A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days.

Objectives: This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT). Methods: From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results. Results: Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful. Conclusion: The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.

Perspective view of allogeneic IgG tumor immunotherapy.

Allogeneic tumors are eradicated by host immunity; however, it is unknown how it is initiated until the report in Nature by Yaron Carmi et al. in 2015. Currently, we know that allogeneic tumors are eradicated by allogeneic IgG via dendritic cells. AlloIgG combined with the dendritic cell stimuli tumor necrosis factor alpha and CD40L induced tumor eradication via the reported and our proposed potential signaling pathways. AlloIgG triggers systematic immune responses targeting multiple antigens, which is proposed to overcome current immunotherapy limitations. The promising perspectives of alloIgG immunotherapy would have advanced from mouse models to clinical trials; however, there are only 6 published articles thus far. Therefore, we hope this perspective view will provide an initiative to promote future discussion.

Global, regional, and national time trends in incidence for tuberculosis, 1990-2019: An age-period-cohort analysis for the Global Burden of Disease 2019 study.

BACKGROUND: Tuberculosis (TB) represents a significant global health concern, being the leading cause of mortality from a single infectious agent worldwide. The investigation of TB incidence and epidemiological trends is critical for evaluating the effectiveness of control strategies and identifying ongoing challenges. OBJECTIVES: This study presents the trend in TB incidence across 204 countries and regions over a 30-year period. METHODS: The study utilises data sourced from the Global Burden of Disease (GBD) database. The age cohort model and gender subgroup analysis were employed to estimate the net drift (overall annual percentage change), local drift (age annual percentage change), longitudinal age curve (expected age ratio), and cycle and cohort effect (relative risk of cycle and birth cohort) of TB incidence from 1990 to 2019. This approach facilitates the examination and differentiation of age, period, and cohort effects in TB incidence trends, potentially identifying disparities in TB prevention across different countries. RESULTS: Over the past three decades, a general downward trend in TB incidence has been observed in most countries. However, in 15 of the 204 countries, the overall incidence rate is still on the rise (net drift ≥0.0 %) or stagnant decline (≥-0.5 %). From 1990 to 2019, the net drift of tuberculosis mortality ranged from -2.2 % [95 % confidence interval (CI): -2.33, -2.05] in high Socio-demographic Index (SDI) countries to -1.7 % [95 % CI: -1.81, -1.62] in low SDI countries. In some below-average SDI countries,men in the birth cohort are at a disadvantage and at risk of deterioration, necessitating comprehensive TB prevention and treatment. CONCLUSIONS: While the global incidence of TB has declined, adverse period and cohort effects have been identified in numerous countries, raising questions about the adequacy of TB healthcare provision across all age groups. Furthermore, this study reveals gender disparities in TB incidence.

Machine learning algorithm to construct cuproptosis- and immune-related prognosis prediction model for colon cancer.

BACKGROUND: Over the past few years, research into the pathogenesis of colon cancer has progressed rapidly, and cuproptosis is an emerging mode of cellular apoptosis. Exploring the relationship between colon cancer and cuproptosis benefits in identifying novel biomarkers and even improving the outcome of the disease. AIM: To look at the prognostic relationship between colon cancer and the genes associated with cuproptosis and the immune system in patients. The main purpose was to assess whether reasonable induction of these biomarkers reduces mortality among patients with colon cancers. METHOD: Data obtained from The Cancer Genome Atlas and Gene Expression Omnibus and the Genotype-Tissue Expression were used in differential analysis to explore differential expression genes associated with cuproptosis and immune activation. The least absolute shrinkage and selection operator and Cox regression algorithm was applied to build a cuproptosis- and immune-related combination model, and the model was utilized for principal component analysis and survival analysis to observe the survival and prognosis of the patients. A series of statistically meaningful transcriptional analysis results demonstrated an intrinsic relationship between cuproptosis and the micro-environment of colon cancer. RESULTS: Once prognostic characteristics were obtained, the CDKN2A and DLAT genes related to cuproptosis were strongly linked to colon cancer: The first was a risk factor, whereas the second was a protective factor. The finding of the validation analysis showed that the comprehensive model associated with cuproptosis and immunity was statistically significant. Within the component expressions, the expressions of HSPA1A, CDKN2A, and UCN3 differed markedly. Transcription analysis primarily reflects the differential activation of related immune cells and pathways. Furthermore, genes linked to immune checkpoint inhibitors were expressed differently between the subgroups, which may reveal the mechanism of worse prognosis and the different sensitivities of chemotherapy. CONCLUSION: The prognosis of the high-risk group evaluated in the combined model was poorer, and cuproptosis was highly correlated with the prognosis of colon cancer. It is possible that we may be able to improve patients' prognosis by regulating the gene expression to intervene the risk score.

