Circular at the very beginning: on the initial genomes in the RNA world.

It is likely that an RNA world existed in early life, when RNA played both the roles of the genome and functional molecules, thereby undergoing Darwinian evolution. However, even with only one type of polymer, it seems quite necessary to introduce a labour division concerning these two roles because folding is required for functional molecules (ribozymes) but unfavourable for the genome (as a template in replication). Notably, while ribozymes tend to have adopted a linear form for folding without constraints, a circular form, which might have been topologically hindered in folding, seems more suitable for an RNA template. Another advantage of involving a circular genome could have been to resist RNA's end-degradation. Here, we explore the scenario of a circular RNA genome plus linear ribozyme(s) at the precellular stage of the RNA world through computer modelling. The results suggest that a one-gene scene could have been 'maintained', albeit with rather a low efficiency for the circular genome to produce the ribozyme, which required precise chain-break or chain-synthesis. This strict requirement may have been relieved by introducing a 'noncoding' sequence into the genome, which had the potential to derive a second gene through mutation. A two-gene scene may have 'run well' with the two corresponding ribozymes promoting the replication of the circular genome from different respects. Circular genomes with more genes might have arisen later in RNA-based protocells. Therefore, circular genomes, which are common in the modern living world, may have had their 'root' at the very beginning of life.

Association of ADAMTS13 activity with cerebral deep medullary vein: A community-based cross-sectional study.

Objectives: This study aims to investigate whether circulating ADAMTS13 activity can offer insights into the mechanism of pathophysiological changes in deep medullary veins (DMVs). Methods: This study was conducted on a community cohort of elderly individuals in Shanghai. Plasma von Willebrand factor (VWF) levels and ADAMTS13 activity were measured. A validated DMV score described the overall burden of DMV on the brain. Through ordinal regression models, we investigated the correlation between VWF levels, ADAMTS13 activity, and increasing severity of DMV score while adjusting for demographics and cardiovascular risk factors. Results: The study enrolled 262 subjects according to the inclusion criteria. The mean VWF level (1.35 ± 0.25) was higher in the DMV group than in the group without DMV (1.25 ± 0.30) (p = 0.025), and ADAMTS13 activity (83.76 ± 7.96) was relatively lower. After adjusting for age, sex, alcohol consumption, smoking, hypertension, and diabetes, reduced ADAMTS13 activity [ß = -7.78; 95 % CI (-10.21, -5.35) p < 0.01] was associated with DMV. Moreover, correlation analysis indicated that ADAMTS13 activity was negatively correlated with the DMV score (Kendall's tau-b = -0.53, p < 0.001). Discussion: In summary, there was an inverse correlation observed between ADAMTS13 activity and the DMV score, which may provide some clinical clues for exploring the potential pathogenesis of DMV.

Cumulative Exposure to Oxidized Low-density Lipoprotein is a Potential Predictor for Prognosis in Acute Ischemic Stroke: A Cohort Study.

BACKGROUND: Oxidized Low-Density Lipoprotein (ox-LDL) is crucial in the recrudescence and prognosis of acute ischemic stroke (AIS). We aimed to probe into the influence of cumulative ox-LDL exposure on the 90-day prognosis of AIS. METHODS: Patients with AIS were recruited in this research. AIS severity at admission was estimated with infarct volumes and National Institute of Health Stroke Scale (NIHSS) scores. AIS prognosis was assessed using Modified Rankin Scale (mRS) scores at 90 days and the change in NIHSS scores from admission to discharge. Cumulative ox-LDL exposure was defined as ox-LDL level (pg/mL) multiplied by age(y). Multivariate logistic regression analysis was employed to reveal the correlation between exposure factors and the prognosis of AIS. The prognostic prediction ability of cumulative ox-LDL exposure was compared with cumulative LDL exposure by the receiver operating characteristic curve (ROC). RESULTS: Higher cumulative ox-LDL exposure was related to worse prognosis, including neurological worsening at discharge (NIHSS increasing more than 2 points) (OR = 3.02, 95% CI, 1.30-6.98, P = 0.01) and poor functional prognosis at 90 days (mRS ≥ 3) (OR = 21.21, 95% CI, 4.72-95.36, P < 0.001). As multivariate regression analysis showed, significantly increased cumulative ox-LDL exposure was relevant to poor functional prognosis at 90 days (OR = 9.92, 95% CI, 1.23-79.76, P = 0.031), but not with neurological worsening at discharge (P = 0.414). ROC curve revealed that cumulative ox-LDL exposure had a higher predictive value (AUC = 0.843, P < 0.001) for functional prognosis of AIS than cumulative LDL exposure (AUC = 0.629, P = 0.023). CONCLUSION: Cumulative ox-LDL exposure has a positive correlation with poor prognosis at 90 days of AIS, and has a more accurate predictive ability than cumulative LDL exposure.

