High-contrast imaging of cellular non-repetitive drug-resistant genes via in situ dead Cas12a-labeled PCR.

In situ imaging of genes of pathogenic bacteria can profile cellular heterogeneity, such as the emergence of drug resistance. Fluorescence in situ hybridization (FISH) serves as a classic approach to image mRNAs inside cells, but it remains challenging to elucidate genomic DNAs and relies on multiple fluorescently labeled probes. Herein, we present a dead Cas12a (dCas12a)-labeled polymerase chain reaction (CasPCR) assay for high-contrast imaging of cellular drug-resistant genes. We employed a syncretic dCas12a-green fluorescent protein (dCas12a-GFP) to tag the amplicons, thereby enabling high-contrast imaging and avoiding multiple fluorescently labeled probes. The CasPCR assay can quantify quinolone-resistant Salmonella enterica in mixed populations and identify them isolated from poultry farms.

Development of Photonic In-Sensor Computing Based on a Mid-Infrared Silicon Waveguide Platform.

Neuromorphic in-sensor computing has provided an energy-efficient solution to smart sensor design and on-chip data processing. In recent years, various free-space-configured optoelectronic chips have been demonstrated for on-chip neuromorphic vision processing. However, on-chip waveguide-based in-sensor computing with different data modalities is still lacking. Here, by integrating a responsivity-tunable graphene photodetector onto the silicon waveguide, an on-chip waveguide-based in-sensor processing unit is realized in the mid-infrared wavelength range. The weighting operation is achieved by dynamically tuning the bias of the photodetector, which could reach 4 bit weighting precision. Three different neural network tasks are performed to demonstrate the capabilities of our device. First, image preprocessing is performed for handwritten digits and fashion product classification as a general task. Next, resistive-type glove sensor signals are reversed and applied to the photodetector as an input for gesture recognition. Finally, spectroscopic data processing for binary gas mixture classification is demonstrated by utilizing the broadband performance of the device from 3.65 to 3.8 µm. By extending the wavelength from near-infrared to mid-infrared, our work shows the capability of a waveguide-integrated tunable graphene photodetector as a viable weighting solution for photonic in-sensor computing. Furthermore, such a solution could be used for large-scale neuromorphic in-sensor computing in photonic integrated circuits at the edge.

Continue using or gathering dust? A mixed method research on the factors influencing the continuous use intention for an AI-powered adaptive learning system for rural middle school students.

This paper investigates the factors influencing the continuous use intention of AI-powered adaptive learning systems among rural middle school students in China. Employing a mixed-method approach, this study integrates Technology Acceptance Model 3 with empirical data collected from rural middle schools in western China. The main contributions of this study include identifying key determinants of usage intention, such as computer self-efficacy, perceived enjoyment, system quality, and the perception of feedback. The findings provide insights into enhancing rural education through AI and suggest strategies for developing more effective and engaging adaptive learning systems. This research not only fills a significant gap in the understanding of AI in education but also offers practical implications for educators and policymakers aiming to improve learning outcomes in rural settings.

Artificial Intelligence-Enhanced Waveguide "Photonic Nose"- Augmented Sensing Platform for VOC Gases in Mid-Infrared.

On-chip nanophotonic waveguide sensor is a promising solution for miniaturization and label-free detection of gas mixtures utilizing the absorption fingerprints in the mid-infrared (MIR) region. However, the quantitative detection and analysis of organic gas mixtures is still challenging and less reported due to the overlapping of the absorption spectrum. Here,an Artificial-Intelligence (AI) assisted waveguide "Photonic nose" is presented as an augmented sensing platform for gas mixture analysis in MIR. With the subwavelength grating cladding supported waveguide design and the help of machine learning algorithms, the MIR absorption spectrum of the binary organic gas mixture is distinguished from arbitrary mixing ratio and decomposed to the single-component spectra for concentration prediction. As a result, the classification of 93.57% for 19 mixing ratios is realized. In addition, the gas mixture spectrum decomposition and concentration prediction show an average root-mean-square error of 2.44 vol%. The work proves the potential for broader sensing and analytical capabilities of the MIR waveguide platform for multiple organic gas components toward MIR on-chip spectroscopy.

