mRNA psi profiling using nanopore DRS reveals cell type-specific pseudouridylation.

Pseudouridine (psi) is one of the most abundant human mRNA modifications generated from the isomerization of uridine via psi synthases, including TRUB1 and PUS7. Nanopore direct RNA sequencing combined with our recent tool, Mod-p ID, enables psi mapping, transcriptome-wide, without chemical derivatization of the input RNA and/or conversion to cDNA. This method is sensitive for detecting changes in positional psi occupancies across cell types, which can inform our understanding of the impact on gene expression. We sequenced, mapped, and compared the positional psi occupancy across six immortalized human cell lines derived from diverse tissue types. We found that lung-derived cells have the highest proportion of psi, while liver-derived cells have the lowest. Further, among a list of highly conserved sites across cell types, most are TRUB1 substrates and fall within the coding sequence. We find that these conserved psi positions correspond to higher levels of protein expression than expected, suggesting translation regulation. Interestingly, we identify cell type-specific sites of psi modification in ubiquitously expressed genes. We validate these sites by ruling out single-nucleotide variants, analyzing current traces, and performing enzymatic knockdowns of psi synthases. Finally, we characterize sites with multiple psi modifications on the same transcript (hypermodification type II) and found that these can be conserved or cell type specific. Among these, we discovered examples of multiple psi modifications within the same k-mer for the first time and analyzed the effect on current distribution. Our data support the hypothesis that motif sequence and the presence of psi synthase are insufficient to drive modifications, that psi modifications contribute to regulating translation and that cell type-specific trans-acting factors play a major role in driving pseudouridylation.

Probing enzyme-dependent pseudouridylation using direct RNA sequencing to assess neuronal epitranscriptome plasticity.

Chemical modifications in mRNAs such as pseudouridine (psi) can regulate gene expression, although our understanding of the functional impact of individual psi modifications, especially in neuronal cells, is limited. We apply nanopore direct RNA sequencing to investigate psi dynamics under cellular perturbations in SH-SY5Y cells. We assign sites to psi synthases using siRNA-based knockdown. A steady-state enzyme-substrate model reveals a strong correlation between psi synthase and mRNA substrate levels and psi modification frequencies. Next, we performed either differentiation or lead-exposure to SH-SY5Y cells and found that, upon lead exposure, not differentiation, the modification frequency is less dependent on enzyme levels suggesting translational control. Finally, we compared the plasticity of psi sites across cellular states and found that plastic sites can be condition-dependent or condition-independent; several of these sites fall within transcripts encoding proteins involved in neuronal processes. Our psi analysis and validation enable investigations into the dynamics and plasticity of RNA modifications.

Developing a novel image marker to predict the clinical outcome of neoadjuvant chemotherapy (NACT) for ovarian cancer patients.

OBJECTIVE: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the clinical outcomes to NACT vary significantly among different subgroups. Partial responses to NACT may lead to suboptimal debulking surgery, which will result in adverse prognosis. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy prognosis prediction of NACT at an early stage. METHODS: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. This cluster was used as input for developing and optimizing support vector machine (SVM) based classifiers, which indicated the likelihood of receiving suboptimal cytoreduction after the NACT treatment. Two different kernels for SVM algorithm were explored and compared. A total of 42 ovarian cancer cases were retrospectively collected to validate the scheme. A nested leave-one-out cross-validation framework was adopted for model performance assessment. RESULTS: The results demonstrated that the model with a Gaussian radial basis function kernel SVM yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.806 ± 0.078. Meanwhile, this model achieved overall accuracy (ACC) of 83.3%, positive predictive value (PPV) of 81.8%, and negative predictive value (NPV) of 83.9%. CONCLUSION: This study provides meaningful information for the development of radiomics based image markers in NACT treatment outcome prediction.

Molecular Electronics: From Nanostructure Assembly to Device Integration.

