bluuebunny/crossref_metadata_2025 huggingface dataset:
Case report: SARS-CoV-2 infection as a trigger for diabetic ketoacidosis and newly detected pancreatic autoantibodies
Authors: Mishra Rahul , Elshimy Ghada , Kannan Lakshmi , Jacob Aasems , Raj Rishi
Published: August 2022

A 39-year-old-woman with a past medical history of type 2 diabetes mellitus (T2DM) on oral hypoglycemic agents presented to the emergency room with nausea, vomiting, shortness of breath, and altered mental status. Seven days prior to presentation, she was diagnosed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Laboratory workup on presentation confirmed the diagnosis of diabetic ketoacidosis (DKA) (blood glucose 523 mg/dl, beta-hydroxybutyrate 8.91 mmol/l, pH 6.9, bicarbonate 11 mEq/l, anion gap 25 mEq/l, and HbA1c 10.8%). She was managed for DKA with hydration and insulin drip and discharged home. However, to our surprise, at the 2-week follow-up visit, she was found to have positive antibodies for zinc transporter 8 (ZnT8) (samples were collected on day of presentation). The rest of her antibodies associated with T1DM were negative. She was therefore started on a basal-bolus regimen and managed as type 1 diabetes mellitus (T1DM). Our case illustrates that there is an increased risk of T1DM following infection with SARS-CoV-2.
Experimental and numerical investigations into molten-pool sloshing motion for severe accident analysis of sodium-cooled fast reactors: A review
Authors: Xu Ruicong , Cheng Songbai
Published: August 2022

Safety issues are particularly crucial for sodium-cooled fast reactors (SFRs). Data obtained from SFR safety analyses over recent years have shown that a specific type of sloshing motion probably occurs in the molten pool during core disruptive accidents (CDAs) of SFRs due to local neutronic power excursion or pressure developments, thereby significantly influencing recriticality. Recognizing the importance of improving the evaluation of CDAs of SFRs, extensive knowledge about this phenomenon has been garnered through experimental studies of their thermal-hydraulic mechanism and characteristics. Based on these studies, simulations using various numerical approaches, such as SIMMER code, the finite volume particle method, and the smoothed particle hydrodynamic method, have attempted to reproduce the sloshing motion under various experimental conditions to verify their reasonability and applicability, thereby promoting the development of SFR safety analysis. To provide useful references for future SFR safety analyses and assessments, we have systematically reviewed and summarized these experimental and numerical investigations into the thermal-hydraulic aspect of molten-pool sloshing motion. In addition, to enhance deeper and more comprehensive research into sloshing motion, we have also discussed future prospects. Knowledge gained from experimental and numerical investigations into molten-pool sloshing motion is valuable not only for improving and verifying SFR safety analysis codes but also for providing reference data for studies of sloshing motion in other fields of engineering.
Carbon Trading or Carbon Tax? A Computable General Equilibrium–Based Study of Carbon Emission Reduction Policy in China
Authors: Shen Jing , Zhao Changheng
Published: August 2022

To solve the environmental problems caused by climate change, the Paris Agreement urges China to accelerate the pace of CO2 emission reduction. Carbon trading and carbon tax have been considered the key instruments in reducing CO2 emissions. The focus of this article is not only to examine the impact of carbon trading and the carbon tax policy on China’s macroeconomy but also to study the “carbon trading–carbon tax” mixed policy and make a comparative analysis based on the computable general equilibrium (CGE) model. We found that the mixed policy is more favorable to China’s macroeconomy than a single carbon emission reduction policy and is conducive to improving people’s welfare. If a carbon tax is carried out, a relatively mild and low carbon tax rate should be adopted to achieve China’s carbon emission reduction goal and have a favorable impact on the macroeconomy. The main purpose of this article is to provide a theoretical basis and policy advices for the Chinese government in formulating innovative carbon reduction policies.
Autonomous underwater vehicle docking system for energy and data transmission in cabled ocean observatory networks
Authors: Sun Kai , Han Zekai
Published: August 2022

Cabled ocean observatory networks (COON) are used for long-term all-weather observation of submarine scientific data, which contribute to low-carbon ocean energy research. Autonomous underwater vehicles (AUV) with clean energy can provide active search capabilities by connecting with the docking station (DS) on the COON to complete energy and data transmission in long-term detection tasks. The AUV is guided by optical active landmarks and a vision system for short-range docking. In this study, we propose an active landmarks tracking framework to solve the problem of detecting failure caused by incomplete observation of landmarks. First, a two-stage docking algorithm based on CNN is used to estimate the 3D relative position and orientation between DS and AUV during docking, including detect phase and PnP pose estimator. Then extended Kalman filter and Hungarian matching algorithm are introduced to improve the robustness of the algorithm. The reliability of the vision-based short-range docking algorithm is verified in the pool, and the robustness of the algorithm to the field environment is shown in the lake field experiment combined with long-range guidance. The experimental results indicate that the algorithm framework can effectively leverage the landmarks information and enhance the scope of the visual guidance algorithm.
For the aged: A novel PM2.5 concentration forecasting method based on spatial-temporal graph ordinary differential equation networks in home-based care parks
Authors: Zeng Qingtian , Wang Chao , Chen Geng , Duan Hua , Wang Shuihua
Published: August 2022

