A silicon photoanode protected with TiO2/stainless steel bilayer stack for solar seawater splitting.

Photoelectrochemical seawater splitting is a promising route for direct utilization of solar energy and abundant seawater resources for H2 production. However, the complex salinity composition in seawater results in intractable challenges for photoelectrodes. This paper describes the fabrication of a bilayer stack consisting of stainless steel and TiO2 as a cocatalyst and protective layer for Si photoanode. The chromium-incorporated NiFe (oxy)hydroxide converted from stainless steel film serves as a protective cocatalyst for efficient oxygen evolution and retarding the adsorption of corrosive ions from seawater, while the TiO2 is capable of avoiding the plasma damage of the surface layer of Si photoanode during the sputtering of stainless steel catalysts. By implementing this approach, the TiO2 layer effectively shields the vulnerable semiconductor photoelectrode from the harsh plasma sputtering conditions in stainless steel coating, preventing surface damages. Finally, the Si photoanode with the bilayer stack inhibits the adsorption of chloride and realizes 167 h stability in chloride-containing alkaline electrolytes. Furthermore, this photoanode also demonstrates stable performance under alkaline natural seawater for over 50 h with an applied bias photon-to-current efficiency of 2.62%.

Retinal Layer Segmentation in OCT Images With Boundary Regression and Feature Polarization.

The geometry of retinal layers is an important imaging feature for the diagnosis of some ophthalmic diseases. In recent years, retinal layer segmentation methods for optical coherence tomography (OCT) images have emerged one after another, and huge progress has been achieved. However, challenges due to interference factors such as noise, blurring, fundus effusion, and tissue artifacts remain in existing methods, primarily manifesting as intra-layer false positives and inter-layer boundary deviation. To solve these problems, we propose a method called Tightly combined Cross-Convolution and Transformer with Boundary regression and feature Polarization (TCCT-BP). This method uses a hybrid architecture of CNN and lightweight Transformer to improve the perception of retinal layers. In addition, a feature grouping and sampling method and the corresponding polarization loss function are designed to maximize the differentiation of the feature vectors of different retinal layers, and a boundary regression loss function is devised to constrain the retinal boundary distribution for a better fit to the ground truth. Extensive experiments on four benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance in dealing with problems of false positives and boundary distortion. The proposed method ranked first in the OCT Layer Segmentation task of GOALS challenge held by MICCAI 2022. The source code is available at https://www.github.com/tyb311/TCCT.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer.

BACKGROUND: Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis. METHODS: Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve. RESULTS: From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients. CONCLUSIONS: This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

A lightweight network guided with differential matched filtering for retinal vessel segmentation.

The geometric morphology of retinal vessels reflects the state of cardiovascular health, and fundus images are important reference materials for ophthalmologists. Great progress has been made in automated vessel segmentation, but few studies have focused on thin vessel breakage and false-positives in areas with lesions or low contrast. In this work, we propose a new network, differential matched filtering guided attention UNet (DMF-AU), to address these issues, incorporating a differential matched filtering layer, feature anisotropic attention, and a multiscale consistency constrained backbone to perform thin vessel segmentation. The differential matched filtering is used for the early identification of locally linear vessels, and the resulting rough vessel map guides the backbone to learn vascular details. Feature anisotropic attention reinforces the vessel features of spatial linearity at each stage of the model. Multiscale constraints reduce the loss of vessel information while pooling within large receptive fields. In tests on multiple classical datasets, the proposed model performed well compared with other algorithms on several specially designed criteria for vessel segmentation. DMF-AU is a high-performance, lightweight vessel segmentation model. The source code is at https://github.com/tyb311/DMF-AU.

Quantitative Ratiometric Biosensors Based on Fluorescent Ferrocene-Modified Histidine Dipeptide Nanoassemblies.