Effect of feruloylated arabinoxylan on the retrogradation and digestibility properties of pea starch during short-term refrigeration: Dependence of polysaccharide structure and bound ferulic acid content.

In this study, arabinoxylans (AX) with various molecular weights (Mw) and bound ferulic acid (FA) contents were prepared to compare their effects on the gelatinization, short-term retrogradation and digestive properties of pea starch (PeS). The results indicated that all AX samples could obviously impede the pasting process of PeS and inhibit the short-term retrogradation of PeS-based gels during refrigeration by hindering the rearrangement and double helical associations of amylose. More precisely, AXs with low Mw and the highest FA content (H-FAX) exhibited the strongest intervention ability on PeS compared with the other samples. According to the Fourier transform infrared spectroscopy and X-ray diffraction results, it might be due to the unique role of bound FA as a noncovalent cross-linking agent, which enhanced the association between AX and starch molecules through extra hydrogen bonding interactions and entanglement behaviour. On these bases, H-FAX clearly improved the hardness, chewiness, moisture content, and sensory acceptance of PeS-base gels (pea jelly), and could also regulate its starch composition during short-term refrigeration to delay starch digestion. Overall, AXs with appropriate structural features might obviously improve the quality and storage stability of PeS-based foods.

Applicability of the low-grade inflammation score in predicting 90-day functional outcomes after acute ischemic stroke.

BACKGROUND AND PURPOSE: The low-grade inflammation (LGI) score, a novel indicator of chronic LGI, combines C-reactive protein (CRP), leukocyte counts, the neutrophil/lymphocyte ratio (NLR), and the platelet (PLT) count to predict outcomes of patients with various conditions, such as cardiovascular diseases, cancers, and neurodegenerative diseases. However, few studies have examined the role of the LGI score in predicting functional outcomes of patients with ischemic stroke. The present study aimed to evaluate the association between the LGI score and functional outcomes of patients with ischemic stroke. METHODS: A total of 1,215 patients were screened in the present study, and 876 patients were finally included in this retrospective observational study based on the inclusion and exclusion criteria. Blood tests were conducted within 24 h of admission. Severity of ischemic stroke was assessed using the NIHSS score with severe stroke denoted by NIHSS > 5. Early neurological deterioration (END) was defined as an increment in the total NIHSS score of ≥ 2 points within 7 days after admission. Patient outcomes were assessed on day 90 after stroke onset using the modified Rankin Scale (mRS). RESULTS: The LGI score was positively correlated with baseline and the day 7 NIHSS scores (R2 = 0.119, p < 0.001;R2 = 0.123, p < 0.001). Multivariate regression analysis showed that the LGI score was an independent predictor of stroke severity and END. In the crude model, the LGI score in the fourth quartile was associated with a higher risk of poor outcomes on day 90 compared with the LGI score in the first quartile (OR = 5.02, 95% CI: 3.09-8.14, p for trend < 0.001). After adjusting for potential confounders, the LGI score in the fourth quartile was independently associated with poor outcomes on day 90 (OR = 2.65, 95% CI: 1.47-4.76, p for trend = 0.001). Finally, the ROC curve analysis showed an AUC of 0.682 for poor outcomes on day 90 after stroke onset. CONCLUSION: The LGI score is strongly correlated with the severity of acute ischemic stroke and that the LGI score might be a good predictor for poor outcomes on day 90 in patients with acute ischemic stroke.

Cerebral Small Vessel Disease Is Associated with Motor, Cognitive, and Emotional Dysfunction in Multiple System Atrophy.