Integrated electronics and optoelectronics based on organic semiconductors have attracted considerable interest in displays, photovoltaics, and biosensing owing to their designable electronic properties, solution processability, and flexibility. Miniaturization and integration of devices are growing trends in molecular electronics and optoelectronics for practical applications, which requires large-scale and versatile assembly strategies for patterning organic micro/nano-structures with simultaneously long-range order, pure orientation, and high resolution. Although various integration methods have been developed in past decades, molecular electronics still needs a versatile platform to avoid defects and disorders due to weak intermolecular interactions in organic materials. In this perspective, a roadmap of organic integration technologies in recent three decades is provided to review the history of molecular electronics. First, we highlight the importance of long-range-ordered molecular packing for achieving exotic electronic and photophysical properties. Second, we classify the strategies for large-scale integration of molecular electronics through the control of nucleation and crystallographic orientation, and evaluate them based on factors of resolution, crystallinity, orientation, scalability, and versatility. Third, we discuss the multifunctional devices and integrated circuits based on organic field-effect transistors (OFETs) and photodetectors. Finally, we explore future research directions and outlines the need for further development of molecular electronics, including assembly of doped organic semiconductors and heterostructures, biological interfaces in molecular electronics and integrated organic logics based on complementary FETs.

Roles of red mud-based biochar carriers in the recovery of anammox activity: characteristics and mechanisms.

Anaerobic ammonium oxidation (anammox) sludge is easily deactivated in the process of treating ammonia-laden wastewater. To investigate an effective recovery method, red mud-based biochar carriers (RMBC) were prepared and added to a deactivated anammox reactor; the operation of this reactor had been interrupted for 6 months with starvation and low temperature. The deactivated sludge with added RMBC was recovered rapidly after 31 days, with the specific anammox activity rapidly increasing to 0.84 g N/(g VSS∙day), and the recovery efficiency of nitrogen removal rate increased by four times compared to the unadded control. The granulation degree and extracellular polymeric substances secretion of the anammox sludge with the added RMBC were significantly higher than that of the control group. In addition, a large number of spherical anammox bacteria were observed moored at the porous channels of RMBC, and the copy numbers of functional genes of anammox bacteria were approximately twice that of the control group. Hence, RMBC is a potential sludge activator, and it can provide a "house" to protect anammox bacteria, enhance the metabolic activity and the agglomerative growth of anammox bacteria, and synergistically achieve rapid recovery of deactivated anammox sludge.

Multi-Interfacial Confined Assembly of Colloidal Quantum Dots Quasisuperlattice Microcavities for High-Resolution Full-Color Microlaser Arrays.

Colloidal quantum dots (CQDs) are considered a promising material for the next generation of integrated display devices due to their designable optical bandgap and low energy consumption. Owing to their dispersibility in solvents, CQD micro/nanostructures are generally fabricated by solution-processing methods. However, the random mass transfer in liquid restricts the programmable construction in macroscopy and ordered assembly in microscopy for the integration of CQD optical structures. Herein, a multi-interfacial confined assembly strategy is developed to fabricate CQDs programmable microstructure arrays with a quasisuperlattice configuration through controlling the dynamics of three-phase contact lines (TPCLs). The motion of TPCLs dominates the division of liquid film for precise positioning of CQD microstructures, while pinned TPCLs control the solvent evaporation and concentration gradient to directionally drive the mass transfer and packing of CQDs. Owing to their long-range order and adjustable structural dimensions, CQD microring arrays function as high-quality-factor (high-Q) lasing resonant cavities with low thresholds and tunable lasing emission modes. Through the further surface treatment and liquid dynamics control, the on-chip integration of red (R), green (G), and blue (B) multicomponent CQD microlaser arrays are demonstrated. The technique establishes a new route to fabricate large-area, ultrahigh-definition, and full-color CQD laser displays.

Crystal Self-Assembly under Confinement: Bridging Nanomaterials to Integrated Devices.

