Virtual histological staining of unlabeled autopsy tissue

Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time. These challenges can become more pronounced during global health crises when the availability of histopathology services is limited, resulting in further delays in tissue fixation and more severe staining artifacts. Here, we report the first demonstration of virtual staining of autopsy tissue and show that a trained neural network can rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin (H&E) stained versions of the same samples, eliminating autolysis-induced severe staining artifacts inherent in traditional histochemical staining of autopsied tissue. Our virtual H&E model was trained using >0.7 TB of image data and a data-efficient collaboration scheme that integrates the virtual staining network with an image registration network. The trained model effectively accentuated nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining failed to provide consistent staining quality. This virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.Comment: 24 Pages, 7 Figure

Diabetes and Pre-Diabetes as Determined by Glycated Haemoglobin A1c and Glucose Levels in a Developing Southern Chinese Population

BACKGROUND: The American Diabetes Association and World Health Organization have recently adopted the HbA1c measurement as one method of diagnostic criteria for diabetes. The change in diagnostic criteria has important implications for diabetes treatment and prevention. We therefore investigate diabetes using HbA1c and glucose criteria together, and assess the prevalent trend in a developing southern Chinese population with 85 million residents. METHODS: A stratified multistage random sampling method was applied and a representative sample of 3590 residents 18 years of age or above was obtained in 2010. Each participant received a full medical check-up, including measurement of fasting plasma glucose, 2-hour post-load plasma glucose, and HbA1c. Information on history of diagnosis and treatment of diabetes was collected. The prevalence of diabetes obtained from the present survey was compared with the data from the survey in 2002. RESULTS: The prevalence of diabetes based on both glucose and HbA1c measurements was 21.7% (95% CI: 17.4%-26.1%) in 2010, which suggests that more than 1 in 5 adult residents were suffering from diabetes in this developing population. Only 12.9% (95% CI: 8.3%-17.6%) of diabetic residents were aware of their condition. The prevalence of pre-diabetes was 66.3% (95% CI: 62.7%-69.8%). The prevalence of diabetes and pre-diabetes which met all the three diagnostic thresholds (fast plasma glucose, 2 hour post-load plasma glucose, and HbA1c) was 3.1% and 5.2%, respectively. Diabetes and pre-diabetes as determined by HbA1c measurement had higher vascular risk than those determined by glucose levels. The prevalence of diabetes increased from 2.9% (95% CI: 2.0%-3.7%) in 2002 to 13.8% (95% CI: 10.2%-17.3%) in 2010 based on the same glucose criteria. CONCLUSIONS: Our results show that the diabetes epidemic is accelerating in China. The awareness of diabetes is extremely low. The glucose test and HbA1c measurement should be used together to increase detection of diabetes and pre-diabetes

Photoluminescent and superparamagnetic reduced graphene oxide-iron oxide quantum dots for dual-modality imaging, drug delivery and photothermal therapy

Reduced graphene oxide–iron oxide quantum dots (QDs) with intrinsic photoluminescent and superparamagnetic properties were synthesized through a green, hydrothermal method that simultaneously reduced and shattered graphene nanosheets to form the dots. The structure, morphology, properties and cell viability of these QDs were investigated. The QDs emitted violet light when excited at 320 nm, possessed no residual magnetization upon magnetic hysteresis tests, and had low cytotoxicity to healthy cells at low concentrations. The suitability of the QDs for fluorescent and magnetic resonance dual-modality imaging was shown by in vitro imaging with dermal fibroblast cells and T2 relaxation time. A drug could be loaded onto the surface of the QDs, with a loading ratio of drug to QD of 0.31:1. The drug achieved a steady but full release from the QDs over 8 h: these drug-loaded QDs could be manipulated by an external magnetic stimulation for targeted drug delivery. The potential for use as a cancer photothermal therapy was demonstrated by both a rapid, ∼50 °C temperature increase by a suspension of 100 μg ml−1 of QDs and the photothermal ablation of HeLa cells in vitro under near infrared irradiation. The stability of the MGQDs in fetal calf serum was shown to improve when an ionic drug was coated on the surface

