Numerical Analysis and Quantification of Transfer Efficiency Coupled with Capillary and Quadrupole Ion Guide in an API-MS System.

Ion trajectory simulation is a significant and useful tool for understanding ion transfer mechanisms within the first vacuum region of the atmospheric pressure ionization mass spectrometer (API-MS). However, the complex dynamic gas field and wide pressure range lead to inaccurate simulation and huge computational costs. In this work, a novel electrohydrodynamic simulation called the statistical diffusion-hard-sphere (SDHS) mixed collision model was developed for characterizing the ion trajectories. For the first time, the influence of the dynamic pressure on the ion trajectory is considered for simulation, which helps to avoid an intolerable computational cost. Comparing with the conventional Monte Carlo collision model, the SDHS method helps to improve the calculation accuracy of ion trajectories under the first vacuum region and reduce the computational cost for at least 12-folds. Simulation results showed that the maximum ion loss came from the gap of the electrodes. The distance of the capillary-quadrupole ion guide was also a non-negligible factor. The trend of quantitative experimental results matches the SDHS simulation results. The maximum ion transfer efficiencies of quantitative experiment and simulation were 55% and 52%, respectively. Moreover, three ions, caffeine, reserpine, and Ultramark 1621, were measured for evaluating the applicability of SDHS in real API-MS. The trend of experimental results showed good agreement with that of computation. And the results of caffeine further illustrated the reason that the small mass ion transfer efficiency decreased with increasing radio frequency voltage. SDHS method is expected to be useful in the design of ion guides for further improvement of the sensitivity of API-MS.

High-throughput single-cell mass spectrometry enables metabolic network analysis by resolving phospholipid C[double bond, length as m-dash]C isomers.

Single-cell mass spectrometry (MS) is an essential technology for sensitive and multiplexed analysis of metabolites and lipids for cell phenotyping and pathway studies. However, the structural elucidation of lipids from single cells remains a challenge, especially in the high-throughput scenario. Technically, there is a contradiction between the inadequate sample amount (i.e. a single cell, 0.5-20 pL) for replicate or multiple analysis, on the one hand, and the high metabolite coverage and multidimensional structure analysis that needs to be performed for each single cell, on the other hand. Here, we have developed a high-throughput single-cell MS platform that can perform both lipid profiling and lipid carbon-carbon double bond (C[double bond, length as m-dash]C) location isomer resolution analysis, aided by C[double bond, length as m-dash]C activation in unsaturated lipids by the Paternò-Büchi (PB) reaction and tandem MS, termed single-cell structural lipidomics analysis. The method can achieve a single-cell analysis throughput of 51 cells per minute. A total of 145 lipids were structurally characterized at the subclass level, of which the relative abundance of 17 isomeric lipids differing in the location of C[double bond, length as m-dash]C from 5 lipid precursors was determined. While cell-to-cell variations in MS1-based lipid profiling can be large, an advantage of quantifying lipid C[double bond, length as m-dash]C location isomers is the significantly improved quantitation accuracy. For example, the relative standard deviations (RSDs) of the relative amounts of PC 34:1 C[double bond, length as m-dash]C position isomers in MDA-MB-468 cells are half smaller than those measured for PC 34:1 as a whole by MS1 abundance profiling. Taken together, the developed method can be effectively used for in-depth structural lipid metabolism network analysis by high-throughput analysis of 142 MDA-MB-468 human breast cancer cells.

The N+ Formation Mechanism of Vibrationally Selected N2O+ Ions in the C2Σ+ State: A TPEPICO Imaging Study.

The N-NO bond fission of N2O+(C2Σ+) ions can produce two major fragment ions, NO+ or N+. In contrast to the dominant NO+ fragment ion, the N+ formation mechanism remains unclear to date. Here, dissociative photoionization of N2O via the C2Σ+ ionic state has been reinvestigated using a combined approach of threshold photoelectron-photoion coincidence (TPEPICO) velocity imaging and quantum chemical calculations. Accompanying the N+(3P) formation, the NO(X2Π) neutral fragment with low and high vi-rotational distributions was identified, based on the N+ speed and angular distributions derived from the TPEPICO images. In particular, the excitation of the symmetric stretching ν1+ mode promotes the formation of high rotational components, while the asymmetric stretching ν3+ mode shows the exact opposite effect. According to our calculated multistate potential energy surfaces, intersystem crossing from C2Σ+ to 14Π exclusively provides feasible decomposition pathways to produce the N+ fragment. In a slightly bent geometry, spin-orbit couplings between C2Σ+ and two substates of 14Π, 14A' or 14A″, play a crucial role in the N+ formation from vibrationally selected N2O+(C2Σ+) ions. The mechanism also provides new insights into the charge transfer reaction of N+ + NO → N + NO+.

SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data.

Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann-Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.

Absolute protein quantification based on calibrated particle counting using electrospray-differential mobility analysis.

The traceability of in vitro diagnostics or drug products is based on the accurate quantification of proteins. In this study, we developed an absolute quantification approach for proteins. This method is based on calibrated particle counting using electrospray-differential mobility analysis (ES-DMA) coupled with a condensation particle counter (CPC). The absolute concentration of proteins was quantified with the observed protein particle number measured with ES-DMA-CPC, and the detection efficiency was determined by calibrators. The measurement performance and quantitative level were verified using two certificated reference materials, BSA and NIMCmAb. The linear regression fit for the detection efficiency values of three reference materials and one highly purified protein (myoglobin, BSA, NIMCmAb and fibrinogen) indicated that the detection efficiency and the particle size distribution of these proteins exhibited a linear relationship. Moreover, to explore the suitability of the detection efficiency-particle size curve for protein quantification, the concentrations of three typical proteinaceous particles, including two high molecular weight proteins (NIST reference material 8671 and D-dimer) and one protein complex (glutathione S-transferase dimer), were determined. This work suggests that this calibrated particle counting method is an efficient approach for nondestructive, rapid and accurate quantification of proteins, especially for measuring proteinaceous particles with tremendous size and without reference standards.

Reliable biological and multi-omics research through biometrology.

Metrology is the science of measurement and its applications, whereas biometrology is the science of biological measurement and its applications. Biometrology aims to achieve accuracy and consistency of biological measurements by focusing on the development of metrological traceability, biological reference measurement procedures, and reference materials. Irreproducibility of biological and multi-omics research results from different laboratories, platforms, and analysis methods is hampering the translation of research into clinical uses and can often be attributed to the lack of biologists' attention to the general principles of metrology. In this paper, the progresses of biometrology including metrology on nucleic acid, protein, and cell measurements and its impacts on the improvement of reliability and comparability in biological research are reviewed. Challenges in obtaining more reliable biological and multi-omics measurements due to the lack of primary reference measurement procedures and new standards for biological reference materials faced by biometrology are discussed. In the future, in addition to establishing reliable reference measurement procedures, developing reference materials from single or multiple parameters to multi-omics scale should be emphasized. Thinking in way of biometrology is warranted for facilitating the translation of high-throughput omics research into clinical practices.

Ti2C3 MXene-based nanocomposite as an intelligent nanoplatform for efficient mild hyperthermia treatment.

Photothermal therapy (PTT) has attracted much attention due to its less invasive, controllable and highly effective nature. However, PTT also suffers from intrinsic cancer resistance mediated by cell survival pathways. These survival pathways are regulated by a variety of proteins, among which heat shock protein (HSP) triggers thermotolerance and protects tumor cells from hyperthermia-induced apoptosis. Confronted by this challenge, we propose and validate here a novel MXene-based HSP-inhibited mild photothermal platform, which significantly enhances the sensitivity of tumor cells to heat-induced stress and thus improves the PPT efficacy. The Ti3C2@Qu nanocomposites are constructed by utilizing the high photothermal conversion ability of Ti3C2 nanosheets in combination with quercetin (Qu) as an inhibitor of HSP70. Qu molecules are loaded onto the nanoplatform in a pH-sensitive controlled release manner. The acidic environment of the tumor causes the burst-release of Qu molecules, which deplete the level of heat shock protein 70 (HSP70) in tumor cells and leave the tumor cells out from the protection of the heat-resistant survival pathway in advance, thus sensitizing the hyperthermia efficacy. The nanostructure, photothermal properties, pH-responsive controlled release, synergistic photothermal ablation of tumor cells in vitro and in vivo, and hyperthermia effect on subcellular structures of the Ti3C2@Qu nanocomposites were systematically investigated.

