Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.

BACKGROUND: Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction. METHODS: In this study, we explored the feasibility of employing deep learning techniques to differentiate murine HSCs and MPPs based solely on their morphology, as observed through light microscopy (DIC) images. RESULTS: After rigorous training and validation using extensive image datasets, we successfully developed a three-class classifier, referred to as the LSM model, capable of reliably distinguishing long-term HSCs, short-term HSCs, and MPPs. The LSM model extracts intrinsic morphological features unique to different cell types, irrespective of the methods used for cell identification and isolation, such as surface markers or intracellular GFP markers. Furthermore, employing the same deep learning framework, we created a two-class classifier that effectively discriminates between aged HSCs and young HSCs. This discovery is particularly significant as both cell types share identical surface markers yet serve distinct functions. This classifier holds the potential to offer a novel, rapid, and efficient means of assessing the functional states of HSCs, thus obviating the need for time-consuming transplantation experiments. CONCLUSION: Our study represents the pioneering use of deep learning to differentiate HSCs and MPPs under steady-state conditions. This novel and robust deep learning-based platform will provide a basis for the future development of a new generation stem cell identification and separation system. It may also provide new insight into the molecular mechanisms underlying stem cell self-renewal

A Hierarchical Control Strategy Based on Dual-Vector Model Predictive Current Control for Railway Energy Router

The multiport and multidirectional energy flow of railway energy routers (RERs) poses a significant challenge when integrating photovoltaic (PV) systems and energy storage systems (ESSs). To address this issue, this paper proposes an improved hierarchical control strategy for RERs with a reference signal generation layer and an inverter control layer. In the reference signal generation layer, a time-segmentation energy allocation strategy based on a state machine is proposed to manage the multidirectional energy flow in RERs resulting from PV systems and ESSs while minimizing peak power demand. In the inverter control layer, a dual-vector model predictive current control (MPCC) strategy is designed for back-to-back inverters. The dual-vector MPCC strategy eliminates the need for individual PWM blocks, thereby enhancing RER current-tracking accuracy and efficiency. The prominent advantage of the dual-vector MPCC strategy is its ability to achieve high current-tracking accuracy while minimizing active power losses. Simulations and hardware-in-the-loop experiments are conducted to validate the feasibility and effectiveness of the proposed method

Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou

Detailed urban land use information is the prerequisite and foundation for implementing urban land policies and urban land development, and is of great importance for solving urban problems, assisting scientific and rational urban planning. The existing results of urban land use mapping have shortcomings in terms of accuracy or recognition scale, and it is difficult to meet the needs of fine urban management and smart city construction. This study aims to explore approaches that mapping urban land use based on multi-source data, to meet the needs of obtaining detailed land use information and, taking Lanzhou as an example, based on the previous study, we proposed a process of urban land use classification based on multi-source data. A combination road network dataset of Gaode and OpenStreetMap (OSM) was synthetically applied to divide urban parcels, while multi-source features using Sentinel-2A images, Sentinel-1A polarization data, night light data, point of interest (POI) data and other data. Simultaneously, a set of comparative experiments were designed to evaluate the contribution and impact of different features. The results showed that: (1) the combination utilization of Gaode and OSM road network could improve the classification results effectively. Specifically, the overall accuracy and kappa coefficient are 83.75% and 0.77 separately for level I and the accuracy of each type reaches more than 70% for level II; (2) the synthetic application of multi-source features is conducive to the improvement of urban land use classification; (3) Internet data, such as point of interest (POI) information and multi-time population information, contribute the most to urban land use mapping. Compared with single-moment population information, the multi-time population distribution makes more contributions to urban land use. The framework developed herein and the results derived therefrom may assist other cities in the detailed mapping and refined management of urban land use

A risk prediction model for renal damage in a hypertensive Chinese Han population

