtained from patients with ALK+ non-small cell lung cancer by using a highly sensitive and specific droplet digital polymerase chain reaction (ddPCR) assay. We enrolled nine patients, including three treatment-naïve patients recently diagnosed with ALK+ non-small cell lung cancer via tissue biopsy and expected to receive ALK TKIs and six patients already receiving ALK TKIs. Plasma samples were collected from these patients every 3 months. cfDNA was extracted from 66 samples during the study period, and 10 ALK mutations were simultaneously evaluated. The numbers of samples showing the G1202R, C1156Y, G1269A, F1174L, T1151ins, and I1171T mutations were 32, 16, 5, 4, 1, and 1, respectively. The L1196M, L1152R, V1180L, and S1206Y mutations were not detected. Correlation analyses between progression-free survival and the time from treatment initiation (or treatment modification) to the detection of resistance mutations revealed that although resistance mutations may occur before a drug change becomes necessary, there is a duration during which the disease does not progress. Our findings suggest that real-time quantitative monitoring of ALK resistance mutations during the response period could provide a time course of changes while acquiring resistance mutations. This information would be beneficial for designing an appropriate treatment strategy.

Comparative Study of Quantitative Real-Time Monitoring QRT-PCR for BCR-ABL Gene in Chronic Myelogenous Leukemia (CML) Between Blood and Bone Marrow Samples

Objectives: This study aimed to assess the quantitative real-time RT-PCR (QRT-PCR) technique as a diagnostic tool for molecular surveillance of the BCR-ABL duplicate in Chronic Myelogenous Leukemia (CML) using both peripheral blood (PB) and bone marrow (BM) samples. Methods: Prospective analysis has been conducted a by quantitative real-time RT-PCR (QRT-PCR) for both PB and BM specimens, from 25 patients with untreated CML. QRT-PCR investigation was carried out previous and during treatment with Imatinib for untreated CML. Statistical examinations showed useful agreement of PB and BM pre- treatment specimens. Nevertheless, using the SPSS statistical method that estimates the agreement between PB and BM data, Results: This study showed low correspond of BCR-ABL measurements in PB and BM for specimens acquired through treatment. PB values tended to be lower than the conformable BM values [average difference = -0.37 (p&lt;0.001) in 36 coupled samples] and the 95% limits of agreement ranged from -1.23 to 0.48. Nevertheless, the present study showed that BM and PB QRT-PCR values followed a similar direction during treatment (Spearman correlation coefficient, 0.83; 95% CI, 0.70, 0.96). Conclusion: Our findings imply that PB and BM measures of BCR-ABL are frequently quantitatively different. The most accurate way to determine whether there is minimal residual disease is through BM sampling because BM results tend to be greater than PB values (MRD). Based on these findings, we advise avoiding switching BM and PB sampling for MRD monitoring during CML treatment because doing so could result in incorrect interpretation of treatment outcomes.

Monitor water quality through retrieving water quality parameters from hyperspectral images using graph convolution network with superposition of multi-point effect: A case study in Maozhou River

Quantitative prediction by unmanned aerial vehicle (UAV) remote sensing on water quality parameters (WQPs) including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a (Chl-a), total suspended solids (TSS), and turbidity provides a flexible and effective approach to monitor the variation in water quality. In this study, a deep learning-based method integrating graph convolution network (GCN), gravity model variant, and dual feedback machine involving parametric probability analysis and spatial distribution pattern analysis, named Graph Convolution Network with Superposition of Multi-point Effect (SMPE-GCN) has been developed to calculate concentrations of WQPs through UAV hyperspectral reflectance data on large scale efficiently. With an end-to-end structure, our proposed method has been applied to assisting environmental protection department to trace potential pollution sources in real time. The proposed method is trained on a real-world dataset and its effectiveness is validated on an equal amount of testing dataset with respect to three evaluation metrics including root of mean squared error (RMSE), mean absolute percent error (MAPE), and coefficient of determination (R2). The experimental results demonstrate that our proposed model achieves better performance in comparison with state-of-the-art baseline models in terms of RMSE, MAPE, and R2. The proposed method is applicable for quantifying seven various WQPs and has achieved good performance for each WQP. The resulting MAPE ranges from 7.16% to 10.96% and R2 ranges from 0.80 to 0.94 for all WQPs. This approach brings a novel and systematic insight into real-time quantitative water quality monitoring of urban rivers, and provides a unified framework for in-situ data acquisition, feature engineering, data conversion, and data modeling for further research. It provides fundamental support to assist environmental managers to efficiently monitor water quality of urban rivers.