B1 inhomogeneity corrected CEST MRI based on direct saturation removed omega plot model at 5T.

PURPOSE: CEST can image macromolecules/compounds via detecting chemical exchange between labile protons and bulk water. B1 field inhomogeneity impairs CEST quantification. Conventional B1 inhomogeneity correction methods depend on interpolation algorithms, B1 choices, acquisition number or calibration curves, making reliable correction challenging. This study proposed a novel B1 inhomogeneity correction method based on a direct saturation (DS) removed omega plot model. METHODS: Four healthy volunteers underwent B1 field mapping and CEST imaging under four nominal B1 levels of 0.75, 1.0, 1.5, and 2.0 µT at 5T. DS was resolved using a multi-pool Lorentzian model and removed from respective Z spectrum. Residual spectral signals were used to construct the omega plot as a linear function of 1/ B 1 2 $$ {B}_1^2 $$ , from which corrected signals at nominal B1 levels were calculated. Routine asymmetry analysis was conducted to quantify amide proton transfer (APT) effect. Its distribution across white matter was compared before and after B1 inhomogeneity correction and also with the conventional interpolation approach. RESULTS: B1 inhomogeneity yielded conspicuous artifact on APT images. Such artifact was mitigated by the proposed method. Homogeneous APT maps were shown with SD consistently smaller than that before B1 inhomogeneity correction and the interpolation method. Moreover, B1 inhomogeneity correction from two and four CEST acquisitions yielded similar results, superior over the interpolation method that derived inconsistent APT contrasts among different B1 choices. CONCLUSION: The proposed method enables reliable B1 inhomogeneity correction from at least two CEST acquisitions, providing an effective way to improve quantitative CEST MRI.

Compact mode converter on SOI based on a polygonal subwavelength grating structure.

In this Letter, we design and experimentally demonstrate compact mode converters with a lightning-like and arrow-like polygonal subwavelength grating (SWG) structure on a silicon-on-insulator (SOI) platform, which can convert the TE0 mode to the TE1 and TE2 modes, respectively. The footprints of the proposed TE0-1 and TE0-2 mode converters are only 4.44 × 1.3 and 5.89 × 1.8 µm2, respectively. The experimental results show the mode converters have a low insertion loss (<1 dB) and a broad bandwidth (>50 nm). The measured cross talks of the TE0-1 and TE0-2 mode converters are -7.2 dB and -10.3 dB, respectively. In addition, the proposed mode converters with the SWG structure have the advantage in fabrication, since only a one-step full-etching process is required.

Study of resonant mode coupling in the transverse-mode-conversion based resonator with an anti-symmetric nanobeam Bragg reflector.

The traveling-wave like Fabry-Perot (F-P) resonators based on transverse-mode-conversion have been extensively studied as on-chip filters. However, the incomplete transverse mode conversion will lead to the coupling between two degenerated resonant modes, which brings additional loss and may further induce the resonance splitting. In this paper, we take the transverse-mode-conversion based resonator with anti-symmetric nanobeam Bragg reflector as an example and study the resonant mode coupling in both the direct-coupled and side-coupled resonators. The coupled mode equations are used to model the incomplete transverse mode conversion of Bragg reflector. The resonant mode coupling can be effectively suppressed by carefully designing the phase shifter length and adding the tapered holes. The insertion loss of less than -1 dB can be achieved in the simulation using the two methods. This work is believed to benefit the design of mode-conversion based resonators with low insertion loss and non-splitting line shape.

Photoredox-Catalyzed Radical-Radical Cross-Coupling of Sulfonyl Chlorides with Trifluoroborate Salts.

Sulfones are widely found in natural products and drug molecules. Here, we disclose a strategy for direct synthesis of sulfone compounds with diverse structures by visible-light-catalyzed radical-radical cross-coupling of sulfonyl chlorides and trifluoroborate salts. Allyl, benzyl, vinyl, and aryl trifluoroborates can be successfully cross-coupled with (hetero)aryl and alkyl sulfonyl chlorides, respectively. This strategy features redox neutrality, good substrate generality, simple operation, and benign reaction conditions.