Fluorescent proteins (FPs) provide a ratiometric readout for quantitative assessment of the destination of internalized biomolecules. FP-inspired peptide nanostructures that can compete with FPs in their capacity are the most preferred building blocks for the synthesis of fluorescent soft matter. However, realizing a ratiometric emission from a single peptide fluorophore remains exclusive since multicolor emission is a rare property in peptide nanostructures. Here, we describe a bioinspired peptidyl platform for ratiometric intracellular quantitation by employing a single ferrocene-modified histidine dipeptide. The intensiometric ratio of green to blue fluorescence correlates linearly with the concentration of the peptide by three orders of magnitude. The ratiometric fluorescence of the peptide is an assembly-induced emission originating from hydrogen bonds and aromatic interactions. Additionally, modular design enables ferrocene-modified histidine dipeptides to use as a general platform for the construction of intricate peptides that retain the ratiometric fluorescent properties. The ratiometric peptide technique promises flexibility in the design of a wide spectrum of stoichiometric biosensors for quantitatively understanding the trafficking and subcellular fate of biomolecules.

The Safety of Cold-Chain Food in Post-COVID-19 Pandemic: Precaution and Quarantine.

Since the outbreak of coronavirus disease-19 (COVID-19), cold-chain food contamination caused by the pathogenic severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has attracted huge concern. Cold-chain foods provide a congenial environment for SARS-CoV-2 survival, which presents a potential risk for public health. Strengthening the SARS-CoV-2 supervision of cold-chain foods has become the top priority in many countries. Methodologically, the potential safety risks and precaution measures of SARS-CoV-2 contamination on cold-chain food are analyzed. To ensure the safety of cold-chain foods, the advances in SARS-CoV-2 detection strategies are summarized based on technical principles and target biomarkers. In particular, the techniques suitable for SARS-CoV-2 detection in a cold-chain environment are discussed. Although many quarantine techniques are available, the field-based quarantine technique on cold-chain food with characteristics of real-time, sensitive, specific, portable, and large-scale application is urgently needed.

Color-Tunable Fluorescent Hierarchical Nanoassemblies with Concentration-Encoded Emission.

Cephalopods possess a dynamic coloration behavior to change their iridescence due to the concentration-induced optical properties of chromatophores and hierarchical assembly of reflectin. However, cephalopods rarely have iridescence in the darkfield. It would be interesting to develop color-tunable fluorescent hierarchical nanoassemblies with concentration-encoded emission. Herein, to construct the bioavailable fluorophore with dynamic coloration properties, a histidine-rich peptide is designed, which can self-assemble into hierarchical nanoassemblies stabilized by hydrogen bonds and π-π stacking interactions. The peptidyl nanoassemblies emit fluorescent iridescence, encompassing the blue to orange region due to the assembly-induced emission. The fluorescence of histidine-rich peptides is color-tunable and reversible, which can be dynamically controlled in a concentration-encoded mode. Due to the coloration ability of histidine-rich peptides, fluorescent polychromatic human cells are developed, highlighting its potential role as a fluorescent candidate for future applications such as bioimaging, implantable light-emitting diodes, and photochromic camouflage.

Retinal Vessel Segmentation With Skeletal Prior and Contrastive Loss.

The morphology of retinal vessels is closely associated with many kinds of ophthalmic diseases. Although huge progress in retinal vessel segmentation has been achieved with the advancement of deep learning, some challenging issues remain. For example, vessels can be disturbed or covered by other components presented in the retina (such as optic disc or lesions). Moreover, some thin vessels are also easily missed by current methods. In addition, existing fundus image datasets are generally tiny, due to the difficulty of vessel labeling. In this work, a new network called SkelCon is proposed to deal with these problems by introducing skeletal prior and contrastive loss. A skeleton fitting module is developed to preserve the morphology of the vessels and improve the completeness and continuity of thin vessels. A contrastive loss is employed to enhance the discrimination between vessels and background. In addition, a new data augmentation method is proposed to enrich the training samples and improve the robustness of the proposed model. Extensive validations were performed on several popular datasets (DRIVE, STARE, CHASE, and HRF), recently developed datasets (UoA-DR, IOSTAR, and RC-SLO), and some challenging clinical images (from RFMiD and JSIEC39 datasets). In addition, some specially designed metrics for vessel segmentation, including connectivity, overlapping area, consistency of vessel length, revised sensitivity, specificity, and accuracy were used for quantitative evaluation. The experimental results show that, the proposed model achieves state-of-the-art performance and significantly outperforms compared methods when extracting thin vessels in the regions of lesions or optic disc. Source code is available at https://www.github.com/tyb311/SkelCon.

A Local and Global Feature Disentangled Network: Toward Classification of Benign-Malignant Thyroid Nodules From Ultrasound Image.