ConspectusSelf-assembly, a spontaneous process that organizes disordered constituents into ordered structures, has revolutionized our fundamental understanding of living matter, nanotechnology, and molecular science. From the perspective of nanomaterials, self-assembly serves as a bottom-up method for creating long-range-ordered materials. This is accomplished by tailoring the geometry, chemistry, and interactions of the components, thereby facilitating the efficient fabrication of high-quality materials and high-performance functional devices. Over the past few decades, we have seen controllable organization and diverse phases in self-assembled materials, such as organic crystals, biomolecular structures, and colloidal nanoparticle supercrystals. However, most self-assembled ordered materials and their assembly mechanisms are derived from constituents in a liquid bulk medium, where the effects of boundaries and interfaces are negligible. In the context of nanostructure patterning, self-assembly occurs in confined spaces, with feature sizes ranging from a few to hundreds of nanometers. In such settings, ubiquitous boundaries and interfaces can trap the system in a kinetically favored but metastable state, devoid of long-range order. This makes it extremely difficult to achieve ordered structures in micro/nano-patterning techniques that rely on sessile microdroplets, such as inkjet printing, dip-pen lithography, and contact printing.In stark contrast to sessile droplets, capillary bridges─formed by liquids confined between two solid surfaces─provide unique opportunities for understanding the long-range-ordered self-assembly of crystalline materials under spatial confinement. Because capillary bridges are stabilized by Laplace pressure, which is inversely proportional to the feature size, the confinement and manipulation of solutions or suspensions of functional materials at the nanoscale become accessible through the rational design of surface chemistry and geometry. Although global thermodynamic equilibrium is unattainable in evaporative systems, ordered nucleation and packing of constituent components can be locally realized at the contact line of capillary bridges. This enables the unprecedented fabrication of long-range-ordered micro/nanostructures with deterministic patterns.In this Account, we review the advancements in long-range-ordered self-assembly of crystalline micro/nanostructures under confinement. First, we briefly introduce crystalline materials characterized by strong intramolecular interactions and relatively weak intermolecular forces, analyzing both the opportunities and challenges inherent to self-assembled nanomaterials. Next, we delve into the construction and manipulation of confined liquids, focusing especially on capillary bridges controlled by engineered chemistry and geometry to regulate Laplace pressure. Through this approach, we have achieved capillary bridges with thicknesses on the order of a few nanometers and wafer-scale homogeneity, facilitating the self-assembly of ordered structures. Supported by factors such as local free-volume entropy, electrostatic interactions, curvilinear geometry, directional microfluidics, and nanoconfinement, we have achieved long-range-ordered, deterministic patterning of organic semiconductors, metal-halide perovskites, and colloidal nanocrystal superlattices using this capillary-bridge platform. These long-range microstructures serve as a bridge between nanomaterials and integrated devices, enabling emergent functionalities like intrinsic stretchability, giant photoconductivity, propagating and interacting exciton polaritons, and spin-valley-locked lasing, which are otherwise unattainable in disordered materials. Finally, we discuss potential directions for both the fundamental understanding and practical applications of confined self-assembly.

Experimental study of a canine model for a newly designed adjustable prefenestration aortic stent graft.

PURPOSE: When performing thoracic aortic endovascular repair (TEVAR) on lesions of the aortic arch, physician- modified fenestration or in situ fenestration is often used to maintain patent branches. We designed a new adjustable prefenestration aortic stent graft that can both isolate pathologies in the aortic arch and obtain patent branches simultaneously. In this study, we use this new type of stent to perform fenestrated TEVAR in a canine's aorta. This study aims to evaluate the safety and feasibility of the new device, which may provide preliminary data for potential human application. METHODS: Eight Labrador Retriever canines underwent fenestrated TEVAR using the new stent device. Digital subtract angiography (DSA) was performed before and after fenestrated TEVAR to evaluate the safety and feasibility of the procedure. For the device deployment, at the "large curvature" side in the endograft, there is a rectangular prefenestration area (2 × 5 cm) without the polytetrafluoroethylene membrane, and at both longer side edges of the fenestration, there are two slide rails. A moveable membrane that covers the same area as the prefenestration area is initially set at the prefenestration position. A stay line is connected from the distal site of the moveable membrane that controls it to the distal position along the slide rail, which releases the fenestration. After the positioning of the prefenestration is determined, the outer sheath of the delivery system is released, and the stay line at the end of the delivery system is pulled outside the body. The animals were divided into a 1-month group (n = 4) and a 3-month group (n = 4) after the fenestrated TEVAR. Computed tomography (CT) was performed before euthanasia, and video of the DSA during the procedures and CT angiography (CTA) images were then studied. RESULTS: The procedure success rate was 100%, but the total survival rate was only 87.5%. There were no aortic-related deaths during follow-up, and during the operation, there were no stent-graft-related accidents. In addition, no stent-graft migrations were observed in the CTA, and all branch arteries were kept patent by the adjustable fenestration. Finally, histological examination and electron microscope results showed no obvious vascular injury or inflammation. CONCLUSION: Based on the results of this study, we judge the safety and feasibility of the use of the newly designed adjustable prefenestration aortic stent graft in a fenestrated-TEVAR canine model to be acceptable. Our preliminary data may provide a first reference for evaluating the new stent's potential use in humans.

Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy.

Background and Objective: 2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian-cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. Methods: For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1403 and 1595 features for the 2D and 3D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM)-based prediction models were developed and optimized for each feature set. Five-fold cross-validation was used to assess the performance of each individual model. Results: The results show that the 2D feature-based model achieved an AUC (area under the ROC curve (receiver operating characteristic)) of 0.84 ± 0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86 ± 0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91 ± 0.01. Conclusions: This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future.

Developing a Novel Image Marker to Predict the Responses of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients.

OBJECTIVE: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the patients' responses to NACT varies significantly among different subgroups. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy response prediction of the NACT at an early stage. METHODS: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. Using this cluster as the input, an SVM based classifier was developed and optimized to create a final marker, indicating the likelihood of the patient being responsive to the NACT treatment. To validate this scheme, a total of 42 ovarian cancer patients were retrospectively collected. A nested leave-one-out cross-validation was adopted for model performance assessment. RESULTS: The results demonstrate that the new method yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.745. Meanwhile, the model achieved overall accuracy of 76.2%, positive predictive value of 70%, and negative predictive value of 78.1%. CONCLUSION: This study provides meaningful information for the development of radiomics based image markers in NACT response prediction.

Evaluation of the antiwear-ability/scratch-resistance efficacy of makeup products by in vitro test method application.

OBJECTIVE: The objective of this study is to propose a method for assessing the antiwear-ability (AW) or surface scratch-resistance (SR) efficacy of makeup products through in vitro experiments. MATERIALS AND METHOD: The method primarily involves measuring the change in weight as a means of evaluating the overall effectiveness. AW/SR effects are evaluated by applying a fixed amount of makeup product on artificial fake skin and comparing the weight difference after simulated friction/scratch. RESULTS: The in vitro results indicate that this method is easy to operate and yields repeatable data. It consistently reflects differences between samples when compared to clinical studies. CONCLUSIONS: This method effectively compares the AW/SR effects of makeup products and demonstrates utility in evaluating product efficacy and difference. It holds great scientific and practical value.

Comparative study of instrumental measurement and sensory evaluation methods for the repairing effect of mildly damaged hair bundles.

OBJECTIVE: This study explores the applicability and scientific accuracy of instrument measurements in repairing hair products on slightly damaged hair bundles. MATERIALS AND METHOD: Sixty hair bundles mildly damaged with hydrogen peroxide and ammonia standards were divided into two groups: the treatment and control groups (30 hair bundles each). The treatment group used commercial hair care essential oil, whereas the control group used tap water to treat the damage. The two groups were measured using an instrument before and after the product application. The objective indicators included the gloss of hair, along with hair cuticle dynamic friction coefficient, and against hair cuticle dynamic friction coefficient. At the same time, two evaluators conducted sensory evaluations on the gloss and frizz levels of the hair bundles. Therefore, data comparison and verification were carried out together with instrumental measurement data. RESULTS: We verified that the instrumental measurement methods could obtain data trends that are consistent with sensory assessment methods; hence, they have the advantages of accuracy, convenience, and quantifiability. CONCLUSION: Thus, the instrumental measurement methods we verified can provide objective evidence for the efficacy of hair care products in repairing hair.

Electrostatic Epitaxy of Orientational Perovskites for Microlasers.

Orientational growth of single-crystalline structures is pivotal in the semiconductor industry, which is achievable by epitaxy for producing thin films, heterostructures, quantum wells, and superlattices. Beyond silicon and III-V semiconductors, solution-processible semiconductors, such as metal-halide perovskites, are emerging for scalable and cost-effective manufacture of optoelectronic devices, whereas the polycrystalline nature of fabricated structures restricts their application toward integrated devices. Here, electrostatic epitaxy, a process sustained by strong electrostatic interactions between self-assembled surfactants (octanoate anions) and Pb2+ , is developed to realize orientational growth of single-crystalline CsPbBr3 microwires. Strong electrostatic interactions localized at the air-liquid interface not only support preferential nucleation for single crystallinity, but also select the crystal facet with the highest Pb2+ areal density for pure crystallographic orientation. Due to the epitaxy at the air-liquid interface, direct growth of oriented single-crystalline microwires onto different substrates without the processes of lift-off and transfer is realized. Photonic lasing emission, waveguide coupling, and on-chip propagation of coherent light are demonstrated based on these single-crystalline microwires. These findings open an avenue for on-chip integration of single-crystalline materials.

Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists.

OBJECTIVE: To help improve radiologists' efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. METHODS: We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method. RESULTS: Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >90% in 45 testing cases. Furthermore, >73% of cases received a high rating score (≥4) from two radiologists. CONCLUSION: This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice.

Establishment and validation of visual assessment for the severity of lacrimal groove wrinkles in Chinese females.

OBJECTIVE: This study was undertaken to establish and validate a new wrinkle clinical assessment scale to measure Chinese Han women`s validated lacrimal groove. METHODS: Three clinical investigators asked to rate lacrimal groove wrinkles severity one each side for 30 photographic images from 15 subjects. Five-grade rating scale has been used in this clinical assessment. Scale definitions was standardized by 6 researchers in visual and descriptive formats. Assessments were conducted independently and were repeated after 1 week. RESULTS: For 30 photos from 15 subjects, test-retest of three investigators analyzed by Spearman's correlation were between 0.967 and 0.993 (p < 0.001), and by ICC Cronbach's α were between 0.989 and 0.997 (p < 0.001); intraobserver agreement of three investigators analyzed by Spearman's correlation were between 0.652 and 0.897 (p < 0.001), and by ICC Cronbach's α were between 0.840 and 0.959 (p < 0.001). CONCLUSION: This lacrimal groove wrinkles visual assessment scale is a valid and reliable instrument for quantitative assessment of China woman skin folds with inter- and intraobserver consistency. This assessment scale should prove a useful clinical tool by allowing objective and reproducible grading for assessing the effectiveness of lacrimal groove area.

Using symmetric illumination and color camera to achieve high throughput Fourier ptychographic microscopy.

This study aims to develop a high throughput Fourier ptychographic microscopy (FPM) technique based on symmetric illumination and a color detector, which is able to accelerate image acquisition by up to 12 times. As an emerging technology, the efficiency of FPM is limited by its data acquisition process, especially for color microscope image reconstruction. To overcome this, we built an FPM prototype equipped with a color camera and a 4×/0.13 NA objective lens. During the image acquisition, two symmetric LEDs illuminate the sample simultaneously using white light, which doubles the light intensity and reduces the total captured raw patterns by half. A standard USAF 1951 resolution target was used to measure the system's modulation transfer function (MTF) curve, and the H&E-stained ovarian cancer samples were then imaged to assess the feature qualities depicted on the reconstructed images. The results showed that the measured MTF curves of red, green, and blue channels are generally comparable to the corresponding curves generated by conventional FPM, while symmetric illumination FPM preserves more tissue details, which is superior to the results captured by conventional 20×/0.4 NA objective lens. This investigation initially verified the feasibility of symmetric illumination based color FPM.

Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism.

Background: The accurate classification between malignant and benign breast lesions detected on mammograms is a crucial but difficult challenge for reducing false-positive recall rates and improving the efficacy of breast cancer screening. Objective: This study aims to optimize a new deep transfer learning model by implementing a novel attention mechanism in order to improve the accuracy of breast lesion classification. Methods: ResNet50 is selected as the base model to develop a new deep transfer learning model. To enhance the accuracy of breast lesion classification, we propose adding a convolutional block attention module (CBAM) to the standard ResNet50 model and optimizing a new model for this task. We assembled a large dataset with 4280 mammograms depicting suspicious soft-tissue mass-type lesions. A region of interest (ROI) is extracted from each image based on lesion center. Among them, 2480 and 1800 ROIs depict verified benign and malignant lesions, respectively. The image dataset is randomly split into two subsets with a ratio of 9:1 five times to train and test two ResNet50 models with and without using CBAM. Results: Using the area under ROC curve (AUC) as an evaluation index, the new CBAM-based ResNet50 model yields AUC = 0.866 ± 0.015, which is significantly higher than that obtained by the standard ResNet50 model (AUC = 0.772 ± 0.008) (p < 0.01). Conclusion: This study demonstrates that although deep transfer learning technology attracted broad research interest in medical-imaging informatic fields, adding a new attention mechanism to optimize deep transfer learning models for specific application tasks can play an important role in further improving model performances.

