Solution-Processable Large-Area Black Phosphorus/Reduced Graphene Oxide Schottky Junction for High-Temperature Broadband Photodetectors

2D materials-based broadband photodetectors have extensive applications in security monitoring and remote sensing fields, especially in supersonic aircraft that require reliable performance under extreme high-temperature conditions. However, the integration of large-area heterostructures with 2D materials often involves high-temperature deposition methods, and also limited options and size of substrates. Herein, a liquid-phase spin-coating method is presented based on the interface engineering to prepare larger-area Van der Waals heterojunctions of black phosphorus (BP)/reduced graphene oxide (RGO) films at room temperature on arbitrary substrates of any required size. Importantly, this method avoids the common requirement of high-temperature, and prevents the curling or stacking in 2D materials during the liquid-phase film formation. The BP/RGO films-based devices exhibit a wide spectral photo-response, ranging from the visible of 532 nm to infrared range of 2200 nm. Additionally, due to Van der Waals interface of Schottky junction, the array devices provide infrared detection at temperatures up to 400 K, with an outstanding photoresponsivity (R) of 12 A W-1 and a specific detectivity (D*) of ≈2.4 × 109 Jones. This work offers an efficient approach to fabricate large-area 2D Schottky junction films by solution-coating for high-temperature infrared photodetectors.

Exercise ameliorates lipid droplet metabolism disorder by the PLIN2-LIPA axis-mediated lipophagy in mouse model of non-alcoholic fatty liver disease

Excessive hepatic lipid droplets (LDs) accumulation-induced lipid metabolism disorder contributes to the development of non-alcoholic fatty liver disease (NAFLD). Exercise is a promising therapeutic strategy for NAFLD. However, the mechanism by which exercise ameliorates NAFLD through regulating the catabolism of hepatic LDs remains unclear. In the present study, we investigated the effect of perilipin2 (PLIN2)-lysosomal acid lipase (LIPA) axis mediating exercise-triggered lipophagy in a high-fat diet (HFD)-induced NAFLD mouse model. Our results showed that exercise could reduce HFD-induced hepatic LDs accumulation and change the expression of lipolysis-related enzymes. Moreover, exercise upregulated the expression of microtubule associated protein 1 light chain 3 (LC3) and autophagy-related proteins, and downregulated sequestosome 1 (P62) expression and promoted autophagosomes formation. Interestingly, exercise downregulated PLIN2 expression, upregulated LIPA expression, and increased the activity of hepatic LIPA and serum levels of LIPA in the NAFLD mouse model. Further mechanistic studies demonstrated that adenosine monophosphate-activated protein kinase (AMPK) activator-5-Aminoimidazole-4-carboxamide ribonucleoside (AICAr) treatment significantly increased mRNA levels and protein expression of LIPA and LC3II and decreased levels of PLIN2 and P62 in palmitic acid (PA)-treated HepG2 cells. PLIN2 silencing and LIPA overexpression notably increased the mRNA level and protein expression of LC3II and decreased the mRNA level and protein expression of p62, respectively. In summary, our findings reveal novel insights into the effect of exercise on improving lipid droplet metabolism disorder in NAFLD. Enhancing the PLIN2-LIPA axis-mediated lipophagy may be one of the key mechanisms involved in NAFLD alleviation by exercise.

Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software

PURPOSE

The assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions.

METHODS

In three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report.

RESULTS

A total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08).

CONCLUSION

The quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.

[Quantitative analysis of risk assessment indicators for re-introduction of imported malaria in China]

OBJECTIVE

To quantitatively analyze the risk indicators of re-introduction of imported malaria in China and their weighting coefficients, so as to investigate the difference in the contribution of risk indicators included in the current risk assessment framework for re-introduction of imported malaria in China to the risk assessment of re-introduction of imported malaria.

METHODS

Publications pertaining to the risk assessment framework for re-introduction of imported malaria in China that reported the risk indicators and their weighting coefficients were retrieved in PubMed, Web of Science, CNKI, Wanfang Data, and VIP with terms of "malaria", "re-introduction/re-transmission/re-establishment", "risk assessment/risk evaluation/risk prediction" from the inception of the database through 3 August 2023, and literature search was performed in Google Scholar to ensure the comprehensiveness of the retrieval. Basic characteristics of included studies were extracted using pre-designed information extraction forms by two investigators, and data pertaining to risk indicators of re-introduction of imported malaria were cross-checked by these two investigators. The risk indicators included in the risk assessment framework for re-introduction of imported malaria in China and their weighting coefficients were visualized with the Nightingale's rose diagrams using the software R 4.2.1, and the importance of risk indictors was evaluated with the frequency of risk indicators included in the risk assessment framework and the ranking of weighting coefficients of risk indicators. In addition, the capability of risk indicators screened by different weighting methods was compared by calculating the ratio of the maximum to the minimum of the weighting coefficients of the risk indicators screened by different weighting methods.

RESULTS

A total of 2 138 publications were retrieved, and following removal of duplications and screening, a total of 8 publications were included in the final analysis. In these 8 studies, 8 risk assessment frameworks for re-introduction of imported malaria in China and 52 risk indicators of re-introduction of imported malaria were reported, in which number of imported malaria cases (n = 8) and species of malaria vectors were more frequently included in the risk assessment frameworks (n = 8), followed by species of imported malaria parasites (n = 6) and population density of local malaria vectors (n = 6), and species of local malaria vectors (n = 6), number of imported malaria cases (n = 5) and species of imported malaria parasites had the three highest weighting coefficients (n = 4). The weighting methods included expert scoring method, combination of expert scoring method and analytic hierarchy process, and combination of expert scoring method and entropy weight method in these 8 studies, and the ratios of the maximum to the minimum of the weighting coefficients of the risk indicators screened by the expert scoring method were 1.143 to 2.241, while the ratios of the maximum to the minimum of the weighting coefficients of the risk indicators screened by combination of the expert scoring method and analytic hierarchy process were 34.970 to 162.000.

CONCLUSIONS

Number of imported malaria cases, species of imported malaria parasites, species of local malaria vectors and population density of local malaria vectors are core indicators in the current risk assessment framework for re-introduction of imported malaria in China. Combination of the expert scoring method and analytic hierarchy process is superior to the expert scoring method alone for weighting the risk indicators.

Enhancing hospital course and outcome prediction in patients with traumatic brain injury: A machine learning study

PURPOSE

We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients.

METHODS

In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables.

RESULTS

Four hundred forty patients were used to predict predictions 1 and 2, while 271 patients were used in prediction 3. Due to Prediction 3's hospitalization requirement, deceased and discharged patients could not be utilized. The Random Forest model achieved an average accuracy of 1.00 for prediction 1 and an accuracy of 0.99 for prediction 2. The Random Forest model achieved a mean accuracy of 0.93 for prediction 3. Key features were extracranial injury, hemorrhage, UCH-L1 for prediction 1; The Glasgow Coma Scale, age, GFAP for prediction 2; and GFAP, subdural hemorrhage volume, and pneumocephalus for prediction 3, per SHAP analysis.

CONCLUSION

Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers.