[Analysis of a child with Marfan syndrome due to a novel variant of FBN1 gene].

OBJECTIVE: To carry out genetic testing for a child with Marfan syndrome (MFS) and explore its genotype-phenotype correlation. METHODS: Peripheral blood samples of the child and his parents were collected for the extraction of genomic DNA and subjected to whole exome sequencing (WES). Candidate variants were verified by Sanger sequencing. Functional impact of the variant was predicted by using bioinformatic software. RESULTS: The child, a 13-year-old male, has featured Marfanoid habitus, with arm span exceeding his height, tapering fingers and toes, pectus excavatum and scoliosis, but absence of typical cardiovascular system diseases such as aortic dilation, thoracic-abdominal aortic aneurysm, mitral valve prolapse, and lens dislocation. The child has harbored a novel splice site variant c.7383_7413del (p. N2461Kfs*211) of the FBN1 gene, which was not found in his parents and younger brother. The variant was unreported previously. CONCLUSION: The novel variant of p. N2461Kfs*211 of the FBN1 gene probably underlay the MFS in this child. Above finding has enriched the genotypic and phenotypic spectrum of MFS.

Enantioselective Cascade Annulation of α-Amino-ynones and Enals Enabled by Gold and Oxidative NHC Relay Catalysis.

We report herein an unprecedented gold and oxidative NHC relay catalysis that enables highly enantioselective cascade annulation between readily available α-amino-ynones with enals. This method utilizes the in situ-generated pyrrolin-4-ones as a novel and versatile synthon, which engage with α,ß-unsaturated acylazolium intermediates generated from enals by oxidative NHC catalysis to produce pyrrole-fused lactones in high yield and excellent enantioselectivity. Synthetic utility of the lactone products is also demonstrated by facile conversion to densely functionalized pyrroles and pyrrolin-4-ones in high yields with excellent stereopurity.

Clinical decision-making framework against over-testing based on modeling implicit evaluation criteria.

Different statistical methods include various subjective criteria that can prevent over-testing. However, no unified framework that defines generalized objective criteria for various diseases is available to determine the appropriateness of diagnostic tests recommended by doctors. We present the clinical decision-making framework against over-testing based on modeling the implicit evaluation criteria (CDFO-MIEC). The CDFO-MIEC quantifies the subjective evaluation process using statistics-based methods to identify over-testing. Furthermore, it determines the test's appropriateness with extracted entities obtained via named entity recognition and entity alignment. More specifically, implicit evaluation criteria are defined-namely, the correlation among the diagnostic tests, symptoms, and diseases, confirmation function, and exclusion function. Additionally, four evaluation strategies are implemented by applying statistical methods, including the multi-label k-nearest neighbor and the conditional probability algorithms, to model the implicit evaluation criteria. Finally, they are combined into a classification and regression tree to make the final decision. The CDFO-MIEC also provides interpretability by decision conditions for supporting each clinical decision of over-testing. We tested the CDFO-MIEC on 2,860 clinical texts obtained from a single respiratory medicine department in China with the appropriate confirmation by physicians. The dataset was supplemented with random inappropriate tests. The proposed framework excelled against the best competing text classification methods with a Mean_F1 of 0.9167. This determined whether the appropriate and inappropriate tests were properly classified. The four evaluation strategies captured the features effectively, and they were imperative. Therefore, the proposed CDFO-MIEC is feasible because it exhibits high performance and can prevent over-testing.

Enhancing unsupervised medical entity linking with multi-instance learning.