Colorimetric and chemiluminescent enzyme immunoassays based on the alkaline phosphatase-tagged single-chain variable fragment fusion tracer for detecting zearalenone in agro-products.

Zearalenone (ZEN) is a non-steroidal estrogenic mycotoxin and seriously threatens food safety, which requires rapid and sensitive detection methods for monitoring ZEN in agro-products. Herein, an alkaline phosphatase-tagged single-chain variable fragment fusion protein (ALP-scFv) was used as a bifunctional tracer to develop a colorimetric enzyme immunoassay (CEIA) and a chemiluminescent enzyme immunoassay (CLEIA) for ZEN. In addition, the interactions between scFv and ZEN were exploited by computer-assisted simulation, and four key amino acid sites were preliminarily identified. After optimization, the CEIA and CLEIA exhibited a limit of detection of 0.02 and 0.006 ng/mL, respectively. Furthermore, both methods showed favorable accuracy in recovery experiments and good selectivity in cross reactions. Moreover, the detection results of the actual samples from both methods correlated well with those from high-performance liquid chromatography. Overall, the ALP-scFv fusion tracer-based CEIA and CLEIA are demonstrated as reliable tools for ZEN detection in food.

In Situ Cas12a-Based Allele-Specific PCR for Imaging Single-Nucleotide Variations in Foodborne Pathogenic Bacteria.

In situ profiling of single-nucleotide variations (SNVs) can elucidate drug-resistant genotypes with single-cell resolution. The capacity to directly "see" genetic information is crucial for investigating the relationship between mutated genes and phenotypes. Fluorescence in situ hybridization serves as a canonical tool for genetic imaging; however, it cannot detect subtle sequence alteration including SNVs. Herein, we develop an in situ Cas12a-based amplification refractory mutation system-PCR (ARMS-PCR) method that allows the visualization of SNVs related to quinolone resistance inside cells. The capacity of discriminating SNVs is enhanced by incorporating optimized mismatched bases in the allele-specific primers, thus allowing to specifically amplify quinolone-resistant related genes. After in situ ARMS-PCR, we employed a modified Cas12a/CRISPR RNA to tag the amplicon, thereby enabling specific binding of fluorophore-labeled DNA probes. The method allows to precisely quantify quinolone-resistant Salmonella enterica in the bacterial mixture. Utilizing this method, we investigated the survival competition capacity of quinolone-resistant and quinolone-sensitive bacteria toward antimicrobial peptides and indicated the enrichment of quinolone-resistant bacteria under colistin sulfate stress. The in situ Cas12a-based ARMS-PCR method holds the potential for profiling cellular phenotypes and gene regulation with single-nucleotide resolution at the single-cell level.

Magnetic Resonance Imaging Features Associated with a High and Low Expression of Tumor-Infiltrating Lymphocytes: A Stratified Analysis According to Molecular Subtypes.

A total of 457 patients, including 241 HR+/HER2- patients, 134 HER2+ patients, and 82 TN patients, were studied. The percentage of TILs in the stroma adjacent to the tumor cells was assessed using a 10% cutoff. The low TIL percentages were 82% in the HR+ patients, 63% in the HER2+ patients, and 56% in the TN patients (p < 0.001). MRI features such as morphology as mass or non-mass enhancement (NME), shape, margin, internal enhancement, presence of peritumoral edema, and the DCE kinetic pattern were assessed. Tumor sizes were smaller in the HR+/HER2- group (p < 0.001); HER2+ was more likely to present as NME (p = 0.031); homogeneous enhancement was mostly seen in HR+ (p < 0.001); and the peritumoral edema was present in 45% HR+, 71% HER2+, and 80% TN (p < 0.001). In each subtype, the MR features between the high- vs. low-TIL groups were compared. In HR+/HER2-, peritumoral edema was more likely to be present in those with high TILs (70%) than in those with low TILs (40%, p < 0.001). In TN, those with high TILs were more likely to present a regular shape (33%) than those with low TILs (13%, p = 0.029) and more likely to present the circumscribed margin (19%) than those with low TILs (2%, p = 0.009).

Age differences in the recruitment of syntactic analysis and semantic plausibility during sentence comprehension.