Hypothalamic FTO promotes high-fat diet-induced leptin resistance in mice through increasing CX3CL1 expression

Long-term consumption of a high-fat diet (HFD) disrupts energy homeostasis and leads to weight gain. The fat mass and obesity-associated (FTO) gene has been consistently identified to be associated with HFD-induced obesity. The hypothalamus is crucial for regulating energy balance, and HFD-induced hypothalamic leptin resistance contributes to obesity. FTO, an N6-methyladenosine (m6A) RNA methylation regulator, may be a key mediator of leptin resistance. However, the exact mechanisms remain unclear. Therefore, the present study aims to investigate the association between FTO and leptin resistance. After HFD or standard diet (SD) feeding in male mice for 22 weeks, m6A-sequencing and western blotting assays were used to identify target genes and assess protein level, and molecular interaction changes. CRISPR/Cas9 gene knockout system was employed to investigate the potential function of FTO in leptin resistance and obesity. Our data showed that chemokine (C-X3-C motif) ligand 1 (CX3CL1) was a direct downstream target of FTO-mediated m6A modification. Furthermore, upregulation of FTO/CX3CL1 and suppressor of cytokine signaling 3 (SOCS3) in the hypothalamus impaired leptin-signal transducer and activator of transcription 3 signaling, resulting in leptin resistance and obesity. Compared to wild-type (WT) mice, FTO deficiency in leptin receptor-expressing neurons of the hypothalamus significantly inhibited the upregulation of CX3CL1 and SOCS3, and partially ameliorating leptin resistance under HFD conditions. Our findings reveal that FTO involved in the hypothalamic leptin resistance and provides novel insight into the function of FTO in the contribution to hypothalamic leptin resistance and obesity.

Individual perceptions of community efficacy for non-communicable disease management in twelve communities in China: cross-sectional and longitudinal analyses

OBJECTIVES

This objective of this study was to use empirical data to assess cross-sectional variation singular and changes over time in community efficacy for non-communicable diseases (NCDs) management (COEN) and to examine individual factors associated with changes in COEN.

STUDY DESIGN

This was a longitudinal observational study.

METHODS

Participants with hypertension and diabetes were randomly selected from 12 communities from three cities in eastern China, and a baseline survey and a 1-year follow-up were conducted. The COEN scale has five dimensions: community physical environment (CPE), behavioral risk factors (BRF), mental health and social relationships (MHSR), community health management (CHM), and community organisations and activities (COA). Mixed-effects models were used to investigate the change in COEN over time and the association between individual factors and changes in COEN.

RESULTS

COEN scores showed significant variation singular among the 12 communities (P < 0.001) at the baseline. In the mixed-effects model, CPE (β coefficient: 1.62, P < 0.001), BRF (0.90, P < 0.001), MHSR (0.86, P < 0.001), CHM (0.46, P < 0.001), and total scores (β = 3.57, P < 0.001) increased significantly over time. The changes in COEN were associated with individual characteristics (e.g., older, men, more educated).

CONCLUSIONS

Cross-sectional variations and changes over time in COEN demonstrated the utility of a sensitive instrument. Factors such as age, gender, marriage, education level, and employment may affect the financial and social resources assignment for NCD management. Our findings suggest that further high-quality studies are needed to better evaluate the effect of community empowerment on the prevention and control of NCDs.

ISX9 loaded thermoresponsive nanoparticles for hair follicle regrowth

There is a high demand for an optimal drug delivery system to treat androgenetic alopecia. Topical application of ISX9, which is a neurogenesis inducer, has been found to stimulate hair follicle (HF) regrowth by upregulating the Wnt/β-catenin signaling pathway, an essential pathway involved in initiating HF growth and development. In the present study, a temperature-sensitive, biopolymer-based, biocompatible, and eco-friendly drug-delivery system was synthesized. This system comprised chitosan-grafted poly(glycidyl methacrylate-co-N-isopropyl acrylamide) (Poly(GMA-co-NIPAAm)@CS-PGNCS) as the shell component and PF127 as the core polymer. The hydrophobic nature of the PF127 block copolymer efficiently dissolved the partially water-soluble drug, ISX9, and the thermos-responsive shell polymer effectively released the drug at a definite skin temperature. The optimized spherical nanoparticles demonstrated the lowest critical solution temperature (LCST) at 32 ± 2 °C with a diameter of 100-250 nm, which delivered encapsulated ISX9 with greater precision than topical ISX9. In a series of in vivo experiments, we demonstrated that ISX9-coated TBNPs upregulated the expression of β-catenin, active β-catenin, Wnt target genes, stemness marker genes, proliferating cell nuclear antigen, HF stem cell markers, and HF markers including VEGF, TGF, and IGF-1 more effectively than topical ISX9. These results suggest that TBNPs could be employed as a platform for effective transdermal delivery of various hydrophobic drugs.

Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography

OBJECTIVES

While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)-based machine learning (ML) model that uses carotid plaques 6-type calcium grading, and clinical parameters to identify CVE patients with bilateral plaques.

MATERIAL AND METHODS

We conducted a multicenter, retrospective diagnostic study (March 2013-May 2020) approved by the institutional review board. We included adults (18 +) with bilateral carotid artery plaques, symptomatic patients having recently experienced a carotid territory ischemic event, and asymptomatic patients either after 3 months from symptom onset or with no such event. Four ML models (clinical factors, calcium configurations, and both with and without plaque grading [ML-All-G and ML-All-NG]) and logistic regression on all variables identified symptomatic patients. Internal validation assessed discrimination and calibration. External validation was also performed, and identified important variables and causes of misclassifications.

RESULTS

We included 790 patients (median age 72, IQR [61-80], 42% male, 64% symptomatic) for training and internal validation, and 159 patients (age 68 [63-76], 36% male, 39% symptomatic) for external testing. The ML-All-G model achieved an area-under-ROC curve of 0.71 (95% CI 0.58-0.78; p < .001) and sensitivity 80% (79-81). Performance was comparable on external testing. Calcified plaque, especially the positive rim sign on the right artery in older and hyperlipidemic patients, had a major impact on identifying symptomatic patients.

CONCLUSION

The developed model can identify symptomatic patients using plaques calcium configuration data and clinical information with reasonable diagnostic accuracy.

CLINICAL RELEVANCE

The analysis of the type of calcium configuration in carotid plaques into 6 classes, combined with clinical variables, allows for an effective identification of symptomatic patients.

KEY POINTS

• While the association between carotid plaques composition and cerebrovascular events is recognized, the role of calcium configuration remains unclear. • Machine learning of 6-type plaque grading can identify symptomatic patients. Calcified plaques on the right artery, advanced age, and hyperlipidemia were the most important predictors. • Fast acquisition of CTA enables rapid grading of plaques upon the patient's arrival at the hospital, which streamlines the diagnosis of symptoms using ML.

Targeting CD24 as a novel immunotherapy for solid cancers

Cluster of differentiation 24 (CD24), a mucin-like highly glycosylated molecule has been extensively studied as a cancer stem cell marker in a variety of solid cancers. The functional role of CD24 is either fulfilled by combining with ligands or participating in signal transduction, which mediate the initiation and progression of neoplasms. Recently, CD24 was also described as an innate immune checkpoint with apparent significance in several types of solid cancers. Herein, we review the current understanding of the molecular fundamentals of CD24, the role of CD24 in tumorigenesis and cancer progression, the possibility as a promising therapeutic target and summarized different therapeutic agents or strategies targeting CD24 in solid cancers. Video Abstract.

Advancing Inorganic Microcapsule Fabrication through Frozen-Assisted Interfacial Reactions Utilizing Liquid Marbles

Inorganic microcapsules (IMs) have gained significant attention as versatile platforms for delivering functional agents in various fields. Traditional template-dependent methods employing hard templates often involve complex and harsh template removal processes. Achieving IMs with diverse composition and structure remains challenging with current preparation strategies. Therefore, in this work, we have for the first time demonstrated an extremely facile and efficient liquid-marbles-based template approach for fabricating pure inorganic microcapsules via interfacial reaction in a mild aqueous solution. The water-water reaction interface is created by changing the wettability of the liquid marble (LM) surface through the icing-melting process. The composition and function of the inorganic shell could be easily adjusted by varying the inorganic reagent species of the interfacial reaction, the hydrophobic particle of the shell, and the reaction environment according to the specific requirements of the application field. Such an approach provides a flexible platform for material preparation.

Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement

PURPOSE

This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast computed tomography (NCCT) scans of patients with suspicion of traumatic brain injury (TBI).