Thyroid nodules are one of the most common nodular lesions. The incidence of thyroid cancer has increased rapidly in the past three decades and is one of the cancers with the highest incidence. As a non-invasive imaging modality, ultrasonography can identify benign and malignant thyroid nodules, and it can be used for large-scale screening. In this study, inspired by the domain knowledge of sonographers when diagnosing ultrasound images, a local and global feature disentangled network (LoGo-Net) is proposed to classify benign and malignant thyroid nodules. This model imitates the dual-pathway structure of human vision and establishes a new feature extraction method to improve the recognition performance of nodules. We use the tissue-anatomy disentangled (TAD) block to connect the dual pathways, which decouples the cues of local and global features based on the self-attention mechanism. To verify the effectiveness of the model, we constructed a large-scale dataset and conducted extensive experiments. The results show that our method achieves an accuracy of 89.33%, which has the potential to be used in the clinical practice of doctors, including early cancer screening procedures in remote or resource-poor areas.

Dynamic 3D radiomics analysis using artificial intelligence to assess the stage of COVID-19 on CT images.

OBJECTIVE: To develop a dynamic 3D radiomics analysis method using artificial intelligence technique for automatically assessing four disease stages (i.e., early, progressive, peak, and absorption stages) of COVID-19 patients on CT images. METHODS: The dynamic 3D radiomics analysis method was composed of three AI algorithms (the lung segmentation, lesion segmentation, and stage-assessing AI algorithms) that were trained and tested on 313,767 CT images from 520 COVID-19 patients. This proposed method used 3D lung lesion that was segmented by the lung and lesion segmentation algorithms to extract radiomics features, and then combined with clinical metadata to assess the possible stage of COVID-19 patients using stage-assessing algorithm. Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used to evaluate diagnostic performance. RESULTS: Of 520 patients, 66 patients (mean age, 57 years ± 15 [standard deviation]; 35 women), including 203 CT scans, were tested. The dynamic 3D radiomics analysis method used 30 features, including 27 radiomics features and 3 clinical features to assess the possible disease stage of COVID-19 with an accuracy of 90%. For the prediction of each stage, the AUC of stage 1 was 0.965 (95% CI: 0.934, 0.997), AUC of stage 2 was 0.958 (95% CI: 0.931, 0.984), AUC of stage 3 was 0.998 (95% CI: 0.994, 1.000), and AUC of stage 4 was 0.975 (95% CI: 0.956, 0.994). CONCLUSION: With high diagnostic performance, the dynamic 3D radiomics analysis using artificial intelligence could represent a potential tool for helping hospitals make appropriate resource allocations and follow-up of treatment response. KEY POINTS: • The AI segmentation algorithms were able to accurately segment the lung and lesion of COVID-19 patients of different stages. • The dynamic 3D radiomics analysis method successfully extracted the radiomics features from the 3D lung lesion. • The stage-assessing AI algorithm combining with clinical metadata was able to assess the four stages with an accuracy of 90%, a macro-average AUC of 0.975.

Observation of Spin-Momentum-Layer Locking in a Centrosymmetric Crystal.

The spin polarization in nonmagnetic materials is conventionally attributed to the outcome of spin-orbit coupling when the global inversion symmetry is broken. The recently discovered hidden spin polarization indicates that a specific atomic site asymmetry could also induce measurable spin polarization, leading to a paradigm shift in research on centrosymmetric crystals for potential spintronic applications. Here, combining spin- and angle-resolved photoemission spectroscopy and theoretical calculations, we report distinct spin-momentum-layer locking phenomena in a centrosymmetric, layered material, BiOI. The measured spin is highly polarized along the Brillouin zone boundary, while the same effect almost vanishes around the zone center due to its nonsymmorphic crystal structure. Our work demonstrates the existence of momentum-dependent hidden spin polarization and uncovers the microscopic mechanism of spin, momentum, and layer locking to each other, thus shedding light on the design metrics for future spintronic materials.

A deep-learning-based framework for severity assessment of COVID-19 with CT images.