The immune ability of the elderly is not strong, and the functions of the body are in a stage of degeneration, the ability to clear PM2.5 is reduced, and the cardiopulmonary system is easily affected. Accurate prediction of PM2.5 can provide guidance for the travel of the elderly, thereby reducing the harm of PM2.5 to the elderly. In PM2.5 prediction, existing works usually used shallow graph neural network (GNN) and temporal extraction module to model spatial and temporal dependencies, respectively, and do not uniformly model temporal and spatial dependencies. In addition, shallow GNN cannot capture long-range spatial correlations. External characteristics such as air humidity are also not considered. We propose a spatial-temporal graph ordinary differential equation network (STGODE-M) to tackle these problems. We capture spatial-temporal dynamics through tensor-based ordinary differential equation, so we can build deeper networks and exploit spatial-temporal features simultaneously. In addition, in the construction of the adjacency matrix, we not only used the Euclidean distance between the stations, but also used the wind direction data. Besides, we propose an external feature fusion strategy that uses air humidity as an auxiliary feature for feature fusion, since air humidity is also an important factor affecting PM2.5 concentration. Finally, our model is evaluated on the home-based care parks atmospheric dataset, and the experimental results show that our STGODE-M can more fully capture the spatial-temporal characteristics of PM2.5, achieving superior performance compared to the baseline. Therefore, it can provide better guarantee for the healthy travel of the elderly.
Effects of enterprise carbon trading mechanism design on willingness to participate——Evidence from China
Authors: Zha Dao-Shun , Feng Tian-Tian , Kong Jia-Jie
Published: August 2022

At this stage, the air pollution problem represented by climate warming has become very serious, and it is important to establish a carbon emission trading system as an innovative policy tool to promote energy savings and emission reduction. China’s carbon trading market is currently in its initial stage, and only power generation enterprises are included in the market. When faced with changes in the carbon trading market mechanism, enterprises are often unable to make rapid adjustments, making it difficult to reflect the carbon market’s role in reducing emissions. Current research on the design of carbon trading mechanisms of enterprises has lacked investigation into the influence of the design of carbon trading mechanisms on the willingness of enterprises to participate from the perspective of enterprises. In this paper, we use choice experiments, a conditional logit model and a multinomial logit model to explore the impacts of changes in the design of enterprise carbon trading mechanisms on enterprise willingness to participate in carbon trading. The impacts of all attributes on the willingness to participate in national carbon trading is ranked from largest to smallest: offset mechanism > carbon product trading > government penalty > carbon quota allocation. Based on the research results, relevant policy recommendations and corporate countermeasures are proposed to improve corporate willingness to participate in carbon trading and encourage active cooperation in energy saving and emission reduction activities.
Development of a gender score in a representative German population sample and its association with diverse social positions
Authors: Wandschneider Lisa , Sauzet Odile , Razum Oliver , Miani Céline
Published: August 2022

Background

Gender as a relational concept is rarely considered in epidemiology. However, an in-depth reflection on gender conceptualisation and operationalisation can advance gender analysis in quantitative health research, allowing for more valid evidence to support public health interventions. We constructed a context-specific gender score to assess how its discriminatory power differed in sub-groups defined by social positions relevant to intersectional analyses, i.e., sex/gender, race, class, age and sexual attraction.

Methods

We created a gender score with the help of multivariable logistic regression models and conditional probabilities based on gendered social practices and expressed on a masculinity-femininity continuum, using data of the German Socioeconomic Panel. With density plots, we exploratively compared distributions of gendered social practices and their variation across social groups.

Results

We included 13 gender-related variables to define a gender score in our sample (n = 20,767). Variables on family and household structures presented with the highest weight for the gender score. When comparing social groups, we saw that young individuals, those without children, not living with a partner or currently living in a same-sex/gender partnership, showed more overlap between feminine/masculine social practices among females and males.

Conclusions

The distribution of gendered social practices differs among social groups, which empirically backs up the theoretical notion of gender being a context-specific construct. Economic participation and household structures remain essential drivers of heterogeneity in practices among women and men in most social positions. The gender score can be used in epidemiology to support concerted efforts to overcome these gender (in)equalities—which are important determinants of health inequalities.
Combining geospatial abundance and ecological niche models to identify high-priority areas for conservation: The neglected role of broadscale interspecific competition
Authors: Cavalcante Thiago , Weber Marcelo M. , Barnett Adrian A.
Published: August 2022