METHODS

This retrospective and multi-reader study included patients with TBI suspicion who were transported to the emergency department and underwent NCCT scans. Eight reviewers, with varying levels of training and experience (two neuroradiology attendings, two neuroradiology fellows, two neuroradiology residents, one neurosurgery attending, and one neurosurgery resident), independently evaluated NCCT head scans. The same scans were evaluated using the version 5.0 of the DL model icobrain tbi. The establishment of the ground truth involved a thorough assessment of all accessible clinical and laboratory data, as well as follow-up imaging studies, including NCCT and magnetic resonance imaging, as a consensus amongst the study reviewers. The outcomes of interest included neuroimaging radiological interpretation system (NIRIS) scores, the presence of midline shift, mass effect, hemorrhagic lesions, hydrocephalus, and severe hydrocephalus, as well as measurements of midline shift and volumes of hemorrhagic lesions. Comparisons using weighted Cohen's kappa coefficient were made. The McNemar test was used to compare the diagnostic performance. Bland-Altman plots were used to compare measurements.

RESULTS

One hundred patients were included, with the DL model successfully categorizing 77 scans. The median age for the total group was 48, with the omitted group having a median age of 44.5 and the included group having a median age of 48. The DL model demonstrated moderate agreement with the ground truth, trainees, and attendings. With the DL model's assistance, trainees' agreement with the ground truth improved. The DL model showed high specificity (0.88) and positive predictive value (0.96) in classifying NIRIS scores as 0-2 or 3-4. Trainees and attendings had the highest accuracy (0.95). The DL model's performance in classifying various TBI CT imaging common data elements was comparable to that of trainees and attendings. The average difference for the DL model in quantifying the volume of hemorrhagic lesions was 6.0 mL with a wide 95% confidence interval (CI) of - 68.32 to 80.22, and for midline shift, the average difference was 1.4 mm with a 95% CI of - 3.4 to 6.2.

CONCLUSION

While the DL model outperformed trainees in some aspects, attendings' assessments remained superior in most instances. Using the DL model as an assistive tool benefited trainees, improving their NIRIS score agreement with the ground truth. Although the DL model showed high potential in classifying some TBI CT imaging common data elements, further refinement and optimization are necessary to enhance its clinical utility.

Preparation of Al3Ti-Al2O3/Al Inoculant and Its Inoculation Effect on Al-Cu-Mn Alloy

The grain size plays a pivotal role in determining the properties of the alloy. The grain size can be significantly decreased by adding inoculants. Aiming to address the shortcomings of existing inoculants, the Al3Ti-Al2O3/Al inoculant was successfully prepared using Al-Ti master alloy and Al2O3 whiskers as raw materials. With the aid of ultrasonic energy, the Al2O3 whiskers were uniformly dispersed within the inoculants. Under the combined action of ultrasonic and titanium, the Al2O3 whiskers were broken into small particles at high temperature. To enhance the morphology of Al3Ti and achieve even particle dispersion throughout the matrix, vacuum rapid quenching treatment was applied to the inoculant. The SEM test results indicated a significant reduction in particle size after vacuum rapid quenching. The Al3Ti-Al2O3/Al inoculants exhibited excellent grain refinement effects on the weldable Al-Cu-Mn alloy. Crystallographic calculations and HRTEM analysis revealed that Al2O3 and Al have orientation relationships, indicating their potential as effective heterogeneous nucleation sites. The mechanical properties of the Al-Cu-Mn alloy were obviously improved after the Al3Ti-Al2O3/Al inoculant was added.

[Risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on the machine learning]

OBJECTIVE

To create risk predictive models of healthcare-seeking delay among imported malaria patients in Jiangsu Province based on machine learning algorithms, so as to provide insights into early identification of imported malaria cases in Jiangsu Province.

METHODS

Case investigation, first symptoms and time of initial diagnosis of imported malaria patients in Jiangsu Province in 2019 were captured from Infectious Disease Report Information Management System and Parasitic Disease Prevention and Control Information Management System of Chinese Center for Disease Control and Prevention. The risk predictive models of healthcare-seeking delay among imported malaria patients were created with the back propagation (BP) neural network model, logistic regression model, random forest model and Bayesian model using thirteen factors as independent variables, including occupation, species of malaria parasite, main clinical manifestations, presence of complications, severity of disease, age, duration of residing abroad, frequency of malaria parasite infections abroad, incubation period, level of institution at initial diagnosis, country of origin, number of individuals travelling with patients and way to go abroad, and time of healthcare-seeking delay as a dependent variable. Logistic regression model was visualized using a nomogram, and the nomogram was evaluated using calibration curves. In addition, the efficiency of the four models for prediction of risk of healthcare-seeking delay among imported malaria patients was evaluated using the area under curve (AUC) of receiver operating characteristic curve (ROC). The importance of each characteristic was quantified and attributed by using SHAP to examine the positive and negative effects of the value of each characteristic on the predictive efficiency.

RESULTS

A total of 244 imported malaria patients were enrolled, including 100 cases (40.98%) with the duration from onset of first symptoms to time of initial diagnosis that exceeded 24 hours. Logistic regression analysis identified a history of malaria parasite infection [odds ratio (OR) = 3.075, 95% confidential interval (CI): (1.597, 5.923)], long incubation period [OR = 1.010, 95% CI: (1.001, 1.018)] and seeking healthcare in provincial or municipal medical facilities [OR = 12.550, 95% CI: (1.158, 135.963)] as risk factors for delay in seeking healthcare among imported malaria cases. BP neural network modeling showed that duration of residing abroad, incubation period and age posed great impacts on delay in healthcare-seek among imported malaria patients. Random forest modeling showed that the top five factors with the greatest impact on healthcare-seeking delay included main clinical manifestations, the way to go abroad, incubation period, duration of residing abroad and age among imported malaria patients, and Bayesian modeling revealed that the top five factors affecting healthcare-seeking delay among imported malaria patients included level of institutions at initial diagnosis, age, country of origin, history of malaria parasite infection and individuals travelling with imported malaria patients. ROC curve analysis showed higher overall performance of the BP neural network model and the logistic regression model for prediction of the risk of healthcare-seeking delay among imported malaria patients (Z = 2.700 to 4.641, all P values < 0.01), with no statistically significant difference in the AUC among four models (Z = 1.209, P > 0.05). The sensitivity (71.00%) and Youden index (43.92%) of the logistic regression model was higher than those of the BP neural network (63.00% and 36.61%, respectively), and the specificity of the BP neural network model (73.61%) was higher than that of the logistic regression model (72.92%).

CONCLUSIONS

Imported malaria cases with long duration of residing abroad, a history of malaria parasite infection, long incubation period, advanced age and seeking healthcare in provincial or municipal medical institutions have a high likelihood of delay in healthcare-seeking in Jiangsu Province. The models created based on the logistic regression and BP neural network show a high efficiency for prediction of the risk of healthcare-seeking among imported malaria patients in Jiangsu Province, which may provide insights into health management of imported malaria patients.

[Acetylcorynoline inhibits microglia activation by regulating EGFR/MAPK signaling to promote functional recovery of injured mouse spinal cord]

OBJECTIVE

To investigate the effect of acetylcorynoline (Ace) for promoting functional recovery of injured spinal cord in rats and explore the underlying mechanism.

METHODS

Rat models of spinal cord injury (SCI) were treated with intraperitoneal injection of different concentrations of Ace, with the sham-operated rats as the control group. After the treatment, the changes in motor function of the rats and the area of spinal cord injury were assessed with BBB score and HE staining, and the changes in pro-inflammatory cytokine levels and microglial activation were determined using PCR, ELISA and immunofluorescence staining. In a lipopolysaccharide (LPS)-treated BV2 cell model, the effects of different concentrations of Ace or DMSO on microglial activation and inflammatory cytokine production were observed. Network pharmacology analysis was performed to predict the target protein and signaling mechanism that mediated the inhibitory effect of Ace on microglia activation, and AutoDock software was used for molecular docking between Ace and the target protein. A signaling pathway blocker (Osimertinib) was used to verify the signaling mechanism in rat models of SCI and LPS-treated BV2 cell model.