Simultaneous determination of diquat, paraquat, glufosinate, and glyphosate in plasma by liquid chromatography/tandem mass spectrometry: from method development to clinical application.

Diquat (DQ), paraquat (PQ), glufosinate (GLU), and glyphosate (GLYP) are commonly used herbicides that have been confirmed to be toxic to humans. Rapid and accurate measurements of these toxicants in clinical practice are beneficial for the correct diagnosis and timely treatment of herbicide-poisoned patients. The present study aimed to establish an efficient, convenient, and reliable method to achieve the simultaneous quantification of DQ, PQ, GLU, and GLYP in human plasma using liquid chromatography-tandem mass spectrometry (LC-MS/MS) without using derivatization or ion-pairing reagents. DQ, PQ, GLU, and GLYP were extracted by the rapid protein precipitation and liquid-liquid extraction method and then separated and detected by LC-MS/MS. Subsequently, linearity, limit of detection (LOD), limit of quantification (LOQ), precision, accuracy, extraction recovery, matrix effect, dilution integrity, and stability were evaluated to validate the method based on the FDA criteria. Finally, the validated method was applied to real plasma samples collected from 166 Chinese patients with herbicide poisoning. The results showed satisfactory linearity with low LOD (1 ng/mL for DQ and PQ, 5 ng/mL for GLU, and 10 ng/mL for GLYP, respectively) and low LOQ (5 ng/mL for DQ and PQ, 25 ng/mL for GLU and GLYP, respectively). In addition, the precision, accuracy, extraction recovery, and stability of the method were acceptable. The matrix effect was not observed in the analyzed samples. Moreover, the developed method was successfully applied to determine the target compounds in real plasma samples. These data provided reliable evidence for the application of this LC-MS/MS method for clinical poisoning detection.

Bone age assessment based on different MRI modalities of the proximal humerus epiphysis: the comparisons of T1WI, T2WI, and PDWI.

Bone age assessment (BAA) is crucial in various fields, including legal proceedings, athletic competitions, and clinical medicine. However, the use of X-ray methods for age estimation without medical indication is subject to ethical debate, especially in forensic and athletic fields. The application of magnetic resonance imaging (MRI) with non-ionizing radiation can overcome this limitation in BAA. This study aimed to compare the application value of several MRI modalities of proximal humeral in BAA. A total of 468 patients with shoulder MRIs were retrospectively collected from a Chinese Han population aged 12-30 years (259 males and 209 females) for training and testing, including T1 weighted MRI (T1WI), T2 weighted MRI (T2WI), and Proton density weighted MRI (PDWI). Optimal regression models were established for age estimation, yielding mean absolute error (MAE) values below 2.0 years. The MAE values of T1WI were the lowest, with 1.700 years in males and 1.798 years in females. The area under the curve (AUC) and accuracy values of different MRI modalities of 16-year and 18-year thresholds were all around 0.9. For the 18-year threshold, T1WI outperformed T2WI and PDWI. In conclusion, the three MRI modalities of the proximal humerus can serve as reliable indicators for age assessment, while the T1WI performed better in age assessment and classification.

Integrated plasma proteomics and N-glycoproteomics reveals alterations in the N-glycosylation in Chinese hepatocellular carcinoma patients.

Hepatocellular carcinoma (HCC) is a life-threatening disease that presents diagnostic challenges due to the absence of reliable biomarkers. Recently, plasma proteomics and glycoproteomics have emerged as powerful tools for identifying potential diagnostic biomarkers for various diseases. In this study, we conducted a comprehensive proteomic and glycoproteomic analysis of plasma samples from 11 HCC patients and 11 healthy control (HC) individuals. We identified 20 differentially expressed (DE) proteins and 32 DE intact glycosylated peptides (IGPs) that can effectively differentiate between HCC patients and HC samples. Our findings demonstrate that IGP profiles had better predictive power than protein profiles for screening HCC. Pathways associated with DE proteins and IGPs were identified. It was reported that the protein expression level of galectin 3 binding protein (LGALS3BP) and its N-linked glycosylation at the N398 and N551 sites might serve as valuable candidates for HCC diagnosis. These results highlight the importance of N-glycoproteomics in advancing our understanding of HCC and suggest possible candidates for the future diagnosis of this disease.

Machine Learning Supported the Modified Gustafson's Criteria for Dental Age Estimation in Southwest China.

