Stain-free histopathology by programmable supercontinuum pulses

The preparation, staining, visualization, and interpretation of histological images of tissue is well-accepted as the gold standard process for the diagnosis of disease. These methods were developed historically, and are used ubiquitously in pathology, despite being highly time and labor intensive. Here we introduce a unique optical imaging platform and methodology for label-free multimodal multiphoton microscopy that uses a novel photonic crystal fiber source to generate tailored chemical contrast based on programmable supercontinuum pulses. We demonstrate collection of optical signatures of the tumor microenvironment, including evidence of mesoscopic biological organization, tumor cell migration, and (lymph-)angiogenesis collected directly from fresh ex vivo mammary tissue. Acquisition of these optical signatures and other cellular or extracellular features, which are largely absent from histologically processed and stained tissue, combined with an adaptable platform for optical alignment-free programmable-contrast imaging, offers the potential to translate stain-free molecular histopathology into routine clinical use

Engineering Composite Structure Optimization Design Method

The design ability of composite structures brings large-scale design variables, which makes it difficult to optimize structures by traditional methods, the optimization design of composite laminates is also one of the key problems. From the perspective of engineering design, multiple models are arranged for laying optimization, based on the design of composite super-ply variable definition, considering the overall engineering design constraints to sizing optimization, automatic strength checking process with the laying sequence library to optimize the laying sequence, forming a set of optimization design method of composite structure with three levels of laying optimization, initial size optimization and detailed laying optimization. Taking the structural design of composite vertical tail box of an aircraft as an example, this method is used to optimize the design of composite vertical tail box. The results show that: the three-level optimization design method proposed in this paper is a general method applicable to the optimization design of aeronautical composite structures. It reduces the weight of the structure and shortens the development cycle

Modification of Ultrafine Wool with Modified Protease

The outermost surface of wool comprises a scale layer which hinders certain fabric processing steps and shrink resistance properties of wool fabrics. How to remove surface scale layer without causing severe damage has been an important topic in wool research for decades. In this study, polyurethane was used to prepare the modified protease (MP), the highest activity was obtained at 50°C and pH 7 for the MP. MP was used to treat the surface of ultrafine wool. The resultant surface morphology, chemical groups, and mechanical performance of the treated wool fabrics were evaluated

Estimating foliar anthocyanin content of purple corn via hyperspectral model

To date, the foliar anthocyanin content was either determined via the pH differential or HPLC methods, both of which are slow and destructive. Here, a hyperspectral model was established to estimate the foliar anthocyanin content of purple corn (Zea mays L. var. Jingzi No. 1). The reflectivity (P) of the foliar hyperspectral was inverted to 1/P, lg P, 1/lg P, urn:x-wiley:20487177:media:fsn3588:fsn3588-math-0001, urn:x-wiley:20487177:media:fsn3588:fsn3588-math-0002, urn:x-wiley:20487177:media:fsn3588:fsn3588-math-0003, and urn:x-wiley:20487177:media:fsn3588:fsn3588-math-0004. The correlation coefficient between these inversions and the foliar anthocyanin content was plotted against the hyperspectral wavelength. The wavelength of inversions around 650 nm was sensitive to the foliar anthocyanin content. The hyperspectral model was fitted via linear, polynomial, power, exponential, and logarithmic functions with the sensitive band as independent variable and the anthocyanin content as function. The hyperspectral model (y = 3,000,000,000 × W6854.5896) fitted via inversion of urn:x-wiley:20487177:media:fsn3588:fsn3588-math-0005 showed the highest determination coefficients (0.768) among all models. The hyperspectral model was well validated with a determination coefficient of 0.932 and an RMSE of 0.0065. Moreover, the accuracy and stability of the hyperspectral model were further enhanced with a determination coefficient of 0.954 and RMSE of 0.0047 when the anthocyanin content of the sample was below 20 mg/g. Hence, the hyperspectral model estimated the foliar anthocyanin content of purple corn quickly and nondestructively

Predictive Model of Type 2 Diabetes Remission after Metabolic Surgery in Chinese Patients