Ecological niche models (ENMs) have become a practical and key mechanism for filling major gaps in spatial information for targeted conservation planning, particularly when only occurrence data are available. Nonetheless, accounting for abundance patterns in the internal structure of species’ ranges, and the role of biotic interactions in such models across broadscale, remains highly challenging. Our study gathered baseline information on abundance data of two Endangered Amazonian primates (Ateles chamekandLagothrix lagotricha cana) to create geospatial abundance models using two spatial interpolation methods: Inverse distance weight (IDW) and Ordinary kriging (OK). The main goals were to: (i) test whether geospatial abundance models are correlated with habitat suitability derived from correlative ENMs; (ii) compare the strength of the abundance-suitability relationships between original and interpolated abundances; (iii) test whether interspecific competition between the two target taxa constrained abundance over broad spatial scales; and (iv) create ensemble models incorporating both habitat suitability and abundance to identify high-priority areas for conservation. We found a significant positive relationship between habitat suitability with observed and predicted abundances of woolly (L. l. cana) and spider (A. chamek) monkeys. Abundance-suitability correlations showed no significant differences when using original relative abundances compared to using interpolated abundances. We also found that the association betweenL. l. canaabundance and habitat suitability depended on the abundance of its putative competitor species,A. chamek. Our final models combining geospatial abundance information with ENMs were able to provide more realistic assessments of hotspots for conservation, especially when accounting for the important, but often neglected, role of interspecific competition in shaping species’ geographic ranges at broader scales. The framework developed here, including general trends in abundance patterns and suitability information, can be used as a surrogate to identify high-priority areas for conservation of poorly known species across their entire geographic ranges.
Identification of lactate metabolism-related subtypes and development of a lactate-related prognostic indicator of lung adenocarcinoma
Authors: Chang Xiaoyan , Lu Tong , Xu Ran , Wang Chenghao , Zhao Jiaying , Zhang Linyou
Published: August 2022

Background: Increasing evidence supports that lactate plays an important role in tumor proliferation, invasion and within the tumor microenvironment (TME). This is particularly relevant in lung adenocarcinoma (LUAD). Therefore, there is a current need to investigate lactate metabolism in LUAD patients and how lactate metabolism is affected by different therapies.

Methods: Data from LUAD patients were collected from The Cancer Genome Atlas (TCGA) and patients were divided into two subtypes according to 12 lactate metabolism-related genes to explore the effect of lactate metabolism in LUAD. We established a lactate-related prognostic indicator (LRPI) based on different gene expression profiles. Subsequently, we investigated associations between this LRPI and patient survival, molecular characteristics and response to therapy. Some analyses were conducted using the Genomics of Drug Sensitivity in Cancer (GDSC) database.

Results: The two LUAD subtypes exhibited different levels of lactate metabolism, in which patients that displayed high lactate metabolism also had a worse prognosis and a poorer immune environment. Indeed, LRPI was shown to accurately predict the prognosis of LUAD patients. Patients with a high LRPI showed a poor prognosis coupled with high sensitivity to chemotherapy using GDSC data. Meanwhile, these patients exhibited a high responsiveness to immunotherapy in TMB (Tumor mutation burden) and TIDE (Tumor Immune Dysfunction and Exclusion) analyses.

Conclusion: We validated the effect of lactate metabolism on the prognosis of LUAD patients and established a promising biomarker. LRPI can predict LUAD patient survival, molecular characteristics and response to therapy, which can aid the individualized treatment of LUAD patients.
Exploring urine:serum fractional excretion ratios as potential biomarkers for lupus nephritis
Authors: Soliman Samar A. , Stanley Samantha , Vanarsa Kamala , Ismail Faten , Mok Chi Chiu , Mohan Chandra
Published: August 2022

Objectives

The goal of this exploratory study is to determine if urine:serum fractional excretion ratios can outperform the corresponding urinary biomarker proteins in identifying active renal disease in systemic lupus erythematosus (SLE).

Methods

Thirty-six adult SLE patients and twelve healthy controls were examined for serum and urine levels of 8 protein markers, namely ALCAM, calpastatin, hemopexin, peroxiredoxin 6 (PRDX6), platelet factor 4 (PF4), properdin, TFPI and VCAM-1, by ELISA. Fractional excretion of analyzed biomarkers was calculated after normalizing both the urine and serum biomarker levels against creatinine. A further validation cohort of fifty SLE patients was included to validate the initial findings.

Results

The FE ratios of all 8 proteins interrogated outperformed conventional disease activity markers such as anti-dsDNA, C3 and C4 in identifying renal disease activity. All but VCAM-1FE were superior to the corresponding urine biomarkers levels in differentiating LN activity, exhibiting positive correlation with renal SLEDAI. ALCAMFE, PF4FE and properdinFE ratios exhibited the highest accuracy (AUC>0.9) in distinguishing active LN from inactive SLE. Four of the FE ratios exhibited perfect sensitivity (calpastatin, PRDX6, PF4 and properdin), while ALCAMFE, PF4FE and properdinFE exhibited the highest specificity values for active LN. In addition, several of these novel biomarkers were associated with higher renal pathology activity indices. In the validation cohort ALCAMFE, PF4FE and properdinFE once again exhibited higher accuracy metrics, surpassing corresponding urine and serum biomarkers levels, with ALCAMFE exhibiting 95% accuracy in distinguishing active LN from inactive SLE.