RESULTS

In rat models of SCI, Ace treatment significantly increased the BBB score, reduced the area of spinal cord injury, and lowered the number of activated microglia cells and the levels of pro-inflammatory cytokines (P < 0.05). The cell experiments showed that Ace treatment significantly lower the level of cell activation and the production of inflammatory cytokines in LPS-treated BV2 cells (P < 0.05). Network pharmacology analysis suggested that EGFR was the main target of Ace, and they bound to each other via hydrogen bonds as shown by molecular docking. Western blotting confirmed that Ace inhibited the activation of the EGFR/MAPK signaling pathway in injured mouse spinal cord tissue and in LPS-treated BV2 cells, and its inhibitory effect was comparable to that of Osimertinib.

CONCLUSION

In rat models of SCI, treatment with Ace can inhibit microglia-mediated inflammatory response by regulating the EGFR/MAPK pathway, thus promoting tissue repair and motor function recovery.

[Regular assessment is an effective approach to maintaining the capacity of prevention of re-establishment from imported malaria in China]

After achieving malaria elimination, preventing re-establishment from imported malaria and consolidating malaria elimination achievements are top priorities of the national malaria control program in China. Due to the long-term existence of overseas imported malaria cases and incomplete eradication of local epidemic conditions, there are multiple challenges for prevention of re-establishment from imported malaria in China. Hereby, we propose that regular assessment is an effective approach to maintaining the capability of prevention of re-establishment from imported malaria, and describe the purpose, significance, management and implementation of the capability assessment for prevention of re-establishment from imported malaria, so as to provide insights into the formulation and adjustment of malaria control strategies during the post-elimination phase.

Prediction of delayed cerebral ischemia after cerebral aneurysm rupture using explainable machine learning approach

BACKGROUND

Aneurysmal subarachnoid hemorrhage results in significant mortality and disability, which is worsened by the development of delayed cerebral ischemia. Tests to identify patients with delayed cerebral ischemia prospectively are of high interest.

OBJECTIVE

We created a machine learning system based on clinical variables to predict delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage patients. We also determined which variables have the most impact on delayed cerebral ischemia prediction using SHapley Additive exPlanations method.

METHODS

500 aneurysmal subarachnoid hemorrhage patients were identified and 369 met inclusion criteria: 70 patients developed delayed cerebral ischemia (delayed cerebral ischemia+) and 299 did not (delayed cerebral ischemia-). The algorithm was trained based upon age, sex, hypertension (HTN), diabetes, hyperlipidemia, congestive heart failure, coronary artery disease, smoking history, family history of aneurysm, Fisher Grade, Hunt and Hess score, and external ventricular drain placement. Random Forest was selected for this project, and prediction outcome of the algorithm was delayed cerebral ischemia+. SHapley Additive exPlanations was used to visualize each feature's contribution to the model prediction.

RESULTS

The Random Forest machine learning algorithm predicted delayed cerebral ischemia: accuracy 80.65% (95% CI: 72.62-88.68), area under the curve 0.780 (95% CI: 0.696-0.864), sensitivity 12.5% (95% CI: -3.7 to 28.7), specificity 94.81% (95% CI: 89.85-99.77), PPV 33.3% (95% CI: -4.39 to 71.05), and NPV 84.1% (95% CI: 76.38-91.82). SHapley Additive exPlanations value demonstrated Age, external ventricular drain placement, Fisher Grade, and Hunt and Hess score, and HTN had the highest predictive values for delayed cerebral ischemia. Lower age, absence of hypertension, higher Hunt and Hess score, higher Fisher Grade, and external ventricular drain placement increased risk of delayed cerebral ischemia.

CONCLUSION

Machine learning models based upon clinical variables predict delayed cerebral ischemia with high specificity and good accuracy.

Prognostic value of combined pre- and postoperative albumin-to-alkaline phosphatase ratio for patients with hepatocellular carcinoma undergoing trans-catheter chemoembolisation

AIM

To reveal the prognostic value of the postoperative and dynamic albumin-to-alkaline phosphatase ratio (AAPR) in hepatocellular carcinoma (HCC) patients undergoing trans-catheter chemoembolisation (TACE).

MATERIALS AND METHODS

In total, 545 HCC patients undergoing initial TACE were enrolled into the study. The receiver operating characteristic (ROC) curve was plotted to determine the best cut-off for AAPR. Univariate and multivariate Cox regression analyses were used to confirm the independent prognostic effect of AAPR on overall survival (OS). The predictive performance of AAPR was assessed by ROC curves, concordance index (C-index), and Akaike information criterion (AIC), and was compared to existing liver function assessment systems.

RESULTS

The optimal cut-off value for the AAPR was 0.26. Elevated AAPR (>0.26) was associated with a low risk of death after adjustment whether before (HR: 0.53; 95% CI: 0.4-0.69) or after (HR: 0.64; 95% CI: 0.43-0.95) TACE treatment. The combined pre- and postoperative AAPR showed much better performance in ROC curve (1-, 3-, and 5-year AUCs: 0.69, 0.71, 0.69), C-index (0.65; 95% CI: 0.59-0.72) and AIC analyses than pre-AAPR and post-AAPR alone or liver function assessment systems.

CONCLUSION

This study demonstrated both preoperative and postoperative AAPR were independent prognostic factors for HCC patients undergoing TACE. In addition, the combined pre- and post-AAPR showed better predictive performance than pre-AAPR and post-AAPR alone or liver function assessment systems.

Large-Area Black Phosphorus/PtSe2 Schottky Junction for High Operating Temperature Broadband Photodetectors

High operating temperature (HOT) broadband photodetectors are urgently necessary for extreme condition applications in infrared-guided missiles, infrared night vision, fire safety imaging, and space exploration sensing. However, conventional photodetectors show dramatic carrier mobility decreases and carrier losses with low photoresponsivity at HOT due to the increased carrier scattering in channels at high temperatures. Herein, the HOT broadband photodetectors from room temperature to 470 K are developed for the first time by large-area black phosphorus (BP)/PtSe₂ films device arrays via a depletion-enhanced photocarrier dynamics strategy. Attributed to the 2D Schottky junction at BP/PtSe₂ interface and resulting in full depleted working channels, the BP/PtSe₂ photodetector arrays exhibit high tolerance to carrier mobility decrease during the increasing operating temperature in a wide wavelength range from 532 to 2200 nm. Thus, the photodetector shows a state-of-the-art operating temperature at 470 K with the photo-responsivity (R) and specific detectivity (D*) of 25 A W^-1 and 6.4 × 10¹¹ Jones under 1850 nm illumination, respectively. Moreover, BP/PtSe₂ photodetector arrays show high-uniformity photo-response in a large area. This work provides new strategies for high-performance broadband photodetector arrays with HOT by Schottky junction of large-area BP/PtSe₂ films.

Comparing blood biomarkers to clinical decision rules to select patients suspected of traumatic brain injury for head computed tomography

INTRODUCTION

Traumatic brain injury (TBI) is a major public health concern in the U.S. Recommendations for patients admitted in the emergency department (ED) to receive head computed tomography (CT) scan are currently guided by various clinical decision rules.

OBJECTIVE

To compare how a blood biomarker approach compares with clinical decision rules in terms of predicting a positive head CT in adult patients suspected of TBI.

METHODS

We retrospectively identified patients transported to our emergency department and underwent a noncontrast head CT due to suspicion of TBI and who had blood samples available. Published thresholds for serum and plasma glial fibrillary acidic protein (GFAP), ubiquitin carboxyl-terminal hydrolase-L1 (UCH-L1), and serum S100β were used to make CT recommendations. These blood biomarker-based recommendations were compared to those achieved under widely used clinical head CT decision rules (Canadian, New Orleans, NEXUS II, and ACEP Clinical Policy).