Improving diagnostic performance of rib fractures for the night shift in radiology department using a computer-aided diagnosis system based on deep learning: A clinical retrospective study.

OBJECTIVE: To investigate the application value of a computer-aided diagnosis (CAD) system based on deep learning (DL) of rib fractures for night shifts in radiology department. METHODS: Chest computed tomography (CT) images and structured reports were retrospectively selected from the picture archiving and communication system (PACS) for 2,332 blunt chest trauma patients. In all CT imaging examinations, two on-duty radiologists (radiologists I and II) completed reports using three different reading patterns namely, P1 = independent reading during the day shift; P2 = independent reading during the night shift; and P3 = reading with the aid of a CAD system as the concurrent reader during the night shift. The locations and types of rib fractures were documented for each reading. In this study, the reference standard for rib fractures was established by an expert group. Sensitivity and false positives per scan (FPS) were counted and compared among P1, P2, and P3. RESULTS: The reference standard verified 6,443 rib fractures in the 2,332 patients. The sensitivity of both radiologists decreased significantly in P2 compared to that in P1 (both p <  0.017). The sensitivities of both radiologists showed no statistical difference between P3 and P1 (both p >  0.017). Radiologist I's FPS increased significantly in P2 compared to P1 (p <  0.017). The FPS of radiologist I showed no statistically significant difference between P3 and P1 (p >  0.017). The FPS of Radiologist II showed no statistical difference among all three reading patterns (p >  0.05). CONCLUSIONS: DL-based CAD systems can be integrated into the workflow of radiology departments during the night shift to improve the diagnostic performance of CT rib fractures.

Integrated assessment of land-to-river Cd fluxes and riverine Cd loads using SWAT-HM to guide management strategies.

In 2011, China invested US$9.8 billion to combat the severe heavy metal pollution in the Xiang River basin (XRB), aiming to reduce 50% of the 2008 industrial metal emissions by 2015. However, river pollution mitigation requires a holistic accounting of both point and diffuse sources, yet the detailed land-to-river metal fluxes in the XRB remain unclear. Here, by combining emissions inventories with the SWAT-HM model, we quantified the land-to-river cadmium (Cd) fluxes and riverine Cd loads across the XRB from 2000 to 2015. The model was validated against long-term historical observations of monthly streamflow and sediment load and Cd concentrations at 42, 11, and 10 gauges, respectively. The analysis of the simulation results showed that the soil erosion flux dominated the Cd exports (23.56-80.14 Mg yr-1). The industrial point flux decreased by 85.5% from 20.84 Mg in 2000 to 3.02 Mg in 2015. Of all the Cd inputs, approximately 54.9% (37.40 Mg yr-1) was finally drained into Dongting Lake; the remaining 45.1% (30.79 Mg yr-1) was deposited within the XRB, increasing the Cd concentration in riverbed sediment. Furthermore, in XRB's 5-order river network, the Cd concentrations in small streams (1st order and 2nd order) showed larger variability due to their low dilution capacity and intense Cd inputs. Our findings highlight the need for multi-path transport modeling to guide future management strategies and better monitoring schemes to restore the small polluted streams.