Syntactic analysis and semantic plausibility provide important cues to build the meaningful representation of sentences. The purpose of this research is to explore the age-related differences in the use of syntactic analysis and semantic plausibility during sentence comprehension under different working memory load conditions. A sentence judgment task was implemented among a group of older and younger adults. Semantic plausibility (plausible, implausible) and syntactic consistency (consistent, inconsistent) were manipulated in the experimental stimuli, and working memory load (high, low) was varied by manipulating the presentation of the stimuli. The study revealed a stronger effect of semantic plausibility in older adults than in younger adults when working memory load was low. But no significant age difference in the effect of syntactic consistency was discovered. When working memory load was high, there was a stronger effect of semantic plausibility and a weaker effect of syntactic consistency in older adults than in younger adults, which suggests that older adults relied more on semantic plausibility and less on syntactic analysis than younger adults. The findings indicate that there is an age-related increase in the use of semantic plausibility, and a reduction in the use of syntactic analysis as working memory load increases.

Single-Cell Genotyping of Single-Nucleotide Mutations Using In Situ Allele-Specific Loop-Mediated Isothermal Amplification.

Single-nucleotide mutations (SNMs) in the bacterial genome may cause antibiotic resistance. The visualization of SNMs can indicate antibiotic resistance phenotypes at the single-cell level but remains challenging. Herein, we proposed an in situ allele-specific isothermal amplification proceeded inside cells, allowing us to image bacterial genes with single-nucleotide resolution. The primer for loop-mediated isothermal amplification (LAMP) was designed with artificial mismatch bases to serve as an allele-specific probe, endowing LAMP to specifically amplify genes with SNMs. Due to the high amplification efficiency of LAMP, the method termed AlleLAMP can generate high gain for imaging SNMs and precisely quantify mutated quinolone-resistant Salmonella in bacterial mixture. We utilized AlleLAMP to survey the selection of antibiotic resistance under the preservative stress and found that the mutant quinolone-resistant strain owned a survival advantage over the wild-type quinolone-sensitive strain under the stress of preservatives. AlleLAMP can serve as a single-cell tool for analyzing the relationship between bacterial genotype and phenotype.

Evaluation of Association Between Menstrual Cycle Timing and Quantitative Background Parenchymal Enhancement on Breast MRI in Premenopausal Women.

OBJECTIVES: To evaluate the influence of menstrual cycle timing on quantitative background parenchymal enhancement and to assess an optimal timing of breast MRI in premenopausal women. METHODS: A total of 197 premenopausal women were enrolled, 120 of which were in the malignant group and 77 in the benign group. Two radiologists depicted the regions of interest (ROI) of the three consecutive biggest slices of glandular tissue in the unaffected side and calculated the ratio (=[SIpost - SIpre]/SIpre) in ROI from the precontrast and early phase to assess BPE quantitatively. Association of BPE with menstrual cycle timing was compared in three categories. The relationships between BPE and age /body mass index (BMI) were also explored. RESULTS: We found that the BPE ratio presented lower in patients with the follicular phase (day1-14) compared to the luteal phase (day15-30) in the benign group (P = .036). Also, the BPE ratio presented significantly lower in the proliferative phase (day5-14) than the menstrual phase (day1-4) and the secretory phase(day15-30) in the benign group (P = .006). While the BPE ratio was not significantly different among the respective weeks (1-4) of the menstrual cycle in the benign group (P > .05). In the malignant group, the BPE ratio did not significantly differ between/among any menstrual cycle phase or week (all P > .05). CONCLUSION: It seems more suitable for Asian women whose lesions need to follow up or are suspected of malignant to undergo breast MRI within the 1st to 14th day of the menstrual cycle, especially on the 5th to 14th day.

Relationship between atmospheric pollution and polycyclic aromatic hydrocarbons in fresh snow during heavy pollution episodes in a cold city, northeast China.