BACKGROUND: A lot of medical mentions can be extracted from a huge amount of medical texts. In order to make use of these medical mentions, a prerequisite step is to link those medical mentions to a medical domain knowledge base (KB). This linkage of mention to a well-defined, unambiguous KB is a necessary part of the downstream application such as disease diagnosis and prescription of drugs. Such demand becomes more urgent in colloquial and informal situations like online medical consultation, where the medical language is more casual and vaguer. In this article, we propose an unsupervised method to link the Chinese medical symptom mentions to the ICD10 classification in a colloquial background. METHODS: We propose an unsupervised entity linking model using multi-instance learning (MIL). Our approach builds on a basic unsupervised entity linking method (named BEL), which is an embedding similarity-based EL model in this paper, and uses MIL training paradigm to boost the performance of BEL. First, we construct a dataset from an unlabeled large-scale Chinese medical consultation corpus with the help of BEL. Subsequently, we use a variety of encoders to obtain the representations of mention-context and the ICD10 entities. Then the representations are fed into a ranking network to score candidate entities. RESULTS: We evaluate the proposed model on the test dataset annotated by professional doctors. The evaluation results show that our method achieves 60.34% accuracy, exceeding the fundamental BEL by 1.72%. CONCLUSIONS: We propose an unsupervised entity linking method to the entity linking in the medical domain, using MIL training manner. We annotate a test set for evaluation. The experimental results show that our model behaves better than the fundamental model BEL, and provides an insight for future research.

Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion.

BACKGROUND: Knowledge graphs (KGs), especially medical knowledge graphs, are often significantly incomplete, so it necessitating a demand for medical knowledge graph completion (MedKGC). MedKGC can find new facts based on the existed knowledge in the KGs. The path-based knowledge reasoning algorithm is one of the most important approaches to this task. This type of method has received great attention in recent years because of its high performance and interpretability. In fact, traditional methods such as path ranking algorithm take the paths between an entity pair as atomic features. However, the medical KGs are very sparse, which makes it difficult to model effective semantic representation for extremely sparse path features. The sparsity in the medical KGs is mainly reflected in the long-tailed distribution of entities and paths. Previous methods merely consider the context structure in the paths of knowledge graph and ignore the textual semantics of the symbols in the path. Therefore, their performance cannot be further improved due to the two aspects of entity sparseness and path sparseness. METHODS: To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC. By using the pre-trained model BERT, combining the textual semantic representations of the entities and the relationships, we model the task of symbolic reasoning in the medical KG as a numerical computing issue in textual semantic representation. RESULTS: Experiments results on the publicly authoritative Chinese symptom knowledge graph demonstrated that the proposed method is significantly better than the state-of-the-art path-based knowledge graph reasoning methods, and the average performance is improved by 5.83% for all relations. CONCLUSIONS: In this paper, we propose two new knowledge graph reasoning algorithms, which adopt textual semantic information of entities and paths and can effectively alleviate the sparsity problem of entities and paths in the MedKGC. As far as we know, it is the first method to use pre-trained language models and text path representations for medical knowledge reasoning. Our method can complete the impaired symptom knowledge graph in an interpretable way, and it outperforms the state-of-the-art path-based reasoning methods.

Relation between blood glucose and the prognosis of severe coronavirus disease 2019. / 血糖与重型2019å† çŠ¶ç— æ¯’ç— é¢„åŽçš„å ³ç³».

OBJECTIVES: To describe the clinical characteristics and outcomes of severely ill patients with coronavirus disease 2019, and to investigate the relationship between plasma glucose level and the prognosis of severely ill patients with coronavirus disease 2019. METHODS: We enrolled 52 severely ill patients with coronavirus disease 2019. Among them, 12 cases progressed to critical illness. The clinical and biochemical characteristics of severely and critically ill patients were compared. RESULTS: Compared with the severely ill patients, critically ill patients had higher white blood cell and neutrophil counts, as well as higher levels of D-dimer, IL-6 and C-reactive protein (all P<0.05). Before treatment, the fasting plasma glucose (FPG) levels were significantly higher in the critically ill patient's group [(10.23±3.71) mmol/L] compared to those in the severely ill patients [(7.12±3.35) mmol/L, P<0.05]. After adjusting for age, gender, and course of the disease, fasting blood glucose at admission (OR=1.308, 95% CI 1.066 to 1.606, P=0.01) and hyperglycemia at admission (OR=29.198, 95% CI 2.903 to 293.639, P=0.004) were closely related to whether severely ill patients progressed to critical patients with coronavirus disease 2019. In our study, 15 (34.8%) of the severely ill and 10 (83.3%) critically ill patients received the steroid treatment. Compared with the severely ill patients, the FPG levels in critically ill patients were higher (P<0.05). CONCLUSIONS: Fasting hyperglycemia at admission is a significant predictor for the prognosis of severely ill patients with coronavirus disease 2019. Closely monitoring and the optimal management of hyperglycemia may improve the prognosis of patients with coronavirus disease 2019.