Organic Single-Crystalline Microwire Arrays toward High-Performance Flexible Near-Infrared Phototransistors.

Flexible organic near-infrared (NIR) phototransistors hold promising prospects for potential applications such as noninvasive bioimaging, health monitoring, and biometric authentication. For integrated circuits of high-performance devices, organic single-crystalline micro-/nanostructures with precise positioning are prominently anticipated. However, the manufacturing of organic single-crystalline arrays remains a conundrum due to difficulties encountered in patterning arrays of dewetting processes at micron-scale confined space and modulating the dewetting dynamics. Herein, we utilize a capillary-bridge lithography strategy to fabricate organic 1D arrays with high quality, homogeneous size, and deterministic location toward high-performance flexible organic NIR phototransistors. Regular micro-liquid stripes and unidirectional dewetting are synchronously achieved by adapting micropillar templates with asymmetric wettability. As a result, high-throughput 1D arrays based organic field-effect transistors exhibit high electron mobility up to 9.82 cm2  V-1  s-1 . Impressively, flexible NIR phototransistors also show outstanding photoelectronic performances with a photosensitivity of 9.87 × 105 , a responsivity of 1.79 × 104  A W-1 , and a specific detectivity of 3.92 × 1014 Jones. This work paves a novel way to pattern high-throughput organic single-crystalline microarrays toward flexible NIR organic optoelectronics.

FTY720 Attenuates Cerebral Vasospasm After Subarachnoid Hemorrhage Through the PI3K/AKT/eNOS and NF-κB Pathways in Rats.

Cerebral vasospasm (CVS) is a major complication of subarachnoid hemorrhage (SAH). Inflammation and nitric oxide (NO) have become increasingly recognized as key pathogenic contributors to brain injury in this condition. We aimed to examine the role of FTY720 in CVS after SAH. Endovascular perforation was used to establish an SAH model. Seventy-five male Sprague-Dawley rats were randomly divided into five groups: sham, sham + FTY720, SAH + saline, and two SAH + FTY720 (0.5 and 1 mg/kg) groups. The results showed that FTY720 treatment in both the surgery and nonsurgery groups decreased the counts of leukocytes and lymphocytes 72 hours after SAH. TNF-α (tumor necrosis factor alpha) and IL-1ß (interleukin 1 beta) in both the cerebrospinal fluid (CSF) and the hippocampus were decreased, and the NF-κB (nuclear factor kappa B) pathway was inhibited. The levels of apoptotic proteins were downregulated. FTY720 promoted NO generation by activating the PI3K/AKT/eNOS pathway. CVS and neurological deficits in the SAH rats were ameliorated after FTY720 treatment. Compared with the sham-only animals, FTY720 treatment in the nonsurgery group did not increase mortality. These results indicated that FTY720 could alleviate CVS due to its anti-inflammatory and antiapoptosis effects and the promotion of NO generation. FTY720 may be effective in the clinical treatment of SAH patients.

Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms.

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operations, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multiple mammograms. This motivated us to leverage the architecture of Multi-view Vision Transformers to capture long-range relationships of multiple mammograms from the same patient in one examination. For this purpose, we employed local transformer blocks to separately learn patch relationships within four mammograms acquired from two-view (CC/MLO) of two-side (right/left) breasts. The outputs from different views and sides were concatenated and fed into global transformer blocks, to jointly learn patch relationships between four images representing two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of mammograms, which included 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our four-image (two-view-two-side) transformer-based model achieves case classification performance with an area under ROC curve (AUC = 0.818 ± 0.039), which significantly outperforms AUC = 0.784 ± 0.016 achieved by the state-of-the-art multi-view CNNs (p = 0.009). It also outperforms two one-view-two-side models that achieve AUC of 0.724 ± 0.013 (CC view) and 0.769 ± 0.036 (MLO view), respectively. The study demonstrates the potential of using transformers to develop high-performing computer-aided diagnosis schemes that combine four mammograms.