Air quality index (AQI) and air pollutants during two typical pollution episodes, and polycyclic aromatic hydrocarbons (PAHs) in fresh snow after each episode in the winter 2019 across Harbin City in northeast China were investigated to explore the co-environmental behaviors. Significantly greater values of AQI and PAHs were found in the more serious atmospheric pollution episode (episode Ⅱ), demonstrating that PAHs in fresh snow is a robust indicator. PM2.5 was the primary air pollutant in both episodes based on PM2.5/PM10 ratios, which might be attributed to fine particulate converted from gas-to-particle process. PM2.5 and 4-ring PAHs significantly positive correlated, indicating that airborne particulate PAHs were co-emitted and co-transported with atmospheric fine particles released from coal combustion and vehicular emission under low temperature and high relative humidity. 3- and 4- rings PAHs were dominant in episode Ⅱ, while 5- and 6- rings PAHs were found the lowest in both episodes. These characteristics reflected that long-range transportation of coal and biomass burning were from the surrounding areas, while vehicle exhausts were mainly from local emissions. Except for the impact of local pollution source emissions, the regional transport could make a greater contribution in a more serious pollution event.

Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer.

BACKGROUND: Noninvasive and accurate prediction of lymph node metastasis (LNM) is very important for patients with early-stage cervical cancer (ECC). Our study aimed to investigate the accuracy and sensitivity of radiomics models with features extracted from both intra- and peritumoral regions in magnetic resonance imaging (MRI) with T2 weighted imaging (T2WI) and diffusion weighted imaging (DWI) for predicting LNM. METHODS: A total of 247 ECC patients with confirmed lymph node status were enrolled retrospectively and randomly divided into training (n = 172) and testing sets (n = 75). Radiomics features were extracted from both intra- and peritumoral regions with different expansion dimensions (3, 5, and 7 mm) in T2WI and DWI. Radiomics signature and combined radiomics models were constructed with selected features. A nomogram was also constructed by combining radiomics model with clinical factors for predicting LNM. RESULTS: The area under curves (AUCs) of radiomics signature with features from tumors in T2WI and DWI were 0.841 vs. 0.791 and 0.820 vs. 0.771 in the training and testing sets, respectively. Combining radiomics features from tumors in the T2WI, DWI and peritumoral 3 mm expansion in T2WI achieved the best performance with an AUC of 0.868 and 0.846 in the training and testing sets, respectively. A nomogram combining age and maximum tumor diameter (MTD) with radiomics signature achieved a C-index of 0.884 in the prediction of LNM for ECC. CONCLUSIONS:  Radiomics features extracted from both intra- and peritumoral regions in T2WI and DWI are feasible and promising for the preoperative prediction of LNM for patients with ECC.

BACH1-induced ferroptosis drives lymphatic metastasis by repressing the biosynthesis of monounsaturated fatty acids.

Esophageal squamous cell carcinoma (ESCC) is one of the fatal malignancies worldwide. It has an increased propensity to metastasize via lymphogenous routes in an early stage. The prognosis of patients with lymph node metastases (LNM) is often worse than that of patients without metastases. Although several factors have been found to influence metastasis, the mechanisms of preference for specific metastatic routes remain poorly understood. Herein, we provide evidence that the intrinsic hypersensitivity of tumor cells to ferroptosis may proactively drive lymphatic metastasis. Serum autoantibodies associated with LNM of early ESCC were screened using a whole-proteome protein array containing 19 394 human recombinant proteins, and an anti-BACH1 autoantibody was first identified. Pan-cancer analysis of ferroptosis-related genes with preferential lymphatic metastasis and preferential hematogenous metastasis based on The Cancer Genome Atlas data was performed. Only BACH1 showed significant overexpression in tumors with preferential lymphatic metastasis, whereas it was downregulated in most tumors with preferential nonlymphatic metastasis. In addition, it was found that the serum levels of autoantibodies against BACH1 were elevated in early-stage patients with LNM. Interestingly, BACH1 overexpression and ferroptosis induction promoted LNM but inhibited hematogenous metastasis in mouse models. Transcriptomic and lipidomic analyses found that BACH1 repressed SCD1-mediated biosynthesis of monounsaturated fatty acids, especially oleic acid (OA). OA significantly attenuated the ferroptotic phenotypes and reversed the metastatic properties of BACH1-overexpressing cells. OA addition significantly rescued the ferroptotic phenotypes and reversed the metastatic properties of BACH1-overexpressing cells. Importantly, the concentration gradient of OA between primary lesions and the lymph resulted in the chemoattraction of tumor cells to promote invasion, thus facilitating lymphatic metastasis. BACH1-induced ferroptosis drives lymphatic metastasis via the BACH1-SCD1-OA axis. More importantly, this study confirms that ferroptosis is a double-edged sword in tumorigenesis and tumor progression. The clinical application of ferroptosis-associated agents requires a great caution.

Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification.

Objective: To develop a multi-modality radiomics nomogram based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions. Material and Methods: In this retrospective study, 345 breast lesions from 305 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n = 241) and testing (n = 104) datasets. Radiomics features were extracted from manually contoured images. The inter-class correlation coefficient (ICC), Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and radiomics signature building. Multivariable logistic regression was used to develop a radiomics nomogram incorporating radiomics signature and clinical factors. The performance of the radiomics nomogram was evaluated by its discrimination, calibration, and clinical usefulness and was compared with BI-RADS classification evaluated by a senior breast radiologist. Results: The All-Combination radiomics signature derived from the combination of DCE-MRI, BMUS and SE images showed better diagnostic performance than signatures derived from single modality alone, with area under the curves (AUCs) of 0.953 and 0.941 in training and testing datasets, respectively. The multi-modality radiomics nomogram incorporating the All-Combination radiomics signature and age showed excellent discrimination with the highest AUCs of 0.964 and 0.951 in two datasets, respectively, which outperformed all single modality radiomics signatures and BI-RADS classification. Furthermore, the specificity of radiomics nomogram was significantly higher than BI-RADS classification (both p < 0.04) with the same sensitivity in both datasets. Conclusion: The proposed multi-modality radiomics nomogram based on DCE-MRI and ultrasound images has the potential to serve as a non-invasive tool for classifying benign and malignant breast lesions and reduce unnecessary biopsy.

Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning.

Purpose: To implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images. Materials and Methods: A total of 298 patients were identified from a retrospective review, and all of them had confirmed pathological diagnoses, 175 malignant and 123 benign. The BI-RADS scores of DBT were obtained from the radiology reports, classified into 2, 3, 4A, 4B, 4C, and 5. The architectural distortion areas on craniocaudal (CC) and mediolateral oblique (MLO) views were manually outlined as the region of interest (ROI) for the radiomics analysis. Features were extracted using PyRadiomics, and then the support vector machine (SVM) was applied to select important features and build the classification model. Deep learning was performed using the ResNet50 algorithm, with the binary output of malignancy and benignity. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was utilized to localize the suspicious areas. The predicted malignancy probability was used to construct the ROC curves, compared by the DeLong test. The binary diagnosis was made using the threshold of ≥ 0.5 as malignant. Results: The majority of malignant lesions had BI-RADS scores of 4B, 4C, and 5 (148/175 = 84.6%). In the benign group, a substantial number of patients also had high BI-RADS ≥ 4B (56/123 = 45.5%), and the majority had BI-RADS ≥ 4A (102/123 = 82.9%). The radiomics model built using the combined CC+MLO features yielded an area under curve (AUC) of 0.82, the sensitivity of 0.78, specificity of 0.68, and accuracy of 0.74. If only features from CC were used, the AUC was 0.77, and if only features from MLO were used, the AUC was 0.72. The deep-learning model yielded an AUC of 0.61, significantly lower than all radiomics models (p<0.01), which was presumably due to the use of the entire image as input. The Grad-CAM could localize the architectural distortion areas. Conclusion: The radiomics model can achieve a satisfactory diagnostic accuracy, and the high specificity in the benign group can be used to avoid unnecessary biopsies. Deep learning can be used to localize the architectural distortion areas, which may provide an automatic method for ROI delineation to facilitate the development of a fully-automatic computer-aided diagnosis system using combined AI strategies.

Correlation research of susceptibility single nucleotide polymorphisms and the severity of clinical symptoms in attention deficit hyperactivity disorder.