Electromagnetic Field and Radio Frequency Circuit Co-Simulation for Magnetic Resonance Imaging Dual-Tuned Radio Frequency Coils.

Electromagnetic (EM) field simulation plays a key role in the design of magnetic resonance imaging radio frequency (RF) coils. However, the values of the components in tuning and matching circuits often need to be iterated repeatedly in the conventional simulation method in order to achieve optimal scattering parameters. This leads to a time-consuming optimization to tune and match RF coils, particularly dual-tuned coils that are comprised of multiple lumped elements. A method combining EM field simulation and circuit simulation was employed in this paper, which can dramatically improve simulation efficiency for the optimization of the values of the lumped elements and corresponding EM field distribution. A systematical theoretical analysis of the co-simulation method was presented. To validate the accuracy and efficiency of the co-simulation method, a comparison study was conducted between the conventional simulation and the proposed co-simulation approaches.

Planar waveguide moiré grating.

We propose a new kind of planar waveguide Bragg grating structure, i.e., planar waveguide moiré grating (MG), which is formed by two transverse adjacent gratings with slightly different Bragg wavelengths. It is found that this kind of structure shows the same light properties as the conventional MG that is realized by superimposing two Bragg gratings. Because the proposed MG structure is a planar pattern, the fabrication becomes much easier if applying a semiconductor microfabrication process, which is very beneficial for its applications in photonic integrated devices. Similar to the well-known Vernier effect, the coupling coefficient distribution can be easily adjusted by the alignment of the two adjacent gratings. Consequently, some special grating profiles can be achieved, such as perfect apodization with two sides of the coupling coefficient approaching zero. One important potential application of these specific features is the distributed feedback (DFB) semiconductor laser for improved properties, such as reduced spatial-hole burning and more power extraction. Some design examples are also given in this paper.

Study on DFB semiconductor laser array integrated with grating reflector based on reconstruction-equivalent-chirp technique.

A 4-channel distributed feedback (DFB) semiconductor laser array with incorporation of a grating reflector utilizing reconstruction-equivalent-chirp technique is theoretically studied and experimentally demonstrated. By integrating with a grating reflector, 40% increase of slope efficiency, about 10mA decrease of threshold current and 7dB increase of side mode suppression ratio (SMSR) are achieved with a deviation of wavelength spacing being less than 0.07nm. The SMSRs of all the lasers are higher than 60dB.

Multi-wavelength distributed feedback laser array with very high wavelength-spacing precision.

Multi-wavelength laser arrays (MLAs) with a high wavelength-spacing precision are designed and fabricated. The MLAs are realized by the reconstruction-equivalent chirp (REC) technique, and anti-reflection structures are used to decrease the wavelength deviation. Six multi-wavelength laser arrays are fabricated and tested. Statistical results show that 81.1% of all 95 lasers have wavelength deviations within ±0.1 nm, and all of them are within ±0.2 nm. The mean wavelength spacing is 1.605 nm, and the standard deviation is 0.132 nm.

[Association of GSTM1 and GSTT1 polymorphisms with noise-induced hearing loss: a meta-analysis].

