Transcriptomic Mapping of Human Parotid Gland at Single-Cell Resolution

As the largest salivary gland in oral cavity, the parotid gland plays an important role in initial digesting and lubricating food. The abnormal secretory function of the parotid gland can lead to dental caries and oral mucosal inflammation. In recent years, single-cell RNA sequencing (scRNA-seq) has been used to explore the heterogeneity and diversity of cells in various organs and tissues. However, the transcription profile of the human parotid gland at single-cell resolution has not been reported yet. In this study, we constructed the cell atlas of human parotid gland using the 10× Genomics platform. Characteristic gene analysis identified the biological functions of serous acinar cell populations in secreting digestive enzymes and antibacterial proteins. We revealed the specificity and similarity of the parotid gland compared to other digestive glands through comparative analyses of other published scRNA-seq data sets. We also identified the cell-specific expression of hub genes for Sjögren syndrome in the human parotid gland by integrating the results of genome-wide association studies and bulk RNA-seq, which highlighted the importance of immune cell dysfunction in parotid Sjögren syndrome pathogenesis.

[Current status and research advances on the use of assisted traction technique in endoscopic full-thickness resection]

Endoscopic full-thickness resection (EFTR) allows completely resecting deep submucosal tumors (SMTs) in the gastrointestinal wall, which has a broad application prospect in clinic. However, its application and promotion are limited by complex surgical procedures and high surgical risk. Various auxiliary traction techniques are expected to reduce the operation difficulty and risk of EFTR and improve its operative success rate. To provide a reference for clinicians, we summarize various auxiliary traction techniques in EFTR in this article. The clip-with-line method is simple to operate and widely used, whereas its traction is limited and there is a risk of clip falling off. The snare traction method and the clip-snare traction method has advantage of large traction force, but its thrust is affected by the hardness of snare. The traction point of the grasping forceps traction method is flexible and easy to adjust. Nevertheless, it requires the use of a dual-channel upper endoscope, which is difficult to operate. The transparent cap traction method and the full-thickness resection device traction method takes a short time and is easy to promote, whereas the resectable lesion is limited, and the size of the lesion may affect the success rate. In contrast, the suture loop needle-T-tag tissue anchors assisted method has a large resection range, but the operation is complicated and the feasibility has not been verified. The robot-assisted method has flexible operation and excellent visualization, whereas it is expensive and difficult to operate. There is no report of the application of magnetic anchor technology in EFTR, but it may have good application prospects in the auxiliary traction of EFTR.

Deep learning for automatic calcium scoring in population based cardiovascular screening

Background

High volumes of standardized coronary artery calcium (CAC) scans are generated in screening that need to be scored accurately and efficiently to risk stratify individuals.

Purpose

To evaluate the performance of deep learning based software for automatic coronary calcium scoring in a screening setting.

Methods

Participants from the Robinsca trial that underwent low-dose ECG-triggered cardiac CT for calcium scoring were included. CAC was measured with fully automated deep learning prototype and compared to the original manual assessment of the Robinsca trial. Detection rate, positive Agatston score and risk categorization (0–99, 100–399, ≥400) were compared using McNemar test, ICC, and Cohen's kappa. False negative (FN), false positive (FP) rate and diagnostic accuracy were determined for preventive treatment initiation (cut-off ≥100 AU).

Results

In total, 997 participants were included between December 2015 and June 2016. Median age was 61.0 y (IQR: 11.0) and 54.4% was male. A high agreement for detection was found between deep learning based and manual scoring, κ=0.87 (95% CI 0.85–0.89). Median Agatston score was 58.4 (IQR: 12.3–200.2) and 61.2 (IQR: 13.9–212.9) for deep learning based and manual assessment respectively, ICC was 0.958 (95% CI 0.951–0.964). Reclassification rate was 2.0%, with a very high agreement with κ=0.960 (95% CI: 0.943–0.997), p<0.001. FN rate was 0.7% and FP rate was 0.1% and diagnostic accuracy was 99.2% for initiation of preventive treatment.

Conclusion

Deep learning based software for automatic CAC scoring can be used in a cardiovascular CT screening setting with high accuracy for risk categorization and initiation of preventive treatment.

Funding Acknowledgement

Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Robinsca trial was supported by advanced grant of European Research Council

Dynamical manipulation of a dual-polarization plasmon-induced transparency employing an anisotropic graphene-black phosphorus heterostructure

Dynamical tunable plasmon-induced transparency (PIT) possesses the unique characteristics of controlling light propagation states, which promises numerous potential applications in efficient optical signal processing chips and nonlinear optical devices. However, previously reported configurations are sensitive to polarization and can merely operate under specific single polarization. In this work we propose an anisotropic PIT metamaterial device based on a graphene-black phosphorus (G-BP) heterostructure to realize a dual-polarization tunable PIT effect. The destructive interference coupling between the bright mode and dark modes under the orthogonal polarization state pronounced anisotropic PIT phenomenon. The coupling strength of the PIT system can be modulated by dynamically manipulating the Fermi energy of the graphene via the external electric field voltage. Moreover, the three-level plasmonic system and the coupled oscillator model are employed to explain the underlying mechanism of the PIT effect, and the analytical results show good consistency with the numerical calculations. Compared to the single-polarization PIT devices, the proposed device offers additional degrees of freedom in realizing universal tunable functionalities, which could significantly promote the development of next-generation integrated optical processing chips, optical modulation and slow light devices.