Millions of positive COVID-19 patients are suffering from the pandemic around the world, a critical step in the management and treatment is severity assessment, which is quite challenging with the limited medical resources. Currently, several artificial intelligence systems have been developed for the severity assessment. However, imprecise severity assessment and insufficient data are still obstacles. To address these issues, we proposed a novel deep-learning-based framework for the fine-grained severity assessment using 3D CT scans, by jointly performing lung segmentation and lesion segmentation. The main innovations in the proposed framework include: 1) decomposing 3D CT scan into multi-view slices for reducing the complexity of 3D model, 2) integrating prior knowledge (dual-Siamese channels and clinical metadata) into our model for improving the model performance. We evaluated the proposed method on 1301 CT scans of 449 COVID-19 cases collected by us, our method achieved an accuracy of 86.7% for four-way classification, with the sensitivities of 92%, 78%, 95%, 89% for four stages. Moreover, ablation study demonstrated the effectiveness of the major components in our model. This indicates that our method may contribute a potential solution to severity assessment of COVID-19 patients using CT images and clinical metadata.

SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images.

Automatic segmentation of lung opacification from computed tomography (CT) images shows excellent potential for quickly and accurately quantifying the infection of Coronavirus disease 2019 (COVID-19) and judging the disease development and treatment response. However, some challenges still exist, including the complexity and variability features of the opacity regions, the small difference between the infected and healthy tissues, and the noise of CT images. Due to limited medical resources, it is impractical to obtain a large amount of data in a short time, which further hinders the training of deep learning models. To answer these challenges, we proposed a novel spatial- and channel-wise coarse-to-fine attention network (SCOAT-Net), inspired by the biological vision mechanism, for the segmentation of COVID-19 lung opacification from CT images. With the UNet++ as basic structure, our SCOAT-Net introduces the specially designed spatial-wise and channel-wise attention modules, which serve to collaboratively boost the attention learning of the network and extract the efficient features of the infected opacification regions at the pixel and channel levels. Experiments show that our proposed SCOAT-Net achieves better results compared to several state-of-the-art image segmentation networks and has acceptable generalization ability.

Bone marrow mesenchymal cells: polymorphism associated with transformation of rough endoplasmic reticulum.

To understand the behavior and function of bone-marrow mesenchymal cells (BMMCs), we overviewed the morphological presentation of BMMCs in bone-marrow granules (b-BMMCs), isolated BMMCs (i-BMMCs), and BMMCs (c-BMMCs) cultured in H4434 methylcellulose semisolid and MEM media. All samples were derived from bone-marrow aspirates of 30 patients with hematocytopenia. Light microscopy exhibited b-BMMCs and i-BMMCs characterized by abundant cytoplasm and irregular shape in bone-marrow smears, as well as c-BMMCs in culture conditions. Scanning electron microscopy demonstrated cultured c-BMMCs with a sheet-like feature enveloping hematopoietic cells. Transmission electron microscopy revealed b-BMMCs constructing a honeycomb-like structure by thin bifurcate processes among hematopoietic cells. Furthermore, i-BMMCs had bifurcate parapodiums on the surface and prominent rough endoplasmic reticulum (rER) connected with the plasmalemma of the parapodiums. The detailed images suggested that rER may serve as a membrane resource for plasmalemmal expansion in BMMCs in bone marrow.

Foam cell origination from degenerated vascular smooth muscle cells in atherosclerosis: An ultrastructural study on hyperlipidemic rabbits.

To clarify foam cell origination in atherosclerosis, a series of morphologic and ultrastructural alterations of vascular smooth muscle cells (VSMCs) and foam cells were studied by light and electron microscopy in atherosclerotic aortas from hyperlipidemic rabbits induced for 5 weeks. The study exhibited that VSMCs were severely degenerated and damaged, including irregular shapes, expanded mitochondria, aplenty lipid droplets, and disarranged myofilaments in cytoplasm in media adjacent to atheromatic bottoms. Most lipid laden cells shared interphase structures of VSMCs and foam cells, and some dissolved spindle cells contained lipid droplets, lipofuscin, and rod-like CCs in cytoplasm also. The result demonstrated that VSMCs were degenerated and transformed into foam cells in atherosclerosis, which was responsible for the accumulation of lipid and cholesterol crystals in atherosclerotic arteries.

Magnetic Order-Induced Polarization Anomaly of Raman Scattering in 2D Magnet CrI3.