OBJECTIVE: To evaluate the association of glutathione S-transferase M1 (GSTM1) and glutathione S-transferase T1 (GSTT1) polymorphisms with noise-induced hearing loss. METHODS: The Cochrane library, PubMed, OVID, EMBASE, Springer, Wanfang Data, VIP, CNKI, and CBM were searched to collect case-control studies on GSTM1 or GSTT1 polymorphism and noise-induced hearing loss. The articles meeting the inclusion criteria were reviewed systematically, and the reported data were aggregated using Revman 5.0. RESULTS: Five studies were included in the meta-analysis. The meta-analysis and subgroup analysis showed that the persons with GSTM1 null genotype had an increased risk of noise-induced hearing loss compared with those with GSTM1 wild genotype (OR = 1.37, 95%CI: 1.13∼1.66); in the Chinese population, the risk of noise-induced hearing loss was higher in persons with GSTM1 null genotype than in those with GSTM1 wild genotype (OR = 1.5, 95%CI: 1.2∼1.86); there was no significant difference in the risk of noise-induced hearing loss between persons with GSTT1 null and wild genotypes. CONCLUSION: GSTM1 polymorphism is related to noise-induced hearing loss, but GSTT1 polymorphism is unrelated to this condition.

Brandt's vole hole detection and counting method based on deep learning and unmanned aircraft system.

Rodents are essential to the balance of the grassland ecosystem, but their population outbreak can cause major economic and ecological damage. Rodent monitoring is crucial for its scientific management, but traditional methods heavily depend on manual labor and are difficult to be carried out on a large scale. In this study, we used UAS to collect high-resolution RGB images of steppes in Inner Mongolia, China in the spring, and used various object detection algorithms to identify the holes of Brandt's vole (Lasiopodomys brandtii). Optimizing the model by adjusting evaluation metrics, specifically, replacing classification strategy metrics such as precision, recall, and F1 score with regression strategy-related metrics FPPI, MR, and MAPE to determine the optimal threshold parameters for IOU and confidence. Then, we mapped the distribution of vole holes in the study area using position data derived from the optimized model. Results showed that the best resolution of UAS acquisition was 0.4 cm pixel-1, and the improved labeling method improved the detection accuracy of the model. The FCOS model had the highest comprehensive evaluation, and an R2 of 0.9106, RMSE of 5.5909, and MAPE of 8.27%. The final accuracy of vole hole counting in the stitched orthophoto was 90.20%. Our work has demonstrated that UAS was able to accurately estimate the population of grassland rodents at an appropriate resolution. Given that the population distribution we focus on is important for a wide variety of species, our work illustrates a general remote sensing approach for mapping and monitoring rodent damage across broad landscapes for studies of grassland ecological balance, vegetation conservation, and land management.

Biotin-new indocyanine green conjugate: Synthesis, in vitro photocytotoxicity and in vivo biodistribution.

New indocyanine green (ICG) (IR820) is one of the ICG derivatives and attracts increasing attention for cancer management. However, the unsatisfactory tumor targeting ability of IR820 significantly limits its applications for cancer theranostics. Biotin receptor is overexpressed on the membrane of various tumor cells and biotin modified nanocarriers have been reported to enhance the tumor targeting ability on several tumor types. In this work, biotin-new ICG conjugate (Biotin-SS-IR820) was prepared for tumor-targeted IR820 delivery. Biotin and IR820 were coupled through cystamine. The synthesized Biotin-SS-IR820 was characterized by 1 H NMR, FT-IR and HRMS. The in vitro singlet oxygen generation study shows that Biotin-SS-IR820 exhibits similar singlet oxygen generation as compared to IR820 upon 660 nm laser irradiation (0.8 W/cm2 ). The cellular uptake study shows that Biotin-SS-IR820 shows enhanced cellular uptake amount as compared to IR820 on 4T1 cells. As a result, Biotin-SS-IR820 displays enhanced in vitro photodynamic therapeutic effect against 4T1 cells as compared to IR820. In in vivo biodistribution study, Biotin-SS-IR820 shows enhanced tumor accumulation as compared to IR820. Biotin-SS-IR820 developed in this work shows promising prospects for targeted delivery of IR820 to biotin receptor overexpressed tumors.

Feasibility of a deep learning algorithm to achieve the low-dose 68Ga-FAPI/the fast-scan PET images: a multicenter study.