Adult age estimation is one of the most challenging problems in forensic science and physical anthropology. In this study, we aimed to develop and evaluate machine learning (ML) methods based on the modified Gustafson's criteria for dental age estimation. In this retrospective study, a total of 851 orthopantomograms were collected from patients aged 15 to 40 years old. The secondary dentin formation (SE), periodontal recession (PE), and attrition (AT) of four mandibular premolars were analyzed according to the modified Gustafson's criteria. Ten ML models were generated and compared for age estimation. The partial least squares regressor outperformed other models in males with a mean absolute error (MAE) of 4.151 years. The support vector regressor (MAE = 3.806 years) showed good performance in females. The accuracy of ML models is better than the single-tooth model provided in the previous studies (MAE = 4.747 years in males and MAE = 4.957 years in females). The Shapley additive explanations method was used to reveal the importance of the 12 features in ML models and found that AT and PE are the most influential in age estimation. The findings suggest that the modified Gustafson method can be effectively employed for adult age estimation in the southwest Chinese population. Furthermore, this study highlights the potential of machine learning models to assist experts in achieving accurate and interpretable age estimation.

Clinical treatment of cryptococcal meningitis: an evidence-based review on the emerging clinical data.

Cryptococcal meningitis (CM) is a fatal fungal central nervous system (CNS) infection caused by Cryptococcus infecting the meninges and/or brain parenchyma, with fever, headache, neck stiffness, and visual disturbances as the primary clinical manifestations. Immunocompromised individuals with human immunodeficiency virus (HIV) infection or who have undergone organ transplantation, as well as immunocompetent people can both be susceptible to CM. Without treatment, patients with CM may have a mortality rate of up to 100% after hospital admission. Even after receiving therapy, CM patients may still suffer from problems such as difficulty to cure, poor prognosis, and high mortality. Therefore, timely and effective treatment is essential to improve the mortality and prognosis of CM patients. Currently, the clinical outcomes of CM are frequently unsatisfactory due to limited drug choices, severe adverse reactions, drug resistance, etc. Here, we review the research progress of CM treatment strategies and discuss the suitable options for managing CM, hoping to provide a reference for physicians to select the most appropriate treatment regimens for CM patients.

Nanoparticle Deep-Subwavelength Dynamics Empowered by Optical Meron-Antimeron Topology.

Optical meron is a type of nonplanar topological texture mainly observed in surface plasmon polaritons and highly symmetric points of photonic crystals in the reciprocal space. Here, we report Poynting-vector merons formed at the real space of a photonic crystal for a Γ-point illumination. Optical merons can be utilized for subwavelength-resolution manipulation of nanoparticles, resembling a topological Hall effect on electrons via magnetic merons. In particular, staggered merons and antimerons impose strong radiation pressure on large gold nanoparticles (AuNPs), while focused hot spots in antimerons generate dominant optical gradient forces on small AuNPs. Synergistically, differently sized AuNPs in a still environment can be trapped or orbit in opposite directions, mimicking a coupled galaxy system. They can also be separated with a 10 nm precision when applying a flow velocity of >1 mm/s. Our study unravels a novel way to exploit topological textures for optical manipulation with deep-subwavelength precision and switchable topology in a lossless environment.

Machine learning and deep learning enabled age estimation on medial clavicle CT images.

The medial clavicle epiphysis is a crucial indicator for bone age estimation (BAE) after hand maturation. This study aimed to develop machine learning (ML) and deep learning (DL) models for BAE based on medial clavicle CT images and evaluate the performance on normal and variant clavicles. This study retrospectively collected 1049 patients (mean± SD: 22.50±4.34 years) and split them into normal training and test sets, and variant training and test sets. An additional 53 variant clavicles were incorporated into the variant test set. The development stages of normal MCE were used to build a linear model and support vector machine (SVM) for BAE. The CT slices of MCE were automatically segmented and used to train DL models for automated BAE. Comparisons were performed by linear versus ML versus DL, and normal versus variant clavicles. Mean absolute error (MAE) and classification accuracy was the primary parameter of comparison. For BAE, the SVM had the best MAE of 1.73 years, followed by the commonly-used CNNs (1.77-1.93 years), the linear model (1.94 years), and the hybrid neural network CoAt Net (2.01 years). In DL models, SE Net 18 was the best-performing DL model with similar results to SVM in the normal test set and achieved an MAE of 2.08 years in the external variant test. For age classification, all the models exhibit superior performance in the classification of 18-, 20-, 21-, and 22-year thresholds with limited value in the 16-year threshold. Both ML and DL models produce desirable performance in BAE based on medial clavicle CT.