Screening of potential hub genes involved in Cutaneous Leishmaniasis infection via bioinformatics analysis.

BACKGROUND: Cutaneous Leishmaniasis (CL) is the most common clinical form of leishmaniasis. Despite its low mortality, CL deserves further attention because its pathogenesis is currently no well-known or well-researched. METHODS: We downloaded the gene expression datasets of GSE55664 and GSE63931 with respect to leishmaniasis from the Gene Expression Synthesis (GEO) database. Additionally, the differentially expressed genes (DEGs) in the infection and control groups were identified by packages of R software. The Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) pathway were utilized for the biological functional analysis. Subsequently, we identified the top ten hub genes from protein-protein interaction (PPI) networks based on STRING and Cytoscape software. The hub genes were validated in GraphPad Prism 8.0 using the GSE162760 dataset. Further, CIBERSORT was used to evaluate the immune cell infiltration proportions between the CL infection samples and the control samples based on the GSE43880 and GSE55664 datasets. RESULTS: The enrichment analysis revealed that DEGs were significantly involved in cell-mediated immune responses, such as leukocyte cell-cell adhesion and T-cell activation. STAT1, CCR7, CCR2, and CXCL10 were identified as hub genes with statistical significance. These hub genes showed close correlations with various immune cells, such as M1 cells and CD4-activated memory T-cells. CONCLUSIONS: In our research, we used bioinformatics analysis to identify some molecular biomarkers and significant pathways in CL infection. These hub genes may provide new options for future diagnosis and treatment.

China economy-wide material flow account database from 1990 to 2020.

Material utilisation has been playing a fundamental role in economic development, but meanwhile, it may have environmental and social consequences. Given China's rapid economic growth, understanding China's material utilisation patterns would inform decisions for researchers and policymakers. However, fragmented data from multiple statistical sources hinder us from comprehensively portraying China's material utilisation dynamics. This study harmonised China-specific official statistics and constructed a China economy-wide material flow accounts database. This database covers hundreds of materials and more than 30 years (1990-2020) from thousands of data sources, which is comprehensive, long-term, up-to-date, and publicly accessed. This database would provide insights into the historical metabolic dynamics of China's economy with elaboration on the production, consumption, and end-of-life disposal of materials. This database also allows for international analyses since it is developed based on an internationally standardised analytical framework. Furthermore, this study would benefit studies on policy impact evaluation, environmental pressure assessment, and sustainable development strategies.

Effects of purifiers on the airborne transmission of droplets inside a bus.

During an airborne infectious disease outbreak, bus passengers can be easily infected by the dispersion of exhaled droplets from an infected passenger. Therefore, measures to control the transport of droplets are necessary, such as a mask or purifier. The current research examined aerosol transport in a bus with air-conditioning. To determine the dispersion path, deposition distribution, and droplet escape time, the computational fluid dynamics were used to predict the flow field and the dispersion of droplets considering the effects of droplet size, location of the infected person, and purifier type. In addition, based on the viability and the number of virus particles in a droplet, the total number of virus particles inhaled by passengers over a 4-h journey was obtained by the superposition method. The Wells-Riley equation was then used to assess the infection risk of the passengers in the bus cabin. The results showed that droplets with a size of 1-20 µm have essentially the same deposition characteristics, and the location of the infected passenger affects the distribution of droplets' transport and the effectiveness of a purifier in removing droplets. A purifier can effectively remove droplets from passengers' coughs and reduce the infection risk of passengers. The performance of the smaller purifiers is not as stable as that of the larger purifiers, and the performance is influenced by the airflow structure where the infected passenger is located.

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.

Investigation of hub gene associated with the infection of Staphylococcus aureus via weighted gene co-expression network analysis.