OBJECTIVES: Our work aims to study the feasibility of a deep learning algorithm to reduce the 68Ga-FAPI radiotracer injected activity and/or shorten the scanning time and to investigate its effects on image quality and lesion detection ability. METHODS: The data of 130 patients who underwent 68Ga-FAPI positron emission tomography (PET)/CT in two centers were studied. Predicted full-dose images (DL-22%, DL-28% and DL-33%) were obtained from three groups of low-dose images using a deep learning method and compared with the standard-dose images (raw data). Injection activity for full-dose images was 2.16 ± 0.61 MBq/kg. The quality of the predicted full-dose PET images was subjectively evaluated by two nuclear physicians using a 5-point Likert scale, and objectively evaluated by the peak signal-to-noise ratio, structural similarity index and root mean square error. The maximum standardized uptake value and the mean standardized uptake value (SUVmean) were used to quantitatively analyze the four volumes of interest (the brain, liver, left lung and right lung) and all lesions, and the lesion detection rate was calculated. RESULTS: Data showed that the DL-33% images of the two test data sets met the clinical diagnosis requirements, and the overall lesion detection rate of the two centers reached 95.9%. CONCLUSION: Through deep learning, we demonstrated that reducing the 68Ga-FAPI injected activity and/or shortening the scanning time in PET/CT imaging was feasible. In addition, 68Ga-FAPI dose as low as 33% of the standard dose maintained acceptable image quality. ADVANCES IN KNOWLEDGE: This is the first study of low-dose 68Ga-FAPI PET images from two centers using a deep learning algorithm.

A Novel and Rapid Smear Cytomorphology Detection Strategy Based on Upconversion Nanoparticles Immunolabeling Integrated with Wright's Staining for Accurate Diagnosis of Leukemia.

Background: Accurate, sensitive, and rapid identification of leukemia cells in blood and bone marrow is of paramount significance for clinical diagnosis. An integrative technique combining traditional cytomorphology with immunophenotyping was proposed to improve the diagnostic efficiency in leukemia. On account of high photostability, biocompatibility, and signal-to-background ratio, upconversion nanoparticles (UCNPs) as luminescent labels have drawn substantial research scrutiny in immunolabeling. Methods: To achieve simultaneous determination, NaYF4:Yb,Er UCNPs were coupled with CD38 antibodies to construct immunofluorescence probes that were developed to bind to diffuse large B cell lymphoma (DLBCL) cells, followed by Wright's staining that has been widely used in clinical work for morphological diagnosis. Further, the experimental conditions were optimized, such as medium, slice-making method, antibody dosage, incubation time, etc. Results: The cell morphology and immunolabeling could be observed simultaneously, and its simple operation rendered it a possibility for clinical diagnosis. The developed immunolabeling assay could achieve DLBCL cell counting with high reproducibility and stability, and the detection limit was as low as 1.54 cell/slice (>3 σ/s). Moreover, the proposed method also realized real blood and bone marrow sample analysis, and the results were consistent with the clinical diagnosis. Conclusion: Overall, this strategy can be carried out after simple laboratory training and has prospective biomedical applications in leukemia classification, diagnosis validation, and differential diagnostics.

Analytic models of spectral responses of fiber-grating-based interferometers on FMC theory.

In this paper the analytic models (AMs) of the spectral responses of fiber-grating-based interferometers are derived from the Fourier mode coupling (FMC) theory proposed recently. The interferometers include Fabry-Perot cavity, Mach-Zehnder and Michelson interferometers, which are constructed by uniform fiber Bragg gratings and long-period fiber gratings, and also by Gaussian-apodized ones. The calculated spectra based on the analytic models are achieved, and compared with the measured cases and those on the transfer matrix (TM) method. The calculations and comparisons have confirmed that the AM-based spectrum is in excellent agreement with the TM-based one and the measured case, of which the efficiency is improved up to ~2990 times that of the TM method for non-uniform-grating-based in-fiber interferometers.