Objective: To analyze the correlation between susceptibility single nucleotide polymorphisms (SNPs) and the severity of clinical symptoms in children with attention deficit hyperactivity disorder (ADHD), so as to supplement the clinical significance of gene polymorphism and increase our understanding of the association between genetic mutations and ADHD phenotypes. Methods: 193 children with ADHD were included in our study from February 2017 to February 2020 in the Children's ADHD Clinic of the author's medical institution. 23 ADHD susceptibility SNPs were selected based on the literature, and multiple polymerase chain reaction (PCR) targeted capture sequencing technology was used for gene analysis. A series of ADHD-related questionnaires were used to reflect the severity of the disease, and the correlation between the SNPs of specific sites and the severity of clinical symptoms was evaluated. R software was used to search for independent risk factors by multivariate logistic regression and the "corplot" package was used for correlation analysis. Results: Among the 23 SNP loci of ADHD children, no mutation was detected in 6 loci, and 2 loci did not conform to Hardy-Weinberg equilibrium. Of the remaining 15 loci, there were 9 SNPs, rs2652511 (SLC6A3 locus), rs1410739 (OBI1-AS1 locus), rs3768046 (TIE1 locus), rs223508 (MANBA locus), rs2906457 (ST3GAL3 locus), rs4916723 (LINC00461 locus), rs9677504 (SPAG16 locus), rs1427829 (intron) and rs11210892 (intron), correlated with the severity of clinical symptoms of ADHD. Specifically, rs1410739 (OBI1-AS1 locus) was found to simultaneously affect conduct problems, control ability and abstract thinking ability of children with ADHD. Conclusion: There were 9 SNPs significantly correlated with the severity of clinical symptoms in children with ADHD, and the rs1410739 (OBI1-AS1 locus) may provide a new direction for ADHD research. Our study builds on previous susceptibility research and further investigates the impact of a single SNP on the severity of clinical symptoms of ADHD. This can help improve the diagnosis, prognosis and treatment of ADHD.

Al-Decorated C2N Monolayer as a Potential Catalyst for NO Reduction with CO Molecules: A DFT Investigation.

Developing efficient and economical catalysts for NO reduction is of great interest. Herein, the catalytic reduction of NO molecules on an Al-decorated C2N monolayer (Al-C2N) is systematically investigated using density functional theory (DFT) calculations. Our results reveal that the Al-C2N catalyst is highly selective for NO, more so than CO, according to the values of the adsorption energy and charge transfer. The NO reduction reaction more preferably undergoes the (NO)2 dimer reduction process instead of the NO direct decomposition process. For the (NO)2 dimer reduction process, two NO molecules initially co-adsorb to form (NO)2 dimers, followed by decomposition into N2O and Oads species. On this basis, five kinds of (NO)2 dimer structures that initiate four reaction paths are explored on the Al-C2N surface. Particularly, the cis-(NO)2 dimer structures (Dcis-N and Dcis-O) are crucial intermediates for NO reduction, where the max energy barrier along the energetically most favorable pathway (path II) is as low as 3.6 kcal/mol. The remaining Oads species on Al-C2N are then easily reduced with CO molecules, being beneficial for a new catalytic cycle. These results, combined with its low-cost nature, render Al-C2N a promising catalyst for NO reduction under mild conditions.

Larger-Than-Unity External Optical Field Confinement Enabled by Metamaterial-Assisted Comb Waveguide for Ultrasensitive Long-Wave Infrared Gas Spectroscopy.

Nanophotonic waveguides that implement long optical pathlengths on chips are promising to enable chip-scale gas sensors. Nevertheless, current absorption-based waveguide sensors suffer from weak interactions with analytes, limiting their adoptions in most demanding applications such as exhaled breath analysis and trace-gas monitoring. Here, we propose an all-dielectric metamaterial-assisted comb (ADMAC) waveguide to greatly boost the sensing capability. By leveraging large longitudinal electric field discontinuity at periodic high-index-contrast interfaces in the subwavelength grating metamaterial and its unique features in refractive index engineering, the ADMAC waveguide features strong field delocalization into the air, pushing the external optical field confinement factor up to 113% with low propagation loss. Our sensor operates in the important but underdeveloped long-wave infrared spectral region, where absorption fingerprints of plentiful chemical bonds are located. Acetone absorption spectroscopy is demonstrated using our sensor around 7.33 µm, showing a detection limit of 2.5 ppm with a waveguide length of only 10 mm.