BACKGROUND: Cerebral small vessel disease (CSVD) has not been systematically studied in patients with multiple system atrophy (MSA). OBJECTIVE: We sought to explore whether MSA patients suffer from a heavier CSVD burden relative to healthy individuals and whether CSVD has a relationship with motor, cognitive, and emotional dysfunction in patients with MSA. METHODS: This study consecutively recruited 190 MSA patients and 190 matched healthy controls whose overall CSVD burden and single CSVD imaging markers (including white matter hyperintensity (WMH), microbleeds, lacunes, and enlarged perivascular spaces (EPVS)) were measured. Of the MSA patients, 118 completed multi-dimensional outcome assessments. Spearman's correlations and multivariable linear regressions were performed. RESULTS: We observed a greater burden of overall CSVD, WMH, and EPVS in MSA patients compared with controls, but not for microbleeds and lacunes. Motor dysfunction and cognitive impairment were significantly worse in subjects with severe CSVD than those with none-to-mild CSVD. In patients with MSA, the severity of CSVD burden was positively associated with motor impairments as measured by the Unified Multiple System Atrophy Rating Scale-II (ß= 2.430, p = 0.039) and Scale for the Assessment and Rating of Ataxia (ß= 1.882, p = 0.015). Of CSVD imaging markers, different associations with MSA outcomes were displayed. WMH was associated with motor, cognitive, and emotional deficits, while the EPVS in the centrum semiovale, basal ganglia, and hippocampus regions was correlated only with motor severity, anxiety, and cognition, respectively. Similar findings were noted in MSA-cerebellar and MSA-parkinsonian patients. CONCLUSIONS: Concomitant CSVD may be correlated with worse multi-dimensional dysfunction in patients with MSA.

Corrigendum to "TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology" [Comput Struct Biotechnol J 20 (2022) 5680-5689].

[This corrects the article DOI: 10.1016/j.scr.2023.103115.].

The oral microbiome of patients with ischemic stroke predicts their severity and prognosis.

Background and objectives: Stroke is a common group of cerebrovascular diseases that can lead to brain damage or death. Several studies have shown a close link between oral health and stroke. However, the oral microbiome profiling of ischemic stroke (IS) and its potential clinical implication are unclear. This study aimed to describe the oral microbiota composition of IS, the high risk of IS, and healthy individuals and to profile the relationship between microbiota and IS prognosis. Methods: This observational study recruited three groups: IS, high-risk IS (HRIS), and healthy control (HC) individuals. Clinical data and saliva were collected from participants. The modified Rankin scale score after 90 days was used to assess the prognosis of stroke. Extracted DNA from saliva and performed 16S ribosomal ribonucleic acid (rRNA) gene amplicon sequencing. Sequence data were analyzed using QIIME2 and R packages to evaluate the association between the oral microbiome and stroke. Results: A total of 146 subjects were enrolled in this study according to the inclusion criteria. Compared with HC, HRIS and IS demonstrated a progressive increase trend in Chao1, observed species richness, and Shannon and Simpson diversity index. On the basis of permutational multivariate analysis of variance, the data indicate a great variation in the saliva microbiota composition between HC and HRIS (F = 2.40, P < 0.001), HC and IS (F = 5.07, P < 0.001), and HRIS and IS (F = 2.79, P < 0.001). The relative abundance of g_Streptococcus, g_Prevotella, g_Veillonella, g_Fusobacterium, and g_Treponema was higher in HRIS and IS compared with that in HC. Furthermore, we constructed the predictive model by differential genera to effectively distinguish patients with IS with poor 90-day prognoses from those with good (area under the curve = 79.7%; 95% CI, 64.41%-94.97%; p < 0.01). Discussion: In summary, the oral salivary microbiome of HRIS and IS subjects have a higher diversity, and the differential bacteria have some predictive value for the severity and prognosis of IS. Oral microbiota may be used as potential biomarkers in patients with IS.

Biological knowledge graph-guided investigation of immune therapy response in cancer with graph neural network.