RESULTS

Our study included 463 patients, of which 122 (26.3%) had one or more abnormalities presenting on head CT. Individual blood biomarkers achieved high negative predictive value (NPV) for abnormal head CT findings (88%-98%), although positive predictive value (PPV) was consistently low (25%-42%). A composite biomarker-based decision rule (GFAP+UCH-L1)'s NPV of 100% and PPV of 29% were comparable or better than those achieved under the clinical decision rules.

CONCLUSION

Blood biomarkers perform at least as well as clinical rules in terms of selecting TBI patients for head CT and may be easier to implement in the clinical setting. A prospective study is necessary to validate this approach.

Preparation of CF/PEEK Composites with High Mechanical Performance Using PEEK Derivatives as the Sizing Agent

Owing to their excellent physical and chemical properties, the carbon fibre reinforced poly(ether-ether-ketone) composites (CF/PEEK) are widely used in aerospace applications such as rockets, missiles, and high-speed vehicles. However, both carbon fibre (CF) and poly(ether-ether-ketone) (PEEK) have inert molecular chain structures, which seriously affect the interfacial properties of CF/PEEK composites. In this study, to improve the properties of CF/PEEK composites, carboxylated PEEK (PEEK-COOH) with different carboxylation degrees is synthesized as the sizing agent by a "two-step" method. Then, the activated CF surface is coated by PEEK-COOH sizing layers with different functionalization degrees to prepare the CF/PEEK composites. The results show that the interfacial properties of CF/PEEK composites are improved after applying the sizing agent. When the carboxylation degree of PEEK-COOH is 19.61%, the flexural strength, flexural modulus, and interlaminar shear strength (ILSS) of CF/PEEK composites reach 489.34 MPa, 25.387 GPa, and 81.3 MPa, respectively. In addition, the use of PEEK-COOH sizing agents can form an excellent transition layer between CF and PEEK, creating an efficient stress transfer system and facilitating an even stress distribution between CF and PEEK. Furthermore, the main mechanism of material fracture changes from CF debonding to CF and resin fracture.

The automatic evaluation of steno-occlusive changes in time-of-flight magnetic resonance angiography of moyamoya patients using a 3D coordinate attention residual network

Background

Moyamoya disease (MMD) is a rare cerebrovascular occlusive disease with progressive stenosis of the terminal portion of internal cerebral artery (ICA) and its main branches, which can cause complications, such as high risks of disability and increased mortality. Accurate and timely diagnosis may be difficult for physicians who are unfamiliar to MMD. Therefore, this study aims to achieve a preoperative deep-learning-based evaluation of MMD by detecting steno-occlusive changes in the middle cerebral artery or distal ICA areas.

Methods

A fine-tuned deep learning model was developed using a three-dimensional (3D) coordinate attention residual network (3D CA-ResNet). This study enrolled 50 preoperative patients with MMD and 50 controls, and the corresponding time of flight magnetic resonance angiography (TOF-MRA) imaging data were acquired. The 3D CA-ResNet was trained based on sub-volumes and tested using patch-based and subject-based methods. The performance of the 3D CA-ResNet, as evaluated by the area under the curve (AUC) of receiving-operator characteristic, was compared with that of three other conventional 3D networks.

Results

With the resulting network, the patch-based test achieved an AUC value of 0.94 for the 3D CA-ResNet in 480 patches from 10 test patients and 10 test controls, which is significantly higher than the results of the others. The 3D CA-ResNet correctly classified the MMD patients and normal healthy controls, and the vascular lesion distribution in subjects with the disease was investigated by generating a stenosis probability map and 3D vascular structure segmentation.

Conclusions

The results demonstrated the reliability of the proposed 3D CA-ResNet in detecting stenotic areas on TOF-MRA imaging, and it outperformed three other models in identifying vascular steno-occlusive changes in patients with MMD.

[Surveillance of Aedes populations in Jiangsu Province in 2020]

OBJECTIVE

To investigate the seasonal Aedes population fluctuation and the resistance of Aedes populations to common insecticides in Jiangsu Province in 2020, so as to provide insights into vector-borne infectious diseases control.

METHODS

One village was randomly sampled from each of Xinbei District of Changzhou City and Zhangjiagang County of Suzhou City in southern Jiangsu Province, Hai'an County of Nantong City and Yandu District of Yancheng City in Central Jiangsu Province, and Suining County of Xuzhou City and Sihong County of Suqian City in northern Jiangsu Province during the period between May and October, 2020. A small ponding container was sampled, and larval Aedes mosquitoes were collected using straws once each in early and late stages of each month. All larvae were bred in laboratory to adults for population identification. In addition, larval breeding were observed in all small ponding containers in and out of 30 households that were randomly sampled from six surveillance sites, and the larval mosquito density was estimated using Breteau index. Larval A. albopictus mosquitoes were sampled around Cuiyuan New Village in Jintan District of Changzhou City, and bred in laboratory to the first offspring generation, and the susceptibility of adult female mosquitoes to deltamethrin, lambda-cyhalothrin, malathion, and propoxur was tested using the filter-paper bioassay recommended by WHO.

RESULTS

A total of 1 165 larval Aedes mosquitoes were captured from small ponding containers in six surveillance sites of Jiangsu Province in 2020, and all were identified as A. albopictus following eclosion. The largest number of Aedes larvae captured was found in July. A total of 1 152 households were investigated in six surveillance sites, and the mean Breteau indexes were 9.58, 13.20, 13.71, 13.20, 12.18 and 5.58 from May to October, respectively, while a high Aedes transmission risk was seen in Xinbei District of Changzhou City, with a higher Breteau index than in Suining (H = 23.667, P_adjusted = 0.001) and Sihong (H = 22.500, P_adjusted = 0.003) counties. The field-captured A. albopictus from Cuiyuan New Village in Jintan District of Changzhou City remained sensitive to malathion, but was resistant to propoxur, and developed high-level resistance to deltamethrin and lambda-cyhalothrin.

CONCLUSIONS

A. albopictus was present in southern, central and northern Jiangsu Province in 2020, and the larval density peaked in July. A. albopictus captured from Cuiyuan New Village in Jintan District of Changzhou City has developed high-level resistance to pyrethroid pesticides.

Extracellular vesicles derived from mesenchymal stem cells confer protection against intervertebral disc degeneration through a microRNA-217-dependent mechanism

OBJECTIVE

Extracellular vesicles released by mesenchymal stem cells (MSC-EVs) can be applied to alleviate intervertebral disc degeneration (IVDD) by curbing apoptosis of nucleus pulposus cells (NPCs). The current study aims to evaluate the effect of MSC-EVs on NPC apoptosis and IVDD and the related regulatory mechanisms involving microRNA (miR)-217.

METHOD

Expression of miR-217 was examined in tumor necrosis factor-α (TNF-α)-induced NPCs and MSC-EVs, followed by identification in the relationship between miR-217, enhancer of zeste homolog 2 (EZH2) and forkhead box O-3 (FOXO3). After isolation of EVs from MSCs and subsequent co-culture with NPCs, we assessed effects of miR-217 on NPC viability, autophagy, senescence and apoptosis along with extracellular matrix (ECM) degradation. Further in vivo experiments were conducted in rat models of IVDD to substantiate the effect of miR-217 on IVDD.

RESULTS

Poor miR-217 expression was found in TNF-α-induced NPCs, while high miR-217 expression was identified in MSC-EVs (P < 0.05). MSC-EVs transferred miR-217 to NPCs and increased its expression, thus attenuating NPC apoptosis and ECM degradation (elevated collagen II and aggrecan but reduced MMP13 and ADAMTS5) (P < 0.05). miR-217 targeted EZH2, and EZH2 bound to the FOXO3 promoter and consequently downregulated its expression. FOXO3 restrained NPC apoptosis and ECM degradation by stimulating cell autophagy (P < 0.05). Furthermore, in vivo experimental results confirmed the suppressive role of miR-217 shuttled by MSC-EVs in IVDD.