Development of cancer biomarker heat shock protein 90α certified reference material using two different isotope dilution mass spectrometry techniques.

Heat shock protein 90α (HSP90α) has been regarded as an important indicator for judging tumor metastasis and prognosis due to its significant upregulation in various tumors. Therefore, the accurate quantification of HSP90α is of great significance for clinical diagnosis and therapy of cancers. However, the lack of HSP90α certified reference material (CRM) leads to the accuracy and consistency of quantification methods not being effectively evaluated. Besides, quantitative results without traceability make comparisons between different studies difficult. In this study, an HSP90α solution CRM was developed from the recombinant protein raw material. The recombinant protein is a dimer, and the purity of the CRM candidate reached 96.71%. Both amino acid analysis-isotope dilution mass spectrometry (AAA-IDMS) and unique peptide analysis-isotope dilution mass spectrometry (UPA-IDMS) were performed to measure the content of HSP90α in the solution CRM candidate, and the certified value was assessed to be 66.2 ± 8.8 µg/g. Good homogeneity of the CRM was identified, and the stability examination suggested that the CRM was stable for at least 4 months at - 80 °C and for 7 days at 4 °C. With traceability to SI unit (kg), this CRM has potential to help establish a metrological traceability chain for quantification of HSP90α, which will make the quantification results standardized and comparable regardless of the quantitative methods.

Automated bone age assessment from knee joint by integrating deep learning and MRI-based radiomics.

Bone age assessment (BAA) is a crucial task in clinical, forensic, and athletic fields. Since traditional age estimation methods are suffered from potential radiation damage, this study aimed to develop and evaluate a deep learning radiomics method based on multiparametric knee MRI for noninvasive and automatic BAA. This retrospective study enrolled 598 patients (age range,10.00-29.99 years) who underwent MR examinations of the knee joint (T1/T2*/PD-weighted imaging). Three-dimensional convolutional neural networks (3D CNNs) were trained to extract and fuse multimodal and multiscale MRI radiomic features for age estimation and compared to traditional machine learning models based on hand-crafted features. The age estimation error was greater in individuals aged 25-30 years; thus, this method may not be suitable for individuals over 25 years old. In the test set aged 10-25 years (n = 95), the 3D CNN (a fusion of T1WI, T2*WI, and PDWI) demonstrated the lowest mean absolute error of 1.32 ± 1.01 years, which is higher than that of other MRI modalities and the hand-crafted models. In the classification for 12-, 14-, 16-, and 18- year thresholds, accuracies and the areas under the ROC curves were all over 0.91 and 0.96, which is similar to the manual methods. Visualization of important features showed that 3D CNN estimated age by focusing on the epiphyseal plates. The deep learning radiomics method enables non-invasive and automated BAA from multimodal knee MR images. The use of 3D CNN and MRI-based radiomics has the potential to assist radiologists or medicolegists in age estimation.

Suppression of pinoid mutant phenotypes by mutations in PIN-FORMED 1 and PIN1-GFP fusion.

Disruption of either the auxin transporter PIN-FORMED 1 (PIN1) or the protein kinase PINOID (PID) leads to the development of pin-like inflorescences. Previous studies have shown that phosphoregulation of PIN1 by AGC kinases including PID directs auxin flux to drive organ initiation. Here, we report unexpected findings on the genetic interactions between these two genes. We deleted the first 2/3 of the PIN1 coding sequence using CRISPR/Cas9, and the resulting pin1 mutant (pin1-27) was a strong allele. Surprisingly, heterozygous pin1-27 suppressed two independent pid null mutants, whereas homozygous pin1-27 enhanced the phenotypes of the pid mutants during embryogenesis. Furthermore, we show that deletion of either the hydrophilic loop or the second half of PIN1 also abolished PIN1 function, yet those heterozygous pin1 mutants were also capable of rescuing pid nulls. Moreover, we inserted green fluorescent protein (GFP) into the hydrophilic loop of PIN1 through CRISPR-mediated homology-directed repair (HDR). The GFP signal and pattern in the PIN1-GFPHDR line are similar to those in the previously reported PIN1-GFP transgenic lines. Interestingly, the PIN1-GFPHDR line also rescued various pid null mutant alleles in a semidominant fashion. We conclude that decreasing the number of functional PIN1 copies is sufficient to suppress the pid mutant phenotype, suggesting that PIN1 is likely part of a larger protein complex required for organogenesis.