The determination of transcriptome profiles that mediate immune therapy in cancer remains a major clinical and biological challenge. Despite responses induced by immune-check points inhibitors (ICIs) in diverse tumor types and all the big breakthroughs in cancer immunotherapy, most patients with solid tumors do not respond to ICI therapies. It still remains a big challenge to predict the ICI treatment response. Here, we propose a framework with multiple prior knowledge networks guided for immune checkpoints inhibitors prediction-DeepOmix-ICI (or ICInet for short). ICInet can predict the immune therapy response by leveraging geometric deep learning and prior biological knowledge graphs of gene-gene interactions. Here, we demonstrate more than 600 ICI-treated patients with ICI response data and gene expression profile to apply on ICInet. ICInet was used for ICI therapy responses prediciton across different cancer types-melanoma, gastric cancer and bladder cancer, which includes 7 cohorts from different data sources. ICInet is able to robustly generalize into multiple cancer types. Moreover, the performance of ICInet in those cancer types can outperform other ICI biomarkers in the clinic. Our model [area under the curve (AUC = 0.85)] generally outperformed other measures, including tumor mutational burden (AUC = 0.62) and programmed cell death ligand-1 score (AUC = 0.74). Therefore, our study presents a prior-knowledge guided deep learning method to effectively select immunotherapy-response-associated biomarkers, thereby improving the prediction of immunotherapy response for precision oncology.

Deep medullary veins: a promising neuroimaging marker for mild cognitive impairment in outpatients.

BACKGROUND AND PURPOSE: Mild cognitive impairment is an age-dependent pre-dementia state caused by varied reasons. Early detection of MCI helps handle dementia. Vascular factors are vital for the occurrence of MCI. This study investigates the correlation between deep medullary veins and multi-dimensional cognitive outcomes. MATERIALS AND METHODS: A total of 73 participants with MCI and 32 controls were enrolled. Minimum Mental State Examination and Montreal Cognitive Assessment were used to examine the global cognitive function, and different cognitive domains were measured by specific neuropsychological tests. MRI was used to assess the visibility of the DMV and other neuroimage markers. RESULTS: DMV score was statistically significantly higher in the MCI group compared with the control group (P = 0.009) and independently related to MCI (P = 0.007). Linear regression analysis verified that DMV score was linearly related to global cognition, memory, attention, and executive function after adjusting for cerebrovascular risk factors. CONCLUSION: DMV score was independently related to the onset of MCI, and correlates with overall cognition, memory, attention, and executive function in outpatients.

TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology.

Recent advances in RNA engineering have enabled the development of RNA-based therapeutics for a broad spectrum of applications. Developing RNA therapeutics start with targeted RNA screening and move to the drug design and optimization. However, existing target screening tools ignore noncoding RNAs and their disease-relevant regulatory relationships. And designing therapeutic RNAs encounters high computational complexity of multi-objective optimization to overcome the immunogenicity, instability and inefficient translational production. To unlock the therapeutic potential of noncoding RNAs and enable one-stop screening and design of therapeutic RNAs, we have built the platform TREAT. It incorporates 43,087,953 regulatory relationships between coding and noncoding genes from 81 biological networks under different physiological conditions. TREAT introduces graph representation learning with Random Walk Diffusions to perform disease-relevant target screening, in addition to the commonly utilized Topological Degree and PageRank algorithms. Design and optimization of large RNAs or interfering RNAs are both available. To reduce the computational complexity of multi-objective optimization for large RNA, we stratified the features into local and global features. The local features are evaluated on the fixed-length or dynamic-length local bins, whereas the latter are inspired by AI language models of protein sequence. Then the global assessment is performed on refined candidates, thus reducing the enormous search space. Overall, TREAT is a one-stop platform for the screening and designing of therapeutic RNAs, with particular attention to noncoding RNAs and cutting-edge AI technology embedded, leading the progress of innovative therapeutics for challenging diseases. TREAT is freely accessible at https://rna.org.cn/treat.

Multi-modality artificial intelligence in digital pathology.

In common medical procedures, the time-consuming and expensive nature of obtaining test results plagues doctors and patients. Digital pathology research allows using computational technologies to manage data, presenting an opportunity to improve the efficiency of diagnosis and treatment. Artificial intelligence (AI) has a great advantage in the data analytics phase. Extensive research has shown that AI algorithms can produce more up-to-date and standardized conclusions for whole slide images. In conjunction with the development of high-throughput sequencing technologies, algorithms can integrate and analyze data from multiple modalities to explore the correspondence between morphological features and gene expression. This review investigates using the most popular image data, hematoxylin-eosin stained tissue slide images, to find a strategic solution for the imbalance of healthcare resources. The article focuses on the role that the development of deep learning technology has in assisting doctors' work and discusses the opportunities and challenges of AI.

The Degree of Plasma Oxidized Low-Density Lipoprotein Level Decrease Is Related to Clinical Outcomes for Patients with Acute Ischemic Stroke.