Conclusions

With most of the tested proteins, urine:serum fractional excretion ratios outperformed corresponding urine and serum protein measurements in identifying active renal involvement in SLE. Hence, this novel class of biomarkers in SLE ought to be systemically evaluated in larger independent cohorts for their diagnostic utility in LN assessment.
Single cell transcriptomics of Atlantic salmon (Salmo salar L.) liver reveals cellular heterogeneity and immunological responses to challenge by Aeromonas salmonicida
Authors: Taylor Richard S. , Ruiz Daniels Rose , Dobie Ross , Naseer Shahmir , Clark Thomas C. , Henderson Neil C. , Boudinot Pierre , Martin Samuel A.M. , Macqueen Daniel J.
Published: August 2022

The liver is a multitasking organ with essential functions for vertebrate health spanning metabolism and immunity. In contrast to mammals, our understanding of liver cellular heterogeneity and its role in regulating immunological status remains poorly defined in fishes. Addressing this knowledge gap, we generated a transcriptomic atlas of 47,432 nuclei isolated from the liver of Atlantic salmon (Salmo salar L.) contrasting control fish with those challenged with a pathogenic strain of Aeromonas salmonicida, a problematic bacterial pathogen in global aquaculture. We identified the major liver cell types and their sub-populations, revealing poor conservation of many hepatic cell marker genes utilized in mammals, while identifying novel heterogeneity within the hepatocyte, lymphoid, and myeloid lineages. This included polyploid hepatocytes, multiple T cell populations including γδ T cells, and candidate populations of monocytes/macrophages and dendritic cells. A dominant hepatocyte population radically remodeled its transcriptome following infection to activate the acute phase response and other defense functions, while repressing routine functions such as metabolism. These defense-specialized hepatocytes showed strong activation of genes controlling protein synthesis and secretion, presumably to support the release of acute phase proteins into circulation. The infection response further involved up-regulation of numerous genes in an immune-cell specific manner, reflecting functions in pathogen recognition and killing, antigen presentation, phagocytosis, regulation of inflammation, B cell differentiation and T cell activation. Overall, this study greatly enhances our understanding of the multifaceted role played by liver immune and non-immune cells in host defense and metabolic remodeling following infection and provides many novel cell-specific marker genes to empower future studies of this organ in fishes.
The Mangal Play: A serious game to experience multi-stakeholder decision-making in complex mangrove social-ecological systems
Authors: Dahdouh-Guebas Farid , Mafaziya Nijamdeen T. W. G. F. , Hugé Jean , Dahdouh-Guebas Yasmin , Di Nitto Diana , Hamza Amina Juma , Kodikara Arachchilage Sunanda , Koedam Nico , Mancilla García María , Mohamed Mohamed O. S. , Mostert Laurence , Munga Cosmas N. , Poti Meenakshi , Satyanarayana Behara , Stiers Iris , Van Puyvelde Karolien , Vanhove Maarten P. M. , Vande Velde Katherine , Ratsimbazafy Hajaniaina A.
Published: August 2022

In order to achieve collaborative action in nature conservation and natural resources management, stakeholders have to understand and acknowledge other stakeholders’ interests, values, world visions and objectives and they have to overcome the problem of irrational decision-making through innate opposition discourses. In this paper we developed the Mangal Play, an experiential learning method to have participants adopt the role of a particular stakeholder in an imaginary mangrove forest social-ecological system (SES). The Mangal Play is a serious game, more specifically a role-play, aimed at promoting oral dialogues between 20 stakeholders involved in governance, fisheries, aquaculture, agriculture, forestry, tourism, transport, conservation and communication sectors. By providing tools to lecturers and scientists to execute it in a public or classroom setting, the Mangal Play stimulates a decision-making process while accepting compromise and distinguishing bottom-line issues from negotiable positions, and instructs about the behaviour of complex real-world systems in a safe learning environment. We exemplify how social network analysis can serve to visualise the outcome and further develop the Mangal Play. In this way we hope to help stakeholders take into account diverse positions in a rational decision-making process.
Biological–physical oceanographic coupling influencing particulate organic matter in the South Yellow Sea
Authors: Guo Congcong , Yang Shu , Zhai Weidong , Niu Yao , Liu Chunli
Published: August 2022

Using total suspended matter (TSM), particulate organic carbon (POC), and particulate nitrogen data, this study investigated the potential vertical POC flux and transport in the South Yellow Sea (SYS). The biogenic production and resuspension fraction (i.e., the proportion of resuspended particles in TSM) were estimated using an ecosystem model and a vertical mixing model. They were verified against reported sediment trap and primary productivity data. The estimates of resuspension fraction showed substantial uncertainty of 50% in summer likely owing to the potential errors of model parameter estimation and the influence of other unexplored biophysical processes such as biological degradation, upwelling, and monsoons; however, the estimates of resuspension fraction showed less uncertainty in other seasons (<20%). Few previous studies have considered the specific influence of resuspension on the dynamics and budget of particulate organic matter (POM) in the SYS. This study proposed a reasonably simple and effective method to address this issue, which was applied to systematic examination of the variation of vertical POM flux with the change of coupled biological–physical oceanographic processes along the Subei coast and in the SYS central basin. The influence of horizontal transport from the Subei coast to the central basin may cause an overestimation of >10% of the resuspension fraction. It will be necessary to acquire additional field data covering a larger spatiotemporal scale to establish an integrated network of the SYS carbon budget.
Characteristics and particularities of bacterial community variation in the offshore shellfish farming waters of the North Yellow Sea
Authors: Gao Lei , Yu Zichao , Liu Chao , Kong Ning , Wang Lingling , Song Linsheng
Published: August 2022