CONCLUSION

Overall, the delivery of miR-217 may be a novel mechanism underlying the effect of MSC-EVs on NPC apoptosis and ECM degradation following IVDD.

Melanoma in women of childbearing age and in pregnancy in California, 1994-2015: a population-based cohort study

BACKGROUND

Melanoma is one of the most common malignancies during pregnancy. There is debate regarding the impact of pregnancy on the prognosis of melanoma. Recent large population-based studies from the United States are lacking.

OBJECTIVES

To determine the characteristics and survival of women with pregnancy-associated melanoma.

METHODS

This population-based, retrospective cohort study used California Cancer Registry data linked with state-wide hospitalization and ambulatory surgery data to identify 15-44-year-old female patients diagnosed with melanoma in 1994-2015, including pregnant patients. Multivariable logistic regression compared demographic and clinical characteristics between pregnant and non-pregnant women with melanoma. Multivariable cox proportional hazards regression models assessed melanoma-specific and overall survival.

RESULTS

We identified 13 108 patients, of which 1406 were pregnant. Pregnancy-associated melanoma was more frequent in Hispanic compared to non-Hispanic White women. Melanoma occurring post-partum was associated with greater tumour thickness (2.01-4.00 vs. 0.01-1.00 mm, odds ratio 1.75, 95% confidence interval: 1.03-2.98). There were otherwise no significant differences between pregnant and non-pregnant women. Worse survival was associated with Asian, Black and Native American race/ethnicity (vs. non-Hispanic White), lower neighbourhood socio-economic status, public insurance, tumour site, greater tumour thickness and lymph node involvement, but not pregnancy.

CONCLUSIONS

Melanoma occurring post-partum was associated with greater tumour thickness, but pregnancy status did not affect survival after melanoma. Race/ethnicity, socio-economic status and health insurance impacted survival, emphasizing the importance of reducing health disparities.

Detection of Early Ischemic Changes with Virtual Noncontrast Dual-Energy CT in Acute Ischemic Stroke: A Noninferiority Analysis

BACKGROUND AND PURPOSE

Dual-energy virtual NCCT has the potential to replace conventional NCCT to detect early ischemic changes in acute ischemic stroke. In this study, we evaluated whether virtual NCCT is noninferior compared with standard linearly blended NCCT, a surrogate of conventional NCCT, regarding the detection of early ischemic changes with ASPECTS.

MATERIALS AND METHODS

Adult patients who presented with suspected acute ischemic stroke and who underwent dual-energy NCCT and CTA and brain MR imaging within 48 hours were included. Standard linearly blended images were reconstructed to match a conventional NCCT. Virtual NCCT images were reconstructed from CTA. ASPECTS was evaluated on conventional NCCT, virtual NCCT, and DWI, which served as the reference standard. Agreement between CT assessments and the reference standard was evaluated with the Lin concordance correlation coefficient. Noninferiority was assessed with bootstrapped estimates of the differences in ASPECTS between conventional and virtual NCCT with 95% CIs.

RESULTS

Of the 193 included patients, 100 patients (52%) had ischemia on DWI. Compared with the reference standard, the ASPECTS concordance correlation coefficient for conventional and virtual NCCT was 0.23 (95% CI, 0.15-0.32) and 0.44 (95% CI, 0.33-0.53), respectively. The difference in the concordance correlation coefficient between virtual and conventional NCCT was 0.20 (95% CI, 0.01-0.39) and did not cross the prespecified noninferiority margin of -0.10.

CONCLUSIONS

Dual-energy virtual NCCT is noninferior compared with conventional NCCT for the detection of early ischemic changes with ASPECTS.

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.

AB0864 A nomogram model combining inflammatory factors and MRI radiomics to assess the disease activity of the patients with axSpA in a prospective study

Background

Clinical and magnetic resonance imaging (MRI) disease activity score (DAS) are measuring different aspects of axial spondyloarthritis (axSpA), they are essential in disease activity assessment. The radiomics was on facilitating readings by clinical specialists via enhancing the medical images in which subtle data differences could be distinguished.

Objectives

If the additional information of MRI imaging can be considered as a predictor for axSpA disease activity? In this study, we sought to construct a nomogram integrating the sacroiliac joint (SIJ)- MRI radiomics features and the inflammatory biomarkers to assess disease activity and compare it with clinical disease acitivity index in axSpA patients.

Methods

203 patients data were collected prospectively and confirmed as axSpA were randomly divided into training (n = 143) and validation cohorts (n = 60). 1316 radiomics features were extracted from the 3.0T SIJ-MRI. A Nomogram model was constructed using multivariate logistic regression analysis Incorporating independent clinical factors and radiomics features score (Rad-score). The performance of clinics, Rad-score and nomogram models were evaluated by ROC analysis, calibration curve and decision curve analysis (DCA), and compared with the disease activity index(Ankylosing Spondylitis DAS (ASDAS)-C reactive protein (CRP), ASDAS-erythrocyte sedimentation rate (ESR), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI)) and Spondyloarthritis Research Consortium of Canada (SPARCC) MRI scoring system.

Results

The Rad-score allowed a good discrimination in the training (AUC, 0.91; 95% CI, 0.85-0.96) and the validation cohort (AUC, 0.84; 95% CI, 0.73-0.96). The CRP-radiomics nomogram model also showed favorable discrimination in the training (AUC, 0.96; 95% CI, 0.93-0.99) and the validation cohort (AUC, 0.89; 95% CI, 0.80-0.98), better than BASDAI(AUC, 0.58), ASDAS-CRP(AUC, 0.72), ASDAS-ESR(AUC, 0.77), ESR(AUC, 0.72), CRP(AUC, 0.77) and BASFI(AUC, 0.73), had no statistical difference with SPARCC(AUC, 0.87). Calibration curves and DCA demonstrated the nomogram fit well (p > 0.05) and was useful for activity evaluation.

Conclusion

Rad-score showed good discriminative ability to assess disease activity in axSpA. The nomogram can increase the efficacy for assessment axSpA disease activity, which might simplify clinical evaluation.

Figure 1.

Comparison of ROC curve analyses in prediction models. ROC curves of the clinical features (green curve), radiomics signature model (blue curve), and hybrid model (gold curve) of axSpA in the training cohort (A) and validation cohort (B), respectively. In addition, there are AUC of ASDAS-CRP(pink curve), ASDAS-ESR(brown curve), BASDAI(purple curve), BASFI(azure curve) and SPARCC scoring system(yellow curve) in the validation cohort (B), respectively. AUC: area under the curve; ROC: receiver operating characteristic; SPARCC: Spondyloarthritis Research Consortium of Canada; BASDAI: Bath Ankylosing Spondylitis Disease Activity Index; ASDAS: Ankylosing Spondylitis Disease Activity Score; CRP: C reactive protein; ESR: erythrocyte sedimentation rate; BASFI: Bath Ankylosing Spondylitis Disease Activity Index.

References

[1]Lee KH, Choi ST, Lee GY, Ha YJ, Choi SI. Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning. Diagnostics (Basel). 2021;11(7).

[2]Zheng Q, Liu W, Huang Y, Gao Z, Wu Y, Wang X, et al. Predictive Value of Active Sacroiliitis in MRI for Flare Among Chinese Patients with Axial Spondyloarthritis in Remission. Rheumatol Ther. 2021;8(1):411-24.

Acknowledgements

No conflict of interest

Disclosure of Interests

None declared

POS0962 CAN RADIOMICS REPLACE SPARCC SCORING SYSTEM IN EVALUATING BONE MARROW OEDEMA OF THE SACROILIAC JOINTS IN AXIAL SPONDYLOARTHRITIS?