Introduction. Metabolic surgery is an effective treatment for type 2 diabetes (T2D). At present, there is no authoritative standard for predicting postoperative T2D remission in clinical use. In general, East Asian patients with T2D have a lower body mass index and worse islet function than westerners. We aimed to look for clinical predictors of T2D remission after metabolic surgery in Chinese patients, which may provide insights for patient selection. Methods. Patients with T2D who underwent metabolic surgery at the Third Xiangya Hospital between October 2008 and March 2017 were enrolled. T2D remission was defined as an HbA1c level below 6.5% and an FPG concentration below 7.1 mmol/L for at least one year in the absence of antidiabetic medications. Results. (1) Independent predictors of short-term T2D remission (1-2 years) were age and C-peptide area under the curve (C-peptide AUC); independent predictors of long-term T2D remission (4–6 years) were C-peptide AUC and fasting plasma glucose (FPG). (2) The optimal cutoff value for C-peptide AUC in predicting T2D remission was 30.93 ng/ml, with a specificity of 67.3% and sensitivity of 75.8% in the short term and with a specificity of 61.9% and sensitivity of 81.5% in the long term, respectively. The areas under the ROC curves are 0.674 and 0.623 in the short term and long term, respectively. (3) We used three variables (age, C-peptide AUC, and FPG) to construct a remission prediction score (ACF), a multidimensional 9-point scale, along which greater scores indicate a better chance of T2D remission. We compared our scoring system with other reported models (ABCD, DiaRem, and IMS). The ACF scoring system had the best distribution of patients and prognostic significance according to the ROC curves. Conclusion. Presurgery age, C-peptide AUC, and FPG are independent predictors of T2D remission after metabolic surgery. Among these, C-peptide AUC plays a decisive role in both short- and long-term remission prediction, and the optimal cutoff value for C-peptide AUC in predicting T2D remission was 30.93 ng/ml, with moderate predictive values. The ACF score is a simple reliable system that can predict T2D remission among Chinese patients

Missing interpolation model for wind power data based on the improved CEEMDAN method and generative adversarial interpolation network

Randomness and fluctuations in wind power output may cause changes in important parameters (e.g., grid frequency and voltage), which in turn affect the stable operation of a power system. However, owing to external factors (such as weather), there are often various anomalies in wind power data, such as missing numerical values and unreasonable data. This significantly affects the accuracy of wind power generation predictions and operational decisions. Therefore, developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry. In this study, the causes of abnormal data in wind power generation were first analyzed from a practical perspective. Second, an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method with a generative adversarial interpolation network (GAIN) network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components. Finally, a complete wind power generation time series was reconstructed. Compared to traditional methods, the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuation

HCAR1/MCT1 regulates tumor ferroptosis through the lactate-mediated AMPK-SCD1 activity and its therapeutic implications

Ferroptosis is a recently discovered form of programed cell death caused by the metabolically regulated lipid peroxidation and holds promise for cancer treatment, but its regulatory mechanisms remain elusive. In this study, we observe that lactate-rich liver cancer cells exhibit enhanced resistance to the ferroptotic damage induced by common ferroptosis inducers such as Ras-selective lethal small molecule 3 (RSL3) and Erastin and that the monocarboxylate transporter 1 (MCT1)-mediated lactate uptake could promote ATP production in hepatocellular carcinoma (HCC) cells and deactivate the energy sensor AMP-activated protein kinase (AMPK), leading to the upregulation of sterol regulatory element-binding protein 1 (SREBP1) and the downstream stearoyl-coenzyme A (CoA) desaturase-1 (SCD1) to enhance the production of anti-ferroptosis monounsaturated fatty acids. Additionally, blocking the lactate uptake via hydroxycarboxylic acid receptor 1 (HCAR1)/MCT1 inhibition promotes ferroptosis by activating the AMPK to downregulate SCD1, which may synergize with its acyl-coenzyme A synthetase 4 (ACSL4)-promoting effect to amplify the ferroptotic susceptibility. In vitro and in vivo evidence confirms that lactate regulates the ferroptosis of HCC cells and highlights its translational potential as a therapeutic target for ferroptosis-based tumor treatment.Published versio