Bacteria in coastal waters drive global biogeochemical cycling and are strongly related to coastal environmental safety. The bacterial community in offshore shellfish farming waters of North China has its own characteristics and particularities, while the knowledge is limited. In this study, the bacterial community characteristics, the particularities of bacterial community in the waters with surface cold patches (SCPs) and the variation of pathogenic bacteria were investigated in the offshore shellfish farming waters in the North Yellow Sea (NYS) from 2017 to 2019. For all studied samples, Desulfobacterales acted as the keystone species taxon in microbial co-occurrence networks, and the proportional abundance of Actinobacteriota was found to be as low as 1.3%. The abundance of Marinobacter and Synechococcus was remarkably prominent in 13 genera with nitrogen-transforming function. The top two different bacterial functions in the spatial analysis (between the waters with SCPs and the ambient waters) were xenobiotics biodegradation and metabolism and metabolism of cofactors and vitamins, which were same with that in the seasonal analysis (between spring and summer). The abundance differences of most pathogenic bacteria analyzed in this study (11 out of 12 genera) also had the same variation dynamics between the spatial analysis and the seasonal analysis. An ANN predictive model for Vibrio abundance was constructed for Vibrio forecasting, with acceptable predictive accuracy. According to the above results, the bacterial community in the shellfish aquaculture waters in this study was characterized by the enhancing ability of nitrogen removal. Temperature was concluded as the predominant environmental factor to drive the variation of bacterial community function and pathogenic bacteria patterns in the offshore shellfish farming waters with SCPs. The results of this study will further our understanding of the bacterial community characteristics in offshore shellfish farming waters, and help for Vibrio forecasting and coastal environmental safety in aquaculture seawater.
Prediction of low cardiac output syndrome in patients following cardiac surgery using machine learning
Authors: Hong Liang , Xu Huan , Ge Chonglin , Tao Hong , Shen Xiao , Song Xiaochun , Guan Donghai , Zhang Cui
Published: August 2022

Background

This study aimed to develop machine learning models to predict Low Cardiac Output Syndrome (LCOS) in patients following cardiac surgery using machine learning algorithms.

Methods

The clinical data of cardiac surgery patients in Nanjing First Hospital between June 2019 and November 2020 were retrospectively extracted from the electronic medical records. Six conventional machine learning algorithms, including logistic regression, support vector machine, decision tree, random forest, extreme gradient boosting and light gradient boosting machine, were employed to construct the LCOS predictive models with all predictive features (full models) and selected predictive features (reduced models). The discrimination of these models was evaluated by the area under the receiver operating characteristic curve (AUC) and the calibration of the models was assessed by the calibration curve. Shapley Additive explanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to interpret the predictive models.

Results

Data from 1,585 patients [982 (62.0%) were male, aged 18 to 88, 212 (13.4%) with LCOS] were employed to train and validate the LCOS models. Among the full models, the RF model (AUC: 0.909, 95% CI: 0.875–0.943; Sensitivity: 0.849, 95% CI: 0.724–0.933; Specificity: 0.835, 95% CI: 0.796–0.869) and the XGB model (AUC: 0.897, 95% CI: 0.859–0.935; Sensitivity: 0.830, 95% CI: 0.702–0.919; Specificity: 0.809, 95% CI: 0.768–0.845) exhibited well predictive power for LCOS. Eleven predictive features including left ventricular ejection fraction (LVEF), first post-operative blood lactate (Lac), left ventricular diastolic diameter (LVDd), cumulative time of mean artery blood pressure (MABP) lower than 65 mmHg (MABP < 65 time), hypertension history, platelets level (PLT), age, blood creatinine (Cr), total area under curve above threshold central venous pressure (CVP) 12 mmHg and 16 mmHg, and blood loss during operation were used to build the reduced models. Among the reduced models, RF model (AUC: 0.895, 95% CI: 0.857–0.933; Sensitivity: 0.830, 95% CI: 0.702–0.919; Specificity: 0.806, 95% CI: 0.765–0.843) revealed the best performance. SHAP and LIME plot showed that LVEF, Lac, LVDd and MABP < 65 time significantly contributed to the prediction model.

Conclusion

In this study, we successfully developed several machine learning models to predict LCOS after surgery, which may avail to risk stratification, early detection and management of LCOS after cardiac surgery.
Corneal morphological changes after small incision lenticule extraction for myopic anisometropia
Authors: Zhu Lu , Ji Yan , Yang Xin , Lu Xiaorong , Wu Qiong , Wang Qing , Xia Jiuyi , Li Meng , Hu Ke , Wan Wenjuan
Published: August 2022

Purpose

This research aims to study the corneal morphological changes in adult patients with myopic anisometropia after small incision lenticule extraction (SMILE) and the safety, efficacy, and predictability of clinical outcomes.