Background

Bone marrow oedema (BMO) of the sacroiliac joints (SIJs) is evaluated to diagnose, classify and monitor disease activity in patients with axial spondyloarthritis (axSpA). Available quantitative methodologies rely on human visual assessment, and errors can’t be completely avoided. Radiomics can extract and select discriminative and quantified features from regions of interest (ROIs), making a more accurate and objective description of BMO.

Objectives

To develop a more objective and efficient method based on radiomics to evaluate BMO of the SIJs by magnetic resonance imaging (MRI) in patients with axSpA in comparison with Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system.

Methods

From September 2013 to July 2021, 523 patients with axSpA underwent 3.0T SIJ-MRI were included, who were randomly classified as training cohort(n=367) and validation cohort(n=156). The optimal radiomics features, selected from the 3.0T SIJ-MRI in the training cohort, were included to build the radiomics model. Four clinical risk predictors were adopted to build the clinical model. The performance of the clinical and radiomics models was evaluated by ROC analysis and decision curve analysis (DCA). Rad-scores were calculated by the radiomics model and SPARCC scores were performed to quantify the BMO of SIJs. We also assessed the correlation between Rad-score and SPARCC score.

Results

The radiomics model, built by 15 optimal features, showed favorable discrimination about SPARCC score <2 or ≥2 both in the training (AUC, 0.91; 95% CI: 0.88-0.94) and the validation cohort (AUC, 0.89; 95% CI, 0.84-0.94). DCA confirmed that the radiomics model was clinically useful. Furthermore, Rad-score has significant correlation with SPARCC score for scoring the status of BMO (rs=0.78, P< 0.001), and moderation correlation for scoring the change (r=0.40, P=0.005).

Conclusion

The radiomics can accurately assess the BMO of the SIJs in axSpA, providing an alternative to SPARCC scoring system. There was a positive correlation between Rad-score and SPARCC score.

References

[1]van der Heijde D, Sieper J, Maksymowych WP, Lambert RG, Chen S, Hojnik M, et al. Clinical and MRI remission in patients with nonradiographic axial spondyloarthritis who received long-term open-label adalimumab treatment: 3-year results of the ABILITY-1 trial. Arthritis Res Ther. 2018;20(1):61.

[2]Landewé RB, Hermann KG, van der Heijde DM, Baraliakos X, Jurik AG, Lambert RG, et al. Scoring sacroiliac joints by magnetic resonance imaging. A multiple-reader reliability experiment. The Journal of rheumatology. 2005;32(10):2050-5.

[3]Cereser L, Zabotti A, Zancan G, Quartuccio L, Cicciò C, Giovannini I, et al. Magnetic resonance imaging assessment of ASAS-defined active sacroiliitis in patients with inflammatory back pain and suspected axial spondyloarthritis: a study of reliability. Clinical and experimental rheumatology. 2021.

[4]Maksymowych WP, Inman RD, Salonen D, Dhillon SS, Williams M, Stone M, et al. Spondyloarthritis research Consortium of Canada magnetic resonance imaging index for assessment of sacroiliac joint inflammation in ankylosing spondylitis. Arthritis Rheum. 2005;53(5):703-9.

[5]Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278(2):563-77.

Table 1.

Rad-scores corresponding to different SPARCC score intervals about the status of SIJ-BMO.

SPARCC scorenRad-scoreMean(sd)Median (iqr)Range0-1170-1.31(1.64)-1.39(2.16)-6.46, 2.352-61250.73(1.86)0.62(2.12)-3.08, 8.487-11552.25(1.80)2.36(1.79)-1.17, 8.3612-16432.65(2.14)2.66(3.21)-0.76, 7.3917-21383.31(2.05)3.25(2.88)-0.88, 7.5522-26263.08(1.55)3.38(2.12)-1.00, 5.3827-31253.77(1.36)3.77(1.59)0.40, 6.27>31414.10(1.51)4.32(2.28)1.00, 6.96

Disclosure of Interests

None declared

Association between Blood and CT Imaging Biomarkers in a Cohort of Mild Traumatic Brain Injury Patients

OBJECTIVE

To analyze the relationships between traumatic brain injury (TBI) on CT imaging and blood concentration of glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), and S100B.

METHODS

This prospective cohort study involved 644 TBI patients referred to Stanford Hospital's Emergency Department between November 2015 and April 2017. Plasma and serum samples of 462 patients were analyzed for levels of GFAP, UCH-L1 and S100B. Glial neuronal ratio (GNR) was calculated as the ratio between GFAP and UCH-L1 concentrations. Admission head CT scans were reviewed for TBI imaging common data elements, and performance of biomarkers for identifying TBI was assessed via area under the receiver operating characteristic curve (ROC). We also dichotomized biomarkers at established thresholds and estimated standard measures of classification accuracy. We assessed the ability of GFAP, UCH-L1 and GNR to discriminate small and large/diffuse lesions based on CT imaging using an ROC analysis.

RESULTS

In our cohort of mostly mild TBI patients, GFAP was significantly more accurate in detecting all types of acute brain injuries than UCH-L1 in terms of area under the curves (AUC) values (P<0.001), and also compared to S100B (P<0.001). UCH-L1 and S100B had similar performance (comparable AUC values, P=0.342). Sensitivity exceeded 0.8 for each biomarker across all different types of TBI injuries, and no significant differences were observed by type of injury. Significant differences of GFAP and GNR distinguished between small lesions and large/diffuse lesions in all injuries (P=0.004, P=0.007).

CONCLUSIONS

GFAP, UCH-L1, and S100B show high sensitivity and negative predictive values for all types of TBI lesions on head CT. A combination of negative blood biomarkers (GFAP and UCH-L1) in a patient suspected of TBI may be used to safely obviate the need for a head CT scan. GFAP is a promising indicator to discriminate between small and large/diffuse TBI lesions.

Application of Deep Learning to Ischemic and Hemorrhagic Stroke Computed Tomography and Magnetic Resonance Imaging

Deep Learning (DL) algorithm holds great potential in the field of stroke imaging. It has been applied not only to the "downstream" side such as lesion detection, treatment decision making, and outcome prediction, but also to the "upstream" side for generation and enhancement of stroke imaging. This paper aims to comprehensively overview the common applications of DL to stroke imaging. In the future, more standardized imaging datasets and more extensive studies are needed to establish and validate the role of DL in stroke imaging.

Abstract TMP12: Artificial Intelligence Prediction Of Delayed Cerebral Ischemia After Cerebral Aneurysm Rupture

Background: Aneurysmal subarachnoid hemorrhage (aSAH) results in significant mortality and disability, which is worsened by the development of Delayed Cerebral Ischemia (DCI). Tests to identify patients with DCI prospectively are needed.

Objective: We created a machine learning (ML) system based on clinical variables to predict DCI in aSAH patients and to determine which variables have the most impact on DCI prediction.

Methods: We performed a retrospective cohort study of aSAH patients from January 2006 to September 2014. The ML algorithm was trained on age, sex, HTN, diabetes, hyperlipidemia, CHF, CAD, smoking history, family history of aneurysm, Fisher Grade, Hunt and Hess score, and external ventricular drain (EVD) placement. Prediction outcome of the ML algorithm was DCI+, which was defined as new neurologic deterioration that could not be attributed to aneurysm re-bleeding, hydrocephalus, infection, seizure, hyponatremia or other metabolic abnormality. SHAP was used to explain and visualize the role of each feature’s contribution to the model prediction.

Results: 500 aSAH patients were identified and 369 met inclusion criteria: 70 patients developed DCI (DCI+) and 299 did not (DCI-). Random Forest was selected for this project after a 5-fold cross validation. 276 cases (222 DCI- and 54 DCI+) were used for training and 93 cases (77 DCI- and 16 DCI+) were used for testing the algorithm. The Random Forest ML algorithm predicted DCI: Accuracy: 81.7%, Sensitivity: 12.5%, Specificity: 96.1%, PPV: 40%, and NPV: 84.1%. SHAP value demonstrated Age, EVD placement, Fisher Grade, and Hunt and Hess score, and HTN had the highest predictive values for DCI. Lower age, absence of hypertension, higher Hunt and Hess score, higher Fisher Grade, and EVD placement increased risk of DCI.