OBJECTIVE: To investigate the relationship between the decrease of plasma oxidized low-density lipoprotein (oxLDL) levels and clinical outcomes in patients with acute atherosclerosis-related ischemic stroke. METHODS: We recruited acute ischemic stroke patients within 3 days of onset consecutively. Plasma oxLDL levels were measured on the second day after admission and before discharge (10-14 days after stroke onset). Initial stroke severity was assessed by the National Institutes of Health Stroke Scale (NIHSS) scores, and infarct volume was measured using diffusion-weighted imaging (DWI) by the ITK-SNAP software. Clinical outcomes were evaluated by DWI volumes in the acute phase, neurological improvement at discharge, and favorable functional prognosis at 90 days. Logistic regression was performed to evaluate the association between oxLDL level decrease and clinical outcomes. RESULTS: 207 patients were enrolled in this study. Compared with the mild decrease of the oxLDL level group, patients with a significant decrease of the oxLDL level group were more likely to have a higher ratio of neurological improvement at discharge (55.07% vs. 14.49%, p < 0.01) and favorable functional prognosis at 90 days (91.30% vs. 55.07%, p < 0.01). In multivariable logistic regression, the degree of oxLDL level decrease was related to neurological improvement at discharge and favorable functional prognosis at 90 days (p < 0.01). Patients with significant decrease were more likely to have neurological improvement at discharge (OR = 7.92, 95% CI, 3.14-19.98, and p < 0.01) and favorable functional prognosis at 90 days (OR = 7.46, 95% CI, 2.40-23.23, and p < 0.01) compared to patients with mild decrease of oxLDL level. The DWI volumes in patients with different oxLDL level decrease groups had no statistical difference (p = 0.41), and the Spearman's rho between oxLDL level decrease and DWI infarct volumes was -0.03, but no statistical difference (p = 0.72). CONCLUSIONS: The degree of oxLDL level decrease is related to neurological improvement at discharge and favorable functional prognosis at 90 days for patients with acute atherosclerosis-related ischemic stroke, but not with infarct volume in the acute phase.

Soluble Lectin-Like Oxidized Low-Density Lipoprotein Receptor-1 Level is Related to Clinical Prognosis In Patients with Acute Atherosclerosis-related Ischemic Stroke.

To investigate the associations between soluble Lectin-like Oxidized Low-density lipoprotein receptor-1 (sLOX-1) and clinical prognosis, especially infarct volume in patients with acute atherosclerosis-related ischemic stroke. We recruited acute ischemic stroke patients within 3 days after onset. Patients were stratified into 3 groups by sLOX-1 level. Initial stroke severity was assessed using the National Institutes of Health Stroke Scale scores, and infarct volume was measured using DWI by ITK-SNAP software. The clinical prognosis was evaluated by DWI volume, clinical response at discharge, and functional outcome at 90 days. Spearman rank correlation analysis was used to examine associations between circulating sLOX-1 levels and infarct volumes. Logistic regression was used to explore the relationship between sLOX-1 levels and clinical prognosis. A total of 207 patients were included in our study. The median DWI volume in the lowest sLOX-1 tertile was 1.98 cm3, smaller than 4.26 cm3 in the highest sLOX-1 group. The Spearman rank correlation coefficient between sLOX-1 levels and DWI volume was 0.47 (P < .01). Compared with the highest sLOX-1 tertiles, patients in the lowest sLOX-1 tertile had a higher risk of favorable functional outcome at 90 days (OR = 3.47, 95% CI, 1.21-9.96) after adjusting traditional risk factors. However, there was no difference between sLOX-1 level and clinical response at discharge. For patients with acute atherosclerosis-related ischemic stroke, circulating sLOX-1 level is correlated with DWI volume in the acute phase and favorable functional outcome at 90 days, but not with the clinical response at discharge.

The Relationship Between ADAMTS13 Activity and Overall Cerebral Small Vessel Disease Burden: A Cross-Sectional Study Based on CSVD.