Assessment of breast lesions by the Kaiser score for differential diagnosis on MRI: the added value of ADC and machine learning modeling.

OBJECTIVES: To evaluate the diagnostic performance of Kaiser score (KS) adjusted with the apparent diffusion coefficient (ADC) (KS+) and machine learning (ML) modeling. METHODS: A dataset of 402 malignant and 257 benign lesions was identified. Two radiologists assigned the KS. If a lesion with KS > 4 had ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 to become KS+. In order to consider the full spectrum of ADC as a continuous variable, the KS and ADC values were used to train diagnostic models using 5 ML algorithms. The performance was evaluated using the ROC analysis, compared by the DeLong test. The sensitivity, specificity, and accuracy achieved using the threshold of KS > 4, KS+ > 4, and ADC ≤ 1.4 × 10-3 mm2/s were obtained and compared by the McNemar test. RESULTS: The ROC curves of KS, KS+, and all ML models had comparable AUC in the range of 0.883-0.921, significantly higher than that of ADC (0.837, p < 0.0001). The KS had sensitivity = 97.3% and specificity = 59.1%; and the KS+ had sensitivity = 95.5% with significantly improved specificity to 68.5% (p < 0.0001). However, when setting at the same sensitivity of 97.3%, KS+ could not improve specificity. In ML analysis, the logistic regression model had the best performance. At sensitivity = 97.3% and specificity = 65.3%, i.e., compared to KS, 16 false-positives may be avoided without affecting true cancer diagnosis (p = 0.0015). CONCLUSION: Using dichotomized ADC to modify KS to KS+ can improve specificity, but at the price of lowered sensitivity. Machine learning algorithms may be applied to consider the ADC as a continuous variable to build more accurate diagnostic models. KEY POINTS: • When using ADC to modify the Kaiser score to KS+, the diagnostic specificity according to the results of two independent readers was improved by 9.4-9.7%, at the price of slightly degraded sensitivity by 1.5-1.8%, and overall had improved accuracy by 2.6-2.9%. • When the KS and the continuous ADC values were combined to train models by machine learning algorithms, the diagnostic specificity achieved by the logistic regression model could be significantly improved from 59.1 to 65.3% (p = 0.0015), while maintaining at the high sensitivity of KS = 97.3%, and thus, the results demonstrated the potential of ML modeling to further evaluate the contribution of ADC. • When setting the sensitivity at the same levels, the modified KS+ and the original KS have comparable specificity; therefore, KS+ with consideration of ADC may not offer much practical help, and the original KS without ADC remains as an excellent robust diagnostic method.

Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks.

Purpose: To standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard. Materials and Methods: A dataset comprising anteroposterior, lateral, and oblique position lumbar spine x-ray images from 1,389 patients was analyzed in this study. The training set consisted of digital radiography images of 1,070 patients (800, 798, and 623 images of the anteroposterior, lateral, and oblique position, respectively) and the validation set included 319 patients (200, 205, and 156 images of the anteroposterior, lateral, and oblique position, respectively). The quality control standard for lumbar spine x-ray radiography in this study was defined using textbook guidelines of as a reference. An enhanced encoder-decoder fully convolutional network with U-net as the backbone was implemented to segment the anatomical structures in the x-ray images. The segmentations were used to build an automatic assessment method to detect unqualified images. The dice similarity coefficient was used to evaluate segmentation performance. Results: The dice similarity coefficient of the anteroposterior position images ranged from 0.82 to 0.96 (mean 0.91 ± 0.06); the dice similarity coefficient of the lateral position images ranged from 0.71 to 0.95 (mean 0.87 ± 0.10); the dice similarity coefficient of the oblique position images ranged from 0.66 to 0.93 (mean 0.80 ± 0.14). The accuracy, sensitivity, and specificity of the assessment method on the validation set were 0.971-0.990 (mean 0.98 ± 0.10), 0.714-0.933 (mean 0.86 ± 0.13), and 0.995-1.000 (mean 0.99 ± 0.12) for the three positions, respectively. Conclusion: This deep learning-based algorithm achieves accurate segmentation of lumbar spine x-ray images. It provides a reliable and efficient method to identify the shape of the lumbar spine while automatically determining the radiographic image quality.