INTRODUCTION: Staphylococcus aureus is a gram-positive bacterium that causes serious infection. With the increasing resistance of bacteria to current antibiotics, it is necessary to learn more about the molecular mechanism and cellular pathways involved in the Staphylococcus aureus infection. METHODS: We downloaded the GSE33341 dataset from the GEO database and applied the weighted gene co-expression network analysis (WGCNA), from which we obtained some critical modules. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were applied to illustrate the biological functions of genes in these modules. We constructed the protein-protein interaction (PPI) network by Cytoscape and selected five candidate hub genes. Five potential hub genes were validated in GSE30119 by GraphPad Prism 8.0. The diagnostic values of these genes were calculated and present in the ROC curve based on the GSE13670 dataset. Their gene functions were analyzed by Gene Set Enrichment Analysis (GSEA). RESULTS: A co-expression network was built with 5000 genes divided into 11 modules. The genes in green and turquoise modules demonstrated a high correlation. According to the KEGG and GO analyses, genes in the green module were closely related to ubiquitination and autophagy. Subsequently, we picked out the top five hub genes in the green module. And UBB was determined as the hub gene in the GSE30119 dataset. The expression level of UBB, ASB, and MKRN1 could significantly differentiate between Staphylococcus aureus infection and healthy controls based on the ROC curve. The GSEA analysis indicated that lower expression levels of UBB were associated with the P53 signal pathway. CONCLUSIONS: We identified some hub genes and significant signal enrichment pathways in Staphylococcus aureus infection via bioinformatics analysis, which may facilitate the development of potential clinical therapeutic strategies.

China material stocks and flows account for 1978-2018.

As the world's top material consumer, China has created intense pressure on national or global demand for natural resources. Building an accurate material stocks and flows account of China is a prerequisite for promoting sustainable resource management. However, there is no annually, officially published material stocks and flows data in China. Existing material stocks and flows estimates conducted by scholars exhibit great discrepancies. In this study, we create the Provincial Material Stocks and Flows Database (PMSFD) for China and its 31 provinces. This dataset describes 13 materials' stocks, demand, and scrap supply in five end-use sectors in each province during 1978-2018. PMSFD is the first version of material stocks and flows inventories in China, and its uniform estimation structure and formatted inventories offer a comprehensive foundation for future accumulation, modification, and enhancement. PMSFD contributes insight into the material metabolism, which is an important database for sustainable development as well as circular economy policy-making in China. This dataset will be updated annually.

A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed. METHODS: Total 400 COVID-19 patients with underlying health conditions were retrospectively recruited from 4 centers, including 54 dead cases (labeled as poor outcomes) and 346 patients discharged or hospitalized for at least 7 days since initial CT scan. Patients were allocated to a training set (n = 271), a test set (n = 68), and an external test set (n = 61). We proposed an initial CT-derived hybrid model by combining a 3D-ResNet10 based deep learning model and a quantitative 3D radiomics model to predict the probability of COVID-19 patients reaching poor outcome. The model performance was assessed by area under the receiver operating characteristic curve (AUC), survival analysis, and subgroup analysis. RESULTS: The hybrid model achieved AUCs of 0.876 (95% confidence interval: 0.752-0.999) and 0.864 (0.766-0.962) in test and external test sets, outperforming other models. The survival analysis verified the hybrid model as a significant risk factor for mortality (hazard ratio, 2.049 [1.462-2.871], P < 0.001) that could well stratify patients into high-risk and low-risk of reaching poor outcomes (P < 0.001). CONCLUSION: The hybrid model that combined deep learning and radiomics could accurately identify poor outcomes in COVID-19 patients with underlying health conditions from initial CT scans. The great risk stratification ability could help alert risk of death and allow for timely surveillance plans.

Spatial-temporal characteristics and driving factors of the human health impacts of five industrial aquatic toxic metals in China.