Objectives: This study aimed to investigate the association between plasma von Willebrand factor (VWF) level, ADAMTS13 activity, and neuroimaging features of cerebral small vessel disease (CSVD), including the CSVD neuroimaging markers and the overall CSVD burden. Methods: CSVD patients admitted to our hospital from 2016 to 2020 were recruited. Plasma VWF level and ADAMTS13 activity were measured. The overall effect of CSVD on the brain was described as a validated CSVD score. We evaluated the association between VWF levels, ADAMTS13 activity, and the increasing severity of CSVD score by the logistic regression model. Results: We enrolled 296 patients into this study. The mean age of the sample was 69.0 years (SD 7.0). The mean VWF level was 1.31 IU/mL, and the ADAMTS13 activity was 88.01 (SD 10.57). In multivariate regression analysis, lower ADAMTS13 activity and higher VWF level was related to white matter hyperintensity (WMH) [ß = -7.31; 95% confidence interval (CI) (-9.40, -4.93); p<0.01; ß = 0.17; 95% confidence interval (0.11, 0.23); p<0.01], subcortical infarction (SI) [(ß = -9.22; 95% CI (-11.37, -7.06); p<0.01); ß = 0.21; 95% confidence interval (0.15, 0.27); p<0.01] independently, but not cerebral microbleed (CMB) [(ß = -2.3; 95% CI (-4.95, 0.05); p = 0.22); ß = 0.02; 95% confidence interval (-0.05, 0.08); p = 0.63]. Furthermore, ADAMTS13 activity was independently negatively correlated with the overall CSVD burden (odd ratio = 21.33; 95% CI (17.46, 54.60); p < 0.01) after adjustment for age, history of hypertension, and current smoking. Conclusions: Reducing ADAMTS13 activity change is related to white matter hyperintensity, subcortical infarction, but not with cerebral microhemorrhage. In addition, ADAMTS13 may have played an essential role in the progression of CSVD.

Optimal design for the connector of bridge prefabricated concrete barriers.

The current optimization research on the connectors of prefabricated barriers uses experimental comparisons only and lacks theoretically based optimization methods as guidance. The primary objective of this study was to propose an efficient optimization approach to the connector design of prefabricated bridge barriers. This paper presents an efficient two-stage optimization approach to the connector design of prefabricated bridge barriers. In the first stage, the hybrid cellular automaton algorithm is used to perform dynamic topology optimization on the connector, and the best material distribution in the design domain is obtained. In the second stage, a kriging metamodel and genetic algorithm are combined to further optimize the size of the connector structure. With a prefabricated bridge as the engineering background, finite element models of a barrier system under impact load caused by a car crash were established. The above approach is utilized to optimize the design of the barrier connector. Results showed that the optimized connector structure greatly improved the overall performance of the barrier system while reducing the material consumption and costs. The proposed optimization approach can determine the optimal material distribution and size of the connector structure, thus providing guiding significance for the design and construction of connectors of prefabricated components.

DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis.

Integrative analysis of multi-omics data can elucidate valuable insights into complex molecular mechanisms for various diseases. However, due to their different modalities and high dimension, utilizing and integrating different types of omics data suffers from great challenges. There is an urgent need to develop a powerful method to improve survival prediction and detect functional gene modules from multi-omics data. To deal with these problems, we present DeepOmix (a scalable and interpretable multi-Omics Deep learning framework and application in cancer survival analysis), a flexible, scalable, and interpretable method for extracting relationships between the clinical survival time and multi-omics data based on a deep learning framework. DeepOmix enables the non-linear combination of variables from different omics datasets and incorporates prior biological information defined by users (such as signaling pathways and tissue networks). Benchmark experiments demonstrate that DeepOmix outperforms the other five cutting-edge prediction methods. Besides, Lower Grade Glioma (LGG) is taken as the case study to perform the prognosis prediction and illustrate the functional module nodes which are associated with the prognostic result in the prediction model.

Insulin Resistance is Significantly Related with Worse Clinical Outcomes in Non-Diabetic Acute Ischemic Stroke Patients Treated with Intravenous Thrombolysis.