Methods

This was a prospective cohort study. Patients with myopic anisometropia [refractive difference >2.0 diopters (D)] were included in this study who underwent SMILE at our hospital from September 2019 to March 2021. For the two eyes of each patient, the one with higher myopia was defined as group A, and the fellow eye was group B. The follow-up time points were set as 1 week, 1 month, 3 months, and 6 months after the surgery. The data collected were uncorrected and best-corrected distance visual acuity (UDVA and CDVA), spherical equivalent (SE), efficacy and safety indexes, posterior corneal elevation (PCE), anterior and posterior corneal radius of curvature in the 3 mm area at the center of the thinnest point of the cornea (ARC and PRC), and higher-order aberrations (HOAs).

Results

The study included 36 patients (72 eyes), and the mean age was 25.2 ± 6.4 years. The preoperative SEs were −6.45 ± 1.25 D in group A and −3.76 ± 1.29 D in group B. Six months after surgery, the SEs in groups A and B were −0.09 ± 0.50 D and 0.07 ± 0.47 (P = 0.059), respectively. The efficacy indexes were 1.06 ± 0.16 in group A and 1.07 ± 0.14 in group B (P = 0.750). The safety indexes were 1.08 ± 0.14 in group A and 1.12 ± 0.15 in group B (P = 0.173). The PCE was significantly reduced at 6 months after surgery in pagebreak both groups (P < 0.05). The ARC was significantly higher than before the surgery (P < 0.05) in the two groups. The two groups showed significant increases in total HOAs, coma 90°, and spherical aberrations (P < 0.05).

Conclusion

SMILE is predictable, effective, and safe in correcting myopic anisometropia. The postoperative changes in HOAs are characteristic.
Clinical presentations, diagnosis, management, and outcomes of renal mucormycosis: An overview of case reports
Authors: Didehdar Mojtaba , Chegini Zahra , Khoshbayan Amin , Moradabadi Alireza , Shariati Aref
Published: August 2022

Background

Renal mucormycosis (RM) is a rare presentation of invasive mucormycosis with a high mortality rate. There is no single systematic review of the literature that indicates the different clinical aspects of RM.

Methods

A systematic search of PubMed/Medline was performed to collect individual case reports of RM in patients of all ages published between 2010 and April 2022.

Results

Seventy-one individual cases were detected through PubMed bibliographic database searches, with a final assessment performed on 60 patients with RM. India and Asia had the largest number of reported cases, with 30 (50%) and 42 (70%) reports, respectively. Also, 74 and 26% of the patients with a mean age of 33 years were male and female, respectively. RM showed 44% mortality rate in the analyzed cases. Immunosuppressive agent therapy followed by tissue transplantation (kidney and liver) and diabetes were the most remarkable risk factors in patients. Nevertheless, 22% of the patients were immunocompetent with no apparent underlying condition. COVID-19 positivity was detected in eight adult patients with an 87% mortality rate. The most common signs of infection were fever, flank pain, and oliguria; additionally, isolated RM was reported in 57% of the cases. In 55% of the patients, histopathologic examination alone was sufficient to diagnose RM, whereas molecular methods and culture were used in only 18 and 35% of patients, respectively. Surgery alone, surgery plus anti-infection therapy, and anti-infection therapy alone were used in 12, 60, and 13% of patients, respectively. Furthermore, 15% of the patients died before any treatment.

Conclusion

The early diagnosis of RM is necessary. In this regard, the use of molecular-based diagnostic assays can help identify the fungus at the genus and species levels and use an appropriate treatment in the shortest possible amount of time. Because of the increase in antibiotic resistance in recent years, determining microbial susceptibility tests can lead to the better infection management. Additionally, withdrawal of immunosuppressant, appropriate surgical intervention, and antifungal therapy are the main factors associated with a successful outcome in RM.
Identifying novel antimicrobial peptides from venom gland of spider Pardosa astrigera by deep multi-task learning
Authors: Lee Byungjo , Shin Min Kyoung , Yoo Jung Sun , Jang Wonhee , Sung Jung-Suk
Published: August 2022

Antimicrobial peptides (AMPs) show promises as valuable compounds for developing therapeutic agents to control the worldwide health threat posed by the increasing prevalence of antibiotic-resistant bacteria. Animal venom can be a useful source for screening AMPs due to its various bioactive components. Here, the deep learning model was developed to predict species-specific antimicrobial activity. To overcome the data deficiency, a multi-task learning method was implemented, achieving F1 scores of 0.818, 0.696, 0.814, 0.787, and 0.719 for Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Staphylococcus epidermidis, respectively. Peptides PA-Full and PA-Win were identified from the model using different inputs of full and partial sequences, broadening the application of transcriptome data of the spider Pardosa astrigera. Two peptides exhibited strong antimicrobial activity against all five strains along with cytocompatibility. Our approach enables excavating AMPs with high potency, which can be expanded into the fields of biology to address data insufficiency.
Interaction network of African swine fever virus structural protein p30 with host proteins
Authors: Chen Xiongnan , Chen Xiaojun , Liang Yifan , Xu Sijia , Weng Zhijun , Gao Qi , Huang Zhao , Zhang Guihong , Gong Lang
Published: August 2022