With the rapid advancement of industrialization without effective supervision, industrial aquatic toxic metal (TM) emissions pose threats to human health in China. Due to differences in socioeconomic development, the regional disparity of industrial aquatic TM emissions is obvious nationwide. In this study, the human health impacts (HHIs) of industrial aquatic TM emissions (i.e., mercury (Hg), cadmium (Cd), hexavalent chromium (Cr(VI)), lead (Pb), and arsenic (As)) in the 31 provinces of China were evaluated based on the ReCiPe method, and the driving factors affecting HHIs from 2000 to 2015 were decomposed using the logarithmic mean Divisia index (LMDI) method. The results showed that the HHIs gradually decreased, with more than an 80% decrease from 2000 to 2015. The order of the TMs contributing to the national HHIs in 2015 was as follows: As (79.5%) > Cr(VI) (19.6%) > Hg (0.4%) > Pb (0.2%) = Cd (0.2%), and 21 (68%) provinces were dominated by industrial aquatic As emissions. Economic development is the major driving factor of the increase in HHIs, while the HHI strength and wastewater discharge intensity are the key driving factors causing reductions in the HHIs. Hunan, Inner Mongolia, Hubei, and Jiangxi accounted for approximately 55% of the total HHIs in 2015. Some suggestions for reducing HHIs based on the local realities of different provinces were put proposed considering two aspects: economic strategy and technical capability.

China's industrial gray water footprint assessment and implications for investment in industrial wastewater treatment.

Industrial wastewater is the largest contributor of toxic pollutants and third-largest contributor of nutrients to bodies of water in China, and understanding the characteristics of such pollution is important for water pollution control. In this study, the industrial gray water footprint (GWF) of each industry sector in China's 31 provinces in 2015 was calculated to identify the pollution characteristics of industrial wastewater discharge and determine how to efficiently allocate investment to pollution reduction. We show that the total industrial GWF of China was 300 billion m3 in 2015 and that the major pollutants were petroleum pollutant (PP), ammonia nitrogen (NH3-N), volatile phenol (VP), and chemical oxygen demand (COD). The water pollution level (WPL) was higher than 1 in Ningxia, Shanxi, Hebei, Tianjin, Shanghai, Henan, and Shandong, indicating that industrial pollution exceeded the carrying capacity of local water bodies in these seven regions. Given equivalent total investment, a scenario that takes the total reduction of the industrial GWF weighted by the WPL in each region as the investment target can better allocate funds to control industrial wastewater pollution in regions with high WPLs relative to a scenario in which investment targets the reduction of the unweighted total industrial GWF. For further industrial GWF reduction in regions with high WPLs, it is crucial to adjust the industrial structure and to upgrade relevant technologies.

CCN2, connective tissue growth factor, stimulates collagen deposition by gingival fibroblasts via module 3 and alpha6- and beta1 integrins.

CCN2, (connective tissue growth factor, CTGF) is a matricellular factor associated with fibrosis that plays an important role in the production and maintenance of fibrotic lesions. Increased collagen deposition and accumulation is a common feature of fibrotic tissues. The mechanisms by which CCN2/CTGF contributes to fibrosis are not well understood. Previous studies suggest that CTGF exerts some of its biological effects at least in part by integrin binding, though this mechanism has not been previously shown to contribute to fibrosis. Utilizing full length CCN2/CTGF, CCN2/CTGF fragments, and integrin neutralizing antibodies, we provide evidence that the effects of CCN2/CTGF to stimulate extracellular matrix deposition by gingival fibroblasts are mediated by the C-terminal half of CCN2/CTGF, and by alpha6 and beta1 integrins. In addition, a synthetic peptide corresponding to a region of CCN2/CTGF domain 3 that binds alpha6beta1 inhibits the collagen-deposition assay. These studies employed a new and relatively rapid assay for CCN2/CTGF-stimulated collagen deposition based on Sirius Red staining of cell layers. Data obtained support a pathway in which CCN2/CTGF could bind to alpha6beta1 integrin and stimulate collagen deposition. These findings provide new experimental methodologies applicable to uncovering the mechanism and signal transduction pathways of CCN2/CTGF-mediated collagen deposition, and may provide insights into potential therapeutic strategies to treat gingival fibrosis and other fibrotic conditions.