Dynamically tunable coherent perfect absorption in topological insulators at oblique incidence

The effective engineering of light absorption has been the focus of intensive research to realize the novel optoelectronic devices based on a topological insulator, a unique topologically protected surface Dirac-state quantum material with excellent prospects in electronics and photonics. Here, we theoretically proposed a versatile platform for manipulating the light-matter interaction employing the dynamically tunable coherent perfect absorption (CPA) in the topological insulator Bi_1.5Sb_0.5Te_1.8Se_1.2(BSTS). By simply varying the phase difference between two coherent counter-propagating beams, the BSTS-based CPA device can be continuously switched from the high transparency state to the strong absorption state, leading to the modulation of absorption ranging from 0.2% to 99.998%. Under the illumination of TE-polarized wave, the high absorption (>90%) can be implemented within a broad range from 0.47 to 1.51 μm through a proper incident angle alteration. In addition, the quasi-CPA wavelength can be flexibly selected by tuning the bulk thickness of BSTS film while maintaining high modulation depth of 10⁴. Such BSTS-based CPA device with flexible tunability, wide absorption modulation range, and high modulation depth is expected to be utilized in a wide range of potential applications such as in next-generation coherent detectors, coherent modulators, all-optical switches, and signal processors.

Highly sensitive detection of Hg2+ employing SPR sensor modified with chitosan/poly (vinyl alcohol)/SnO2 film

Water contamination by mercury ions (Hg²⁺) causes irreversible and serious effect on the ambient environment, ecological systems, and human health, necessitating further improvement of Hg²⁺ monitoring at low concentrations. Here, we proposed a novel surface plasmon resonance (SPR) sensor for Hg²⁺ detection with desirable advantages of high sensitivity, simple operation, label-free, and low cost, in which the chitosan/poly (vinyl alcohol)/SnO₂ composite film was modified on sensing surface as the active layer for sensitivity enhancement. Benefiting from the relatively high refractive index of SnO₂ nanoparticles, the evanescent field generated at the metal-solution interface can be significantly enhanced, which results in a 5 times improvement of sensitivity. Through appropriate optimization in the aspects of componential constitutions, the sensor exhibits excellent sensitivity of 25.713 nm/μg/L and ultra-low calculated detection limit of 6.61 ng/L(32.95 pM). Such detection limit is strikingly lower than the limitation (10 nM) in drinking water set by the US Environmental Protection Agency. In addition, the as-prepared sensor presents relatively high selectivity for Hg²⁺, attributing to plenty of binding sites for specific adsorption produced by functionalized chitosan/poly (vinyl alcohol) composites, which have been furtherly verified by characterization of FTIR and XPS spectra. The proposed sensor also exhibits great repeatability and good time stability for 15 days. This work provides a promising strategy for developing high-performance SPR sensor for Hg²⁺ detection and a prospective application in environmental monitoring.

Deep Learning in Biomedical Optics

This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC.

Unusually thick shear-softening surface of micrometer-size metallic glasses

The surface of glass is crucial for understanding many fundamental processes in glassy solids. A common notion is that a glass surface is a thin layer with liquid-like atomic dynamics and a thickness of a few tens of nanometers. Here, we measured the shear modulus at the surface of both millimeter-size and micrometer-size metallic glasses (MGs) through high-sensitivity torsion techniques. We found a pronounced shear-modulus softening at the surface of MGs. Compared with the bulk, the maximum decrease in the surface shear modulus (G) for the micro-scale MGs reaches ~27%, which is close to the decrease in the G upon glass transition, yet it still behaves solid-like. Strikingly, the surface thickness estimated from the shear-modulus softening is at least 400 nm, which is approximately one order of magnitude larger than that revealed from the glass dynamics. The unusually thick surface is also confirmed by measurements using X-ray nano-computed tomography, and this may account for the brittle-to-ductile transition of the MGs with size reductions. The unique and unusual properties at the surface of the micrometer-size MGs are physically related to the negative pressure effect during the thermoplastic formation process, which can dramatically reduce the density of the proximate surface region in the supercooled liquid state.

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The shear modulus and thickness of metallic glass (MG) surface is determined through torsion testing on micrometer-size wires

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The surface region of MG wires has a significant shear-modulus softening close to the supercooled liquid, yet still behaves solid-like

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The thickness of the soft surface of MG wires is at least 400 nm, which is about one order of magnitude larger than those revealed from surface dynamics

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The unusually thick surface accounts for the brittle-to-ductile transition of the MGs with size reduction

Resolved Infrared Spectroscopy of Aqueous Molecules Employing Tunable Graphene Plasmons in an Otto Prism

Real-time and in situ detection of aqueous solution is essential for bioanalysis and chemical reactions. However, it is extremely challenging for infrared microscopic measurement because of the large background of water absorption. Here, we proposed a wideband-tunable graphene plasmonic infrared biosensor to detect biomolecules in an aqueous environment, employing attenuated total reflection in an Otto prism configuration and tightly confined plasmons in graphene nanoribbons. Benefiting from the graphene plasmonic electric field enhancement, such a biosensor is able to identify the molecular chemical fingerprints without the interference of water absorption. As a proof of concept, the recombinant protein AG and goat anti-mouse immunoglobulin G (IgG) are used as the sensing analytes, of which the vibrational modes (1669 and 1532 cm^-1) are very close to the OH-bending mode of water (1640 cm^-1). Simulation results show that the fingerprints of protein molecules in the water environment can be selectively enhanced. Therefore, the water absorption is successfully suppressed so that two protein modes can be resolved by sweeping graphene Fermi energy in a wide waveband. By further optimizing the incident angle and graphene mobility to improve the mode energy of graphene plasmons, maximum enhancement factors of 112 and 130 can be achieved for amide I and II bands. Our work provides an effective approach for the highly sensitive and selective in situ identification of aqueous-phase molecular fingerprints in fields of healthcare, food safety, and biochemical sensing.