African swine fever virus (ASFV) is a complex nucleocytoplasmic large DNA virus (NCLDV) that causes a lethal hemorrhagic disease that is currently threatening the global pig industry. ASFV structural protein p30 is a membrane phosphoprotein that suggests it may play a regulatory role, possibly in signal transduction. Despite its significance in internalization into host cells, the interaction between p30 and host proteins is relatively unknown. In this study, we describe the application of a DUALmembrane yeast two-hybrid assay to screen a primary porcine alveolar macrophages cDNA library and analyze the interactome of p30 protein. Our data identify seven host cellular proteins (DAB2, RPSA, OAS1, PARP9, CAPG, ARPC5, and VBP1) that putatively interact with the p30. We further verified the interaction between p30 and host proteins by laser confocal microscopy, co-immunoprecipitation, and GST-pulldown assay. To further understand the relationship between host proteins and p30, we drew the interaction network diagram and analyzed the functional enrichment of each host protein. Enrichment analysis of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes indicated that host proteins were mainly related to endocytosis, actin cytoskeleton regulation, and innate immunity. Collectively, we identified the interaction between p30 and host cell protein using a membrane protein yeast two-hybrid system, which increases our knowledge of the interaction between ASFV and the host and informs future research on antiviral strategies.
Dynamic changes of metabolic characteristics in neonatal intrahepatic cholestasis caused by citrin deficiency
Authors: Zhang Ting , Zhu Shasha , Miao Haixia , Yang Jianbin , Shi Yezhen , Yue Yuwei , Zhang Yu , Yang Rulai , Wu Benqing , Huang Xinwen
Published: August 2022

Introduction: Neonatal intrahepatic cholestasis caused by citrin deficiency (NICCD) is a pan-ethnic complicated inborn error of metabolism but the specific mechanism is not fully understood.

Methods: A total of 169 patients with NICCD who have biallelic pathogenic SLC25A13 variants detected by targeted next-generation sequencing were collected. They were divided into the “Newborn-screen Group” and “Clinical diagnosed Group” depending on the newborn screening results. Amino acid and acylcarnitine profiles were measured by MS/MS. The total bile acids, blood amino acids and acylcarnitines, general biochemistry, blood count, and coagulation parameters were monitored every 2–3 months. We compared the differences in metabolic indices and their dynamic changes between these two groups. The Mann–Whitney test and orthogonal partial least squares discrimination analysis (OPLS-DA) were used for statistical analysis.

Results: At the onset of NICCD, we found that the “Clinical diagnosed Group” had higher levels of intermediate products of the urea cycle, free carnitine, and short-chain and long-chain acylcarnitines than those in the “Newborn-screen Group,” but the levels of ketogenic/glucogenic amino acids and several medium-chain acylcarnitines were lower. Furthermore, concentrations of direct bilirubin, total bile acid, lactate, prothrombin time, and several liver enzymes were significantly higher while total protein, amylase, and hemoglobin were lower in the “Clinical diagnosed Group” than in the “Newborn-screen Group.” Dynamic change analysis showed that direct bilirubin, albumin, arginine, and citrulline were the earliest metabolic derangements to reach peak levels in NICCD groups, followed by acylcarnitine profiles, and finally with the elevation of liver enzymes. All abnormal characteristic metabolic indicators in the “Newborn-screen Group” came back to normal levels at earlier ages than the “Clinical diagnosed Group.” c.852_855del (41.2%), IVS16ins3kb (17.6%), c.615 + 5G>A (9.6%), 1638_1660dup (4.4%), and c.1177 + 1G>A (3.7%) accounted for 76.5% of all the mutated SLC25A13 alleles in our population.

Conclusion: Argininosuccinate synthesis, gluconeogenesis, ketogenesis, fatty acid oxidation, liver function, and cholestasis were more severely affected in the “Clinical diagnosed Group.” The “Newborn-screen Group” had a better prognosis which highlighted the importance of newborn screening of NICCD.
Metabolomics reveals mouse plasma metabolite responses to acute exercise and effects of disrupting AMPK-glycogen interactions
Authors: Belhaj Mehdi R. , Lawler Nathan G. , Hawley John A. , Broadhurst David I. , Hoffman Nolan J. , Reinke Stacey N.
Published: August 2022

Introduction: The AMP-activated protein kinase (AMPK) is a master regulator of energy homeostasis that becomes activated by exercise and binds glycogen, an important energy store required to meet exercise-induced energy demands. Disruption of AMPK-glycogen interactions in mice reduces exercise capacity and impairs whole-body metabolism. However, the mechanisms underlying these phenotypic effects at rest and following exercise are unknown. Furthermore, the plasma metabolite responses to an acute exercise challenge in mice remain largely uncharacterized.

Methods: Plasma samples were collected from wild type (WT) and AMPK double knock-in (DKI) mice with disrupted AMPK-glycogen binding at rest and following 30-min submaximal treadmill running. An untargeted metabolomics approach was utilized to determine the breadth of plasma metabolite changes occurring in response to acute exercise and the effects of disrupting AMPK-glycogen binding.