Potential Efficacy of Herbal Medicine-Derived Carbon Dots in the Treatment of Diseases: From Mechanism to Clinic.

Carbon dots (CDs), a crucial component of nanomaterials, are zero-dimensional nanomaterials with carbon as the backbone structure and smaller than 10 nm. Due to their beneficial characteristics, they are widely used in biomedical fields such as biosensors, drug delivery, bio-imaging, and interactions with DNA. Interestingly, a novel type of carbon dot, generated by using herbal medicines as synthetic raw materials, has emerged as the most recent incomer in the family of CDs with the extensive growth in the number of materials selected for carbon dots synthesis. Herbal medicine-derived carbon dots (HM-CDs) have been employed in the biomedical industry, and are rapidly emerging as "modern nanomaterials" due to their unique structures and exceptional capabilities. Emerging trends suggest that their specific properties can be used in bleeding disorders, gastrointestinal disorders, inflammation-related diseases, and other common intractable diseases including cancer, menopausal syndrome, central nervous system disorders, and pain of various forms and causes. In addition, HM-CDs have been found to have organ-protective and antioxidant properties, as evidenced by extensive studies. This research provides a more comprehensive understanding of the biomedical applications of HM-CDs for the aforementioned disorders and investigates the intrinsic pharmacological activities and mechanisms of these HM-CDs to further advance their clinical applications.

Highly Sensitive and Quantitative Magnetic Nanoparticle-Based Lateral Flow Immunoassay with an Atomic Magnetometer.

Lateral flow immunoassay (LFIA) is a simple point-of-care method for detecting various analytes. However, the lack of test result precision and poor quantification are the main bottlenecks of LFIA. Although magnetic nanoparticles (MNPs) have gained prominence as potent labels in LIFA, the quantitative detection method for trace biomarkers remains to be improved. Here, we propose a promising real-time biosensing platform based on a highly sensitive atomic magnetometer to fulfill the quantitative detection of MNP-based lateral flow immunochromatographic assays. The strategy entails obtaining the residual flux density component spectrum by continuously and linearly scanning the trace MNP label and then resolving the magnetization and quantity from the spectrum. Moreover, we exploit the theoretical model of the magnetic dipole to verify the method's reliability. Regarding carcinoembryonic antigen detection, the atomic magnetometer exhibits a low detection limit of ∼0.01 ng mL-1 with a 100-fold enhancement factor compared to optical detection methods and a more straightforward mechanism than other magnetic detection approaches. Together, these results provide valuable insight for the potential application of atomic magnetometer quantum measurement techniques in intelligent diagnosis and treatment.

Integrated Microwell Array-Based Microfluidic Chip with a Hand-Held Smartphone-Controlled Device for Nucleic Acid Detection.

In this study, we designed a highly integrated microfluidic chip for nucleic acid extraction, amplification, and detection. Magnetic beads, which are used to capture nucleic acids on the chip, are trapped in the microwell arrays in a one-well-one-bead manner after local surface modification of the inner faces of the microwells. On-chip liquid introduction, delivery, and mixing are all carried out manually with one syringe and no other equipment. A hand-held device with precise temperature control and high-quality imaging is developed, which is only 2.3 cubic decimeters in volume and 1.2 kg in weight. Via the use of the Internet for wireless communication, the experiment and data analysis after inserting the chip into the device can be conducted by a smartphone anywhere there is an Internet connection. We carried out reverse transcription loop-mediated isothermal amplification (RT-LAMP) on the chip with the hand-held device. SARS-CoV-2 pseudoviruses are extracted, reverse transcribed, amplified, and detected on the chip with the hand-held device with satisfactory results. Thus, a highly integrated, easy-to-operate, and rapid nucleic acid detection microfluidic chip with a hand-held smartphone-controlled device is proposed, and this new platform for nucleic acid detection shows great potential for mobile point-of-care testing (POCT).