OBJECTIVES: to investigate the relationship between insulin resistance (IR) and clinical outcomes in non-diabetic ischemic stroke patients treated with intravenous thrombolysis. METHODS: We recruited non-diabetic ischemic stroke patients treated with intravenous thrombolysis prospectively. IR was defined as homeostasis model assessment-estimated insulin resistance index ≥2.80. Initial stroke severity was assessed using the National Institutes of Health Stroke Scale scores, and infarct volume was measured using DWI. Clinical outcomes were evaluated by neurological improvement and hemorrhagic transformation at 24 hours, and favorable functional prognosis at 90 days. RESULTS: 232 patients were enrolled into this study. IR group was 67 patients, non-IR group was 165 patients. Compared with the non-IR group, the probability of neurological improvement at 24 h ours and favorable functional outcome at 90 days in IR group were all significantly lower (41.79% vs 63.03%, p<0.01; 73.13% vs 89.09%, p<0.01 respectively), whereas the ratio of hemorrhagic transformation was much higher (16.42% vs 4.85%, p<0.01). In multivariable logistic regression, IR was negatively associated with neurological improvement and favorable functional prognosis (OR=0.39, 95%CI, 0.20-0.76, p<0.01; OR= 0.26, 95%CI, 0.07-0.91, p=0.04, respectively), but was positively correlated with hemorrhagic transformation (OR=4.07, 95%CI, 1.13-14.59, p=0.03) after adjusting traditional risk factors. We analyzed 108 infarct volume data further, the median of volume in IR group was 2.27 cm3, higher than that in non-IR group (1.96 cm3), but no statistical difference (p=0.65). CONCLUSIONS: In non-diabetic ischemic stroke patients treated with intravenous thrombolysis, IR was related with worse clinical outcomes, but not with infarct volume.

Systemic immune-inflammation index (SII) but not platelet-albumin-bilirubin (PALBI) grade is associated with severity of acute ischemic stroke (AIS).

INTRODUCTION: Inflammation plays an important role in stroke. Many inflammatory markers in peripheral blood are proved to be associated with stroke severity or prognosis. But few comprehensive models or scales to evaluate the severity of stroke have been reported. Systemic immune-inflammation index (SII) and platelet-albumin-bilirubin (PALBI) grade as new markers of inflammation have shown their positive association with liver cancer. The relation between SII, or PALBI and stroke remains uncertain. OBJECTIVE: To investigate the relationship between SII, PALBI grade and stroke severity. METHODS: Patients with ischemic stroke with hospital admission <24 h after symptom onset were prospectively included in a stroke registry. Demographic, clinical, and laboratory data were collected immediately after admission in all patients. The National Institutes of Health Stroke Scale (NIHSS) was used to assess stroke severity upon admission. Minor stroke was defined as NIHSS score < =5, moderate-to-severe stroke as NIHSS score >5. SII, calculated as platelet × neutrophil/lymphocyte was divided into four groups according to interquartile range: lowest SII (SII < 353.9 × 109/L), low SII (353.9-532.8 × 109/L), high SII (532.8-783.9 × 109/L), and highest SII (>783.9 × 109/L) group. RESULTS: A total of 362 patients with ischemic stroke were included, and between minor and moderate-to-severe stroke significant difference was found in SII (p < 0.0001), NLR (p < 0.0001), and PLR (p = 0.001), respectively. After multivariate regression analyses, SII groups (Odd ratio = 1.351, 95% confidence interval 1.084-1.684, p = 0.007) not PALBI was an independent risk factor for stroke severity. CONCLUSION: We found that SII but not PALBI, which both are markers of inflammation, was independently associated with stroke severity.

DeepACEv2: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks.

Chromosome enumeration is an essential but tedious procedure in karyotyping analysis. To automate the enumeration process, we develop a chromosome enumeration framework, DeepACEv2, based on the region based object detection scheme. The framework is developed following three steps. Firstly, we take the classical ResNet-101 as the backbone and attach the Feature Pyramid Network (FPN) to the backbone. The FPN takes full advantage of the multiple level features, and we only output the level of feature map that most of the chromosomes are assigned to. Secondly, we enhance the region proposal network's ability by adding a newly proposed Hard Negative Anchors Sampling to extract unapparent but essential information about highly confusing partial chromosomes. Next, to alleviate serious occlusion problems, besides the traditional detection branch, we novelly introduce an isolated Template Module branch to extract unique embeddings of each proposal by utilizing the chromosome's geometric information. The embeddings are further incorporated into the No Maximum Suppression (NMS) procedure to improve the detection of overlapping chromosomes. Finally, we design a Truncated Normalized Repulsion Loss and add it to the loss function to avoid inaccurate localization caused by occlusion. In the newly collected 1375 metaphase images that came from a clinical laboratory, a series of ablation studies validate the effectiveness of each proposed module. Combining them, the proposed DeepACEv2 outperforms all the previous methods, yielding the Whole Correct Ratio(WCR)(%) with respect to images as 71.39, and the Average Error Ratio(AER)(%) with respect to chromosomes as about 1.17.