Results: Relative to WT mice, DKI mice had reduced maximal running speed (p < 0.0001) concomitant with increased body mass (p < 0.01) and adiposity (p < 0.001). A total of 83 plasma metabolites were identified/annotated, with 17 metabolites significantly different (p < 0.05; FDR<0.1) in exercised (↑6; ↓11) versus rested mice, including amino acids, acylcarnitines and steroid hormones. Pantothenic acid was reduced in DKI mice versus WT. Distinct plasma metabolite profiles were observed between the rest and exercise conditions and between WT and DKI mice at rest, while metabolite profiles of both genotypes converged following exercise. These differences in metabolite profiles were primarily explained by exercise-associated increases in acylcarnitines and steroid hormones as well as decreases in amino acids and derivatives following exercise. DKI plasma showed greater decreases in amino acids following exercise versus WT.

Conclusion: This is the first study to map mouse plasma metabolomic changes following a bout of acute exercise in WT mice and the effects of disrupting AMPK-glycogen interactions in DKI mice. Untargeted metabolomics revealed alterations in metabolite profiles between rested and exercised mice in both genotypes, and between genotypes at rest. This study has uncovered known and previously unreported plasma metabolite responses to acute exercise in WT mice, as well as greater decreases in amino acids following exercise in DKI plasma. Reduced pantothenic acid levels may contribute to differences in fuel utilization in DKI mice.
Research on smooth path planning method based on improved ant colony algorithm optimized by Floyd algorithm
Authors: Wang Lina , Wang Hejing , Yang Xin , Gao Yanfeng , Cui Xiaohong , Wang Binrui
Published: August 2022

Aiming at the problems of slow convergence and easy fall into local optimal solution of the classic ant colony algorithm in path planning, an improved ant colony algorithm is proposed. Firstly, the Floyd algorithm is introduced to generate the guiding path, and increase the pheromone content on the guiding path. Through the difference in initial pheromone, the ant colony is guided to quickly find the target node. Secondly, the fallback strategy is applied to reduce the number of ants who die due to falling into the trap to increase the probability of ants finding the target node. Thirdly, the gravity concept in the artificial potential field method and the concept of distance from the optional node to the target node are introduced to improve the heuristic function to make up for the fallback strategy on the convergence speed of the algorithm. Fourthly, a multi-objective optimization function is proposed, which comprehensively considers the three indexes of path length, security, and energy consumption and combines the dynamic optimization idea to optimize the pheromone update method, to avoid the algorithm falling into the local optimal solution and improve the comprehensive quality of the path. Finally, according to the connectivity principle and quadratic B-spline curve optimization method, the path nodes are optimized to shorten the path length effectively.
Frictional force analysis of stent retriever devices using a realistic vascular model: Pilot study
Authors: Kwak Youngseok , Son Wonsoo , Kim Byoung-Joon , Kim Myungsoo , Yoon Sang-Youl , Park Jaechan , Lim Jongkyeong , Kim Joonwon , Kang Dong-Hun
Published: August 2022

Objective

To date, no vascular model to analyze frictional forces between stent retriever devices and vessel walls has been designed to be similar to the real human vasculature. We developed a novel in vitro intracranial cerebrovascular model and analyzed frictional forces of three stent retriever devices.

Methods

A vascular mold was created based on digital subtraction angiography of a patient's cerebral vessels. The vascular model was constructed using polydimethylsiloxane (PDMS, Dow Corning, Inc.) as a silicone elastomer. The vascular model was coated on its inner surface with a lubricating layer to create a low coefficient of friction (~0.037) to closely approximate the intima. A pulsatile blood pump was used to produce blood flow inside the model to approximate real vascular conditions. The frictional forces of Trevo XP, Solitaire 2, and Eric 4 were analyzed for initial and maximal friction retrieval forces using this vascular model. The total pulling energy generated during the 3 cm movement was also obtained.

Results

Results for initial retrieval force were as follows: Trevo, 0.09 ± 0.04 N; Solitaire, 0.25 ± 0.07 N; and Eric, 0.33 ± 0.21 N. Results for maximal retrieval force were as follows: Trevo, 0.36 ± 0.07 N; Solitaire, 0.54 ± 0.06 N; and Eric, 0.80 ± 0.13 N. Total pulling energy (N·cm) was 0.40 ± 0.10 in Trevo, 0.65 ± 0.10 in Solitaire, and 0.87 ± 0.14 in Eric, respectively.

Conclusions

Using a realistic vascular model, different stent retriever devices were shown to have statistically different frictional forces. Future studies using a realistic vascular model are warranted to assess SRT devices.
Linear leaky-integrate-and-fire neuron model based spiking neural networks and its mapping relationship to deep neural networks
Authors: Lu Sijia , Xu Feng
Published: August 2022

Spiking neural networks (SNNs) are brain-inspired machine learning algorithms with merits such as biological plausibility and unsupervised learning capability. Previous works have shown that converting Artificial Neural Networks (ANNs) into SNNs is a practical and efficient approach for implementing an SNN. However, the basic principle and theoretical groundwork are lacking for training a non-accuracy-loss SNN. This paper establishes a precise mathematical mapping between the biological parameters of the Linear Leaky-Integrate-and-Fire model (LIF)/SNNs and the parameters of ReLU-AN/Deep Neural Networks (DNNs). Such mapping relationship is analytically proven under certain conditions and demonstrated by simulation and real data experiments. It can serve as the theoretical basis for the potential combination of the respective merits of the two categories of neural networks.