Identifying Economic Growth Convergence Clubs and Their Influencing Factors in China

Balanced and coordinated economic development across regions is a critical goal of regional economic development and new-type urbanization in China. However, few studies have examined economic growth convergence clubs at the county level. To extend the research on convergence clubs, this research applies a log t convergence test and a dynamic spatial ordered probit model (DSOP) to endogenously identify economic growth convergence clubs in counties and to examine the influence of initial states and structures on club convergence probability. The study sample covers 2286 counties of China from 1992 to 2010. The results show significant convergence club patterns at the county levels, resulting in the gradual formation of six convergence clubs. The DSOP estimation results show that per capita fixed assets, population density, and industrialization have promoted convergence club formation to varying degrees

Research on Risk Assessment and Control for Urban Rainwater Resources Utilization

Strengthening risk management and control of urban rainwater resources utilization is the key to ensuring the sustainable and efficient use of urban rainwater resources. On the basis of comprehensive research on urban rainwater utilization, the definition and connotation of urban rainwater utilization emphasizing the attributes of resource utilization are proposed, and the definition and function model of urban rainwater utilization risk are established. Based on the whole process of the development and utilization of urban rainwater resources including external environment, urban natural and social characteristics, rainwater utilization engineering systems, and project operation management, the risk assessment index system for urban rainwater utilization and risk evaluation model by using Analytic Hierarchy Process are established, and a comprehensive risk assessment standard based on risk value and risk tolerance, as well as residual risk management and control theory and risk management methods are put forward. The results of research on risk assessment and control of rainwater resources utilization in Xifeng District of Qingyang City shows that the main risks of rainwater utilization in this district are as follows in order of severity: policies and regulations, economy developing level, maintenance costs, precipitation and natural eutrophication. Risk assessment shows that the risks are low and acceptable and thus can be reduced by strengthening daily monitoring and control

Early Warning of the Carbon-Neutral Pressure Caused by Urban Agglomeration Growth: Evidence from an Urban Network-Based Cellular Automata Model in the Greater Bay Area

Carbon neutrality is becoming an important development goal for regions and countries around the world. Land-use cover/change (LUCC), especially urban growth, as a major source of carbon emissions, has been extensively studied to support carbon-neutral planning. However, studies have typically used methods of small-scale urban growth simulation to model urban agglomeration growth to assist in carbon-neutral planning, ignoring the significant characteristics of the process to achieve carbon neutrality: large-scale and long-term. This paper proposes a framework to model large-scale and long-term urban growth, which couples a quantity module and a spatial module to model the quantity and spatial allocation of urban land, respectively. This framework integrates the inertia of historical land-use change, the driving effects of the urbanization law (S-curve), and the traction of the urban agglomeration network to model the long-term quantity change of urban land. Moreover, it couples a partitioned modeling framework, spatially heterogeneous rules derived by geographically weighted regression (GWR), and quantified land-use planning orientations to build a cellular automata (CA) model to accurately allocate the urbanized cells in a large-scale spatial domain. Taking the Guangdong–Hong Kong–Macao Greater Bay Area (GHMGBA) as an example, the proposed framework is calibrated by the urban growth from 2000 to 2010 and validated by that from 2010 to 2020. The figure of merit (FoM) of the results simulated by the framework is 0.2926, and the simulated results are also assessed by some evidence, which both confirm the good performance of the framework to model large-scale and long-term urban growth. Coupling with the coefficients proposed by the Intergovernmental Panel on Climate Change (IPCC), this framework is used to project the carbon emissions caused by urban growth in the GHMGBA from 2020 to 2050. The results indicate that Guangzhou, Foshan, Huizhou, and Jiangmen are under great pressure to achieve the carbon-neutral targets in the future, while Hong Kong, Macao, Shenzhen, and Zhuhai are relatively easy to bring up to the standard. This research contributes to the ability of land-use models to simulate large-scale and long-term urban growth to predict carbon emissions and to support the carbon-neutral planning of the GHMGBA