Conclusion: ML models based upon clinical variable predict DCI with high specificity and modest accuracy. The addition of imaging or other biomarkers may improve the sensitivity of the ML algorithm.

Impact Analysis of Different CT Configurations of Carotid Artery Plaque Calcifications on Cerebrovascular Events

BACKGROUND AND PURPOSE

CT is considered the standard reference both for quantification and characterization of carotid artery calcifications. Our aim was to investigate the relationship among different types of calcium configurations detected with CT within the plaque with a novel classification and to investigate the prevalence of cerebrovascular events.

MATERIALS AND METHODS

Seven hundred ninety patients (men = 332; mean age, 69.7 [SD, 13] years; 508 symptomatic for cerebrovascular symptoms and 282 asymptomatic) who underwent computed tomography of the carotid arteries were retrospectively included in this institutional review board-approved study. The plaque was classified into 6 types according to the different types of calcium configurations as the following: type 1, complete absence of calcification within the plaque; type 2, intimal or superficial calcifications; type 3, deep or bulky calcifications; type 4, adventitial calcifications with internal soft plaque of <2 mm thickness; type 5, mixed patterns with intimal and bulky calcifications; and type 6, positive rim sign.

RESULTS

The highest prevalence of cerebrovascular events was observed for type 6, for which 89 of the 99 cases were symptomatic. Type 6 plaque had the highest degree of correlation with TIA, stroke, symptoms, and ipsilateral infarct for both sides with a higher prevalence in younger patients. The frequency of symptoms observed by configuration type significantly differed between right and left plaques, with symptoms observed more frequently in type 6 calcification on the right side (50/53; 94%) than on the left side (39/46; 85%, P < .001).

CONCLUSIONS

We propose a novel carotid artery plaque configuration classification that is associated with the prevalence of cerebrovascular events. If confirmed in longitudinal analysis, this classification could be used to stratify the risk of occurrence of ischemic events.

MEDAS: an open-source platform as a service to help break the walls between medicine and informatics

In the past decade, deep learning (DL) has achieved unprecedented success in numerous fields, such as computer vision and healthcare. Particularly, DL is experiencing an increasing development in advanced medical image analysis applications in terms of segmentation, classification, detection, and other tasks. On the one hand, tremendous needs that leverage DL’s power for medical image analysis arise from the research community of a medical, clinical, and informatics background to share their knowledge, skills, and experience jointly. On the other hand, barriers between disciplines are on the road for them, often hampering a full and efficient collaboration. To this end, we propose our novel open-source platform, i.e., MEDAS–the MEDical open-source platform As Service. To the best of our knowledge, MEDAS is the first open-source platform providing collaborative and interactive services for researchers from a medical background using DL-related toolkits easily and for scientists or engineers from informatics modeling faster. Based on tools and utilities from the idea of RINV (Rapid Implementation aNd Verification), our proposed platform implements tools in pre-processing, post-processing, augmentation, visualization, and other phases needed in medical image analysis. Five tasks, concerning lung, liver, brain, chest, and pathology, are validated and demonstrated to be efficiently realizable by using MEDAS. MEDAS is available at http://medas.bnc.org.cn/.

Multi-Modal Co-Learning for Liver Lesion Segmentation on PET-CT Images

Liver lesion segmentation is an essential process to assist doctors in hepatocellular carcinoma diagnosis and treatment planning. Multi-modal positron emission tomography and computed tomography (PET-CT) scans are widely utilized due to their complementary feature information for this purpose. However, current methods ignore the interaction of information across the two modalities during feature extraction, omit the co-learning of the feature maps of different resolutions, and do not ensure that shallow and deep features complement each others sufficiently. In this paper, our proposed model can achieve feature interaction across multi-modal channels by sharing the down-sampling blocks between two encoding branches to eliminate misleading features. Furthermore, we combine feature maps of different resolutions to derive spatially varying fusion maps and enhance the lesions information. In addition, we introduce a similarity loss function for consistency constraint in case that predictions of separated refactoring branches for the same regions vary a lot. We evaluate our model for liver tumor segmentation using a PET-CT scans dataset, compare our method with the baseline techniques for multi-modal (multi-branches, multi-channels and cascaded networks) and then demonstrate that our method has a significantly higher accuracy ( ) than the baseline models.

A Dataset of Pulmonary Lesions With Multiple-Level Attributes and Fine Contours

Lung cancer is a life-threatening disease and its diagnosis is of great significance. Data scarcity and unavailability of datasets is a major bottleneck in lung cancer research. In this paper, we introduce a dataset of pulmonary lesions for designing the computer-aided diagnosis (CAD) systems. The dataset has fine contour annotations and nine attribute annotations. We define the structure of the dataset in detail, and then discuss the relationship of the attributes and pathology, and the correlation between the nine attributes with the chi-square test. To demonstrate the contribution of our dataset to computer-aided system design, we define four tasks that can be developed using our dataset. Then, we use our dataset to model multi-attribute classification tasks. We discuss the performance in 2D, 2.5D, and 3D input modes of the classification model. To improve performance, we introduce two attention mechanisms and verify the principles of the attention mechanisms through visualization. Experimental results show the relationship between different models and different levels of attributes.

An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) is an irreversible brain disease that severely damages human thinking and memory. Early diagnosis plays an important part in the prevention and treatment of AD. Neuroimaging-based computer-aided diagnosis (CAD) has shown that deep learning methods using multimodal images are beneficial to guide AD detection. In recent years, many methods based on multimodal feature learning have been proposed to extract and fuse latent representation information from different neuroimaging modalities including magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose positron emission tomography (FDG-PET). However, these methods lack the interpretability required to clearly explain the specific meaning of the extracted information. To make the multimodal fusion process more persuasive, we propose an image fusion method to aid AD diagnosis. Specifically, we fuse the gray matter (GM) tissue area of brain MRI and FDG-PET images by registration and mask coding to obtain a new fused modality called "GM-PET." The resulting single composite image emphasizes the GM area that is critical for AD diagnosis, while retaining both the contour and metabolic characteristics of the subject's brain tissue. In addition, we use the three-dimensional simple convolutional neural network (3D Simple CNN) and 3D Multi-Scale CNN to evaluate the effectiveness of our image fusion method in binary classification and multi-classification tasks. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset indicate that the proposed image fusion method achieves better overall performance than unimodal and feature fusion methods, and that it outperforms state-of-the-art methods for AD diagnosis.

Era-like GTP protein gene expression in rice

The mutations are genetic changes in the genome sequences and have a significant role in biotechnology, genetics, and molecular biology even to find out the genome sequences of a cell DNA along with the viral RNA sequencing. The mutations are the alterations in DNA that may be natural or spontaneous and induced due to biochemical reactions or radiations which damage cell DNA. There is another cause of mutations which is known as transposons or jumping genes which can change their position in the genome during meiosis or DNA replication. The transposable elements can induce by self in the genome due to cellular and molecular mechanisms including hypermutation which caused the localization of transposable elements to move within the genome. The use of induced mutations for studying the mutagenesis in crop plants is very common as well as a promising method for screening crop plants with new and enhanced traits for the improvement of yield and production. The utilization of insertional mutations through transposons or jumping genes usually generates stable mutant alleles which are mostly tagged for the presence or absence of jumping genes or transposable elements. The transposable elements may be used for the identification of mutated genes in crop plants and even for the stable insertion of transposable elements in mutated crop plants. The guanine nucleotide-binding (GTP) proteins have an important role in inducing tolerance in rice plants to combat abiotic stress conditions.