Backgroud: While numerous risk factors for renal damage in the hypertensive population have been reported, there is no single prediction model. The purpose of this study was to develop a model to comprehensively evaluate renal damage risk among hypertensive patients. Methods: We analyzed the data of 582 Chinese hypertensive patients from 1 January 2013 to 30 June 2016. Basic patient information was collected along with laboratory test results. According to the albumin-to-creatinine ratio, the subjects were divided into a hypertension with renal damage group and a hypertension without renal damage group. The prediction model was established by logistic regression based on principal component analysis, and the area under the receiver operating characteristic curve was used to evaluate the predictive performance of the model.Results: There are 11 indicators have statistically significant difference between the two groups (P < 0.05); The equation expressed including all 11 risk factors was as follows: Y = (–0.236) – 0.1705 (sex) – 0.0098 (age) – 0.1067 (smoking history) + 0.0303 (drinking history) – 0.3031 (CHD) + 0.1276 (diabetes history) – 0.0596 (CRP level) – 0.0732 (CysC level) + 0.0949 (β2-MG level) + 0.5407 (blood pressure type) + 0.6470 (RRI). The calculated AUC was 74.4%; The risk in males was much higher than that in females of the same age. However, with increasing age, the male:female risk ratio gradually decreased. Conclusion: Eleven  indicators (including sex, age, smoking history, drinking history, coronary heart disease, diabetes history, C-reactive protein, CystatinC,  β2-microglobulin protein, blood pressure type, renal artery resistance index)  may be the risk factors of renal damage in hypertension. Our regression equation provides a feasible means of predicting renal damage in Chinese hypertensive populations, and the model showed good predictive power. In addition, estrogen may confer a protective effect on the kidney. Abbreviations: PCA: principal component analysis; SLPs: synthetic latent predictors; CKD: chronic kidney disease; RRI: renal artery resistance index; MLR: multivariate logistic regression; CHD: coronary heart disease; UACR: urine trace albumin/uric creatinine ratio; CysC: CystatinC; TG: Triglyceride; CHO: cholesterol; HDL: high-density lipoprotein cholesterol; LDL: low-density lipoprotein cholesterol; CRP: C-reactive protein; HCY: homocysteine; UA: uric acid; AUC: area under the ROC curve; CVE: cardiovascular events; RFF: renal function related factor; PHF: personal history related factor; CVF: cardiovascular factor; GMF: glucose metabolism factor; IF: inflammatory factor; BPF: blood pressure facto

Progress in the biological function of alpha-enolase

Alpha-enolase (ENO1), also known as 2-phospho-D-glycerate hydrolase, is a metalloenzyme that catalyzes the conversion of 2-phosphoglyceric acid to phosphoenolpyruvic acid in the glycolytic pathway. It is a multifunctional glycolytic enzyme involved in cellular stress, bacterial and fungal infections, autoantigen activities, the occurrence and metastasis of cancer, parasitic infections, and the growth, development and reproduction of organisms. This article mainly reviews the basic characteristics and biological functions of ENO1

High-sensitivity and throughput optical fiber SERS probes based on laser-induced fractional reaction method

Surface-enhanced Raman scattering (SERS) is widely used in many fields, such as biosensors, medical diagnostics, materials science, and food security. Here, we report a low-cost, high-throughput laser-induced fractional reaction method for optical fiber SERS probes. Under laser irradiation, the local thermal effect and the electromagnetic interaction between nanoparticles effectively contribute to the formation and growth of silver nanoparticles on the optical fiber facet. Sodium dodecyl sulfate (SDS) solution with a concentration of 2 mM is employed as a surfactant to control the shape and size of the silver nanoparticles. A detection limit of 1.0 × 10−11 M for R6G is achieved, which is, as far as we know, the highest sensitivity that laser-induced fabricated optical fiber SERS probes have achieved. The SERS enhancement factors (EFs) are calculated to be 6.795 × 1011. The SERS intensity of R6G at peaks of 621 cm−1, 1281 cm−1, and 1359 cm−1 are measured with probes fabricated under the same condition, and showed perfect repeatability with an RSD of less than 4.5%. This new method shows effectively in fabricating optical fiber SERS probes with high sensitivity and good repeatability