The recent discovery of 2D magnets has revealed various intriguing phenomena due to the coupling between spin and other degrees of freedoms (such as helical photoluminescence, nonreciprocal SHG). Previous research on the spin-phonon coupling effect mainly focuses on the renormalization of phonon frequency. Here we demonstrate that the Raman polarization selection rules of optical phonons can be greatly modified by the magnetic ordering in 2D magnet CrI3. For monolayer samples, the dominant A1g peak shows an abnormally high intensity in the cross-polarization channel at low temperatures, which is forbidden by the selection rule based on the lattice symmetry. For the bilayer, this peak is absent in the cross-polarization channel for the layered antiferromagnetic (AFM) state and reappears when it is tuned to the ferromagnetic (FM) state by an external magnetic field. Our findings shed light on exploring the emergent magneto-optical effects in 2D magnets.

One cell one niche: hematopoietic microenvironments constructed by bone marrow stromal cells with fibroblastic and histiocytic features.

Hematopoietic microenvironments have been extensively studied, especially focusing on regulation of hematopoietic stem cells (HSCs) in HSC niche following progress of molecular biology in resent years. Based on prior morphological achievements from 1970s, the characteristics of cellular compartments and bone marrow stromal cells (BMSCs) were studied ultrastructurally in human and mice bone marrow in the present study. The samples, human bone marrow granules, were collected from bone marrow aspirations (BMAs) of 20 patients with hematocytopenia and isolated BMSCs were found undesignedly in nucleated cells of BMAs of the patients. Femoral bone marrow samples were collected from 6-week-old three sacrificed mice. Detailed images illustrated maturing hematopoietic cells harbored individually in honeycomb-like microenvironment constituted by BMSCs that shared of fibroblastic and histiocytic characteristics in hematopoietic microenvironments of human and mice bone marrow.

Thrombopoietin knock-in augments platelet generation from human embryonic stem cells.

BACKGROUND: Refinement of therapeutic-scale platelet production in vitro will provide a new source for transfusion in patients undergoing chemotherapy or radiotherapy. However, procedures for cost-effective and scalable platelet generation remain to be established. METHODS: In this study, we established human embryonic stem cell (hESC) lines containing knock-in of thrombopoietin (TPO) via CRISPR/Cas9-mediated genome editing. The expression and secretion of TPO was detected by western blotting and enzyme-linked immunosorbent assay. Then, we tested the potency for hematopoietic differentiation by coculturing the cells with mAGM-S3 cells and measured the generation of CD43+ and CD45+ hematopoietic progenitor cells (HPCs). The potency for megakaryocytic differentiation and platelet generation of TPO knock-in hESCs were further detected by measuring the expression of CD41a and CD42b. The morphology and function of platelets were analyzed with electronic microscopy and aggregation assay. RESULTS: The TPO gene was successfully inserted into the AAVS1 locus of the hESC genome and two cell lines with stable TPO expression and secretion were established. TPO knock-in exerts minimal effects on pluripotency but enhances early hematopoiesis and generation of more HPCs. More importantly, upon its knock-in, TPO augments megakaryocytic differentiation and platelet generation. In addition, the platelets derived from hESCs in vitro are functionally and morphologically comparable to those found in peripheral blood. Furthermore, TPO knock-in can partially replace the large quantities of extrinsic TPO necessary for megakaryocytic differentiation and platelet generation. CONCLUSIONS: Our results demonstrate that autonomous production of cytokines in hESCs may become a powerful approach for cost-effective and large-scale platelet generation in translational medicine.

A novel anemia associated with membranous cytoplasm degeneration in 16 patients: an ultrastructural study.

Sixteen patients with mild anemia and hemolysis were difficult to be classified into any known category based on laboratory examinations and light microscopy. To make a definite diagnosis and investigate the pathomechanism, ultrastructural study was performed on erythroid cells from 16 patients. Transmission electron microscopy demonstrated a series of alterations of cytoplasm, including cytoplasm sequestration, membranous transformation, and degeneration in erythroblasts and reticulocytes at different stages. The affected erythroblasts were usually complicated with chromatin condensation, karyorrhexis, nuclear membrane lysis, and megaloblastic changes. The reticulocytes with the cytoplasm alterations had a huge size from 10 um to 15 um in diameter. The membranous cytoplasm degeneration revealed a unique pathomechanism of dyserythropoiesis and ineffective erythropoiesis in 16 patients with anemia, and suggested a novel anemia category though more details remained to be investigated.